autoclass-3.3.6.dfsg.1/0000755000175000017500000000000011667632321012711 5ustar areareautoclass-3.3.6.dfsg.1/version-3-3-3.text0000644000175000017500000000403711247310756015747 0ustar areare AUTOCLASS C VERSION 3.3.3 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/reports-c.text - document that report log messages will go into a ".rlog" file, rather than the ".log" which is used during search runs. Also minor typos corrected. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.3. 2. autoclass-c/prog/init.c, intf-reports.c, intf-sigma-contours.c - Sun Solaris CC compiler breaks when #ifdef, etc preprocessor directives do not start in column 1. All preprocessor directives now start in column 1. 3. autoclass-c/prog/autoclass.make.alpha.cc, autoclass-c/load-ac - A Makefile for the Dec Alpha (OSF1 v4.0) has been added. 4. autoclass-c/prog/prints.c - Modified PRINT_VECTOR_F to eliminate compiler warning. 5. autoclass-c/prog/search-control.c - Conditionalize two warning tests to fail in batch mode (.s-params parameter interactive_p = false), rather than attempt to ask the user whether to proceed. 6. autoclass-c/prog/autoclass.h, autoclass.c, io-results.c - To make it convenient to generate reports while the search is still running, so you can decide whether or not to stop the search, but not have the search log file be overwritten with the report log file, the report log file will now be written to a file with the extension ".rlog". The search output will continue to be directed to a file with the extension ".log". 7. autoclass-c/prog/getparams.c, init.c, io-read-model.c, struct-class.c - Change sizeof(int) to sizeof(void *), so that 64-bit architectures will be handled properly. This corrects the core dump which occurs on Dec Alpha platforms at the end of each search or reports run, when AutoClass C attempts to free allocated storage. autoclass-3.3.6.dfsg.1/data/0000755000175000017500000000000011667631535013630 5ustar areareautoclass-3.3.6.dfsg.1/data/soybean/0000755000175000017500000000000011667631535015270 5ustar areareautoclass-3.3.6.dfsg.1/data/soybean/soyc.influ-o-text-20000644000175000017500000011617611247310756020666 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 47 cases in: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.db2 /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -660.007 from classification results file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.results-bin and using models: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY: 4 tries over 2 seconds _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY: exp(-645.604) N_CLASSES: 4 FOUND ON TRY: 3 *SAVED* -1 PROBABILITY: exp(-660.007) N_CLASSES: 5 FOUND ON TRY: 4 *SAVED* -2 PROBABILITY: exp(-710.824) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-727.858) N_CLASSES: 2 FOUND ON TRY: 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 5 POPULATED CLASSES: (max global influence value = 1.277) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -9.40e+00 6.12e-01 10 0.213 1 -9.39e+00 6.16e-01 10 0.213 2 -9.29e+00 6.85e-01 10 0.213 3 -9.76e+00 4.24e-01 10 0.213 4 -8.91e+00 1.00e+00 7 0.149 CLASS DIVERGENCES: The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 7.66e+00 10 0.213 1 1.16e+01 10 0.213 2 6.18e+00 10 0.213 3 5.13e+00 10 0.213 4 6.31e+00 7 0.149 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES: This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 22: stem cankers 1.000 23: canker lesion color 0.885 2: time of occurance 0.620 8: damaged area 0.545 29: fruit pod condition 0.511 5: temperature 0.475 36: root condition 0.473 3: plant stand 0.448 4: precipitation 0.435 25: outer stem decay 0.366 24: fruiting bodies of stem 0.366 27: internal discoloration of stem 0.366 28: scerotia internal or external 0.366 13: leaf condition 0.297 10: seed treatment 0.135 9: damage severity 0.131 7: number years crop repeated 0.116 11: seed germination 0.095 6: occurance of hail 0.094 21: stem lodging 0.039 26: mycelium on stem 0.038 0: case number ----- 1: diagnosis ----- 12: plant growth ----- 14: leaf spot halos ----- 15: leaf spot margin ----- 16: size of leaf spots ----- 17: slot holing ----- 18: leaf malformation ----- 19: leaf mildew growth ----- 20: condition of stem ----- 30: fruit spots ----- 31: seed condition ----- 32: seed mold growth ----- 33: seed discoloration ----- 34: seed size ----- 35: seed shriveling ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value. CLASS 0 - weight 10 normalized weight 0.213 relative strength 6.12e-01 ******* class cross entropy w.r.t. global class 7.66e+00 ******* Model file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 14 24 D SM fruiting bodies of s 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 tem 2 .................. 1.47e+00 9.55e-01 2.19e-01 12 22 D SM stem cankers ....... 1.209 2 .................. -2.82e+00 2.27e-02 3.80e-01 1 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 4 .................. 1.47e+00 9.32e-01 2.14e-01 13 23 D SM canker lesion color 0.750 3 .................. -2.76e+00 2.27e-02 3.59e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. 1.47e+00 3.86e-01 8.85e-02 2 .................. 5.18e-01 5.68e-01 3.39e-01 6 8 D SM damaged area ....... 0.682 4 .................. -1.75e+00 2.27e-02 1.30e-01 3 .................. -1.57e+00 2.27e-02 1.09e-01 1 .................. 1.47e+00 6.59e-01 1.51e-01 2 .................. -7.24e-01 2.95e-01 6.09e-01 19 29 D SM fruit pod condition 0.653 4 .................. -2.53e+00 4.55e-02 5.73e-01 1 .................. 8.04e-01 9.55e-01 4.27e-01 1 3 D SM plant stand ........ 0.567 2 .................. -2.46e+00 4.55e-02 5.31e-01 1 .................. 7.11e-01 9.55e-01 4.69e-01 0 2 D SM time of occurance .. 0.483 1 .................. -2.79e+00 1.30e-02 2.11e-01 3 .................. -2.44e+00 1.30e-02 1.49e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 7 .................. 8.03e-01 2.86e-01 1.28e-01 6 .................. 5.98e-01 1.95e-01 1.07e-01 5 .................. 5.98e-01 1.95e-01 1.07e-01 4 .................. 4.05e-01 2.86e-01 1.90e-01 3 5 D SM temperature ........ 0.460 1 .................. -2.48e+00 3.03e-02 3.61e-01 3 .................. -1.47e+00 3.03e-02 1.32e-01 2 .................. 6.17e-01 9.39e-01 5.07e-01 15 25 D SM outer stem decay ... 0.419 1 .................. -2.29e+00 4.55e-02 4.48e-01 2 .................. 5.48e-01 9.55e-01 5.52e-01 20 36 D SM root condition ..... 0.323 2 .................. -2.14e+00 4.55e-02 3.85e-01 1 .................. 4.40e-01 9.55e-01 6.15e-01 2 4 D SM precipitation ...... 0.191 1 .................. -1.96e+00 3.03e-02 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 3 .................. 3.02e-01 9.39e-01 6.94e-01 17 27 D SM internal discolorati 0.120 3 .................. -1.57e+00 4.55e-02 2.19e-01 on of stem 1 .................. 2.00e-01 9.55e-01 7.81e-01 18 28 D SM scerotia internal or 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 external 1 .................. 2.00e-01 9.55e-01 7.81e-01 5 7 D SM number years crop re 0.106 1 .................. -1.89e+00 2.27e-02 1.51e-01 peated 2 .................. 2.63e-01 3.86e-01 2.97e-01 3 .................. 2.32e-01 2.95e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 10 13 D SM leaf condition ..... 0.099 1 .................. -1.47e+00 4.55e-02 1.98e-01 2 .................. 1.74e-01 9.55e-01 8.02e-01 11 21 D SM stem lodging ....... 0.091 2 .................. 6.26e-01 4.09e-01 2.19e-01 1 .................. -2.79e-01 5.91e-01 7.81e-01 4 6 D SM occurance of hail .. 0.076 2 .................. -7.95e-01 1.36e-01 3.02e-01 1 .................. 2.13e-01 8.64e-01 6.98e-01 9 11 D SM seed germination ... 0.017 1 .................. -2.70e-01 2.12e-01 2.78e-01 3 .................. 1.85e-01 4.85e-01 4.03e-01 2 .................. -5.28e-02 3.03e-01 3.19e-01 DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 7 9 D SM damage severity .... 0.017 3 .................. -2.44e-01 3.18e-01 4.06e-01 2 .................. 1.38e-01 6.82e-01 5.94e-01 16 26 D SM mycelium on stem ... 0.000 2 .................. -1.36e-01 4.55e-02 5.21e-02 1 .................. 6.97e-03 9.55e-01 9.48e-01 8 10 D SM seed treatment ..... 0.000 2 .................. 2.11e-02 5.00e-01 4.90e-01 1 .................. -2.06e-02 5.00e-01 5.10e-01 CLASS 1 - weight 10 normalized weight 0.213 relative strength 6.16e-01 ******* class cross entropy w.r.t. global class 1.16e+01 ******* Model file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 17 27 D SM internal discolorati 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 on of stem 3 .................. 1.47e+00 9.55e-01 2.19e-01 18 28 D SM scerotia internal or 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 external 2 .................. 1.47e+00 9.55e-01 2.19e-01 2 4 D SM precipitation ...... 1.256 3 .................. -3.13e+00 3.03e-02 6.94e-01 1 .................. 1.47e+00 9.39e-01 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 13 23 D SM canker lesion color 1.218 3 .................. -2.76e+00 2.27e-02 3.59e-01 2 .................. -2.70e+00 2.27e-02 3.39e-01 4 .................. 1.47e+00 9.32e-01 2.14e-01 1 .................. -1.36e+00 2.27e-02 8.85e-02 12 22 D SM stem cankers ....... 1.209 2 .................. -2.82e+00 2.27e-02 3.80e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 1 .................. 1.47e+00 9.32e-01 2.14e-01 6 8 D SM damaged area ....... 1.205 2 .................. -3.29e+00 2.27e-02 6.09e-01 1 .................. -1.89e+00 2.27e-02 1.51e-01 3 .................. 1.47e+00 4.77e-01 1.09e-01 4 .................. 1.30e+00 4.77e-01 1.30e-01 3 5 D SM temperature ........ 0.674 1 .................. -2.48e+00 3.03e-02 3.61e-01 3 .................. 1.47e+00 5.76e-01 1.32e-01 2 .................. -2.52e-01 3.94e-01 5.07e-01 19 29 D SM fruit pod condition 0.653 4 .................. -2.53e+00 4.55e-02 5.73e-01 1 .................. 8.04e-01 9.55e-01 4.27e-01 15 25 D SM outer stem decay ... 0.609 2 .................. -2.50e+00 4.55e-02 5.52e-01 1 .................. 7.57e-01 9.55e-01 4.48e-01 1 3 D SM plant stand ........ 0.567 2 .................. -2.46e+00 4.55e-02 5.31e-01 1 .................. 7.11e-01 9.55e-01 4.69e-01 0 2 D SM time of occurance .. 0.535 1 .................. -2.79e+00 1.30e-02 2.11e-01 3 .................. -2.44e+00 1.30e-02 1.49e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 6 .................. 9.81e-01 2.86e-01 1.07e-01 7 .................. 8.03e-01 2.86e-01 1.28e-01 5 .................. 5.98e-01 1.95e-01 1.07e-01 4 .................. 2.25e-02 1.95e-01 1.90e-01 7 9 D SM damage severity .... 0.354 3 .................. -2.19e+00 4.55e-02 4.06e-01 2 .................. 4.75e-01 9.55e-01 5.94e-01 20 36 D SM root condition ..... 0.323 2 .................. -2.14e+00 4.55e-02 3.85e-01 1 .................. 4.40e-01 9.55e-01 6.15e-01 4 6 D SM occurance of hail .. 0.178 2 .................. 6.71e-01 5.91e-01 3.02e-01 1 .................. -5.34e-01 4.09e-01 6.98e-01 14 24 D SM fruiting bodies of s 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 tem 1 .................. 2.00e-01 9.55e-01 7.81e-01 10 13 D SM leaf condition ..... 0.099 1 .................. -1.47e+00 4.55e-02 1.98e-01 2 .................. 1.74e-01 9.55e-01 8.02e-01 9 11 D SM seed germination ... 0.035 1 .................. 3.49e-01 3.94e-01 2.78e-01 3 .................. -2.85e-01 3.03e-01 4.03e-01 2 .................. -5.28e-02 3.03e-01 3.19e-01 5 7 D SM number years crop re 0.011 1 .................. 3.03e-01 2.05e-01 1.51e-01 peated 3 .................. -1.36e-01 2.05e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 2 .................. -4.80e-03 2.95e-01 2.97e-01 DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 16 26 D SM mycelium on stem ... 0.000 2 .................. -1.36e-01 4.55e-02 5.21e-02 1 .................. 6.97e-03 9.55e-01 9.48e-01 8 10 D SM seed treatment ..... 0.000 2 .................. 2.11e-02 5.00e-01 4.90e-01 1 .................. -2.06e-02 5.00e-01 5.10e-01 11 21 D SM stem lodging ....... 0.000 2 .................. 3.82e-02 2.27e-01 2.19e-01 1 .................. -1.10e-02 7.73e-01 7.81e-01 CLASS 2 - weight 10 normalized weight 0.213 relative strength 6.85e-01 ******* class cross entropy w.r.t. global class 6.18e+00 ******* Model file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 10 13 D SM leaf condition ..... 1.031 2 .................. -1.77e+00 1.36e-01 8.02e-01 1 .................. 1.47e+00 8.64e-01 1.98e-01 13 23 D SM canker lesion color 0.799 3 .................. -2.76e+00 2.27e-02 3.59e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -1.36e+00 2.27e-02 8.85e-02 2 .................. 1.01e+00 9.32e-01 3.39e-01 3 5 D SM temperature ........ 0.768 2 .................. -2.82e+00 3.03e-02 5.07e-01 3 .................. -1.47e+00 3.03e-02 1.32e-01 1 .................. 9.56e-01 9.39e-01 3.61e-01 12 22 D SM stem cankers ....... 0.685 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 2 .................. 8.96e-01 9.32e-01 3.80e-01 15 25 D SM outer stem decay ... 0.419 1 .................. -2.29e+00 4.55e-02 4.48e-01 2 .................. 5.48e-01 9.55e-01 5.52e-01 0 2 D SM time of occurance .. 0.407 7 .................. -2.29e+00 1.30e-02 1.28e-01 6 .................. -2.11e+00 1.30e-02 1.07e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 1 .................. 7.94e-01 4.68e-01 2.11e-01 3 .................. 6.52e-01 2.86e-01 1.49e-01 4 .................. -6.06e-01 1.04e-01 1.90e-01 5 .................. -3.08e-02 1.04e-01 1.07e-01 19 29 D SM fruit pod condition 0.385 1 .................. -2.24e+00 4.55e-02 4.27e-01 4 .................. 5.10e-01 9.55e-01 5.73e-01 6 8 D SM damaged area ....... 0.277 1 .................. -1.89e+00 2.27e-02 1.51e-01 4 .................. -1.75e+00 2.27e-02 1.30e-01 3 .................. -1.57e+00 2.27e-02 1.09e-01 2 .................. 4.25e-01 9.32e-01 6.09e-01 9 11 D SM seed germination ... 0.225 1 .................. -2.22e+00 3.03e-02 2.78e-01 2 .................. 4.17e-01 4.85e-01 3.19e-01 3 .................. 1.85e-01 4.85e-01 4.03e-01 2 4 D SM precipitation ...... 0.191 1 .................. -1.96e+00 3.03e-02 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 3 .................. 3.02e-01 9.39e-01 6.94e-01 16 26 D SM mycelium on stem ... 0.177 2 .................. 1.47e+00 2.27e-01 5.21e-02 1 .................. -2.04e-01 7.73e-01 9.48e-01 20 36 D SM root condition ..... 0.152 2 .................. -1.04e+00 1.36e-01 3.85e-01 1 .................. 3.40e-01 8.64e-01 6.15e-01 1 3 D SM plant stand ........ 0.125 1 .................. -7.24e-01 2.27e-01 4.69e-01 2 .................. 3.75e-01 7.73e-01 5.31e-01 14 24 D SM fruiting bodies of s 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 tem 1 .................. 2.00e-01 9.55e-01 7.81e-01 17 27 D SM internal discolorati 0.120 3 .................. -1.57e+00 4.55e-02 2.19e-01 on of stem 1 .................. 2.00e-01 9.55e-01 7.81e-01 18 28 D SM scerotia internal or 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 external 1 .................. 2.00e-01 9.55e-01 7.81e-01 4 6 D SM occurance of hail .. 0.076 2 .................. -7.95e-01 1.36e-01 3.02e-01 1 .................. 2.13e-01 8.64e-01 6.98e-01 5 7 D SM number years crop re 0.073 1 .................. 6.71e-01 2.95e-01 1.51e-01 peated 2 .................. -3.73e-01 2.05e-01 2.97e-01 3 .................. -1.36e-01 2.05e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 7 9 D SM damage severity .... 0.018 3 .................. 2.08e-01 5.00e-01 4.06e-01 2 .................. -1.72e-01 5.00e-01 5.94e-01 8 10 D SM seed treatment ..... 0.013 2 .................. -1.80e-01 4.09e-01 4.90e-01 1 .................. 1.46e-01 5.91e-01 5.10e-01 11 21 D SM stem lodging ....... 0.000 2 .................. 3.82e-02 2.27e-01 2.19e-01 1 .................. -1.10e-02 7.73e-01 7.81e-01 CLASS 3 - weight 10 normalized weight 0.213 relative strength 4.24e-01 ******* class cross entropy w.r.t. global class 5.13e+00 ******* Model file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 20 36 D SM root condition ..... 0.747 1 .................. -2.60e+00 4.55e-02 6.15e-01 2 .................. 9.07e-01 9.55e-01 3.85e-01 13 23 D SM canker lesion color 0.745 2 .................. -2.70e+00 2.27e-02 3.39e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -1.36e+00 2.27e-02 8.85e-02 3 .................. 9.53e-01 9.32e-01 3.59e-01 0 2 D SM time of occurance .. 0.597 4 .................. -2.68e+00 1.31e-02 1.90e-01 7 .................. -2.29e+00 1.30e-02 1.28e-01 6 .................. -2.11e+00 1.30e-02 1.07e-01 5 .................. -2.11e+00 1.30e-02 1.07e-01 3 .................. 9.26e-01 3.76e-01 1.49e-01 1 .................. 7.93e-01 4.67e-01 2.11e-01 2 .................. -1.80e-02 1.05e-01 1.07e-01 1 3 D SM plant stand ........ 0.453 1 .................. -2.33e+00 4.55e-02 4.69e-01 2 .................. 5.86e-01 9.55e-01 5.31e-01 12 22 D SM stem cankers ....... 0.420 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -2.24e+00 2.27e-02 2.14e-01 2 .................. 6.80e-01 7.50e-01 3.80e-01 3 .................. 5.87e-02 2.04e-01 1.93e-01 19 29 D SM fruit pod condition 0.385 1 .................. -2.24e+00 4.55e-02 4.27e-01 4 .................. 5.10e-01 9.55e-01 5.73e-01 2 4 D SM precipitation ...... 0.280 1 .................. -1.96e+00 3.03e-02 2.15e-01 2 .................. 1.21e+00 3.02e-01 9.03e-02 3 .................. -3.99e-02 6.67e-01 6.94e-01 6 8 D SM damaged area ....... 0.277 1 .................. -1.89e+00 2.27e-02 1.51e-01 4 .................. -1.73e+00 2.31e-02 1.30e-01 3 .................. -1.57e+00 2.27e-02 1.09e-01 2 .................. 4.24e-01 9.31e-01 6.09e-01 5 7 D SM number years crop re 0.171 1 .................. -1.89e+00 2.28e-02 1.51e-01 peated 3 .................. -7.27e-01 1.13e-01 2.34e-01 4 .................. 4.07e-01 4.77e-01 3.18e-01 2 .................. 2.64e-01 3.87e-01 2.97e-01 8 10 D SM seed treatment ..... 0.146 2 .................. -7.68e-01 2.27e-01 4.90e-01 1 .................. 4.15e-01 7.73e-01 5.10e-01 3 5 D SM temperature ........ 0.146 3 .................. -1.47e+00 3.03e-02 1.32e-01 1 .................. -5.27e-01 2.13e-01 3.61e-01 2 .................. 4.00e-01 7.56e-01 5.07e-01 9 11 D SM seed germination ... 0.143 2 .................. -9.66e-01 1.22e-01 3.19e-01 1 .................. 5.56e-01 4.84e-01 2.78e-01 3 .................. -2.19e-02 3.94e-01 4.03e-01 14 24 D SM fruiting bodies of s 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 tem 1 .................. 2.00e-01 9.55e-01 7.81e-01 17 27 D SM internal discolorati 0.120 3 .................. -1.57e+00 4.55e-02 2.19e-01 on of stem 1 .................. 2.00e-01 9.55e-01 7.81e-01 18 28 D SM scerotia internal or 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 external 1 .................. 2.00e-01 9.55e-01 7.81e-01 10 13 D SM leaf condition ..... 0.099 1 .................. -1.47e+00 4.55e-02 1.98e-01 2 .................. 1.74e-01 9.55e-01 8.02e-01 4 6 D SM occurance of hail .. 0.084 2 .................. 5.01e-01 4.99e-01 3.02e-01 1 .................. -3.31e-01 5.01e-01 6.98e-01 7 9 D SM damage severity .... 0.068 3 .................. 3.72e-01 5.90e-01 4.06e-01 2 .................. -3.69e-01 4.10e-01 5.94e-01 DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 15 25 D SM outer stem decay ... 0.006 1 .................. 1.12e-01 5.01e-01 4.48e-01 2 .................. -1.02e-01 4.99e-01 5.52e-01 16 26 D SM mycelium on stem ... 0.000 2 .................. -1.36e-01 4.55e-02 5.21e-02 1 .................. 6.95e-03 9.55e-01 9.48e-01 11 21 D SM stem lodging ....... 0.000 2 .................. 3.52e-02 2.27e-01 2.19e-01 1 .................. -1.01e-02 7.73e-01 7.81e-01 CLASS 4 - weight 7 normalized weight 0.149 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 6.31e+00 ******* Model file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 12 22 D SM stem cankers ....... 1.205 2 .................. -2.50e+00 3.13e-02 3.80e-01 1 .................. -1.92e+00 3.12e-02 2.14e-01 4 .................. -1.92e+00 3.12e-02 2.14e-01 3 .................. 1.55e+00 9.06e-01 1.93e-01 0 2 D SM time of occurance .. 0.911 1 .................. -2.42e+00 1.89e-02 2.11e-01 3 .................. -2.05e+00 1.92e-02 1.49e-01 7 .................. -1.97e+00 1.78e-02 1.28e-01 6 .................. -1.79e+00 1.78e-02 1.07e-01 5 .................. -1.79e+00 1.78e-02 1.07e-01 2 .................. 1.57e+00 5.16e-01 1.07e-01 4 .................. 7.23e-01 3.93e-01 1.90e-01 20 36 D SM root condition ..... 0.691 1 .................. -2.29e+00 6.25e-02 6.15e-01 2 .................. 8.89e-01 9.38e-01 3.85e-01 13 23 D SM canker lesion color 0.671 2 .................. -2.38e+00 3.12e-02 3.39e-01 4 .................. -1.92e+00 3.12e-02 2.14e-01 1 .................. -1.04e+00 3.12e-02 8.85e-02 3 .................. 9.25e-01 9.06e-01 3.59e-01 8 10 D SM seed treatment ..... 0.478 1 .................. -2.10e+00 6.26e-02 5.10e-01 2 .................. 6.50e-01 9.37e-01 4.90e-01 1 3 D SM plant stand ........ 0.407 1 .................. -2.02e+00 6.25e-02 4.69e-01 2 .................. 5.68e-01 9.38e-01 5.31e-01 19 29 D SM fruit pod condition 0.342 1 .................. -1.92e+00 6.25e-02 4.27e-01 4 .................. 4.93e-01 9.38e-01 5.73e-01 15 25 D SM outer stem decay ... 0.278 2 .................. -1.07e+00 1.89e-01 5.52e-01 1 .................. 5.93e-01 8.11e-01 4.48e-01 3 5 D SM temperature ........ 0.198 3 .................. -1.15e+00 4.16e-02 1.32e-01 1 .................. 6.11e-01 6.65e-01 3.61e-01 2 .................. -5.47e-01 2.93e-01 5.07e-01 5 7 D SM number years crop re 0.187 4 .................. -7.10e-01 1.56e-01 3.18e-01 peated 2 .................. -6.43e-01 1.56e-01 2.97e-01 1 .................. 6.21e-01 2.81e-01 1.51e-01 3 .................. 5.51e-01 4.07e-01 2.34e-01 7 9 D SM damage severity .... 0.163 2 .................. -6.48e-01 3.11e-01 5.94e-01 3 .................. 5.29e-01 6.89e-01 4.06e-01 2 4 D SM precipitation ...... 0.138 1 .................. -1.64e+00 4.16e-02 2.15e-01 2 .................. 6.19e-01 1.68e-01 9.03e-02 3 .................. 1.30e-01 7.91e-01 6.94e-01 6 8 D SM damaged area ....... 0.134 1 .................. -1.58e+00 3.12e-02 1.51e-01 3 .................. -1.25e+00 3.12e-02 1.09e-01 2 .................. 2.49e-01 7.82e-01 6.09e-01 4 .................. 1.79e-01 1.56e-01 1.30e-01 14 24 D SM fruiting bodies of s 0.093 2 .................. -1.25e+00 6.25e-02 2.19e-01 tem 1 .................. 1.82e-01 9.38e-01 7.81e-01 17 27 D SM internal discolorati 0.093 3 .................. -1.25e+00 6.25e-02 2.19e-01 on of stem 1 .................. 1.82e-01 9.38e-01 7.81e-01 18 28 D SM scerotia internal or 0.093 2 .................. -1.25e+00 6.25e-02 2.19e-01 external 1 .................. 1.82e-01 9.38e-01 7.81e-01 11 21 D SM stem lodging ....... 0.091 2 .................. -1.24e+00 6.35e-02 2.19e-01 1 .................. 1.81e-01 9.36e-01 7.81e-01 10 13 D SM leaf condition ..... 0.074 1 .................. -1.15e+00 6.25e-02 1.98e-01 2 .................. 1.56e-01 9.38e-01 8.02e-01 DISCRETE ATTRIBUTE: (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 4 6 D SM occurance of hail .. 0.033 2 .................. -4.67e-01 1.89e-01 3.02e-01 1 .................. 1.50e-01 8.11e-01 6.98e-01 9 11 D SM seed germination ... 0.031 3 .................. -3.23e-01 2.92e-01 4.03e-01 2 .................. 2.64e-01 4.16e-01 3.19e-01 1 .................. 5.14e-02 2.92e-01 2.78e-01 16 26 D SM mycelium on stem ... 0.001 2 .................. 1.82e-01 6.25e-02 5.21e-02 1 .................. -1.10e-02 9.38e-01 9.48e-01 autoclass-3.3.6.dfsg.1/data/soybean/soyc.case-text-20000644000175000017500000000371611247310756020223 0ustar areare CROSS REFERENCE: CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 47 cases in: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.db2 /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -660.007 from classification results file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.results-bin and using models: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 0 1.00 17 1 1.00 33 4 0.99 2 0 1.00 18 1 1.00 34 3 1.00 3 0 1.00 19 1 1.00 35 4 0.99 4 0 1.00 20 1 1.00 36 4 0.99 5 0 1.00 21 2 1.00 37 3 0.99 6 0 1.00 22 2 1.00 38 3 0.99 7 0 1.00 23 2 1.00 39 4 0.99 8 0 1.00 24 2 1.00 40 4 0.99 9 0 1.00 25 2 1.00 41 3 1.00 10 0 1.00 26 2 1.00 42 3 0.99 11 1 1.00 27 2 0.99 43 4 0.99 12 1 1.00 28 2 0.99 44 4 0.99 13 1 1.00 29 2 1.00 45 3 0.99 14 1 1.00 30 2 1.00 46 3 0.99 15 1 1.00 31 3 0.99 47 3 0.99 16 1 1.00 32 3 1.00 autoclass-3.3.6.dfsg.1/data/soybean/soyc-predict.class-text-10000644000175000017500000000344211247310756022040 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 4 "TEST" cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2 based on the "TRAINING" classification of 47 cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -645.604 from classification results file /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 CLASS = 0 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 4 Phytophthora_Rot 1 2 1.000 CLASS = 1 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 1 Diaporthe_Stem_Canker 7 1 1.000 CLASS = 2 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 2 Charcoal_Rot 7 1 1.000 CLASS = 3 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 3 Rhizoctonia_Root_Rot 1 2 1.000autoclass-3.3.6.dfsg.1/data/soybean/soyc.case-text-10000644000175000017500000000363711247310756020224 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 47 cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -645.604 from classification results file /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 1 1.000 17 2 1.000 33 0 1.000 2 1 1.000 18 2 1.000 34 0 0.999 3 1 1.000 19 2 1.000 35 0 1.000 4 1 1.000 20 2 1.000 36 0 1.000 5 1 1.000 21 3 1.000 37 0 1.000 6 1 1.000 22 3 1.000 38 0 1.000 7 1 1.000 23 3 1.000 39 0 1.000 8 1 1.000 24 3 1.000 40 0 1.000 9 1 1.000 25 3 1.000 41 0 1.000 10 1 1.000 26 3 1.000 42 0 1.000 11 2 1.000 27 3 1.000 43 0 1.000 12 2 1.000 28 3 0.999 44 0 1.000 13 2 1.000 29 3 1.000 45 0 0.999 14 2 1.000 30 3 1.000 46 0 1.000 15 2 1.000 31 0 1.000 47 0 1.000 16 2 1.000 32 0 1.000 autoclass-3.3.6.dfsg.1/data/soybean/soyc.class-text-20000644000175000017500000001311211247310756020404 0ustar areare CROSS REFERENCE: CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 47 cases in: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.db2 /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -660.007 from classification results file: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.results-bin and using models: /home/copernicus/id2/taylor/autoclass-c/data/soybean/soyc.model - index = 0 CLASS = 0 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 1 Diaporthe_Stem_Canker 5 1 1.00 2 Diaporthe_Stem_Canker 6 1 1.00 3 Diaporthe_Stem_Canker 4 1 1.00 4 Diaporthe_Stem_Canker 7 1 1.00 5 Diaporthe_Stem_Canker 5 1 1.00 6 Diaporthe_Stem_Canker 6 1 1.00 7 Diaporthe_Stem_Canker 4 1 1.00 8 Diaporthe_Stem_Canker 4 1 1.00 9 Diaporthe_Stem_Canker 7 1 1.00 10 Diaporthe_Stem_Canker 7 1 1.00 CLASS = 1 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 11 Charcoal_Rot 7 1 1.00 12 Charcoal_Rot 5 1 1.00 13 Charcoal_Rot 6 1 1.00 14 Charcoal_Rot 7 1 1.00 15 Charcoal_Rot 4 1 1.00 16 Charcoal_Rot 5 1 1.00 17 Charcoal_Rot 4 1 1.00 18 Charcoal_Rot 6 1 1.00 19 Charcoal_Rot 7 1 1.00 20 Charcoal_Rot 6 1 1.00 CLASS = 2 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 21 Rhizoctonia_Root_Rot 1 2 1.00 22 Rhizoctonia_Root_Rot 3 2 1.00 23 Rhizoctonia_Root_Rot 3 2 1.00 24 Rhizoctonia_Root_Rot 1 2 1.00 25 Rhizoctonia_Root_Rot 1 2 1.00 CLASS = 2 (continued) Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 26 Rhizoctonia_Root_Rot 5 1 1.00 27 Rhizoctonia_Root_Rot 3 2 1.00 28 Rhizoctonia_Root_Rot 1 2 1.00 29 Rhizoctonia_Root_Rot 4 1 1.00 30 Rhizoctonia_Root_Rot 1 2 1.00 CLASS = 3 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 31 Phytophthora_Rot 3 2 0.99 4 0.01 32 Phytophthora_Rot 1 2 1.00 34 Phytophthora_Rot 3 2 1.00 37 Phytophthora_Rot 1 2 1.00 38 Phytophthora_Rot 3 2 1.00 41 Phytophthora_Rot 1 2 1.00 42 Phytophthora_Rot 2 2 1.00 45 Phytophthora_Rot 3 2 1.00 46 Phytophthora_Rot 1 2 0.99 47 Phytophthora_Rot 1 2 1.00 CLASS = 4 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 33 Phytophthora_Rot 4 2 1.00 35 Phytophthora_Rot 2 2 0.99 3 0.01 36 Phytophthora_Rot 2 2 1.00 39 Phytophthora_Rot 4 2 1.00 40 Phytophthora_Rot 4 2 1.00 43 Phytophthora_Rot 2 2 1.00 44 Phytophthora_Rot 2 2 1.00autoclass-3.3.6.dfsg.1/data/soybean/soyc-predict.db20000644000175000017500000000167611247310756020271 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; Soybean Case Histories ;;; from UC Irvine Technical Report 87-22 ;;; Fisher & Kibler PhD Dissertation - Knowledge Acquisition ;;; Via Incremental Conceptual Clustering ;;; ; 4 Data, 37 attributes for test database 10,Diaporthe_Stem_Canker,7,1,3,2,1,2,1,2,1,3,2,2,1,3,3,1,1,1,2,1,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 11,Charcoal_Rot,7,1,1,3,2,1,3,2,1,1,2,2,1,3,3,1,1,1,2,2,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 21,Rhizoctonia_Root_Rot,1,2,3,1,1,2,2,2,2,2,2,1,1,3,3,1,1,1,2,1,2,2,1,2,2,1,1,4,5,1,1,1,1,1,1 47,Phytophthora_Rot,1,2,3,2,1,4,2,2,1,3,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 autoclass-3.3.6.dfsg.1/data/soybean/soyc.s-params0000644000175000017500000002236711247310756017715 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/soybean/semantic.cache0000644000175000017500000000070711247310756020055 0ustar areare;; Object soybean/ ;; SEMANTICDB Tags save file (semanticdb-project-database-file "soybean/" :tables (list (semanticdb-table "soyc.model" :major-mode 'lisp-mode :tags 'nil :file "soyc.model" :pointmax 666 ) (semanticdb-table "soyc.hd2" :major-mode 'lisp-mode :tags 'nil :file "soyc.hd2" :pointmax 2445 ) ) :file "semantic.cache" :semantic-tag-version "2.0beta3" :semanticdb-version "2.0beta3" ) autoclass-3.3.6.dfsg.1/data/soybean/soyc-predict.case-text-10000644000175000017500000000163311247310756021646 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 4 "TEST" cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2 based on the "TRAINING" classification of 47 cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -645.604 from classification results file /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 1 1.000 3 3 1.000 2 2 1.000 4 0 1.000 autoclass-3.3.6.dfsg.1/data/soybean/soyc.class-text-10000644000175000017500000001167311247310756020415 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 47 cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -645.604 from classification results file /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 CLASS = 0 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 31 Phytophthora_Rot 3 2 1.000 32 Phytophthora_Rot 1 2 1.000 33 Phytophthora_Rot 4 2 1.000 34 Phytophthora_Rot 3 2 1.000 35 Phytophthora_Rot 2 2 1.000 36 Phytophthora_Rot 2 2 1.000 37 Phytophthora_Rot 1 2 1.000 38 Phytophthora_Rot 3 2 1.000 39 Phytophthora_Rot 4 2 1.000 40 Phytophthora_Rot 4 2 1.000 41 Phytophthora_Rot 1 2 1.000 42 Phytophthora_Rot 2 2 1.000 43 Phytophthora_Rot 2 2 1.000 44 Phytophthora_Rot 2 2 1.000 45 Phytophthora_Rot 3 2 1.000 46 Phytophthora_Rot 1 2 1.000 47 Phytophthora_Rot 1 2 1.000 CLASS = 1 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 1 Diaporthe_Stem_Canker 5 1 1.000 2 Diaporthe_Stem_Canker 6 1 1.000 3 Diaporthe_Stem_Canker 4 1 1.000 4 Diaporthe_Stem_Canker 7 1 1.000 5 Diaporthe_Stem_Canker 5 1 1.000 6 Diaporthe_Stem_Canker 6 1 1.000 7 Diaporthe_Stem_Canker 4 1 1.000 8 Diaporthe_Stem_Canker 4 1 1.000 9 Diaporthe_Stem_Canker 7 1 1.000 10 Diaporthe_Stem_Canker 7 1 1.000 CLASS = 2 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 11 Charcoal_Rot 7 1 1.000 12 Charcoal_Rot 5 1 1.000 13 Charcoal_Rot 6 1 1.000 14 Charcoal_Rot 7 1 1.000 15 Charcoal_Rot 4 1 1.000 16 Charcoal_Rot 5 1 1.000 17 Charcoal_Rot 4 1 1.000 18 Charcoal_Rot 6 1 1.000 19 Charcoal_Rot 7 1 1.000 20 Charcoal_Rot 6 1 1.000 CLASS = 3 Case # diagnosis time of occurance plant stand (Cls Prob) ------------------------------------------------------------------------------------------ 21 Rhizoctonia_Root_Rot 1 2 1.000 22 Rhizoctonia_Root_Rot 3 2 1.000 23 Rhizoctonia_Root_Rot 3 2 1.000 24 Rhizoctonia_Root_Rot 1 2 1.000 25 Rhizoctonia_Root_Rot 1 2 1.000 26 Rhizoctonia_Root_Rot 5 1 1.000 27 Rhizoctonia_Root_Rot 3 2 1.000 28 Rhizoctonia_Root_Rot 1 2 1.000 29 Rhizoctonia_Root_Rot 4 1 1.000 30 Rhizoctonia_Root_Rot 1 2 1.000autoclass-3.3.6.dfsg.1/data/soybean/soyc.results0000644000175000017500000036675311247310756017705 0ustar areare# ordered sequence of clsf_DS's: 0 -> 1 # clsf_DS 0: log_a_x_h = -6.4560419e+02 # clsf_DS 1: log_a_x_h = -6.6000694e+02 ac_version 3.3.5unx clsf_DS 0 log_p_x_h_pi_theta, log_a_x_h -4.5590982e+02 -6.4560419e+02 database_DS data_file, header_file data/soybean/soyc.db2 data/soybean/soyc.hd2 n_data, n_atts, input_n_atts 47 37 37 att_DS 0 type, subtype, dscrp dummy nil "case number" dummy_stats_DS 0 att_DS 1 type, subtype, dscrp discrete nominal "diagnosis" discrete_stats_DS range, n_observed 4 4 0 10 1 10 2 10 3 17 n_props, range, zero_point, n_trans 0 4 0.000000 4 translations_DS 0 Diaporthe_Stem_Canker 1 Charcoal_Rot 2 Rhizoctonia_Root_Rot 3 Phytophthora_Rot props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 2 type, subtype, dscrp discrete nominal "time of occurance" discrete_stats_DS range, n_observed 7 7 0 5 1 5 2 9 3 6 4 10 5 7 6 5 n_props, range, zero_point, n_trans 0 7 0.000000 7 translations_DS 0 5 1 6 2 4 3 7 4 1 5 3 6 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 3 type, subtype, dscrp discrete nominal "plant stand" discrete_stats_DS range, n_observed 2 2 0 22 1 25 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 4 type, subtype, dscrp discrete nominal "precipitation" discrete_stats_DS range, n_observed 3 3 0 33 1 10 2 4 n_props, range, zero_point, n_trans 0 3 0.000000 3 translations_DS 0 3 1 1 2 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 5 type, subtype, dscrp discrete nominal "temperature" discrete_stats_DS range, n_observed 3 3 0 24 1 6 2 17 n_props, range, zero_point, n_trans 0 3 0.000000 3 translations_DS 0 2 1 3 2 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 6 type, subtype, dscrp discrete nominal "occurance of hail" discrete_stats_DS range, n_observed 2 2 0 14 1 33 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 2 1 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 7 type, subtype, dscrp discrete nominal "number years crop repeated" discrete_stats_DS range, n_observed 4 4 0 14 1 15 2 11 3 7 n_props, range, zero_point, n_trans 0 4 0.000000 4 translations_DS 0 2 1 4 2 3 3 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 8 type, subtype, dscrp discrete nominal "damaged area" discrete_stats_DS range, n_observed 4 4 0 7 1 29 2 5 3 6 n_props, range, zero_point, n_trans 0 4 0.000000 4 translations_DS 0 1 1 2 2 3 3 4 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 9 type, subtype, dscrp discrete nominal "damage severity" discrete_stats_DS range, n_observed 2 2 0 28 1 19 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 2 1 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 10 type, subtype, dscrp discrete nominal "seed treatment" discrete_stats_DS range, n_observed 2 2 0 24 1 23 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 11 type, subtype, dscrp discrete nominal "seed germination" discrete_stats_DS range, n_observed 3 3 0 19 1 15 2 13 n_props, range, zero_point, n_trans 0 3 0.000000 3 translations_DS 0 3 1 2 2 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 12 type, subtype, dscrp discrete nominal "plant growth" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 13 type, subtype, dscrp discrete nominal "leaf condition" discrete_stats_DS range, n_observed 2 2 0 38 1 9 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 2 1 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 14 type, subtype, dscrp discrete nominal "leaf spot halos" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 15 type, subtype, dscrp discrete nominal "leaf spot margin" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 16 type, subtype, dscrp discrete nominal "size of leaf spots" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 17 type, subtype, dscrp discrete nominal "slot holing" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 18 type, subtype, dscrp discrete nominal "leaf malformation" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 19 type, subtype, dscrp discrete nominal "leaf mildew growth" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 20 type, subtype, dscrp discrete nominal "condition of stem" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 21 type, subtype, dscrp discrete nominal "stem lodging" discrete_stats_DS range, n_observed 2 2 0 37 1 10 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 22 type, subtype, dscrp discrete nominal "stem cankers" discrete_stats_DS range, n_observed 4 4 0 10 1 10 2 18 3 9 n_props, range, zero_point, n_trans 0 4 0.000000 4 translations_DS 0 4 1 1 2 2 3 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 23 type, subtype, dscrp discrete nominal "canker lesion color" discrete_stats_DS range, n_observed 4 4 0 16 1 4 2 10 3 17 n_props, range, zero_point, n_trans 0 4 0.000000 4 translations_DS 0 2 1 1 2 4 3 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 24 type, subtype, dscrp discrete nominal "fruiting bodies of stem" discrete_stats_DS range, n_observed 2 2 0 10 1 37 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 2 1 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 25 type, subtype, dscrp discrete nominal "outer stem decay" discrete_stats_DS range, n_observed 2 2 0 26 1 21 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 2 1 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 26 type, subtype, dscrp discrete nominal "mycelium on stem" discrete_stats_DS range, n_observed 2 2 0 45 1 2 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 27 type, subtype, dscrp discrete nominal "internal discoloration of stem" discrete_stats_DS range, n_observed 2 2 0 37 1 10 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 3 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 28 type, subtype, dscrp discrete nominal "scerotia internal or external" discrete_stats_DS range, n_observed 2 2 0 37 1 10 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 29 type, subtype, dscrp discrete nominal "fruit pod condition" discrete_stats_DS range, n_observed 2 2 0 20 1 27 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 4 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 30 type, subtype, dscrp discrete nominal "fruit spots" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 5 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 31 type, subtype, dscrp discrete nominal "seed condition" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 32 type, subtype, dscrp discrete nominal "seed mold growth" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 33 type, subtype, dscrp discrete nominal "seed discoloration" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 34 type, subtype, dscrp discrete nominal "seed size" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 35 type, subtype, dscrp discrete nominal "seed shriveling" discrete_stats_DS range, n_observed 1 1 0 47 n_props, range, zero_point, n_trans 0 1 0.000000 1 translations_DS 0 1 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL true 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 att_DS 36 type, subtype, dscrp discrete nominal "root condition" discrete_stats_DS range, n_observed 2 2 0 29 1 18 n_props, range, zero_point, n_trans 0 2 0.000000 2 translations_DS 0 1 1 2 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 0.0000000e+00 0 num_models 1 model_DS 0 id, file_index MODEL-0 0 model_file data/soybean/soyc.model data_file, header_file, n_data data/soybean/soyc.db2 data/soybean/soyc.hd2 47 n_classes 4 class_DS 0 w_j, pi_j 1.0000000e+01 2.1354167e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5439233e+00 -9.3927383e+01 -1.2284684e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 2.0000000e+00 2.0000000e+00 3.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 1.9480520e-01 1.9480520e-01 2.8571430e-01 2.8571430e-01 1.2987014e-02 1.2987014e-02 1.2987014e-02 val_log_probs -1.6357552e+00 -1.6357552e+00 -1.2527629e+00 -1.2527629e+00 -4.3438053e+00 -4.3438053e+00 -4.3438053e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 9.3939400e-01 3.0303031e-02 3.0303031e-02 val_log_probs -6.2520325e-02 -3.4965074e+00 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 9.3939400e-01 3.0303031e-02 3.0303031e-02 val_log_probs -6.2520325e-02 -3.4965074e+00 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+00 9.0000000e+00 val_probs 1.3636364e-01 8.6363637e-01 val_log_probs -1.9924301e+00 -1.4660345e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 4.0000000e+00 3.0000000e+00 3.0000000e+00 0.0000000e+00 val_probs 3.8636366e-01 2.9545456e-01 2.9545456e-01 2.2727273e-02 val_log_probs -9.5097625e-01 -1.2192402e+00 -1.2192402e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 7.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 6.5909094e-01 2.9545456e-01 2.2727273e-02 2.2727273e-02 val_log_probs -4.1689378e-01 -1.2192402e+00 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0000000e+00 3.0000000e+00 val_probs 6.8181819e-01 3.1818181e-01 val_log_probs -3.8299221e-01 -1.1451323e+00 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.0000000e+00 5.0000000e+00 val_probs 5.0000000e-01 5.0000000e-01 val_log_probs -6.9314712e-01 -6.9314712e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 5.0000000e+00 3.0000000e+00 2.0000000e+00 val_probs 4.8484850e-01 3.0303031e-01 2.1212122e-01 val_log_probs -7.2391880e-01 -1.1939224e+00 -1.5505974e+00 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000000e+00 4.0000000e+00 val_probs 5.9090912e-01 4.0909094e-01 val_log_probs -5.2609307e-01 -8.9381784e-01 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 9.3181819e-01 2.2727273e-02 2.2727273e-02 2.2727273e-02 val_log_probs -7.0617534e-02 -3.7841897e+00 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 6.0000000e+00 4.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 5.6818181e-01 3.8636366e-01 2.2727273e-02 2.2727273e-02 val_log_probs -5.6531376e-01 -9.5097625e-01 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 1 w_j, pi_j 1.0000000e+01 2.1354167e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5439233e+00 -9.3865911e+01 -1.2238210e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 2.0000000e+00 3.0000000e+00 2.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 1.9480520e-01 2.8571430e-01 1.9480520e-01 2.8571430e-01 1.2987014e-02 1.2987014e-02 1.2987014e-02 val_log_probs -1.6357552e+00 -1.2527629e+00 -1.6357552e+00 -1.2527629e+00 -4.3438053e+00 -4.3438053e+00 -4.3438053e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 0.0000000e+00 1.0000000e+01 0.0000000e+00 val_probs 3.0303031e-02 9.3939400e-01 3.0303031e-02 val_log_probs -3.4965074e+00 -6.2520325e-02 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 4.0000000e+00 6.0000000e+00 0.0000000e+00 val_probs 3.9393941e-01 5.7575762e-01 3.0303031e-02 val_log_probs -9.3155819e-01 -5.5206853e-01 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000000e+00 4.0000000e+00 val_probs 5.9090912e-01 4.0909094e-01 val_log_probs -5.2609307e-01 -8.9381784e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 3.0000000e+00 3.0000000e+00 2.0000000e+00 2.0000000e+00 val_probs 2.9545456e-01 2.9545456e-01 2.0454547e-01 2.0454547e-01 val_log_probs -1.2192402e+00 -1.2192402e+00 -1.5869651e+00 -1.5869651e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 5.0000000e+00 5.0000000e+00 val_probs 2.2727273e-02 2.2727273e-02 4.7727275e-01 4.7727275e-01 val_log_probs -3.7841897e+00 -3.7841897e+00 -7.3966718e-01 -7.3966718e-01 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.0000000e+00 5.0000000e+00 val_probs 5.0000000e-01 5.0000000e-01 val_log_probs -6.9314712e-01 -6.9314712e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 3.0000000e+00 3.0000000e+00 4.0000000e+00 val_probs 3.0303031e-01 3.0303031e-01 3.9393941e-01 val_log_probs -1.1939224e+00 -1.1939224e+00 -9.3155819e-01 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 8.0000000e+00 2.0000000e+00 val_probs 7.7272731e-01 2.2727273e-01 val_log_probs -2.5782907e-01 -1.4816045e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 2.2727273e-02 9.3181819e-01 2.2727273e-02 2.2727273e-02 val_log_probs -3.7841897e+00 -7.0617534e-02 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 1.0000000e+01 0.0000000e+00 val_probs 2.2727273e-02 2.2727273e-02 9.3181819e-01 2.2727273e-02 val_log_probs -3.7841897e+00 -3.7841897e+00 -7.0617534e-02 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 2 w_j, pi_j 9.9999990e+00 2.1354164e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5439234e+00 -9.2802030e+01 -1.2362161e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 1.0000000e+00 0.0000000e+00 1.0000000e+00 0.0000000e+00 4.9999628e+00 3.0000362e+00 0.0000000e+00 val_probs 1.0389612e-01 1.2987015e-02 1.0389612e-01 1.2987015e-02 4.6752915e-01 2.8571761e-01 1.2987015e-02 val_log_probs -2.2643638e+00 -4.3438053e+00 -2.2643638e+00 -4.3438053e+00 -7.6029360e-01 -1.2527514e+00 -4.3438053e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 2.0000000e+00 7.9999995e+00 val_probs 2.2727275e-01 7.7272731e-01 val_log_probs -1.4816045e+00 -2.5782904e-01 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 9.9999876e+00 0.0000000e+00 1.2196219e-05 val_probs 9.3939292e-01 3.0303035e-02 3.0304143e-02 val_log_probs -6.2521443e-02 -3.4965074e+00 -3.4964709e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 2.4063527e-05 0.0000000e+00 9.9999762e+00 val_probs 3.0305222e-02 3.0303035e-02 9.3939185e-01 val_log_probs -3.4964352e+00 -3.4965074e+00 -6.2522553e-02 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000241e+00 8.9999762e+00 val_probs 1.3636585e-01 8.6363429e-01 val_log_probs -1.9924140e+00 -1.4660588e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 2.0000241e+00 3.0000122e+00 2.0000000e+00 2.9999630e+00 val_probs 2.0454767e-01 2.9545569e-01 2.0454548e-01 2.9545122e-01 val_log_probs -1.5869542e+00 -1.2192364e+00 -1.5869650e+00 -1.2192515e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 2.2727275e-02 9.3181831e-01 2.2727275e-02 2.2727275e-02 val_log_probs -3.7841895e+00 -7.0617452e-02 -3.7841895e+00 -3.7841895e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 4.9999633e+00 5.0000362e+00 val_probs 4.9999672e-01 5.0000334e-01 val_log_probs -6.9315374e-01 -6.9314051e-01 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.9999995e+00 4.0000000e+00 val_probs 5.9090912e-01 4.0909097e-01 val_log_probs -5.2609307e-01 -8.9381778e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 5.0000362e+00 4.9999628e+00 0.0000000e+00 val_probs 4.8485184e-01 4.8484516e-01 3.0303035e-02 val_log_probs -7.2391194e-01 -7.2392571e-01 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000362e+00 8.9999638e+00 val_probs 1.3636695e-01 8.6363316e-01 val_log_probs -1.9924059e+00 -1.4660718e-01 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.9999995e+00 2.0000000e+00 val_probs 7.7272731e-01 2.2727275e-01 val_log_probs -2.5782904e-01 -1.4816045e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 1.0000000e+01 0.0000000e+00 val_probs 2.2727275e-02 2.2727275e-02 9.3181831e-01 2.2727275e-02 val_log_probs -3.7841895e+00 -3.7841895e+00 -7.0617452e-02 -3.7841895e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 9.9999638e+00 0.0000000e+00 0.0000000e+00 3.6259746e-05 val_probs 9.3181497e-01 2.2727275e-02 2.2727275e-02 2.2730572e-02 val_log_probs -7.0620991e-02 -3.7841895e+00 -3.7841895e+00 -3.7840445e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454551e-02 9.5454556e-01 val_log_probs -3.0910423e+00 -4.6519905e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9999876e+00 1.2196219e-05 val_probs 9.5454443e-01 4.5455661e-02 val_log_probs -4.6521086e-02 -3.0910180e+00 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.9999995e+00 2.0000000e+00 val_probs 7.7272731e-01 2.2727275e-01 val_log_probs -2.5782904e-01 -1.4816045e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454556e-01 4.5454551e-02 val_log_probs -4.6519905e-02 -3.0910423e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454556e-01 4.5454551e-02 val_log_probs -4.6519905e-02 -3.0910423e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454551e-02 9.5454556e-01 val_log_probs -3.0910423e+00 -4.6519905e-02 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.0000000e+00 9.9999940e-01 val_probs 8.6363643e-01 1.3636360e-01 val_log_probs -1.4660336e-01 -1.9924304e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999990e+00 0.0000000e+00 9.9999990e+00 9.0909101e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 3 w_j, pi_j 1.7000000e+01 3.5937500e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.0233889e+00 -1.7531534e+02 -2.1159652e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 0.0000000e+00 0.0000000e+00 3.0000000e+00 0.0000000e+00 5.0000372e+00 3.9999638e+00 5.0000000e+00 val_probs 7.9365084e-03 7.9365084e-03 1.7460318e-01 7.9365084e-03 2.8571635e-01 2.3015672e-01 2.8571430e-01 val_log_probs -4.8362818e+00 -4.8362818e+00 -1.7452395e+00 -4.8362818e+00 -1.2527558e+00 -1.4689949e+00 -1.2527629e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.7000000e+01 val_probs 2.7777778e-02 9.7222221e-01 val_log_probs -3.5835190e+00 -2.8170869e-02 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 1.3000013e+01 0.0000000e+00 3.9999878e+00 val_probs 7.4074149e-01 1.8518519e-02 2.4074006e-01 val_log_probs -3.0010357e-01 -3.9889841e+00 -1.4240375e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 9.9999762e+00 0.0000000e+00 7.0000248e+00 val_probs 5.7407278e-01 1.8518519e-02 4.0740877e-01 val_log_probs -5.5499911e-01 -3.9889841e+00 -8.9793819e-01 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.9999762e+00 1.1000025e+01 val_probs 3.6110979e-01 6.3889027e-01 val_log_probs -1.0185733e+00 -4.4802254e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 4.9999762e+00 5.9999876e+00 4.0000000e+00 2.0000370e+00 val_probs 2.9166535e-01 3.4722152e-01 2.3611112e-01 1.2500206e-01 val_log_probs -1.2321482e+00 -1.0577923e+00 -1.4434527e+00 -2.0794251e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 1.6000000e+01 0.0000000e+00 1.0000000e+00 val_probs 1.3888889e-02 9.0277779e-01 1.3888889e-02 6.9444448e-02 val_log_probs -4.2766662e+00 -1.0227884e-01 -4.2766662e+00 -2.6672282e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000372e+00 1.0999964e+01 val_probs 3.6111319e-01 6.3888687e-01 val_log_probs -1.0185639e+00 -4.4802788e-01 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 8.0000000e+00 9.0000000e+00 val_probs 4.7222224e-01 5.2777779e-01 val_log_probs -7.5030559e-01 -6.3907993e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 5.9999638e+00 4.0000372e+00 7.0000000e+00 val_probs 3.5184985e-01 2.4074280e-01 4.0740740e-01 val_log_probs -1.0445508e+00 -1.4240261e+00 -8.9794159e-01 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.6999964e+01 3.6911388e-05 val_probs 9.7222024e-01 2.7779829e-02 val_log_probs -2.8172940e-02 -3.5834451e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.5000001e+01 2.0000000e+00 val_probs 8.6111116e-01 1.3888890e-01 val_log_probs -1.4953166e-01 -1.9740810e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 8.0000000e+00 9.0000000e+00 val_probs 1.3888889e-02 1.3888889e-02 4.5833334e-01 5.1388890e-01 val_log_probs -4.2766662e+00 -4.2766662e+00 -7.8015858e-01 -6.6574818e-01 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 3.6911388e-05 0.0000000e+00 0.0000000e+00 1.6999964e+01 val_probs 1.3890940e-02 1.3888889e-02 1.3888889e-02 9.5833135e-01 val_log_probs -4.2765183e+00 -4.2766662e+00 -4.2766662e+00 -4.2561706e-02 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.7000000e+01 val_probs 2.7777778e-02 9.7222221e-01 val_log_probs -3.5835190e+00 -2.8170869e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000129e+00 1.0999988e+01 val_probs 3.6111182e-01 6.3888818e-01 val_log_probs -1.0185676e+00 -4.4802579e-01 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.7000000e+01 0.0000000e+00 val_probs 9.7222221e-01 2.7777778e-02 val_log_probs -2.8170869e-02 -3.5835190e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.7000000e+01 0.0000000e+00 val_probs 9.7222221e-01 2.7777778e-02 val_log_probs -2.8170869e-02 -3.5835190e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.7000000e+01 0.0000000e+00 val_probs 9.7222221e-01 2.7777778e-02 val_log_probs -2.8170869e-02 -3.5835190e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.7000000e+01 val_probs 2.7777778e-02 9.7222221e-01 val_log_probs -3.5835190e+00 -2.8170869e-02 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.7000000e+01 val_probs 2.7777778e-02 9.7222221e-01 val_log_probs -3.5835190e+00 -2.8170869e-02 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 1.7000000e+01 0.0000000e+00 1.7000000e+01 5.5555556e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 min_class_wt 2.0999999 chkpt_DS accumulated_try_time, current_try_j_in, current_cycle 0 0 0 clsf_DS 1 log_p_x_h_pi_theta, log_a_x_h -4.4051856e+02 -6.6000694e+02 database_DS_ptr num_models 1 model_DS_ptr 0 n_classes 5 class_DS 0 w_j, pi_j 1.0000000e+01 2.1250001e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5488132e+00 -9.3976283e+01 -1.2284684e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 2.0000000e+00 2.0000000e+00 3.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 1.9480520e-01 1.9480520e-01 2.8571430e-01 2.8571430e-01 1.2987014e-02 1.2987014e-02 1.2987014e-02 val_log_probs -1.6357552e+00 -1.6357552e+00 -1.2527629e+00 -1.2527629e+00 -4.3438053e+00 -4.3438053e+00 -4.3438053e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 9.3939400e-01 3.0303031e-02 3.0303031e-02 val_log_probs -6.2520325e-02 -3.4965074e+00 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 9.3939400e-01 3.0303031e-02 3.0303031e-02 val_log_probs -6.2520325e-02 -3.4965074e+00 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+00 9.0000000e+00 val_probs 1.3636364e-01 8.6363637e-01 val_log_probs -1.9924301e+00 -1.4660345e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 4.0000000e+00 3.0000000e+00 3.0000000e+00 0.0000000e+00 val_probs 3.8636366e-01 2.9545456e-01 2.9545456e-01 2.2727273e-02 val_log_probs -9.5097625e-01 -1.2192402e+00 -1.2192402e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 7.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 6.5909094e-01 2.9545456e-01 2.2727273e-02 2.2727273e-02 val_log_probs -4.1689378e-01 -1.2192402e+00 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0000000e+00 3.0000000e+00 val_probs 6.8181819e-01 3.1818181e-01 val_log_probs -3.8299221e-01 -1.1451323e+00 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.0000000e+00 5.0000000e+00 val_probs 5.0000000e-01 5.0000000e-01 val_log_probs -6.9314712e-01 -6.9314712e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 5.0000000e+00 3.0000000e+00 2.0000000e+00 val_probs 4.8484850e-01 3.0303031e-01 2.1212122e-01 val_log_probs -7.2391880e-01 -1.1939224e+00 -1.5505974e+00 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000000e+00 4.0000000e+00 val_probs 5.9090912e-01 4.0909094e-01 val_log_probs -5.2609307e-01 -8.9381784e-01 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 1.0000000e+01 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 9.3181819e-01 2.2727273e-02 2.2727273e-02 2.2727273e-02 val_log_probs -7.0617534e-02 -3.7841897e+00 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 6.0000000e+00 4.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 5.6818181e-01 3.8636366e-01 2.2727273e-02 2.2727273e-02 val_log_probs -5.6531376e-01 -9.5097625e-01 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 1 w_j, pi_j 1.0000000e+01 2.1250001e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5488132e+00 -9.3914810e+01 -1.2238210e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 2.0000000e+00 3.0000000e+00 2.0000000e+00 3.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 val_probs 1.9480520e-01 2.8571430e-01 1.9480520e-01 2.8571430e-01 1.2987014e-02 1.2987014e-02 1.2987014e-02 val_log_probs -1.6357552e+00 -1.2527629e+00 -1.6357552e+00 -1.2527629e+00 -4.3438053e+00 -4.3438053e+00 -4.3438053e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 0.0000000e+00 1.0000000e+01 0.0000000e+00 val_probs 3.0303031e-02 9.3939400e-01 3.0303031e-02 val_log_probs -3.4965074e+00 -6.2520325e-02 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 4.0000000e+00 6.0000000e+00 0.0000000e+00 val_probs 3.9393941e-01 5.7575762e-01 3.0303031e-02 val_log_probs -9.3155819e-01 -5.5206853e-01 -3.4965074e+00 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.0000000e+00 4.0000000e+00 val_probs 5.9090912e-01 4.0909094e-01 val_log_probs -5.2609307e-01 -8.9381784e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 3.0000000e+00 3.0000000e+00 2.0000000e+00 2.0000000e+00 val_probs 2.9545456e-01 2.9545456e-01 2.0454547e-01 2.0454547e-01 val_log_probs -1.2192402e+00 -1.2192402e+00 -1.5869651e+00 -1.5869651e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 5.0000000e+00 5.0000000e+00 val_probs 2.2727273e-02 2.2727273e-02 4.7727275e-01 4.7727275e-01 val_log_probs -3.7841897e+00 -3.7841897e+00 -7.3966718e-01 -7.3966718e-01 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.0000000e+00 5.0000000e+00 val_probs 5.0000000e-01 5.0000000e-01 val_log_probs -6.9314712e-01 -6.9314712e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 3.0000000e+00 3.0000000e+00 4.0000000e+00 val_probs 3.0303031e-01 3.0303031e-01 3.9393941e-01 val_log_probs -1.1939224e+00 -1.1939224e+00 -9.3155819e-01 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 8.0000000e+00 2.0000000e+00 val_probs 7.7272731e-01 2.2727273e-01 val_log_probs -2.5782907e-01 -1.4816045e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 1.0000000e+01 0.0000000e+00 0.0000000e+00 val_probs 2.2727273e-02 9.3181819e-01 2.2727273e-02 2.2727273e-02 val_log_probs -3.7841897e+00 -7.0617534e-02 -3.7841897e+00 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 1.0000000e+01 0.0000000e+00 val_probs 2.2727273e-02 2.2727273e-02 9.3181819e-01 2.2727273e-02 val_log_probs -3.7841897e+00 -3.7841897e+00 -7.0617534e-02 -3.7841897e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 1.0000000e+01 val_probs 4.5454547e-02 9.5454550e-01 val_log_probs -3.0910425e+00 -4.6519987e-02 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+01 0.0000000e+00 val_probs 9.5454550e-01 4.5454547e-02 val_log_probs -4.6519987e-02 -3.0910425e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 1.0000000e+01 0.0000000e+00 1.0000000e+01 9.0909094e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 2 w_j, pi_j 9.9999886e+00 2.1249977e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5488144e+00 -9.2850521e+01 -1.2362125e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 1.0000000e+00 0.0000000e+00 1.0000000e+00 0.0000000e+00 4.9999886e+00 2.9999995e+00 0.0000000e+00 val_probs 1.0389621e-01 1.2987027e-02 1.0389621e-01 1.2987027e-02 4.6753189e-01 2.8571454e-01 1.2987027e-02 val_log_probs -2.2643628e+00 -4.3438044e+00 -2.2643628e+00 -4.3438044e+00 -7.6028770e-01 -1.2527621e+00 -4.3438044e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 2.0000000e+00 7.9999886e+00 val_probs 2.2727296e-01 7.7272701e-01 val_log_probs -1.4816035e+00 -2.5782943e-01 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 9.9999781e+00 0.0000000e+00 1.0495686e-05 val_probs 9.3939292e-01 3.0303061e-02 3.0304017e-02 val_log_probs -6.2521465e-02 -3.4965065e+00 -3.4964750e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 0.0000000e+00 0.0000000e+00 9.9999886e+00 val_probs 3.0303061e-02 3.0303061e-02 9.3939388e-01 val_log_probs -3.4965065e+00 -3.4965065e+00 -6.2520452e-02 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000000e+00 8.9999886e+00 val_probs 1.3636377e-01 8.6363620e-01 val_log_probs -1.9924291e+00 -1.4660366e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 2.0000000e+00 2.9999995e+00 2.0000000e+00 2.9999888e+00 val_probs 2.0454566e-01 2.9545480e-01 2.0454566e-01 2.9545382e-01 val_log_probs -1.5869640e+00 -1.2192394e+00 -1.5869640e+00 -1.2192427e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 9.9999886e+00 0.0000000e+00 0.0000000e+00 val_probs 2.2727296e-02 9.3181807e-01 2.2727296e-02 2.2727296e-02 val_log_probs -3.7841885e+00 -7.0617668e-02 -3.7841885e+00 -3.7841885e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 4.9999886e+00 4.9999995e+00 val_probs 4.9999946e-01 5.0000048e-01 val_log_probs -6.9314826e-01 -6.9314623e-01 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 5.9999886e+00 4.0000000e+00 val_probs 5.9090865e-01 4.0909132e-01 val_log_probs -5.2609384e-01 -8.9381689e-01 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 4.9999995e+00 4.9999886e+00 0.0000000e+00 val_probs 4.8484895e-01 4.8484793e-01 3.0303061e-02 val_log_probs -7.2391790e-01 -7.2391999e-01 -3.4965065e+00 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0000105e+00 8.9999781e+00 val_probs 1.3636473e-01 8.6363524e-01 val_log_probs -1.9924221e+00 -1.4660478e-01 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.9999886e+00 2.0000000e+00 val_probs 7.7272701e-01 2.2727296e-01 val_log_probs -2.5782943e-01 -1.4816035e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 9.9999886e+00 0.0000000e+00 val_probs 2.2727296e-02 2.2727296e-02 9.3181807e-01 2.2727296e-02 val_log_probs -3.7841885e+00 -3.7841885e+00 -7.0617668e-02 -3.7841885e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 9.9999781e+00 0.0000000e+00 0.0000000e+00 1.0495686e-05 val_probs 9.3181711e-01 2.2727296e-02 2.2727296e-02 2.2728249e-02 val_log_probs -7.0618697e-02 -3.7841885e+00 -3.7841885e+00 -3.7841465e+00 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 9.9999886e+00 val_probs 4.5454592e-02 9.5454538e-01 val_log_probs -3.0910413e+00 -4.6520092e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9999781e+00 1.0495686e-05 val_probs 9.5454443e-01 4.5455545e-02 val_log_probs -4.6521090e-02 -3.0910203e+00 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.9999886e+00 2.0000000e+00 val_probs 7.7272701e-01 2.2727296e-01 val_log_probs -2.5782943e-01 -1.4816035e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9999886e+00 0.0000000e+00 val_probs 9.5454538e-01 4.5454592e-02 val_log_probs -4.6520092e-02 -3.0910413e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9999886e+00 0.0000000e+00 val_probs 9.5454538e-01 4.5454592e-02 val_log_probs -4.6520092e-02 -3.0910413e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 9.9999886e+00 val_probs 4.5454592e-02 9.5454538e-01 val_log_probs -3.0910413e+00 -4.6520092e-02 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 8.9999886e+00 9.9999928e-01 val_probs 8.6363620e-01 1.3636371e-01 val_log_probs -1.4660366e-01 -1.9924296e+00 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 9.9999886e+00 0.0000000e+00 9.9999886e+00 9.0909183e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 3 w_j, pi_j 7.0033579e+00 1.5006995e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.8966538e+00 -6.2373236e+01 -8.7715148e+01 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 0.0000000e+00 0.0000000e+00 2.9986699e+00 0.0000000e+00 8.0887033e-03 1.0930325e-02 3.9856691e+00 val_probs 1.7849652e-02 1.7849652e-02 3.9252612e-01 1.7849652e-02 1.8860316e-02 1.9215370e-02 5.1584929e-01 val_log_probs -4.0257711e+00 -4.0257711e+00 -9.3515217e-01 -4.0257711e+00 -3.9706953e+00 -3.9520450e+00 -6.6194069e-01 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 7.0033584e+00 val_probs 6.2473778e-02 9.3752629e-01 val_log_probs -2.7730083e+00 -6.4510487e-02 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 5.9953833e+00 0.0000000e+00 1.0079746e+00 val_probs 7.9075766e-01 4.1649185e-02 1.6759315e-01 val_log_probs -2.3476371e-01 -3.1784735e+00 -1.7862159e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 2.0147762e+00 0.0000000e+00 4.9885817e+00 val_probs 2.9339054e-01 4.1649185e-02 6.6496027e-01 val_log_probs -1.2262506e+00 -3.1784735e+00 -4.0802798e-01 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0150537e+00 5.9883041e+00 val_probs 1.8930227e-01 8.1069773e-01 val_log_probs -1.6644102e+00 -2.0986000e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 9.9919444e-01 1.0005852e+00 3.0041077e+00 1.9994708e+00 val_probs 1.5608379e-01 1.5625757e-01 4.0659282e-01 2.8106588e-01 val_log_probs -1.8573623e+00 -1.8562496e+00 -8.9994305e-01 -1.2691662e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 6.0070696e+00 0.0000000e+00 9.9628866e-01 val_probs 3.1236889e-02 7.8180557e-01 3.1236889e-02 1.5572073e-01 val_log_probs -3.4661555e+00 -2.4614923e-01 -3.4661555e+00 -1.8596911e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.9860305e+00 5.0173278e+00 val_probs 3.1062344e-01 6.8937659e-01 val_log_probs -1.1691740e+00 -3.7196755e-01 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.5591844e-04 7.0027022e+00 val_probs 6.2555730e-02 9.3744433e-01 val_log_probs -2.7716973e+00 -6.4597934e-02 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 2.0001543e+00 2.9961576e+00 2.0070462e+00 val_probs 2.9156357e-01 4.1601175e-01 2.9242471e-01 val_log_probs -1.2324972e+00 -8.7704176e-01 -1.2295481e+00 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0033584e+00 0.0000000e+00 val_probs 9.3752629e-01 6.2473778e-02 val_log_probs -6.4510487e-02 -2.7730083e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 6.9951015e+00 8.2566738e-03 val_probs 9.3649459e-01 6.3505426e-02 val_log_probs -6.5611519e-02 -2.7566299e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 6.5591844e-04 7.0027022e+00 val_probs 3.1236889e-02 3.1236889e-02 3.1318843e-02 9.0620744e-01 val_log_probs -3.4661555e+00 -3.4661555e+00 -3.4635353e+00 -9.8487064e-02 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.0033584e+00 val_probs 3.1236889e-02 3.1236889e-02 3.1236889e-02 9.0628940e-01 val_log_probs -3.4661555e+00 -3.4661555e+00 -3.4661555e+00 -9.8396599e-02 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 7.0033584e+00 val_probs 6.2473778e-02 9.3752629e-01 val_log_probs -2.7730083e+00 -6.4510487e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0150537e+00 5.9883041e+00 val_probs 1.8930227e-01 8.1069773e-01 val_log_probs -1.6644102e+00 -2.0986000e-01 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0033584e+00 0.0000000e+00 val_probs 9.3752629e-01 6.2473778e-02 val_log_probs -6.4510487e-02 -2.7730083e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0033584e+00 0.0000000e+00 val_probs 9.3752629e-01 6.2473778e-02 val_log_probs -6.4510487e-02 -2.7730083e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.0033584e+00 0.0000000e+00 val_probs 9.3752629e-01 6.2473778e-02 val_log_probs -6.4510487e-02 -2.7730083e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 7.0033584e+00 val_probs 6.2473778e-02 9.3752629e-01 val_log_probs -2.7730083e+00 -6.4510487e-02 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 7.0033584e+00 val_probs 6.2473778e-02 9.3752629e-01 val_log_probs -2.7730083e+00 -6.4510487e-02 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 7.0033579e+00 0.0000000e+00 7.0033579e+00 1.2494756e-01 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 class_DS 4 w_j, pi_j 9.9966536e+00 2.1243028e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.5491414e+00 -9.7613040e+01 -1.2765960e+02 known_parms_p, num_tparms 0 21 tparm_DS 0 n_atts, tppt(type) 7 5 sm_params gamma_term, range, range_m1, inv_range, range_factor 4.0837226e+00 7 6.0000000e+00 1.4285715e-01 8.5714287e-01 val_wts 0.0000000e+00 0.0000000e+00 1.3301438e-03 0.0000000e+00 4.9919224e+00 3.9890699e+00 1.0143307e+00 val_probs 1.2990966e-02 1.2990966e-02 1.3111925e-02 1.2990966e-02 4.6694019e-01 3.7574404e-01 1.0523091e-01 val_log_probs -4.3435011e+00 -4.3435011e+00 -4.3342333e+00 -4.3435011e+00 -7.6155406e-01 -9.7884709e-01 -2.2515981e+00 n_term, n_att, n_att_indices, n_datum, n_data 0 2 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 1 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 9.9966545e+00 val_probs 4.5468379e-02 9.5453173e-01 val_log_probs -3.0907381e+00 -4.6534397e-02 n_term, n_att, n_att_indices, n_datum, n_data 1 3 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 2 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 7.0046387e+00 0.0000000e+00 2.9920149e+00 val_probs 6.6729140e-01 3.0312253e-02 3.0239639e-01 val_log_probs -4.0452847e-01 -3.4962032e+00 -1.1960166e+00 n_term, n_att, n_att_indices, n_datum, n_data 2 4 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 3 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 7.9852233e+00 0.0000000e+00 2.0114298e+00 val_probs 7.5646257e-01 3.0312253e-02 2.1322516e-01 val_log_probs -2.7910224e-01 -3.4962032e+00 -1.5454066e+00 n_term, n_att, n_att_indices, n_datum, n_data 3 5 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 4 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 4.9849463e+00 5.0117073e+00 val_probs 4.9878323e-01 5.0121677e-01 val_log_probs -6.9558370e-01 -6.9071656e-01 n_term, n_att, n_att_indices, n_datum, n_data 4 6 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 5 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 4.0008054e+00 4.9994154e+00 9.9589223e-01 5.4038106e-04 val_probs 3.8655445e-01 4.7736481e-01 1.1329740e-01 2.2783330e-02 val_log_probs -9.5048255e-01 -7.3947430e-01 -2.1777391e+00 -3.7817261e+00 n_term, n_att, n_att_indices, n_datum, n_data 5 7 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 6 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 9.9929428e+00 0.0000000e+00 3.7113177e-03 val_probs 2.2734189e-02 9.3146002e-01 2.2734189e-02 2.3071684e-02 val_log_probs -3.7838852e+00 -7.1002021e-02 -3.7838852e+00 -3.7691493e+00 n_term, n_att, n_att_indices, n_datum, n_data 6 8 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 7 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 4.0139804e+00 5.9826727e+00 val_probs 4.1048673e-01 5.8951324e-01 val_log_probs -8.9041167e-01 -5.2845812e-01 n_term, n_att, n_att_indices, n_datum, n_data 7 9 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 8 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 7.9993558e+00 1.9972978e+00 val_probs 7.7290386e-01 2.2709616e-01 val_log_probs -2.5760061e-01 -1.4823817e+00 n_term, n_att, n_att_indices, n_datum, n_data 8 10 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 9 n_atts, tppt(type) 3 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -9.0944779e-01 3 2.0000000e+00 3.3333334e-01 6.6666669e-01 val_wts 3.9998460e+00 1.0038537e+00 4.9929538e+00 val_probs 3.9404526e-01 1.2159945e-01 4.8435527e-01 val_log_probs -9.3128943e-01 -2.1070228e+00 -7.2493660e-01 n_term, n_att, n_att_indices, n_datum, n_data 9 11 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 10 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9966326e+00 2.2024988e-05 val_probs 9.5452970e-01 4.5470383e-02 val_log_probs -4.6536487e-02 -3.0906942e+00 n_term, n_att, n_att_indices, n_datum, n_data 10 13 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 11 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 8.0049105e+00 1.9917433e+00 val_probs 7.7340901e-01 2.2659107e-01 val_log_probs -2.5694728e-01 -1.4846084e+00 n_term, n_att, n_att_indices, n_datum, n_data 11 21 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 12 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 0.0000000e+00 0.0000000e+00 7.9993558e+00 1.9972978e+00 val_probs 2.2734189e-02 2.2734189e-02 7.5016969e-01 2.0436198e-01 val_log_probs -3.7838852e+00 -3.7838852e+00 -2.8745589e-01 -1.5878625e+00 n_term, n_att, n_att_indices, n_datum, n_data 12 22 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 13 n_atts, tppt(type) 4 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1997312e-01 4 3.0000000e+00 2.5000000e-01 7.5000000e-01 val_wts 2.2024988e-05 0.0000000e+00 0.0000000e+00 9.9966326e+00 val_probs 2.2736192e-02 2.2734189e-02 2.2734189e-02 9.3179554e-01 val_log_probs -3.7837973e+00 -3.7838852e+00 -3.7838852e+00 -7.0641860e-02 n_term, n_att, n_att_indices, n_datum, n_data 13 23 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 14 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 9.9966545e+00 val_probs 4.5468379e-02 9.5453173e-01 val_log_probs -3.0907381e+00 -4.6534397e-02 n_term, n_att, n_att_indices, n_datum, n_data 14 24 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 15 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 4.9849682e+00 5.0116854e+00 val_probs 4.9878523e-01 5.0121480e-01 val_log_probs -6.9557971e-01 -6.9072056e-01 n_term, n_att, n_att_indices, n_datum, n_data 15 25 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 16 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9966545e+00 0.0000000e+00 val_probs 9.5453173e-01 4.5468379e-02 val_log_probs -4.6534397e-02 -3.0907381e+00 n_term, n_att, n_att_indices, n_datum, n_data 16 26 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 17 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9966545e+00 0.0000000e+00 val_probs 9.5453173e-01 4.5468379e-02 val_log_probs -4.6534397e-02 -3.0907381e+00 n_term, n_att, n_att_indices, n_datum, n_data 17 27 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 18 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 9.9966545e+00 0.0000000e+00 val_probs 9.5453173e-01 4.5468379e-02 val_log_probs -4.6534397e-02 -3.0907381e+00 n_term, n_att, n_att_indices, n_datum, n_data 18 28 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 19 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 0.0000000e+00 9.9966545e+00 val_probs 4.5468379e-02 9.5453173e-01 val_log_probs -3.0907381e+00 -4.6534397e-02 n_term, n_att, n_att_indices, n_datum, n_data 19 29 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 tparm_DS 20 n_atts, tppt(type) 2 5 sm_params gamma_term, range, range_m1, inv_range, range_factor -1.1447273e+00 2 1.0000000e+00 5.0000000e-01 5.0000000e-01 val_wts 1.0820259e-05 9.9966431e+00 val_probs 4.5469362e-02 9.5453072e-01 val_log_probs -3.0907166e+00 -4.6535488e-02 n_term, n_att, n_att_indices, n_datum, n_data 20 36 0 0 47 w_j, ranges, class_wt, disc_scale 9.9966536e+00 0.0000000e+00 9.9966536e+00 9.0936758e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 num_wts 47 model_DS_ptr 0 min_class_wt 2.0999999 chkpt_DS accumulated_try_time, current_try_j_in, current_cycle 0 0 0 autoclass-3.3.6.dfsg.1/data/soybean/soyc.log0000644000175000017500000015124111247310756016745 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:52:10 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Thu Jun 7 11:52:10 2001 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] best3->3(2) [j_in=5] [cs-3: cycles 6] best5->4(3) [j_in=7] [cs-3: cycles 10] 7->5(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:52:10 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 6 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent try 1 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:52:10 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:53:07 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Thu Jun 7 11:53:07 2001 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] best3->3(2) [j_in=5] [cs-3: cycles 6] best5->4(3) [j_in=7] [cs-3: cycles 8] 7->5(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:53:07 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.010) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 6 max_cycles 200 convergent try 4 num_cycles 8 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent try 1 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:53:07 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:53:57 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Thu Jun 7 11:53:58 2001 [j_in=2] [cs-4: cycles 6] best2->2(1) [j_in=3] [cs-4: cycles 6] best3->3(2) [j_in=5] [cs-4: cycles 8] best5->4(3) [j_in=7] [cs-4: cycles 11] 7->5(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:53:58 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 8 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 6 max_cycles 200 convergent try 1 num_cycles 6 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:53:58 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:54:44 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 4 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:54:44 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 14:33:20 2002 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; save_compact_p=false; read_compact_p=false; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Mon May 13 14:33:20 2002 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] best3->3(2) [j_in=5] [cs-3: cycles 6] best5->4(3) [j_in=7] [cs-3: cycles 10] 7->5(4) ENDING SEARCH because max number of tries reached at Mon May 13 14:33:20 2002 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 6 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent try 1 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 14:33:20 2002 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:03:23 2004 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 4 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:03:23 2004 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:06:15 2004 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 4 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:06:15 2004 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:09:21 2004 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Tue Sep 28 12:09:30 2004 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] best3->3(2) [j_in=5] [cs-3: cycles 6] best5->4(3) [j_in=7] [cs-3: cycles 10] 7->5(4) ENDING SEARCH because max number of tries reached at Tue Sep 28 12:09:30 2004 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 6 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent try 1 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:09:30 2004 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:12:54 2004 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #1, "diagnosis": [ (0 Diaporthe_Stem_Canker):10 (1 Charcoal_Rot):10 (2 Rhizoctonia_Root_Rot):10 (3 Phytophthora_Rot):17 ] Attribute #2, "time of occurance": [ (0 5):5 (1 6):5 (2 4):9 (3 7):6 (4 1):10 (5 3):7 (6 2):5 ] Attribute #3, "plant stand": [ (0 1):22 (1 2):25 ] Attribute #4, "precipitation": [ (0 3):33 (1 1):10 (2 2):4 ] Attribute #5, "temperature": [ (0 2):24 (1 3):6 (2 1):17 ] Attribute #6, "occurance of hail": [ (0 2):14 (1 1):33 ] Attribute #7, "number years crop repeated": [ (0 2):14 (1 4):15 (2 3):11 (3 1):7 ] Attribute #8, "damaged area": [ (0 1):7 (1 2):29 (2 3):5 (3 4):6 ] Attribute #9, "damage severity": [ (0 2):28 (1 3):19 ] Attribute #10, "seed treatment": [ (0 1):24 (1 2):23 ] Attribute #11, "seed germination": [ (0 3):19 (1 2):15 (2 1):13 ] Attribute #12, "plant growth": [ (0 2):47 ] Attribute #13, "leaf condition": [ (0 2):38 (1 1):9 ] Attribute #14, "leaf spot halos": [ (0 1):47 ] Attribute #15, "leaf spot margin": [ (0 3):47 ] Attribute #16, "size of leaf spots": [ (0 3):47 ] Attribute #17, "slot holing": [ (0 1):47 ] Attribute #18, "leaf malformation": [ (0 1):47 ] Attribute #19, "leaf mildew growth": [ (0 1):47 ] Attribute #20, "condition of stem": [ (0 2):47 ] Attribute #21, "stem lodging": [ (0 1):37 (1 2):10 ] Attribute #22, "stem cankers": [ (0 4):10 (1 1):10 (2 2):18 (3 3):9 ] Attribute #23, "canker lesion color": [ (0 2):16 (1 1):4 (2 4):10 (3 3):17 ] Attribute #24, "fruiting bodies of stem": [ (0 2):10 (1 1):37 ] Attribute #25, "outer stem decay": [ (0 2):26 (1 1):21 ] Attribute #26, "mycelium on stem": [ (0 1):45 (1 2):2 ] Attribute #27, "internal discoloration of stem": [ (0 1):37 (1 3):10 ] Attribute #28, "scerotia internal or external": [ (0 1):37 (1 2):10 ] Attribute #29, "fruit pod condition": [ (0 1):20 (1 4):27 ] Attribute #30, "fruit spots": [ (0 5):47 ] Attribute #31, "seed condition": [ (0 1):47 ] Attribute #32, "seed mold growth": [ (0 1):47 ] Attribute #33, "seed discoloration": [ (0 1):47 ] Attribute #34, "seed size": [ (0 1):47 ] Attribute #35, "seed shriveling": [ (0 1):47 ] Attribute #36, "root condition": [ (0 1):29 (1 2):18 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## ADVISORY: start_j_list has been modified to: (2,3,5,7,10,15,20) WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log BEGINNING SEARCH at Tue Sep 28 12:12:57 2004 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] best3->3(2) [j_in=5] [cs-3: cycles 6] best5->4(3) [j_in=7] [cs-3: cycles 10] 7->5(4) ENDING SEARCH because max number of tries reached at Tue Sep 28 12:12:57 2004 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 6 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent try 1 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:12:57 2004 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:13:52 2004 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2, [2] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 4 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc-predict.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:13:52 2004 autoclass-3.3.6.dfsg.1/data/soybean/soyc.rlog0000644000175000017500000000707511247310756017134 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:54:19 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/soybean/soyc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:54:20 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 14:34:22 2002 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY: read 2 classifications from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.influ-o-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 14:34:22 2002 AUTOCLASS C (version 3.3.5unx) STARTING at Tue Sep 28 12:13:27 2004 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(1,2,3) ADVISORY[2]: read 37 attribute defs from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 ADVISORY[1]: read 47 datum from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/soybean/soyc.search File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.influ-o-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Tue Sep 28 12:13:27 2004 autoclass-3.3.6.dfsg.1/data/soybean/soyc.r-params0000644000175000017500000001121211247310756017677 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; xref_class_report_att_list = 1, 2, 3 ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 autoclass-3.3.6.dfsg.1/data/soybean/soyc.results-bin0000644000175000017500000016705411247310756020444 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -6.4560416e+02(# clsf_DS 1: log_a_x_h = -6.6000695e+02ac_version 3.3.5unx 4+ƃ~|Ā!S,p X ff@H  |data/soybean/soyc.db2(@ZBdata/soybean/soyc.hd2/%%2 ,;? dummynilcase number discretenominaldiagnosis И Diaporthe_Stem_Canker Charcoal_RotRhizoctonia_Root_RotPhytophthora_RotlNULLNULL discretenominaltime of occurancex X  5647132lNULLNULL discretenominalplant standHȝ 12lNULLNULL discretenominalprecipitation8  ! 312lNULLNULL discretenominaltemperaturex(  231lNULLNULL discretenominaloccurance of hailx !21lNULLNULL discretenominalnumber years crop repeatedȢ  2431lNULLNULL discretenominaldamaged area 1234lNULLNULL discretenominaldamage severityH(h X23lNULLNULL discretenominalseed treatmentH 12lNULLNULL discretenominalseed germinationȧ ا  321lNULLNULL discretenominalplant growth( /2ltrueNULLtrue discretenominalleaf conditionHh X& 21lNULLNULL discretenominalleaf spot halos0 /1ltrueNULLtrue discretenominalleaf spot marginȬP ج/3ltrueNULLtrue discretenominalsize of leaf spotsp( /3ltrueNULLtrue discretenominalslot holingXx h/1ltrueNULLtrue discretenominalleaf malformation 8/1ltrueNULLtrue discretenominalleaf mildew growthر ȱ/1ltrueNULLtrue discretenominalcondition of stem0  /2ltrueNULLtrue discretenominalstem lodgingPp `% 12lNULLNULL discretenominalstem cankers    4123lNULLNULL discretenominalcanker lesion colorض  H 2143lNULLNULL discretenominalfruiting bodies of stemp(  %21lNULLNULL discretenominalouter stem decay` p21lNULLNULL discretenominalmycelium on stem -12lNULLNULL discretenominalinternal discoloration of stem( % 13lNULLNULL discretenominalscerotia internal or externalHh X% 12lNULLNULL discretenominalfruit pod condition 14lNULLNULL discretenominalfruit spotsؿ0 /5ltrueNULLtrue discretenominalseed conditionhP /1ltrueNULLtrue discretenominalseed mold growthp8 0/1ltrueNULLtrue discretenominalseed discoloration@h X/1ltrueNULLtrue discretenominalseed size /1ltrueNULLtrue discretenominalseed shriveling /1ltrueNULLtrue discretenominalroot condition( 12lNULLNULL LMODEL-0YAdata/soybean/soyc.model% %   0 L A AZ>Gſ0=Z{Wh2^ / ۭ@@%I>m[?  x  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./ A A.=[  @@@@@@{G>{G>%I>%I>|T<|T<|T<m`ѿm`ѿZZtttm??? x h / A A.=[   A]t?.:=>Eh@>*?   / A A.=[  @ A |p?><>< __h@>*?   / A A.=[  ` A |p?><>< __m???   / A A.=[  ?A >F]?7x@@>@?` H 0 / A A.=[  p@@@@@u>E>E>.<.ss*0rx@@>@?h P 8 / A A.=[  @@@/(?E>.<.<sվ*0r*0rm???    / A A.=[   @@@.?>ľm???(    / A A.=[   @@??r1r1h@>*?H 8 (  / A A.=[   @@@@ >>&>N6Y> R9sҘyƿm???x h X  / A A.=[    A]t?.:=>Em???  x  / A A.=[   @@E?^t> ?dx@@>@?    / A A.=[   An?.<.<.<ퟐ*0r*0r*0rx@@>@?    / A A.=[  (@@]t?u>.<.<g.ss*0r*0rm???   / A A.=[   A]t?.:=>Em???   / A A.=[   A]t?.:=>Em???   / A A.=[    A]t?.:=>Em???(   / A A.=[    A]t?.:=>Em???H 8 ( / A A.=[   A]t?.:=>Em???h X H / A A.=[   A]t?.:=>Em???  0H $/ A A.=[ Xh  A]t?.:=>EL A AZ>GſkwWFt^ / ۭ@@%I>m[?p P 0  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./ A A.=pm  @@@@@@{G>%I>{G>%I>|T<|T<|T<m`ѿZm`ѿZtttm???(" " " / A A.=pm   A]t?.:=>Eh@>*?H!   / A A.=pm   A >< |p?>< _ _h@>*?  p / A A.=pm   @@ m>d?>< zn]T _m???` P  / A A.=pm  @@E?^t> ?dx@@>@? x ` / A A.=pm  @@@@@@E>E>^tQ>^tQ>!˿!˿x@@>@?H 0  / A A.=pm  @@.<.<]>]>*0r*0rZ=Z=m???    / A A.=pm   A]t?.:=>Em???    / A A.=pm  @@??r1r1h@>*?  @  / A A.=pm   @@@@@ &>&>m> sҘsҘznm???0   `  / A A.=pm   A]t?.:=>Em???P @   / A A.=pm  A@uE?/h>,7x@@>@?    / A A.=pm   A.@?    / A A.=pm P  A.<.m???   / A A.=pm P  A.:=]t?E>m???   / A A.=pm   A]t?.:=>Em???   / A A.=pm   A.:=]t?E>m???  0 / A A.=pm   A.:=]t?E>m???   P / A A.=pm   A]t?.:=>Em???@ H 8" $/ A A.=pm   A]t?.:=>ELAAZ>HſxT3W^$ / ۭ@@%I>m[?# # #  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./AA.=ȩ  ??@@@}=}T<}=}T<_>I>}T<VtVtB(Ztm???5 5 5 /AA.=ȩ  @@0h>uE?7+h@>*?4 4 4 /AA.=ȩ   A:L7 |p?> _._h@>*?3 3 3 /AA.=ȩ   7A B<><{p? __ m???3 3 2 /AA.=ȩ  ?A} >#]?lx@@>@?/ x/ `/ /AA.=ȩ  e@3@@@e?@tQ>E>_tQ>bE>Q!˿!˿ox@@>@?. . h. /AA.=ȩ   A.8?r1q1m???X1 H1 81  /AA.=ȩ  @@E?_t> >dh@>*?x0 h0 X0  /AA.=ȩ   L@@ >>=>>< KR92S9_m???- - -  /AA.=ȩ  0?Aǣ >]?(1 m???, , ,  /AA.=ȩ  @@uE?0h>+7x@@>@?@) () )  /AA.=ȩ   A.<.@?H( 0(   /AA.=ȩ  A~8mn?.<.m???+ * * /AA.=ȩ  A:L7]t?/:=>=Em???(* * * /AA.=ȩ  @@uE?0h>+7m???p' `' P' /AA.=ȩ   A]t?.:=>Em???& & p& /AA.=ȩ   A]t?.:=>Em???% % % /AA.=ȩ   A.:=]t?E>m???$ $ $ $/AA.=ȩ  A?G]? >1LAA>ht=e$+=sjH# / ۭ@@%I>m[?h7 H7 (7  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./AA9c=  @@N@h@@!!4k>%I>ššd߿šMZZm???XI HI 8I /AA9c=  A9*?xH hH XH /AA9c=   PA@ <=?& -KFh@>*?G G xG /AA9c=   A4@ o?&<> lKGem???F F F /AA9c=  @0Ab>P#?`6cx@@>@?0C C C /AA9c=  @@@@)U>DZ>q>>eøMx@@>@?8B  B B /AA9c=  A?9c#?M`cm???D D D  /AA9c=  AA>r?@#h@>*?D D C  /AA9c=   @N@@ %>Hv>> ׳}Fem???HA 8A (A  /AA9c=  A8mx?<*Wem???h@ X@ H@  /AA9c=  pA@q\?8>x@@>@?< < <  /AA9c=  AA9c<9c<>9?sڈsڈyGyn*x@@>@?; ; ;  /AA9c=  8AӖc<9c<9c<4Uu?=وsڈsڈ/U.m???? x? h? /AA9c=  A9 > > /AA9c=  @/A>-#?l`cm???= = = /AA9c=  Ax?9<`Xem???: : : /AA9c=  Ax?9<`Xem???: : 9 /AA9c=  Ax?9<`Xem???89 (9 9 /AA9c=  A9?ƿ0i{~Wh2^ / ۭ@@%I>m[?    /home/wtaylor/AC/autoclass-c/data/soybean/soyc./ A A.=[  @@@@@@{G>{G>%I>%I>|T<|T<|T<m`ѿm`ѿZZtttm???   / A A.=[   A]t?.:=>Eh@>*?   / A A.=[  @ A |p?><>< __h@>*?   / A A.=[  ` A |p?><>< __m???@ 0  / A A.=[  ?A >F]?7x@@>@?   / A A.=[  p@@@@@u>E>E>.<.ss*0rx@@>@?   / A A.=[  @@@/(?E>.<.<sվ*0r*0rm???` P @  / A A.=[   @@@.?>ľm??? p `  / A A.=[   @@??r1r1h@>*?    / A A.=[   @@@@ >>&>N6Y> R9sҘyƿm???    / A A.=[    A]t?.:=>Em???    / A A.=[   @@E?^t> ?dx@@>@?h P 8  / A A.=[   An?.<.<.<ퟐ*0r*0r*0rx@@>@?p X @  / A A.=[  (@@]t?u>.<.<g.ss*0r*0rm???   / A A.=[   A]t?.:=>Em???0    / A A.=[   A]t?.:=>Em???P @ 0 / A A.=[    A]t?.:=>Em??? p ` / A A.=[    A]t?.:=>Em???   / A A.=[   A]t?.:=>Em???   / A A.=[   A]t?.:=>Em???   $/ A A.=[ Xh  A]t?.:=>EL A AY>?ƿ?zWFt^h / ۭ@@%I>m[?0\  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./ A A.=ȩ  @@@@@@{G>%I>{G>%I>|T<|T<|T<m`ѿZm`ѿZtttm???`m Pm @m / A A.=ȩ   A]t?.:=>Eh@>*?l pl `l / A A.=ȩ   A >< |p?>< _ _h@>*?k k k / A A.=ȩ   @@ m>d?>< zn]T _m???j j j / A A.=ȩ  @@E?^t> ?dx@@>@?8g  g g / A A.=ȩ  @@@@@@E>E>^tQ>^tQ>!˿!˿x@@>@?@f (f f / A A.=ȩ  @@.<.<]>]>*0r*0rZ=Z=m???i i i  / A A.=ȩ   A]t?.:=>Em???i h h  / A A.=ȩ  @@??r1r1h@>*? h h h  / A A.=ȩ   @@@@@ &>&>m> sҘsҘznm???Pe @e 0e  / A A.=ȩ   A]t?.:=>Em???pd `d Pd  / A A.=ȩ  A@uE?/h>,7x@@>@?` ` `  / A A.=ȩ   A.@?_ _ _  / A A.=ȩ P  A.<.m???b b b / A A.=ȩ   A.:=]t?E>m???a a a / A A.=ȩ   A]t?.:=>Em???p`P/ A A.=ȩ   A.:=]t?E>m???@0 / A A.=ȩ   A.:=]t?E>m???\ / A A.=ȩ   A]t?.:=>Em???\ \  $/ A A.=ȩ   A]t?.:=>ELAAY>?ƿbn6WGe^X / ۭ@@%I>m[?`_ ؎ /home/wtaylor/AC/autoclass-c/data/soybean/soyc./AA.=]  ??@?@=T<=T-I>T<RrRr7BZrm??? ؙ ș /AA.=]  @@>h>pE?/8h@>*?   /AA.=]   A07 |p? >*?(   /AA.=]   A >< ><|p? __ m???H 8 ( /AA.=]  ?A >C]?Ex@@>@?   /AA.=]  @?@@?@ktQ>E>ktQ>E>!˿ !˿%x@@>@?Ȓ   /AA.=]  A.?*r1r1m??? x h  /AA.=]  @@E?kt>/dh@>*?    /AA.=]   @@ >>=> >< R9R9_m???ؑ ȑ   /AA.=]  X?A2 >3]?m???  ؐ  /AA.=]  @@pE?>h>8/x@@>@?p X @  /AA.=]  A.<.@?x `  /AA.=]  A07n?.<.<0<%0r%0ru/rm???   /AA.=]  A.:=]t?Eڋ>m???8 (  /AA.=]  A07]t?/:=>GEm???X H 8 /AA.=]  @@pE?>h>8/m???   /AA.=]  A]t?.:=ڋ>Em???   /AA.=]  A]t?.:=ڋ>Em???_ _ _ /AA.=]  A.:=]t?Eڋ>m???_  n  $/AA.=]  A?C]? >EL@@>~<1/OIU / ۭ@@%I>m[?  `  /home/wtaylor/AC/autoclass-c/data/soybean/soyc./@@=`q  5?@l<3<4@o9o9<*? Ы  /@@=`q   .ڿ@P? oJ?V*=+> eplKh@>*?   /@@=`q   @v@ I7>V*=:*? lK оm???   /@@=`q  H?0@tA>O?e տVx@@>@?  h /@@=`q  5?-?MC@@?n> >,>> bf tx@@>@?  p /@@=`q  9@ ?~]|~]\ m???@ 0   /@@=`q  ?6?@ >z0?~rm???` P @  /@@=`q  +:#@8=Zo?}c1Kh@>*? p `  /@@=`q   @ ?@rs@ G>~>> xυ`am???    /@@=`q  @p?=x1m???У    /@@=`q  @F<o?"=U_l0x@@>@?    /@@=`q  +:#@<<0H=6g?~]~]]ɽx@@>@?x P 8  /@@=`q  @<<<h?~]~]~](ɽm???  Т /@@=`q  @=p?x1m???   /@@=`q  H?0@tA>O?e տVm???І  0 /@@=`q  @p?=x1m???  P /@@=`q  @p?=x1m???@ 0 p /@@=`q  @p?=x1m???` P  /@@=`q  @=p?x1m??? p  $/@@=`q  @=p?x1LKAKASY>DJƿ} m[? (   /home/wtaylor/AC/autoclass-c/data/soybean/soyc./KAKA ==  /X:Խ@L@Ձ?T<Ta>N= 5Bz/m???h X H /KAKA ==  LA =:=1\t?Eښ>h@>*? x h /KAKA ==   &@,}?@ *?gQ<Ӛ> [Ͼ_h@>*?   /KAKA ==   @D@ A?gQ }掾_ſm???Ƚ   /KAKA ==  @_@`>O?20x@@>@?@ (  /KAKA ==  @6@~?N :w>)i>x=<Rs0N=` rx@@>@?H 0  /KAKA ==  A9s; =<*tn? =<<-+ri-+r9qm??? ؼ ȼ  /KAKA ==  r@r@Q+>W?cIm???    /KAKA ==  @t?E?h>:䃾h@>*?(    /KAKA ==   z@G~?GƟ@ M>" =j> hnvr9m???X H 8  /KAKA ==  5A5¸7\t?'?:= >Em???x h X  /KAKA ==  Ar?"E?}h>x@@>@? س   /KAKA ==  @t? =< =< @?DDQ>-+r-+rk-?˿x@@>@?  Ȳ  /KAKA ==  5¸75A@A< =< =<'n?)r-+r-+rm???  x /KAKA ==  LA =:=1\t?Eښ>m???   /KAKA ==  ܄@_@`>O?20m???ش ȴ  /KAKA ==  LA1\t? =:=ښ>Em???   /KAKA ==  LA1\t? =:=ښ>Em???(   /KAKA ==  LA1\t? =:=ښ>Em???H 8 x /KAKA ==  LA =:=1\t?Eښ>m???h X  $/KAKA ==  57@A>:= \t?ME> autoclass-3.3.6.dfsg.1/data/soybean/soyc.model0000644000175000017500000000123111247310756017255 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; Soybean Case Histories ;;; from UC Irvine Technical Report 87-22 ;;; Fisher & Kibler PhD Dissertation - Knowledge Acquisition ;;; Via Incremental Conceptual Clustering ;;; ;; 1 or more model definitions ;; model_index model_index 0 3 ;; ... ignore 1 single_multinomial 2 3 4 5 6 7 8 9 10 11 13 21 22 23 24 25 26 27 28 29 36 ignore 12 14 15 16 17 18 19 20 30 31 32 33 34 35 autoclass-3.3.6.dfsg.1/data/soybean/soyc.hd20000644000175000017500000000461411247310756016642 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; Soybean Case Histories ;;; from UC Irvine Technical Report 87-22 ;;; Fisher & Kibler PhD Dissertation - Knowledge Acquisition ;;; Via Incremental Conceptual Clustering ;;; ; 47 Data, 37 attributes ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 37 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 dummy nil "case number" 1 discrete nominal diagnosis range 4 2 discrete nominal "time of occurance" range 7 3 discrete nominal "plant stand" range 2 4 discrete nominal precipitation range 3 5 discrete nominal temperature range 3 6 discrete nominal "occurance of hail" range 2 7 discrete nominal "number years crop repeated" range 4 8 discrete nominal "damaged area" range 4 9 discrete nominal "damage severity" range 2 10 discrete nominal "seed treatment" range 2 11 discrete nominal "seed germination" range 3 12 discrete nominal "plant growth" range 1 13 discrete nominal "leaf condition" range 2 14 discrete nominal "leaf spot halos" range 1 15 discrete nominal "leaf spot margin" range 1 16 discrete nominal "size of leaf spots" range 1 17 discrete nominal "slot holing" range 1 18 discrete nominal "leaf malformation" range 1 19 discrete nominal "leaf mildew growth" range 1 20 discrete nominal "condition of stem" range 1 21 discrete nominal "stem lodging" range 2 22 discrete nominal "stem cankers" range 4 23 discrete nominal "canker lesion color" range 4 24 discrete nominal "fruiting bodies of stem" range 2 25 discrete nominal "outer stem decay" range 2 26 discrete nominal "mycelium on stem" range 2 27 discrete nominal "internal discoloration of stem" range 2 28 discrete nominal "scerotia internal or external" range 2 29 discrete nominal "fruit pod condition" range 2 30 discrete nominal "fruit spots" range 1 31 discrete nominal "seed condition" range 1 32 discrete nominal "seed mold growth" range 1 33 discrete nominal "seed discoloration" range 1 34 discrete nominal "seed size" range 1 35 discrete nominal "seed shriveling" range 1 36 discrete nominal "root condition" range 2 autoclass-3.3.6.dfsg.1/data/soybean/soyc.influ-o-text-10000644000175000017500000010364111247310756020656 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 47 cases in /home/wtaylor/AC/autoclass-c/data/soybean/soyc.db2 /home/wtaylor/AC/autoclass-c/data/soybean/soyc.hd2 with log-A (approximate marginal likelihood) = -645.604 from classification results file /home/wtaylor/AC/autoclass-c/data/soybean/soyc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 1 second _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-645.604) N_CLASSES 4 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-660.007) N_CLASSES 5 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-710.824) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-727.858) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 4 POPULATED CLASSES (max global influence value = 1.277) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -1.03e+01 3.56e-01 17 0.362 1 -9.39e+00 8.94e-01 10 0.213 2 -9.39e+00 8.99e-01 10 0.213 3 -9.28e+00 1.00e+00 10 0.213 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 4.93e+00 17 0.362 1 7.66e+00 10 0.213 2 1.16e+01 10 0.213 3 6.18e+00 10 0.213 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 022 stem cankers 1.000 023 canker lesion color 0.995 008 damaged area 0.667 029 fruit pod condition 0.588 005 temperature 0.549 004 precipitation 0.519 002 time of occurance 0.501 003 plant stand 0.489 027 internal discoloration of stem 0.462 028 scerotia internal or external 0.462 024 fruiting bodies of stem 0.462 036 root condition 0.446 025 outer stem decay 0.420 013 leaf condition 0.376 009 damage severity 0.138 006 occurance of hail 0.093 011 seed germination 0.088 007 number years crop repeated 0.054 026 mycelium on stem 0.051 021 stem lodging 0.031 010 seed treatment 0.005 000 case number ----- 001 diagnosis ----- 012 plant growth ----- 014 leaf spot halos ----- 015 leaf spot margin ----- 016 size of leaf spots ----- 017 slot holing ----- 018 leaf malformation ----- 019 leaf mildew growth ----- 020 condition of stem ----- 030 fruit spots ----- 031 seed condition ----- 032 seed mold growth ----- 033 seed discoloration ----- 034 seed size ----- 035 seed shriveling ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 17 normalized weight 0.362 relative strength 3.56e-01 ******* class cross entropy w.r.t. global class 4.93e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 13 23 D SM canker lesion color 0.832 2 .................. -3.19e+00 1.39e-02 3.39e-01 4 .................. -2.73e+00 1.39e-02 2.14e-01 1 .................. -1.85e+00 1.39e-02 8.85e-02 3 .................. 9.81e-01 9.58e-01 3.59e-01 20 36 D SM root condition ..... 0.814 1 .................. -3.10e+00 2.78e-02 6.15e-01 2 .................. 9.25e-01 9.72e-01 3.85e-01 12 22 D SM stem cankers ....... 0.514 4 .................. -2.73e+00 1.39e-02 2.14e-01 1 .................. -2.73e+00 1.39e-02 2.14e-01 3 .................. 9.81e-01 5.14e-01 1.93e-01 2 .................. 1.87e-01 4.58e-01 3.80e-01 01 03 D SM plant stand ........ 0.509 1 .................. -2.83e+00 2.78e-02 4.69e-01 2 .................. 6.04e-01 9.72e-01 5.31e-01 19 29 D SM fruit pod condition 0.438 1 .................. -2.73e+00 2.78e-02 4.27e-01 4 .................. 5.29e-01 9.72e-01 5.73e-01 00 02 D SM time of occurance .. 0.388 7 .................. -2.78e+00 7.94e-03 1.28e-01 5 .................. -2.60e+00 7.94e-03 1.07e-01 6 .................. -2.60e+00 7.94e-03 1.07e-01 2 .................. 9.81e-01 2.86e-01 1.07e-01 3 .................. 4.36e-01 2.30e-01 1.49e-01 1 .................. 3.02e-01 2.86e-01 2.11e-01 4 .................. -8.70e-02 1.75e-01 1.90e-01 06 08 D SM damaged area ....... 0.249 1 .................. -2.39e+00 1.39e-02 1.51e-01 3 .................. -2.06e+00 1.39e-02 1.09e-01 4 .................. -6.29e-01 6.94e-02 1.30e-01 2 .................. 3.93e-01 9.03e-01 6.09e-01 02 04 D SM precipitation ...... 0.239 1 .................. -2.45e+00 1.85e-02 2.15e-01 2 .................. 9.81e-01 2.41e-01 9.03e-02 3 .................. 6.45e-02 7.41e-01 6.94e-01 17 27 D SM internal discolorati 0.155 3 .................. -2.06e+00 2.78e-02 2.19e-01 on of stem 1 .................. 2.19e-01 9.72e-01 7.81e-01 18 28 D SM scerotia internal or 0.155 2 .................. -2.06e+00 2.78e-02 2.19e-01 external 1 .................. 2.19e-01 9.72e-01 7.81e-01 14 24 D SM fruiting bodies of s 0.155 2 .................. -2.06e+00 2.78e-02 2.19e-01 tem 1 .................. 2.19e-01 9.72e-01 7.81e-01 10 13 D SM leaf condition ..... 0.132 1 .................. -1.96e+00 2.78e-02 1.98e-01 2 .................. 1.92e-01 9.72e-01 8.02e-01 07 09 D SM damage severity .... 0.110 2 .................. -4.97e-01 3.61e-01 5.94e-01 3 .................. 4.53e-01 6.39e-01 4.06e-01 03 05 D SM temperature ........ 0.084 3 .................. -1.96e+00 1.85e-02 1.32e-01 2 .................. 1.24e-01 5.74e-01 5.07e-01 1 .................. 1.21e-01 4.07e-01 3.61e-01 15 25 D SM outer stem decay ... 0.074 2 .................. -4.25e-01 3.61e-01 5.52e-01 1 .................. 3.55e-01 6.39e-01 4.48e-01 09 11 D SM seed germination ... 0.040 1 .................. 3.83e-01 4.07e-01 2.78e-01 2 .................. -2.83e-01 2.41e-01 3.19e-01 3 .................. -1.35e-01 3.52e-01 4.03e-01 11 21 D SM stem lodging ....... 0.021 2 .................. -4.54e-01 1.39e-01 2.19e-01 1 .................. 9.73e-02 8.61e-01 7.81e-01 04 06 D SM occurance of hail .. 0.008 2 .................. 1.78e-01 3.61e-01 3.02e-01 1 .................. -8.84e-02 6.39e-01 6.98e-01 16 26 D SM mycelium on stem ... 0.007 2 .................. -6.29e-01 2.78e-02 5.21e-02 1 .................. 2.53e-02 9.72e-01 9.48e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 05 07 D SM number years crop re 0.004 1 .................. -1.89e-01 1.25e-01 1.51e-01 peated 4 .................. 8.88e-02 3.47e-01 3.18e-01 2 .................. -1.77e-02 2.92e-01 2.97e-01 3 .................. 7.38e-03 2.36e-01 2.34e-01 08 10 D SM seed treatment ..... 0.003 1 .................. -7.78e-02 4.72e-01 5.10e-01 2 .................. 7.51e-02 5.28e-01 4.90e-01 CLASS 1 - weight 10 normalized weight 0.213 relative strength 8.94e-01 ******* class cross entropy w.r.t. global class 7.66e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 14 24 D SM fruiting bodies of s 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 tem 2 .................. 1.47e+00 9.55e-01 2.19e-01 12 22 D SM stem cankers ....... 1.209 2 .................. -2.82e+00 2.27e-02 3.80e-01 1 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 4 .................. 1.47e+00 9.32e-01 2.14e-01 13 23 D SM canker lesion color 0.750 3 .................. -2.76e+00 2.27e-02 3.59e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. 1.47e+00 3.86e-01 8.85e-02 2 .................. 5.18e-01 5.68e-01 3.39e-01 06 08 D SM damaged area ....... 0.682 4 .................. -1.75e+00 2.27e-02 1.30e-01 3 .................. -1.57e+00 2.27e-02 1.09e-01 1 .................. 1.47e+00 6.59e-01 1.51e-01 2 .................. -7.24e-01 2.95e-01 6.09e-01 19 29 D SM fruit pod condition 0.653 4 .................. -2.53e+00 4.55e-02 5.73e-01 1 .................. 8.04e-01 9.55e-01 4.27e-01 01 03 D SM plant stand ........ 0.567 2 .................. -2.46e+00 4.55e-02 5.31e-01 1 .................. 7.11e-01 9.55e-01 4.69e-01 00 02 D SM time of occurance .. 0.483 1 .................. -2.79e+00 1.30e-02 2.11e-01 3 .................. -2.44e+00 1.30e-02 1.49e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 7 .................. 8.03e-01 2.86e-01 1.28e-01 5 .................. 5.98e-01 1.95e-01 1.07e-01 6 .................. 5.98e-01 1.95e-01 1.07e-01 4 .................. 4.05e-01 2.86e-01 1.90e-01 03 05 D SM temperature ........ 0.460 1 .................. -2.48e+00 3.03e-02 3.61e-01 3 .................. -1.47e+00 3.03e-02 1.32e-01 2 .................. 6.17e-01 9.39e-01 5.07e-01 15 25 D SM outer stem decay ... 0.419 1 .................. -2.29e+00 4.55e-02 4.48e-01 2 .................. 5.48e-01 9.55e-01 5.52e-01 20 36 D SM root condition ..... 0.323 2 .................. -2.14e+00 4.55e-02 3.85e-01 1 .................. 4.40e-01 9.55e-01 6.15e-01 02 04 D SM precipitation ...... 0.191 1 .................. -1.96e+00 3.03e-02 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 3 .................. 3.02e-01 9.39e-01 6.94e-01 17 27 D SM internal discolorati 0.120 3 .................. -1.57e+00 4.55e-02 2.19e-01 on of stem 1 .................. 2.00e-01 9.55e-01 7.81e-01 18 28 D SM scerotia internal or 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 external 1 .................. 2.00e-01 9.55e-01 7.81e-01 05 07 D SM number years crop re 0.106 1 .................. -1.89e+00 2.27e-02 1.51e-01 peated 2 .................. 2.63e-01 3.86e-01 2.97e-01 3 .................. 2.32e-01 2.95e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 10 13 D SM leaf condition ..... 0.099 1 .................. -1.47e+00 4.55e-02 1.98e-01 2 .................. 1.74e-01 9.55e-01 8.02e-01 11 21 D SM stem lodging ....... 0.091 2 .................. 6.26e-01 4.09e-01 2.19e-01 1 .................. -2.79e-01 5.91e-01 7.81e-01 04 06 D SM occurance of hail .. 0.076 2 .................. -7.95e-01 1.36e-01 3.02e-01 1 .................. 2.13e-01 8.64e-01 6.98e-01 09 11 D SM seed germination ... 0.017 1 .................. -2.70e-01 2.12e-01 2.78e-01 3 .................. 1.85e-01 4.85e-01 4.03e-01 2 .................. -5.28e-02 3.03e-01 3.19e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 07 09 D SM damage severity .... 0.017 3 .................. -2.44e-01 3.18e-01 4.06e-01 2 .................. 1.38e-01 6.82e-01 5.94e-01 16 26 D SM mycelium on stem ... 0.000 2 .................. -1.36e-01 4.55e-02 5.21e-02 1 .................. 6.97e-03 9.55e-01 9.48e-01 08 10 D SM seed treatment ..... 0.000 2 .................. 2.11e-02 5.00e-01 4.90e-01 1 .................. -2.06e-02 5.00e-01 5.10e-01 CLASS 2 - weight 10 normalized weight 0.213 relative strength 8.99e-01 ******* class cross entropy w.r.t. global class 1.16e+01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 17 27 D SM internal discolorati 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 on of stem 3 .................. 1.47e+00 9.55e-01 2.19e-01 18 28 D SM scerotia internal or 1.277 1 .................. -2.84e+00 4.55e-02 7.81e-01 external 2 .................. 1.47e+00 9.55e-01 2.19e-01 02 04 D SM precipitation ...... 1.256 3 .................. -3.13e+00 3.03e-02 6.94e-01 1 .................. 1.47e+00 9.39e-01 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 13 23 D SM canker lesion color 1.218 3 .................. -2.76e+00 2.27e-02 3.59e-01 2 .................. -2.70e+00 2.27e-02 3.39e-01 4 .................. 1.47e+00 9.32e-01 2.14e-01 1 .................. -1.36e+00 2.27e-02 8.85e-02 12 22 D SM stem cankers ....... 1.209 2 .................. -2.82e+00 2.27e-02 3.80e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 1 .................. 1.47e+00 9.32e-01 2.14e-01 06 08 D SM damaged area ....... 1.205 2 .................. -3.29e+00 2.27e-02 6.09e-01 1 .................. -1.89e+00 2.27e-02 1.51e-01 3 .................. 1.47e+00 4.77e-01 1.09e-01 4 .................. 1.30e+00 4.77e-01 1.30e-01 03 05 D SM temperature ........ 0.674 1 .................. -2.48e+00 3.03e-02 3.61e-01 3 .................. 1.47e+00 5.76e-01 1.32e-01 2 .................. -2.52e-01 3.94e-01 5.07e-01 19 29 D SM fruit pod condition 0.653 4 .................. -2.53e+00 4.55e-02 5.73e-01 1 .................. 8.04e-01 9.55e-01 4.27e-01 15 25 D SM outer stem decay ... 0.609 2 .................. -2.50e+00 4.55e-02 5.52e-01 1 .................. 7.57e-01 9.55e-01 4.48e-01 01 03 D SM plant stand ........ 0.567 2 .................. -2.46e+00 4.55e-02 5.31e-01 1 .................. 7.11e-01 9.55e-01 4.69e-01 00 02 D SM time of occurance .. 0.535 1 .................. -2.79e+00 1.30e-02 2.11e-01 3 .................. -2.44e+00 1.30e-02 1.49e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 6 .................. 9.81e-01 2.86e-01 1.07e-01 7 .................. 8.03e-01 2.86e-01 1.28e-01 5 .................. 5.98e-01 1.95e-01 1.07e-01 4 .................. 2.25e-02 1.95e-01 1.90e-01 07 09 D SM damage severity .... 0.354 3 .................. -2.19e+00 4.55e-02 4.06e-01 2 .................. 4.75e-01 9.55e-01 5.94e-01 20 36 D SM root condition ..... 0.323 2 .................. -2.14e+00 4.55e-02 3.85e-01 1 .................. 4.40e-01 9.55e-01 6.15e-01 04 06 D SM occurance of hail .. 0.178 2 .................. 6.71e-01 5.91e-01 3.02e-01 1 .................. -5.34e-01 4.09e-01 6.98e-01 14 24 D SM fruiting bodies of s 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 tem 1 .................. 2.00e-01 9.55e-01 7.81e-01 10 13 D SM leaf condition ..... 0.099 1 .................. -1.47e+00 4.55e-02 1.98e-01 2 .................. 1.74e-01 9.55e-01 8.02e-01 09 11 D SM seed germination ... 0.035 1 .................. 3.49e-01 3.94e-01 2.78e-01 3 .................. -2.85e-01 3.03e-01 4.03e-01 2 .................. -5.28e-02 3.03e-01 3.19e-01 05 07 D SM number years crop re 0.011 1 .................. 3.03e-01 2.05e-01 1.51e-01 peated 3 .................. -1.36e-01 2.05e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 2 .................. -4.80e-03 2.95e-01 2.97e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 16 26 D SM mycelium on stem ... 0.000 2 .................. -1.36e-01 4.55e-02 5.21e-02 1 .................. 6.97e-03 9.55e-01 9.48e-01 08 10 D SM seed treatment ..... 0.000 2 .................. 2.11e-02 5.00e-01 4.90e-01 1 .................. -2.06e-02 5.00e-01 5.10e-01 11 21 D SM stem lodging ....... 0.000 2 .................. 3.82e-02 2.27e-01 2.19e-01 1 .................. -1.10e-02 7.73e-01 7.81e-01 CLASS 3 - weight 10 normalized weight 0.213 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 6.18e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/soybean/soyc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 10 13 D SM leaf condition ..... 1.031 2 .................. -1.77e+00 1.36e-01 8.02e-01 1 .................. 1.47e+00 8.64e-01 1.98e-01 13 23 D SM canker lesion color 0.799 3 .................. -2.76e+00 2.27e-02 3.59e-01 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -1.36e+00 2.27e-02 8.85e-02 2 .................. 1.01e+00 9.32e-01 3.39e-01 03 05 D SM temperature ........ 0.768 2 .................. -2.82e+00 3.03e-02 5.07e-01 3 .................. -1.47e+00 3.03e-02 1.32e-01 1 .................. 9.56e-01 9.39e-01 3.61e-01 12 22 D SM stem cankers ....... 0.685 4 .................. -2.24e+00 2.27e-02 2.14e-01 1 .................. -2.24e+00 2.27e-02 2.14e-01 3 .................. -2.14e+00 2.27e-02 1.93e-01 2 .................. 8.96e-01 9.32e-01 3.80e-01 15 25 D SM outer stem decay ... 0.419 1 .................. -2.29e+00 4.55e-02 4.48e-01 2 .................. 5.48e-01 9.55e-01 5.52e-01 00 02 D SM time of occurance .. 0.407 7 .................. -2.29e+00 1.30e-02 1.28e-01 6 .................. -2.11e+00 1.30e-02 1.07e-01 2 .................. -2.11e+00 1.30e-02 1.07e-01 1 .................. 7.94e-01 4.68e-01 2.11e-01 3 .................. 6.52e-01 2.86e-01 1.49e-01 4 .................. -6.06e-01 1.04e-01 1.90e-01 5 .................. -3.08e-02 1.04e-01 1.07e-01 19 29 D SM fruit pod condition 0.385 1 .................. -2.24e+00 4.55e-02 4.27e-01 4 .................. 5.10e-01 9.55e-01 5.73e-01 06 08 D SM damaged area ....... 0.277 1 .................. -1.89e+00 2.27e-02 1.51e-01 4 .................. -1.75e+00 2.27e-02 1.30e-01 3 .................. -1.57e+00 2.27e-02 1.09e-01 2 .................. 4.25e-01 9.32e-01 6.09e-01 09 11 D SM seed germination ... 0.225 1 .................. -2.22e+00 3.03e-02 2.78e-01 2 .................. 4.17e-01 4.85e-01 3.19e-01 3 .................. 1.85e-01 4.85e-01 4.03e-01 02 04 D SM precipitation ...... 0.191 1 .................. -1.96e+00 3.03e-02 2.15e-01 2 .................. -1.09e+00 3.03e-02 9.03e-02 3 .................. 3.02e-01 9.39e-01 6.94e-01 16 26 D SM mycelium on stem ... 0.177 2 .................. 1.47e+00 2.27e-01 5.21e-02 1 .................. -2.04e-01 7.73e-01 9.48e-01 20 36 D SM root condition ..... 0.152 2 .................. -1.04e+00 1.36e-01 3.85e-01 1 .................. 3.40e-01 8.64e-01 6.15e-01 01 03 D SM plant stand ........ 0.125 1 .................. -7.24e-01 2.27e-01 4.69e-01 2 .................. 3.75e-01 7.73e-01 5.31e-01 14 24 D SM fruiting bodies of s 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 tem 1 .................. 2.00e-01 9.55e-01 7.81e-01 17 27 D SM internal discolorati 0.120 3 .................. -1.57e+00 4.55e-02 2.19e-01 on of stem 1 .................. 2.00e-01 9.55e-01 7.81e-01 18 28 D SM scerotia internal or 0.120 2 .................. -1.57e+00 4.55e-02 2.19e-01 external 1 .................. 2.00e-01 9.55e-01 7.81e-01 04 06 D SM occurance of hail .. 0.076 2 .................. -7.95e-01 1.36e-01 3.02e-01 1 .................. 2.13e-01 8.64e-01 6.98e-01 05 07 D SM number years crop re 0.073 1 .................. 6.71e-01 2.95e-01 1.51e-01 peated 2 .................. -3.73e-01 2.05e-01 2.97e-01 3 .................. -1.36e-01 2.05e-01 2.34e-01 4 .................. -7.26e-02 2.95e-01 3.18e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl 07 09 D SM damage severity .... 0.018 3 .................. 2.08e-01 5.00e-01 4.06e-01 2 .................. -1.72e-01 5.00e-01 5.94e-01 08 10 D SM seed treatment ..... 0.013 2 .................. -1.80e-01 4.09e-01 4.90e-01 1 .................. 1.46e-01 5.91e-01 5.10e-01 11 21 D SM stem lodging ....... 0.000 2 .................. 3.82e-02 2.27e-01 2.19e-01 1 .................. -1.10e-02 7.73e-01 7.81e-01 autoclass-3.3.6.dfsg.1/data/soybean/soyc.search0000644000175000017500000000111411247310756017422 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 1 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 4 -6.45604162e+02 6 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 5 -6.60006947e+02 10 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -7.10824396e+02 4 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -7.27857655e+02 4 200 n_dups 0 start_j_list 10 15 20 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/soybean/soyc.db20000644000175000017500000001135711247310756016636 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; Soybean Case Histories ;;; from UC Irvine Technical Report 87-22 ;;; Fisher & Kibler PhD Dissertation - Knowledge Acquisition ;;; Via Incremental Conceptual Clustering ;;; ; 47 Data, 37 attributes 1,Diaporthe_Stem_Canker,5,1,3,2,2,2,1,2,1,3,2,2,1,3,3,1,1,1,2,1,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 2,Diaporthe_Stem_Canker,6,1,3,2,1,4,2,2,2,3,2,2,1,3,3,1,1,1,2,2,4,1,2,2,1,1,1,1,5,1,1,1,1,1,1 3,Diaporthe_Stem_Canker,4,1,3,2,1,3,1,3,2,2,2,2,1,3,3,1,1,1,2,1,4,1,2,2,1,1,1,1,5,1,1,1,1,1,1 4,Diaporthe_Stem_Canker,7,1,3,2,1,2,2,2,1,1,2,2,1,3,3,1,1,1,2,2,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 5,Diaporthe_Stem_Canker,5,1,3,2,1,4,1,3,1,3,2,2,1,3,3,1,1,1,2,1,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 6,Diaporthe_Stem_Canker,6,1,3,2,1,3,1,2,2,1,2,2,1,3,3,1,1,1,2,2,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 7,Diaporthe_Stem_Canker,4,1,3,2,1,3,2,2,1,2,2,2,1,3,3,1,1,1,2,2,4,1,2,2,1,1,1,1,5,1,1,1,1,1,1 8,Diaporthe_Stem_Canker,4,1,3,2,1,2,1,3,2,3,2,2,1,3,3,1,1,1,2,1,4,1,2,2,1,1,1,1,5,1,1,1,1,1,1 9,Diaporthe_Stem_Canker,7,1,3,2,1,4,1,2,2,2,2,2,1,3,3,1,1,1,2,1,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 10,Diaporthe_Stem_Canker,7,1,3,2,1,2,1,2,1,3,2,2,1,3,3,1,1,1,2,1,4,2,2,2,1,1,1,1,5,1,1,1,1,1,1 11,Charcoal_Rot,7,1,1,3,2,1,3,2,1,1,2,2,1,3,3,1,1,1,2,2,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 12,Charcoal_Rot,5,1,1,2,1,3,4,2,2,2,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 13,Charcoal_Rot,6,1,1,3,1,4,3,2,1,3,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 14,Charcoal_Rot,7,1,1,2,2,4,4,2,2,1,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 15,Charcoal_Rot,4,1,1,3,2,1,3,2,1,2,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 16,Charcoal_Rot,5,1,1,2,2,2,4,2,2,2,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 17,Charcoal_Rot,4,1,1,2,1,2,3,2,1,1,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 18,Charcoal_Rot,6,1,1,3,2,3,3,2,1,3,2,2,1,3,3,1,1,1,2,2,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 19,Charcoal_Rot,7,1,1,3,1,2,4,2,2,1,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 20,Charcoal_Rot,6,1,1,3,2,4,4,2,2,3,2,2,1,3,3,1,1,1,2,1,1,4,1,1,1,3,2,1,5,1,1,1,1,1,1 21,Rhizoctonia_Root_Rot,1,2,3,1,1,2,2,2,2,2,2,1,1,3,3,1,1,1,2,1,2,2,1,2,2,1,1,4,5,1,1,1,1,1,1 22,Rhizoctonia_Root_Rot,3,2,3,1,1,4,2,3,1,2,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 23,Rhizoctonia_Root_Rot,3,2,3,1,1,3,2,2,1,3,2,1,1,3,3,1,1,1,2,1,2,2,1,2,2,1,1,4,5,1,1,1,1,1,1 24,Rhizoctonia_Root_Rot,1,2,3,1,1,1,2,2,2,3,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 25,Rhizoctonia_Root_Rot,1,2,3,1,1,3,2,2,2,2,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 26,Rhizoctonia_Root_Rot,5,1,3,1,2,1,2,3,1,3,2,2,1,3,3,1,1,1,2,2,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 27,Rhizoctonia_Root_Rot,3,2,3,1,1,4,2,3,1,3,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 28,Rhizoctonia_Root_Rot,1,2,3,1,1,1,2,2,1,2,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,2 29,Rhizoctonia_Root_Rot,4,1,3,1,1,4,2,3,1,2,2,1,1,3,3,1,1,1,2,2,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 30,Rhizoctonia_Root_Rot,1,2,3,1,1,2,2,3,2,3,2,1,1,3,3,1,1,1,2,1,2,2,1,2,1,1,1,4,5,1,1,1,1,1,1 31,Phytophthora_Rot,3,2,3,2,2,4,2,3,2,3,2,2,1,3,3,1,1,1,2,1,3,3,1,2,1,1,1,4,5,1,1,1,1,1,2 32,Phytophthora_Rot,1,2,2,2,1,2,2,2,1,1,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 33,Phytophthora_Rot,4,2,3,1,1,2,2,3,2,1,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 34,Phytophthora_Rot,3,2,3,2,2,2,2,3,1,3,2,2,1,3,3,1,1,1,2,1,2,3,1,2,1,1,1,4,5,1,1,1,1,1,2 35,Phytophthora_Rot,2,2,3,1,1,4,2,2,2,3,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 36,Phytophthora_Rot,2,2,3,2,1,1,2,3,2,2,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 37,Phytophthora_Rot,1,2,3,2,1,4,2,2,1,1,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 38,Phytophthora_Rot,3,2,3,1,1,2,2,3,1,1,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 39,Phytophthora_Rot,4,2,3,1,1,3,2,3,2,2,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 40,Phytophthora_Rot,4,2,2,1,1,3,2,3,2,3,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 41,Phytophthora_Rot,1,2,3,2,2,2,2,2,1,1,2,2,1,3,3,1,1,1,2,1,2,3,1,2,1,1,1,4,5,1,1,1,1,1,2 42,Phytophthora_Rot,2,2,3,2,2,4,2,3,1,2,2,2,1,3,3,1,1,1,2,2,2,3,1,2,1,1,1,4,5,1,1,1,1,1,2 43,Phytophthora_Rot,2,2,3,1,1,1,2,3,2,1,2,2,1,3,3,1,1,1,2,1,3,3,1,1,1,1,1,4,5,1,1,1,1,1,2 44,Phytophthora_Rot,2,2,3,2,2,3,4,2,2,2,2,2,1,3,3,1,1,1,2,1,3,3,1,2,1,1,1,4,5,1,1,1,1,1,2 45,Phytophthora_Rot,3,2,2,1,1,4,2,3,1,3,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 46,Phytophthora_Rot,1,2,2,2,2,3,2,3,2,1,2,2,1,3,3,1,1,1,2,2,3,3,1,2,1,1,1,4,5,1,1,1,1,1,2 47,Phytophthora_Rot,1,2,3,2,1,4,2,2,1,3,2,2,1,3,3,1,1,1,2,1,2,3,1,1,1,1,1,4,5,1,1,1,1,1,2 autoclass-3.3.6.dfsg.1/data/tests.c0000644000175000017500000004511511247310756015135 0ustar areare;;;-------------------------------------------------------------------- ;; AUTOCLASS C Test suite: ;; when running with CodeCenter, ObjectCenter, or TestCenter replace "autoclass" with "run" ;;;------------------------------------------------------------------------ ;;; input checking tests ;;; S-PARAMS ERROR MSGS % yes % autoclass -search data/input-check/test.db2 data/input-check/test.hd2 \ data/input-check/test.model data/input-check/first-test-0.s-params ;;; HD2 & DB2 WARNING & ERROR MSGS % yes % autoclass -search data/input-check/test.db2 data/input-check/test.hd2 \ data/input-check/test.model data/input-check/first-test.s-params % yes % autoclass -search data/input-check/test.db2 data/input-check/test-0.hd2 \ data/input-check/test.model data/input-check/first-test.s-params % yes % autoclass -search data/input-check/test-00.db2 data/input-check/test-0.hd2 \ data/input-check/test.model data/input-check/first-test.s-params % yes % autoclass -search data/input-check/test-0.db2 data/input-check/test-0.hd2 \ data/input-check/test.model data/input-check/first-test.s-params ;;; RESPOND TO ERROR MSGS IN .DB2 => TEST-1.DB2 % yes % autoclass -search data/input-check/test-1.db2 data/input-check/test-0.hd2 \ data/input-check/test.model data/input-check/first-test.s-params ;;; RESPOND TO WARNING MSGS & ERROR MSGS IN .HD2 => TEST-1.HD2 TOO ;;; check errors flagged in generate_attribute_info, extend_terms, & ;;; extend_default_terms by looking at code and preturbing test-1.model % yes % autoclass -search data/input-check/test-1.db2 data/input-check/test-1.hd2 \ data/input-check/test-1.model data/input-check/first-test.s-params ;;; WARNING MSGS ONLY IN .HD2 => TEST-2.HD2 TOO % yes % autoclass -search data/input-check/test-1.db2 data/input-check/test-2.hd2 \ data/input-check/test-2.model data/input-check/first-test.s-params ;;; WRONG MODEL TERM TYPE % yes % autoclass -search data/input-check/test-1.db2 data/input-check/test-2.hd2 \ data/input-check/test-3.model data/input-check/first-test.s-params ;;; MODEL TERM EXPANSION MSGS -- SINGLE-NORMAL-CM MODEL & TOO MAY CLASSES FOR DATA % yes % autoclass -search data/input-check/test-cm.db2 data/input-check/test-cm.hd2 \ data/input-check/test-cm.model data/input-check/first-test.s-params % yes reply no to proceed from warnings % autoclass -search data/input-check/test-cm-1.db2 data/input-check/test-cm.hd2 \ data/input-check/test-cm.model data/input-check/first-test-1.s-params ;;; MODEL TERM EXPANSION MSGS -- SINGLE-NORMAL-CN MODEL % yes % autoclass -search data/input-check/test-cn.db2 data/input-check/test-cn.hd2 \ data/input-check/test-cn.model data/input-check/first-test.s-params % yes % autoclass -search data/input-check/test-cn-1.db2 data/input-check/test-cn.hd2 \ data/input-check/test-cn.model data/input-check/first-test.s-params % yes % autoclass -search data/input-check/test-cn-2.db2 data/input-check/test-cn.hd2 \ data/input-check/test-cn.model data/input-check/first-test.s-params ;;;--------------------------------------------------------------------------------------- ;;; CHECK-POINTING ;;;--------------------------------------------------------------------------------------- % autoclass -search data/glass/glassc.db2 data/glass/glass-3c.hd2 data/glass/glass-mnc.model data/glass/glassc-chkpt.s-params Run 1) ## glassc-chkpt.s-params max_n_tries = 2 force_new_search_p = true ## -------------------- ;; run to completion Run 2) ## glassc-chkpt.s-params force_new_search_p = false max_n_tries = 10 checkpoint_p = true min_checkpoint_period = 2 ## -------------------- ;; after first checkpoint, ctrl-C to abort Run 3) ## glassc-chkpt.s-params force_new_search_p = false max_n_tries = 1 checkpoint_p = true min_checkpoint_period = 1 reconverge_type = "chkpt" ## -------------------- ;; checkpointed trial should finish Run 4) ## reconverge checkpointed clsf with another try function ## glassc-chkpt.s-params force_new_search_p = false try_fn_type = "converge_search_4" max_n_tries = 1 reconverge_type = "results" ## -------------------- ;; this trial should start and complete with a slightly better log marginal value ;; than the previous trial ;;;------------------------------------------------------------- ;;; BLOCK-SET-CLSF TESTS (.s-params files configured for **non**-random trials) ;;;------------------------------------------------------------- ;;; MODEL: SINGLE-NORMAL-CN START-FN: BLOCK-SET-CLSF MODEL: -- RNA ;;; (att-type = real, att-subtype = scalar) ;;; yes % autoclass -search data/rna/rnac.db2 data/rna/rnac.hd2 data/rna/rnac.model data/rna/rnac.s-params % autoclass -reports data/rna/rnac.results-bin data/rna/rnac.search data/rna/rnac.r-params % autoclass -predict data/rna/rnac-predict.db2 data/rna/rnac.results-bin data/rna/rnac.search data/rna/rnac.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 5 CLASSES with WEIGHTS 22 20 11 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2270.933) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2297.160) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2317.474) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2350.139) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 5 CLASSES with WEIGHTS 22 20 11 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2270.935) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2297.157) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2321.813) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2350.340) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 5 CLASSES with WEIGHTS 22 20 11 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2270.932) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2297.160) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2317.475) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2350.139) N_CLASSES: 2 FOUND ON TRY: 1 ;;;------------------------------------------------------------- ;;; MODEL: SINGLE-NORMAL-CN START-FN: BLOCK-SET-CLSF MODEL: -- RNA ;;; (att-type = real, att-subtype = location) ;;; yes % autoclass -search data/rna/rnac.db2 data/rna/rnac-location.hd2 data/rna/rnac.model data/rna/rnac-location.s-params % autoclass -reports data/rna/rnac-location.results-bin data/rna/rnac-location.search data/rna/rnac-location.r-params % autoclass -predict data/rna/rnac-location-predict.db2 data/rna/rnac-location.results-bin data/rna/rnac-location.search data/rna/rnac-location.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 5 CLASSES with WEIGHTS 23 16 11 10 8 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2514.293) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2532.702) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2546.108) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2559.346) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 5 CLASSES with WEIGHTS 23 16 11 10 8 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2514.343) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2532.458) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2546.141) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2559.150) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 5 CLASSES with WEIGHTS 23 16 11 10 8 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2514.293) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2532.702) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2546.108) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2559.346) N_CLASSES: 2 FOUND ON TRY: 1 ;;;------------------------------------------------------------- ;;; MODEL: SINGLE-NORMAL-CM, START-FN: BLOCK-SET-CLSF, MODEL: -- RNA-UNK ;;; (att-type = real, att-subtype = scalar) ;;; yes % autoclass -search data/rna/rnac-unk.db2 data/rna/rnac.hd2 data/rna/rnac-unk.model data/rna/rnac-unk.s-params % autoclass -reports data/rna/rnac-unk.results-bin data/rna/rnac-unk.search data/rna/rnac-unk.r-params % autoclass -predict data/rna/rnac-unk-predict.db2 data/rna/rnac-unk.results-bin data/rna/rnac-unk.search data/rna/rnac-unk.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 5 CLASSES with WEIGHTS 22 21 10 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2315.537) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2343.858) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2345.158) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2359.724) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 5 CLASSES with WEIGHTS 22 21 10 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2315.332) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2343.914) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2345.153) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2359.718) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 5 CLASSES with WEIGHTS 22 21 10 8 7 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-2315.537) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-2343.858) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-2345.158) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-2359.724) N_CLASSES: 2 FOUND ON TRY: 1 ;;;------------------------------------------------------------- ;;; MODEL: SINGLE-MULTINOMIAL, START-FN: BLOCK-SET-CLSF, - SOYBEAN == ;;; (att-type = discrete, att-subtype = nominal) ;;; yes % autoclass -search data/soybean/soyc.db2 data/soybean/soyc.hd2 data/soybean/soyc.model data/soybean/soyc.s-params % autoclass -reports data/soybean/soyc.results-bin data/soybean/soyc.search data/soybean/soyc.r-params % autoclass -predict data/soybean/soyc-predict.db2 data/soybean/soyc.results-bin data/soybean/soyc.search data/soybean/soyc.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 4 CLASSES with WEIGHTS 17 10 10 10 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-645.604) N_CLASSES: 4 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-660.007) N_CLASSES: 5 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-710.824) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-727.858) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 4 CLASSES with WEIGHTS 17 10 10 10 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-645.604) N_CLASSES: 4 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-660.010) N_CLASSES: 5 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-710.824) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-727.858) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 4 CLASSES with WEIGHTS 17 10 10 10 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-645.604) N_CLASSES: 4 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-660.007) N_CLASSES: 5 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-710.824) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-727.858) N_CLASSES: 2 FOUND ON TRY: 1 ;;;------------------------------------------------------------- ;;; MODEL: MULTI-NORMAL-CN, START-FN: BLOCK-SET-CLSF, - 3-DIM == ;;; (att-type = real, att-subtype = location) ;;; yes % autoclass -search data/3-dim/3-dimc.db2 data/3-dim/3-dimc.hd2 data/3-dim/3-dimc.model data/3-dim/3-dimc.s-params % autoclass -reports data/3-dim/3-dimc.results-bin data/3-dim/3-dimc.search data/3-dim/3-dimc.r-params % autoclass -predict data/3-dim/3-dimc-predict.db2 data/3-dim/3-dimc.results-bin data/3-dim/3-dimc.search data/3-dim/3-dimc.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 2 CLASSES with WEIGHTS 58 42 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-4255.623) N_CLASSES: 2 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-4257.323) N_CLASSES: 2 FOUND ON TRY: 2 *SAVED* PROBABILITY: exp(-4257.496) N_CLASSES: 2 FOUND ON TRY: 1 PROBABILITY: exp(-4257.508) N_CLASSES: 2 FOUND ON TRY: 3 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 2 CLASSES with WEIGHTS 50 50 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-4259.769) N_CLASSES: 2 FOUND ON TRY: 1 *SAVED* PROBABILITY: exp(-4270.297) N_CLASSES: 3 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-4272.345) N_CLASSES: 3 FOUND ON TRY: 4 PROBABILITY: exp(-4274.939) N_CLASSES: 3 FOUND ON TRY: 2 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 2 CLASSES with WEIGHTS 58 42 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-4255.622) N_CLASSES: 2 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-4257.310) N_CLASSES: 2 FOUND ON TRY: 2 *SAVED* PROBABILITY: exp(-4257.496) N_CLASSES: 2 FOUND ON TRY: 1 PROBABILITY: exp(-4257.508) N_CLASSES: 2 FOUND ON TRY: 3 autoclass -reports data/3-dim/3-dimc.results-bin data/3-dim/3-dimc.search data/3-dim/3-dimc.r-params ;;;------------------------------------------------------------- ;;; MODEL: MULTI-NORMAL-CN, START-FN: BLOCK-SET-CLSF, - GLASS == ;;; (att-type = real, att-subtype = scalar) ;;; yes % autoclass -search data/glass/glassc.db2 data/glass/glass-3c.hd2 data/glass/glass-mnc.model data/glass/glassc.s-params % autoclass -reports data/glass/glassc.results-bin data/glass/glassc.search data/glass/glassc.r-params % autoclass -predict data/glass/glassc-predict.db2 data/glass/glassc.results-bin data/glass/glassc.search data/glass/glassc.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 5 CLASSES with WEIGHTS 97 46 35 19 17 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.745) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.516) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.431) N_CLASSES 2 FOUND ON TRY 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 5 CLASSES with WEIGHTS 100 43 35 19 17 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10907.367) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.751) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.554) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.418) N_CLASSES 2 FOUND ON TRY 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 5 CLASSES with WEIGHTS 97 46 35 19 17 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.745) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.516) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.432) N_CLASSES 2 FOUND ON TRY 1 ;;;------------------------------------------------------------- ;;; MODEL-SINGLE-NORMAL-CM & MODEL-SINGLE-MULTINOMIAL - IMPORTS-85 ;;; yes % autoclass -search data/autos/imports-85.db2 data/autos/imports-85.hd2 data/autos/imports-85.model data/autos/imports-85.s-params % autoclass -reports data/autos/imports-85.results-bin data/autos/imports-85.search data/autos/imports-85.r-params % autoclass -predict data/autos/imports-85-predict.db2 data/autos/imports-85.results-bin data/autos/imports-85.search data/autos/imports-85.r-params try_fn_type = "converge_search_3" & rel_delta_range = 0.0025 & n_average = 3 It has 7 CLASSES with WEIGHTS 50 46 37 31 18 13 11 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-16453.536) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-16654.238) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-16816.658) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-17041.867) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_3" & rel_delta_range = 0.05 & n_average = 3 It has 7 CLASSES with WEIGHTS 50 46 40 28 18 13 11 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-16464.989) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-16674.829) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-16816.729) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-17042.456) N_CLASSES: 2 FOUND ON TRY: 1 try_fn_type = "converge_search_4" & cs4_delta_range = 0.0025 & sigma_beta_n_values = 6 It has 7 CLASSES with WEIGHTS 50 46 37 31 18 13 11 ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY: exp(-16453.532) N_CLASSES: 7 FOUND ON TRY: 4 *SAVED* PROBABILITY: exp(-16654.237) N_CLASSES: 5 FOUND ON TRY: 3 *SAVED* PROBABILITY: exp(-16816.658) N_CLASSES: 3 FOUND ON TRY: 2 PROBABILITY: exp(-17041.867) N_CLASSES: 2 FOUND ON TRY: 1 ;;;------------------------------------------------------------- ;;; MODEL: MULTI-NORMAL-CN. REPORTS CASE - HUNG ;;; 3 MULTI-NORMAL-CN TERMS ;;; (att-type = real, att-subtype = scalar) ;;; yes % autoclass -search data/hung/testsum.db2-bin data/hung/testsum.hd2 data/hung/testsum.model data/hung/testsum.s-params % autoclass -reports data/hung/testsum.results-bin data/hung/testsum.search data/hung/testsum.r-params ;;; ordering & non-ordering of attributes in influence values report order_attributes_by_influence_p = true order_attributes_by_influence_p = false ;;;------------------------------------------------------------- ;;; MODEL: SINGLE_MULTINOMIAL. REPORTS CASE - ROMKE ;;; LARGE NUMBER OF DISCRETE ATTRIBUTES ;;; (att-type = discrete, att-subtype = nominal) ;;; yes % autoclass -reports data/romke/big.results data/romke/big.search data/romke/big.r-params autoclass-3.3.6.dfsg.1/data/semantic.cache0000644000175000017500000000050211247310756016406 0ustar areare;; Object data/ ;; SEMANTICDB Tags save file (semanticdb-project-database-file "data/" :tables (list (semanticdb-table "tests.c" :major-mode 'c-mode :tags 'nil :file "tests.c" :pointmax 19022 ) ) :file "semantic.cache" :semantic-tag-version "2.0beta3" :semanticdb-version "2.0beta3" ) autoclass-3.3.6.dfsg.1/data/rna/0000755000175000017500000000000011247310756014401 5ustar areareautoclass-3.3.6.dfsg.1/data/rna/rnac.influ-o-text-10000644000175000017500000003242611247310756017746 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2270.933 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 1 second _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 5 POPULATED CLASSES (max global influence value = 2.463) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -3.17e+01 1.09e-02 22 0.320 1 -2.72e+01 1.00e+00 20 0.292 2 -3.19e+01 8.82e-03 11 0.162 3 -3.27e+01 3.85e-03 8 0.117 4 -3.82e+01 1.63e-05 7 0.109 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 4.25e+00 22 0.320 1 7.14e+00 20 0.292 2 5.38e+00 11 0.162 3 7.24e+00 8 0.117 4 2.01e+00 7 0.109 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 010 Log x(v) 1.000 009 Log z(p) 0.883 012 Log z(v) 0.817 007 Log x(p) 0.669 011 Log y(v) 0.513 008 Log y(p) 0.438 000 numero ----- 001 x(p) ----- 002 y(p) ----- 003 z(p) ----- 004 x(v) ----- 005 y(v) ----- 006 z(v) ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 22 normalized weight 0.320 relative strength 1.09e-02 ******* class cross entropy w.r.t. global class 4.25e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 12 R SNcn Log z(v) ........... 1.404 ( 4.99e-01 1.61e-01) 2.90e+00 ( 3.22e-02 9.41e-01) 01 08 R SNcn Log y(p) ........... 1.007 ( 1.89e+00 3.48e-01) 2.51e+00 ( 1.02e+00 1.06e+00) 03 10 R SNcn Log x(v) ........... 0.894 ( 1.87e-01 2.82e-01) 1.18e+00 (-1.45e-01 1.04e+00) 02 09 R SNcn Log z(p) ........... 0.549 ( 7.19e-01 4.27e-01) 1.41e+00 ( 1.32e+00 7.80e-01) 04 11 R SNcn Log y(v) ........... 0.360 (-5.84e-01 8.89e-01) 5.72e-01 (-7.54e-02 6.83e-01) 00 07 R SNcn Log x(p) ........... 0.038 ( 1.81e+00 5.16e-01) 2.67e-01 ( 1.95e+00 5.68e-01) CLASS 1 - weight 20 normalized weight 0.292 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 7.14e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 12 R SNcn Log z(v) ........... 2.345 ( 6.08e-01 6.61e-02) 8.71e+00 ( 3.22e-02 9.41e-01) 04 11 R SNcn Log y(v) ........... 1.288 ( 2.80e-01 1.33e-01) 2.67e+00 (-7.54e-02 6.83e-01) 00 07 R SNcn Log x(p) ........... 1.214 ( 2.18e+00 1.13e-01) 2.04e+00 ( 1.95e+00 5.68e-01) 03 10 R SNcn Log x(v) ........... 1.039 ( 1.78e-01 2.41e-01) 1.34e+00 (-1.45e-01 1.04e+00) 02 09 R SNcn Log z(p) ........... 0.685 ( 1.37e+00 2.52e-01) 1.83e-01 ( 1.32e+00 7.80e-01) 01 08 R SNcn Log y(p) ........... 0.569 ( 3.26e-01 5.04e-01) 1.37e+00 ( 1.02e+00 1.06e+00) CLASS 2 - weight 11 normalized weight 0.162 relative strength 8.82e-03 ******* class cross entropy w.r.t. global class 5.38e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 03 10 R SNcn Log x(v) ........... 1.882 ( 4.28e-01 1.13e-01) 5.08e+00 (-1.45e-01 1.04e+00) 04 11 R SNcn Log y(v) ........... 1.001 ( 3.19e-01 1.87e-01) 2.11e+00 (-7.54e-02 6.83e-01) 02 09 R SNcn Log z(p) ........... 0.956 ( 1.99e+00 2.79e-01) 2.39e+00 ( 1.32e+00 7.80e-01) 05 12 R SNcn Log z(v) ........... 0.707 (-1.08e+00 9.91e-01) 1.13e+00 ( 3.22e-02 9.41e-01) 01 08 R SNcn Log y(p) ........... 0.493 (-3.35e-02 1.02e+00) 1.03e+00 ( 1.02e+00 1.06e+00) 00 07 R SNcn Log x(p) ........... 0.340 ( 1.81e+00 2.87e-01) 4.79e-01 ( 1.95e+00 5.68e-01) CLASS 3 - weight 8 normalized weight 0.117 relative strength 3.85e-03 ******* class cross entropy w.r.t. global class 7.24e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 02 09 R SNcn Log z(p) ........... 2.463 ( 2.05e+00 6.28e-02) 1.17e+01 ( 1.32e+00 7.80e-01) 03 10 R SNcn Log x(v) ........... 2.097 (-2.18e+00 1.53e+00) 1.34e+00 (-1.45e-01 1.04e+00) 00 07 R SNcn Log x(p) ........... 1.478 ( 2.47e+00 1.22e-01) 4.25e+00 ( 1.95e+00 5.68e-01) 01 08 R SNcn Log y(p) ........... 0.551 ( 1.72e+00 5.21e-01) 1.35e+00 ( 1.02e+00 1.06e+00) 05 12 R SNcn Log z(v) ........... 0.359 (-7.12e-01 1.15e+00) 6.49e-01 ( 3.22e-02 9.41e-01) 04 11 R SNcn Log y(v) ........... 0.295 (-2.54e-01 3.70e-01) 4.84e-01 (-7.54e-02 6.83e-01) CLASS 4 - weight 7 normalized weight 0.109 relative strength 1.63e-05 ******* class cross entropy w.r.t. global class 2.01e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 07 R SNcn Log x(p) ........... 0.957 ( 1.41e+00 1.01e+00) 5.39e-01 ( 1.95e+00 5.68e-01) 02 09 R SNcn Log z(p) ........... 0.667 ( 1.19e+00 1.48e+00) 8.99e-02 ( 1.32e+00 7.80e-01) 04 11 R SNcn Log y(v) ........... 0.144 ( 8.43e-02 4.65e-01) 3.43e-01 (-7.54e-02 6.83e-01) 03 10 R SNcn Log x(v) ........... 0.112 (-5.62e-01 8.64e-01) 4.83e-01 (-1.45e-01 1.04e+00) 05 12 R SNcn Log z(v) ........... 0.108 (-3.45e-01 7.87e-01) 4.79e-01 ( 3.22e-02 9.41e-01) 01 08 R SNcn Log y(p) ........... 0.018 ( 1.13e+00 1.18e+00) 9.65e-02 ( 1.02e+00 1.06e+00) autoclass-3.3.6.dfsg.1/data/rna/rnac-predict.case-text-10000644000175000017500000000201711247310756020731 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 8 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2270.933 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 4 1.000 4 2 0.999 7 4 1.000 2 4 1.000 5 1 0.999 8 4 0.998 3 3 0.999 6 0 0.999 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk-predict.case-text-10000644000175000017500000000213111247310756021521 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2315.537 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 4 1.000 5 0 0.991 9 0 0.999 2 1 0.999 6 0 0.951 10 1 0.563 3 2 0.966 7 1 0.962 4 2 0.604 8 0 0.999 autoclass-3.3.6.dfsg.1/data/rna/rnac.r-params0000644000175000017500000001116611247310756016775 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.hd20000644000175000017500000000145711247310756017540 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 68 Data, 7 attributes ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 7 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 real location "numero" error 0.0 1 real location "x(p)" error .01 2 real location "y(p)" error .01 3 real location "z(p)" error .01 4 real location "x(v)" error .01 5 real location "y(v)" error .01 6 real location "z(v)" error .01 autoclass-3.3.6.dfsg.1/data/rna/rnac-predict.db20000644000175000017500000000153611247310756017352 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 5 Data, 7 attributes for prediction test database 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 4,12.8610,7.0418,-5.3478,-0.4860,0.0997,-0.8683 6,6.9577,-0.8914,-3.8977,0.5327,0.5117,-0.6741 16,9.0270,-0.0953,-8.8529,0.3500,0.4757,0.8070 17,8.8524,9.2938,-11.8409,0.5178,-0.3210,0.7930 33,6.9082,1.5865,-8.7953,-0.8354,0.3378,0.4336 34,4.2023,1.4894,-7.1171,-0.5162,0.7284,0.4505 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.influ-o-text-10000644000175000017500000003156211247310756021554 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 with log-A (approximate marginal likelihood) = -2514.293 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 1 second _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-2514.294) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-2532.702) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-2546.108) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2559.346) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 5 POPULATED CLASSES (max global influence value = 2.294) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -3.31e+01 1.00e+00 23 0.341 1 -3.65e+01 3.38e-02 16 0.239 2 -3.60e+01 5.24e-02 11 0.162 3 -3.67e+01 2.64e-02 10 0.147 4 -3.35e+01 6.62e-01 8 0.111 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 4.03e+00 23 0.341 1 2.62e+00 16 0.239 2 4.73e+00 11 0.162 3 4.85e+00 10 0.147 4 6.10e+00 8 0.111 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 006 z(v) 1.000 003 z(p) 0.719 004 x(v) 0.534 002 y(p) 0.451 001 x(p) 0.448 005 y(v) 0.247 000 numero ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 23 normalized weight 0.341 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 4.03e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 01 02 R SNcn y(p) ............... 1.520 ( 5.10e-01 6.23e-01) 4.35e+00 ( 3.22e+00 3.27e+00) 05 06 R SNcn z(v) ............... 0.942 ( 7.62e-01 1.94e-01) 2.14e+00 ( 3.46e-01 6.29e-01) 04 05 R SNcn y(v) ............... 0.672 ( 3.81e-01 1.89e-01) 1.56e+00 ( 8.62e-02 4.52e-01) 02 03 R SNcn z(p) ............... 0.436 (-8.68e+00 1.11e+00) 2.85e-01 (-8.36e+00 2.56e+00) 00 01 R SNcn x(p) ............... 0.404 ( 8.50e+00 1.39e+00) 4.33e-01 ( 7.90e+00 3.03e+00) 03 04 R SNcn x(v) ............... 0.052 ( 1.88e-01 3.92e-01) 1.48e-01 ( 1.30e-01 4.94e-01) CLASS 1 - weight 16 normalized weight 0.239 relative strength 3.38e-02 ******* class cross entropy w.r.t. global class 2.62e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 02 03 R SNcn z(p) ............... 0.870 (-1.05e+01 9.87e-01) 2.15e+00 (-8.36e+00 2.56e+00) 05 06 R SNcn z(v) ............... 0.641 ( 5.78e-01 2.30e-01) 1.01e+00 ( 3.46e-01 6.29e-01) 01 02 R SNcn y(p) ............... 0.388 ( 5.48e+00 2.10e+00) 1.08e+00 ( 3.22e+00 3.27e+00) 04 05 R SNcn y(v) ............... 0.337 (-2.56e-01 5.59e-01) 6.12e-01 ( 8.62e-02 4.52e-01) 00 01 R SNcn x(p) ............... 0.248 ( 5.78e+00 2.90e+00) 7.32e-01 ( 7.90e+00 3.03e+00) 03 04 R SNcn x(v) ............... 0.131 ( 2.73e-01 3.54e-01) 4.03e-01 ( 1.30e-01 4.94e-01) CLASS 2 - weight 11 normalized weight 0.162 relative strength 5.24e-02 ******* class cross entropy w.r.t. global class 4.73e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 06 R SNcn z(v) ............... 2.050 (-6.81e-01 1.96e-01) 5.25e+00 ( 3.46e-01 6.29e-01) 03 04 R SNcn x(v) ............... 1.013 ( 5.09e-01 1.53e-01) 2.47e+00 ( 1.30e-01 4.94e-01) 02 03 R SNcn z(p) ............... 0.916 (-5.12e+00 1.76e+00) 1.85e+00 (-8.36e+00 2.56e+00) 00 01 R SNcn x(p) ............... 0.456 ( 5.15e+00 2.42e+00) 1.14e+00 ( 7.90e+00 3.03e+00) 01 02 R SNcn y(p) ............... 0.219 ( 1.64e+00 2.28e+00) 6.91e-01 ( 3.22e+00 3.27e+00) 04 05 R SNcn y(v) ............... 0.072 ( 2.18e-01 3.77e-01) 3.49e-01 ( 8.62e-02 4.52e-01) CLASS 3 - weight 10 normalized weight 0.147 relative strength 2.64e-02 ******* class cross entropy w.r.t. global class 4.85e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 03 04 R SNcn x(v) ............... 1.812 (-7.30e-01 2.55e-01) 3.38e+00 ( 1.30e-01 4.94e-01) 02 03 R SNcn z(p) ............... 1.101 (-5.46e+00 1.07e+00) 2.71e+00 (-8.36e+00 2.56e+00) 00 01 R SNcn x(p) ............... 1.051 ( 1.16e+01 1.53e+00) 2.41e+00 ( 7.90e+00 3.03e+00) 05 06 R SNcn z(v) ............... 0.637 (-2.82e-01 4.11e-01) 1.53e+00 ( 3.46e-01 6.29e-01) 04 05 R SNcn y(v) ............... 0.136 (-4.56e-02 3.22e-01) 4.09e-01 ( 8.62e-02 4.52e-01) 01 02 R SNcn y(p) ............... 0.109 ( 4.71e+00 3.51e+00) 4.24e-01 ( 3.22e+00 3.27e+00) CLASS 4 - weight 8 normalized weight 0.111 relative strength 6.62e-01 ******* class cross entropy w.r.t. global class 6.10e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cn SNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 06 R SNcn z(v) ............... 2.294 ( 9.12e-01 5.79e-02) 9.78e+00 ( 3.46e-01 6.29e-01) 02 03 R SNcn z(p) ............... 1.397 (-1.14e+01 8.25e-01) 3.70e+00 (-8.36e+00 2.56e+00) 00 01 R SNcn x(p) ............... 0.785 ( 9.84e+00 1.10e+00) 1.76e+00 ( 7.90e+00 3.03e+00) 01 02 R SNcn y(p) ............... 0.723 ( 6.84e+00 2.26e+00) 1.60e+00 ( 3.22e+00 3.27e+00) 03 04 R SNcn x(v) ............... 0.495 ( 2.33e-01 2.03e-01) 5.04e-01 ( 1.30e-01 4.94e-01) 04 05 R SNcn y(v) ............... 0.403 (-8.17e-02 2.21e-01) 7.59e-01 ( 8.62e-02 4.52e-01) autoclass-3.3.6.dfsg.1/data/rna/rnac.rlog0000644000175000017500000007177611247310756016233 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 14:49:06 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 14:49:06 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:23:11 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:29:32 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:30:44 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:31:03 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:33:35 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 15:33:35 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:34:44 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 15:34:44 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:50:01 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 15:58:32 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:16:59 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:18:09 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:24:16 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:26:47 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:30:45 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:33:29 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:34:20 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:34:21 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:40:30 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:41:18 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:41:19 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:46:19 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:46:19 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:47:04 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:56:04 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:59:43 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:59:43 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:02:51 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 17:02:51 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:06:00 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 17:06:00 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:16:29 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 17:16:29 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:17:44 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:21:47 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:22:53 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 17:22:53 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:25:27 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:28:57 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:16:56 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:24:32 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:26:28 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:38:59 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Tue Jun 5 17:38:59 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 17:58:43 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Wed Jun 6 17:58:43 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:27:30 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:27:30 2001 autoclass-3.3.6.dfsg.1/data/rna/rnac-location-predict.class-text-10000644000175000017500000000351211247310756022732 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 8 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 with log-A (approximate marginal likelihood) = -2514.293 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 5 1.000 7 0.995 1 0.005 8 0.976 1 0.024 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 4 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 2 1.000 3 1.000 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 0.901 1 0.099 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.model0000644000175000017500000000067711247310756017153 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ;; 1 or more model definitions ;; model_index model_index 0 2 ;; ... ignore 0 single_normal_cm 1 2 3 4 5 6 autoclass-3.3.6.dfsg.1/data/rna/rnac-location-predict.case-text-10000644000175000017500000000205211247310756022536 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 8 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 with log-A (approximate marginal likelihood) = -2514.293 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 3 1.000 4 2 1.000 7 0 0.995 2 3 1.000 5 0 0.999 8 0 0.976 3 3 1.000 6 4 0.901 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk-predict.class-text-10000644000175000017500000000372011247310756021720 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2315.537 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 5 0.991 2 0.009 6 0.951 2 0.048 4 0.001 8 0.999 2 0.001 9 0.999 2 0.001 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 0.999 7 0.962 2 0.038 10 0.563 2 0.437 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 3 0.966 1 0.034 4 0.604 1 0.395 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.s-params0000644000175000017500000002236511247310756017574 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.db20000644000175000017500000000707011247310756016514 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 68 Data, 7 attributes 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 2,3.3717,6.2885,-10.5401,0.1322,-0.8102,0.5711 3,5.0184,4.6393,-11.2117,-0.0073,-0.5806,0.8142 4,12.8610,7.0418,-5.3478,-0.4860,0.0997,-0.8683 5,12.3403,5.4943,-10.5624,-0.3290,0.5859,0.7406 6,?,-0.8914,-3.8977,0.5327,0.5117,-0.6741 7,4.2251,1.2770,-8.9367,0.6231,0.7303,-0.2801 8,7.8351,7.9843,-12.9357,0.4055,0.0990,0.9087 9,7.8416,-0.8153,-4.6193,0.5723,0.6039,-0.5547 10,4.5589,-0.2088,-3.9610,0.8749,0.2014,-0.4405 11,0.4334,6.5764,-4.9003,0.5279,-0.6362,-0.5626 12,10.9123,?,-11.2686,0.1146,-0.0641,0.9913 13,13.7526,3.1565,-5.3233,-0.9512,-0.2950,0.0909 14,9.4074,2.4833,-2.0792,0.3880,-0.0936,-0.9169 15,5.7826,1.5037,-4.9871,0.1940,0.2504,-0.9485 16,9.0270,-0.0953,-8.8529,0.3500,0.4757,0.8070 17,8.8524,9.2938,-11.8409,0.5178,-0.3210,0.7930 18,3.6560,4.3409,?,0.5809,-0.4352,0.6878 19,2.3908,9.4845,-9.6016,0.5135,0.1241,0.8491 20,11.1601,7.6421,-11.2482,-0.0299,0.1716,0.9847 21,10.2860,-0.3494,-6.9610,-0.0480,0.6753,0.7360 22,10.8998,8.5427,-11.1533,0.1002,-0.4681,0.8780 23,4.2783,2.3259,-10.4358,0.4691,-0.0440,0.8820 24,8.8627,7.6604,-12.2208,?,0.1097,0.8747 25,10.3666,0.7718,-9.1882,-0.0522,0.2162,0.9750 26,10.3666,0.7718,-9.1882,-0.0522,0.2162,0.9750 27,8.3373,-0.6567,-7.8716,-0.1328,0.3873,0.9123 28,10.3979,2.8571,-10.5576,-0.0444,-0.3009,0.9526 29,9.5540,0.9464,-9.3461,-0.3528,-0.0808,0.9322 30,6.7947,6.0667,-12.1396,-0.2642,-0.6909,0.6729 31,10.7148,9.7124,-8.8671,0.2541,-0.9556,0.1495 32,11.7587,1.8787,-5.2227,-0.9469,?,0.1608 33,6.9082,1.5865,-8.7953,-0.8354,0.3378,0.4336 34,4.2023,1.4894,-7.1171,-0.5162,0.7284,0.4505 35,11.2721,12.0003,-6.2916,-0.9958,-0.0742,0.0529 36,11.5720,6.4747,-4.3818,-0.1396,-0.2213,-0.9652 37,8.7213,2.3700,-4.8825,-0.9276,-0.0596,-0.3687 38,8.7198,0.7693,-7.1437,-0.0534,0.4339,0.8994 39,5.6592,0.0370,-3.9632,0.4658,0.1502,-0.8721 40,3.3965,2.1506,-7.8867,0.4006,0.6673,? 41,7.6410,0.4475,-10.1836,0.3936,0.3630,0.8446 42,12.0817,4.2518,-8.4655,-0.6654,0.4801,-0.5716 43,6.4402,0.0661,-4.6974,0.5486,0.2322,-0.8032 44,9.0661,0.9876,-10.6172,0.2741,0.3298,0.9034 45,7.5654,-0.1753,-9.5899,0.2168,0.3398,0.9152 46,1.5457,5.6684,-6.0399,0.4812,-0.2003,-0.8534 47,8.9653,0.3247,-10.0728,0.0924,0.2921,0.9519 48,8.9653,0.3247,-10.0728,0.0924,0.2921,0.9519 49,10.2241,6.4498,-11.2915,-0.2959,0.7632,? 50,9.0737,0.1021,-7.7521,0.6000,0.7295,0.3282 51,6.6366,-0.4600,-6.7527,0.7888,0.4583,0.4096 52,3.8250,3.7715,-10.3711,0.9410,-0.1730,0.2907 53,12.4519,2.0353,-5.0957,-0.9112,0.1310,0.3907 54,8.2489,1.1837,-9.5679,0.7235,0.3626,? 55,8.2489,1.1837,-9.5679,0.7235,0.3626,0.5875 56,6.7405,0.5263,-8.7933,0.6840,0.1256,0.7185 57,2.6143,6.0357,-10.0313,0.5391,-0.7735,0.3332 58,7.3507,2.2775,-10.1005,0.4996,-0.2910,0.8159 59,5.2306,7.8844,-10.9373,0.4213,-0.8944,0.1505 60,9.9296,0.6538,-8.1603,0.3578,0.5769,? 61,9.9296,0.6538,-8.1603,0.3578,0.5769,0.7342 62,7.7086,-0.3975,-6.9777,0.4323,0.4952,0.7536 63,4.8080,3.7794,-10.8582,0.6629,-0.3437,0.6652 64,12.8371,9.1619,-5.0408,-0.5013,-0.6971,-0.5125 65,9.8706,2.2357,-9.7856,0.3855,0.1467,? 66,8.6698,0.7492,-8.6831,0.2311,0.1315,0.9640 67,5.9728,5.6075,-11.5282,0.3562,-0.6924,0.6274 68,2.4298,3.8136,-7.9598,-0.0687,0.9271,0.3684 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.s-params0000644000175000017500000002236611247310756020610 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/rna/rnac.log0000644000175000017500000036001011247310756016027 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:21:37 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:22:26 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:24:00 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Thu May 31 16:24:00 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu May 31 16:24:00 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:24:00 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:26:40 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Thu May 31 16:26:41 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu May 31 16:26:41 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:26:41 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:30:38 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Thu May 31 16:30:38 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu May 31 16:30:39 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:30:39 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:33:40 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, ignore is not a valid default model term type AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 16:34:15 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Thu May 31 16:34:16 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu May 31 16:34:16 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 16:34:16 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:26:47 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, ignore is not a valid default model term type AUTOCLASS C (version 3.3.4unx) STARTING at Thu May 31 17:28:35 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Thu May 31 17:28:36 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu May 31 17:28:36 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu May 31 17:28:36 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:16:41 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Tue Jun 5 17:16:45 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Tue Jun 5 17:16:46 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Tue Jun 5 17:16:46 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Tue Jun 5 17:34:30 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Tue Jun 5 17:34:32 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Tue Jun 5 17:34:32 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Tue Jun 5 17:34:32 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 17:58:18 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Wed Jun 6 17:58:19 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Wed Jun 6 17:58:19 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Wed Jun 6 17:58:20 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:03:52 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:13:49 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: the default model term type, ignore, will be used for these attributes: #6: "z(v)" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log BEGINNING SEARCH at Wed Jun 6 18:13:50 2001 [j_in=2] [cs-3: cycles 11] best2->2(1) [j_in=3] [cs-3: cycles 16] best3->3(2) [j_in=5] [cs-3: cycles 18] 5->5(3) [j_in=7] [cs-3: cycles 22] 7->7(4) ENDING SEARCH because max number of tries reached at Wed Jun 6 18:13:50 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-1913.830) N_CLASSES 3 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-1931.156) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-1937.671) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-1938.981) N_CLASSES 7 FOUND ON TRY 4 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 2 num_cycles 16 max_cycles 200 convergent try 3 num_cycles 18 max_cycles 200 convergent try 1 num_cycles 11 max_cycles 200 convergent try 4 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Wed Jun 6 18:13:50 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:16:12 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:16:58 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true; allow_ignore_default_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:17:33 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Wed Jun 6 18:18:25 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true; allow_ignore_default_p=false USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ERROR[3]: for model index = 0, model term type = default, is not handled AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:22:42 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log BEGINNING SEARCH at Thu Jun 7 11:22:43 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:22:43 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:22:44 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:24:17 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log BEGINNING SEARCH at Thu Jun 7 11:24:18 2001 [j_in=2] [cs-3: cycles 7] best2->2(1) [j_in=3] [cs-3: cycles 6] best3->3(2) [j_in=5] [cs-3: cycles 13] best5->5(3) [j_in=7] [cs-3: cycles 9] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:24:18 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.935) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.157) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2321.813) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.340) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 13 max_cycles 200 convergent try 4 num_cycles 9 max_cycles 200 convergent try 2 num_cycles 6 max_cycles 200 convergent try 1 num_cycles 7 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:24:18 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:26:32 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log BEGINNING SEARCH at Thu Jun 7 11:26:33 2001 [j_in=2] [cs-4: cycles 14] best2->2(1) [j_in=3] [cs-4: cycles 13] best3->3(2) [j_in=5] [cs-4: cycles 16] best5->5(3) [j_in=7] [cs-4: cycles 12] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:26:33 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.932) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.475) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 16 max_cycles 200 convergent try 4 num_cycles 12 max_cycles 200 convergent try 2 num_cycles 13 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:26:33 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:27:18 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log BEGINNING SEARCH at Thu Jun 7 11:27:18 2001 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:27:18 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:27:18 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:28:25 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 8 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:28:25 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Thu Aug 14 17:14:34 2003 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/rna/rnac.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9497e+00 : 3.2722e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 1.0165e+00 : 1.1376e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3199e+00 : 6.1795e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.4527e-01 : 1.1051e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.5405e-02 : 4.7385e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 3.2201e-02 : 8.9771e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.results-bin and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.log BEGINNING SEARCH at Thu Aug 14 17:14:40 2003 [j_in=2] [cs-3: cycles 14] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 15] best5->5(3) [j_in=7] [cs-3: cycles 11] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Aug 14 17:14:41 2003 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/rna/rnac.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2270.933) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2297.160) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2317.474) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2350.139) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 15 max_cycles 200 convergent try 4 num_cycles 11 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 14 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Thu Aug 14 17:14:41 2003 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.case-text-10000644000175000017500000000475311247310756020105 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2315.537 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 2 1.000 24 1 0.997 47 0 0.999 2 1 1.000 25 0 0.999 48 0 0.999 3 1 0.999 26 0 0.999 49 2 0.604 4 3 0.999 27 0 0.999 50 0 0.991 5 1 0.949 28 1 0.993 51 0 0.951 6 4 1.000 29 0 0.948 52 1 0.962 7 2 1.000 30 1 0.999 53 3 0.999 8 2 1.000 31 1 1.000 54 0 0.999 9 4 0.999 32 3 1.000 55 0 0.999 10 4 0.990 33 2 1.000 56 0 0.999 11 2 1.000 34 2 0.999 57 1 0.999 12 1 0.999 35 3 1.000 58 1 0.991 13 3 0.999 36 3 0.999 59 1 1.000 14 4 0.988 37 3 0.994 60 0 0.999 15 4 0.999 38 0 0.999 61 0 0.999 16 0 0.999 39 4 0.999 62 0 0.999 17 1 0.998 40 2 0.966 63 1 0.998 18 1 0.999 41 0 0.999 64 3 0.999 19 1 0.963 42 2 1.000 65 0 0.989 20 1 0.993 43 4 0.999 66 0 0.999 21 0 0.999 44 0 0.999 67 1 0.999 22 1 0.999 45 0 0.999 68 1 0.563 23 1 0.979 46 2 1.000 autoclass-3.3.6.dfsg.1/data/rna/rnac-location-predict.db20000644000175000017500000000153611247310756021160 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 5 Data, 7 attributes for prediction test database 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 4,12.8610,7.0418,-5.3478,-0.4860,0.0997,-0.8683 6,6.9577,-0.8914,-3.8977,0.5327,0.5117,-0.6741 16,9.0270,-0.0953,-8.8529,0.3500,0.4757,0.8070 17,8.8524,9.2938,-11.8409,0.5178,-0.3210,0.7930 33,6.9082,1.5865,-8.7953,-0.8354,0.3378,0.4336 34,4.2023,1.4894,-7.1171,-0.5162,0.7284,0.4505 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.rlog0000644000175000017500000000227311247310756017010 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:51:13 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:51:13 2001 autoclass-3.3.6.dfsg.1/data/rna/rnac.model0000644000175000017500000000067611247310756016357 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ;; 1 or more model definitions ;; model_index model_index 0 2 ;; ... ignore 0 single_normal_cn 1 2 3 4 5 6 autoclass-3.3.6.dfsg.1/data/rna/rnac.s-params0000644000175000017500000002237111247310756016776 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 1200 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 20 minutes) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/rna/rnac-location.results-bin0000644000175000017500000004301511247310756021326 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -2.5142935e+03(# clsf_DS 1: log_a_x_h = -2.5327020e+03ac_version 3.3.4unx 4ٷLFHTSEB B ff@  |data/rna/rnac.db2x9*data/rna/rnac-location.hd2D2`x ,;? reallocationnumeroXpDB? BClNULLNULL reallocationx(p) #<D \A>@AlNULLNULL reallocationy(p) #<D;@A2d N@L-AlNULLNULL reallocationz(p) #<DN@lNULLNULL reallocationx(v)8P #<D`p?~Z>(}>lNULLNULL reallocationy(v)p #<DmVm?4tz=T>lNULLNULL reallocationz(v) #<D}?Yw'>a>lNULLNULL LMODEL-0;data/rna/rnac.model  @  L/A/A4&>¼^nxq2,yC D<@R@ @f@b?@?`.>;! #;@q@1f1Bf1Bclass-c/data/rna/rnaD/A/Am=]Ű Hx?k+@?43@zS?@z?:D>&! #; N@hD@"}*2&B*2&BD/A/Am=]Ű Xx ϢW@ Σb??X%E@?B6>X% #;:@ֈ+A+AD/A/Am=]Ű hxj?5S Gw?yp?p?D/A/Am=]Ű x`>>^>t>͚y>͚@ #;z=Pp?b?b?D/A/Am=]Ű xX/W+=Nb.aH>п=PA #;'>nz?"1t?1t?D/A/Am=]Ű xL@@y=o oi#tpG DA׮?bAM?5=m?5@>0T?m #;@q@1f1Bf1Bclass-c/data/rna/rnaD@@=]Ű8 x@ @@p@μP?r@μ?zH>r#! #; N@hD@"}*2&B*2&BD@@=]Ű8 x6^.E?S6&S?9E(.?9ž=&? #;:@ֈ+A+AD@@=]Ű8 x7n>>=n>CO>D-̿}(=D-L[A #;Z>w?yp?p?D@@=]Ű8 x/⩽0c=2Zb>qH=qALA #;z=Pp?b?b?D@@=]Ű8 xQi?ky;i?Gm=5N6[;5N7C #;'>nz?"1t?1t?D@@=]Ű8 xL A A`>r vd9Tw0L D9AA#@!J9A:?#>@#X?o> #;@q@1f1Bf1Bclass-c/data/rna/rnaD A A-=]Űx' x@lXA@{`@?#EA @7=#! #; N@hD@"}*2&B*2&BD A A-=]Űx' xB?ۮ ?=vj? >_?vj #;:@ֈ+A+AD A A-=]Űx' xI<=:fU>"="/4vA #;Z>w?yp?p?D A A-=]Űx' x^As=C:Ѥ>:=0fA #;z=Pp?b?b?D A A-=]Űx' x '>>>^>Oc\,>O썽@ #;'>nz?"1t?1t?D A A-=]Űx' hxL9A9AmI>4H,P D^ AK"@A?>1?(?[?1w #;@q@1f1Bf1Bclass-c/data/rna/rnaD9A9A)=]Űp; x=?>?_?o>r!%@ #; N@hD@"}*2&B*2&BD9A9A)=]Űp; x ?m J?=.?X>u'O?. #;:@ֈ+A+AD9A9A)=]Űp; xZA>>k$> @>>o>N@ #;Z>w?yp?p?D9A9A)=]Űp; xw->=!>mA>=տ U==UA #;z=Pp?b?b?D9A9A)=]Űp; xHC? =JC?F>Yѿ;B=YQ lA #;'>nz?"1t?1t?D9A9A)=]Űp; hxLMBAMBAt>Ed|k;PU D@4AGѸ@49@Gt?AGt@u=! #;@q@1f1Bf1Bclass-c/data/rna/rnaDMBAMBA&m=]ŰA x@@y@v@>? 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#; N@hD@"}*2&B*2&BDMAMA]i=]Űp; x?9Gj?"X=H?"X>^?H #;:@ֈ+A+ADMAMA]i=]Űp; x9֨=7_>N=*'cA #;Z>w?yp?p?DMAMA]i=]Űp; x2ͽ}=ɽp>m=-h6A #;z=Pp?b?b?DMAMA]i=]Űp; hxsBR>}H>W=>׿?߬@ #;'>nz?"1t?1t?DMAMA]i=]Űp; hxL^@^@k=7Tf8m8L'n(x D@}?@m?^C>=?^>.?=; #;@q@1f1Bf1Bclass-c/data/rna/rnaD^@^@,Q>]ŰA xѡ%~?=X;?5ڟ! ?5d? #; N@hD@"}*2&B*2&BD^@^@,Q>]ŰA x#>5ȓ>{]>@ #;:@ֈ+A+AD^@^@,Q>]ŰA x{>'>ch>17>s>g>saύ@ #;Z>w?yp?p?D^@^@,Q>]ŰA x5ܮ>R<:_>r>I'\7]ŰA x)\5=!(I>'п5='PdA #;'>nz?"1t?1t?D^@^@,Q>]ŰA xL`UA`UAQ{>γƁ1M| D@^@)@?f+?K1t@f?o0>K1 #;@q@1f1Bf1Bclass-c/data/rna/rnaD`UA`UAJ8f=]Ű@^ xBQ?_?B?hC?[݈?[x? #; N@hD@"}*2&B*2&BD`UA`UAJ8f=]Ű@^ xZp$?qx? roq? rhWy? #;:@ֈ+A+AD`UA`UAJ8f=]Ű@^ x>}DJ>'>>SW>>S׿@ #;Z>w?yp?p?D`UA`UAJ8f=]Ű@^ x#a><0>}(>I<IgnB #;z=Pp?b?b?D`UA`UAJ8f=]Ű@^ x&?&>?|>olA>_@ #;'>nz?"1t?1t?D`UA`UAJ8f=]Ű@^ xL`;A`;A/>vHzyOE_yH Df@}U@U@c%?m?rAD@m?E>rA #;@q@1f1Bf1Bclass-c/data/rna/rnaD`;A`;A7=]Űc xȮ@b@l @@EO?p@E?J>p!! #; N@hD@"}*2&B*2&BD`;A`;A7=]Űc xߎ+z>D+?o>ox(@ #;:@ֈ+A+AD`;A`;A7=]Űc x>D]R=>o^>XAÿA=XACA #;Z>w?yp?p?D`;A`;A7=]Űc xqݾT=g۾>R=N6A #;z=Pp?b?b?D`;A`;A7=]Űc x?<_=Q?Uve>1tM=1t?QA #;'>nz?"1t?1t?D`;A`;A7=]Űc x autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.class-text-10000644000175000017500000001072611247310756020274 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2315.537 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 16 1.000 21 0.999 25 1.000 26 1.000 27 1.000 29 0.948 2 0.050 1 0.002 38 1.000 41 1.000 44 1.000 45 1.000 47 1.000 48 1.000 50 0.991 2 0.009 51 0.951 2 0.048 4 0.001 54 0.999 2 0.001 55 1.000 56 0.999 60 0.999 2 0.001 61 1.000 62 1.000 65 0.989 2 0.007 1 0.003 66 1.000 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 1.000 3 1.000 5 0.949 2 0.049 0 0.002 12 0.999 17 0.998 2 0.002 18 1.000 19 0.963 2 0.037 20 0.993 2 0.006 22 1.000 23 0.979 2 0.021 24 0.997 2 0.003 CLASS = 1 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 28 0.993 2 0.007 30 1.000 31 1.000 52 0.962 2 0.038 57 1.000 58 0.991 2 0.009 59 1.000 63 0.998 2 0.002 67 1.000 68 0.563 2 0.437 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 7 1.000 8 1.000 11 1.000 33 1.000 34 0.999 40 0.966 1 0.034 42 1.000 46 1.000 49 0.604 1 0.395 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 4 0.999 2 0.001 13 1.000 32 1.000 35 1.000 36 1.000 37 0.994 2 0.006 53 0.999 64 0.999 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 1.000 9 1.000 10 0.990 2 0.010 14 0.988 2 0.012 15 1.000 CLASS = 4 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 39 1.000 43 1.000 autoclass-3.3.6.dfsg.1/data/rna/rnac.search0000644000175000017500000000111711247310756016513 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 2 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -2.27093264e+03 15 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 1 7 7 -2.29716003e+03 11 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -2.31747393e+03 12 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -2.35013931e+03 14 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/rna/rnac.hd20000644000175000017500000000165411247310756015731 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 68 Data, 7 attributes ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 7 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 real scalar "numero" zero_point 0.0 rel_error 0.0 1 real scalar "x(p)" zero_point 0.0 rel_error .01 2 real scalar "y(p)" zero_point -1.0 rel_error .01 3 real scalar "z(p)" zero_point -13.0 rel_error .01 4 real scalar "x(v)" zero_point -1.0 rel_error .01 5 real scalar "y(v)" zero_point -1.0 rel_error .01 6 real scalar "z(v)" zero_point -1.0 rel_error .01 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk-predict.db20000644000175000017500000000157711247310756020152 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 10 Data, 7 attributes 6,?,-0.8914,-3.8977,0.5327,0.5117,-0.6741 12,10.9123,?,-11.2686,0.1146,-0.0641,0.9913 40,3.3965,2.1506,-7.8867,0.4006,0.6673,? 49,10.2241,6.4498,-11.2915,-0.2959,0.7632,? 50,9.0737,0.1021,-7.7521,0.6000,0.7295,0.3282 51,6.6366,-0.4600,-6.7527,0.7888,0.4583,0.4096 52,3.8250,3.7715,-10.3711,0.9410,-0.1730,0.2907 54,8.2489,1.1837,-9.5679,0.7235,0.3626,? 60,9.9296,0.6538,-8.1603,0.3578,0.5769,? 68,2.4298,3.8136,-7.9598,-0.0687,0.9271,0.3684 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.influ-o-text-10000644000175000017500000004013011247310756020530 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2315.537 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 1 second _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-2315.537) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-2343.858) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-2345.158) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2359.724) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 5 POPULATED CLASSES (max global influence value = 2.451) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -2.77e+01 1.00e+00 22 0.322 1 -3.16e+01 1.95e-02 21 0.306 2 -3.68e+01 1.05e-04 10 0.152 3 -3.25e+01 7.58e-03 8 0.118 4 -3.06e+01 5.41e-02 7 0.103 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 6.33e+00 22 0.322 1 4.29e+00 21 0.306 2 1.53e+00 10 0.152 3 7.29e+00 8 0.118 4 8.07e+00 7 0.103 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 009 Log z(p) 1.000 010 Log x(v) 0.934 012 Log z(v) 0.804 007 Log x(p) 0.636 011 Log y(v) 0.530 008 Log y(p) 0.446 000 numero ----- 001 x(p) ----- 002 y(p) ----- 003 z(p) ----- 004 x(v) ----- 005 y(v) ----- 006 z(v) ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 22 normalized weight 0.322 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 6.33e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cm SNcm) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 12 R SNcm Log z(v) ........... 1.646 ( 5.85e-01 1.07e-01) 5.31e+00 ( 1.69e-02 9.61e-01) Prob-jk is known 8.66e-01 Prob-*k is known 9.26e-01 04 11 R SNcm Log y(v) ........... 1.275 ( 2.98e-01 1.39e-01) 2.66e+00 (-7.17e-02 6.88e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 00 07 R SNcm Log x(p) ........... 1.141 ( 2.17e+00 1.23e-01) 1.78e+00 ( 1.95e+00 5.72e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 03 10 R SNcm Log x(v) ........... 1.036 ( 2.11e-01 2.49e-01) 1.46e+00 (-1.53e-01 1.05e+00) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 02 09 R SNcm Log z(p) ........... 0.653 ( 1.40e+00 2.65e-01) 2.62e-01 ( 1.33e+00 7.80e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 01 08 R SNcm Log y(p) ........... 0.579 ( 2.71e-01 5.19e-01) 1.40e+00 ( 9.99e-01 1.06e+00) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 CLASS 1 - weight 21 normalized weight 0.306 relative strength 1.95e-02 ******* class cross entropy w.r.t. global class 4.29e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cm SNcm) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 12 R SNcm Log z(v) ........... 1.402 ( 5.04e-01 1.64e-01) 2.97e+00 ( 1.69e-02 9.61e-01) Prob-jk is known 9.76e-01 Prob-*k is known 9.26e-01 01 08 R SNcm Log y(p) ........... 0.966 ( 1.87e+00 3.55e-01) 2.46e+00 ( 9.99e-01 1.06e+00) Prob-jk is known 9.53e-01 Prob-*k is known 9.85e-01 03 10 R SNcm Log x(v) ........... 0.910 ( 1.97e-01 2.74e-01) 1.28e+00 (-1.53e-01 1.05e+00) Prob-jk is known 9.53e-01 Prob-*k is known 9.85e-01 02 09 R SNcm Log z(p) ........... 0.543 ( 7.24e-01 4.33e-01) 1.40e+00 ( 1.33e+00 7.80e-01) Prob-jk is known 9.53e-01 Prob-*k is known 9.85e-01 04 11 R SNcm Log y(v) ........... 0.412 (-6.33e-01 8.75e-01) 6.42e-01 (-7.17e-02 6.88e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 00 07 R SNcm Log x(p) ........... 0.055 ( 1.81e+00 5.09e-01) 2.80e-01 ( 1.95e+00 5.72e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.85e-01 CLASS 2 - weight 10 normalized weight 0.152 relative strength 1.05e-04 ******* class cross entropy w.r.t. global class 1.53e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cm SNcm) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 07 R SNcm Log x(p) ........... 0.699 ( 1.44e+00 9.03e-01) 5.67e-01 ( 1.95e+00 5.72e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 02 09 R SNcm Log z(p) ........... 0.385 ( 1.22e+00 1.29e+00) 8.63e-02 ( 1.33e+00 7.80e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 04 11 R SNcm Log y(v) ........... 0.233 ( 2.08e-01 4.47e-01) 6.24e-01 (-7.17e-02 6.88e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 05 12 R SNcm Log z(v) ........... 0.123 (-3.06e-01 7.52e-01) 4.30e-01 ( 1.69e-02 9.61e-01) Prob-jk is known 8.54e-01 Prob-*k is known 9.26e-01 03 10 R SNcm Log x(v) ........... 0.073 (-3.50e-01 8.31e-01) 2.37e-01 (-1.53e-01 1.05e+00) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 01 08 R SNcm Log y(p) ........... 0.020 ( 1.16e+00 1.05e+00) 1.53e-01 ( 9.99e-01 1.06e+00) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 CLASS 3 - weight 8 normalized weight 0.118 relative strength 7.58e-03 ******* class cross entropy w.r.t. global class 7.29e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cm SNcm) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 02 09 R SNcm Log z(p) ........... 2.451 ( 2.05e+00 6.28e-02) 1.15e+01 ( 1.33e+00 7.80e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 03 10 R SNcm Log x(v) ........... 2.058 (-2.18e+00 1.53e+00) 1.33e+00 (-1.53e-01 1.05e+00) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 00 07 R SNcm Log x(p) ........... 1.483 ( 2.47e+00 1.22e-01) 4.25e+00 ( 1.95e+00 5.72e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 01 08 R SNcm Log y(p) ........... 0.568 ( 1.72e+00 5.21e-01) 1.38e+00 ( 9.99e-01 1.06e+00) Prob-jk is known 9.98e-01 Prob-*k is known 9.85e-01 05 12 R SNcm Log z(v) ........... 0.369 (-7.13e-01 1.15e+00) 6.36e-01 ( 1.69e-02 9.61e-01) Prob-jk is known 9.91e-01 Prob-*k is known 9.26e-01 04 11 R SNcm Log y(v) ........... 0.361 (-2.44e-01 3.94e-01) 4.37e-01 (-7.17e-02 6.88e-01) Prob-jk is known 8.86e-01 Prob-*k is known 9.85e-01 CLASS 4 - weight 7 normalized weight 0.103 relative strength 5.41e-02 ******* class cross entropy w.r.t. global class 8.07e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_normal_cm SNcm) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 02 09 R SNcm Log z(p) ........... 2.292 ( 2.19e+00 8.81e-02) 9.73e+00 ( 1.33e+00 7.80e-01) Prob-jk is known 9.97e-01 Prob-*k is known 9.85e-01 03 10 R SNcm Log x(v) ........... 1.833 ( 4.04e-01 1.18e-01) 4.72e+00 (-1.53e-01 1.05e+00) Prob-jk is known 9.97e-01 Prob-*k is known 9.85e-01 05 12 R SNcm Log z(v) ........... 1.542 (-1.63e+00 7.69e-01) 2.14e+00 ( 1.69e-02 9.61e-01) Prob-jk is known 9.90e-01 Prob-*k is known 9.26e-01 04 11 R SNcm Log y(v) ........... 1.069 ( 2.21e-01 1.62e-01) 1.81e+00 (-7.17e-02 6.88e-01) Prob-jk is known 9.97e-01 Prob-*k is known 9.85e-01 01 08 R SNcm Log y(p) ........... 0.686 (-2.32e-01 1.10e+00) 1.12e+00 ( 9.99e-01 1.06e+00) Prob-jk is known 9.97e-01 Prob-*k is known 9.85e-01 00 07 R SNcm Log x(p) ........... 0.646 ( 1.86e+00 2.19e-01) 4.04e-01 ( 1.95e+00 5.72e-01) Prob-jk is known 8.72e-01 Prob-*k is known 9.85e-01 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.class-text-10000644000175000017500000001064411247310756021306 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 with log-A (approximate marginal likelihood) = -2514.293 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 16 1.000 21 1.000 25 0.999 26 0.999 27 1.000 29 0.990 1 0.007 4 0.003 33 0.995 1 0.005 34 0.976 1 0.024 38 1.000 41 0.998 1 0.002 44 0.974 4 0.023 1 0.004 45 1.000 47 0.998 4 0.002 48 0.998 4 0.002 50 1.000 51 1.000 54 0.995 1 0.005 55 0.995 1 0.005 56 0.998 1 0.002 60 1.000 61 1.000 62 1.000 66 1.000 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 1.000 3 1.000 5 0.999 18 1.000 19 1.000 23 0.999 30 1.000 31 1.000 49 1.000 52 1.000 CLASS = 1 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 57 1.000 58 0.962 4 0.037 0 0.002 59 1.000 63 1.000 67 1.000 68 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 1.000 7 0.999 9 1.000 10 1.000 11 1.000 14 1.000 15 1.000 39 1.000 40 1.000 43 1.000 46 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 4 1.000 13 1.000 32 1.000 35 1.000 36 1.000 37 1.000 42 1.000 53 1.000 64 1.000 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 8 0.987 1 0.013 12 0.997 1 0.003 17 0.901 1 0.099 20 0.992 1 0.008 22 0.987 1 0.013 24 0.992 1 0.008 CLASS = 4 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 28 0.956 1 0.044 65 0.674 0 0.246 1 0.080 autoclass-3.3.6.dfsg.1/data/rna/rnac.db20000644000175000017500000000715611247310756015726 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;;; From: gautheret@bch.umontreal.ca (Daniel Gautheret) ; 68 Data, 7 attributes 1,8.6928,-0.8365,-4.4779,-0.8300,0.4427,-0.3392 2,3.3717,6.2885,-10.5401,0.1322,-0.8102,0.5711 3,5.0184,4.6393,-11.2117,-0.0073,-0.5806,0.8142 4,12.8610,7.0418,-5.3478,-0.4860,0.0997,-0.8683 5,12.3403,5.4943,-10.5624,-0.3290,0.5859,0.7406 6,6.9577,-0.8914,-3.8977,0.5327,0.5117,-0.6741 7,4.2251,1.2770,-8.9367,0.6231,0.7303,-0.2801 8,7.8351,7.9843,-12.9357,0.4055,0.0990,0.9087 9,7.8416,-0.8153,-4.6193,0.5723,0.6039,-0.5547 10,4.5589,-0.2088,-3.9610,0.8749,0.2014,-0.4405 11,0.4334,6.5764,-4.9003,0.5279,-0.6362,-0.5626 12,10.9123,8.0080,-11.2686,0.1146,-0.0641,0.9913 13,13.7526,3.1565,-5.3233,-0.9512,-0.2950,0.0909 14,9.4074,2.4833,-2.0792,0.3880,-0.0936,-0.9169 15,5.7826,1.5037,-4.9871,0.1940,0.2504,-0.9485 16,9.0270,-0.0953,-8.8529,0.3500,0.4757,0.8070 17,8.8524,9.2938,-11.8409,0.5178,-0.3210,0.7930 18,3.6560,4.3409,-11.3199,0.5809,-0.4352,0.6878 19,2.3908,9.4845,-9.6016,0.5135,0.1241,0.8491 20,11.1601,7.6421,-11.2482,-0.0299,0.1716,0.9847 21,10.2860,-0.3494,-6.9610,-0.0480,0.6753,0.7360 22,10.8998,8.5427,-11.1533,0.1002,-0.4681,0.8780 23,4.2783,2.3259,-10.4358,0.4691,-0.0440,0.8820 24,8.8627,7.6604,-12.2208,0.4721,0.1097,0.8747 25,10.3666,0.7718,-9.1882,-0.0522,0.2162,0.9750 26,10.3666,0.7718,-9.1882,-0.0522,0.2162,0.9750 27,8.3373,-0.6567,-7.8716,-0.1328,0.3873,0.9123 28,10.3979,2.8571,-10.5576,-0.0444,-0.3009,0.9526 29,9.5540,0.9464,-9.3461,-0.3528,-0.0808,0.9322 30,6.7947,6.0667,-12.1396,-0.2642,-0.6909,0.6729 31,10.7148,9.7124,-8.8671,0.2541,-0.9556,0.1495 32,11.7587,1.8787,-5.2227,-0.9469,-0.2785,0.1608 33,6.9082,1.5865,-8.7953,-0.8354,0.3378,0.4336 34,4.2023,1.4894,-7.1171,-0.5162,0.7284,0.4505 35,11.2721,12.0003,-6.2916,-0.9958,-0.0742,0.0529 36,11.5720,6.4747,-4.3818,-0.1396,-0.2213,-0.9652 37,8.7213,2.3700,-4.8825,-0.9276,-0.0596,-0.3687 38,8.7198,0.7693,-7.1437,-0.0534,0.4339,0.8994 39,5.6592,0.0370,-3.9632,0.4658,0.1502,-0.8721 40,3.3965,2.1506,-7.8867,0.4006,0.6673,-0.6279 41,7.6410,0.4475,-10.1836,0.3936,0.3630,0.8446 42,12.0817,4.2518,-8.4655,-0.6654,0.4801,-0.5716 43,6.4402,0.0661,-4.6974,0.5486,0.2322,-0.8032 44,9.0661,0.9876,-10.6172,0.2741,0.3298,0.9034 45,7.5654,-0.1753,-9.5899,0.2168,0.3398,0.9152 46,1.5457,5.6684,-6.0399,0.4812,-0.2003,-0.8534 47,8.9653,0.3247,-10.0728,0.0924,0.2921,0.9519 48,8.9653,0.3247,-10.0728,0.0924,0.2921,0.9519 49,10.2241,6.4498,-11.2915,-0.2959,0.7632,0.5745 50,9.0737,0.1021,-7.7521,0.6000,0.7295,0.3282 51,6.6366,-0.4600,-6.7527,0.7888,0.4583,0.4096 52,3.8250,3.7715,-10.3711,0.9410,-0.1730,0.2907 53,12.4519,2.0353,-5.0957,-0.9112,0.1310,0.3907 54,8.2489,1.1837,-9.5679,0.7235,0.3626,0.5875 55,8.2489,1.1837,-9.5679,0.7235,0.3626,0.5875 56,6.7405,0.5263,-8.7933,0.6840,0.1256,0.7185 57,2.6143,6.0357,-10.0313,0.5391,-0.7735,0.3332 58,7.3507,2.2775,-10.1005,0.4996,-0.2910,0.8159 59,5.2306,7.8844,-10.9373,0.4213,-0.8944,0.1505 60,9.9296,0.6538,-8.1603,0.3578,0.5769,0.7342 61,9.9296,0.6538,-8.1603,0.3578,0.5769,0.7342 62,7.7086,-0.3975,-6.9777,0.4323,0.4952,0.7536 63,4.8080,3.7794,-10.8582,0.6629,-0.3437,0.6652 64,12.8371,9.1619,-5.0408,-0.5013,-0.6971,-0.5125 65,9.8706,2.2357,-9.7856,0.3855,0.1467,0.9110 66,8.6698,0.7492,-8.6831,0.2311,0.1315,0.9640 67,5.9728,5.6075,-11.5282,0.3562,-0.6924,0.6274 68,2.4298,3.8136,-7.9598,-0.0687,0.9271,0.3684 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.r-params0000644000175000017500000001114511247310756020600 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.case-text-10000644000175000017500000000476111247310756021117 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 with log-A (approximate marginal likelihood) = -2514.293 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 3 1.000 24 4 0.992 47 0 0.998 2 1 1.000 25 0 0.999 48 0 0.998 3 1 0.999 26 0 0.999 49 1 1.000 4 3 1.000 27 0 0.999 50 0 0.999 5 1 0.999 28 4 0.956 51 0 0.999 6 2 1.000 29 0 0.990 52 1 1.000 7 2 0.999 30 1 1.000 53 3 1.000 8 4 0.987 31 1 0.999 54 0 0.995 9 2 1.000 32 3 1.000 55 0 0.995 10 2 1.000 33 0 0.995 56 0 0.998 11 2 1.000 34 0 0.976 57 1 1.000 12 4 0.997 35 3 1.000 58 1 0.962 13 3 1.000 36 3 0.999 59 1 1.000 14 2 1.000 37 3 1.000 60 0 0.999 15 2 1.000 38 0 1.000 61 0 0.999 16 0 0.999 39 2 1.000 62 0 1.000 17 4 0.901 40 2 1.000 63 1 1.000 18 1 1.000 41 0 0.998 64 3 1.000 19 1 1.000 42 3 1.000 65 4 0.674 20 4 0.992 43 2 1.000 66 0 0.999 21 0 0.999 44 0 0.974 67 1 1.000 22 4 0.987 45 0 0.999 68 1 1.000 23 1 0.999 46 2 1.000 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.log0000644000175000017500000004621511247310756017645 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:30:24 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log BEGINNING SEARCH at Thu Jun 7 11:30:24 2001 [j_in=2] [cs-3: cycles 37] best2->2(1) [j_in=3] [cs-3: cycles 11] best3->3(2) [j_in=5] [cs-3: cycles 18] best5->5(3) [j_in=7] [cs-3: cycles 19] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:30:24 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2514.293) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2532.702) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2546.108) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2559.346) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 18 max_cycles 200 convergent try 4 num_cycles 19 max_cycles 200 convergent try 2 num_cycles 11 max_cycles 200 convergent try 1 num_cycles 37 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:30:24 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:31:11 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 8 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:31:11 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:32:03 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log BEGINNING SEARCH at Thu Jun 7 11:32:04 2001 [j_in=2] [cs-3: cycles 9] best2->2(1) [j_in=3] [cs-3: cycles 7] best3->3(2) [j_in=5] [cs-3: cycles 9] best5->5(3) [j_in=7] [cs-3: cycles 10] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:32:04 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2514.343) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2532.458) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2546.141) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2559.150) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 9 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 7 max_cycles 200 convergent try 1 num_cycles 9 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:32:04 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:32:49 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9003e+00 : 9.3144e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.2193e+00 : 1.0831e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3630e+00 : 6.6417e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.3023e-01 : 2.4722e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 8.6172e-02 : 2.0754e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.4600e-01 : 4.0113e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log BEGINNING SEARCH at Thu Jun 7 11:32:50 2001 [j_in=2] [cs-4: cycles 37] best2->2(1) [j_in=3] [cs-4: cycles 11] best3->3(2) [j_in=5] [cs-4: cycles 18] best5->5(3) [j_in=7] [cs-4: cycles 19] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:32:50 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2514.293) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2532.702) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2546.108) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2559.346) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 18 max_cycles 200 convergent try 4 num_cycles 19 max_cycles 200 convergent try 2 num_cycles 11 max_cycles 200 convergent try 1 num_cycles 37 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:32:50 2001 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.search0000644000175000017500000000111711247310756017306 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 1 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -2.31553681e+03 42 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 7 -2.34385794e+03 28 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -2.34515783e+03 10 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -2.35972394e+03 12 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/rna/rnac.class-text-10000644000175000017500000001074611247310756017503 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2270.933 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 1.000 3 1.000 5 0.983 4 0.015 1 0.002 12 0.998 4 0.002 17 0.999 4 0.001 18 0.999 19 0.985 4 0.015 20 0.997 4 0.003 22 1.000 23 0.990 4 0.010 24 0.991 4 0.009 28 0.996 4 0.004 30 0.999 31 1.000 49 0.992 4 0.008 52 0.984 4 0.016 57 1.000 58 0.994 4 0.005 59 1.000 63 0.999 67 1.000 68 0.812 4 0.188 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 16 1.000 21 0.999 25 1.000 26 1.000 27 1.000 29 0.987 4 0.013 38 1.000 41 1.000 44 1.000 45 1.000 47 1.000 CLASS = 1 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 48 1.000 54 0.998 2 0.001 55 0.998 2 0.001 56 0.976 2 0.023 60 0.999 61 0.999 62 0.918 2 0.082 65 0.995 0 0.005 66 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 1.000 7 0.947 4 0.053 9 1.000 10 0.995 4 0.005 14 0.987 4 0.013 15 0.999 39 1.000 40 0.956 4 0.044 43 1.000 50 0.993 4 0.006 51 0.999 4 0.001 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 4 0.999 4 0.001 13 1.000 32 1.000 35 1.000 36 1.000 37 0.992 4 0.008 53 0.999 64 0.998 4 0.002 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 8 1.000 11 1.000 33 1.000 34 0.998 0 0.002 CLASS = 4 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 42 1.000 46 1.000 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.results-bin0000644000175000017500000005007411247310756020316 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -2.3155368e+03(# clsf_DS 1: log_a_x_h = -2.3438579e+03ac_version 3.3.4unx 4rHɂ7I V ff@p  |data/rna/rnac-unk.db2x9*data/rna/rnac.hd2qD 2`X,;? realscalarnumeroDB? 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D A ARy=XJ`w  A~?'^>>7=y>*0>SyMUS6v@ k7";hP?=}xc@}xc@Noz? D A ARy=XJ`w  A&~?gF˝j>x>'߻<'qj.B k7";Z #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.search0000644000175000017500000000111711247310756020321 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 1 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -2.51429350e+03 18 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 7 -2.53270203e+03 19 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -2.54610835e+03 11 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -2.55934611e+03 37 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/rna/rnac.results-bin0000644000175000017500000005007411247310756017523 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -2.2709326e+03(# clsf_DS 1: log_a_x_h = -2.2971600e+03ac_version 3.3.5unx 4G}âXݽ[ [ ff@(  |rnac.db2@X@@rnac.hd2D 2x,;? realscalarnumeroDB? 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H{?4? k7"; =j@zCV@V@ D/A/A-=XJX2 X L8@8@I=Oj]Vp p8e D @Bl<@Q=Ct<9&B k7";3?D?:6??@??@D8@8@=XJ@ X R?ug>?7c?#&{>#䦿k@ k7";d?.@Q,@,@D8@8@=XJ@   V@ga;!U@ٙ=Z%1k4;Z%}C k7";?P$@ڳVl@l@ D8@8@=XJ@   \'@k d?>"@X?Y>" k7";YD@dAA D8@8@=XJ@  y>*%H>~ >[#@ k7";!nhP?=}xc@}xc@ D8@8@=XJ@  ?V6ܒ?g >?gÌ>vB?( k7"; =j@zCV@V@ D8@8@=XJ@  L!A!A> $ހ׉_Di D  @I]< @}==vQ Rq>ɦ>6?x/:>x=|@ k7";d?.@Q,@,@D!A!AC?D=XJxT    ٮ?s=eخ?>-t=-0;|A k7";?P$@ڳVl@l@ D!A!AC?D=XJxT   [ 6>az=5>( w>bn=6DuA k7";YD@dAA D!A!AC?D=XJxT   _> <>><`B k7";!nhP?=}xc@}xc@ D!A!AC?D=XJxT   ?H;?U=}-;}#eC k7"; =j@zCV@V@ D!A!AC?D=XJxT p LϭAϭA>ڽ|LnG:ŅXn D ?>V?F)?qA)/u>qA"p@ k7";3?D?:6??@??@DϭAϭAG;4=XJ Z X ?q>?>?=?eA k7";d?.@Q,@,@DϭAϭAG;4=XJ Z X 7?B>/8?r> Zg:> ڿʯ@ k7";?P$@ڳVl@l@ DϭAϭAG;4=XJ Z X 3?>^ݪ=7v?>ޘ>֡X=!HA k7";YD@dAA DϭAϭAG;4=XJ Z X 8yS?l'c?&HJ?&qA? k7";!nhP?=}xc@}xc@ DϭAϭAG;4=XJ Z X :>͛$>鿀4 qxQ~ p~ ff@O database_DS_ptrmodel_DS_ptr 0L@@6V='7 pgh;=sp~ D v?Y $@Pr?}> k7";d?.@Q,@,@D@@ v=XJ0/   P k|?6@#~?Lj?->#< @-j?>#< k7";?P$@ڳVl@l@ D@@ v=XJ0/    鲖v??mk?X?+;Y? k7";YD@dAA D@@ v=XJ0/   P ;r=7c>o=?;08>;ە}@ k7";!nhP?=}xc@}xc@ D@@ v=XJ0/   ` @|dR>Xw*?;>}B+@ k7"; =j@zCV@V@ D@@ v=XJ0/   LY @Y @_Ø=q &(Ie\ߋf D O??϶?S??S>_??>Gt)?_A k7";3?D?:6??@??@DY @Y @*>XJX2 X A?et=?Kf>{="X'IA k7";d?.@Q,@,@DY @Y @*>XJX2 X R@ֻ<@ >GXJX2 X "=5>3=>t^>z@ k7";YD@dAA DY @Y @*>XJX2 X 㯘w>엾>JN|=J.A k7";!nhP?=}xc@}xc@ DY @Y @*>XJX2 X E -?T<@?ڑ?G? k7"; =j@zCV@V@ DY @Y @*>XJX2 X LAA=>^]4# ;Ԇ D !@$<,@ >f٘׎?>O?e>O@ k7";?P$@ڳVl@l@ DAAz' =XJ@   >n=>">yta=yt+iA k7";YD@dAA DAAz' =XJ@   tr5>R=>5>>?{rC=?{!|GA k7";!nhP?=}xc@}xc@ DAAz' =XJ@   ?p;?=,/;6K\C k7"; =j@zCV@V@ DAAz' =XJ@   LI@I@{=p q D Q@<3@U=r<ņB k7";3?D?:6??@??@DI@I@=XJxT X ?>{? ?JW>JX@ k7";d?.@Q,@,@DI@I@=XJxT   Pz? C=e?v;v>lTl=l6L[A k7";?P$@ڳVl@l@ DI@I@=XJxT   *Ne,@f ?q>1@q_?>1 k7";YD@dAA DI@I@=XJxT p V?e/z~?70û|?70C? k7";!nhP?=}xc@}xc@ DI@I@=XJxT p ~!4>̐ h>m >@ k7"; =j@zCV@V@ DI@I@=XJxT p L@@Au=^41;\>`0 D ?fXJ Z X H>?>+N>">7 sY>7 b@ k7";d?.@Q,@,@D@@qL>XJ Z X  @ 6; @7A=>[CE;>[lC k7";?P$@ڳVl@l@ D@@qL>XJ Z X >?<>9>ApVXJ Z  sA>k7";jA>OD6=/G3;/C k7";!nhP?=}xc@}xc@ D@@qL>XJ Z   mO翾OXJ Z   L*@*@c=1&V0cfΗ+d D ?&@>θ?u>lm >lЦ@ k7";3?D?:6??@??@D*@*@1*>XJHw X 'i?%m>Vi?>q^RE>q^ҿ@ k7";d?.@Q,@,@D*@*@1*>XJHw X P?olXJHw X =vW>=d>^|3>޿@ k7";YD@dAA D*@*@1*>XJHw X ;?k7";a?:=E,;iC k7";!nhP?=}xc@}xc@ D*@*@1*>XJHw X OO>ŋ}><_k>#@ k7"; =j@zCV@V@ D*@*@1*>XJHw X L A A4->swUsP D  ?Uv=ư?s>Ώ$=Ώ%ޝTA k7";3?D?:6??@??@D A AQq=XJ| X w?=-N?>@U=@ i-A k7";d?.@Q,@,@D A AQq=XJ| X 2O?z_=NO?}t>|Ui=|U7UA k7";?P$@ڳVl@l@ D A AQq=XJ| X >Z=ֹ>30>Bῼ%^6|@ k7";!nhP?=}xc@}xc@ D A AQq=XJ| X t><~>>x CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 8 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-predict.db2 based on the "TRAINING" classification of 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2270.933 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 0.999 4 0.001 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 5 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 4 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 3 0.999 4 0.001 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 2 1.000 7 1.000 8 0.998 0 0.002 autoclass-3.3.6.dfsg.1/data/rna/rnac-unk.log0000644000175000017500000005662711247310756016642 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:48:43 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9143e+00 : 9.4399e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.1479e+00 : 1.0645e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3188e+00 : 6.6083e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.2512e-01 : 2.4914e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 9.1615e-02 : 2.0862e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.3887e-01 : 4.0865e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9499e+00 : 3.3210e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 9.9889e-01 : 1.1335e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3319e+00 : 6.1745e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.5321e-01 : 1.1174e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.1659e-02 : 4.7997e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 1.6889e-02 : 9.3615e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log BEGINNING SEARCH at Thu Jun 7 11:48:43 2001 [j_in=2] [cs-3: cycles 11] best2->2(1) [j_in=3] [cs-3: cycles 10] best3->3(2) [j_in=5] [cs-3: cycles 42] best5->5(3) [j_in=7] [cs-3: cycles 28] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:48:43 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2315.537) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2343.858) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2345.158) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2359.724) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 42 max_cycles 200 convergent try 4 num_cycles 28 max_cycles 200 convergent try 2 num_cycles 10 max_cycles 200 convergent try 1 num_cycles 11 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:48:43 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:49:55 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9143e+00 : 9.4399e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.1479e+00 : 1.0645e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3188e+00 : 6.6083e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.2512e-01 : 2.4914e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 9.1615e-02 : 2.0862e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.3887e-01 : 4.0865e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9499e+00 : 3.3210e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 9.9889e-01 : 1.1335e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3319e+00 : 6.1745e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.5321e-01 : 1.1174e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.1659e-02 : 4.7997e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 1.6889e-02 : 9.3615e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log BEGINNING SEARCH at Thu Jun 7 11:49:56 2001 [j_in=2] [cs-3: cycles 8] best2->2(1) [j_in=3] [cs-3: cycles 8] best3->3(2) [j_in=5] [cs-3: cycles 19] best5->5(3) [j_in=7] [cs-3: cycles 12] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:49:56 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2315.332) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2343.914) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2345.153) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2359.718) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 19 max_cycles 200 convergent try 4 num_cycles 12 max_cycles 200 convergent try 2 num_cycles 8 max_cycles 200 convergent try 1 num_cycles 8 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:49:56 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:50:44 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "numero": [ 1.0000e+00 < ( 3.4500e+01 : 3.8525e+02) < 6.8000e+01 ] Attribute #1, "x(p)": [ 4.3340e-01 < ( 7.9143e+00 : 9.4399e+00) < 1.3753e+01 ] Attribute #2, "y(p)": [ -8.9140e-01 < ( 3.1479e+00 : 1.0645e+01) < 1.2000e+01 ] Attribute #3, "z(p)": [ -1.2936e+01 < (-8.3188e+00 : 6.6083e+00) < -2.0792e+00 ] Attribute #4, "x(v)": [ -9.9580e-01 < ( 1.2512e-01 : 2.4914e-01) < 9.4100e-01 ] Attribute #5, "y(v)": [ -9.5560e-01 < ( 9.1615e-02 : 2.0862e-01) < 9.2710e-01 ] Attribute #6, "z(v)": [ -9.6520e-01 < ( 3.3887e-01 : 4.0865e-01) < 9.9130e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. Attribute #7, "Log x(p)": [ -8.3609e-01 < ( 1.9499e+00 : 3.3210e-01) < 2.6212e+00 ] ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. Attribute #8, "Log y(p)": [ -2.2201e+00 < ( 9.9889e-01 : 1.1335e+00) < 2.5650e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. Attribute #9, "Log z(p)": [ -2.7442e+00 < ( 1.3319e+00 : 6.1745e-01) < 2.3907e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. Attribute #10, "Log x(v)": [ -5.4727e+00 < (-1.5321e-01 : 1.1174e+00) < 6.6320e-01 ] ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. Attribute #11, "Log y(v)": [ -3.1145e+00 < (-7.1659e-02 : 4.7997e-01) < 6.5602e-01 ] ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. Attribute #12, "Log z(v)": [ -3.3581e+00 < ( 1.6889e-02 : 9.3615e-01) < 6.8879e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log BEGINNING SEARCH at Thu Jun 7 11:50:45 2001 [j_in=2] [cs-4: cycles 12] best2->2(1) [j_in=3] [cs-4: cycles 10] best3->3(2) [j_in=5] [cs-4: cycles 42] best5->5(3) [j_in=7] [cs-4: cycles 28] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:50:45 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-2315.537) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-2343.858) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-2345.158) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-2359.724) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 42 max_cycles 200 convergent try 4 num_cycles 28 max_cycles 200 convergent try 2 num_cycles 10 max_cycles 200 convergent try 1 num_cycles 12 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:50:45 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:51:28 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 ADVISORY[1]: read 10 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk.model ADVISORY[2]: log_transform is being applied to attribute #1: "x(p)" and will be stored as attribute #7. ADVISORY[2]: log_transform is being applied to attribute #2: "y(p)" and will be stored as attribute #8. ADVISORY[2]: log_transform is being applied to attribute #3: "z(p)" and will be stored as attribute #9. ADVISORY[2]: log_transform is being applied to attribute #4: "x(v)" and will be stored as attribute #10. ADVISORY[2]: log_transform is being applied to attribute #5: "y(v)" and will be stored as attribute #11. ADVISORY[2]: log_transform is being applied to attribute #6: "z(v)" and will be stored as attribute #12. ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-unk-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:51:28 2001 autoclass-3.3.6.dfsg.1/data/rna/rnac-location.rlog0000644000175000017500000000233111247310756020016 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:30:59 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 7 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.hd2 ADVISORY[1]: read 68 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac-location.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:30:59 2001 autoclass-3.3.6.dfsg.1/data/rna/rnac.case-text-10000644000175000017500000000473711247310756017314 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 68 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.hd2 with log-A (approximate marginal likelihood) = -2270.933 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/rna/rnac.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 4 1.000 24 0 0.991 47 1 0.999 2 0 1.000 25 1 0.999 48 1 0.999 3 0 0.999 26 1 0.999 49 0 0.992 4 3 0.999 27 1 0.999 50 2 0.993 5 0 0.983 28 0 0.996 51 2 0.999 6 2 0.999 29 1 0.987 52 0 0.984 7 2 0.947 30 0 0.999 53 3 0.999 8 4 1.000 31 0 1.000 54 1 0.998 9 2 0.999 32 3 0.999 55 1 0.998 10 2 0.995 33 4 1.000 56 1 0.976 11 4 1.000 34 4 0.998 57 0 0.999 12 0 0.998 35 3 1.000 58 0 0.994 13 3 0.999 36 3 0.999 59 0 1.000 14 2 0.987 37 3 0.992 60 1 0.999 15 2 0.999 38 1 0.999 61 1 0.999 16 1 0.999 39 2 0.999 62 1 0.918 17 0 0.999 40 2 0.956 63 0 0.999 18 0 0.999 41 1 0.999 64 3 0.998 19 0 0.985 42 4 1.000 65 1 0.995 20 0 0.997 43 2 0.999 66 1 0.999 21 1 0.999 44 1 0.999 67 0 0.999 22 0 0.999 45 1 0.999 68 0 0.812 23 0 0.990 46 4 0.999 autoclass-3.3.6.dfsg.1/data/uci-dbs-readme.text0000644000175000017500000002646511247310756017325 0ustar areare-*- Mode: Text -*- =============================================================================== This is the UCI Repository Of Machine Learning Databases 3 March 1990 ics.uci.edu: /usr2/spool/ftp/pub/machine-learning-databases Site Librarian: David W. Aha (aha@ics.uci.edu) 48 databases (5884K plus 1 offline database of unknown size) =============================================================================== Included in this directory are data sets that have been or can be used to evaluate learning algorithms. Each data file (*.data) consists of individual records described in terms of attribute-value pairs. See the corresponding *.names file for voluminous documentation. (Some files _generate_ databases; they do not have *.data files.) The contents of this repository can be remotely copied to other network sites via ftp to ics.uci.edu. Both the userid and password are "anonymous". Notes: 1. We're always looking for additional databases. Please send yours, with documentation. Thanks. Current documentation requirements are located in file DOC-REQUIREMENTS. Complaints and suggestions for improvements are welcome anytime. Presently, all databases except 4 with unusual formats have the following format: 1 instance per line, no spaces, commas separate attribute values, and missing values are denoted by "?". Exceptions: audioogy, labor-negotiations, spectrometer, university, and the undocumented databases. 2. There is also the "undocumented" sub-directory which contains six databases that require attention before being incorporated into the repository. You are welcome to access them. 3. Ivan Bratko has asked me to restrict the access on the databases he donated from the Ljubljana Oncology Institute. These databases, under the breast-cancer, lymphography, and primary-tumor directories, are unreadable to you. However, we are allowed to share them with academic institutions upon request. If used, these databases (like several others) require providing proper citations be made in published articles that use them. The citation requirements can be found in each database's corresponding documentation file. 4. Finally, I'm maintaining a list of CORRESPONDENTS and TRANSACTIONS. Perhaps someone on your site is listed among the CORRESPONDENTS and can provide you with some of these databases and related information. TRANSACTIONS is a log of my correspondence with others. David W. Aha Repository Librarian ---------------------------------------------------------------------- Brief Overview of Databases: Quick Listing: 1. annealing 2. audiology 3. autos 4. breast-cancer (restricted access) 5-6. chess-end-games 7. cpu-performance 8. echocardiogram 9. glass 10. hayes-roth 11-14. heart-disease 15. hepatitis 16. iris 17. labor-negotiations 18-19. led-display-creator 20. lymphography (restricted access) 21. mushroom 22. primary-tumor (restricted access) 23. shuttle-landing-control 24-25. soybean 26. spectrometer 27-34. thyroid-disease 35. university 36. voting-records 37-38. waveform domain 39-47. Undocumented databases: sub-directory undocumented 1. Bradshaw's flare data 2. Pat Langley's data generator 3. David Lewis's information retrieval (IR) data collection (offline) 4. Mike Pazzani's economic sanctions database 5. Ross Quinlan's latest version of the thyroid database 6. Philippe Collard's database on cloud cover images 7. Mary McLeish & Matt Cecile's database on horse colic 8. Paul O'Rorke's database containing theorems from Principia Mathematica 9. John Gennari's program for creating structured objects ("animals") 48. Nine small EBL domain theories and examples in sub-directory ebl Quick Summaries of Each Database: 1. Annealing data (unknown source) -- Documentation: On everything except database statistics -- Background information on this database: unknown -- Many missing attribute values 2. Audiology data (Baylor College) -- Documentation: On everything except database statistics -- Non-standardized attributes (differs between instances) -- All attributes are nominally-valued 3. Automobile data (1985 Ward's Automotive Yearbook) -- Documentation: On everything except statistics and class distribution -- Good mix of numeric and nominal-valued attributes -- More than 1 attribute can be used as a class attribute in this database 4. Breast cancer database (Ljubljana Oncology Institute) -- Documentation: On everything except database statistics -- Well-used database -- 286 instances, 2 classes, 9 attributes + the class attribute 5-6. Chess endgames data creator 1. king-rook-vs-king-knight -- Documentation: limited (nothing on class distribution, statistics) -- This concerns king-knight versus king-rook end games -- The database creator is coded in Common Lisp 2. king-rook-vs-king-pawn -- Documentation: sufficient -- This concerns king-rook versus king-pawn end games -- Originally described by Alen Shapiro 7. Computer hardware described in terms of its cycle time, memory size, etc. and classified in terms of their relative performance capabilities (CACM 4/87) -- Documentation: complete -- Contains integer-valued concept labels -- All attributes are integer-valued 8. Echocardiogram database (Reed Institute, Miami) -- Documentation: sufficient -- 13 numeric-valued attributes -- Binary classification: patient either alive or dead after survival period 9. Glass Identification database (USA Forensic Science Service) -- Documentation: completed -- 6 types of glass -- Defined in terms of their oxide content (i.e. Na, Fe, K, etc) -- All attributes are numeric-valued 10. Hayes-Roth and Hayes-Roth's database -- Described in their 1977 paper -- Topic: human subjects study 11-14. Heart Disease databases (Sources listed below) -- Documentation: extensive, but statistics and missing attribute information not yet furnished (perhaps later) -- 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach -- 13 of the 75 attributes were used for prediction in 2 separate tests, each of which achieved approximately 75%-80% classification accuracy -- The chosen 13 attributes are all continuously valued 15. Hepatitis database (G.Gong: CMU) -- Documentation: incomplete -- 155 instances with 20 attributes each; 2 classes -- Mostly Boolean or numeric-valued attribute types 16. Iris Plant database (Fisher, 1936) -- Documentation: complete -- 3 classes, 4 numeric attributes, 150 instances -- 1 class is linearly separable from the other 2, but the other 2 are not linearly separable from each other (simple database) 17. Labor relations database (Collective Bargaining Review) -- Documentation: no statistics -- Please see the labor directory for more information 18-19. LED display domains (Classification and Regression Trees book) -- Documentation: sufficient, but missing statistical information -- All attributes are Boolean-valued -- Two versions: 7 and 24 attributes -- Optimal Baye's rate known for the 10% probability of noise problem -- Several ML researchers have used this domain for testing noise tolerancy -- We provide here 2 C programs for generating sample databases 20. Lymphography database (Ljubljana Oncology Institute) -- Documentation: incomplete -- CITATION REQUIREMENT: Please use (see the documentation file) -- 148 instances; 19 attributes; 4 classes; no missing data values 21. Mushrooms in terms of their physical characteristics and classified as poisonous or edible (Audobon Society Field Guide) -- Documentation: complete, but missing statistical information -- All attributes are nominal-valued -- Large database: 8124 instances (2480 missing values for attribute #12) 22. Primary Tumor database (Ljubljana Oncology Institute) -- Documentation: incomplete -- CITATION REQUIREMENT: Please use (see the documentation file) -- 339 instances; 18 attributes; 22 classes; lots of missing data values 23. Shuttle Landing Control database -- tiny, 15-instance database with 7 attributes per instance; 2 classes -- appears to be well-known in the decision-tree community 24-25. Soybean data (Michalski) -- Documentation: Only the statistics is missing -- (2 sizes) -- Michalski's famous soybean disease databases 26. Low resolution spectrometer data (IRAS data -- NASA Ames Research Center) -- Documentation: no statistics nor class distribution given -- LARGE database...and this is only 531 of the instances -- 98 attributes per instance (all numeric) -- Contact NASA-Ames Research Center for more information 27-34. Thyroid patient records classified into disjoint disease classes (Garavan Institute) -- Documentation: as given by Ross Quinlan -- 6 databases from the Garavan Institute in Sydney, Australia -- Approximately the following for each database: -- 2800 training (data) instances and 972 test instances -- plenty of missing data -- 29 or so attributes, either Boolean or continuously-valued -- 2 additional databases, also from Ross Quinlan, are also here -- hypothyroid.data and sick-euthyroid.data -- Quinlan believes that these databases have been corrupted -- Their format is highly similar to the other databases 35. University data (Lebowitz) -- Documentation: scant; we've left it in its original (LISP-readable) form -- 285 instances, including some duplicates -- At least one attribute, academic-emphasis, can have multiple values per instance -- The user is encouraged to pursue the Lebowitz reference for more information on the database 36. Congressional voting records classified into Republican or Democrat (1984 United Stated Congressional Voting Records) -- Documentation: completed -- All attributes are Boolean valued; plenty of missing values; 2 classes -- Also, their is a 2nd, undocumented database containing 1986 voting records here. (will be) 37-38. Waveform data generator (Classification and Regression Trees book) -- Documentation: no statistics -- CART book's waveform domains -- 21 and 40 continuous attributes respectively -- difficult concepts to learn, but known Bayes optimal classification rate of 86% accuracy 39-47. Undocumented databases: see the sub-directory named undocumented 1. Bradshaw's flare data 2. Pat Langley's data generator 3. David Lewis's information retrieval (IR) data collection (offline) 4. Mike Pazzani's economic sanctions database 5. Ross Quinlan's latest version of the thyroid database 6. Philippe Collard's database on cloud cover images 7. Mary McLeish & Matt Cecile's database on hormse colic 8. Paul O'Rorke's database containing theorems from Principia Mathematica 9. John Gennari's program for creating structured objects ("animals") es 48. Nine simple small EBL domain theories and examples in sub-directory ebl 1. cup 2. deductive.assumable (contains three domain theories) 3. emotion 4. ice 5. pople 6. safe-to-stack 7. suicide autoclass-3.3.6.dfsg.1/data/glass/0000755000175000017500000000000011247310756014732 5ustar areareautoclass-3.3.6.dfsg.1/data/glass/glassc-predict.class-text-20000644000175000017500000000314211247310756022006 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 10 "TEST" cases in /home/tove/p/autoclass-c/data/glass/glassc-predict.db2 based on the "TRAINING" classification of 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11187.745 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 2 1.000 3 1.000 4 0.989 5 0.011 5 0.999 6 0.001 7 1.000 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 10 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 1.000 8 1.000 9 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.case-text-10000644000175000017500000001501611247310756020166 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 214 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -10897.738 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 1 1.000 47 0 0.996 93 1 1.000 2 0 0.896 48 1 1.000 94 1 1.000 3 1 1.000 49 1 1.000 95 0 0.999 4 0 0.999 50 0 0.975 96 0 0.969 5 0 0.999 51 1 1.000 97 0 0.990 6 0 0.999 52 0 0.976 98 0 0.983 7 0 0.999 53 1 0.999 99 1 0.999 8 0 0.999 54 1 0.999 100 3 1.000 9 0 0.975 55 1 0.998 101 3 1.000 10 0 0.999 56 1 0.995 102 1 0.995 11 0 0.999 57 1 1.000 103 1 0.995 12 0 0.999 58 0 0.999 104 1 1.000 13 0 0.999 59 0 0.999 105 1 1.000 14 0 0.999 60 0 0.999 106 4 1.000 15 0 0.999 61 1 1.000 107 4 1.000 16 0 0.999 62 3 0.999 108 4 1.000 17 0 0.999 63 1 1.000 109 4 1.000 18 1 1.000 64 1 1.000 110 4 1.000 19 1 1.000 65 1 1.000 111 4 1.000 20 0 0.995 66 1 1.000 112 4 1.000 21 0 0.999 67 1 1.000 113 4 1.000 22 1 1.000 68 1 1.000 114 0 0.999 23 0 0.999 69 1 1.000 115 0 0.998 24 0 0.999 70 1 1.000 116 0 0.999 25 0 0.997 71 1 1.000 117 0 0.999 26 0 0.999 72 0 0.999 118 0 0.999 27 0 0.999 73 0 0.999 119 0 0.999 28 0 0.999 74 0 0.999 120 0 0.999 29 0 0.999 75 0 0.999 121 0 0.999 30 0 0.999 76 0 0.999 122 0 0.999 31 0 0.999 77 0 0.999 123 0 0.999 32 0 0.999 78 0 0.999 124 0 0.999 33 3 1.000 79 1 0.999 125 1 0.999 34 0 0.999 80 0 0.999 126 0 0.999 35 0 0.999 81 0 0.998 127 0 0.999 36 0 0.966 82 0 0.999 128 3 0.978 37 3 1.000 83 0 0.997 129 3 1.000 38 0 0.999 84 0 0.999 130 3 1.000 39 1 1.000 85 1 0.999 131 3 1.000 40 1 1.000 86 1 0.663 132 4 1.000 41 0 0.999 87 1 1.000 133 0 0.999 42 0 0.999 88 0 0.999 134 0 0.999 43 0 0.999 89 0 0.999 135 0 0.999 44 1 1.000 90 0 0.999 136 0 0.999 45 0 0.999 91 0 0.995 137 0 0.999 46 0 0.998 92 1 0.999 138 0 0.999 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 139 0 0.999 165 3 0.999 191 2 1.000 140 0 0.999 166 3 1.000 192 2 1.000 141 0 0.999 167 3 1.000 193 2 0.999 142 3 1.000 168 4 1.000 194 2 1.000 143 3 1.000 169 4 1.000 195 2 1.000 144 0 0.999 170 4 1.000 196 2 0.999 145 0 0.975 171 4 1.000 197 2 0.991 146 0 0.999 172 2 1.000 198 2 1.000 147 1 1.000 173 2 1.000 199 2 0.999 148 0 0.999 174 4 1.000 200 2 0.999 149 0 0.999 175 3 1.000 201 2 1.000 150 1 0.998 176 3 1.000 202 4 1.000 151 0 0.999 177 2 1.000 203 2 1.000 152 1 1.000 178 2 0.999 204 2 1.000 153 1 1.000 179 2 1.000 205 2 1.000 154 0 0.993 180 2 1.000 206 2 1.000 155 0 0.999 181 2 1.000 207 2 1.000 156 0 0.996 182 2 1.000 208 2 0.993 157 0 0.993 183 2 0.999 209 2 1.000 158 1 1.000 184 4 1.000 210 2 0.999 159 0 0.999 185 4 1.000 211 2 1.000 160 0 0.998 186 2 1.000 212 2 1.000 161 0 0.995 187 2 1.000 213 2 1.000 162 3 1.000 188 1 1.000 214 2 1.000 163 1 1.000 189 3 1.000 164 2 1.000 190 3 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.case-data-20000644000175000017500000001201611247310756020111 0ustar areare CROSS REFERENCE: CASE NUMBER => MOST PROBABLE CLASS DATA_CLSF_HEADER AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11510.965 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 DATA_CASE_TO_CLASS Case # Class Prob Case # Class Prob Case # Class Prob 001 3 0.99 002 1 0.98 003 1 0.99 004 0 0.99 005 0 0.99 006 0 0.99 007 0 0.99 008 0 0.99 009 0 0.97 010 0 0.99 011 0 0.99 012 0 0.99 013 0 0.99 014 0 0.99 015 0 0.99 016 0 0.99 017 0 0.99 018 1 0.99 019 1 0.99 020 0 0.97 021 0 0.99 022 6 0.99 023 0 0.99 024 0 0.99 025 0 0.88 026 0 0.99 027 0 0.99 028 0 0.99 029 0 0.99 030 0 0.99 031 0 0.99 032 0 0.99 033 4 0.99 034 0 0.99 035 0 0.99 036 0 0.99 037 4 0.99 038 0 0.99 039 1 0.99 040 1 0.99 041 0 0.99 042 0 0.99 043 0 0.99 044 1 1.00 045 0 0.99 046 0 0.99 047 0 0.99 048 1 1.00 049 1 0.99 050 0 0.99 051 1 1.00 052 0 0.99 053 4 0.97 054 4 0.98 055 4 0.99 056 4 0.99 057 0 0.99 058 0 0.99 059 0 0.99 060 0 0.99 061 1 0.99 062 6 0.99 063 1 1.00 064 1 1.00 065 1 1.00 066 1 0.99 067 1 1.00 068 1 1.00 069 1 1.00 070 1 1.00 071 1 0.99 072 0 0.99 073 0 0.99 074 0 0.99 075 0 0.99 076 0 0.99 077 0 0.99 078 0 0.99 079 1 1.00 080 0 0.99 081 0 0.99 082 0 0.98 083 0 0.94 084 0 0.99 085 1 0.94 086 1 0.98 087 1 0.99 088 0 0.98 089 0 0.99 090 0 0.99 091 0 0.92 092 1 0.99 093 1 0.99 094 1 0.99 095 0 0.99 096 0 0.97 097 1 0.60 098 0 0.99 099 0 0.97 100 4 0.99 101 4 0.99 102 4 0.94 103 6 0.99 104 5 0.99 105 3 0.99 106 5 0.99 107 5 1.00 108 5 1.00 109 5 0.99 110 5 0.99 111 5 1.00 112 5 1.00 113 5 0.99 114 0 0.99 115 0 0.99 116 0 0.99 117 0 0.99 118 0 0.99 119 0 0.99 120 0 0.99 121 0 0.99 122 0 0.99 123 0 0.99 124 0 0.99 125 0 0.99 126 0 0.99 127 0 0.99 128 4 0.99 129 4 1.00 130 4 0.99 131 3 0.99 132 5 0.98 133 0 0.99 134 0 0.99 135 0 0.99 136 0 0.99 137 0 0.99 138 0 0.99 139 0 0.99 140 0 0.99 141 0 0.99 142 4 0.99 143 4 1.00 144 0 0.99 145 0 0.99 146 0 0.99 147 1 0.99 148 0 0.99 149 0 0.99 150 0 0.99 151 0 0.99 152 1 0.99 153 1 0.99 154 0 0.99 155 0 0.99 156 0 0.98 157 0 0.99 158 1 1.00 159 0 0.99 160 0 0.99 161 0 0.99 162 6 1.00 163 1 0.99 164 2 1.00 165 4 0.96 166 3 1.00 167 3 1.00 168 3 1.00 169 3 1.00 170 3 1.00 171 3 0.99 172 3 1.00 173 3 1.00 174 3 0.99 175 4 1.00 176 3 0.99 177 3 1.00 178 3 1.00 179 3 1.00 180 3 1.00 181 3 1.00 182 3 1.00 183 3 1.00 184 5 1.00 185 2 0.99 186 2 1.00 187 2 1.00 188 0 0.99 189 3 1.00 190 5 1.00 191 2 0.99 192 2 1.00 193 2 0.99 194 2 1.00 195 2 1.00 196 2 0.99 197 2 0.99 198 2 1.00 199 2 1.00 200 2 1.00 201 2 1.00 202 3 1.00 203 2 1.00 204 2 1.00 205 2 1.00 206 2 1.00 207 2 1.00 208 2 1.00 209 2 1.00 210 2 1.00 211 2 1.00 212 2 1.00 213 2 1.00 214 2 1.00 autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.case-text-20000644000175000017500000000206211247310756021614 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 10 "TEST" cases in /home/tove/p/autoclass-c/data/glass/glassc-predict.db2 based on the "TRAINING" classification of 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11187.745 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 0 0.999 5 0 0.999 9 2 1.000 2 0 0.999 6 2 1.000 10 1 0.999 3 0 0.999 7 0 1.000 4 0 0.989 8 2 0.999 autoclass-3.3.6.dfsg.1/data/glass/glassc.class-text-20000644000175000017500000002262111247310756020361 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11187.745 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 1.000 3 1.000 4 1.000 5 1.000 6 1.000 7 1.000 8 1.000 9 0.994 6 0.006 10 1.000 11 1.000 12 0.999 13 1.000 14 1.000 15 1.000 16 1.000 17 0.999 18 1.000 20 1.000 21 1.000 23 0.999 24 0.999 25 1.000 26 1.000 27 1.000 28 1.000 29 1.000 30 1.000 31 1.000 32 1.000 34 1.000 35 1.000 36 1.000 38 1.000 39 0.989 5 0.011 40 0.989 5 0.011 41 1.000 42 0.999 43 1.000 44 1.000 45 1.000 46 1.000 CLASS = 0 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 47 0.999 2 0.001 49 1.000 50 0.999 6 0.001 51 0.999 52 0.997 2 0.002 58 0.999 59 1.000 60 1.000 61 0.891 5 0.108 63 1.000 65 1.000 67 1.000 68 1.000 69 1.000 70 1.000 72 1.000 73 1.000 74 1.000 75 1.000 76 1.000 77 1.000 78 1.000 79 1.000 80 1.000 81 1.000 82 1.000 83 1.000 84 1.000 86 1.000 87 1.000 88 1.000 89 1.000 90 1.000 91 1.000 92 1.000 94 1.000 95 1.000 96 1.000 97 1.000 98 0.999 114 1.000 115 1.000 116 1.000 117 1.000 118 1.000 119 1.000 120 1.000 121 1.000 122 1.000 123 1.000 124 1.000 126 1.000 127 0.999 133 1.000 134 1.000 135 1.000 136 1.000 CLASS = 0 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 137 1.000 138 1.000 139 1.000 140 1.000 141 1.000 144 1.000 145 0.985 2 0.013 5 0.002 146 1.000 148 1.000 149 1.000 151 1.000 154 1.000 155 0.999 156 1.000 157 1.000 158 1.000 159 1.000 160 1.000 161 1.000 163 1.000 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 181 0.999 3 0.001 185 1.000 186 1.000 187 1.000 191 1.000 192 1.000 193 1.000 194 1.000 195 1.000 196 1.000 197 1.000 198 1.000 199 1.000 200 1.000 201 1.000 202 1.000 203 1.000 204 1.000 205 1.000 206 1.000 207 1.000 208 1.000 209 1.000 210 1.000 211 1.000 212 1.000 213 1.000 214 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 33 0.998 5 0.002 53 1.000 54 1.000 55 0.996 5 0.004 99 0.998 5 0.002 100 1.000 101 1.000 102 1.000 128 1.000 129 1.000 130 1.000 131 1.000 142 0.996 5 0.004 143 0.998 5 0.002 150 1.000 165 1.000 175 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 164 1.000 166 1.000 167 1.000 168 1.000 169 0.999 1 0.001 170 0.999 171 0.959 4 0.041 172 1.000 173 1.000 174 0.993 4 0.007 176 0.995 4 0.005 177 1.000 178 0.999 4 0.001 179 1.000 180 1.000 182 1.000 183 1.000 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 104 1.000 105 0.997 3 0.003 106 1.000 107 1.000 108 1.000 109 1.000 110 1.000 CLASS = 4 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 111 1.000 112 1.000 113 1.000 132 1.000 184 1.000 189 1.000 190 1.000 CLASS = 5 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 22 1.000 48 1.000 56 1.000 57 1.000 93 1.000 103 1.000 125 0.998 0 0.002 147 0.998 0 0.002 153 1.000 162 1.000 188 0.998 6 0.002 CLASS = 6 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 19 0.958 5 0.042 37 1.000 62 1.000 64 1.000 66 0.980 0 0.015 5 0.005 71 1.000 85 1.000 152 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.results-bin0000644000175000017500000006706211247310756020412 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -1.0897738e+04(# clsf_DS 1: log_a_x_h = -1.1187745e+04ac_version 3.3.4unx 4BQ'tH. `H ff@.  |data/glass/glassc.db2x9*data/glass/glass-3c.hd2 2`@ ;? dummynilId number( realscalarRI: refractive index?u]6  W?]m?Y?q7log_transform int 11lNULLNULL realscalarNa: Wt.% Sodium oxide@9hX= A+AVA)?log_transform int 12lNULLNULL realscalarMg: Wt.% Magnesium oxidex :@b+@I@log_transform int 13lNULLNULL realscalarAl: Wt.% Aluminum oxideb; `@z>?U~>log_transform int 14lNULLNULL realscalarSi: Wt.% Silicon oxideT88- іBBGMB?log_transform int 15lNULLNULL realscalar K: Wt.% Potassium oxide $<8R@"~>>log_transform int 16lNULLNULL realscalarCa: Wt.% Calcium oxideXU: pA­@OA@log_transform int 17lNULLNULL realscalarBa: Wt.% Barium oxide< I@s?3>;{>log_transform int 18lNULLNULL realscalarFe: Wt.% Iron oxide-= \?i=<log_transform int 19lNULLNULL discretenominalType of glass  FL 123567lNULLNULL reallog_transformLog RI: refractive indexh? 02 yu]6: ++7( 8 source int 1source_sub_type str scalarlNULLNULL reallog_transformLog Na: Wt.% Sodium oxideP3 gz96@@>&@ȵo; source int 2source_sub_type str scalarlNULLNULL reallog_transformLog Mg: Wt.% Magnesium oxideP? |:.a?濽Y@ source int 3source_sub_type str scalarlNULLNULL reallog_transformLog Al: Wt.% Aluminum oxide( X @ rdb;Z?r> > source int 4source_sub_type str scalarlNULLNULL reallog_transformLog Si: Wt.% Silicon oxideh1 ( N hS8U@h݇@#@88 source int 5source_sub_type str scalarlNULLNULL reallog_transformLog K: Wt.% Potassium oxide80 > O #<?̫?@ source int 6source_sub_type str scalarlNULLNULL reallog_transformLog Ca: Wt.% Calcium oxide@Y VKd-ܯ`]CpM ($  Au?( P x     @ h  [B[B@'<Ipœ} X $($@?ۢ>1@n8@̃$mYK66,7jyᆸw$8UQ9$6:|918i潸01@n8@̃$ Q66+7­7:=ո|'89$6+7k#:.<9A8N3Pkֻ$­7.<9Т:Yqwt!{WE:;$:鸚A8Yc<%3׸_;ky2ۍ$=Nqw%3׸7Fv8/׽A$ո3t!_;Fv8q; ̘;$|'8Pk{Wky2/ F:Y><$V:$9ֻE:;ۍ׽A̘;Y><h7@$ Q66+7­7:=ո|'89$l@:VT9I9L ۩$Q @ܖ>:ޤb^Ϣ|Թ%p;$O?P 4+;m V:$beLA= w{ɾ> km9bK$V:$uBjC-5@?GH+>YPF#@$ ?-C4__h65H7໢1 834z P:6;L)8B)8B<\>)Ŀ~,3~M ($  wAu?0 X      H p  )8B)8B%<Ipœ0 X $'&@｟?Mb@$pۿ @$" 8&8uc9vƺP"BZq9$&8Y:Ƞ(;𻿼a뼸p7$A$uc9Ƞ(;_T<ϠDU9>GF$vƺϠl$ >;5> VI$P"BD;rT8;w;$Zqa5>;Ϲ?B`iN>$9p7U9 VwBD<]p,<$z P:$$A>GFI;`iN>]p,<-@$'&@｟?Mb@$pۿ @$+8;f>ɼ$=Jia;R 8;'v ;$|]󄖽f>;pl?K W>$5:z7 9ɼ'KP<E4<$Q5^:$3JYHOv ; W>E4<s9@$+8׽dmV: ;$Q5^:$qQ\`*>nĀAW=d@8^/@$ ?-C4__h65H7໢1 834z P:6;L$A$A=r!h X ($  Q˟Au?8 `     ( P x  $A$A.Tc=Ipœ X $&Ϣ#@=։=6@N@A<ӕ$F8|(V :#'M7:j;; <0<$|(V<\<u2#:a~|Tm?D>$#'M2#:|9 <'TmR˼$7:aTm? $;ee>m2+?=@H?$ <0<^D>R˼A*? >H?2@$&Ϣ#@=։=6@N@A<ӕ$9r:Syhޟ:[;;TG<$r< a":1$ؕ6^y|$Qe6$: 3W>S?V=ԝ>@>$Syha":S=:U[p̓nf$ޟ:1$?Ut@^9zB??$[;ؕV=[p^>wS=+1=>$;6^ԝ>̓9zB?+1=R2@?$TGnf?>?V@$9r:Syhޟ:[;;TG<$S&#<zܗ @KU:<$Qe6$>A[u>>ch^?s: >7>$˿ _ms%8PwMŻՎ$_ AEJTRh@I??Թ>$AڤV;,d缰i5^P'@(}Ut@@K=2E, 2=[B?;\$m9|=2ES=b>v= ~*<"R$۪ҷJ+L, bZz9S8Y9Ԙ\<$w42=>v=S8 > N[<$r9e[B ~*$2{;W;\R\<[<@7ʑ>mn@$.l'q%@U??~'>'@(}Ut@@Ke\7i=9Yi?E%v$B9e\y=ƌ,ȉ=>><l' {$t0d7ƌ,Cc39d8 7s9Ϫy<$1i=ȉ=d8\>ѻ%z #<$ڭ99Y>>< 7s9ѻk8M$E9v=i?l'Ϫ%d>@l>$Q E%v {yP@$ €7q08HğB9tڭ9EQ$@;$<uB8D9=B+<$Ӟ?i>r伧㺒="ż*׾>$A6-ֽ$S=OL;=: _1O$K꿏ܽW#ջƷw-7e8^"N::$m›>?m@->˒]91,g=$cA[Vz*="q&:3T;sx(q$`-Asʲ?wBZ~"I?k3>$SNPÞ?b'{PBZ?J2=@@$ ?-C4__h65H7໢1 834z P:6;L B B'>S֋˟`>+ ($  RAu?H p     8 `   B Bש<Ipœ` X $)~*@HdbB?6@r@+$n7 18v]Q:qĹs6'9<:$ 184: ҼӺM79a:k=tP;$v]Q: Ҽ@əG%?+5<$qĹӺə2KS=X>*>/(=$s6M79G%X )9-Ыw:;1;$'a?>-ЫUb@(B ='M&$9:+w:(BI<ν<$; =ν|@7>$:tP;Ʌs?$)~*@HdbB?6@r@+$7WB<8x^:йߞ6C9,nHKE$WB<8֬:߼y9Ęoծ:@=p];$x^:߼uAeM/?rM@iH$йf=ןY>PWm4>t2=$ߞ6y9eM/ן>99C;m:2;<;$Ęo?Y>C;?@]A=b0$C9ծ:rPm:]1<(۽X6<$,nH@=M@Wm4>2;A=(۽Q@?$KEp];iHt2=<;b0X6<?ք?$7WB<8x^:йߞ6C9,nHKE$9@ޞ:)9Ժ9w:^#=k;$lZB\@"tHnGM.$Q<ܾQ)#=G>=H<$J>k M>/|8һg9: ;$U hּ @)?DP۽mv?\<$VAU:?t1 @\SAR(@ė>$|oB@@5ú1> A;U>>A=b?$ ?-C4__h65H7໢1 834z P:6; 4g s&Px PC ff@h database_DS_ptrmodel_DS_ptr 0LKBKB} ? 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This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -4.21e+01 1.00e+00 97 0.453 1 -5.03e+01 2.77e-04 46 0.215 2 -5.82e+01 1.01e-07 35 0.163 3 -5.54e+01 1.62e-06 19 0.089 4 -5.39e+01 6.97e-06 17 0.080 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 1.31e+01 97 0.453 1 8.16e+00 46 0.215 2 5.68e+00 35 0.163 3 6.25e+00 19 0.089 4 1.73e+01 17 0.080 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 011 Log RI: refractive index 1.000 012 Log Na: Wt.% Sodium oxide 1.000 013 Log Mg: Wt.% Magnesium oxide 1.000 014 Log Al: Wt.% Aluminum oxide 1.000 015 Log Si: Wt.% Silicon oxide 1.000 016 Log K: Wt.% Potassium oxide 1.000 017 Log Ca: Wt.% Calcium oxide 1.000 018 Log Ba: Wt.% Barium oxide 1.000 019 Log Fe: Wt.% Iron oxide 1.000 000 Id number ----- 001 RI: refractive index ----- 002 Na: Wt.% Sodium oxide ----- 003 Mg: Wt.% Magnesium oxide ----- 004 Al: Wt.% Aluminum oxide ----- 005 Si: Wt.% Silicon oxide ----- 006 K: Wt.% Potassium oxide ----- 007 Ca: Wt.% Calcium oxide ----- 008 Ba: Wt.% Barium oxide ----- 009 Fe: Wt.% Iron oxide ----- 010 Type of glass ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 97 normalized weight 0.453 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 1.31e+01 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.454 ( 1.27e+00 4.25e-02) 3.21e+01 (-9.37e-02 2.60e+00) um oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.454 (-5.30e+00 2.86e-02) 3.03e+01 (-4.43e+00 1.93e+00) oxide 00 16 R MNcn Log K: Wt.% Potassi 1.454 (-5.10e-01 8.42e-02) 1.14e+01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.454 ( 2.12e+00 3.88e-02) 1.48e+00 ( 2.18e+00 1.45e-01) oxide 00 11 R MNcn Log RI: refractive i 1.454 (-6.59e-01 1.77e-03) 1.12e+00 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 1.454 ( 2.57e+00 2.50e-02) 8.66e-01 ( 2.59e+00 6.09e-02) oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.454 ( 4.29e+00 5.21e-03) 3.25e-01 ( 4.29e+00 1.07e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.454 ( 3.18e-01 1.41e-01) 8.24e-02 ( 3.06e-01 3.70e-01) m oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.454 (-4.12e+00 1.69e+00) 3.34e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.176 0.276 -0.445 -0.434 -0.441 0.582 0.000 0.114 12 0.176 1.000 0.239 0.025 -0.710 -0.326 -0.232 0.000 -0.155 13 0.276 0.239 1.000 -0.139 -0.266 -0.172 -0.287 0.000 0.060 14 -0.445 0.025 -0.139 1.000 -0.139 0.364 -0.497 0.000 -0.005 15 -0.434 -0.710 -0.266 -0.139 1.000 0.249 -0.207 0.000 -0.084 16 -0.441 -0.326 -0.172 0.364 0.249 1.000 -0.300 0.000 0.033 17 0.582 -0.232 -0.287 -0.497 -0.207 -0.300 1.000 0.000 0.177 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.114 -0.155 0.060 -0.005 -0.084 0.033 0.177 0.000 1.000 CLASS 1 - weight 46 normalized weight 0.215 relative strength 2.77e-04 ******* class cross entropy w.r.t. global class 8.16e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.906 (-5.30e+00 2.91e-02) 2.97e+01 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.906 ( 1.25e+00 1.18e-01) 1.14e+01 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.906 (-4.90e-05 4.09e-01) 7.49e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.906 ( 4.28e+00 1.01e-02) 4.38e-01 ( 4.29e+00 1.07e-02) oxide 00 11 R MNcn Log RI: refractive i 0.906 (-6.55e-01 6.40e-03) 3.95e-01 (-6.57e-01 5.82e-03) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 0.906 ( 2.20e+00 9.66e-02) 2.28e-01 ( 2.18e+00 1.45e-01) oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.906 ( 2.60e+00 4.55e-02) 2.15e-01 ( 2.59e+00 6.09e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 0.906 (-1.71e+00 1.20e+00) 2.01e-01 (-1.47e+00 1.74e+00) um oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.906 (-4.11e+00 1.70e+00) 3.70e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.308 0.301 -0.605 -0.747 -0.503 0.816 0.000 -0.029 12 0.308 1.000 0.502 -0.413 -0.661 -0.551 0.003 0.000 -0.159 13 0.301 0.502 1.000 -0.426 -0.484 -0.521 0.041 0.000 -0.063 14 -0.605 -0.413 -0.426 1.000 0.496 0.547 -0.621 0.000 -0.013 15 -0.747 -0.661 -0.484 0.496 1.000 0.584 -0.556 0.000 0.122 16 -0.503 -0.551 -0.521 0.547 0.584 1.000 -0.430 0.000 0.103 17 0.816 0.003 0.041 -0.621 -0.556 -0.430 1.000 0.000 0.067 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 -0.029 -0.159 -0.063 -0.013 0.122 0.103 0.067 0.000 1.000 CLASS 2 - weight 35 normalized weight 0.163 relative strength 1.01e-07 ******* class cross entropy w.r.t. global class 5.68e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 14 R MNcn Log Al: Wt.% Aluminu 0.631 ( 7.59e-01 2.37e-01) 1.91e+00 ( 3.06e-01 3.70e-01) m oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.631 ( 2.66e+00 3.78e-02) 1.85e+00 ( 2.59e+00 6.09e-02) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.631 (-1.34e+00 2.48e+00) 1.25e+00 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.631 (-3.57e+00 2.87e+00) 1.21e+00 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.631 (-3.78e+00 2.47e+00) 9.34e-01 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.631 (-6.61e-01 3.93e-03) 9.02e-01 (-6.57e-01 5.82e-03) ndex 00 19 R MNcn Log Fe: Wt.% Iron ox 0.631 (-4.87e+00 1.02e+00) 6.84e-01 (-4.18e+00 1.66e+00) ide 00 17 R MNcn Log Ca: Wt.% Calcium 0.631 ( 2.12e+00 1.61e-01) 3.53e-01 ( 2.18e+00 1.45e-01) oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.631 ( 4.29e+00 1.33e-02) 1.76e-01 ( 4.29e+00 1.07e-02) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.301 0.075 -0.427 0.098 -0.466 0.481 -0.078 -0.047 12 0.301 1.000 -0.252 -0.191 0.548 -0.625 0.301 0.402 0.088 13 0.075 -0.252 1.000 -0.469 -0.281 0.084 -0.254 -0.423 -0.268 14 -0.427 -0.191 -0.469 1.000 -0.387 0.502 -0.213 0.299 0.181 15 0.098 0.548 -0.281 -0.387 1.000 -0.677 0.469 0.235 0.213 16 -0.466 -0.625 0.084 0.502 -0.677 1.000 -0.721 0.017 -0.068 17 0.481 0.301 -0.254 -0.213 0.469 -0.721 1.000 -0.268 0.145 18 -0.078 0.402 -0.423 0.299 0.235 0.017 -0.268 1.000 0.206 19 -0.047 0.088 -0.268 0.181 0.213 -0.068 0.145 0.206 1.000 CLASS 3 - weight 19 normalized weight 0.089 relative strength 1.62e-06 ******* class cross entropy w.r.t. global class 6.25e+00 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 0.695 ( 7.47e-01 6.16e-01) 1.37e+00 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.695 (-8.34e-01 5.87e-01) 1.09e+00 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.695 (-6.54e-01 3.92e-03) 7.91e-01 (-6.57e-01 5.82e-03) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 0.695 ( 2.26e+00 1.20e-01) 6.30e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.695 (-3.18e+00 2.10e+00) 5.95e-01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.695 (-3.28e+00 2.09e+00) 4.28e-01 (-4.18e+00 1.66e+00) ide 00 15 R MNcn Log Si: Wt.% Silicon 0.695 ( 4.28e+00 1.31e-02) 3.23e-01 ( 4.29e+00 1.07e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.695 ( 3.67e-01 2.47e-01) 2.49e-01 ( 3.06e-01 3.70e-01) m oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.695 ( 2.59e+00 8.41e-02) 1.06e-01 ( 2.59e+00 6.09e-02) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.278 -0.505 0.262 -0.548 -0.213 0.551 -0.215 -0.390 12 0.278 1.000 0.054 -0.107 -0.792 -0.220 -0.387 0.342 -0.049 13 -0.505 0.054 1.000 -0.354 -0.074 0.306 -0.718 0.482 -0.047 14 0.262 -0.107 -0.354 1.000 -0.204 0.465 0.393 -0.316 -0.145 15 -0.548 -0.792 -0.074 -0.204 1.000 0.013 0.148 -0.190 0.329 16 -0.213 -0.220 0.306 0.465 0.013 1.000 -0.091 -0.131 0.008 17 0.551 -0.387 -0.718 0.393 0.148 -0.091 1.000 -0.738 -0.199 18 -0.215 0.342 0.482 -0.316 -0.190 -0.131 -0.738 1.000 0.110 19 -0.390 -0.049 -0.047 -0.145 0.329 0.008 -0.199 0.110 1.000 CLASS 4 - weight 17 normalized weight 0.080 relative strength 6.97e-06 ******* class cross entropy w.r.t. global class 1.73e+01 ******* Model file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.917 (-5.30e+00 2.04e-03) 2.55e+03 (-9.37e-02 2.60e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.917 ( 2.48e+00 2.27e-01) 1.32e+00 ( 2.18e+00 1.45e-01) oxide 00 11 R MNcn Log RI: refractive i 1.917 (-6.49e-01 1.18e-02) 7.03e-01 (-6.57e-01 5.82e-03) ndex 00 14 R MNcn Log Al: Wt.% Aluminu 1.917 ( 6.73e-02 5.58e-01) 4.28e-01 ( 3.06e-01 3.70e-01) m oxide 00 16 R MNcn Log K: Wt.% Potassi 1.917 (-2.29e+00 2.32e+00) 3.50e-01 (-1.47e+00 1.74e+00) um oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.917 ( 2.56e+00 1.27e-01) 2.93e-01 ( 2.59e+00 6.09e-02) oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.917 (-4.91e+00 1.67e+00) 2.88e-01 (-4.43e+00 1.93e+00) oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.917 ( 4.29e+00 2.25e-02) 9.96e-02 ( 4.29e+00 1.07e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.917 (-4.24e+00 1.83e+00) 3.37e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.618 0.000 0.252 -0.834 0.044 0.862 0.351 0.564 12 -0.618 1.000 0.000 -0.477 0.408 -0.279 -0.634 -0.369 -0.265 13 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 14 0.252 -0.477 0.000 1.000 -0.430 0.814 0.278 0.316 0.352 15 -0.834 0.408 0.000 -0.430 1.000 -0.249 -0.716 -0.479 -0.681 16 0.044 -0.279 0.000 0.814 -0.249 1.000 -0.002 0.196 0.294 17 0.862 -0.634 0.000 0.278 -0.716 -0.002 1.000 0.114 0.368 18 0.351 -0.369 0.000 0.316 -0.479 0.196 0.114 1.000 0.418 19 0.564 -0.265 0.000 0.352 -0.681 0.294 0.368 0.418 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.influ-o-data-20000644000175000017500000004761411247310756020563 0ustar areareDATA_CLSF_HEADER AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11510.965 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 DATA_SEARCH_SUMMARY SEARCH SUMMARY 4 tries over 9 seconds SUMMARY OF 10 BEST RESULTS PROBABILITY exp(-11434.215) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-11510.965) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-11663.794) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11670.677) N_CLASSES 2 FOUND ON TRY 1 DATA_POP_CLASSES CLASSIFICATION HAS 7 POPULATED CLASSES(max global influence value = 1.430) Class Log of class Relative Class Normalized num strength class strength weight class weight 00 -4.68e+01 1.00e+00 100 0.466 01 -5.15e+01 9.26e-03 33 0.155 02 -5.90e+01 4.91e-06 27 0.126 03 -5.79e+01 1.43e-05 22 0.104 04 -5.39e+01 7.77e-04 16 0.074 05 -6.17e+01 3.37e-07 12 0.056 06 -5.80e+01 1.31e-05 4 0.019 DATA_CLASS_DIVS CLASS DIVERGENCES Class class cross entropy Class Normalized num w.r.t. global class weight class weight 00 1.15e+01 100 0.466 01 8.89e+00 33 0.155 02 5.70e+00 27 0.126 03 8.56e+00 22 0.104 04 7.72e+00 16 0.074 05 1.05e+01 12 0.056 06 1.29e+01 4 0.019 DATA_NORM_INF_VALS ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES num description I-*k 011 Log RI: refractive index 1.000 012 Log Na: Wt.% Sodium oxide 1.000 013 Log Mg: Wt.% Magnesium oxide 1.000 014 Log Al: Wt.% Aluminum oxide 1.000 015 Log Si: Wt.% Silicon oxide 1.000 016 Log K: Wt.% Potassium oxide 1.000 017 Log Ca: Wt.% Calcium oxide 1.000 018 Log Ba: Wt.% Barium oxide 1.000 019 Log Fe: Wt.% Iron oxide 1.000 000 Id number ----- 001 RI: refractive index ----- 002 Na: Wt.% Sodium oxide ----- 003 Mg: Wt.% Magnesium oxide ----- 004 Al: Wt.% Aluminum oxide ----- 005 Si: Wt.% Silicon oxide ----- 006 K: Wt.% Potassium oxide ----- 007 Ca: Wt.% Calcium oxide ----- 008 Ba: Wt.% Barium oxide ----- 009 Fe: Wt.% Iron oxide ----- 010 Type of glass ----- DATA_CLASS 0 CLASS 0 - weight 100 normalized weight 0.466 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 1.15e+01 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 1.276 (-5.30e+00 2.86e-02) 3.03e+01 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 1.276 ( 1.27e+00 4.76e-02) 2.86e+01 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 1.276 (-5.06e-01 8.16e-02) 1.19e+01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.276 ( 2.13e+00 3.76e-02) 1.49e+00 ( 2.18e+00 1.45e-01) oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.276 ( 2.57e+00 2.54e-02) 9.06e-01 ( 2.59e+00 6.09e-02) oxide 00 11 R MNcn Log RI: refractive i 1.276 (-6.59e-01 1.31e-02) 1.47e-01 (-6.57e-01 1.30e-02) ndex 00 15 R MNcn Log Si: Wt.% Silicon 1.276 ( 4.29e+00 1.31e-02) 1.37e-01 ( 4.29e+00 1.30e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.276 ( 3.16e-01 1.42e-01) 7.13e-02 ( 3.06e-01 3.70e-01) m oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.276 (-4.13e+00 1.69e+00) 2.41e-02 (-4.18e+00 1.66e+00) ide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.041 0.032 -0.061 -0.028 -0.073 0.092 0.000 -0.010 12 0.041 1.000 0.252 -0.003 -0.285 -0.275 -0.190 0.000 -0.115 13 0.032 0.252 1.000 -0.172 -0.107 -0.268 -0.303 0.000 0.076 14 -0.061 -0.003 -0.172 1.000 -0.051 0.421 -0.477 0.000 -0.010 15 -0.028 -0.285 -0.107 -0.051 1.000 0.112 -0.080 0.000 -0.025 16 -0.073 -0.275 -0.268 0.421 0.112 1.000 -0.364 0.000 0.004 17 0.092 -0.190 -0.303 -0.477 -0.080 -0.364 1.000 0.000 0.123 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 -0.010 -0.115 0.076 -0.010 -0.025 0.004 0.123 0.000 1.000 DATA_CLASS 1 CLASS 1 - weight 33 normalized weight 0.155 relative strength 9.26e-03 ******* class cross entropy w.r.t. global class 8.89e+00 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.988 (-5.30e+00 2.95e-02) 2.94e+01 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.988 ( 1.29e+00 4.90e-02) 2.83e+01 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.988 (-1.90e-02 3.92e-01) 8.28e-01 ( 3.06e-01 3.70e-01) m oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.988 ( 2.62e+00 3.61e-02) 5.90e-01 ( 2.59e+00 6.09e-02) oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.988 ( 4.28e+00 1.35e-02) 4.26e-01 ( 4.29e+00 1.30e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 0.988 (-1.90e+00 1.20e+00) 3.56e-01 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.988 (-6.55e-01 1.35e-02) 1.91e-01 (-6.57e-01 1.30e-02) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 0.988 ( 2.19e+00 1.01e-01) 1.20e-01 ( 2.18e+00 1.45e-01) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.988 (-4.02e+00 1.72e+00) 8.81e-02 (-4.18e+00 1.66e+00) ide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.062 0.310 -0.353 -0.213 -0.229 0.394 0.000 0.102 12 0.062 1.000 0.331 -0.175 -0.352 -0.348 -0.145 0.000 -0.163 13 0.310 0.331 1.000 -0.698 -0.414 -0.542 0.646 0.000 0.034 14 -0.353 -0.175 -0.698 1.000 0.361 0.466 -0.810 0.000 -0.043 15 -0.213 -0.352 -0.414 0.361 1.000 0.302 -0.363 0.000 -0.088 16 -0.229 -0.348 -0.542 0.466 0.302 1.000 -0.490 0.000 0.077 17 0.394 -0.145 0.646 -0.810 -0.363 -0.490 1.000 0.000 0.202 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.102 -0.163 0.034 -0.043 -0.088 0.077 0.202 0.000 1.000 DATA_CLASS 2 CLASS 2 - weight 27 normalized weight 0.126 relative strength 4.91e-06 ******* class cross entropy w.r.t. global class 5.70e+00 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.634 (-1.61e-01 1.20e+00) 3.57e+00 (-4.43e+00 1.93e+00) oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.634 ( 2.68e+00 4.63e-02) 1.86e+00 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.634 (-4.37e+00 2.36e+00) 1.81e+00 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.634 (-3.86e+00 2.23e+00) 1.07e+00 (-1.47e+00 1.74e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.634 ( 7.39e-01 4.17e-01) 1.04e+00 ( 3.06e-01 3.70e-01) m oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.634 ( 2.10e+00 1.61e-01) 5.13e-01 ( 2.18e+00 1.45e-01) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.634 (-4.75e+00 1.14e+00) 4.99e-01 (-4.18e+00 1.66e+00) ide 00 15 R MNcn Log Si: Wt.% Silicon 0.634 ( 4.29e+00 1.36e-02) 4.27e-01 ( 4.29e+00 1.30e-02) oxide 00 11 R MNcn Log RI: refractive i 0.634 (-6.61e-01 1.36e-02) 3.09e-01 (-6.57e-01 1.30e-02) ndex DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.077 -0.058 0.110 -0.062 -0.041 0.082 0.164 0.010 12 -0.077 1.000 -0.392 -0.736 0.514 -0.447 -0.011 -0.701 -0.097 13 -0.058 -0.392 1.000 0.071 -0.577 0.572 -0.692 0.167 -0.206 14 0.110 -0.736 0.071 1.000 -0.553 0.238 0.280 0.693 0.147 15 -0.062 0.514 -0.577 -0.553 1.000 -0.562 0.382 -0.557 0.111 16 -0.041 -0.447 0.572 0.238 -0.562 1.000 -0.648 0.215 -0.071 17 0.082 -0.011 -0.692 0.280 0.382 -0.648 1.000 0.003 0.254 18 0.164 -0.701 0.167 0.693 -0.557 0.215 0.003 1.000 -0.011 19 0.010 -0.097 -0.206 0.147 0.111 -0.071 0.254 -0.011 1.000 DATA_CLASS 3 CLASS 3 - weight 22 normalized weight 0.104 relative strength 1.43e-05 ******* class cross entropy w.r.t. global class 8.56e+00 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.951 (-5.30e+00 3.02e-02) 2.87e+01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.951 (-5.11e+00 9.02e-01) 1.04e+00 (-4.18e+00 1.66e+00) ide 00 14 R MNcn Log Al: Wt.% Aluminu 0.951 ( 4.89e-01 2.75e-01) 6.68e-01 ( 3.06e-01 3.70e-01) m oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.951 ( 2.30e+00 1.76e-01) 6.51e-01 ( 2.18e+00 1.45e-01) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.951 (-1.82e+00 3.12e+00) 5.52e-01 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.951 (-2.08e+00 2.60e+00) 2.34e-01 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.951 (-6.55e-01 1.38e-02) 1.20e-01 (-6.57e-01 1.30e-02) ndex 00 15 R MNcn Log Si: Wt.% Silicon 0.951 ( 4.28e+00 1.77e-02) 6.83e-02 ( 4.29e+00 1.30e-02) oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.951 ( 2.59e+00 8.07e-02) 9.12e-03 ( 2.59e+00 6.09e-02) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.012 0.135 -0.239 -0.064 -0.042 0.363 0.000 0.064 12 -0.012 1.000 0.249 0.043 -0.346 -0.581 -0.196 0.000 -0.082 13 0.135 0.249 1.000 -0.423 -0.114 -0.515 -0.044 0.000 0.047 14 -0.239 0.043 -0.423 1.000 -0.412 0.336 -0.434 0.000 -0.066 15 -0.064 -0.346 -0.114 -0.412 1.000 -0.175 0.225 0.000 0.139 16 -0.042 -0.581 -0.515 0.336 -0.175 1.000 -0.067 0.000 0.007 17 0.363 -0.196 -0.044 -0.434 0.225 -0.067 1.000 0.000 0.164 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.064 -0.082 0.047 -0.066 0.139 0.007 0.164 0.000 1.000 DATA_CLASS 4 CLASS 4 - weight 16 normalized weight 0.074 relative strength 7.77e-04 ******* class cross entropy w.r.t. global class 7.72e+00 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 16 R MNcn Log K: Wt.% Potassi 0.858 (-5.59e-01 1.65e-01) 5.54e+00 (-1.47e+00 1.74e+00) um oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.858 ( 9.57e-01 3.11e-01) 3.38e+00 (-9.37e-02 2.60e+00) um oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.858 (-2.95e+00 2.02e+00) 6.09e-01 (-4.18e+00 1.66e+00) ide 00 12 R MNcn Log Na: Wt.% Sodium 0.858 ( 2.57e+00 3.98e-02) 5.04e-01 ( 2.59e+00 6.09e-02) oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.858 ( 2.21e+00 7.27e-02) 4.41e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.858 (-3.66e+00 1.83e+00) 4.22e-01 (-4.43e+00 1.93e+00) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.858 ( 3.63e-01 1.91e-01) 2.95e-01 ( 3.06e-01 3.70e-01) m oxide 00 11 R MNcn Log RI: refractive i 0.858 (-6.57e-01 1.42e-02) 3.04e-02 (-6.57e-01 1.30e-02) ndex 00 15 R MNcn Log Si: Wt.% Silicon 0.858 ( 4.29e+00 1.42e-02) 2.76e-02 ( 4.29e+00 1.30e-02) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.117 -0.129 0.088 -0.088 -0.065 0.138 0.011 0.028 12 0.117 1.000 -0.199 -0.128 -0.418 -0.715 0.302 0.004 -0.003 13 -0.129 -0.199 1.000 -0.728 0.269 0.233 -0.883 0.198 -0.182 14 0.088 -0.128 -0.728 1.000 -0.210 0.275 0.614 0.126 0.096 15 -0.088 -0.418 0.269 -0.210 1.000 0.214 -0.303 -0.069 0.009 16 -0.065 -0.715 0.233 0.275 0.214 1.000 -0.379 0.342 -0.016 17 0.138 0.302 -0.883 0.614 -0.303 -0.379 1.000 -0.377 0.006 18 0.011 0.004 0.198 0.126 -0.069 0.342 -0.377 1.000 0.291 19 0.028 -0.003 -0.182 0.096 0.009 -0.016 0.006 0.291 1.000 DATA_CLASS 5 CLASS 5 - weight 12 normalized weight 0.056 relative strength 3.37e-07 ******* class cross entropy w.r.t. global class 1.05e+01 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 17 R MNcn Log Ca: Wt.% Calcium 1.166 ( 2.54e+00 1.91e-01) 1.88e+00 ( 2.18e+00 1.45e-01) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 1.166 (-4.27e+00 2.63e+00) 1.59e+00 (-9.37e-02 2.60e+00) um oxide 00 11 R MNcn Log RI: refractive i 1.166 (-6.43e-01 1.47e-02) 9.60e-01 (-6.57e-01 1.30e-02) ndex 00 14 R MNcn Log Al: Wt.% Aluminu 1.166 (-4.79e-02 4.94e-01) 7.17e-01 ( 3.06e-01 3.70e-01) m oxide 00 16 R MNcn Log K: Wt.% Potassi 1.166 (-2.86e+00 2.13e+00) 6.49e-01 (-1.47e+00 1.74e+00) um oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.166 ( 4.28e+00 2.27e-02) 4.37e-01 ( 4.29e+00 1.30e-02) oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.166 ( 2.55e+00 1.38e-01) 2.94e-01 ( 2.59e+00 6.09e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.166 (-3.80e+00 2.07e+00) 1.83e-01 (-4.18e+00 1.66e+00) ide 00 18 R MNcn Log Ba: Wt.% Barium 1.166 (-4.27e+00 2.61e+00) 6.11e-02 (-4.43e+00 1.93e+00) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.340 -0.013 0.231 -0.425 0.239 0.392 0.116 0.258 12 -0.340 1.000 0.463 -0.273 0.033 0.001 -0.724 0.010 -0.306 13 -0.013 0.463 1.000 -0.035 -0.414 0.225 -0.568 0.318 -0.340 14 0.231 -0.273 -0.035 1.000 -0.561 0.781 0.029 0.540 0.731 15 -0.425 0.033 -0.414 -0.561 1.000 -0.717 -0.030 -0.534 -0.432 16 0.239 0.001 0.225 0.781 -0.717 1.000 -0.090 0.448 0.659 17 0.392 -0.724 -0.568 0.029 -0.030 -0.090 1.000 -0.392 0.313 18 0.116 0.010 0.318 0.540 -0.534 0.448 -0.392 1.000 0.141 19 0.258 -0.306 -0.340 0.731 -0.432 0.659 0.313 0.141 1.000 DATA_CLASS 6 CLASS 6 - weight 4 normalized weight 0.019 relative strength 1.31e-05 ******* class cross entropy w.r.t. global class 1.29e+01 ******* INTEGER or REAL ATTRIBUTE (t = I or R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.430 ( 1.22e+00 1.89e-01) 6.94e+00 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.430 (-3.56e-01 8.69e-01) 7.62e-01 ( 3.06e-01 3.70e-01) m oxide 00 16 R MNcn Log K: Wt.% Potassi 1.430 (-2.06e+00 1.32e+00) 4.46e-01 (-1.47e+00 1.74e+00) um oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.430 (-3.21e+00 3.41e+00) 3.59e-01 (-4.43e+00 1.93e+00) oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.430 ( 2.20e+00 4.75e-02) 3.09e-01 ( 2.18e+00 1.45e-01) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.430 (-3.39e+00 3.00e+00) 2.60e-01 (-4.18e+00 1.66e+00) ide 00 12 R MNcn Log Na: Wt.% Sodium 1.430 ( 2.62e+00 8.82e-02) 2.53e-01 ( 2.59e+00 6.09e-02) oxide 00 11 R MNcn Log RI: refractive i 1.430 (-6.55e-01 2.03e-02) 8.36e-02 (-6.57e-01 1.30e-02) ndex 00 15 R MNcn Log Si: Wt.% Silicon 1.430 ( 4.28e+00 2.03e-02) 6.74e-02 ( 4.29e+00 1.30e-02) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.065 0.073 -0.011 -0.063 -0.058 -0.068 0.041 -0.055 12 0.065 1.000 0.738 -0.491 -0.574 -0.782 -0.475 0.054 -0.593 13 0.073 0.738 1.000 -0.262 -0.629 -0.662 -0.662 0.334 -0.511 14 -0.011 -0.491 -0.262 1.000 0.062 0.574 -0.203 0.566 0.174 15 -0.063 -0.574 -0.629 0.062 1.000 0.515 0.618 -0.371 0.569 16 -0.058 -0.782 -0.662 0.574 0.515 1.000 0.351 0.093 0.610 17 -0.068 -0.475 -0.662 -0.203 0.618 0.351 1.000 -0.671 0.451 18 0.041 0.054 0.334 0.566 -0.371 0.093 -0.671 1.000 -0.124 19 -0.055 -0.593 -0.511 0.174 0.569 0.610 0.451 -0.124 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.class-text-10000644000175000017500000002226311247310756020362 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 214 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -10897.738 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 0.896 1 0.104 4 1.000 5 1.000 6 1.000 7 1.000 8 0.999 9 0.975 1 0.025 10 1.000 11 1.000 12 1.000 13 0.999 1 0.001 14 1.000 15 1.000 16 1.000 17 1.000 20 0.995 1 0.005 21 0.999 23 1.000 24 1.000 25 0.997 1 0.003 26 1.000 27 1.000 28 1.000 29 1.000 30 1.000 31 1.000 32 1.000 34 0.999 35 1.000 36 0.966 1 0.034 38 1.000 41 0.999 42 1.000 43 1.000 45 0.999 46 0.998 1 0.002 47 0.996 1 0.004 50 0.975 1 0.025 52 0.976 1 0.024 58 0.999 1 0.001 59 0.999 1 0.001 CLASS = 0 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 60 0.999 72 0.999 73 1.000 74 1.000 75 0.999 76 0.999 77 1.000 78 1.000 80 0.999 81 0.998 1 0.002 82 0.999 83 0.997 1 0.003 84 1.000 88 0.999 89 1.000 90 0.999 91 0.995 1 0.005 95 1.000 96 0.969 1 0.031 97 0.990 1 0.010 98 0.983 1 0.017 114 0.999 1 0.001 115 0.998 1 0.002 116 0.999 1 0.001 117 1.000 118 0.999 119 1.000 120 0.999 121 1.000 122 1.000 123 1.000 124 0.999 126 1.000 127 0.999 1 0.001 133 0.999 134 0.999 135 0.999 136 1.000 137 0.999 138 1.000 139 1.000 140 1.000 141 1.000 144 1.000 145 0.975 1 0.025 146 1.000 148 0.999 1 0.001 149 0.999 151 1.000 154 0.993 1 0.007 155 0.999 1 0.001 156 0.996 1 0.004 157 0.993 1 0.007 159 0.999 1 0.001 160 0.998 1 0.002 161 0.995 1 0.005 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 3 1.000 18 1.000 19 1.000 22 1.000 39 1.000 40 1.000 44 1.000 48 1.000 49 1.000 51 1.000 53 0.999 54 0.999 55 0.998 3 0.002 56 0.995 3 0.005 57 1.000 61 1.000 63 1.000 64 1.000 65 1.000 66 1.000 67 1.000 68 1.000 69 1.000 70 1.000 71 1.000 79 1.000 85 0.999 86 0.663 0 0.337 87 1.000 92 0.999 0 0.001 93 1.000 94 1.000 99 1.000 102 0.995 3 0.005 103 0.995 3 0.005 104 1.000 105 1.000 125 1.000 147 1.000 150 0.998 3 0.002 152 1.000 153 1.000 158 1.000 163 1.000 188 1.000 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 164 1.000 172 1.000 173 1.000 CLASS = 2 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 177 1.000 178 1.000 179 1.000 180 1.000 181 1.000 182 1.000 183 0.999 186 1.000 187 1.000 191 1.000 192 1.000 193 1.000 194 1.000 195 1.000 196 0.999 197 0.991 4 0.009 198 1.000 199 1.000 200 1.000 201 1.000 203 1.000 204 1.000 205 1.000 206 1.000 207 1.000 208 0.993 4 0.007 209 1.000 210 1.000 211 1.000 212 1.000 213 1.000 214 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 33 1.000 37 1.000 62 1.000 100 1.000 101 1.000 128 0.978 1 0.022 129 1.000 130 1.000 131 1.000 142 1.000 143 1.000 162 1.000 165 1.000 166 1.000 167 1.000 175 1.000 176 1.000 CLASS = 3 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 189 1.000 190 1.000 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 106 1.000 107 1.000 108 1.000 109 1.000 110 1.000 111 1.000 112 1.000 113 1.000 132 1.000 168 1.000 169 1.000 170 1.000 171 1.000 174 1.000 184 1.000 185 1.000 202 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.rlog0000644000175000017500000001371711247310756017104 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:06:01 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:06:01 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:07:31 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:07:31 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:08:23 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:08:23 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:09:15 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:09:15 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:09:54 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:09:54 2001 autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.db20000644000175000017500000000202411247310756020225 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;; This is ics.uci.edu:/pub/machine-learning-databases/glass/glass.data ;; 10 instances, 11 attributes 10 1.51755 13.00 3.60 1.36 72.99 0.57 8.40 0.00 0.11 1 20 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0.00 0.07 1 30 1.51784 13.08 3.49 1.28 72.86 0.60 8.49 0.00 0.00 1 40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.00 0.00 1 50 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0.00 0.00 1 100 1.51811 12.96 2.96 1.43 72.92 0.60 8.79 0.14 0.00 2 120 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0.00 0.00 2 150 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0.00 0.00 3 175 1.52058 12.85 1.61 2.17 72.18 0.76 9.70 0.24 0.51 5 200 1.51609 15.01 0.00 2.51 73.05 0.05 8.83 0.53 0.00 7 autoclass-3.3.6.dfsg.1/data/glass/glass-3c.hd20000644000175000017500000000662211247310756016753 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;; This is ics.uci.edu:/pub/machine-learning-databases/glass/glass.data ;; 214 instances, 11 attributes ;#! num_db2_format_defs num_db2_format_defs 1 ;; required number_of_attributes 11 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' ;; 0 dummy nil "Id number" 10 discrete nominal "Type of glass" range 6 1 real scalar "RI: refractive index" rel_error 3.3e-6 zero_point 1.0 2 real scalar "Na: Wt.% Sodium oxide" rel_error 3.7291448e-4 zero_point 0.0 3 real scalar "Mg: Wt.% Magnesium oxide" rel_error 0.0018625442 zero_point 0.0 4 real scalar "Al: Wt.% Aluminum oxide" rel_error 0.0034604468 zero_point 0.0 5 real scalar "Si: Wt.% Silicon oxide" rel_error 6.882227e-5 zero_point 0.0 6 real scalar " K: Wt.% Potassium oxide" rel_error 0.010058338 zero_point 0.0 7 real scalar "Ca: Wt.% Calcium oxide" rel_error 5.5822264e-4 zero_point 0.0 8 real scalar "Ba: Wt.% Barium oxide" rel_error 0.028571429 zero_point 0.0 9 real scalar "Fe: Wt.% Iron oxide" rel_error 0.0877193 zero_point 0.0 ;(DEFINE-DISCRETE-TRANSLATIONS ;'((10 (1 building_windows_float_processed) (2 building_windows_non_float_processed) ; (3 vehicle_windows_float_processed) ;;(4 vehicle_windows_non_float_processed) ; (5 containers) (6 tableware) (7 headlamps)) ;)) ;#| ;5. Number of Instances: 214 ; ;6. Number of Attributes: 10 (including an Id#) plus the class attribute ; -- all attributes are continuously valued ; ;7. Attribute Information: ; 1. Id number: 1 to 214 ; 2. RI: refractive index ; 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as ; are attributes 4-10) ; 4. Mg: Magnesium ; 5. Al: Aluminum ; 6. Si: Silicon ; 7. K: Potassium ; 8. Ca: Calcium ; 9. Ba: Barium ; 10. Fe: Iron ; 11. Type of glass: (class attribute) ; -- 1 building_windows_float_processed ; -- 2 building_windows_non_float_processed ; -- 3 vehicle_windows_float_processed ; -- 4 vehicle_windows_non_float_processed (none in this database) ; -- 5 containers ; -- 6 tableware ; -- 7 headlamps ; ;8. Missing Attribute Values: None ; ;Summary Statistics: ;Attribute: Min Max Mean SD Correlation with class ; 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 ; 3. Na: 10.73 17.38 13.4079 0.8166 0.5030 ; 4. Mg: 0 4.49 2.6845 1.4424 -0.7447 ; 5. Al: 0.29 3.5 1.4449 0.4993 0.5988 ; 6. Si: 69.81 75.41 72.6509 0.7745 0.1515 ; 7. K: 0 6.21 0.4971 0.6522 -0.0100 ; 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 ; 9. Ba: 0 3.15 0.1750 0.4972 0.5751 ;10. Fe: 0 0.51 0.0570 0.0974 -0.1879 ; ;9. Class Distribution: (out of 214 total instances) ; -- 163 Window glass (building windows and vehicle windows) ; -- 87 float processed ; -- 70 building windows ; -- 17 vehicle windows ; -- 76 non-float processed ; -- 76 building windows ; -- 0 vehicle windows ; -- 51 Non-window glass ; -- 13 containers ; -- 9 tableware ; -- 29 headlamps ;|# autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.case-text-10000644000175000017500000000214411247310756021614 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.db2 based on the "TRAINING" classification of 214 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -10897.738 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 0 0.999 5 0 0.975 9 3 1.000 2 0 0.995 6 3 1.000 10 2 0.999 3 0 0.999 7 0 0.999 4 1 1.000 8 1 0.998 autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.case-data-10000644000175000017500000000176211247310756021546 0ustar areare # CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS #DATA_CLSF_HEADER # AutoClass PREDICTION for the 10 "TEST" cases in # /home/tove/p/autoclass-c/data/glass/glassc-predict.db2 # based on the "TRAINING" classification of 214 cases in # /home/tove/p/autoclass-c/data/glass/glassc.db2 # /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 # with log-A (approximate marginal likelihood) = -11434.215 # from classification results file # /home/tove/p/autoclass-c/data/glass/glassc.results-bin # and using models # /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 #DATA_CASE_TO_CLASS # Case # Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 0 0.999 002 0 0.988 003 0 0.999 004 1 1.000 005 0 0.959 006 3 1.000 007 0 0.997 008 0 0.999 009 3 1.000 010 2 0.999 autoclass-3.3.6.dfsg.1/data/glass/glassc.class-data-10000644000175000017500000001573211247310756020312 0ustar areare # CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP #DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 214 cases in # /home/tove/p/autoclass-c/data/glass/glassc.db2 # /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 # with log-A (approximate marginal likelihood) = -11434.215 # from classification results file # /home/tove/p/autoclass-c/data/glass/glassc.results-bin # and using models # /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 DATA_CLASS 0 # CLASS = 0 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 004 0.999 005 0.997 1 0.003 006 0.999 007 0.997 1 0.003 008 0.995 1 0.005 009 0.988 1 0.012 010 0.999 011 0.999 1 0.001 012 1.000 013 0.994 1 0.006 014 0.997 1 0.003 015 1.000 016 1.000 017 0.999 020 0.988 1 0.012 021 0.997 1 0.003 023 0.999 024 0.999 025 0.969 1 0.031 026 0.998 1 0.002 027 0.999 028 0.999 029 0.999 030 0.999 031 0.999 032 0.999 034 0.999 035 0.999 036 0.998 1 0.002 038 0.999 1 0.001 041 0.997 1 0.003 042 0.999 043 0.998 1 0.002 045 0.998 1 0.002 046 0.981 1 0.019 047 0.990 1 0.010 050 0.959 1 0.041 052 0.957 1 0.043 057 0.992 1 0.008 058 1.000 059 0.996 1 0.004 060 0.997 1 0.003 072 0.995 1 0.005 073 0.999 1 0.001 074 0.998 1 0.002 075 0.996 1 0.004 076 0.999 1 0.001 077 0.998 1 0.002 078 1.000 080 0.997 1 0.003 081 0.983 1 0.017 082 0.993 1 0.007 083 0.983 1 0.017 084 0.998 1 0.002 088 0.994 1 0.006 089 0.999 090 0.996 1 0.004 091 0.965 1 0.035 095 0.998 1 0.002 096 0.981 1 0.019 097 0.873 1 0.127 098 0.983 1 0.017 114 0.998 1 0.002 115 0.993 1 0.007 116 0.992 1 0.008 117 0.997 1 0.003 118 0.991 1 0.009 119 0.999 1 0.001 120 0.997 1 0.003 121 0.999 122 0.999 123 0.998 1 0.002 124 0.996 1 0.004 125 0.999 1 0.001 126 0.998 1 0.002 127 0.999 133 0.994 1 0.006 134 0.986 1 0.014 135 0.995 1 0.005 136 0.998 1 0.002 137 0.998 1 0.002 138 1.000 139 0.999 140 0.999 141 0.997 1 0.003 144 0.998 1 0.002 145 0.882 1 0.118 146 0.999 148 0.999 1 0.001 149 0.999 1 0.001 150 0.999 151 0.996 1 0.004 154 0.992 1 0.008 155 0.999 1 0.001 156 0.991 1 0.009 157 0.986 1 0.014 159 0.983 1 0.017 160 0.959 1 0.041 161 0.942 1 0.058 188 0.979 1 0.021 DATA_CLASS 1 # CLASS = 1 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 1.000 002 0.837 0 0.163 003 1.000 018 1.000 019 1.000 022 1.000 039 1.000 040 1.000 044 1.000 048 1.000 049 1.000 051 1.000 053 0.999 054 0.999 055 0.998 3 0.002 056 0.992 3 0.008 061 1.000 063 1.000 064 1.000 065 1.000 066 1.000 067 1.000 068 1.000 069 1.000 070 1.000 071 1.000 079 1.000 085 1.000 086 0.880 0 0.120 087 1.000 092 0.997 0 0.003 093 1.000 094 1.000 099 0.995 0 0.005 102 0.996 3 0.004 103 0.997 3 0.003 104 1.000 105 1.000 147 1.000 152 1.000 153 1.000 158 1.000 163 1.000 DATA_CLASS 2 # CLASS = 2 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 164 1.000 172 1.000 173 1.000 177 1.000 178 1.000 179 1.000 180 1.000 181 1.000 182 1.000 183 1.000 186 1.000 187 1.000 191 1.000 192 1.000 193 1.000 194 1.000 195 1.000 196 1.000 197 0.997 4 0.003 198 1.000 199 1.000 200 1.000 201 1.000 203 1.000 204 1.000 205 1.000 206 1.000 207 1.000 208 0.996 4 0.004 209 1.000 210 1.000 211 1.000 212 1.000 213 1.000 214 1.000 DATA_CLASS 3 # CLASS = 3 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 033 1.000 037 1.000 062 1.000 100 1.000 101 1.000 128 0.994 1 0.006 129 1.000 130 1.000 131 1.000 142 1.000 143 1.000 162 1.000 165 1.000 166 1.000 167 1.000 175 1.000 176 1.000 189 1.000 190 1.000 DATA_CLASS 4 # CLASS = 4 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 106 1.000 107 1.000 108 1.000 109 1.000 110 1.000 111 1.000 112 1.000 113 1.000 132 1.000 168 1.000 169 1.000 170 1.000 171 1.000 174 1.000 184 1.000 185 1.000 202 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.class-text-10000644000175000017500000000360711247310756022013 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.db2 based on the "TRAINING" classification of 214 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -10897.738 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 1.000 2 0.995 1 0.005 3 1.000 5 0.975 1 0.025 7 0.999 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 4 1.000 8 0.998 3 0.002 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 10 1.000 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 1.000 9 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.search0000644000175000017500000000111711247310756017375 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 2 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -1.08977379e+04 32 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 1 7 7 -1.11877446e+04 13 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -1.12295156e+04 15 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -1.12364317e+04 10 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/glass/glassc-chkpt.s-params0000644000175000017500000002273211247310756020770 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false uses 1 as the seed for rand, the pseudo-random number function (this ! facilitates producing repeatable test cases); true uses universal time ! clock as the seed # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 1200 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 20 minutes) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ## for block_set_clsf test ;;; start_fn_type "block" ;;; randomize_random_p = false ## for detailed printout of convergence ;; converge_print_p = true ## for source output files ;; save_compact_p = false ;; read_compact_p = false ## Checkpoint test: Run 1) ;; max_n_tries = 2 ;; force_new_search_p = true ## run to completion ## Checkpoint test: Run 2) ;; force_new_search_p = false ;; max_n_tries = 10 ;; checkpoint_p = true ;; min_checkpoint_period = 2 ## after first checkpoint, ctrl-C to abort ## Checkpoint test: Run 3) ;; force_new_search_p = false ;; max_n_tries = 1 ;; checkpoint_p = true ;; min_checkpoint_period = 1 ;; reconverge_type = "chkpt" ## checkpointed trial should finish ## reconverge checkpointed clsf with another try function: Run 4) ;; force_new_search_p = false ;; try_fn_type = "converge_search_4" ;; max_n_tries = 1 ;; reconverge_type = "results" ## this trial should start and complete with a slightly better log marginal value ## than the previous trial autoclass-3.3.6.dfsg.1/data/glass/glassc.class-data-20000644000175000017500000002010111247310756020275 0ustar areare CROSS REFERENCE: CLASS => CASE NUMBER MEMBERSHIP DATA_CLSF_HEADER AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11510.965 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 4 1.00 5 1.00 6 1.00 7 0.99 8 0.99 9 0.97 1 0.03 10 1.00 11 1.00 12 1.00 13 1.00 14 1.00 15 1.00 16 1.00 17 1.00 20 0.97 1 0.03 21 0.99 23 1.00 24 1.00 25 0.88 1 0.12 26 0.99 27 1.00 28 1.00 29 1.00 30 1.00 31 1.00 32 1.00 34 1.00 35 1.00 36 0.99 38 1.00 41 0.99 1 0.01 42 1.00 43 1.00 45 1.00 46 0.99 47 0.99 50 1.00 52 0.99 57 0.99 58 1.00 59 1.00 60 1.00 72 1.00 73 1.00 74 1.00 75 1.00 76 1.00 77 1.00 78 1.00 80 1.00 81 0.99 1 0.01 82 0.98 1 0.02 83 0.94 1 0.06 84 1.00 88 0.98 1 0.02 89 1.00 90 1.00 91 0.92 1 0.07 95 1.00 96 0.97 1 0.03 98 1.00 99 0.97 3 0.02 114 1.00 115 1.00 116 1.00 117 1.00 118 0.99 119 1.00 120 0.99 121 1.00 122 1.00 123 0.99 124 0.99 1 0.01 125 1.00 126 1.00 127 1.00 133 1.00 134 1.00 135 1.00 136 1.00 137 1.00 138 1.00 139 1.00 140 1.00 141 0.99 144 1.00 145 0.99 146 1.00 148 1.00 149 1.00 150 1.00 151 0.99 1 0.01 154 0.99 1 0.01 155 1.00 156 0.98 1 0.02 157 0.99 1 0.01 159 1.00 160 0.99 161 1.00 188 1.00 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 2 0.98 0 0.02 3 1.00 18 1.00 19 1.00 39 1.00 40 1.00 44 1.00 48 1.00 49 1.00 51 1.00 61 1.00 63 1.00 64 1.00 65 1.00 66 1.00 67 1.00 68 1.00 69 1.00 70 1.00 71 1.00 79 1.00 85 0.94 3 0.06 86 0.98 0 0.02 87 1.00 92 1.00 93 1.00 94 1.00 97 0.60 0 0.40 147 1.00 152 1.00 153 1.00 158 1.00 163 1.00 CLASS = 2 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 164 1.00 185 1.00 186 1.00 187 1.00 191 1.00 192 1.00 193 1.00 194 1.00 195 1.00 196 1.00 197 1.00 198 1.00 199 1.00 200 1.00 201 1.00 203 1.00 204 1.00 205 1.00 206 1.00 207 1.00 208 1.00 209 1.00 210 1.00 211 1.00 212 1.00 213 1.00 214 1.00 CLASS = 3 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 1 1.00 105 1.00 131 1.00 166 1.00 167 1.00 168 1.00 169 1.00 170 1.00 171 1.00 172 1.00 173 1.00 174 1.00 176 1.00 177 1.00 178 1.00 179 1.00 180 1.00 181 1.00 182 1.00 183 1.00 189 1.00 202 1.00 CLASS = 4 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 33 1.00 37 1.00 53 0.97 3 0.02 0 0.01 54 0.98 3 0.01 55 1.00 56 1.00 100 1.00 101 1.00 102 0.94 3 0.06 128 1.00 129 1.00 130 1.00 142 1.00 143 1.00 165 0.96 3 0.04 175 1.00 CLASS = 5 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 104 0.99 3 0.01 106 1.00 107 1.00 108 1.00 109 0.99 110 1.00 111 1.00 112 1.00 113 1.00 132 0.98 3 0.02 184 1.00 190 1.00 CLASS = 6 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 22 1.00 62 1.00 103 1.00 162 1.00 autoclass-3.3.6.dfsg.1/data/glass/glassc.db20000644000175000017500000003045511247310756016606 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ;; This is ics.uci.edu:/pub/machine-learning-databases/glass/glass.data ;; 214 instances, 11 attributes 1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0.00 0.00 1 2 1.51761 13.89 3.60 1.36 72.73 0.48 7.83 0.00 0.00 1 3 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0.00 0.00 1 4 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0.00 0.00 1 5 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0.00 0.00 1 6 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0.00 0.26 1 7 1.51743 13.30 3.60 1.14 73.09 0.58 8.17 0.00 0.00 1 8 1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0.00 0.00 1 9 1.51918 14.04 3.58 1.37 72.08 0.56 8.30 0.00 0.00 1 10 1.51755 13.00 3.60 1.36 72.99 0.57 8.40 0.00 0.11 1 11 1.51571 12.72 3.46 1.56 73.20 0.67 8.09 0.00 0.24 1 12 1.51763 12.80 3.66 1.27 73.01 0.60 8.56 0.00 0.00 1 13 1.51589 12.88 3.43 1.40 73.28 0.69 8.05 0.00 0.24 1 14 1.51748 12.86 3.56 1.27 73.21 0.54 8.38 0.00 0.17 1 15 1.51763 12.61 3.59 1.31 73.29 0.58 8.50 0.00 0.00 1 16 1.51761 12.81 3.54 1.23 73.24 0.58 8.39 0.00 0.00 1 17 1.51784 12.68 3.67 1.16 73.11 0.61 8.70 0.00 0.00 1 18 1.52196 14.36 3.85 0.89 71.36 0.15 9.15 0.00 0.00 1 19 1.51911 13.90 3.73 1.18 72.12 0.06 8.89 0.00 0.00 1 20 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0.00 0.07 1 21 1.51750 12.82 3.55 1.49 72.75 0.54 8.52 0.00 0.19 1 22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.00 0.00 1 23 1.51736 12.78 3.62 1.29 72.79 0.59 8.70 0.00 0.00 1 24 1.51751 12.81 3.57 1.35 73.02 0.62 8.59 0.00 0.00 1 25 1.51720 13.38 3.50 1.15 72.85 0.50 8.43 0.00 0.00 1 26 1.51764 12.98 3.54 1.21 73.00 0.65 8.53 0.00 0.00 1 27 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0.00 0.00 1 28 1.51721 12.87 3.48 1.33 73.04 0.56 8.43 0.00 0.00 1 29 1.51768 12.56 3.52 1.43 73.15 0.57 8.54 0.00 0.00 1 30 1.51784 13.08 3.49 1.28 72.86 0.60 8.49 0.00 0.00 1 31 1.51768 12.65 3.56 1.30 73.08 0.61 8.69 0.00 0.14 1 32 1.51747 12.84 3.50 1.14 73.27 0.56 8.55 0.00 0.00 1 33 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.22 1 34 1.51753 12.57 3.47 1.38 73.39 0.60 8.55 0.00 0.06 1 35 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0.00 0.00 1 36 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0.00 0.00 1 37 1.51909 13.89 3.53 1.32 71.81 0.51 8.78 0.11 0.00 1 38 1.51797 12.74 3.48 1.35 72.96 0.64 8.68 0.00 0.00 1 39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.00 0.00 1 40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.00 0.00 1 41 1.51793 12.79 3.50 1.12 73.03 0.64 8.77 0.00 0.00 1 42 1.51755 12.71 3.42 1.20 73.20 0.59 8.64 0.00 0.00 1 43 1.51779 13.21 3.39 1.33 72.76 0.59 8.59 0.00 0.00 1 44 1.52210 13.73 3.84 0.72 71.76 0.17 9.74 0.00 0.00 1 45 1.51786 12.73 3.43 1.19 72.95 0.62 8.76 0.00 0.30 1 46 1.51900 13.49 3.48 1.35 71.95 0.55 9.00 0.00 0.00 1 47 1.51869 13.19 3.37 1.18 72.72 0.57 8.83 0.00 0.16 1 48 1.52667 13.99 3.70 0.71 71.57 0.02 9.82 0.00 0.10 1 49 1.52223 13.21 3.77 0.79 71.99 0.13 10.02 0.00 0.00 1 50 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0.00 0.00 1 51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.00 0.16 1 52 1.51926 13.20 3.33 1.28 72.36 0.60 9.14 0.00 0.11 1 53 1.51808 13.43 2.87 1.19 72.84 0.55 9.03 0.00 0.00 1 54 1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0.00 0.00 1 55 1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0.00 0.09 1 56 1.51769 12.45 2.71 1.29 73.70 0.56 9.06 0.00 0.24 1 57 1.51215 12.99 3.47 1.12 72.98 0.62 8.35 0.00 0.31 1 58 1.51824 12.87 3.48 1.29 72.95 0.60 8.43 0.00 0.00 1 59 1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0.00 0.00 1 60 1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0.00 0.11 1 61 1.51905 13.60 3.62 1.11 72.64 0.14 8.76 0.00 0.00 1 62 1.51977 13.81 3.58 1.32 71.72 0.12 8.67 0.69 0.00 1 63 1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0.00 0.11 1 64 1.52227 14.17 3.81 0.78 71.35 0.00 9.69 0.00 0.00 1 65 1.52172 13.48 3.74 0.90 72.01 0.18 9.61 0.00 0.07 1 66 1.52099 13.69 3.59 1.12 71.96 0.09 9.40 0.00 0.00 1 67 1.52152 13.05 3.65 0.87 72.22 0.19 9.85 0.00 0.17 1 68 1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0.00 0.17 1 69 1.52152 13.12 3.58 0.90 72.20 0.23 9.82 0.00 0.16 1 70 1.52300 13.31 3.58 0.82 71.99 0.12 10.17 0.00 0.03 1 71 1.51574 14.86 3.67 1.74 71.87 0.16 7.36 0.00 0.12 2 72 1.51848 13.64 3.87 1.27 71.96 0.54 8.32 0.00 0.32 2 73 1.51593 13.09 3.59 1.52 73.10 0.67 7.83 0.00 0.00 2 74 1.51631 13.34 3.57 1.57 72.87 0.61 7.89 0.00 0.00 2 75 1.51596 13.02 3.56 1.54 73.11 0.72 7.90 0.00 0.00 2 76 1.51590 13.02 3.58 1.51 73.12 0.69 7.96 0.00 0.00 2 77 1.51645 13.44 3.61 1.54 72.39 0.66 8.03 0.00 0.00 2 78 1.51627 13.00 3.58 1.54 72.83 0.61 8.04 0.00 0.00 2 79 1.51613 13.92 3.52 1.25 72.88 0.37 7.94 0.00 0.14 2 80 1.51590 12.82 3.52 1.90 72.86 0.69 7.97 0.00 0.00 2 81 1.51592 12.86 3.52 2.12 72.66 0.69 7.97 0.00 0.00 2 82 1.51593 13.25 3.45 1.43 73.17 0.61 7.86 0.00 0.00 2 83 1.51646 13.41 3.55 1.25 72.81 0.68 8.10 0.00 0.00 2 84 1.51594 13.09 3.52 1.55 72.87 0.68 8.05 0.00 0.09 2 85 1.51409 14.25 3.09 2.08 72.28 1.10 7.08 0.00 0.00 2 86 1.51625 13.36 3.58 1.49 72.72 0.45 8.21 0.00 0.00 2 87 1.51569 13.24 3.49 1.47 73.25 0.38 8.03 0.00 0.00 2 88 1.51645 13.40 3.49 1.52 72.65 0.67 8.08 0.00 0.10 2 89 1.51618 13.01 3.50 1.48 72.89 0.60 8.12 0.00 0.00 2 90 1.51640 12.55 3.48 1.87 73.23 0.63 8.08 0.00 0.09 2 91 1.51841 12.93 3.74 1.11 72.28 0.64 8.96 0.00 0.22 2 92 1.51605 12.90 3.44 1.45 73.06 0.44 8.27 0.00 0.00 2 93 1.51588 13.12 3.41 1.58 73.26 0.07 8.39 0.00 0.19 2 94 1.51590 13.24 3.34 1.47 73.10 0.39 8.22 0.00 0.00 2 95 1.51629 12.71 3.33 1.49 73.28 0.67 8.24 0.00 0.00 2 96 1.51860 13.36 3.43 1.43 72.26 0.51 8.60 0.00 0.00 2 97 1.51841 13.02 3.62 1.06 72.34 0.64 9.13 0.00 0.15 2 98 1.51743 12.20 3.25 1.16 73.55 0.62 8.90 0.00 0.24 2 99 1.51689 12.67 2.88 1.71 73.21 0.73 8.54 0.00 0.00 2 100 1.51811 12.96 2.96 1.43 72.92 0.60 8.79 0.14 0.00 2 101 1.51655 12.75 2.85 1.44 73.27 0.57 8.79 0.11 0.22 2 102 1.51730 12.35 2.72 1.63 72.87 0.70 9.23 0.00 0.00 2 103 1.51820 12.62 2.76 0.83 73.81 0.35 9.42 0.00 0.20 2 104 1.52725 13.80 3.15 0.66 70.57 0.08 11.64 0.00 0.00 2 105 1.52410 13.83 2.90 1.17 71.15 0.08 10.79 0.00 0.00 2 106 1.52475 11.45 0.00 1.88 72.19 0.81 13.24 0.00 0.34 2 107 1.53125 10.73 0.00 2.10 69.81 0.58 13.30 3.15 0.28 2 108 1.53393 12.30 0.00 1.00 70.16 0.12 16.19 0.00 0.24 2 109 1.52222 14.43 0.00 1.00 72.67 0.10 11.52 0.00 0.08 2 110 1.51818 13.72 0.00 0.56 74.45 0.00 10.99 0.00 0.00 2 111 1.52664 11.23 0.00 0.77 73.21 0.00 14.68 0.00 0.00 2 112 1.52739 11.02 0.00 0.75 73.08 0.00 14.96 0.00 0.00 2 113 1.52777 12.64 0.00 0.67 72.02 0.06 14.40 0.00 0.00 2 114 1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0.00 0.14 2 115 1.51847 13.10 3.97 1.19 72.44 0.60 8.43 0.00 0.00 2 116 1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0.00 0.00 2 117 1.51829 13.24 3.90 1.41 72.33 0.55 8.31 0.00 0.10 2 118 1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0.00 0.00 2 119 1.51673 13.30 3.64 1.53 72.53 0.65 8.03 0.00 0.29 2 120 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0.00 0.00 2 121 1.51844 13.25 3.76 1.32 72.40 0.58 8.42 0.00 0.00 2 122 1.51663 12.93 3.54 1.62 72.96 0.64 8.03 0.00 0.21 2 123 1.51687 13.23 3.54 1.48 72.84 0.56 8.10 0.00 0.00 2 124 1.51707 13.48 3.48 1.71 72.52 0.62 7.99 0.00 0.00 2 125 1.52177 13.20 3.68 1.15 72.75 0.54 8.52 0.00 0.00 2 126 1.51872 12.93 3.66 1.56 72.51 0.58 8.55 0.00 0.12 2 127 1.51667 12.94 3.61 1.26 72.75 0.56 8.60 0.00 0.00 2 128 1.52081 13.78 2.28 1.43 71.99 0.49 9.85 0.00 0.17 2 129 1.52068 13.55 2.09 1.67 72.18 0.53 9.57 0.27 0.17 2 130 1.52020 13.98 1.35 1.63 71.76 0.39 10.56 0.00 0.18 2 131 1.52177 13.75 1.01 1.36 72.19 0.33 11.14 0.00 0.00 2 132 1.52614 13.70 0.00 1.36 71.24 0.19 13.44 0.00 0.10 2 133 1.51813 13.43 3.98 1.18 72.49 0.58 8.15 0.00 0.00 2 134 1.51800 13.71 3.93 1.54 71.81 0.54 8.21 0.00 0.15 2 135 1.51811 13.33 3.85 1.25 72.78 0.52 8.12 0.00 0.00 2 136 1.51789 13.19 3.90 1.30 72.33 0.55 8.44 0.00 0.28 2 137 1.51806 13.00 3.80 1.08 73.07 0.56 8.38 0.00 0.12 2 138 1.51711 12.89 3.62 1.57 72.96 0.61 8.11 0.00 0.00 2 139 1.51674 12.79 3.52 1.54 73.36 0.66 7.90 0.00 0.00 2 140 1.51674 12.87 3.56 1.64 73.14 0.65 7.99 0.00 0.00 2 141 1.51690 13.33 3.54 1.61 72.54 0.68 8.11 0.00 0.00 2 142 1.51851 13.20 3.63 1.07 72.83 0.57 8.41 0.09 0.17 2 143 1.51662 12.85 3.51 1.44 73.01 0.68 8.23 0.06 0.25 2 144 1.51709 13.00 3.47 1.79 72.72 0.66 8.18 0.00 0.00 2 145 1.51660 12.99 3.18 1.23 72.97 0.58 8.81 0.00 0.24 2 146 1.51839 12.85 3.67 1.24 72.57 0.62 8.68 0.00 0.35 2 147 1.51769 13.65 3.66 1.11 72.77 0.11 8.60 0.00 0.00 3 148 1.51610 13.33 3.53 1.34 72.67 0.56 8.33 0.00 0.00 3 149 1.51670 13.24 3.57 1.38 72.70 0.56 8.44 0.00 0.10 3 150 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0.00 0.00 3 151 1.51665 13.14 3.45 1.76 72.48 0.60 8.38 0.00 0.17 3 152 1.52127 14.32 3.90 0.83 71.50 0.00 9.49 0.00 0.00 3 153 1.51779 13.64 3.65 0.65 73.00 0.06 8.93 0.00 0.00 3 154 1.51610 13.42 3.40 1.22 72.69 0.59 8.32 0.00 0.00 3 155 1.51694 12.86 3.58 1.31 72.61 0.61 8.79 0.00 0.00 3 156 1.51646 13.04 3.40 1.26 73.01 0.52 8.58 0.00 0.00 3 157 1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0.00 0.00 3 158 1.52121 14.03 3.76 0.58 71.79 0.11 9.65 0.00 0.00 3 159 1.51776 13.53 3.41 1.52 72.04 0.58 8.79 0.00 0.00 3 160 1.51796 13.50 3.36 1.63 71.94 0.57 8.81 0.00 0.09 3 161 1.51832 13.33 3.34 1.54 72.14 0.56 8.99 0.00 0.00 3 162 1.51934 13.64 3.54 0.75 72.65 0.16 8.89 0.15 0.24 3 163 1.52211 14.19 3.78 0.91 71.36 0.23 9.14 0.00 0.37 3 164 1.51514 14.01 2.68 3.50 69.89 1.68 5.87 2.20 0.00 5 165 1.51915 12.73 1.85 1.86 72.69 0.60 10.09 0.00 0.00 5 166 1.52171 11.56 1.88 1.56 72.86 0.47 11.41 0.00 0.00 5 167 1.52151 11.03 1.71 1.56 73.44 0.58 11.62 0.00 0.00 5 168 1.51969 12.64 0.00 1.65 73.75 0.38 11.53 0.00 0.00 5 169 1.51666 12.86 0.00 1.83 73.88 0.97 10.17 0.00 0.00 5 170 1.51994 13.27 0.00 1.76 73.03 0.47 11.32 0.00 0.00 5 171 1.52369 13.44 0.00 1.58 72.22 0.32 12.24 0.00 0.00 5 172 1.51316 13.02 0.00 3.04 70.48 6.21 6.96 0.00 0.00 5 173 1.51321 13.00 0.00 3.02 70.70 6.21 6.93 0.00 0.00 5 174 1.52043 13.38 0.00 1.40 72.25 0.33 12.50 0.00 0.00 5 175 1.52058 12.85 1.61 2.17 72.18 0.76 9.70 0.24 0.51 5 176 1.52119 12.97 0.33 1.51 73.39 0.13 11.27 0.00 0.28 5 177 1.51905 14.00 2.39 1.56 72.37 0.00 9.57 0.00 0.00 6 178 1.51937 13.79 2.41 1.19 72.76 0.00 9.77 0.00 0.00 6 179 1.51829 14.46 2.24 1.62 72.38 0.00 9.26 0.00 0.00 6 180 1.51852 14.09 2.19 1.66 72.67 0.00 9.32 0.00 0.00 6 181 1.51299 14.40 1.74 1.54 74.55 0.00 7.59 0.00 0.00 6 182 1.51888 14.99 0.78 1.74 72.50 0.00 9.95 0.00 0.00 6 183 1.51916 14.15 0.00 2.09 72.74 0.00 10.88 0.00 0.00 6 184 1.51969 14.56 0.00 0.56 73.48 0.00 11.22 0.00 0.00 6 185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.00 0.00 6 186 1.51131 13.69 3.20 1.81 72.81 1.76 5.43 1.19 0.00 7 187 1.51838 14.32 3.26 2.22 71.25 1.46 5.79 1.63 0.00 7 188 1.52315 13.44 3.34 1.23 72.38 0.60 8.83 0.00 0.00 7 189 1.52247 14.86 2.20 2.06 70.26 0.76 9.76 0.00 0.00 7 190 1.52365 15.79 1.83 1.31 70.43 0.31 8.61 1.68 0.00 7 191 1.51613 13.88 1.78 1.79 73.10 0.00 8.67 0.76 0.00 7 192 1.51602 14.85 0.00 2.38 73.28 0.00 8.76 0.64 0.09 7 193 1.51623 14.20 0.00 2.79 73.46 0.04 9.04 0.40 0.09 7 194 1.51719 14.75 0.00 2.00 73.02 0.00 8.53 1.59 0.08 7 195 1.51683 14.56 0.00 1.98 73.29 0.00 8.52 1.57 0.07 7 196 1.51545 14.14 0.00 2.68 73.39 0.08 9.07 0.61 0.05 7 197 1.51556 13.87 0.00 2.54 73.23 0.14 9.41 0.81 0.01 7 198 1.51727 14.70 0.00 2.34 73.28 0.00 8.95 0.66 0.00 7 199 1.51531 14.38 0.00 2.66 73.10 0.04 9.08 0.64 0.00 7 200 1.51609 15.01 0.00 2.51 73.05 0.05 8.83 0.53 0.00 7 201 1.51508 15.15 0.00 2.25 73.50 0.00 8.34 0.63 0.00 7 202 1.51653 11.95 0.00 1.19 75.18 2.70 8.93 0.00 0.00 7 203 1.51514 14.85 0.00 2.42 73.72 0.00 8.39 0.56 0.00 7 204 1.51658 14.80 0.00 1.99 73.11 0.00 8.28 1.71 0.00 7 205 1.51617 14.95 0.00 2.27 73.30 0.00 8.71 0.67 0.00 7 206 1.51732 14.95 0.00 1.80 72.99 0.00 8.61 1.55 0.00 7 207 1.51645 14.94 0.00 1.87 73.11 0.00 8.67 1.38 0.00 7 208 1.51831 14.39 0.00 1.82 72.86 1.41 6.47 2.88 0.00 7 209 1.51640 14.37 0.00 2.74 72.85 0.00 9.45 0.54 0.00 7 210 1.51623 14.14 0.00 2.88 72.61 0.08 9.18 1.06 0.00 7 211 1.51685 14.92 0.00 1.99 73.06 0.00 8.40 1.59 0.00 7 212 1.52065 14.36 0.00 2.02 73.42 0.00 8.44 1.64 0.00 7 213 1.51651 14.38 0.00 1.94 73.61 0.00 8.48 1.57 0.00 7 214 1.51711 14.23 0.00 2.08 73.36 0.00 8.62 1.67 0.00 7 autoclass-3.3.6.dfsg.1/data/glass/glassc.influ-o-text-20000644000175000017500000006322711247310756020634 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11187.745 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 3 seconds _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-11187.745) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-11229.517) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.431) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 7 POPULATED CLASSES (max global influence value = 1.225) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -4.41e+01 1.00e+00 118 0.551 1 -5.80e+01 9.75e-07 28 0.131 2 -5.06e+01 1.57e-03 17 0.079 3 -5.89e+01 3.75e-07 17 0.079 4 -6.03e+01 9.76e-08 14 0.066 5 -5.40e+01 5.36e-05 12 0.057 6 -5.16e+01 6.02e-04 8 0.037 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 1.10e+01 118 0.551 1 5.45e+00 28 0.131 2 8.01e+00 17 0.079 3 8.48e+00 17 0.079 4 1.03e+01 14 0.066 5 8.97e+00 12 0.057 6 9.60e+00 8 0.037 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 011 Log RI: refractive index 1.000 012 Log Na: Wt.% Sodium oxide 1.000 013 Log Mg: Wt.% Magnesium oxide 1.000 014 Log Al: Wt.% Aluminum oxide 1.000 015 Log Si: Wt.% Silicon oxide 1.000 016 Log K: Wt.% Potassium oxide 1.000 017 Log Ca: Wt.% Calcium oxide 1.000 018 Log Ba: Wt.% Barium oxide 1.000 019 Log Fe: Wt.% Iron oxide 1.000 000 Id number ----- 001 RI: refractive index ----- 002 Na: Wt.% Sodium oxide ----- 003 Mg: Wt.% Magnesium oxide ----- 004 Al: Wt.% Aluminum oxide ----- 005 Si: Wt.% Silicon oxide ----- 006 K: Wt.% Potassium oxide ----- 007 Ca: Wt.% Calcium oxide ----- 008 Ba: Wt.% Barium oxide ----- 009 Fe: Wt.% Iron oxide ----- 010 Type of glass ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 118 normalized weight 0.551 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 1.10e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.225 ( 1.28e+00 4.35e-02) 3.15e+01 (-9.37e-02 2.60e+00) um oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.225 (-5.30e+00 2.85e-02) 3.04e+01 (-4.43e+00 1.93e+00) oxide 00 16 R MNcn Log K: Wt.% Potassi 1.225 (-6.98e-01 4.70e-01) 1.65e+00 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.225 ( 2.14e+00 6.19e-02) 6.66e-01 ( 2.18e+00 1.45e-01) oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.225 ( 2.58e+00 2.96e-02) 5.20e-01 ( 2.59e+00 6.09e-02) oxide 00 11 R MNcn Log RI: refractive i 1.225 (-6.58e-01 3.46e-03) 2.91e-01 (-6.57e-01 5.82e-03) ndex 00 14 R MNcn Log Al: Wt.% Aluminu 1.225 ( 2.44e-01 2.58e-01) 2.41e-01 ( 3.06e-01 3.70e-01) m oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.225 (-4.07e+00 1.69e+00) 5.96e-02 (-4.18e+00 1.66e+00) ide 00 15 R MNcn Log Si: Wt.% Silicon 1.225 ( 4.29e+00 6.50e-03) 5.33e-02 ( 4.29e+00 1.07e-02) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.431 0.464 -0.804 -0.721 -0.814 0.857 0.000 0.176 12 0.431 1.000 0.383 -0.411 -0.738 -0.525 0.171 0.000 -0.107 13 0.464 0.383 1.000 -0.393 -0.463 -0.366 0.146 0.000 0.077 14 -0.804 -0.411 -0.393 1.000 0.475 0.816 -0.777 0.000 -0.066 15 -0.721 -0.738 -0.463 0.475 1.000 0.599 -0.576 0.000 -0.136 16 -0.814 -0.525 -0.366 0.816 0.599 1.000 -0.740 0.000 -0.053 17 0.857 0.171 0.146 -0.777 -0.576 -0.740 1.000 0.000 0.210 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.176 -0.107 0.077 -0.066 -0.136 -0.053 0.210 0.000 1.000 CLASS 1 - weight 28 normalized weight 0.131 relative strength 9.75e-07 ******* class cross entropy w.r.t. global class 5.45e+00 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.606 (-5.62e-01 1.81e+00) 2.14e+00 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.606 (-4.42e+00 2.28e+00) 1.89e+00 (-9.37e-02 2.60e+00) um oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.606 ( 2.67e+00 5.95e-02) 1.34e+00 ( 2.59e+00 6.09e-02) oxide 00 11 R MNcn Log RI: refractive i 0.606 (-6.61e-01 3.84e-03) 1.13e+00 (-6.57e-01 5.82e-03) ndex 00 16 R MNcn Log K: Wt.% Potassi 0.606 (-3.89e+00 2.24e+00) 1.08e+00 (-1.47e+00 1.74e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.606 ( 6.89e-01 4.13e-01) 9.29e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.606 ( 4.29e+00 1.07e-02) 8.51e-01 ( 4.29e+00 1.07e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.606 (-4.77e+00 1.12e+00) 5.25e-01 (-4.18e+00 1.66e+00) ide 00 17 R MNcn Log Ca: Wt.% Calcium 0.606 ( 2.11e+00 1.44e-01) 4.85e-01 ( 2.18e+00 1.45e-01) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.232 -0.295 0.439 -0.438 -0.047 0.305 0.532 0.050 12 -0.232 1.000 -0.210 -0.355 0.058 -0.557 -0.136 0.032 -0.021 13 -0.295 -0.210 1.000 -0.084 -0.234 0.302 -0.610 -0.134 -0.197 14 0.439 -0.355 -0.084 1.000 -0.637 0.038 0.404 0.623 0.206 15 -0.438 0.058 -0.234 -0.637 1.000 -0.190 0.155 -0.786 -0.021 16 -0.047 -0.557 0.302 0.038 -0.190 1.000 -0.452 -0.074 -0.061 17 0.305 -0.136 -0.610 0.404 0.155 -0.452 1.000 0.055 0.232 18 0.532 0.032 -0.134 0.623 -0.786 -0.074 0.055 1.000 0.106 19 0.050 -0.021 -0.197 0.206 -0.021 -0.061 0.232 0.106 1.000 CLASS 2 - weight 17 normalized weight 0.079 relative strength 1.57e-03 ******* class cross entropy w.r.t. global class 8.01e+00 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 16 R MNcn Log K: Wt.% Potassi 0.890 (-5.67e-01 2.21e-01) 4.09e+00 (-1.47e+00 1.74e+00) um oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.890 ( 9.05e-01 3.86e-01) 2.58e+00 (-9.37e-02 2.60e+00) um oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.890 ( 2.57e+00 4.10e-02) 5.93e-01 ( 2.59e+00 6.09e-02) oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.890 ( 2.22e+00 9.01e-02) 4.30e-01 ( 2.18e+00 1.45e-01) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.890 (-3.33e+00 2.06e+00) 4.09e-01 (-4.18e+00 1.66e+00) ide 00 14 R MNcn Log Al: Wt.% Aluminu 0.890 ( 3.78e-01 1.87e-01) 3.83e-01 ( 3.06e-01 3.70e-01) m oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.890 (-3.96e+00 1.79e+00) 2.65e-01 (-4.43e+00 1.93e+00) oxide 00 11 R MNcn Log RI: refractive i 0.890 (-6.57e-01 3.45e-03) 1.14e-01 (-6.57e-01 5.82e-03) ndex 00 15 R MNcn Log Si: Wt.% Silicon 0.890 ( 4.29e+00 6.54e-03) 7.60e-02 ( 4.29e+00 1.07e-02) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.702 -0.779 0.267 -0.846 -0.538 0.800 0.021 0.178 12 0.702 1.000 -0.521 -0.156 -0.661 -0.702 0.563 -0.071 0.270 13 -0.779 -0.521 1.000 -0.470 0.728 0.562 -0.905 0.173 0.000 14 0.267 -0.156 -0.470 1.000 -0.326 0.301 0.375 0.106 0.074 15 -0.846 -0.661 0.728 -0.326 1.000 0.521 -0.760 0.059 -0.270 16 -0.538 -0.702 0.562 0.301 0.521 1.000 -0.631 0.329 0.039 17 0.800 0.563 -0.905 0.375 -0.760 -0.631 1.000 -0.295 -0.062 18 0.021 -0.071 0.173 0.106 0.059 0.329 -0.295 1.000 0.561 19 0.178 0.270 0.000 0.074 -0.270 0.039 -0.062 0.561 1.000 CLASS 3 - weight 17 normalized weight 0.079 relative strength 3.75e-07 ******* class cross entropy w.r.t. global class 8.48e+00 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 14 R MNcn Log Al: Wt.% Aluminu 0.942 ( 5.97e-01 3.12e-01) 9.31e-01 ( 3.06e-01 3.70e-01) m oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.942 (-5.06e+00 1.04e+00) 8.50e-01 (-4.18e+00 1.66e+00) ide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.942 (-2.24e+00 3.19e+00) 6.71e-01 (-9.37e-02 2.60e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.942 ( 2.29e+00 2.30e-01) 4.51e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.942 (-4.94e+00 1.57e+00) 3.22e-01 (-4.43e+00 1.93e+00) oxide 00 16 R MNcn Log K: Wt.% Potassi 0.942 (-2.05e+00 2.83e+00) 2.05e-01 (-1.47e+00 1.74e+00) um oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.942 ( 4.28e+00 1.65e-02) 1.88e-01 ( 4.29e+00 1.07e-02) oxide 00 11 R MNcn Log RI: refractive i 0.942 (-6.56e-01 5.89e-03) 1.39e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 0.942 ( 2.59e+00 8.22e-02) 7.93e-02 ( 2.59e+00 6.09e-02) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.220 0.148 -0.761 0.631 -0.331 0.834 -0.310 0.203 12 -0.220 1.000 0.081 0.087 -0.270 -0.562 -0.266 0.165 -0.078 13 0.148 0.081 1.000 -0.232 -0.005 -0.468 -0.148 0.262 0.093 14 -0.761 0.087 -0.232 1.000 -0.745 0.526 -0.790 0.545 -0.153 15 0.631 -0.270 -0.005 -0.745 1.000 -0.335 0.795 -0.558 0.208 16 -0.331 -0.562 -0.468 0.526 -0.335 1.000 -0.269 0.236 0.005 17 0.834 -0.266 -0.148 -0.790 0.795 -0.269 1.000 -0.582 0.154 18 -0.310 0.165 0.262 0.545 -0.558 0.236 -0.582 1.000 -0.059 19 0.203 -0.078 0.093 -0.153 0.208 0.005 0.154 -0.059 1.000 CLASS 4 - weight 14 normalized weight 0.066 relative strength 9.76e-08 ******* class cross entropy w.r.t. global class 1.03e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 17 R MNcn Log Ca: Wt.% Calcium 1.141 ( 2.51e+00 1.92e-01) 1.71e+00 ( 2.18e+00 1.45e-01) oxide 00 11 R MNcn Log RI: refractive i 1.141 (-6.44e-01 8.62e-03) 1.56e+00 (-6.57e-01 5.82e-03) ndex 00 13 R MNcn Log Mg: Wt.% Magnesi 1.141 (-3.53e+00 3.13e+00) 1.10e+00 (-9.37e-02 2.60e+00) um oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.141 ( 4.27e+00 2.18e-02) 5.68e-01 ( 4.29e+00 1.07e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.141 ( 2.35e-02 5.01e-01) 5.64e-01 ( 3.06e-01 3.70e-01) m oxide 00 16 R MNcn Log K: Wt.% Potassi 1.141 (-2.64e+00 2.07e+00) 5.62e-01 (-1.47e+00 1.74e+00) um oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.141 ( 2.57e+00 1.33e-01) 1.86e-01 ( 2.59e+00 6.09e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.141 (-4.01e+00 1.97e+00) 8.27e-02 (-4.18e+00 1.66e+00) ide 00 18 R MNcn Log Ba: Wt.% Barium 1.141 (-4.43e+00 2.40e+00) 2.89e-03 (-4.43e+00 1.93e+00) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.569 -0.139 0.228 -0.556 0.275 0.640 0.212 0.442 12 -0.569 1.000 0.526 -0.091 -0.076 0.110 -0.757 -0.042 -0.366 13 -0.139 0.526 1.000 0.198 -0.485 0.325 -0.649 0.140 -0.429 14 0.228 -0.091 0.198 1.000 -0.612 0.808 -0.152 0.419 0.521 15 -0.556 -0.076 -0.485 -0.612 1.000 -0.743 0.110 -0.454 -0.307 16 0.275 0.110 0.325 0.808 -0.743 1.000 -0.211 0.371 0.512 17 0.640 -0.757 -0.649 -0.152 0.110 -0.211 1.000 -0.283 0.390 18 0.212 -0.042 0.140 0.419 -0.454 0.371 -0.283 1.000 0.181 19 0.442 -0.366 -0.429 0.521 -0.307 0.512 0.390 0.181 1.000 CLASS 5 - weight 12 normalized weight 0.057 relative strength 5.36e-05 ******* class cross entropy w.r.t. global class 8.97e+00 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 0.996 ( 1.25e+00 1.46e-01) 9.20e+00 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.996 (-9.09e-02 4.87e-01) 8.14e-01 ( 3.06e-01 3.70e-01) m oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.996 (-5.01e+00 1.07e+00) 5.42e-01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.996 (-3.46e+00 2.14e+00) 3.35e-01 (-4.18e+00 1.66e+00) ide 00 16 R MNcn Log K: Wt.% Potassi 0.996 (-1.86e+00 1.28e+00) 2.99e-01 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.996 (-6.55e-01 7.68e-03) 2.21e-01 (-6.57e-01 5.82e-03) ndex 00 15 R MNcn Log Si: Wt.% Silicon 0.996 ( 4.29e+00 1.05e-02) 8.96e-02 ( 4.29e+00 1.07e-02) oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.996 ( 2.60e+00 4.99e-02) 5.52e-02 ( 2.59e+00 6.09e-02) oxide 00 17 R MNcn Log Ca: Wt.% Calcium 0.996 ( 2.18e+00 5.09e-02) 2.57e-02 ( 2.18e+00 1.45e-01) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.387 0.251 -0.197 -0.603 -0.304 0.571 0.007 -0.334 12 0.387 1.000 0.654 -0.669 -0.751 -0.689 0.144 0.101 -0.507 13 0.251 0.654 1.000 -0.191 -0.745 -0.521 -0.227 0.033 -0.538 14 -0.197 -0.669 -0.191 1.000 0.356 0.499 -0.423 -0.125 0.219 15 -0.603 -0.751 -0.745 0.356 1.000 0.549 -0.194 -0.026 0.433 16 -0.304 -0.689 -0.521 0.499 0.549 1.000 -0.326 0.016 0.209 17 0.571 0.144 -0.227 -0.423 -0.194 -0.326 1.000 0.011 0.150 18 0.007 0.101 0.033 -0.125 -0.026 0.016 0.011 1.000 0.303 19 -0.334 -0.507 -0.538 0.219 0.433 0.209 0.150 0.303 1.000 CLASS 6 - weight 8 normalized weight 0.037 relative strength 6.02e-04 ******* class cross entropy w.r.t. global class 9.60e+00 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.066 ( 1.28e+00 8.12e-02) 1.70e+01 (-9.37e-02 2.60e+00) um oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.066 ( 4.27e+00 4.90e-03) 2.32e+00 ( 4.29e+00 1.07e-02) oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.066 ( 2.65e+00 3.03e-02) 1.74e+00 ( 2.59e+00 6.09e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.066 (-4.89e+00 1.31e+00) 5.48e-01 (-4.18e+00 1.66e+00) ide 00 16 R MNcn Log K: Wt.% Potassi 1.066 (-2.51e+00 2.24e+00) 4.63e-01 (-1.47e+00 1.74e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.066 ( 2.11e-01 3.84e-01) 2.47e-01 ( 3.06e-01 3.70e-01) m oxide 00 11 R MNcn Log RI: refractive i 1.066 (-6.56e-01 6.23e-03) 2.08e-01 (-6.57e-01 5.82e-03) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 1.066 ( 2.15e+00 1.34e-01) 2.07e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.066 (-4.28e+00 2.22e+00) 6.73e-02 (-4.43e+00 1.93e+00) oxide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.428 0.670 -0.843 -0.659 -0.691 0.877 0.090 -0.422 12 -0.428 1.000 0.063 0.234 -0.092 -0.076 -0.481 -0.387 0.708 13 0.670 0.063 1.000 -0.721 -0.652 -0.750 0.643 -0.092 0.092 14 -0.843 0.234 -0.721 1.000 0.705 0.809 -0.837 0.106 0.369 15 -0.659 -0.092 -0.652 0.705 1.000 0.712 -0.584 -0.120 0.054 16 -0.691 -0.076 -0.750 0.809 0.712 1.000 -0.657 0.268 0.131 17 0.877 -0.481 0.643 -0.837 -0.584 -0.657 1.000 0.050 -0.485 18 0.090 -0.387 -0.092 0.106 -0.120 0.268 0.050 1.000 -0.189 19 -0.422 0.708 0.092 0.369 0.054 0.131 -0.485 -0.189 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.r-params0000644000175000017500000001125211247310756017653 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; free_storage_p = false ;; n_clsfs = 2 ;; report_mode = "data" autoclass-3.3.6.dfsg.1/data/glass/glassc.s-params0000644000175000017500000002237011247310756017657 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/glass/glassc.influ-no-text-10000644000175000017500000005057411247310756021012 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = false ============================================= AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11434.215 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 4 seconds _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-11434.215) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-11510.965) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-11663.794) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11670.677) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 5 POPULATED CLASSES (max global influence value = 3.141) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -4.67e+01 1.00e+00 99 0.465 1 -5.27e+01 2.48e-03 44 0.204 2 -5.99e+01 1.93e-06 35 0.164 3 -5.81e+01 1.19e-05 19 0.089 4 -5.69e+01 3.79e-05 17 0.079 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 2.83e+01 99 0.465 1 2.14e+01 44 0.204 2 2.18e+01 35 0.164 3 2.35e+01 19 0.089 4 2.09e+01 17 0.079 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 011 Log RI: refractive index 1.000 012 Log Na: Wt.% Sodium oxide 1.000 013 Log Mg: Wt.% Magnesium oxide 1.000 014 Log Al: Wt.% Aluminum oxide 1.000 015 Log Si: Wt.% Silicon oxide 1.000 016 Log K: Wt.% Potassium oxide 1.000 017 Log Ca: Wt.% Calcium oxide 1.000 018 Log Ba: Wt.% Barium oxide 1.000 019 Log Fe: Wt.% Iron oxide 1.000 000 Id number ----- 001 RI: refractive index ----- 002 Na: Wt.% Sodium oxide ----- 003 Mg: Wt.% Magnesium oxide ----- 004 Al: Wt.% Aluminum oxide ----- 005 Si: Wt.% Silicon oxide ----- 006 K: Wt.% Potassium oxide ----- 007 Ca: Wt.% Calcium oxide ----- 008 Ba: Wt.% Barium oxide ----- 009 Fe: Wt.% Iron oxide ----- 010 Type of glass ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 99 normalized weight 0.465 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 2.83e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 11 R MNcn Log RI: refractive i 3.141 (-6.59e-01 1.31e-02) 1.46e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 3.141 ( 2.57e+00 2.54e-02) 8.92e-01 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 3.141 ( 1.27e+00 4.24e-02) 3.22e+01 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 3.141 ( 3.13e-01 1.42e-01) 4.75e-02 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 3.141 ( 4.29e+00 1.31e-02) 1.31e-01 ( 4.29e+00 1.07e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 3.141 (-5.09e-01 8.06e-02) 1.20e+01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 3.141 ( 2.13e+00 3.80e-02) 1.47e+00 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 3.141 (-5.30e+00 2.86e-02) 3.03e+01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 3.141 (-4.12e+00 1.70e+00) 3.28e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.040 0.034 -0.061 -0.028 -0.072 0.092 0.000 -0.009 12 0.040 1.000 0.222 0.017 -0.278 -0.261 -0.194 0.000 -0.128 13 0.034 0.222 1.000 -0.124 -0.099 -0.184 -0.295 0.000 0.059 14 -0.061 0.017 -0.124 1.000 -0.054 0.383 -0.502 0.000 -0.010 15 -0.028 -0.278 -0.099 -0.054 1.000 0.100 -0.085 0.000 -0.026 16 -0.072 -0.261 -0.184 0.383 0.100 1.000 -0.359 0.000 0.033 17 0.092 -0.194 -0.295 -0.502 -0.085 -0.359 1.000 0.000 0.141 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 -0.009 -0.128 0.059 -0.010 -0.026 0.033 0.141 0.000 1.000 CLASS 1 - weight 44 normalized weight 0.204 relative strength 2.48e-03 ******* class cross entropy w.r.t. global class 2.14e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 11 R MNcn Log RI: refractive i 2.383 (-6.54e-01 1.33e-02) 1.96e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 2.383 ( 2.61e+00 4.34e-02) 3.19e-01 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 2.383 ( 1.25e+00 1.21e-01) 1.11e+01 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 2.383 (-6.89e-03 4.21e-01) 7.44e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 2.383 ( 4.28e+00 1.33e-02) 3.61e-01 ( 4.29e+00 1.07e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 2.383 (-1.79e+00 1.19e+00) 2.61e-01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 2.383 ( 2.21e+00 1.00e-01) 2.38e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 2.383 (-5.30e+00 2.92e-02) 2.97e+01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 2.383 (-4.11e+00 1.69e+00) 3.81e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.118 0.149 -0.301 -0.272 -0.247 0.392 0.000 0.039 12 0.118 1.000 0.557 -0.389 -0.499 -0.515 -0.072 0.000 -0.208 13 0.149 0.557 1.000 -0.423 -0.384 -0.538 0.038 0.000 -0.064 14 -0.301 -0.389 -0.423 1.000 0.377 0.545 -0.622 0.000 -0.009 15 -0.272 -0.499 -0.384 0.377 1.000 0.439 -0.418 0.000 0.084 16 -0.247 -0.515 -0.538 0.545 0.439 1.000 -0.417 0.000 0.112 17 0.392 -0.072 0.038 -0.622 -0.418 -0.417 1.000 0.000 0.099 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.039 -0.208 -0.064 -0.009 0.084 0.112 0.099 0.000 1.000 CLASS 2 - weight 35 normalized weight 0.164 relative strength 1.93e-06 ******* class cross entropy w.r.t. global class 2.18e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 11 R MNcn Log RI: refractive i 2.417 (-6.61e-01 1.35e-02) 2.64e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 2.417 ( 2.66e+00 3.78e-02) 1.85e+00 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 2.417 (-3.57e+00 2.87e+00) 1.21e+00 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 2.417 ( 7.59e-01 2.37e-01) 1.91e+00 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 2.417 ( 4.29e+00 1.35e-02) 1.74e-01 ( 4.29e+00 1.07e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 2.417 (-3.78e+00 2.47e+00) 9.33e-01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 2.417 ( 2.12e+00 1.61e-01) 3.53e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 2.417 (-1.34e+00 2.48e+00) 1.25e+00 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 2.417 (-4.87e+00 1.02e+00) 6.84e-01 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.088 0.022 -0.125 0.028 -0.136 0.141 -0.023 -0.014 12 0.088 1.000 -0.252 -0.191 0.541 -0.625 0.300 0.402 0.087 13 0.022 -0.252 1.000 -0.469 -0.277 0.083 -0.254 -0.423 -0.268 14 -0.125 -0.191 -0.469 1.000 -0.382 0.502 -0.213 0.299 0.181 15 0.028 0.541 -0.277 -0.382 1.000 -0.668 0.464 0.232 0.210 16 -0.136 -0.625 0.083 0.502 -0.668 1.000 -0.720 0.017 -0.068 17 0.141 0.300 -0.254 -0.213 0.464 -0.720 1.000 -0.268 0.145 18 -0.023 0.402 -0.423 0.299 0.232 0.017 -0.268 1.000 0.206 19 -0.014 0.087 -0.268 0.181 0.210 -0.068 0.145 0.206 1.000 CLASS 3 - weight 19 normalized weight 0.089 relative strength 1.19e-05 ******* class cross entropy w.r.t. global class 2.35e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 11 R MNcn Log RI: refractive i 2.607 (-6.54e-01 1.40e-02) 2.22e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 2.607 ( 2.59e+00 8.40e-02) 1.06e-01 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 2.607 ( 7.47e-01 6.16e-01) 1.37e+00 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 2.607 ( 3.68e-01 2.46e-01) 2.50e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 2.607 ( 4.28e+00 1.40e-02) 3.02e-01 ( 4.29e+00 1.07e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 2.607 (-8.34e-01 5.87e-01) 1.09e+00 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 2.607 ( 2.26e+00 1.20e-01) 6.31e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 2.607 (-3.18e+00 2.10e+00) 5.94e-01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 2.607 (-3.28e+00 2.09e+00) 4.29e-01 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.078 -0.142 0.074 -0.144 -0.060 0.154 -0.060 -0.109 12 0.078 1.000 0.054 -0.108 -0.741 -0.220 -0.387 0.341 -0.049 13 -0.142 0.054 1.000 -0.354 -0.069 0.306 -0.718 0.482 -0.046 14 0.074 -0.108 -0.354 1.000 -0.191 0.465 0.393 -0.316 -0.145 15 -0.144 -0.741 -0.069 -0.191 1.000 0.012 0.138 -0.177 0.307 16 -0.060 -0.220 0.306 0.465 0.012 1.000 -0.091 -0.131 0.008 17 0.154 -0.387 -0.718 0.393 0.138 -0.091 1.000 -0.738 -0.199 18 -0.060 0.341 0.482 -0.316 -0.177 -0.131 -0.738 1.000 0.109 19 -0.109 -0.049 -0.046 -0.145 0.307 0.008 -0.199 0.109 1.000 CLASS 4 - weight 17 normalized weight 0.079 relative strength 3.79e-05 ******* class cross entropy w.r.t. global class 2.09e+01 ******* Model file: /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 11 R MNcn Log RI: refractive i 2.322 (-6.49e-01 1.41e-02) 5.88e-01 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 2.322 ( 2.56e+00 1.27e-01) 2.93e-01 ( 2.59e+00 6.09e-02) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 2.322 (-5.30e+00 1.41e-02) 3.69e+02 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 2.322 ( 6.68e-02 5.58e-01) 4.29e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 2.322 ( 4.29e+00 2.26e-02) 9.95e-02 ( 4.29e+00 1.07e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 2.322 (-2.29e+00 2.32e+00) 3.50e-01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 2.322 ( 2.48e+00 2.27e-01) 1.32e+00 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 2.322 (-4.92e+00 1.67e+00) 2.91e-01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 2.322 (-4.24e+00 1.83e+00) 3.36e-02 (-4.18e+00 1.66e+00) ide Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.517 0.000 0.212 -0.697 0.037 0.721 0.296 0.472 12 -0.517 1.000 0.000 -0.479 0.408 -0.279 -0.634 -0.372 -0.265 13 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 14 0.212 -0.479 0.000 1.000 -0.431 0.814 0.280 0.314 0.353 15 -0.697 0.408 0.000 -0.431 1.000 -0.249 -0.717 -0.481 -0.681 16 0.037 -0.279 0.000 0.814 -0.249 1.000 -0.001 0.196 0.294 17 0.721 -0.634 0.000 0.280 -0.717 -0.001 1.000 0.118 0.368 18 0.296 -0.372 0.000 0.314 -0.481 0.196 0.118 1.000 0.419 19 0.472 -0.265 0.000 0.353 -0.681 0.294 0.368 0.419 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.influ-o-data-10000644000175000017500000007407211247310756020560 0ustar areareDATA_CLSF_HEADER AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -10897.738 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 DATA_SEARCH_SUMMARY SEARCH SUMMARY 4 tries over 3 seconds SUMMARY OF 10 BEST RESULTS PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -1 PROBABILITY exp(-11187.746) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -2 PROBABILITY exp(-11229.516) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.432) N_CLASSES 2 FOUND ON TRY 1 DATA_POP_CLASSES CLASSIFICATION HAS 5 POPULATED CLASSES (max global influence value = 1.912) Class Log of class Relative Class Normalized num strength class strength weight class weight 00 -4.21e+01 1.00e+00 97 0.453 01 -5.03e+01 2.77e-04 46 0.215 02 -5.82e+01 1.01e-07 35 0.163 03 -5.54e+01 1.62e-06 19 0.089 04 -5.39e+01 6.97e-06 17 0.080 DATA_CLASS_DIVS CLASS DIVERGENCES Class class cross entropy Class Normalized num w.r.t. global class weight class weight 00 1.31e+01 97 0.453 01 8.13e+00 46 0.215 02 5.64e+00 35 0.163 03 6.26e+00 19 0.089 04 1.72e+01 17 0.080 DATA_NORM_INF_VALS ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES num description I-*k 011 Log RI: refractive index 1.000 012 Log Na: Wt.% Sodium oxide 1.000 013 Log Mg: Wt.% Magnesium oxide 1.000 014 Log Al: Wt.% Aluminum oxide 1.000 015 Log Si: Wt.% Silicon oxide 1.000 016 Log K: Wt.% Potassium oxide 1.000 017 Log Ca: Wt.% Calcium oxide 1.000 018 Log Ba: Wt.% Barium oxide 1.000 019 Log Fe: Wt.% Iron oxide 1.000 000 Id number ----- 001 RI: refractive index ----- 002 Na: Wt.% Sodium oxide ----- 003 Mg: Wt.% Magnesium oxide ----- 004 Al: Wt.% Aluminum oxide ----- 005 Si: Wt.% Silicon oxide ----- 006 K: Wt.% Potassium oxide ----- 007 Ca: Wt.% Calcium oxide ----- 008 Ba: Wt.% Barium oxide ----- 009 Fe: Wt.% Iron oxide ----- 010 Type of glass ----- DATA_CLASS 0 CLASS 0 - weight 97 normalized weight 0.453 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 1.31e+01 ******* REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.455 ( 1.27e+00 4.25e-02) 3.21e+01 (-9.37e-02 2.60e+00) um oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.455 (-5.30e+00 2.86e-02) 3.03e+01 (-4.43e+00 1.93e+00) oxide 00 16 R MNcn Log K: Wt.% Potassi 1.455 (-5.10e-01 8.42e-02) 1.14e+01 (-1.47e+00 1.74e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.455 ( 2.12e+00 3.88e-02) 1.48e+00 ( 2.18e+00 1.45e-01) oxide 00 11 R MNcn Log RI: refractive i 1.455 (-6.59e-01 1.77e-03) 1.12e+00 (-6.57e-01 5.82e-03) ndex 00 12 R MNcn Log Na: Wt.% Sodium 1.455 ( 2.57e+00 2.50e-02) 8.66e-01 ( 2.59e+00 6.09e-02) oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.455 ( 4.29e+00 5.21e-03) 3.25e-01 ( 4.29e+00 1.07e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 1.455 ( 3.18e-01 1.41e-01) 8.24e-02 ( 3.06e-01 3.70e-01) m oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.455 (-4.12e+00 1.69e+00) 3.34e-02 (-4.18e+00 1.66e+00) ide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.176 0.276 -0.445 -0.434 -0.441 0.582 0.000 0.114 12 0.176 1.000 0.239 0.025 -0.710 -0.326 -0.232 0.000 -0.155 13 0.276 0.239 1.000 -0.139 -0.266 -0.172 -0.287 0.000 0.060 14 -0.445 0.025 -0.139 1.000 -0.139 0.364 -0.497 0.000 -0.005 15 -0.434 -0.710 -0.266 -0.139 1.000 0.249 -0.207 0.000 -0.084 16 -0.441 -0.326 -0.172 0.364 0.249 1.000 -0.300 0.000 0.033 17 0.582 -0.232 -0.287 -0.497 -0.207 -0.300 1.000 0.000 0.177 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 0.114 -0.155 0.060 -0.005 -0.084 0.033 0.177 0.000 1.000 SIGMA CONTOURS att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000011 00012 -6.590722e-01 2.498446e-02 2.572384e+00 1.740388e-03 1.558305e+00 000011 00013 -6.590722e-01 4.249269e-02 1.271025e+00 1.699370e-03 1.559305e+00 000011 00014 -6.590722e-01 1.412292e-01 3.176487e-01 1.582801e-03 1.576373e+00 000011 00015 -6.590722e-01 5.275176e-03 4.287304e+00 1.574136e-03 1.731364e+00 000011 00016 -6.590722e-01 8.422710e-02 -5.097567e-01 1.586767e-03 1.580057e+00 000011 00017 -6.590722e-01 3.883135e-02 2.124220e+00 1.437427e-03 1.544265e+00 000011 00018 -6.590722e-01 2.861241e-02 -5.298050e+00 1.768012e-03 1.570796e+00 000011 00019 -6.590722e-01 1.692859e+00 -4.118752e+00 1.726335e-03 1.570677e+00 000012 00013 2.572384e+00 4.310152e-02 1.271025e+00 2.391184e-02 1.367637e+00 000012 00014 2.572384e+00 1.412284e-01 3.176487e-01 2.497468e-02 1.566298e+00 000012 00015 2.572384e+00 2.526101e-02 4.287304e+00 3.631383e-03 -1.501987e-01 000012 00016 2.572384e+00 8.464921e-02 -5.097567e-01 2.349966e-02 1.675250e+00 000012 00017 2.572384e+00 3.950692e-02 2.124220e+00 2.387769e-02 1.806404e+00 000012 00018 2.572384e+00 2.861241e-02 -5.298050e+00 2.498252e-02 1.570796e+00 000012 00019 2.572384e+00 1.692863e+00 -4.118752e+00 2.468116e-02 1.573086e+00 000013 00014 1.271025e+00 1.413616e-01 3.176487e-01 4.203998e-02 1.616505e+00 000013 00015 1.271025e+00 4.251291e-02 4.287304e+00 5.022217e-03 -3.314270e-02 000013 00016 1.271025e+00 8.464267e-02 -5.097567e-01 4.164859e-02 1.685223e+00 000013 00017 1.271025e+00 4.639769e-02 2.124220e+00 3.405083e-02 -6.327401e-01 000013 00018 1.271025e+00 4.248989e-02 -5.298050e+00 2.861241e-02 0.000000e+00 000013 00019 1.271025e+00 1.692860e+00 -4.118752e+00 4.241307e-02 1.569291e+00 000014 00015 3.176487e-01 1.412289e-01 4.287304e+00 5.162434e-03 -5.151733e-03 000014 00016 3.176487e-01 1.458394e-01 -5.097567e-01 7.595740e-02 2.966070e-01 000014 00017 3.176487e-01 1.426145e-01 2.124220e+00 3.336163e-02 -1.436143e-01 000014 00018 3.176487e-01 1.412270e-01 -5.298050e+00 2.861241e-02 0.000000e+00 000014 00019 3.176487e-01 1.692859e+00 -4.118752e+00 1.412260e-01 1.571181e+00 000015 00016 4.287304e+00 8.423354e-02 -5.097567e-01 5.048354e-03 1.555317e+00 000015 00017 4.287304e+00 3.883297e-02 2.124220e+00 5.098417e-03 1.599085e+00 000015 00018 4.287304e+00 2.861241e-02 -5.298050e+00 5.213400e-03 1.570796e+00 000015 00019 4.287304e+00 1.692859e+00 -4.118752e+00 5.190501e-03 1.571054e+00 000016 00017 -5.097567e-01 8.520963e-02 2.124220e+00 3.660231e-02 -1.687863e-01 000016 00018 -5.097567e-01 8.422349e-02 -5.298050e+00 2.861241e-02 0.000000e+00 000016 00019 -5.097567e-01 1.692861e+00 -4.118752e+00 8.417861e-02 1.569165e+00 000017 00018 2.124220e+00 3.881770e-02 -5.298050e+00 2.861241e-02 0.000000e+00 000017 00019 2.124220e+00 1.692873e+00 -4.118752e+00 3.820469e-02 1.566735e+00 000018 00019 -5.298050e+00 1.692859e+00 -4.118752e+00 2.861345e-02 1.570796e+00 DATA_CLASS 1 CLASS 1 - weight 46 normalized weight 0.215 relative strength 2.77e-04 ******* class cross entropy w.r.t. global class 8.13e+00 ******* REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 18 R MNcn Log Ba: Wt.% Barium 0.903 (-5.30e+00 2.91e-02) 2.97e+01 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.903 ( 1.25e+00 1.18e-01) 1.14e+01 (-9.37e-02 2.60e+00) um oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.903 (-4.84e-05 4.09e-01) 7.49e-01 ( 3.06e-01 3.70e-01) m oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.903 ( 4.28e+00 1.01e-02) 4.38e-01 ( 4.29e+00 1.07e-02) oxide 00 11 R MNcn Log RI: refractive i 0.903 (-6.55e-01 6.40e-03) 3.95e-01 (-6.57e-01 5.82e-03) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 0.903 ( 2.20e+00 9.66e-02) 2.28e-01 ( 2.18e+00 1.45e-01) oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.903 ( 2.60e+00 4.55e-02) 2.15e-01 ( 2.59e+00 6.09e-02) oxide 00 16 R MNcn Log K: Wt.% Potassi 0.903 (-1.71e+00 1.20e+00) 2.01e-01 (-1.47e+00 1.74e+00) um oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.903 (-4.11e+00 1.70e+00) 3.70e-02 (-4.18e+00 1.66e+00) ide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.308 0.301 -0.605 -0.747 -0.503 0.816 0.000 -0.029 12 0.308 1.000 0.502 -0.413 -0.661 -0.551 0.003 0.000 -0.159 13 0.301 0.502 1.000 -0.426 -0.484 -0.521 0.041 0.000 -0.063 14 -0.605 -0.413 -0.426 1.000 0.496 0.547 -0.621 0.000 -0.013 15 -0.747 -0.661 -0.484 0.496 1.000 0.584 -0.556 0.000 0.122 16 -0.503 -0.551 -0.521 0.547 0.584 1.000 -0.430 0.000 0.103 17 0.816 0.003 0.041 -0.621 -0.556 -0.430 1.000 0.000 0.067 18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 19 -0.029 -0.159 -0.063 -0.013 0.122 0.103 0.067 0.000 1.000 SIGMA CONTOURS att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000011 00012 -6.545673e-01 4.557043e-02 2.603818e+00 6.080871e-03 1.526711e+00 000011 00013 -6.545673e-01 1.177785e-01 1.247984e+00 6.100654e-03 1.554395e+00 000011 00014 -6.545673e-01 4.087260e-01 -4.842137e-05 5.092688e-03 1.580274e+00 000011 00015 -6.545673e-01 1.136215e-02 4.281175e+00 3.792241e-03 2.072761e+00 000011 00016 -6.545673e-01 1.197082e+00 -1.714358e+00 5.524272e-03 1.573486e+00 000011 00017 -6.545673e-01 9.678531e-02 2.203559e+00 3.696849e-03 1.516773e+00 000011 00018 -6.545673e-01 2.911455e-02 -5.298050e+00 6.398329e-03 1.570796e+00 000011 00019 -6.545673e-01 1.703992e+00 -4.112256e+00 6.403748e-03 1.570904e+00 000012 00013 2.603818e+00 1.202186e-01 1.247984e+00 3.857766e-02 1.356841e+00 000012 00014 2.603818e+00 4.091448e-01 -4.842137e-05 4.141441e-02 1.617268e+00 000012 00015 2.603818e+00 4.602941e-02 4.281175e+00 7.519092e-03 -1.499218e-01 000012 00016 2.603818e+00 1.197341e+00 -1.714358e+00 3.797858e-02 1.591776e+00 000012 00017 2.603818e+00 9.664448e-02 2.203559e+00 4.552660e-02 1.568738e+00 000012 00018 2.603818e+00 4.552694e-02 -5.298050e+00 2.911455e-02 0.000000e+00 000012 00019 2.603818e+00 1.704008e+00 -4.112256e+00 4.494575e-02 1.575059e+00 000013 00014 1.247984e+00 4.119995e-01 -4.842137e-05 1.056711e-01 1.701696e+00 000013 00015 1.247984e+00 1.178653e-01 4.281175e+00 8.851965e-03 -4.185277e-02 000013 00016 1.247984e+00 1.198663e+00 -1.714358e+00 1.003590e-01 1.622402e+00 000013 00017 1.247984e+00 1.179600e-01 2.203559e+00 9.640338e-02 1.004962e-01 000013 00018 1.247984e+00 1.177627e-01 -5.298050e+00 2.911455e-02 0.000000e+00 000013 00019 1.247984e+00 1.704008e+00 -4.112256e+00 1.175273e-01 1.575174e+00 000014 00015 -4.842137e-05 4.087386e-01 4.281175e+00 8.789062e-03 1.230535e-02 000014 00016 -4.842137e-05 1.219539e+00 -1.714358e+00 3.358018e-01 1.370744e+00 000014 00017 -4.842137e-05 4.132478e-01 2.203559e+00 7.488842e-02 -1.508869e-01 000014 00018 -4.842137e-05 4.087076e-01 -5.298050e+00 2.911447e-02 0.000000e+00 000014 00019 -4.842137e-05 1.704001e+00 -4.112256e+00 4.086708e-01 1.574114e+00 000015 00016 4.281175e+00 1.197093e+00 -1.714358e+00 8.217789e-03 1.565854e+00 000015 00017 4.281175e+00 9.680948e-02 2.203559e+00 8.401706e-03 1.629440e+00 000015 00018 4.281175e+00 2.911455e-02 -5.298050e+00 1.012625e-02 1.570796e+00 000015 00019 4.281175e+00 1.703993e+00 -4.112256e+00 1.004840e-02 1.570072e+00 000016 00017 -1.714358e+00 1.197803e+00 2.203559e+00 8.720025e-02 -3.488717e-02 000016 00018 -1.714358e+00 1.197078e+00 -5.298050e+00 2.911524e-02 0.000000e+00 000016 00019 -1.714358e+00 1.712656e+00 -4.112256e+00 1.184650e+00 1.431244e+00 000017 00018 2.203559e+00 9.664432e-02 -5.298050e+00 2.911455e-02 0.000000e+00 000017 00019 2.203559e+00 1.704004e+00 -4.112256e+00 9.642720e-02 1.566995e+00 000018 00019 -5.298050e+00 1.703992e+00 -4.112256e+00 2.911319e-02 1.570796e+00 DATA_CLASS 2 CLASS 2 - weight 35 normalized weight 0.163 relative strength 1.01e-07 ******* class cross entropy w.r.t. global class 5.64e+00 ******* REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 14 R MNcn Log Al: Wt.% Aluminu 0.627 ( 7.59e-01 2.37e-01) 1.91e+00 ( 3.06e-01 3.70e-01) m oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.627 ( 2.66e+00 3.78e-02) 1.85e+00 ( 2.59e+00 6.09e-02) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.627 (-1.34e+00 2.48e+00) 1.25e+00 (-4.43e+00 1.93e+00) oxide 00 13 R MNcn Log Mg: Wt.% Magnesi 0.627 (-3.57e+00 2.87e+00) 1.21e+00 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.627 (-3.78e+00 2.47e+00) 9.34e-01 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.627 (-6.61e-01 3.93e-03) 9.02e-01 (-6.57e-01 5.82e-03) ndex 00 19 R MNcn Log Fe: Wt.% Iron ox 0.627 (-4.87e+00 1.02e+00) 6.84e-01 (-4.18e+00 1.66e+00) ide 00 17 R MNcn Log Ca: Wt.% Calcium 0.627 ( 2.12e+00 1.61e-01) 3.53e-01 ( 2.18e+00 1.45e-01) oxide 00 15 R MNcn Log Si: Wt.% Silicon 0.627 ( 4.29e+00 1.33e-02) 1.76e-01 ( 4.29e+00 1.07e-02) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.301 0.075 -0.427 0.098 -0.466 0.481 -0.078 -0.047 12 0.301 1.000 -0.252 -0.191 0.548 -0.625 0.301 0.402 0.088 13 0.075 -0.252 1.000 -0.469 -0.281 0.084 -0.254 -0.423 -0.268 14 -0.427 -0.191 -0.469 1.000 -0.387 0.502 -0.213 0.299 0.181 15 0.098 0.548 -0.281 -0.387 1.000 -0.677 0.469 0.235 0.213 16 -0.466 -0.625 0.084 0.502 -0.677 1.000 -0.721 0.017 -0.068 17 0.481 0.301 -0.254 -0.213 0.469 -0.721 1.000 -0.268 0.145 18 -0.078 0.402 -0.423 0.299 0.235 0.017 -0.268 1.000 0.206 19 -0.047 0.088 -0.268 0.181 0.213 -0.068 0.145 0.206 1.000 SIGMA CONTOURS att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000011 00012 -6.606410e-01 3.785854e-02 2.663985e+00 3.749970e-03 1.539149e+00 000011 00013 -6.606410e-01 2.866369e+00 -3.566931e+00 3.906250e-03 1.570693e+00 000011 00014 -6.606410e-01 2.372345e-01 7.593212e-01 3.558407e-03 1.577875e+00 000011 00015 -6.606410e-01 1.329766e-02 4.287948e+00 3.913973e-03 1.538922e+00 000011 00016 -6.606410e-01 2.472981e+00 -3.782718e+00 3.452670e-03 1.571538e+00 000011 00017 -6.606410e-01 1.607398e-01 2.124689e+00 3.448351e-03 1.559004e+00 000011 00018 -6.606410e-01 2.480572e+00 -1.336065e+00 3.906250e-03 1.570921e+00 000011 00019 -6.606410e-01 1.018716e+00 -4.871840e+00 3.936649e-03 1.570978e+00 000012 00013 2.663985e+00 2.866385e+00 -3.566931e+00 3.661133e-02 1.574120e+00 000012 00014 2.663985e+00 2.373414e-01 7.593212e-01 3.712546e-02 1.602018e+00 000012 00015 2.663985e+00 3.859639e-02 4.287948e+00 1.090093e-02 2.068697e-01 000012 00016 2.663985e+00 2.473093e+00 -3.782718e+00 2.953597e-02 1.580362e+00 000012 00017 2.663985e+00 1.611520e-01 2.124689e+00 3.599407e-02 1.496412e+00 000012 00018 2.663985e+00 2.480619e+00 -1.336065e+00 3.464045e-02 1.564655e+00 000012 00019 2.663985e+00 1.018722e+00 -4.871840e+00 3.769424e-02 1.567541e+00 000013 00014 -3.566931e+00 2.868535e+00 7.593212e-01 2.094231e-01 -3.896358e-02 000013 00015 -3.566931e+00 2.866371e+00 4.287948e+00 1.275153e-02 -1.302303e-03 000013 00016 -3.566931e+00 2.893406e+00 -3.782718e+00 2.441292e+00 2.569148e-01 000013 00017 -3.566931e+00 2.866660e+00 2.124689e+00 1.554514e-01 -1.427140e-02 000013 00018 -3.566931e+00 3.219390e+00 -1.336065e+00 2.001209e+00 -6.202287e-01 000013 00019 -3.566931e+00 2.881052e+00 -4.871840e+00 9.764198e-01 -1.073783e-01 000014 00015 7.593212e-01 2.372844e-01 4.287948e+00 1.225432e-02 -2.172408e-02 000014 00016 7.593212e-01 2.475863e+00 -3.782718e+00 2.049700e-01 1.522371e+00 000014 00017 7.593212e-01 2.414646e-01 2.124689e+00 1.542918e-01 -2.448788e-01 000014 00018 7.593212e-01 2.481597e+00 -1.336065e+00 2.262513e-01 1.541929e+00 000014 00019 7.593212e-01 1.019667e+00 -4.871840e+00 2.331108e-01 1.526448e+00 000015 00016 4.287948e+00 2.472996e+00 -3.782718e+00 9.777824e-03 1.574434e+00 000015 00017 4.287948e+00 1.608503e-01 2.124689e+00 1.172761e-02 1.531794e+00 000015 00018 4.287948e+00 2.480574e+00 -1.336065e+00 1.290948e-02 1.569536e+00 000015 00019 4.287948e+00 1.018720e+00 -4.871840e+00 1.298773e-02 1.568023e+00 000016 00017 -3.782718e+00 2.475696e+00 2.124689e+00 1.113238e-01 -4.689319e-02 000016 00018 -3.782718e+00 2.497638e+00 -1.336065e+00 2.455742e+00 8.765097e-01 000016 00019 -3.782718e+00 2.474160e+00 -4.871840e+00 1.015848e+00 -3.387023e-02 000017 00018 2.124689e+00 2.480948e+00 -1.336065e+00 1.548067e-01 1.588254e+00 000017 00019 2.124689e+00 1.018989e+00 -4.871840e+00 1.589925e-01 1.547386e+00 000018 00019 -1.336065e+00 2.491082e+00 -4.871840e+00 9.927391e-01 1.002182e-01 DATA_CLASS 3 CLASS 3 - weight 19 normalized weight 0.089 relative strength 1.62e-06 ******* class cross entropy w.r.t. global class 6.26e+00 ******* REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 0.696 ( 7.47e-01 6.16e-01) 1.37e+00 (-9.37e-02 2.60e+00) um oxide 00 16 R MNcn Log K: Wt.% Potassi 0.696 (-8.34e-01 5.87e-01) 1.09e+00 (-1.47e+00 1.74e+00) um oxide 00 11 R MNcn Log RI: refractive i 0.696 (-6.54e-01 3.92e-03) 7.91e-01 (-6.57e-01 5.82e-03) ndex 00 17 R MNcn Log Ca: Wt.% Calcium 0.696 ( 2.26e+00 1.20e-01) 6.30e-01 ( 2.18e+00 1.45e-01) oxide 00 18 R MNcn Log Ba: Wt.% Barium 0.696 (-3.18e+00 2.10e+00) 5.95e-01 (-4.43e+00 1.93e+00) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 0.696 (-3.28e+00 2.09e+00) 4.28e-01 (-4.18e+00 1.66e+00) ide 00 15 R MNcn Log Si: Wt.% Silicon 0.696 ( 4.28e+00 1.31e-02) 3.23e-01 ( 4.29e+00 1.07e-02) oxide 00 14 R MNcn Log Al: Wt.% Aluminu 0.696 ( 3.67e-01 2.47e-01) 2.49e-01 ( 3.06e-01 3.70e-01) m oxide 00 12 R MNcn Log Na: Wt.% Sodium 0.696 ( 2.59e+00 8.41e-02) 1.06e-01 ( 2.59e+00 6.09e-02) oxide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 0.278 -0.505 0.262 -0.548 -0.213 0.551 -0.215 -0.390 12 0.278 1.000 0.054 -0.107 -0.792 -0.220 -0.387 0.342 -0.049 13 -0.505 0.054 1.000 -0.354 -0.074 0.306 -0.718 0.482 -0.047 14 0.262 -0.107 -0.354 1.000 -0.204 0.465 0.393 -0.316 -0.145 15 -0.548 -0.792 -0.074 -0.204 1.000 0.013 0.148 -0.190 0.329 16 -0.213 -0.220 0.306 0.465 0.013 1.000 -0.091 -0.131 0.008 17 0.551 -0.387 -0.718 0.393 0.148 -0.091 1.000 -0.738 -0.199 18 -0.215 0.342 0.482 -0.316 -0.190 -0.131 -0.738 1.000 0.110 19 -0.390 -0.049 -0.047 -0.145 0.329 0.008 -0.199 0.110 1.000 SIGMA CONTOURS att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000011 00012 -6.539944e-01 8.406447e-02 2.585070e+00 3.762738e-03 1.557801e+00 000011 00013 -6.539944e-01 6.157874e-01 7.474042e-01 3.380709e-03 1.574011e+00 000011 00014 -6.539944e-01 2.465721e-01 3.674888e-01 3.780486e-03 1.566626e+00 000011 00015 -6.539944e-01 1.326582e-02 4.281391e+00 3.231814e-03 1.743785e+00 000011 00016 -6.539944e-01 5.871632e-01 -8.339410e-01 3.827250e-03 1.572219e+00 000011 00017 -6.539944e-01 1.199914e-01 2.257113e+00 3.269941e-03 1.552805e+00 000011 00018 -6.539944e-01 2.099104e+00 -3.183365e+00 3.813599e-03 1.571198e+00 000011 00019 -6.539944e-01 2.094002e+00 -3.279189e+00 3.588120e-03 1.571526e+00 000012 00013 2.585070e+00 6.158012e-01 7.474042e-01 8.393309e-02 1.563305e+00 000012 00014 2.585070e+00 2.467565e-01 3.674888e-01 8.350832e-02 1.612118e+00 000012 00015 2.585070e+00 8.469857e-02 4.281391e+00 7.929228e-03 -1.236705e-01 000012 00016 2.585070e+00 5.874596e-01 -8.339410e-01 8.195574e-02 1.602913e+00 000012 00017 2.585070e+00 1.268272e-01 2.257113e+00 7.330612e-02 1.979510e+00 000012 00018 2.585070e+00 2.099300e+00 -3.183365e+00 7.899449e-02 1.557098e+00 000012 00019 2.585070e+00 2.094006e+00 -3.279189e+00 8.395740e-02 1.572762e+00 000013 00014 7.474042e-01 6.229268e-01 3.674888e-01 2.279236e-01 -1.629709e-01 000013 00015 7.474042e-01 6.157849e-01 4.281391e+00 1.304383e-02 -1.562183e-03 000013 00016 7.474042e-01 6.883754e-01 -8.339410e-01 5.000893e-01 7.081595e-01 000013 00017 7.474042e-01 6.218872e-01 2.257113e+00 8.270279e-02 -1.414776e-01 000013 00018 7.474042e-01 2.121398e+00 -3.183365e+00 5.339457e-01 1.420830e+00 000013 00019 7.474042e-01 2.094217e+00 -3.279189e+00 6.150516e-01 1.585794e+00 000014 00015 3.674888e-01 2.465845e-01 4.281391e+00 1.280357e-02 -1.085550e-02 000014 00016 3.674888e-01 5.999108e-01 -8.339410e-01 2.136905e-01 1.349543e+00 000014 00017 3.674888e-01 2.520884e-01 2.257113e+00 1.078957e-01 2.323318e-01 000014 00018 3.674888e-01 2.100566e+00 -3.183365e+00 2.337817e-01 1.608351e+00 000014 00019 3.674888e-01 2.094312e+00 -3.279189e+00 2.439228e-01 1.588121e+00 000015 00016 4.281391e+00 5.871626e-01 -8.339410e-01 1.307806e-02 1.570515e+00 000015 00017 4.281391e+00 1.199877e-01 2.257113e+00 1.293427e-02 1.554493e+00 000015 00018 4.281391e+00 2.099105e+00 -3.183365e+00 1.284467e-02 1.571979e+00 000015 00019 4.281391e+00 2.094006e+00 -3.279189e+00 1.236229e-02 1.568743e+00 000016 00017 -8.339410e-01 5.872681e-01 2.257113e+00 1.194545e-01 -1.936016e-02 000016 00018 -8.339410e-01 2.100637e+00 -3.183365e+00 5.816516e-01 1.610568e+00 000016 00019 -8.339410e-01 2.094007e+00 -3.279189e+00 5.871417e-01 1.568332e+00 000017 00018 2.257113e+00 2.100974e+00 -3.183365e+00 8.084557e-02 1.613031e+00 000017 00019 2.257113e+00 2.094139e+00 -3.279189e+00 1.175542e-01 1.582258e+00 000018 00019 -3.183365e+00 2.208920e+00 -3.279189e+00 1.977815e+00 7.743424e-01 DATA_CLASS 4 CLASS 4 - weight 17 normalized weight 0.080 relative strength 6.97e-06 ******* class cross entropy w.r.t. global class 1.72e+01 ******* REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 13 R MNcn Log Mg: Wt.% Magnesi 1.912 (-5.30e+00 2.04e-03) 2.55e+03 (-9.37e-02 2.60e+00) um oxide 00 17 R MNcn Log Ca: Wt.% Calcium 1.912 ( 2.48e+00 2.27e-01) 1.32e+00 ( 2.18e+00 1.45e-01) oxide 00 11 R MNcn Log RI: refractive i 1.912 (-6.49e-01 1.18e-02) 7.03e-01 (-6.57e-01 5.82e-03) ndex 00 14 R MNcn Log Al: Wt.% Aluminu 1.912 ( 6.73e-02 5.58e-01) 4.28e-01 ( 3.06e-01 3.70e-01) m oxide 00 16 R MNcn Log K: Wt.% Potassi 1.912 (-2.29e+00 2.32e+00) 3.50e-01 (-1.47e+00 1.74e+00) um oxide 00 12 R MNcn Log Na: Wt.% Sodium 1.912 ( 2.56e+00 1.27e-01) 2.93e-01 ( 2.59e+00 6.09e-02) oxide 00 18 R MNcn Log Ba: Wt.% Barium 1.912 (-4.91e+00 1.67e+00) 2.88e-01 (-4.43e+00 1.93e+00) oxide 00 15 R MNcn Log Si: Wt.% Silicon 1.912 ( 4.29e+00 2.25e-02) 9.96e-02 ( 4.29e+00 1.07e-02) oxide 00 19 R MNcn Log Fe: Wt.% Iron ox 1.912 (-4.24e+00 1.83e+00) 3.37e-02 (-4.18e+00 1.66e+00) ide DATA_CORR_MATRIX Correlation matrix (row & column indices are attribute numbers) 11 12 13 14 15 16 17 18 19 11 1.000 -0.618 0.000 0.252 -0.834 0.044 0.862 0.351 0.564 12 -0.618 1.000 0.000 -0.477 0.408 -0.279 -0.634 -0.369 -0.265 13 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 14 0.252 -0.477 0.000 1.000 -0.430 0.814 0.278 0.316 0.352 15 -0.834 0.408 0.000 -0.430 1.000 -0.249 -0.716 -0.479 -0.681 16 0.044 -0.279 0.000 0.814 -0.249 1.000 -0.002 0.196 0.294 17 0.862 -0.634 0.000 0.278 -0.716 -0.002 1.000 0.114 0.368 18 0.351 -0.369 0.000 0.316 -0.479 0.196 0.114 1.000 0.418 19 0.564 -0.265 0.000 0.352 -0.681 0.294 0.368 0.418 1.000 SIGMA CONTOURS att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000011 00012 -6.488007e-01 1.271575e-01 2.556812e+00 9.258100e-03 1.628484e+00 000011 00013 -6.488007e-01 1.179728e-02 -5.298305e+00 2.038216e-03 0.000000e+00 000011 00014 -6.488007e-01 5.578719e-01 6.730316e-02 1.141537e-02 1.565459e+00 000011 00015 -6.488007e-01 2.474241e-02 4.287856e+00 5.938086e-03 2.009099e+00 000011 00016 -6.488007e-01 2.319128e+00 -2.286049e+00 1.178974e-02 1.570574e+00 000011 00017 -6.488007e-01 2.274440e-01 2.480597e+00 5.982691e-03 1.526061e+00 000011 00018 -6.488007e-01 1.670608e+00 -4.913605e+00 1.104854e-02 1.568318e+00 000011 00019 -6.488007e-01 1.831233e+00 -4.237039e+00 9.741181e-03 1.567161e+00 000012 00013 2.556812e+00 1.269471e-01 -5.298305e+00 2.038063e-03 0.000000e+00 000012 00014 2.556812e+00 5.612820e-01 6.730316e-02 1.108612e-01 1.683442e+00 000012 00015 2.556812e+00 1.272892e-01 4.287856e+00 2.052556e-02 7.430550e-02 000012 00016 2.556812e+00 2.319398e+00 -2.286049e+00 1.218973e-01 1.586102e+00 000012 00017 2.556812e+00 2.436471e-01 2.480597e+00 9.153721e-02 1.970929e+00 000012 00018 2.556812e+00 1.671263e+00 -4.913605e+00 1.179308e-01 1.598986e+00 000012 00019 2.556812e+00 1.831532e+00 -4.237039e+00 1.223829e-01 1.589258e+00 000013 00014 -5.298305e+00 5.578640e-01 6.730316e-02 2.035319e-03 1.570796e+00 000013 00015 -5.298305e+00 2.254488e-02 4.287856e+00 2.038219e-03 1.570796e+00 000013 00016 -5.298305e+00 2.319127e+00 -2.286049e+00 2.071602e-03 1.570796e+00 000013 00017 -5.298305e+00 2.272166e-01 2.480597e+00 2.038063e-03 1.570796e+00 000013 00018 -5.298305e+00 1.670603e+00 -4.913605e+00 2.013235e-03 1.570796e+00 000013 00019 -5.298305e+00 1.831221e+00 -4.237039e+00 2.013235e-03 1.570796e+00 000014 00015 6.730316e-02 5.579484e-01 4.287856e+00 2.034843e-02 -1.741020e-02 000014 00016 6.730316e-02 2.363961e+00 -2.286049e+00 3.182024e-01 1.373914e+00 000014 00017 6.730316e-02 5.620595e-01 2.480597e+00 2.166302e-01 1.325567e-01 000014 00018 6.730316e-02 1.680873e+00 -4.913605e+00 5.261099e-01 1.454318e+00 000014 00019 6.730316e-02 1.842651e+00 -4.237039e+00 5.188655e-01 1.454638e+00 000015 00016 4.287856e+00 2.319134e+00 -2.286049e+00 2.183660e-02 1.573215e+00 000015 00017 4.287856e+00 2.277920e-01 2.480597e+00 1.569968e-02 1.642055e+00 000015 00018 4.287856e+00 1.670637e+00 -4.913605e+00 1.979196e-02 1.577262e+00 000015 00019 4.287856e+00 1.831286e+00 -4.237039e+00 1.650434e-02 1.579184e+00 000016 00017 -2.286049e+00 2.319128e+00 2.480597e+00 2.272156e-01 -1.861367e-04 000016 00018 -2.286049e+00 2.363240e+00 -4.913605e+00 1.607594e+00 2.654788e-01 000016 00019 -2.286049e+00 2.444230e+00 -4.237039e+00 1.660561e+00 4.449420e-01 000017 00018 2.480597e+00 1.670808e+00 -4.913605e+00 2.256995e-01 1.554962e+00 000017 00019 2.480597e+00 1.833159e+00 -4.237039e+00 2.110152e-01 1.524507e+00 000018 00019 -4.913605e+00 2.092861e+00 -4.237039e+00 1.328239e+00 8.937541e-01 autoclass-3.3.6.dfsg.1/data/glass/glassc.case-data-10000644000175000017500000001456011247310756020116 0ustar areare # CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS #DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 214 cases in # /home/tove/p/autoclass-c/data/glass/glassc.db2 # /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 # with log-A (approximate marginal likelihood) = -11434.215 # from classification results file # /home/tove/p/autoclass-c/data/glass/glassc.results-bin # and using models # /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 #DATA_CASE_TO_CLASS # Case # Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 1 1.000 002 1 0.837 0 0.163 003 1 0.999 004 0 0.999 005 0 0.997 1 0.003 006 0 0.999 007 0 0.997 1 0.003 008 0 0.995 1 0.005 009 0 0.988 1 0.012 010 0 0.999 011 0 0.999 1 0.001 012 0 0.999 013 0 0.994 1 0.006 014 0 0.997 1 0.003 015 0 0.999 016 0 0.999 017 0 0.999 018 1 1.000 019 1 1.000 020 0 0.988 1 0.012 021 0 0.997 1 0.003 022 1 1.000 023 0 0.999 024 0 0.999 025 0 0.969 1 0.031 026 0 0.998 1 0.002 027 0 0.999 028 0 0.999 029 0 0.999 030 0 0.999 031 0 0.999 032 0 0.999 033 3 1.000 034 0 0.999 035 0 0.999 036 0 0.998 1 0.002 037 3 1.000 038 0 0.999 1 0.001 039 1 1.000 040 1 1.000 041 0 0.997 1 0.003 042 0 0.999 043 0 0.998 1 0.002 044 1 1.000 045 0 0.998 1 0.002 046 0 0.981 1 0.019 047 0 0.990 1 0.010 048 1 1.000 049 1 1.000 050 0 0.959 1 0.041 051 1 1.000 052 0 0.957 1 0.043 053 1 0.999 054 1 0.999 055 1 0.998 3 0.002 056 1 0.992 3 0.008 057 0 0.992 1 0.008 058 0 0.999 059 0 0.996 1 0.004 060 0 0.997 1 0.003 061 1 1.000 062 3 0.999 063 1 0.999 064 1 1.000 065 1 0.999 066 1 1.000 067 1 0.999 068 1 0.999 069 1 0.999 070 1 1.000 071 1 1.000 072 0 0.995 1 0.005 073 0 0.999 1 0.001 074 0 0.998 1 0.002 075 0 0.996 1 0.004 076 0 0.999 1 0.001 077 0 0.998 1 0.002 078 0 0.999 079 1 0.999 080 0 0.997 1 0.003 081 0 0.983 1 0.017 082 0 0.993 1 0.007 083 0 0.983 1 0.017 084 0 0.998 1 0.002 085 1 0.999 086 1 0.880 0 0.120 087 1 1.000 088 0 0.994 1 0.006 089 0 0.999 090 0 0.996 1 0.004 091 0 0.965 1 0.035 092 1 0.997 0 0.003 093 1 1.000 094 1 1.000 095 0 0.998 1 0.002 096 0 0.981 1 0.019 097 0 0.873 1 0.127 098 0 0.983 1 0.017 099 1 0.995 0 0.005 100 3 1.000 101 3 1.000 102 1 0.996 3 0.004 103 1 0.997 3 0.003 104 1 1.000 105 1 0.999 106 4 1.000 107 4 1.000 108 4 1.000 109 4 1.000 110 4 1.000 111 4 1.000 112 4 1.000 113 4 1.000 114 0 0.998 1 0.002 115 0 0.993 1 0.007 116 0 0.992 1 0.008 117 0 0.997 1 0.003 118 0 0.991 1 0.009 119 0 0.999 1 0.001 120 0 0.997 1 0.003 121 0 0.999 122 0 0.999 123 0 0.998 1 0.002 124 0 0.996 1 0.004 125 0 0.999 1 0.001 126 0 0.998 1 0.002 127 0 0.999 128 3 0.994 1 0.006 129 3 1.000 130 3 1.000 131 3 1.000 132 4 1.000 133 0 0.994 1 0.006 134 0 0.986 1 0.014 135 0 0.995 1 0.005 136 0 0.998 1 0.002 137 0 0.998 1 0.002 138 0 0.999 139 0 0.999 140 0 0.999 141 0 0.997 1 0.003 142 3 1.000 143 3 1.000 144 0 0.998 1 0.002 145 0 0.882 1 0.118 146 0 0.999 147 1 1.000 148 0 0.999 1 0.001 149 0 0.999 1 0.001 150 0 0.999 151 0 0.996 1 0.004 152 1 1.000 153 1 1.000 154 0 0.992 1 0.008 155 0 0.999 1 0.001 156 0 0.991 1 0.009 157 0 0.986 1 0.014 158 1 1.000 159 0 0.983 1 0.017 160 0 0.959 1 0.041 161 0 0.942 1 0.058 162 3 1.000 163 1 0.999 164 2 1.000 165 3 0.999 166 3 0.999 167 3 1.000 168 4 1.000 169 4 1.000 170 4 1.000 171 4 1.000 172 2 1.000 173 2 1.000 174 4 1.000 175 3 1.000 176 3 1.000 177 2 1.000 178 2 0.999 179 2 1.000 180 2 1.000 181 2 1.000 182 2 1.000 183 2 0.999 184 4 1.000 185 4 1.000 186 2 1.000 187 2 1.000 188 0 0.979 1 0.021 189 3 1.000 190 3 1.000 191 2 1.000 192 2 1.000 193 2 0.999 194 2 1.000 195 2 1.000 196 2 0.999 197 2 0.997 4 0.003 198 2 1.000 199 2 0.999 200 2 0.999 201 2 1.000 202 4 1.000 203 2 1.000 204 2 1.000 205 2 1.000 206 2 1.000 207 2 1.000 208 2 0.996 4 0.004 209 2 1.000 210 2 0.999 211 2 1.000 212 2 1.000 213 2 1.000 214 2 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.case-text-20000644000175000017500000001474611247310756020200 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 214 cases in /home/tove/p/autoclass-c/data/glass/glassc.db2 /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 with log-A (approximate marginal likelihood) = -11187.745 from classification results file /home/tove/p/autoclass-c/data/glass/glassc.results-bin and using models /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 5 0.999 47 0 0.999 93 5 0.999 2 0 1.000 48 5 1.000 94 0 0.999 3 0 1.000 49 0 0.999 95 0 0.999 4 0 1.000 50 0 0.999 96 0 0.999 5 0 0.999 51 0 0.999 97 0 0.999 6 0 0.999 52 0 0.997 98 0 0.999 7 0 0.999 53 2 0.999 99 2 0.998 8 0 0.999 54 2 0.999 100 2 1.000 9 0 0.994 55 2 0.996 101 2 0.999 10 0 0.999 56 5 0.999 102 2 0.999 11 0 0.999 57 5 1.000 103 5 1.000 12 0 0.999 58 0 0.999 104 4 1.000 13 0 0.999 59 0 0.999 105 4 0.997 14 0 0.999 60 0 0.999 106 4 1.000 15 0 0.999 61 0 0.891 107 4 1.000 16 0 0.999 62 6 0.999 108 4 1.000 17 0 0.999 63 0 1.000 109 4 0.999 18 0 0.999 64 6 0.999 110 4 1.000 19 6 0.958 65 0 1.000 111 4 1.000 20 0 0.999 66 6 0.980 112 4 1.000 21 0 0.999 67 0 1.000 113 4 1.000 22 5 1.000 68 0 1.000 114 0 0.999 23 0 0.999 69 0 1.000 115 0 0.999 24 0 0.999 70 0 1.000 116 0 0.999 25 0 0.999 71 6 1.000 117 0 1.000 26 0 0.999 72 0 1.000 118 0 1.000 27 0 0.999 73 0 1.000 119 0 1.000 28 0 0.999 74 0 1.000 120 0 1.000 29 0 0.999 75 0 1.000 121 0 0.999 30 0 0.999 76 0 1.000 122 0 1.000 31 0 0.999 77 0 1.000 123 0 1.000 32 0 0.999 78 0 1.000 124 0 1.000 33 2 0.998 79 0 0.999 125 5 0.998 34 0 0.999 80 0 1.000 126 0 0.999 35 0 0.999 81 0 0.999 127 0 0.999 36 0 0.999 82 0 1.000 128 2 1.000 37 6 0.999 83 0 1.000 129 2 1.000 38 0 0.999 84 0 1.000 130 2 1.000 39 0 0.989 85 6 1.000 131 2 0.999 40 0 0.989 86 0 0.999 132 4 0.999 41 0 0.999 87 0 0.999 133 0 0.999 42 0 0.999 88 0 1.000 134 0 1.000 43 0 0.999 89 0 1.000 135 0 0.999 44 0 0.999 90 0 0.999 136 0 0.999 45 0 0.999 91 0 0.999 137 0 0.999 46 0 0.999 92 0 0.999 138 0 1.000 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 139 0 1.000 165 2 0.999 191 1 0.999 140 0 1.000 166 3 0.999 192 1 1.000 141 0 1.000 167 3 0.999 193 1 1.000 142 2 0.996 168 3 0.999 194 1 1.000 143 2 0.998 169 3 0.999 195 1 1.000 144 0 1.000 170 3 0.999 196 1 0.999 145 0 0.985 171 3 0.959 197 1 0.999 146 0 0.999 172 3 1.000 198 1 1.000 147 5 0.998 173 3 1.000 199 1 1.000 148 0 0.999 174 3 0.993 200 1 0.999 149 0 0.999 175 2 1.000 201 1 1.000 150 2 0.999 176 3 0.995 202 1 0.999 151 0 0.999 177 3 0.999 203 1 1.000 152 6 0.999 178 3 0.999 204 1 1.000 153 5 1.000 179 3 1.000 205 1 1.000 154 0 0.999 180 3 1.000 206 1 1.000 155 0 0.999 181 1 0.999 207 1 1.000 156 0 0.999 182 3 0.999 208 1 1.000 157 0 0.999 183 3 0.999 209 1 1.000 158 0 0.999 184 4 1.000 210 1 1.000 159 0 0.999 185 1 0.999 211 1 1.000 160 0 1.000 186 1 1.000 212 1 1.000 161 0 0.999 187 1 0.999 213 1 1.000 162 5 1.000 188 5 0.998 214 1 1.000 163 0 1.000 189 4 1.000 164 3 1.000 190 4 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc-predict.class-data-10000644000175000017500000000270011247310756021731 0ustar areare # CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP #DATA_CLSF_HEADER # AutoClass PREDICTION for the 10 "TEST" cases in # /home/tove/p/autoclass-c/data/glass/glassc-predict.db2 # based on the "TRAINING" classification of 214 cases in # /home/tove/p/autoclass-c/data/glass/glassc.db2 # /home/tove/p/autoclass-c/data/glass/glass-3c.hd2 # with log-A (approximate marginal likelihood) = -11434.215 # from classification results file # /home/tove/p/autoclass-c/data/glass/glassc.results-bin # and using models # /home/tove/p/autoclass-c/data/glass/glass-mnc.model - index = 0 DATA_CLASS 0 # CLASS = 0 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 0.999 002 0.988 003 0.999 005 0.959 007 0.997 008 0.999 DATA_CLASS 1 # CLASS = 1 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 004 1.000 DATA_CLASS 2 # CLASS = 2 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 010 1.000 DATA_CLASS 3 # CLASS = 3 # Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 006 1.000 009 1.000 autoclass-3.3.6.dfsg.1/data/glass/glassc.log0000644000175000017500000007066011247310756016722 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:05:25 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #10, "Type of glass": [ (0 1):70 (1 2):76 (2 3):17 (3 5):13 (4 6):9 (5 7):29 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "RI: refractive index": [ 1.5112e+00 < ( 1.5184e+00 : 9.1795e-06) < 1.5339e+00 ] Attribute #2, "Na: Wt.% Sodium oxide": [ 1.0730e+01 < ( 1.3408e+01 : 6.6373e-01) < 1.7380e+01 ] Attribute #3, "Mg: Wt.% Magnesium oxide": [ 0.0000e+00 < ( 2.6845e+00 : 2.0708e+00) < 4.4900e+00 ] Attribute #4, "Al: Wt.% Aluminum oxide": [ 2.9000e-01 < ( 1.4449e+00 : 2.4811e-01) < 3.5000e+00 ] Attribute #5, "Si: Wt.% Silicon oxide": [ 6.9810e+01 < ( 7.2651e+01 : 5.9712e-01) < 7.5410e+01 ] Attribute #6, " K: Wt.% Potassium oxide": [ 0.0000e+00 < ( 4.9706e-01 : 4.2337e-01) < 6.2100e+00 ] Attribute #7, "Ca: Wt.% Calcium oxide": [ 5.4300e+00 < ( 8.9570e+00 : 2.0159e+00) < 1.6190e+01 ] Attribute #8, "Ba: Wt.% Barium oxide": [ 0.0000e+00 < ( 1.7505e-01 : 2.4607e-01) < 3.1500e+00 ] Attribute #9, "Fe: Wt.% Iron oxide": [ 0.0000e+00 < ( 5.7009e-02 : 9.4499e-03) < 5.1000e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model ADVISORY[2]: log_transform is being applied to attribute #1: "RI: refractive index" and will be stored as attribute #11. Attribute #11, "Log RI: refractive index": [ -6.7109e-01 < (-6.5709e-01 : 3.3848e-05) < -6.2749e-01 ] ADVISORY[2]: log_transform is being applied to attribute #2: "Na: Wt.% Sodium oxide" and will be stored as attribute #12. Attribute #12, "Log Na: Wt.% Sodium oxide": [ 2.3730e+00 < ( 2.5940e+00 : 3.6577e-03) < 2.8553e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "Mg: Wt.% Magnesium oxide" and will be stored as attribute #13. Attribute #13, "Log Mg: Wt.% Magnesium oxide": [ -5.2983e+00 < (-9.3701e-02 : 6.6834e+00) < 1.5030e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "Al: Wt.% Aluminum oxide" and will be stored as attribute #14. Attribute #14, "Log Al: Wt.% Aluminum oxide": [ -1.2379e+00 < ( 3.0602e-01 : 1.3473e-01) < 1.2528e+00 ] ADVISORY[2]: log_transform is being applied to attribute #5: "Si: Wt.% Silicon oxide" and will be stored as attribute #15. Attribute #15, "Log Si: Wt.% Silicon oxide": [ 4.2458e+00 < ( 4.2856e+00 : 1.1407e-04) < 4.3229e+00 ] ADVISORY[2]: log_transform is being applied to attribute #6: " K: Wt.% Potassium oxide" and will be stored as attribute #16. Attribute #16, "Log K: Wt.% Potassium oxide": [ -5.2984e+00 < (-1.4740e+00 : 2.9845e+00) < 1.8270e+00 ] ADVISORY[2]: log_transform is being applied to attribute #7: "Ca: Wt.% Calcium oxide" and will be stored as attribute #17. Attribute #17, "Log Ca: Wt.% Calcium oxide": [ 1.6919e+00 < ( 2.1815e+00 : 2.0663e-02) < 2.7844e+00 ] ADVISORY[2]: log_transform is being applied to attribute #8: "Ba: Wt.% Barium oxide" and will be stored as attribute #18. Attribute #18, "Log Ba: Wt.% Barium oxide": [ -5.2981e+00 < (-4.4321e+00 : 3.6644e+00) < 1.1490e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "Fe: Wt.% Iron oxide" and will be stored as attribute #19. Attribute #19, "Log Fe: Wt.% Iron oxide": [ -5.2982e+00 < (-4.1753e+00 : 2.7054e+00) < -6.6359e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log BEGINNING SEARCH at Thu Jun 7 12:05:25 2001 [j_in=2] [cs-3: cycles 9] best2->2(1) [j_in=3] [cs-3: cycles 14] best3->3(2) [j_in=5] [cs-3: cycles 32] best5->5(3) [j_in=7] [cs-3: cycles 12] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:05:25 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.745) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.516) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.431) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 32 max_cycles 200 convergent try 4 num_cycles 12 max_cycles 200 convergent try 2 num_cycles 14 max_cycles 200 convergent try 1 num_cycles 9 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:05:25 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:07:38 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #10, "Type of glass": [ (0 1):70 (1 2):76 (2 3):17 (3 5):13 (4 6):9 (5 7):29 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "RI: refractive index": [ 1.5112e+00 < ( 1.5184e+00 : 9.1795e-06) < 1.5339e+00 ] Attribute #2, "Na: Wt.% Sodium oxide": [ 1.0730e+01 < ( 1.3408e+01 : 6.6373e-01) < 1.7380e+01 ] Attribute #3, "Mg: Wt.% Magnesium oxide": [ 0.0000e+00 < ( 2.6845e+00 : 2.0708e+00) < 4.4900e+00 ] Attribute #4, "Al: Wt.% Aluminum oxide": [ 2.9000e-01 < ( 1.4449e+00 : 2.4811e-01) < 3.5000e+00 ] Attribute #5, "Si: Wt.% Silicon oxide": [ 6.9810e+01 < ( 7.2651e+01 : 5.9712e-01) < 7.5410e+01 ] Attribute #6, " K: Wt.% Potassium oxide": [ 0.0000e+00 < ( 4.9706e-01 : 4.2337e-01) < 6.2100e+00 ] Attribute #7, "Ca: Wt.% Calcium oxide": [ 5.4300e+00 < ( 8.9570e+00 : 2.0159e+00) < 1.6190e+01 ] Attribute #8, "Ba: Wt.% Barium oxide": [ 0.0000e+00 < ( 1.7505e-01 : 2.4607e-01) < 3.1500e+00 ] Attribute #9, "Fe: Wt.% Iron oxide": [ 0.0000e+00 < ( 5.7009e-02 : 9.4499e-03) < 5.1000e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model ADVISORY[2]: log_transform is being applied to attribute #1: "RI: refractive index" and will be stored as attribute #11. Attribute #11, "Log RI: refractive index": [ -6.7109e-01 < (-6.5709e-01 : 3.3848e-05) < -6.2749e-01 ] ADVISORY[2]: log_transform is being applied to attribute #2: "Na: Wt.% Sodium oxide" and will be stored as attribute #12. Attribute #12, "Log Na: Wt.% Sodium oxide": [ 2.3730e+00 < ( 2.5940e+00 : 3.6577e-03) < 2.8553e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "Mg: Wt.% Magnesium oxide" and will be stored as attribute #13. Attribute #13, "Log Mg: Wt.% Magnesium oxide": [ -5.2983e+00 < (-9.3701e-02 : 6.6834e+00) < 1.5030e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "Al: Wt.% Aluminum oxide" and will be stored as attribute #14. Attribute #14, "Log Al: Wt.% Aluminum oxide": [ -1.2379e+00 < ( 3.0602e-01 : 1.3473e-01) < 1.2528e+00 ] ADVISORY[2]: log_transform is being applied to attribute #5: "Si: Wt.% Silicon oxide" and will be stored as attribute #15. Attribute #15, "Log Si: Wt.% Silicon oxide": [ 4.2458e+00 < ( 4.2856e+00 : 1.1407e-04) < 4.3229e+00 ] ADVISORY[2]: log_transform is being applied to attribute #6: " K: Wt.% Potassium oxide" and will be stored as attribute #16. Attribute #16, "Log K: Wt.% Potassium oxide": [ -5.2984e+00 < (-1.4740e+00 : 2.9845e+00) < 1.8270e+00 ] ADVISORY[2]: log_transform is being applied to attribute #7: "Ca: Wt.% Calcium oxide" and will be stored as attribute #17. Attribute #17, "Log Ca: Wt.% Calcium oxide": [ 1.6919e+00 < ( 2.1815e+00 : 2.0663e-02) < 2.7844e+00 ] ADVISORY[2]: log_transform is being applied to attribute #8: "Ba: Wt.% Barium oxide" and will be stored as attribute #18. Attribute #18, "Log Ba: Wt.% Barium oxide": [ -5.2981e+00 < (-4.4321e+00 : 3.6644e+00) < 1.1490e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "Fe: Wt.% Iron oxide" and will be stored as attribute #19. Attribute #19, "Log Fe: Wt.% Iron oxide": [ -5.2982e+00 < (-4.1753e+00 : 2.7054e+00) < -6.6359e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log BEGINNING SEARCH at Thu Jun 7 12:07:38 2001 [j_in=2] [cs-3: cycles 7] best2->2(1) [j_in=3] [cs-3: cycles 11] best3->3(2) [j_in=5] [cs-3: cycles 20] best5->5(3) [j_in=7] [cs-3: cycles 10] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:07:39 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10907.367) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.751) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.554) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.418) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 20 max_cycles 200 convergent try 4 num_cycles 10 max_cycles 200 convergent try 2 num_cycles 11 max_cycles 200 convergent try 1 num_cycles 7 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:07:39 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:09:20 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #10, "Type of glass": [ (0 1):70 (1 2):76 (2 3):17 (3 5):13 (4 6):9 (5 7):29 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "RI: refractive index": [ 1.5112e+00 < ( 1.5184e+00 : 9.1795e-06) < 1.5339e+00 ] Attribute #2, "Na: Wt.% Sodium oxide": [ 1.0730e+01 < ( 1.3408e+01 : 6.6373e-01) < 1.7380e+01 ] Attribute #3, "Mg: Wt.% Magnesium oxide": [ 0.0000e+00 < ( 2.6845e+00 : 2.0708e+00) < 4.4900e+00 ] Attribute #4, "Al: Wt.% Aluminum oxide": [ 2.9000e-01 < ( 1.4449e+00 : 2.4811e-01) < 3.5000e+00 ] Attribute #5, "Si: Wt.% Silicon oxide": [ 6.9810e+01 < ( 7.2651e+01 : 5.9712e-01) < 7.5410e+01 ] Attribute #6, " K: Wt.% Potassium oxide": [ 0.0000e+00 < ( 4.9706e-01 : 4.2337e-01) < 6.2100e+00 ] Attribute #7, "Ca: Wt.% Calcium oxide": [ 5.4300e+00 < ( 8.9570e+00 : 2.0159e+00) < 1.6190e+01 ] Attribute #8, "Ba: Wt.% Barium oxide": [ 0.0000e+00 < ( 1.7505e-01 : 2.4607e-01) < 3.1500e+00 ] Attribute #9, "Fe: Wt.% Iron oxide": [ 0.0000e+00 < ( 5.7009e-02 : 9.4499e-03) < 5.1000e-01 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model ADVISORY[2]: log_transform is being applied to attribute #1: "RI: refractive index" and will be stored as attribute #11. Attribute #11, "Log RI: refractive index": [ -6.7109e-01 < (-6.5709e-01 : 3.3848e-05) < -6.2749e-01 ] ADVISORY[2]: log_transform is being applied to attribute #2: "Na: Wt.% Sodium oxide" and will be stored as attribute #12. Attribute #12, "Log Na: Wt.% Sodium oxide": [ 2.3730e+00 < ( 2.5940e+00 : 3.6577e-03) < 2.8553e+00 ] ADVISORY[2]: log_transform is being applied to attribute #3: "Mg: Wt.% Magnesium oxide" and will be stored as attribute #13. Attribute #13, "Log Mg: Wt.% Magnesium oxide": [ -5.2983e+00 < (-9.3701e-02 : 6.6834e+00) < 1.5030e+00 ] ADVISORY[2]: log_transform is being applied to attribute #4: "Al: Wt.% Aluminum oxide" and will be stored as attribute #14. Attribute #14, "Log Al: Wt.% Aluminum oxide": [ -1.2379e+00 < ( 3.0602e-01 : 1.3473e-01) < 1.2528e+00 ] ADVISORY[2]: log_transform is being applied to attribute #5: "Si: Wt.% Silicon oxide" and will be stored as attribute #15. Attribute #15, "Log Si: Wt.% Silicon oxide": [ 4.2458e+00 < ( 4.2856e+00 : 1.1407e-04) < 4.3229e+00 ] ADVISORY[2]: log_transform is being applied to attribute #6: " K: Wt.% Potassium oxide" and will be stored as attribute #16. Attribute #16, "Log K: Wt.% Potassium oxide": [ -5.2984e+00 < (-1.4740e+00 : 2.9845e+00) < 1.8270e+00 ] ADVISORY[2]: log_transform is being applied to attribute #7: "Ca: Wt.% Calcium oxide" and will be stored as attribute #17. Attribute #17, "Log Ca: Wt.% Calcium oxide": [ 1.6919e+00 < ( 2.1815e+00 : 2.0663e-02) < 2.7844e+00 ] ADVISORY[2]: log_transform is being applied to attribute #8: "Ba: Wt.% Barium oxide" and will be stored as attribute #18. Attribute #18, "Log Ba: Wt.% Barium oxide": [ -5.2981e+00 < (-4.4321e+00 : 3.6644e+00) < 1.1490e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "Fe: Wt.% Iron oxide" and will be stored as attribute #19. Attribute #19, "Log Fe: Wt.% Iron oxide": [ -5.2982e+00 < (-4.1753e+00 : 2.7054e+00) < -6.6359e-01 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log BEGINNING SEARCH at Thu Jun 7 12:09:21 2001 [j_in=2] [cs-4: cycles 10] best2->2(1) [j_in=3] [cs-4: cycles 15] best3->3(2) [j_in=5] [cs-4: cycles 32] best5->5(3) [j_in=7] [cs-4: cycles 13] 7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:09:22 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-10897.738) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-11187.745) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-11229.516) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-11236.432) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 3 num_cycles 32 max_cycles 200 convergent try 4 num_cycles 13 max_cycles 200 convergent try 2 num_cycles 15 max_cycles 200 convergent try 1 num_cycles 10 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:09:22 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:10:32 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 214 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 11 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-3c.hd2 ADVISORY[1]: read 10 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glass-mnc.model ADVISORY[2]: log_transform is being applied to attribute #1: "RI: refractive index" and will be stored as attribute #11. ADVISORY[2]: log_transform is being applied to attribute #2: "Na: Wt.% Sodium oxide" and will be stored as attribute #12. ADVISORY[2]: log_transform is being applied to attribute #3: "Mg: Wt.% Magnesium oxide" and will be stored as attribute #13. ADVISORY[2]: log_transform is being applied to attribute #4: "Al: Wt.% Aluminum oxide" and will be stored as attribute #14. ADVISORY[2]: log_transform is being applied to attribute #5: "Si: Wt.% Silicon oxide" and will be stored as attribute #15. ADVISORY[2]: log_transform is being applied to attribute #6: " K: Wt.% Potassium oxide" and will be stored as attribute #16. ADVISORY[2]: log_transform is being applied to attribute #7: "Ca: Wt.% Calcium oxide" and will be stored as attribute #17. ADVISORY[2]: log_transform is being applied to attribute #8: "Ba: Wt.% Barium oxide" and will be stored as attribute #18. ADVISORY[2]: log_transform is being applied to attribute #9: "Fe: Wt.% Iron oxide" and will be stored as attribute #19. ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/glass/glassc-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:10:32 2001 autoclass-3.3.6.dfsg.1/data/glass/glass-mnc.model0000644000175000017500000000065011247310756017641 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;; This is ics.uci.edu:/pub/machine-learning-databases/glass/glass.data ;; 214 instances, 11 attributes ;; 1 or more model definitions ;; model_index model_index 0 2 multi_normal_cn 1 2 3 4 5 6 7 8 9 ignore 0 10 autoclass-3.3.6.dfsg.1/data/3-dim/0000755000175000017500000000000011667631535014541 5ustar areareautoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.results0000644000175000017500000001527111247310756017237 0ustar areare# ordered sequence of clsf_DS's: 0 -> 1 # clsf_DS 0: log_a_x_h = -4.2556230e+03 # clsf_DS 1: log_a_x_h = -4.2573228e+03 ac_version 3.3.5unx clsf_DS 0 log_p_x_h_pi_theta, log_a_x_h -4.2127875e+03 -4.2556230e+03 database_DS data_file, header_file data/3-dim/3-dimc.db2 data/3-dim/3-dimc.hd2 n_data, n_atts, input_n_atts 100 3 3 att_DS 0 type, subtype, dscrp real location "X_coordinate" real_stats_DS count, max, min, mean, var 100 2.3584290e+01 -1.5994501e+01 3.6691794e+00 8.0234909e+01 n_props, range, zero_point, n_trans 0 0 0.000000 0 translations_DS 0 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 9.9999997e-06 0 att_DS 1 type, subtype, dscrp real location "Y_coordinate" real_stats_DS count, max, min, mean, var 100 1.4270947e+01 -8.2093115e+00 2.7497025e+00 3.1099710e+01 n_props, range, zero_point, n_trans 0 0 0.000000 0 translations_DS 0 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 9.9999997e-06 0 att_DS 2 type, subtype, dscrp real location "Z_coordinate" real_stats_DS count, max, min, mean, var 100 2.5810928e+00 -2.5362384e+00 6.1429828e-02 9.2657948e-01 n_props, range, zero_point, n_trans 0 0 0.000000 0 translations_DS 0 props_DS 0 warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL NULL 0 0 rel_error, error, missing 0.0000000e+00 9.9999997e-06 0 num_models 1 model_DS 0 id, file_index MODEL-0 0 model_file data/3-dim/3-dimc.model data_file, header_file, n_data data/3-dim/3-dimc.db2 data/3-dim/3-dimc.hd2 100 n_classes 2 class_DS 0 w_j, pi_j 4.1464123e+01 4.1548637e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -8.7830549e-01 -1.7811032e+03 -1.7643246e+03 known_parms_p, num_tparms 0 1 tparm_DS 0 n_atts, tppt(type) 3 4 mn_cn_params ln_root log_ranges -3.4431796e+00 8.4235640e+00 emp_means 1.3498044e+00 -1.0699624e+00 2.6782152e-01 emp_covar row 0 9.3848335e+01 3.9686771e+01 7.5130212e-01 row 1 3.9686771e+01 2.2802656e+01 9.4851720e-01 row 2 7.5130224e-01 9.4851714e-01 1.2938308e+00 means 1.3498044e+00 -1.0699624e+00 2.6782152e-01 covariance row 0 1.0098254e+02 4.1698055e+01 7.8937733e-01 row 1 4.1698055e+01 2.4536077e+01 9.9658710e-01 row 2 7.8937745e-01 9.9658704e-01 1.3921857e+00 factor row 0 1.0098254e+02 4.1698055e+01 7.8937733e-01 row 1 4.1292343e-01 7.3179736e+00 6.7063469e-01 row 2 7.8169703e-03 9.1642112e-02 1.3245568e+00 min_sigma_2s 9.9999994e-11 9.9999994e-11 9.9999994e-11 n_term, n_att, n_att_indices, n_datum, n_data 0 0 3 0 100 w_j, ranges, class_wt, disc_scale 4.1464123e+01 0.0000000e+00 4.1464123e+01 2.3549292e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 -3.4538776e+01 -3.7295593e+01 0.0000000e+00 0.0000000e+00 num_wts 100 model_DS_ptr 0 class_DS 1 w_j, pi_j 5.8535877e+01 5.8451366e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -5.3697515e-01 -2.4652070e+03 -2.4551331e+03 known_parms_p, num_tparms 0 1 tparm_DS 0 n_atts, tppt(type) 3 4 mn_cn_params ln_root log_ranges -2.8842058e+00 8.4235640e+00 emp_means 5.3121185e+00 5.4553771e+00 -8.4768571e-02 emp_covar row 0 6.4081947e+01 2.7813499e+01 1.9040642e+00 row 1 2.7813499e+01 1.9321522e+01 7.1098280e-01 row 2 1.9040642e+00 7.1098280e-01 6.1488724e-01 means 5.3121185e+00 5.4553771e+00 -8.4768571e-02 covariance row 0 6.7482368e+01 2.8797422e+01 1.9714218e+00 row 1 2.8797422e+01 2.0346790e+01 7.3613435e-01 row 2 1.9714218e+00 7.3613435e-01 6.4751536e-01 factor row 0 6.7482368e+01 2.8797422e+01 1.9714218e+00 row 1 4.2673996e-01 8.0577793e+00 -1.0515013e-01 row 2 2.9213881e-02 -1.3049517e-02 5.8855033e-01 min_sigma_2s 9.9999994e-11 9.9999994e-11 9.9999994e-11 n_term, n_att, n_att_indices, n_datum, n_data 0 0 3 0 100 w_j, ranges, class_wt, disc_scale 5.8535877e+01 0.0000000e+00 5.8535877e+01 1.6796594e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 -3.4538776e+01 -3.7295593e+01 0.0000000e+00 0.0000000e+00 num_wts 100 model_DS_ptr 0 min_class_wt 2.0999999 chkpt_DS accumulated_try_time, current_try_j_in, current_cycle 0 0 0 clsf_DS 1 log_p_x_h_pi_theta, log_a_x_h -4.2153761e+03 -4.2573228e+03 database_DS_ptr num_models 1 model_DS_ptr 0 n_classes 2 class_DS 0 w_j, pi_j 6.5299828e+01 6.5148342e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -4.2850333e-01 -2.7590146e+03 -2.7525334e+03 known_parms_p, num_tparms 0 1 tparm_DS 0 n_atts, tppt(type) 3 4 mn_cn_params ln_root log_ranges -3.1156652e+00 8.4235640e+00 emp_means 4.5151072e+00 3.6829031e+00 -3.7732077e-01 emp_covar row 0 7.2460709e+01 3.5099854e+01 1.7822133e+00 row 1 3.5099854e+01 2.9329479e+01 1.3417912e+00 row 2 1.7822132e+00 1.3417912e+00 5.3651321e-01 means 4.5151072e+00 3.6829031e+00 -3.7732077e-01 covariance row 0 7.5894875e+01 3.6208858e+01 1.8385236e+00 row 1 3.6208858e+01 3.0719507e+01 1.3841860e+00 row 2 1.8385235e+00 1.3841860e+00 5.6194043e-01 factor row 0 7.5894875e+01 3.6208858e+01 1.8385236e+00 row 1 4.7709227e-01 1.3444541e+01 5.0704062e-01 row 2 2.4224607e-02 3.7713498e-02 4.9828067e-01 min_sigma_2s 9.9999994e-11 9.9999994e-11 9.9999994e-11 n_term, n_att, n_att_indices, n_datum, n_data 0 0 3 0 100 w_j, ranges, class_wt, disc_scale 6.5299828e+01 0.0000000e+00 6.5299828e+01 1.5082995e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 -3.4538776e+01 -3.7295593e+01 0.0000000e+00 0.0000000e+00 num_wts 100 model_DS_ptr 0 class_DS 1 w_j, pi_j 3.4700176e+01 3.4851658e-01 log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j -1.0540695e+00 -1.4919659e+03 -1.4739988e+03 known_parms_p, num_tparms 0 1 tparm_DS 0 n_atts, tppt(type) 3 4 mn_cn_params ln_root log_ranges -3.3195086e+00 8.4235640e+00 emp_means 2.0772872e+00 9.9357754e-01 8.8708383e-01 emp_covar row 0 9.0983841e+01 4.2093201e+01 1.7914997e+00 row 1 4.2093201e+01 2.9708189e+01 4.1890052e-01 row 2 1.7914997e+00 4.1890046e-01 6.1665660e-01 means 2.0772872e+00 9.9357754e-01 8.8708383e-01 covariance row 0 9.9330933e+01 4.4667694e+01 1.9010710e+00 row 1 4.4667694e+01 3.2433697e+01 4.4452122e-01 row 2 1.9010710e+00 4.4452116e-01 6.7323029e-01 factor row 0 9.9330933e+01 4.4667694e+01 1.9010710e+00 row 1 4.4968563e-01 1.2347277e+01 -4.1036308e-01 row 2 1.9138761e-02 -3.3235114e-02 6.2320769e-01 min_sigma_2s 9.9999994e-11 9.9999994e-11 9.9999994e-11 n_term, n_att, n_att_indices, n_datum, n_data 0 0 3 0 100 w_j, ranges, class_wt, disc_scale 3.4700176e+01 0.0000000e+00 3.4700176e+01 2.8011067e-02 log_pi, log_att_delta, log_delta, wt_m, log_marginal 0.0000000e+00 -3.4538776e+01 -3.7295593e+01 0.0000000e+00 0.0000000e+00 num_wts 100 model_DS_ptr 0 min_class_wt 2.0999999 chkpt_DS accumulated_try_time, current_try_j_in, current_cycle 0 0 0 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.model0000644000175000017500000000223011247310756016625 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;(let* ((n-data 250) ; (db (gen-formatted-data `((,n-data ((0.0 9.0) (0.0 3.0) (0.0 1.0))) ; (,n-data ((0.0 9.0) (0.0 1.5) (0.0 1.0))) ; (,n-data ((0.0 2.0) (0.0 1.0) (0.0 1.0))) ; (,n-data ((0.0 0.5) (0.0 0.5) (0.0 1.0)))) ; )) ; (total-data (cadr db)) ; (data (map 'vector #'(lambda (x) (coerce x 'vector)) (cddr db)))) ; (Rotate-Data data (/ *single-pi* 6.0) :start 0 :end (* 1 n-data)) ; (Rotate-Data data (/ *single-pi* -4.0) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(5.0 3.5 nil) :start 0 :end (* 1 n-data)) ; (Shift-Data data #(3.0 4.5 nil) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(8.0 5.0 nil) :start (* 2 n-data) :end (* 3 n-data)) ; (Shift-Data data #(4.0 1.0 nil) :start (* 3 n-data)) ; (format nil "; ~A data~2%~A" total-data data)) ; 100 Data, 3 attributes ;; 1 or more model definitions ;; model_index model_index 0 1 multi_normal_cn 0 1 2 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.db20000644000175000017500000001062611247310756016204 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ; Two dimensional distribution generated by: ;(let* ((n-data 250) ; (db (gen-formatted-data `((,n-data ((0.0 9.0) (0.0 3.0) (0.0 1.0))) ; (,n-data ((0.0 9.0) (0.0 1.5) (0.0 1.0))) ; (,n-data ((0.0 2.0) (0.0 1.0) (0.0 1.0))) ; (,n-data ((0.0 0.5) (0.0 0.5) (0.0 1.0)))) ; )) ; (total-data (cadr db)) ; (data (map 'vector #'(lambda (x) (coerce x 'vector)) (cddr db)))) ; (Rotate-Data data (/ *single-pi* 6.0) :start 0 :end (* 1 n-data)) ; (Rotate-Data data (/ *single-pi* -4.0) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(5.0 3.5 nil) :start 0 :end (* 1 n-data)) ; (Shift-Data data #(3.0 4.5 nil) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(8.0 5.0 nil) :start (* 2 n-data) :end (* 3 n-data)) ; (Shift-Data data #(4.0 1.0 nil) :start (* 3 n-data)) (format nil "; ~A data~2%~A" total-data data)) ; 100 Data, 3 attributes 7.951312 5.690361 -0.75982195 -1.3695183 2.334321 -0.53833467 6.2042465 3.0605597 0.4272124 0.6274195 7.259904 -1.8430382 2.0255766 0.5444553 -0.5050058 -1.1098604 -3.5191784 -0.43494883 9.138485 8.578201 1.3337282 -8.283234 -0.6700516 0.6567967 9.774305 10.41371 -0.23650117 9.134876 6.049179 1.859805 6.175076 -0.003753662 0.4494005 -3.7194166 -3.8287969 1.0713747 -7.852168 1.5780549 -0.86744237 1.4980352 -1.8360863 2.045566 6.045492 5.9253244 -0.08853189 6.8769097 7.200096 -0.7966329 -2.751563 6.012211 -0.15060054 -1.13552 -1.0574231 -1.1535416 13.435176 7.7271485 0.29232067 10.016619 10.84547 -0.9010529 -9.2561865 -5.5462046 0.5760337 -4.3650055 -5.5392675 -0.59126216 -13.002132 -6.8768253 0.0053548147 -0.20501232 -1.3630047 1.5073024 0.9362278 -2.604473 -0.91468656 11.816528 8.463099 -0.4294865 -4.377281 -0.5509634 0.27032024 4.873208 9.632997 5.6278385e-4 -8.746294 -6.6707335 -0.5796882 17.24001 9.96601 0.8749883 -3.3427677 1.3219748 -1.0413249 11.915267 11.970855 0.67498475 0.4102912 -2.740281 -1.8189163 -4.4644365 5.288473 1.069092 -9.914025 -7.0774946 -0.046298236 6.7088933 1.7932739 0.26689765 -0.46714926 -2.1242514 1.3675662 -1.7784338 1.2004709 1.0439152 -3.085495 1.576549 1.382772 -15.994501 -8.135718 0.80787647 15.498349 5.684474 -0.06649601 0.82385445 -6.1690273 1.8856454 -7.4469347 -0.46325302 -1.4907236 19.773502 2.9479136 -1.4481831 7.3303595 -1.7889595 0.88229054 -13.759708 -5.2984715 1.0254989 -6.8384714 4.658766 -1.1285433 -1.174581 -0.8683076 0.03853487 -3.2183828 0.76212406 0.15420344 -3.768199 2.550647 -0.49896044 20.956896 7.931507 -0.3157244 15.09057 4.1050596 1.113673 5.7089353 6.4998455 -0.43293402 10.046911 3.8150883 -0.039431136 13.268089 10.262918 -0.40493596 8.019163 1.6235664 -0.05664601 8.543041 2.7547326 -0.25572217 -2.8785658 -8.2093115 -0.16097406 5.166216 2.6615896 2.5810928 -11.673483 -7.674114 -0.23295523 16.104584 2.7005606 1.0004565 3.1933324 -1.0191588 0.64302784 7.9011726 5.850755 -0.6312827 4.9305663 8.202309 -0.5484908 12.969362 7.1007133 1.4084051 19.555374 12.559574 0.14021482 10.577393 8.547583 0.5485992 23.58429 9.731125 -0.8878539 0.23407125 -4.7910557 -2.5362384 1.4615703 -0.4979577 -0.7849662 17.89262 7.2994184 2.0382276 4.684598 8.653381 -0.79996 -8.471826 -6.0668564 -1.5666307 6.0448985 5.1200314 -0.6270158 13.633059 13.744501 0.9549455 15.975548 12.700053 0.5293713 5.13043 5.04814 -0.33016968 -12.678328 -7.702018 1.3827145 -0.9751935 2.4671507 -0.93602544 15.407906 8.627243 -0.74877954 -6.671095 0.104660034 -0.757127 2.0521111 3.8031554 0.72480845 21.655602 14.270947 0.9439139 10.142155 1.7463455 -0.021443013 3.8023899 -0.39892864 -0.8445116 -0.13447571 2.7754488 0.08105283 -4.400223 -0.7874036 0.6308106 -4.6101933 2.984815 -0.8051411 -2.1147718 2.9249349 0.2913745 3.5484169 2.5386896 0.026939131 2.5796793 4.0973506 1.3317822 0.3462267 1.2038426 -0.64918035 10.0413265 8.869856 0.06998943 17.353739 4.3556833 1.6768079 3.9950674 6.127659 0.19470645 9.530268 7.562671 1.3614054 9.152258 5.157537 0.19134295 22.12666 14.182836 -0.41267285 -7.5192833 -6.496298 -0.77933353 1.8351529 1.5959675 -0.7965551 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.results-bin0000644000175000017500000001060511247310756020001 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -4.2556230e+03(# clsf_DS 1: log_a_x_h = -4.2573228e+03ac_version 3.3.5unx 4L~t79z0? h X ff@H  |data/3-dim/3-dimc.db29 ? `*data/3-dim/3-dimc.hd2d2@ G W  ;? reallocationX_coordinateC C '7dAzj@FxBlNULLNULL reallocationY_coordinate(; E '7dUdAWY /@5AlNULLNULL reallocationZ_coordinate(F @F '7d0%@Q"؝{=P4m?lNULLNULL LMODEL-0j`жiԛ`Lu dl  ]\A@v Pv `v pv v v v v v me/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.dC%BC%Br<' °. l k G  dƬ?> YBABVU@? 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F@[~?c? pB2BKV? 2BBI> KV?G>X,? pB2BKV? 2=>rEAҾ Ȝ #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! a segmentation fault. If specified as false, AutoClass will not attempt ! to free storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ;; converge_print_p = true ;; min_report_period = 0 ;; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc-predict.case-text-10000644000175000017500000000213611247310756021215 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.db2 based on the "TRAINING" classification of 100 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 with log-A (approximate marginal likelihood) = -4255.622 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 1 0.556 5 0 0.960 9 0 0.767 2 0 0.997 6 1 0.922 10 0 0.761 3 0 0.894 7 1 0.559 4 1 0.979 8 0 0.891 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.r-params0000644000175000017500000001126711247310756017261 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! core dump. If specified as false, AutoClass will not attempt to free ! storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 ;; report_mode = "data" ;; comment_data_headers_p = true ;; sigma_contours_att_list = 0, 1, 2 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.rlog0000644000175000017500000000674611247310756016510 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:58:22 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:58:22 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 14:38:51 2002 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY: read 2 classifications from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.search File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.influ-o-text-1 File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 14:38:51 2002 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 18:39:56 2002 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.search File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.influ-o-text-1 File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 18:39:56 2002 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.class-text-10000644000175000017500000001332211247310756017756 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 100 cases in /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 with log-A (approximate marginal likelihood) = -4255.623 from classification results file /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 0.919 1 0.081 2 0.923 1 0.077 3 0.662 1 0.338 4 0.998 1 0.002 5 0.592 1 0.408 7 0.913 1 0.087 8 0.550 1 0.450 9 0.996 1 0.004 13 0.973 1 0.027 15 0.954 1 0.046 16 0.982 1 0.018 17 0.995 1 0.005 19 0.890 1 0.110 20 0.997 1 0.003 26 0.966 1 0.034 27 0.602 1 0.398 28 0.998 1 0.002 30 0.894 1 0.106 31 0.913 1 0.087 32 0.994 1 0.006 34 0.938 1 0.062 41 0.535 1 0.465 43 0.881 1 0.119 47 0.998 1 0.002 49 0.771 1 0.229 50 0.960 1 0.040 53 0.976 1 0.024 54 0.615 1 0.385 55 0.986 1 0.014 57 0.543 1 0.457 63 0.930 1 0.070 64 0.995 1 0.005 65 0.653 1 0.347 66 0.977 1 0.023 67 0.961 1 0.039 72 0.997 1 0.003 74 0.931 1 0.069 75 0.996 1 0.004 76 0.992 1 0.008 77 0.940 1 0.060 79 0.930 1 0.070 CLASS = 0 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 80 0.891 1 0.109 81 0.896 1 0.104 82 0.827 1 0.173 83 0.978 1 0.022 86 0.889 1 0.111 88 0.980 1 0.020 89 0.909 1 0.091 90 0.767 1 0.233 91 0.643 1 0.357 92 0.780 1 0.220 93 0.982 1 0.018 95 0.969 1 0.031 96 0.832 1 0.168 97 0.821 1 0.179 98 0.984 1 0.016 100 0.761 1 0.239 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 6 0.829 0 0.171 10 0.556 0 0.444 11 0.765 0 0.235 12 0.932 0 0.068 14 0.979 0 0.021 18 0.517 0 0.483 21 0.914 0 0.086 22 0.899 0 0.101 23 0.890 0 0.110 24 0.918 0 0.082 25 0.831 0 0.169 29 0.893 0 0.107 33 0.916 0 0.084 35 0.921 0 0.079 36 0.530 0 0.470 37 0.925 0 0.075 38 0.511 0 0.489 39 0.607 0 0.393 40 0.979 0 0.021 42 0.994 0 0.006 44 0.993 0 0.007 45 0.931 0 0.069 46 0.948 0 0.052 48 0.520 0 0.480 51 0.565 0 0.435 52 0.754 0 0.246 56 0.607 0 0.393 58 0.981 0 0.019 59 0.969 0 0.031 60 0.922 0 0.078 61 0.881 0 0.119 62 0.798 0 0.202 68 0.626 0 0.374 CLASS = 1 (continued) Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 69 0.994 0 0.006 70 0.559 0 0.441 71 0.761 0 0.239 73 0.883 0 0.117 78 0.992 0 0.008 84 0.711 0 0.289 85 0.696 0 0.304 87 0.554 0 0.446 94 0.880 0 0.120 99 0.901 0 0.099 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.hd20000644000175000017500000000276611247310756016220 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ; Two dimensional distribution generated by: ;(let* ((n-data 250) ; (db (gen-formatted-data `((,n-data ((0.0 9.0) (0.0 3.0) (0.0 1.0))) ; (,n-data ((0.0 9.0) (0.0 1.5) (0.0 1.0))) ; (,n-data ((0.0 2.0) (0.0 1.0) (0.0 1.0))) ; (,n-data ((0.0 0.5) (0.0 0.5) (0.0 1.0)))) ; )) ; (total-data (cadr db)) ; (data (map 'vector #'(lambda (x) (coerce x 'vector)) (cddr db)))) ; (Rotate-Data data (/ *single-pi* 6.0) :start 0 :end (* 1 n-data)) ; (Rotate-Data data (/ *single-pi* -4.0) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(5.0 3.5 nil) :start 0 :end (* 1 n-data)) ; (Shift-Data data #(3.0 4.5 nil) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(8.0 5.0 nil) :start (* 2 n-data) :end (* 3 n-data)) ; (Shift-Data data #(4.0 1.0 nil) :start (* 3 n-data)) ; (format nil "; ~A data~2%~A" total-data data)) ; 100 Data, 3 attributes ;#! num_db2_format_defs num_db2_format_defs 1 ;; required number_of_attributes 3 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' ;; 0 real location X_coordinate error 0.00001 1 real location Y_coordinate error 0.00001 2 real location Z_coordinate error 0.00001 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.search0000644000175000017500000000112011247310756016767 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 2 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 2 -4.25562296e+03 107 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 2 -4.25732283e+03 55 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -4.25749609e+03 24 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 1 5 2 -4.25750761e+03 63 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.log0000644000175000017500000010344211247310756016315 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:55:48 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Thu Jun 7 11:55:48 2001 [j_in=2] [cs-3: cycles 24] best2->2(1) [j_in=3] [cs-3: cycles 55] best3->2(2) [j_in=5] [cs-3: cycles 63] 5->2(3) [j_in=7] [cs-3: cycles 107] best7->2(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:55:48 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4255.623) N_CLASSES 2 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-4257.323) N_CLASSES 2 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 107 max_cycles 200 convergent try 2 num_cycles 55 max_cycles 200 convergent try 1 num_cycles 24 max_cycles 200 convergent try 3 num_cycles 63 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:55:48 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:56:42 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Thu Jun 7 11:56:43 2001 [j_in=2] [cs-3: cycles 4] best2->2(1) [j_in=3] [cs-3: cycles 4] 3->3(2) [j_in=5] [cs-3: cycles 18] 5->3(3) [j_in=7] [cs-3: cycles 20] 7->3(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:56:43 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4259.769) N_CLASSES 2 FOUND ON TRY 1 *SAVED* PROBABILITY exp(-4270.297) N_CLASSES 3 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-4272.345) N_CLASSES 3 FOUND ON TRY 4 PROBABILITY exp(-4274.939) N_CLASSES 3 FOUND ON TRY 2 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 1 num_cycles 4 max_cycles 200 convergent try 3 num_cycles 18 max_cycles 200 convergent try 4 num_cycles 20 max_cycles 200 convergent try 2 num_cycles 4 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:56:43 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:57:47 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Thu Jun 7 11:57:48 2001 [j_in=2] [cs-4: cycles 24] best2->2(1) [j_in=3] [cs-4: cycles 56] best3->2(2) [j_in=5] [cs-4: cycles 63] 5->2(3) [j_in=7] [cs-4: cycles 107] best7->2(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 11:57:48 2001 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4255.622) N_CLASSES 2 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-4257.310) N_CLASSES 2 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 107 max_cycles 200 convergent try 2 num_cycles 56 max_cycles 200 convergent try 1 num_cycles 24 max_cycles 200 convergent try 3 num_cycles 63 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:57:48 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 11:58:37 2001 AUTOCLASS -PREDICT default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: None. ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.search ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 10 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.db2 ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 11:58:37 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 14:38:13 2002 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; save_compact_p=false; read_compact_p=false; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Mon May 13 14:38:13 2002 [j_in=2] [cs-3: cycles 24] best2->2(1) [j_in=3] [cs-3: cycles 55] best3->2(2) [j_in=5] [cs-3: cycles 63] 5->2(3) [j_in=7] [cs-3: cycles 107] best7->2(4) ENDING SEARCH because max number of tries reached at Mon May 13 14:38:13 2002 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4255.623) N_CLASSES 2 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-4257.323) N_CLASSES 2 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 107 max_cycles 200 convergent try 2 num_cycles 55 max_cycles 200 convergent try 1 num_cycles 24 max_cycles 200 convergent try 3 num_cycles 63 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 14:38:13 2002 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 17:45:36 2002 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; save_compact_p=false; read_compact_p=false; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Mon May 13 17:45:43 2002 [j_in=2] [cs-3: cycles 24] best2->2(1) [j_in=3] [cs-3: cycles 55] best3->2(2) [j_in=5] [cs-3: cycles 63] 5->2(3) [j_in=7] [cs-3: cycles 107] best7->2(4) ENDING SEARCH because max number of tries reached at Mon May 13 17:45:44 2002 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4255.623) N_CLASSES 2 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-4257.323) N_CLASSES 2 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 107 max_cycles 200 convergent try 2 num_cycles 55 max_cycles 200 convergent try 1 num_cycles 24 max_cycles 200 convergent try 3 num_cycles 63 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 17:45:44 2002 AUTOCLASS C (version 3.3.5unx) STARTING at Mon May 13 18:38:26 2002 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2, [2] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2, [3] /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ' ', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 3 attribute defs from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 ADVISORY[1]: read 100 datum from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #0, "X_coordinate": [ -1.5995e+01 < ( 3.6692e+00 : 8.0235e+01) < 2.3584e+01 ] Attribute #1, "Y_coordinate": [ -8.2093e+00 < ( 2.7497e+00 : 3.1100e+01) < 1.4271e+01 ] Attribute #2, "Z_coordinate": [ -2.5362e+00 < ( 6.1430e-02 : 9.2658e-01) < 2.5811e+00 ] ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log BEGINNING SEARCH at Mon May 13 18:38:30 2002 [j_in=2] [cs-3: cycles 24] best2->2(1) [j_in=3] [cs-3: cycles 55] best3->2(2) [j_in=5] [cs-3: cycles 63] 5->2(3) [j_in=7] [cs-3: cycles 107] best7->2(4) ENDING SEARCH because max number of tries reached at Mon May 13 18:38:31 2002 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-4255.623) N_CLASSES 2 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-4257.323) N_CLASSES 2 FOUND ON TRY 2 *SAVED* PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 107 max_cycles 200 convergent try 2 num_cycles 55 max_cycles 200 convergent try 1 num_cycles 24 max_cycles 200 convergent try 3 num_cycles 63 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Mon May 13 18:38:31 2002 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc-predict.db20000644000175000017500000000317611247310756017636 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ; Two dimensional distribution generated by: ;(let* ((n-data 250) ; (db (gen-formatted-data `((,n-data ((0.0 9.0) (0.0 3.0) (0.0 1.0))) ; (,n-data ((0.0 9.0) (0.0 1.5) (0.0 1.0))) ; (,n-data ((0.0 2.0) (0.0 1.0) (0.0 1.0))) ; (,n-data ((0.0 0.5) (0.0 0.5) (0.0 1.0)))) ; )) ; (total-data (cadr db)) ; (data (map 'vector #'(lambda (x) (coerce x 'vector)) (cddr db)))) ; (Rotate-Data data (/ *single-pi* 6.0) :start 0 :end (* 1 n-data)) ; (Rotate-Data data (/ *single-pi* -4.0) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(5.0 3.5 nil) :start 0 :end (* 1 n-data)) ; (Shift-Data data #(3.0 4.5 nil) :start (* 1 n-data) :end (* 2 n-data)) ; (Shift-Data data #(8.0 5.0 nil) :start (* 2 n-data) :end (* 3 n-data)) ; (Shift-Data data #(4.0 1.0 nil) :start (* 3 n-data)) (format nil "; ~A data~2%~A" total-data data)) ; 10 Data( 1-based case #'s: 10 20 30 40 50 60 70 80 90 100, 3 attributes 9.134876 6.049179 1.859805 10.016619 10.84547 -0.9010529 17.24001 9.96601 0.8749883 -15.994501 -8.135718 0.80787647 -3.768199 2.550647 -0.49896044 -11.673483 -7.674114 -0.23295523 1.4615703 -0.4979577 -0.7849662 15.407906 8.627243 -0.74877954 3.5484169 2.5386896 0.026939131 1.8351529 1.5959675 -0.7965551 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.influ-o-text-10000644000175000017500000002056211247310756020226 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 100 cases in /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 with log-A (approximate marginal likelihood) = -4255.623 from classification results file /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 2 seconds _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-4255.623) N_CLASSES 2 FOUND ON TRY 4 *SAVED* -1 PROBABILITY exp(-4257.323) N_CLASSES 2 FOUND ON TRY 2 *SAVED* -2 PROBABILITY exp(-4257.496) N_CLASSES 2 FOUND ON TRY 1 PROBABILITY exp(-4257.508) N_CLASSES 2 FOUND ON TRY 3 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 2 POPULATED CLASSES (max global influence value = 0.172) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -4.21e+01 1.00e+00 58 0.584 1 -4.30e+01 4.31e-01 42 0.416 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 2.86e-01 58 0.584 1 5.16e-01 42 0.416 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 000 X_coordinate 1.000 001 Y_coordinate 1.000 002 Z_coordinate 1.000 CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 58 normalized weight 0.584 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 2.86e-01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 01 R MNcn Y_coordinate ....... 0.095 ( 5.46e+00 4.51e+00) 6.00e-01 ( 2.75e+00 5.66e+00) 00 00 R MNcn X_coordinate ....... 0.095 ( 5.31e+00 8.21e+00) 2.00e-01 ( 3.67e+00 9.09e+00) 00 02 R MNcn Z_coordinate ....... 0.095 (-8.48e-02 8.05e-01) 1.82e-01 ( 6.14e-02 9.77e-01) Correlation matrix (row & column indices are attribute numbers) 0 1 2 0 1.000 0.777 0.298 1 0.777 1.000 0.203 2 0.298 0.203 1.000 CLASS 1 - weight 42 normalized weight 0.416 relative strength 4.31e-01 ******* class cross entropy w.r.t. global class 5.16e-01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (multi_normal_cn MNcn) REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 01 R MNcn Y_coordinate ....... 0.172 (-1.07e+00 4.95e+00) 7.71e-01 ( 2.75e+00 5.66e+00) 00 00 R MNcn X_coordinate ....... 0.172 ( 1.35e+00 1.00e+01) 2.31e-01 ( 3.67e+00 9.09e+00) 00 02 R MNcn Z_coordinate ....... 0.172 ( 2.68e-01 1.18e+00) 1.75e-01 ( 6.14e-02 9.77e-01) Correlation matrix (row & column indices are attribute numbers) 0 1 2 0 1.000 0.838 0.067 1 0.838 1.000 0.171 2 0.067 0.171 1.000 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc.case-text-10000644000175000017500000000647311247310756017575 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 100 cases in /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.db2 /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.hd2 with log-A (approximate marginal likelihood) = -4255.623 from classification results file /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.results-bin and using models /home/wtaylor/AC/autoclass-c/data/3-dim/3-dimc.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 0 0.919 35 1 0.921 69 1 0.994 2 0 0.923 36 1 0.530 70 1 0.559 3 0 0.662 37 1 0.925 71 1 0.761 4 0 0.998 38 1 0.511 72 0 0.997 5 0 0.592 39 1 0.607 73 1 0.883 6 1 0.829 40 1 0.979 74 0 0.931 7 0 0.913 41 0 0.535 75 0 0.996 8 0 0.550 42 1 0.994 76 0 0.992 9 0 0.996 43 0 0.881 77 0 0.940 10 1 0.556 44 1 0.993 78 1 0.992 11 1 0.765 45 1 0.931 79 0 0.930 12 1 0.932 46 1 0.948 80 0 0.891 13 0 0.973 47 0 0.998 81 0 0.896 14 1 0.979 48 1 0.520 82 0 0.827 15 0 0.954 49 0 0.771 83 0 0.978 16 0 0.982 50 0 0.960 84 1 0.711 17 0 0.995 51 1 0.565 85 1 0.696 18 1 0.517 52 1 0.754 86 0 0.889 19 0 0.890 53 0 0.976 87 1 0.554 20 0 0.997 54 0 0.615 88 0 0.980 21 1 0.914 55 0 0.986 89 0 0.909 22 1 0.899 56 1 0.607 90 0 0.767 23 1 0.890 57 0 0.543 91 0 0.643 24 1 0.918 58 1 0.981 92 0 0.780 25 1 0.831 59 1 0.969 93 0 0.982 26 0 0.966 60 1 0.922 94 1 0.880 27 0 0.602 61 1 0.881 95 0 0.969 28 0 0.998 62 1 0.798 96 0 0.832 29 1 0.893 63 0 0.930 97 0 0.821 30 0 0.894 64 0 0.995 98 0 0.984 31 0 0.913 65 0 0.653 99 1 0.901 32 0 0.994 66 0 0.977 100 0 0.761 33 1 0.916 67 0 0.961 34 0 0.938 68 1 0.626 autoclass-3.3.6.dfsg.1/data/3-dim/3-dimc-predict.class-text-10000644000175000017500000000303111247310756021402 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 10 "TEST" cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc-predict.db2 based on the "TRAINING" classification of 100 cases in /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.db2 /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.hd2 with log-A (approximate marginal likelihood) = -4255.622 from classification results file /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.results-bin and using models /home/wtaylor/AC/3.3.4/autoclass-c/data/3-dim/3-dimc.model - index = 0 CLASS = 0 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 2 0.997 1 0.003 3 0.894 1 0.106 5 0.960 1 0.040 8 0.891 1 0.109 9 0.767 1 0.233 10 0.761 1 0.239 CLASS = 1 Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ 1 0.556 0 0.444 4 0.979 0 0.021 6 0.922 0 0.078 7 0.559 0 0.441 autoclass-3.3.6.dfsg.1/data/autos/0000755000175000017500000000000011667631535014763 5ustar areareautoclass-3.3.6.dfsg.1/data/autos/imports-85.results-bin0000644000175000017500000024603611247310756021107 0ustar areare(# ordered sequence of clsf_DS's: 0 -> 1(# clsf_DS 0: log_a_x_h = -1.6453536e+04(# clsf_DS 1: log_a_x_h = -1.6654238e+04ac_version 3.3.5unx 4hQ&[?Nb8@B  B ff@B  |data/autos/imports-85.db2data/autos/imports-85.hd2)2аx,;? discretenominalsymboling؃P; 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ETBETBq9 w аCTBO?ksPAH <QA=8G>l\=i=|?ETBETBq9<{|> w аCTB?ӹ_AY=[A17>ۿ=[|A v7A(?3u?u?chz?ETBETBq9 w аO@=O@F>ѿI=QfA =Y@7-?9Ed>f> ETBETBq9 w а@@=m@@F>ѿI=QfA =xL@7-?XZ>f>!ETBETBq9 w аBt@=u@ט>7=̊3A G9k9@KD?iW">W">"ETBETBq9 w а"x@m7<7"fB k7";@V?X3?3?#ETBETBq9 w а Aա;A=S*؞;S2INC 05qp@H?} uW>uW>$ETBETBq9 w аf@Ee3;w@PT=k=}0;kC v7~@[=x< |M< |M<%ETBETBq9 w аΟ@9@<^ 9^:E v7@ ٹ=hS><<&ETBETBq9 w аIæ@:¦@K1=lH :lD v7@F>42!=!='ETBETBq9 w а@){;@7{=2Ԇv;/C v7p@*><<(ETBETBq9 w аL\RB\RBQ>l+ ~>.i#@( b v&BA.:=]t?@   @` 3 A?@B\RB\RBZ< w аX~Y+9A=<,?[?@@@@?< ?@H<@Aщ@C@X^:I]:=>)^:с<=4=>)^:=>)^:>)^:=1=>)^:ڹ<>)^:4=|ݵ:e>v>>>)^:X:,Q XI{ Q X+M X X MQ X={ XQL+Y[qD Xm???  0 @@3 A?B\RB\RBZ< w а`RB}?Z<rzm???   @ 3 @??B\RB\RBZ< w а|BBdl@]uj?;U=iih@>*?   \RB\RBZ< w а &#A'n B*< m,>&?; L?   \RB\RBZ< w аQ?"hAAJAg9M<{>W>4/>t;z/ss z@:Έh@>*?   \RB\RBZ< w а #k@w"Bj)@ T=tC?O= )Um???   \RB\RBZ< w а`RB}?Z<rzۭ@@%I>m[?h   \RB\RBZ< w а@1B@An#=.;9?.;.;g>.;GN#ۭ@@%I>m[?x   \RB\RBZ< w аRB+T;{?.;̂2;.;.;.;.;syq@@>`?    \RB\RBZ< w а Ar]A?@?H@@ G>/>Z;}=ӫ0=0}=A k7";p@ v/?S)п6>6>\mL? \RB\RBZ*;fC G9k9Ӊ?`>V\E=\E=chz? \RB\RBZ$=$=chz? \RB\RBZ6y<̂[nB k7";@`Xe?:wM?:wM?|? \RB\RBZG>l\=i=|?\RB\RBZ<{| w а`RBT?mչ}TA q(=TA M>pͿM%=pMv;A v7A(?3u?u?chz?\RB\RBZѿٔ=QA =Y@7-?9Ed>f> \RB\RBZѿٔ=QA =xL@7-?XZ>f>!\RB\RBZW">"\RB\RBZuW>$\RB\RBZ%D v7~@[=x< |M< |M<%\RB\RBZ<<&\RB\RBZ42!=!='\RB\RBZ<<(\RB\RBZGh> ;Xy׍yyy=uyyDyycPD:yyyyyy\pym???   @@3 A?BAAMU= w а ?A A > ?J$m???h x  @ 3 @??BAAMU= w аTNٿh@>*?H X h AAMU= w а @SA? d>B?h3= ]2p,V*?@L>L?( @ X AAMU= w аa:I6@0EAS;>?@+H'?05ڑ^پph@>*?  0 @ AAMU= w а å@1GAh< s>3j*?B< x^Zоam???    AAMU= w аAUy?M< ؼ>|iۭ@@%I>m[?    AAMU= w аA;;t?;;;;ԜԜ ;ԜԜԜԜۭ@@%I>m[?  0 AAMU= w аcA&?Y?;Ec=;;;;#$Ԝc ԜԜԜԜq@@>`? ( P  AAMU= w а 3An; A?: (>\;M;M;M;M;?r; U֔#bSA:?mdۖ@S=ޖ@T>DZ~=1A k7";p@ v/?S)п6>6>\mL? AAMU=XJ w аAs?U0i'?;(?=+&;4UC G9k9Ӊ?`>V\E=\E=chz? AAMU=X w аAs?U0i?9:?<3-x+93-E G9k9jo?HK>$=$=chz? AAMU=X w аA?%%@@O=K@r> =z1)A k7";@`Xe?:wM?:wM?|? AAMU=XJ w аA?%%AH <Am=(A<(6B H <A>G>l\=i=|?AAMU={| w аAs?U0i A0=; At>l=l/[xA v7A(?3u?u?chz?AAMU=m_x w аyc@ >=c@W0W>ǿ;4=G'A =Y@7-?9Ed>f> AAMU=hDO& w а5W@tx=1W@u>hȶok=h6!A =xL@7-?XZ>f>!AAMU=hDO& w а-@CP>,@>;RjE>;ҿ@ G9k9@KD?iW">W">"AAMU=X w а@?RuW>$AAMU=F w а1@c%::0@`<is0:YD v7~@[=x< |M< |M<%AAMU=m_x w а@0%9@kH<<&AAMU=m_x (t а@':@]<my%:D v7@F>42!=!='AAMU=m_x (t а^@:[@f<<(AAMU=m_x hT а autoclass-3.3.6.dfsg.1/data/autos/imports-85.log0000644000175000017500000013507511247310756017421 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:11:03 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_normal_cn, will be used for these attributes: #9: "wheel-base" #10: "length" #11: "width" #12: "height" #13: "curb-weight" #16: "engine-size" #20: "compression-ratio" #23: "city-mpg" #24: "highway-mpg" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #32. Attribute #32, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #33. Attribute #33, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #34. Attribute #34, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #35. Attribute #35, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #37. Attribute #37, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #38. Attribute #38, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #39. Attribute #39, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #40. Attribute #40, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log BEGINNING SEARCH at Thu Jun 7 12:11:03 2001 [j_in=2] [cs-3: cycles 22] best2->2(1) [j_in=3] [cs-3: cycles 16] best3->3(2) [j_in=5] [cs-3: cycles 37] best5->5(3) [j_in=7] [cs-3: cycles 32] best7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:11:04 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16654.238) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-16816.658) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-17041.867) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 32 max_cycles 200 convergent try 3 num_cycles 37 max_cycles 200 convergent try 2 num_cycles 16 max_cycles 200 convergent try 1 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:11:04 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:12:56 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; rel_delta_range=5.000000e-02; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_normal_cn, will be used for these attributes: #9: "wheel-base" #10: "length" #11: "width" #12: "height" #13: "curb-weight" #16: "engine-size" #20: "compression-ratio" #23: "city-mpg" #24: "highway-mpg" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #32. Attribute #32, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #33. Attribute #33, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #34. Attribute #34, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #35. Attribute #35, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #37. Attribute #37, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #38. Attribute #38, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #39. Attribute #39, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #40. Attribute #40, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log BEGINNING SEARCH at Thu Jun 7 12:12:57 2001 [j_in=2] [cs-3: cycles 12] best2->2(1) [j_in=3] [cs-3: cycles 12] best3->3(2) [j_in=5] [cs-3: cycles 28] best5->5(3) [j_in=7] [cs-3: cycles 20] best7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:12:58 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16464.989) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16674.829) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-16816.729) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-17042.456) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 20 max_cycles 200 convergent try 3 num_cycles 28 max_cycles 200 convergent try 2 num_cycles 12 max_cycles 200 convergent try 1 num_cycles 12 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:12:58 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:14:17 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; try_fn_type="converge_search_4"; randomize_random_p=false; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_normal_cn, will be used for these attributes: #9: "wheel-base" #10: "length" #11: "width" #12: "height" #13: "curb-weight" #16: "engine-size" #20: "compression-ratio" #23: "city-mpg" #24: "highway-mpg" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #32. Attribute #32, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #33. Attribute #33, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #34. Attribute #34, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #35. Attribute #35, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #37. Attribute #37, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #38. Attribute #38, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #39. Attribute #39, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #40. Attribute #40, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log BEGINNING SEARCH at Thu Jun 7 12:14:18 2001 [j_in=2] [cs-4: cycles 22] best2->2(1) [j_in=3] [cs-4: cycles 16] best3->3(2) [j_in=5] [cs-4: cycles 38] best5->5(3) [j_in=7] [cs-4: cycles 33] best7->7(4) ENDING SEARCH because max number of tries reached at Thu Jun 7 12:14:19 2001 after a total of 4 tries over 2 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16453.532) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16654.237) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-16816.658) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-17041.867) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 33 max_cycles 200 convergent try 3 num_cycles 38 max_cycles 200 convergent try 2 num_cycles 16 max_cycles 200 convergent try 1 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:14:19 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Wed Mar 1 11:55:08 2006 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: min_report_period=30000; max_n_tries=4; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.log During loading of: [1] /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2, [2] /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2, [3] /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_normal_cn, will be used for these attributes: #9: "wheel-base" #10: "length" #11: "width" #12: "height" #13: "curb-weight" #16: "engine-size" #20: "compression-ratio" #23: "city-mpg" #24: "highway-mpg" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #32. Attribute #32, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #33. Attribute #33, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #34. Attribute #34, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #35. Attribute #35, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #37. Attribute #37, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #38. Attribute #38, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #39. Attribute #39, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #40. Attribute #40, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 8 hours 20 minutes have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (4). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin and a description of the search to file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.search 9) A record of this search will be printed to file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.log BEGINNING SEARCH at Wed Mar 1 11:55:15 2006 [j_in=2] [cs-3: cycles 22] best2->2(1) [j_in=3] [cs-3: cycles 16] best3->3(2) [j_in=5] [cs-3: cycles 37] best5->5(3) [j_in=7] [cs-3: cycles 32] best7->7(4) ENDING SEARCH because max number of tries reached at Wed Mar 1 11:55:15 2006 after a total of 4 tries over 1 second A log of this search is in file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.log The search results are stored in file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16654.238) N_CLASSES 5 FOUND ON TRY 3 *SAVED* PROBABILITY exp(-16816.657) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-17041.867) N_CLASSES 2 FOUND ON TRY 1 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 4 num_cycles 32 max_cycles 200 convergent try 3 num_cycles 37 max_cycles 200 convergent try 2 num_cycles 16 max_cycles 200 convergent try 1 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.5unx) STOPPING at Wed Mar 1 11:55:15 2006 autoclass-3.3.6.dfsg.1/data/autos/imports-85.db20000644000175000017500000006420411247310756017302 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,13495 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,16500 1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.50,171.20,65.50,52.40,2823,ohcv,six,152,mpfi,2.68,3.47,9.00,154,5000,19,26,16500 2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950 2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450 2,?,audi,gas,std,two,sedan,fwd,front,99.80,177.30,66.30,53.10,2507,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,15250 1,158,audi,gas,std,four,sedan,fwd,front,105.80,192.70,71.40,55.70,2844,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,17710 1,?,audi,gas,std,four,wagon,fwd,front,105.80,192.70,71.40,55.70,2954,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,18920 1,158,audi,gas,turbo,four,sedan,fwd,front,105.80,192.70,71.40,55.90,3086,ohc,five,131,mpfi,3.13,3.40,8.30,140,5500,17,20,23875 0,?,audi,gas,turbo,two,hatchback,4wd,front,99.50,178.20,67.90,52.00,3053,ohc,five,131,mpfi,3.13,3.40,7.00,160,5500,16,22,? 2,192,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16430 0,192,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16925 0,188,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2710,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,20970 0,188,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2765,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,21105 1,?,bmw,gas,std,four,sedan,rwd,front,103.50,189.00,66.90,55.70,3055,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,20,25,24565 0,?,bmw,gas,std,four,sedan,rwd,front,103.50,189.00,66.90,55.70,3230,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,16,22,30760 0,?,bmw,gas,std,two,sedan,rwd,front,103.50,193.80,67.90,53.70,3380,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,16,22,41315 0,?,bmw,gas,std,four,sedan,rwd,front,110.00,197.00,70.90,56.30,3505,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,15,20,36880 2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.40,141.10,60.30,53.20,1488,l,three,61,2bbl,2.91,3.03,9.50,48,5100,47,53,5151 1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.50,155.90,63.60,52.00,1874,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6295 0,81,chevrolet,gas,std,four,sedan,fwd,front,94.50,158.80,63.60,52.00,1909,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6575 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6377 1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,mpfi,3.03,3.39,7.60,102,5500,24,30,7957 1,148,dodge,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 1,148,dodge,gas,turbo,?,sedan,fwd,front,93.70,157.30,63.80,50.60,2191,ohc,four,98,mpfi,3.03,3.39,7.60,102,5500,24,30,8558 -1,110,dodge,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.34,3.46,8.50,88,5000,24,30,8921 3,145,dodge,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2811,ohc,four,156,mfi,3.60,3.90,7.00,145,5000,19,24,12964 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1713,ohc,four,92,1bbl,2.91,3.41,9.60,58,4800,49,54,6479 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1819,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,31,38,6855 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1837,ohc,four,79,1bbl,2.91,3.07,10.10,60,5500,38,42,5399 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1940,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,6529 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1956,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7129 0,110,honda,gas,std,four,sedan,fwd,front,96.50,163.40,64.00,54.50,2010,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7295 0,78,honda,gas,std,four,wagon,fwd,front,96.50,157.10,63.90,58.30,2024,ohc,four,92,1bbl,2.92,3.41,9.20,76,6000,30,34,7295 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2236,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,7895 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2289,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,9095 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2304,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,8845 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,62.50,54.10,2372,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,10295 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2465,ohc,four,110,mpfi,3.15,3.58,9.00,101,5800,24,28,12945 1,107,honda,gas,std,two,sedan,fwd,front,96.50,169.10,66.00,51.00,2293,ohc,four,110,2bbl,3.15,3.58,9.10,100,5500,25,31,10345 0,?,isuzu,gas,std,four,sedan,rwd,front,94.30,170.70,61.80,53.50,2337,ohc,four,111,2bbl,3.31,3.23,8.50,78,4800,24,29,6785 1,?,isuzu,gas,std,two,sedan,fwd,front,94.50,155.90,63.60,52.00,1874,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,? 0,?,isuzu,gas,std,four,sedan,fwd,front,94.50,155.90,63.60,52.00,1909,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,? 2,?,isuzu,gas,std,two,hatchback,rwd,front,96.00,172.60,65.20,51.40,2734,ohc,four,119,spfi,3.43,3.23,9.20,90,5000,24,29,11048 0,145,jaguar,gas,std,four,sedan,rwd,front,113.00,199.60,69.60,52.80,4066,dohc,six,258,mpfi,3.63,4.17,8.10,176,4750,15,19,32250 0,?,jaguar,gas,std,four,sedan,rwd,front,113.00,199.60,69.60,52.80,4066,dohc,six,258,mpfi,3.63,4.17,8.10,176,4750,15,19,35550 0,?,jaguar,gas,std,two,sedan,rwd,front,102.00,191.70,70.60,47.80,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.50,262,5000,13,17,36000 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1890,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,30,31,5195 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1900,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6095 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1905,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6795 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1945,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6695 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1950,ohc,four,91,2bbl,3.08,3.15,9.00,68,5000,31,38,7395 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2380,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,10945 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2380,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,11845 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2385,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,13645 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2500,rotor,two,80,mpfi,?,?,9.40,135,6000,16,23,15645 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8845 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8495 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10595 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10245 0,?,mazda,diesel,std,?,sedan,fwd,front,98.80,177.80,66.50,55.50,2443,ohc,four,122,idi,3.39,3.39,22.70,64,4650,36,42,10795 0,115,mazda,gas,std,four,hatchback,fwd,front,98.80,177.80,66.50,55.50,2425,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,11245 0,118,mazda,gas,std,four,sedan,rwd,front,104.90,175.00,66.10,54.40,2670,ohc,four,140,mpfi,3.76,3.16,8.00,120,5000,19,27,18280 0,?,mazda,diesel,std,four,sedan,rwd,front,104.90,175.00,66.10,54.40,2700,ohc,four,134,idi,3.43,3.64,22.00,72,4200,31,39,18344 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,110.00,190.90,70.30,56.50,3515,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,25552 -1,93,mercedes-benz,diesel,turbo,four,wagon,rwd,front,110.00,190.90,70.30,58.70,3750,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28248 0,93,mercedes-benz,diesel,turbo,two,hardtop,rwd,front,106.70,187.50,70.30,54.90,3495,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28176 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,115.60,202.60,71.70,56.30,3770,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,31600 -1,?,mercedes-benz,gas,std,four,sedan,rwd,front,115.60,202.60,71.70,56.50,3740,ohcv,eight,234,mpfi,3.46,3.10,8.30,155,4750,16,18,34184 3,142,mercedes-benz,gas,std,two,convertible,rwd,front,96.60,180.30,70.50,50.80,3685,ohcv,eight,234,mpfi,3.46,3.10,8.30,155,4750,16,18,35056 0,?,mercedes-benz,gas,std,four,sedan,rwd,front,120.90,208.10,71.70,56.70,3900,ohcv,eight,308,mpfi,3.80,3.35,8.00,184,4500,14,16,40960 1,?,mercedes-benz,gas,std,two,hardtop,rwd,front,112.00,199.20,72.00,55.40,3715,ohcv,eight,304,mpfi,3.80,3.35,8.00,184,4500,14,16,45400 1,?,mercury,gas,turbo,two,hatchback,rwd,front,102.70,178.40,68.00,54.80,2910,ohc,four,140,mpfi,3.78,3.12,8.00,175,5000,19,24,16503 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1918,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,37,41,5389 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1944,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6189 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,2004,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6669 1,161,mitsubishi,gas,turbo,two,hatchback,fwd,front,93,157.30,63.80,50.80,2145,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7689 3,153,mitsubishi,gas,turbo,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2370,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9959 3,153,mitsubishi,gas,std,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2328,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8499 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2833,ohc,four,156,spdi,3.58,3.86,7.00,145,5000,19,24,12629 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2921,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,14869 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2926,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,14489 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2365,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,6989 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2405,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8189 1,125,mitsubishi,gas,turbo,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 -1,137,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1889,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,5499 1,128,nissan,diesel,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,2017,ohc,four,103,idi,2.99,3.47,21.90,55,4800,45,50,7099 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1918,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6649 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1938,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6849 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2024,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7349 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1951,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7299 1,128,nissan,gas,std,two,hatchback,fwd,front,94.50,165.60,63.80,53.30,2028,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7799 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1971,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7499 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2037,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7999 2,168,nissan,gas,std,two,hardtop,fwd,front,95.10,162.40,63.80,53.30,2008,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,8249 0,106,nissan,gas,std,four,hatchback,fwd,front,97.20,173.40,65.20,54.70,2324,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,8949 0,106,nissan,gas,std,four,sedan,fwd,front,97.20,173.40,65.20,54.70,2302,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,9549 0,128,nissan,gas,std,four,sedan,fwd,front,100.40,181.70,66.50,55.10,3095,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,13499 0,108,nissan,gas,std,four,wagon,fwd,front,100.40,184.60,66.50,56.10,3296,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,14399 0,108,nissan,gas,std,four,sedan,fwd,front,100.40,184.60,66.50,55.10,3060,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,19,25,13499 3,194,nissan,gas,std,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3071,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,17199 3,194,nissan,gas,turbo,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,7.80,200,5200,17,23,19699 1,231,nissan,gas,std,two,hatchback,rwd,front,99.20,178.50,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,18399 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3020,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,11900 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3197,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,13200 0,?,peugot,gas,std,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3230,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,12440 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3430,l,four,152,idi,3.70,3.52,21.00,95,4150,25,25,13860 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,2.19,8.40,95,5000,19,24,15580 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,16900 0,?,peugot,gas,std,four,wagon,rwd,front,114.20,198.90,68.40,56.70,3285,l,four,120,mpfi,3.46,2.19,8.40,95,5000,19,24,16695 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3485,l,four,152,idi,3.70,3.52,21.00,95,4150,25,25,17075 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,16630 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,17950 0,161,peugot,gas,turbo,four,sedan,rwd,front,108.00,186.70,68.30,56.00,3130,l,four,134,mpfi,3.61,3.21,7.00,142,5600,18,24,18150 1,119,plymouth,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1918,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,37,41,5572 1,119,plymouth,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7957 1,154,plymouth,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,2191,ohc,four,98,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 -1,74,plymouth,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,24,30,8921 3,?,plymouth,gas,turbo,two,hatchback,rwd,front,95.90,173.20,66.30,50.20,2818,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,12764 3,186,porsche,gas,std,two,hatchback,rwd,front,94.50,168.90,68.30,50.20,2778,ohc,four,151,mpfi,3.94,3.11,9.50,143,5500,19,27,22018 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.50,168.90,65.00,51.60,2756,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,32528 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.50,168.90,65.00,51.60,2756,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,34028 3,?,porsche,gas,std,two,convertible,rwd,rear,89.50,168.90,65.00,51.60,2800,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,37028 1,?,porsche,gas,std,two,hatchback,rwd,front,98.40,175.70,72.30,50.50,3366,dohcv,eight,203,mpfi,3.94,3.11,10.00,288,5750,17,28,? 0,?,renault,gas,std,four,wagon,fwd,front,96.10,181.50,66.50,55.20,2579,ohc,four,132,mpfi,3.46,3.90,8.70,?,?,23,31,9295 2,?,renault,gas,std,two,hatchback,fwd,front,96.10,176.80,66.60,50.50,2460,ohc,four,132,mpfi,3.46,3.90,8.70,?,?,23,31,9895 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2695,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,12170 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2707,ohc,four,121,mpfi,2.54,2.07,9.30,110,5250,21,28,15040 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2758,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,15510 3,150,saab,gas,turbo,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2808,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18150 2,104,saab,gas,turbo,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2847,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18620 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,156.90,63.40,53.70,2050,ohcf,four,97,2bbl,3.62,2.36,9.00,69,4900,31,36,5118 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,157.90,63.60,53.70,2120,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7053 2,83,subaru,gas,std,two,hatchback,4wd,front,93.30,157.30,63.80,55.70,2240,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7603 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2145,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4800,32,37,7126 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2190,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4400,28,33,7775 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2340,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,26,32,9960 0,102,subaru,gas,std,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2385,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,24,25,9233 0,102,subaru,gas,turbo,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2510,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,24,29,11259 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2290,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,28,32,7463 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2455,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,25,31,10198 0,85,subaru,gas,std,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2420,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,23,29,8013 0,85,subaru,gas,turbo,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2650,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,23,23,11694 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,1985,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,35,39,5348 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,2040,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6338 1,74,toyota,gas,std,four,hatchback,fwd,front,95.70,158.70,63.60,54.50,2015,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6488 0,77,toyota,gas,std,four,wagon,fwd,front,95.70,169.70,63.60,59.10,2280,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,37,6918 0,81,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,2290,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,7898 0,91,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,3110,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,8778 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2081,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,6938 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2109,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,7198 0,91,toyota,diesel,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,34,36,7898 0,91,toyota,diesel,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,38,47,7788 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2094,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,38,47,7738 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2122,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,8358 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,52.80,2140,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,9258 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2169,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8058 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2204,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8238 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2265,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9298 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2300,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9538 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2540,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,8449 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2536,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9639 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2551,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9989 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2679,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11199 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2714,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11549 2,134,toyota,gas,std,two,convertible,rwd,front,98.40,176.20,65.60,53.00,2975,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,17669 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2326,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,29,34,8948 -1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2480,ohc,four,110,idi,3.27,3.35,22.50,73,4500,30,33,10698 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,9988 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,10898 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2458,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,11248 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,2976,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,20,24,16558 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,3016,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,19,24,15998 -1,90,toyota,gas,std,four,sedan,rwd,front,104.50,187.80,66.50,54.10,3131,dohc,six,171,mpfi,3.27,3.35,9.20,156,5200,20,24,15690 -1,?,toyota,gas,std,four,wagon,rwd,front,104.50,187.80,66.50,54.10,3151,dohc,six,161,mpfi,3.27,3.35,9.20,156,5200,19,24,15750 2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2261,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7775 2,122,volkswagen,gas,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2209,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,7975 2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2264,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7995 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2212,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8195 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2275,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8495 2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2319,ohc,four,97,idi,3.01,3.40,23.00,68,4500,37,42,9495 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2300,ohc,four,109,mpfi,3.19,3.40,10.00,100,5500,26,32,9995 3,?,volkswagen,gas,std,two,convertible,fwd,front,94.50,159.30,64.20,55.60,2254,ohc,four,109,mpfi,3.19,3.40,8.50,90,5500,24,29,11595 3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.50,165.70,64.00,51.40,2221,ohc,four,109,mpfi,3.19,3.40,8.50,90,5500,24,29,9980 0,?,volkswagen,gas,std,four,sedan,fwd,front,100.40,180.20,66.90,55.10,2661,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,24,13295 0,?,volkswagen,diesel,turbo,four,sedan,fwd,front,100.40,180.20,66.90,55.10,2579,ohc,four,97,idi,3.01,3.40,23.00,68,4500,33,38,13845 0,?,volkswagen,gas,std,four,wagon,fwd,front,100.40,183.10,66.90,55.10,2563,ohc,four,109,mpfi,3.19,3.40,9.00,88,5500,25,31,12290 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2912,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,12940 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3034,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,13415 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2935,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,15985 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3042,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,16515 -2,103,volvo,gas,turbo,four,sedan,rwd,front,104.30,188.80,67.20,56.20,3045,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18420 -1,74,volvo,gas,turbo,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3157,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18950 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,2952,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,16845 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.80,55.50,3049,ohc,four,141,mpfi,3.78,3.15,8.70,160,5300,19,25,19045 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3012,ohcv,six,173,mpfi,3.58,2.87,8.80,134,5500,18,23,21485 -1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3217,ohc,six,145,idi,3.01,3.40,23.00,106,4800,26,27,22470 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3062,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,19,25,22625 autoclass-3.3.6.dfsg.1/data/autos/imports-85.class-data-10000644000175000017500000004500611247310756021004 0ustar areare # CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP #DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 # with log-A (approximate marginal likelihood) = -16453.536 # from classification results file # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin # and using models # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 DATA_CLASS 0 # CLASS = 0 #Case # curb-weight make price engine-type (Cls Prob) 019 1488 chevrolet 5151 l 1.000 020 1874 chevrolet 6295 ohc 1.000 021 1909 chevrolet 6575 ohc 1.000 022 1876 dodge 5572 ohc 1.000 023 1876 dodge 6377 ohc 1.000 025 1967 dodge 6229 ohc 1.000 026 1989 dodge 6692 ohc 1.000 027 1989 dodge 7609 ohc 1.000 031 1713 honda 6479 ohc 1.000 032 1819 honda 6855 ohc 1.000 033 1837 honda 5399 ohc 1.000 034 1940 honda 6529 ohc 1.000 035 1956 honda 7129 ohc 1.000 036 2010 honda 7295 ohc 1.000 037 2024 honda 7295 ohc 1.000 045 1874 isuzu ? ohc 1.000 046 1909 isuzu ? ohc 1.000 051 1890 mazda 5195 ohc 1.000 052 1900 mazda 6095 ohc 1.000 053 1905 mazda 6795 ohc 1.000 054 1945 mazda 6695 ohc 1.000 055 1950 mazda 7395 ohc 1.000 077 1918 mitsubishi 5389 ohc 1.000 078 1944 mitsubishi 6189 ohc 1.000 079 2004 mitsubishi 6669 ohc 1.000 090 1889 nissan 5499 ohc 1.000 092 1918 nissan 6649 ohc 1.000 093 1938 nissan 6849 ohc 1.000 094 2024 nissan 7349 ohc 1.000 095 1951 nissan 7299 ohc 1.000 096 2028 nissan 7799 ohc 1.000 097 1971 nissan 7499 ohc 1.000 098 2037 nissan 7999 ohc 1.000 099 2008 nissan 8249 ohc 1.000 119 1918 plymouth 5572 ohc 1.000 121 1967 plymouth 6229 ohc 1.000 122 1989 plymouth 6692 ohc 1.000 123 2191 plymouth 7609 ohc 1.000 151 1985 toyota 5348 ohc 1.000 152 2040 toyota 6338 ohc 1.000 153 2015 toyota 6488 ohc 1.000 154 2280 toyota 6918 ohc 0.999 155 2290 toyota 7898 ohc 0.939 1 0.061 157 2081 toyota 6938 ohc 0.998 1 0.002 158 2109 toyota 7198 ohc 0.998 1 0.002 161 2094 toyota 7738 ohc 0.999 162 2122 toyota 8358 ohc 0.978 1 0.022 163 2140 toyota 9258 ohc 0.756 1 0.244 164 2169 toyota 8058 ohc 0.991 1 0.009 165 2204 toyota 8238 ohc 0.993 1 0.007 DATA_CLASS 1 # CLASS = 1 #Case # curb-weight make price engine-type (Cls Prob) 024 2128 dodge 7957 ohc 1.000 028 2191 dodge 8558 ohc 1.000 038 2236 honda 7895 ohc 1.000 039 2289 honda 9095 ohc 1.000 040 2304 honda 8845 ohc 1.000 041 2372 honda 10295 ohc 1.000 042 2465 honda 12945 ohc 0.998 4 0.002 043 2293 honda 10345 ohc 1.000 044 2337 isuzu 6785 ohc 1.000 080 2145 mitsubishi 7689 ohc 1.000 081 2370 mitsubishi 9959 ohc 0.953 3 0.047 082 2328 mitsubishi 8499 ohc 1.000 086 2365 mitsubishi 6989 ohc 1.000 087 2405 mitsubishi 8189 ohc 1.000 088 2403 mitsubishi 9279 ohc 1.000 089 2403 mitsubishi 9279 ohc 1.000 091 2017 nissan 7099 ohc 1.000 100 2324 nissan 8949 ohc 0.982 4 0.018 101 2302 nissan 9549 ohc 0.978 4 0.022 120 2128 plymouth 7957 ohc 1.000 139 2050 subaru 5118 ohcf 1.000 140 2120 subaru 7053 ohcf 1.000 141 2240 subaru 7603 ohcf 1.000 142 2145 subaru 7126 ohcf 1.000 143 2190 subaru 7775 ohcf 1.000 144 2340 subaru 9960 ohcf 1.000 145 2385 subaru 9233 ohcf 1.000 146 2510 subaru 11259 ohcf 1.000 147 2290 subaru 7463 ohcf 1.000 148 2455 subaru 10198 ohcf 1.000 149 2420 subaru 8013 ohcf 1.000 150 2650 subaru 11694 ohcf 1.000 156 3110 toyota 8778 ohc 1.000 159 2275 toyota 7898 ohc 1.000 160 2275 toyota 7788 ohc 1.000 166 2265 toyota 9298 dohc 0.995 3 0.005 183 2261 volkswagen 7775 ohc 1.000 184 2209 volkswagen 7975 ohc 0.999 4 0.001 185 2264 volkswagen 7995 ohc 1.000 186 2212 volkswagen 8195 ohc 0.992 4 0.008 187 2275 volkswagen 8495 ohc 0.986 4 0.014 188 2319 volkswagen 9495 ohc 0.925 4 0.075 189 2300 volkswagen 9995 ohc 0.985 4 0.015 190 2254 volkswagen 11595 ohc 1.000 191 2221 volkswagen 9980 ohc 1.000 DATA_CLASS 2 # CLASS = 2 #Case # curb-weight make price engine-type (Cls Prob) 005 2824 audi 17450 ohc 1.000 006 2507 audi 15250 ohc 0.999 007 2844 audi 17710 ohc 1.000 008 2954 audi 18920 ohc 1.000 009 3086 audi 23875 ohc 1.000 010 3053 audi ? ohc 0.997 3 0.003 011 2395 bmw 16430 ohc 0.999 3 0.001 012 2395 bmw 16925 ohc 1.000 013 2710 bmw 20970 ohc 1.000 014 2765 bmw 21105 ohc 1.000 015 3055 bmw 24565 ohc 1.000 016 3230 bmw 30760 ohc 0.974 5 0.026 066 2670 mazda 18280 ohc 1.000 076 2910 mercury 16503 ohc 1.000 102 3095 nissan 13499 ohcv 1.000 103 3296 nissan 14399 ohcv 1.000 104 3060 nissan 13499 ohcv 1.000 133 2658 saab 11850 ohc 1.000 134 2695 saab 12170 ohc 1.000 136 2758 saab 15510 ohc 1.000 137 2808 saab 18150 dohc 1.000 138 2847 saab 18620 dohc 1.000 179 2976 toyota 16558 dohc 0.982 3 0.018 180 3016 toyota 15998 dohc 0.983 3 0.017 181 3131 toyota 15690 dohc 1.000 182 3151 toyota 15750 dohc 1.000 192 2661 volkswagen 13295 ohc 0.998 4 0.002 195 2912 volvo 12940 ohc 1.000 196 3034 volvo 13415 ohc 1.000 197 2935 volvo 15985 ohc 1.000 198 3042 volvo 16515 ohc 1.000 199 3045 volvo 18420 ohc 1.000 200 3157 volvo 18950 ohc 1.000 201 2952 volvo 16845 ohc 1.000 202 3049 volvo 19045 ohc 1.000 203 3012 volvo 21485 ohcv 1.000 205 3062 volvo 22625 ohc 1.000 DATA_CLASS 3 # CLASS = 3 #Case # curb-weight make price engine-type (Cls Prob) 001 2548 alfa-romero 13495 dohc 1.000 002 2548 alfa-romero 16500 dohc 1.000 003 2823 alfa-romero 16500 ohcv 1.000 030 2811 dodge 12964 ohc 1.000 047 2734 isuzu 11048 ohc 1.000 056 2380 mazda 10945 rotor 1.000 057 2380 mazda 11845 rotor 1.000 058 2385 mazda 13645 rotor 1.000 059 2500 mazda 15645 rotor 1.000 083 2833 mitsubishi 12629 ohc 1.000 084 2921 mitsubishi 14869 ohc 1.000 085 2926 mitsubishi 14489 ohc 1.000 105 3071 nissan 17199 ohcv 1.000 106 3139 nissan 19699 ohcv 1.000 107 3139 nissan 18399 ohcv 0.997 2 0.003 125 2818 plymouth 12764 ohc 1.000 126 2778 porsche 22018 ohc 1.000 127 2756 porsche 32528 ohcf 1.000 128 2756 porsche 34028 ohcf 1.000 129 2800 porsche 37028 ohcf 1.000 130 3366 porsche ? dohcv 1.000 131 2579 renault 9295 ohc 1.000 132 2460 renault 9895 ohc 1.000 135 2707 saab 15040 ohc 1.000 167 2300 toyota 9538 dohc 0.621 1 0.379 168 2540 toyota 8449 ohc 1.000 169 2536 toyota 9639 ohc 1.000 170 2551 toyota 9989 ohc 1.000 171 2679 toyota 11199 ohc 1.000 172 2714 toyota 11549 ohc 1.000 173 2975 toyota 17669 ohc 0.993 2 0.007 DATA_CLASS 4 # CLASS = 4 #Case # curb-weight make price engine-type (Cls Prob) 004 2337 audi 13950 ohc 0.999 029 2535 dodge 8921 ohc 1.000 060 2385 mazda 8845 ohc 1.000 061 2410 mazda 8495 ohc 1.000 062 2385 mazda 10595 ohc 1.000 063 2410 mazda 10245 ohc 1.000 064 2443 mazda 10795 ohc 1.000 065 2425 mazda 11245 ohc 1.000 067 2700 mazda 18344 ohc 1.000 124 2535 plymouth 8921 ohc 1.000 174 2326 toyota 8948 ohc 1.000 175 2480 toyota 10698 ohc 1.000 176 2414 toyota 9988 ohc 1.000 177 2414 toyota 10898 ohc 1.000 178 2458 toyota 11248 ohc 1.000 193 2579 volkswagen 13845 ohc 1.000 194 2563 volkswagen 12290 ohc 1.000 204 3217 volvo 22470 ohc 1.000 DATA_CLASS 5 # CLASS = 5 #Case # curb-weight make price engine-type (Cls Prob) 017 3380 bmw 41315 ohc 1.000 018 3505 bmw 36880 ohc 1.000 048 4066 jaguar 32250 dohc 1.000 049 4066 jaguar 35550 dohc 1.000 050 3950 jaguar 36000 ohcv 1.000 068 3515 mercedes-benz 25552 ohc 1.000 069 3750 mercedes-benz 28248 ohc 1.000 070 3495 mercedes-benz 28176 ohc 1.000 071 3770 mercedes-benz 31600 ohc 1.000 072 3740 mercedes-benz 34184 ohcv 1.000 073 3685 mercedes-benz 35056 ohcv 1.000 074 3900 mercedes-benz 40960 ohcv 1.000 075 3715 mercedes-benz 45400 ohcv 1.000 DATA_CLASS 6 # CLASS = 6 #Case # curb-weight make price engine-type (Cls Prob) 108 3020 peugot 11900 l 1.000 109 3197 peugot 13200 l 1.000 110 3230 peugot 12440 l 1.000 111 3430 peugot 13860 l 1.000 112 3075 peugot 15580 l 1.000 113 3252 peugot 16900 l 1.000 114 3285 peugot 16695 l 1.000 115 3485 peugot 17075 l 1.000 116 3075 peugot 16630 l 1.000 117 3252 peugot 17950 l 1.000 118 3130 peugot 18150 l 1.000autoclass-3.3.6.dfsg.1/data/autos/imports-85-predict.case-text-10000644000175000017500000000213511247310756022311 0ustar areare CROSS REFERENCE: CASE NUMBER => MOST PROBABLE CLASS AutoClass PREDICTION for the 11 "TEST" cases in: /home/tove/p/autoclass-c/data/autos/imports-85-predict.db2 based on the "TRAINING" classification of 205 cases in: /home/tove/p/autoclass-c/data/autos/imports-85.db2 /home/tove/p/autoclass-c/data/autos/imports-85.hd2 with log-A (approximate marginal likelihood) = -16453.535 from classification results file: /home/tove/p/autoclass-c/data/autos/imports-85.results-bin and using models: /home/tove/p/autoclass-c/data/autos/imports-85.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 3 1.00 5 2 1.00 9 2 1.00 2 3 1.00 6 2 0.99 10 2 0.99 3 3 1.00 7 2 1.00 11 2 0.99 4 4 0.99 8 2 1.00 autoclass-3.3.6.dfsg.1/data/autos/imports-85.s-params0000644000175000017500000002103111247310756020345 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.0001 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. True uses universal time clock ! as the seed, giving semi-random searches. # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; max_n_tries = 4 start_fn_type "block" randomize_random_p = false force_new_search_p = true ;; specify a time greater than duration of run min_report_period = 30000 ; force_new_search_p = true ; converge_print_p = true ; max_n_tries = 1 ;; min_report_period = 0 ; try_fn_type = "converge_search_4" autoclass-3.3.6.dfsg.1/data/autos/imports-85-predict.db20000644000175000017500000000424511247310756020731 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,13495 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,16500 1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.50,171.20,65.50,52.40,2823,ohcv,six,152,mpfi,2.68,3.47,9.00,154,5000,19,26,16500 2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950 2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450 2,?,audi,gas,std,two,sedan,fwd,front,99.80,177.30,66.30,53.10,2507,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,15250 1,158,audi,gas,std,four,sedan,fwd,front,105.80,192.70,71.40,55.70,2844,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,17710 1,?,audi,gas,std,four,wagon,fwd,front,105.80,192.70,71.40,55.70,2954,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,18920 1,158,audi,gas,turbo,four,sedan,fwd,front,105.80,192.70,71.40,55.90,3086,ohc,five,131,mpfi,3.13,3.40,8.30,140,5500,17,20,23875 0,?,audi,gas,turbo,two,hatchback,4wd,front,99.50,178.20,67.90,52.00,3053,ohc,five,131,mpfi,3.13,3.40,7.00,160,5500,16,22,? 2,192,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16430 autoclass-3.3.6.dfsg.1/data/autos/imports-85.class-text-10000644000175000017500000005103111247310756021052 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 205 cases in /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 with log-A (approximate marginal likelihood) = -16453.536 from classification results file /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin and using models /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 CLASS = 0 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 19 1488 chevrolet 5151 l 1.000 20 1874 chevrolet 6295 ohc 1.000 21 1909 chevrolet 6575 ohc 1.000 22 1876 dodge 5572 ohc 1.000 23 1876 dodge 6377 ohc 1.000 25 1967 dodge 6229 ohc 1.000 26 1989 dodge 6692 ohc 1.000 27 1989 dodge 7609 ohc 1.000 31 1713 honda 6479 ohc 1.000 32 1819 honda 6855 ohc 1.000 33 1837 honda 5399 ohc 1.000 34 1940 honda 6529 ohc 1.000 35 1956 honda 7129 ohc 1.000 36 2010 honda 7295 ohc 1.000 37 2024 honda 7295 ohc 1.000 45 1874 isuzu ? ohc 1.000 46 1909 isuzu ? ohc 1.000 51 1890 mazda 5195 ohc 1.000 52 1900 mazda 6095 ohc 1.000 53 1905 mazda 6795 ohc 1.000 54 1945 mazda 6695 ohc 1.000 55 1950 mazda 7395 ohc 1.000 77 1918 mitsubishi 5389 ohc 1.000 78 1944 mitsubishi 6189 ohc 1.000 79 2004 mitsubishi 6669 ohc 1.000 90 1889 nissan 5499 ohc 1.000 92 1918 nissan 6649 ohc 1.000 93 1938 nissan 6849 ohc 1.000 94 2024 nissan 7349 ohc 1.000 95 1951 nissan 7299 ohc 1.000 96 2028 nissan 7799 ohc 1.000 97 1971 nissan 7499 ohc 1.000 98 2037 nissan 7999 ohc 1.000 99 2008 nissan 8249 ohc 1.000 119 1918 plymouth 5572 ohc 1.000 121 1967 plymouth 6229 ohc 1.000 122 1989 plymouth 6692 ohc 1.000 123 2191 plymouth 7609 ohc 1.000 151 1985 toyota 5348 ohc 1.000 152 2040 toyota 6338 ohc 1.000 153 2015 toyota 6488 ohc 1.000 CLASS = 0 (continued) Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 154 2280 toyota 6918 ohc 0.999 155 2290 toyota 7898 ohc 0.939 1 0.061 157 2081 toyota 6938 ohc 0.998 1 0.002 158 2109 toyota 7198 ohc 0.998 1 0.002 161 2094 toyota 7738 ohc 0.999 162 2122 toyota 8358 ohc 0.978 1 0.022 163 2140 toyota 9258 ohc 0.756 1 0.244 164 2169 toyota 8058 ohc 0.991 1 0.009 165 2204 toyota 8238 ohc 0.993 1 0.007 CLASS = 1 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 24 2128 dodge 7957 ohc 1.000 28 2191 dodge 8558 ohc 1.000 38 2236 honda 7895 ohc 1.000 39 2289 honda 9095 ohc 1.000 40 2304 honda 8845 ohc 1.000 41 2372 honda 10295 ohc 1.000 42 2465 honda 12945 ohc 0.998 4 0.002 43 2293 honda 10345 ohc 1.000 44 2337 isuzu 6785 ohc 1.000 80 2145 mitsubishi 7689 ohc 1.000 81 2370 mitsubishi 9959 ohc 0.953 3 0.047 82 2328 mitsubishi 8499 ohc 1.000 86 2365 mitsubishi 6989 ohc 1.000 87 2405 mitsubishi 8189 ohc 1.000 88 2403 mitsubishi 9279 ohc 1.000 89 2403 mitsubishi 9279 ohc 1.000 91 2017 nissan 7099 ohc 1.000 100 2324 nissan 8949 ohc 0.982 4 0.018 101 2302 nissan 9549 ohc 0.978 4 0.022 120 2128 plymouth 7957 ohc 1.000 139 2050 subaru 5118 ohcf 1.000 140 2120 subaru 7053 ohcf 1.000 141 2240 subaru 7603 ohcf 1.000 142 2145 subaru 7126 ohcf 1.000 143 2190 subaru 7775 ohcf 1.000 144 2340 subaru 9960 ohcf 1.000 145 2385 subaru 9233 ohcf 1.000 146 2510 subaru 11259 ohcf 1.000 147 2290 subaru 7463 ohcf 1.000 CLASS = 1 (continued) Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 148 2455 subaru 10198 ohcf 1.000 149 2420 subaru 8013 ohcf 1.000 150 2650 subaru 11694 ohcf 1.000 156 3110 toyota 8778 ohc 1.000 159 2275 toyota 7898 ohc 1.000 160 2275 toyota 7788 ohc 1.000 166 2265 toyota 9298 dohc 0.995 3 0.005 183 2261 volkswagen 7775 ohc 1.000 184 2209 volkswagen 7975 ohc 0.999 4 0.001 185 2264 volkswagen 7995 ohc 1.000 186 2212 volkswagen 8195 ohc 0.992 4 0.008 187 2275 volkswagen 8495 ohc 0.986 4 0.014 188 2319 volkswagen 9495 ohc 0.925 4 0.075 189 2300 volkswagen 9995 ohc 0.985 4 0.015 190 2254 volkswagen 11595 ohc 1.000 191 2221 volkswagen 9980 ohc 1.000 CLASS = 2 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 5 2824 audi 17450 ohc 1.000 6 2507 audi 15250 ohc 0.999 7 2844 audi 17710 ohc 1.000 8 2954 audi 18920 ohc 1.000 9 3086 audi 23875 ohc 1.000 10 3053 audi ? ohc 0.997 3 0.003 11 2395 bmw 16430 ohc 0.999 3 0.001 12 2395 bmw 16925 ohc 1.000 13 2710 bmw 20970 ohc 1.000 14 2765 bmw 21105 ohc 1.000 15 3055 bmw 24565 ohc 1.000 16 3230 bmw 30760 ohc 0.974 5 0.026 66 2670 mazda 18280 ohc 1.000 76 2910 mercury 16503 ohc 1.000 102 3095 nissan 13499 ohcv 1.000 103 3296 nissan 14399 ohcv 1.000 104 3060 nissan 13499 ohcv 1.000 133 2658 saab 11850 ohc 1.000 134 2695 saab 12170 ohc 1.000 136 2758 saab 15510 ohc 1.000 137 2808 saab 18150 dohc 1.000 138 2847 saab 18620 dohc 1.000 179 2976 toyota 16558 dohc 0.982 3 0.018 CLASS = 2 (continued) Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 180 3016 toyota 15998 dohc 0.983 3 0.017 181 3131 toyota 15690 dohc 1.000 182 3151 toyota 15750 dohc 1.000 192 2661 volkswagen 13295 ohc 0.998 4 0.002 195 2912 volvo 12940 ohc 1.000 196 3034 volvo 13415 ohc 1.000 197 2935 volvo 15985 ohc 1.000 198 3042 volvo 16515 ohc 1.000 199 3045 volvo 18420 ohc 1.000 200 3157 volvo 18950 ohc 1.000 201 2952 volvo 16845 ohc 1.000 202 3049 volvo 19045 ohc 1.000 203 3012 volvo 21485 ohcv 1.000 205 3062 volvo 22625 ohc 1.000 CLASS = 3 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 1 2548 alfa-romero 13495 dohc 1.000 2 2548 alfa-romero 16500 dohc 1.000 3 2823 alfa-romero 16500 ohcv 1.000 30 2811 dodge 12964 ohc 1.000 47 2734 isuzu 11048 ohc 1.000 56 2380 mazda 10945 rotor 1.000 57 2380 mazda 11845 rotor 1.000 58 2385 mazda 13645 rotor 1.000 59 2500 mazda 15645 rotor 1.000 83 2833 mitsubishi 12629 ohc 1.000 84 2921 mitsubishi 14869 ohc 1.000 85 2926 mitsubishi 14489 ohc 1.000 105 3071 nissan 17199 ohcv 1.000 106 3139 nissan 19699 ohcv 1.000 107 3139 nissan 18399 ohcv 0.997 2 0.003 125 2818 plymouth 12764 ohc 1.000 126 2778 porsche 22018 ohc 1.000 127 2756 porsche 32528 ohcf 1.000 128 2756 porsche 34028 ohcf 1.000 129 2800 porsche 37028 ohcf 1.000 130 3366 porsche ? dohcv 1.000 131 2579 renault 9295 ohc 1.000 132 2460 renault 9895 ohc 1.000 135 2707 saab 15040 ohc 1.000 167 2300 toyota 9538 dohc 0.621 1 0.379 168 2540 toyota 8449 ohc 1.000 169 2536 toyota 9639 ohc 1.000 170 2551 toyota 9989 ohc 1.000 171 2679 toyota 11199 ohc 1.000 172 2714 toyota 11549 ohc 1.000 CLASS = 3 (continued) Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 173 2975 toyota 17669 ohc 0.993 2 0.007 CLASS = 4 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 4 2337 audi 13950 ohc 0.999 29 2535 dodge 8921 ohc 1.000 60 2385 mazda 8845 ohc 1.000 61 2410 mazda 8495 ohc 1.000 62 2385 mazda 10595 ohc 1.000 63 2410 mazda 10245 ohc 1.000 64 2443 mazda 10795 ohc 1.000 65 2425 mazda 11245 ohc 1.000 67 2700 mazda 18344 ohc 1.000 124 2535 plymouth 8921 ohc 1.000 174 2326 toyota 8948 ohc 1.000 175 2480 toyota 10698 ohc 1.000 176 2414 toyota 9988 ohc 1.000 177 2414 toyota 10898 ohc 1.000 178 2458 toyota 11248 ohc 1.000 193 2579 volkswagen 13845 ohc 1.000 194 2563 volkswagen 12290 ohc 1.000 204 3217 volvo 22470 ohc 1.000 CLASS = 5 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 17 3380 bmw 41315 ohc 1.000 18 3505 bmw 36880 ohc 1.000 48 4066 jaguar 32250 dohc 1.000 49 4066 jaguar 35550 dohc 1.000 50 3950 jaguar 36000 ohcv 1.000 68 3515 mercedes-benz 25552 ohc 1.000 69 3750 mercedes-benz 28248 ohc 1.000 70 3495 mercedes-benz 28176 ohc 1.000 71 3770 mercedes-benz 31600 ohc 1.000 72 3740 mercedes-benz 34184 ohcv 1.000 73 3685 mercedes-benz 35056 ohcv 1.000 74 3900 mercedes-benz 40960 ohcv 1.000 75 3715 mercedes-benz 45400 ohcv 1.000 CLASS = 6 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 108 3020 peugot 11900 l 1.000 109 3197 peugot 13200 l 1.000 110 3230 peugot 12440 l 1.000 111 3430 peugot 13860 l 1.000 112 3075 peugot 15580 l 1.000 113 3252 peugot 16900 l 1.000 114 3285 peugot 16695 l 1.000 115 3485 peugot 17075 l 1.000 116 3075 peugot 16630 l 1.000 117 3252 peugot 17950 l 1.000 118 3130 peugot 18150 l 1.000autoclass-3.3.6.dfsg.1/data/autos/imports-85.influ-o-data-10000644000175000017500000024012311247310756021245 0ustar areare#DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 # with log-A (approximate marginal likelihood) = -16453.536 # from classification results file # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin # and using models # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 #DATA_SEARCH_SUMMARY #SEARCH SUMMARY 4 tries over 1 second #SUMMARY OF 10 BEST RESULTS #PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -1 #PROBABILITY exp(-16654.238) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -2 #PROBABILITY exp(-16816.657) N_CLASSES 3 FOUND ON TRY 2 #PROBABILITY exp(-17041.867) N_CLASSES 2 FOUND ON TRY 1 DATA_POP_CLASSES #CLASSIFICATION HAS 7 POPULATED CLASSES (max global influence value = 3.119) # Class Log of class Relative Class Normalized # num strength class strength weight class weight 00 -6.98e+01 1.00e+00 50 0.242 01 -7.81e+01 2.56e-04 46 0.222 02 -7.65e+01 1.32e-03 37 0.180 03 -8.00e+01 3.85e-05 31 0.150 04 -7.53e+01 4.13e-03 18 0.089 05 -7.92e+01 8.46e-05 13 0.064 06 -7.14e+01 2.15e-01 11 0.054 DATA_CLASS_DIVS #CLASS DIVERGENCES # Class class cross entropy Class Normalized # num w.r.t. global class weight class weight 00 1.65e+01 50 0.242 01 7.20e+00 46 0.222 02 9.87e+00 37 0.180 03 9.53e+00 31 0.150 04 9.59e+00 18 0.089 05 2.76e+01 13 0.064 06 2.38e+01 11 0.054 DATA_NORM_INF_VALS #ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES # num description I-*k 036 Log curb-weight 1.000 002 make 0.957 031 Log price 0.877 038 Log width 0.861 040 Log wheel-base 0.840 035 Log engine-size 0.815 039 Log length 0.777 029 Log horse-power 0.658 034 Log compression-ratio 0.563 027 Log bore 0.501 014 engine-type 0.474 017 fuel-system 0.424 037 Log height 0.380 028 Log stroke 0.363 032 Log highway-mpg 0.354 033 Log city-mpg 0.352 015 num-of-cylinders 0.325 007 drive-wheels 0.322 026 Log normalized-loses 0.315 006 body-style 0.226 005 num-of-doors 0.169 030 Log peak-rpm 0.165 003 fuel-type 0.109 004 aspiration 0.064 008 engine-location 0.017 000 symboling ----- 001 normalized-loses ----- 009 wheel-base ----- 010 length ----- 011 width ----- 012 height ----- 013 curb-weight ----- 016 engine-size ----- 018 bore ----- 019 stroke ----- 020 compression-ratio ----- 021 horse-power ----- 022 peak-rpm ----- 023 city-mpg ----- 024 highway-mpg ----- 025 price ----- DATA_CLASS 0 #CLASS 0 - weight 50 normalized weight 0.242 relative strength 1.00e+00 ******* # class cross entropy w.r.t. global class 1.65e+01 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 18 34 R SNcn Log compression-rati 1.913 ( 2.23e+00 2.55e-02) 1.55e+00 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power 1.635 ( 4.22e+00 7.40e-02) 4.95e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 19 35 R SNcn Log engine-size 1.394 ( 4.52e+00 7.09e-02) 3.91e+00 ( 4.80e+00 2.82e-01) 15 31 R SNcm Log price 1.374 ( 8.82e+00 1.33e-01) 3.98e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.60e-01 Prob-*k is known 9.80e-01 20 36 R SNcn Log curb-weight 1.362 ( 7.59e+00 6.80e-02) 3.52e+00 ( 7.83e+00 1.97e-01) 22 38 R SNcn Log width 1.274 ( 4.16e+00 9.08e-03) 3.43e+00 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore 1.126 ( 1.11e+00 3.10e-02) 2.81e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 09 17 D SM fuel-system 0.914 mpfi -5.22e+00 2.47e-03 4.57e-01 idi -3.68e+00 2.47e-03 9.77e-02 spdi -2.89e+00 2.47e-03 4.43e-02 4bbl -1.82e+00 2.47e-03 1.52e-02 2bbl 9.67e-01 8.45e-01 3.21e-01 1bbl 9.57e-01 1.41e-01 5.40e-02 mfi -7.94e-01 2.47e-03 5.46e-03 spfi -7.94e-01 2.47e-03 5.46e-03 24 40 R SNcn Log wheel-base 0.914 ( 4.54e+00 2.12e-02) 2.25e+00 ( 4.59e+00 5.89e-02) 23 39 R SNcn Log length 0.812 ( 5.08e+00 4.35e-02) 1.83e+00 ( 5.16e+00 7.06e-02) 17 33 R SNcn Log city-mpg 0.666 ( 3.48e+00 1.94e-01) 1.45e+00 ( 3.19e+00 2.56e-01) 16 32 R SNcn Log highway-mpg 0.596 ( 3.64e+00 1.94e-01) 1.24e+00 ( 3.40e+00 2.23e-01) 12 28 R SNcm Log stroke 0.575 ( 1.16e+00 3.86e-02) 4.09e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 00 02 D SM make 0.566 subaru -4.18e+00 8.97e-04 5.85e-02 volkswagen -4.18e+00 8.97e-04 5.85e-02 peugot -4.09e+00 8.97e-04 5.36e-02 volvo -4.09e+00 8.97e-04 5.36e-02 bmw -3.77e+00 8.97e-04 3.91e-02 mercedes-benz -3.77e+00 8.97e-04 3.91e-02 audi -3.64e+00 8.97e-04 3.42e-02 saab -3.49e+00 8.97e-04 2.93e-02 porsche -3.31e+00 8.97e-04 2.45e-02 alfa-romero -2.80e+00 8.97e-04 1.48e-02 jaguar -2.80e+00 8.97e-04 1.48e-02 renault -2.40e+00 8.97e-04 9.93e-03 mercury -1.73e+00 8.97e-04 5.08e-03 chevrolet 1.40e+00 6.01e-02 1.48e-02 plymouth 8.48e-01 7.99e-02 3.42e-02 dodge 8.19e-01 9.96e-02 4.39e-02 honda 7.87e-01 1.39e-01 6.33e-02 isuzu 7.21e-01 4.04e-02 1.96e-02 nissan 7.12e-01 1.79e-01 8.76e-02 toyota 3.95e-01 2.31e-01 1.56e-01 mazda 1.85e-01 9.96e-02 8.27e-02 mitsubishi -5.19e-02 6.01e-02 6.33e-02 05 07 D SM drive-wheels 0.321 rwd -2.09e+00 4.58e-02 3.71e-01 4wd -5.90e-01 2.51e-02 4.53e-02 fwd 4.64e-01 9.29e-01 5.84e-01 07 14 D SM engine-type 0.226 ohcf -3.26e+00 2.82e-03 7.35e-02 ohcv -3.12e+00 2.82e-03 6.38e-02 dohc -3.04e+00 2.82e-03 5.89e-02 rotor -1.96e+00 2.82e-03 2.01e-02 l -9.60e-01 2.26e-02 5.89e-02 dohcv -6.77e-01 2.82e-03 5.55e-03 ohc 2.92e-01 9.63e-01 7.19e-01 08 15 D SM num-of-cylinders 0.212 six -3.73e+00 2.82e-03 1.17e-01 five -2.95e+00 2.82e-03 5.41e-02 eight -2.18e+00 2.82e-03 2.50e-02 two -1.96e+00 2.82e-03 2.01e-02 three 1.40e+00 2.26e-02 5.55e-03 twelve -6.77e-01 2.82e-03 5.55e-03 four 2.21e-01 9.63e-01 7.73e-01 02 04 D SM aspiration 0.160 turbo -2.91e+00 9.87e-03 1.82e-01 std 1.91e-01 9.90e-01 8.18e-01 10 26 R SNcm Log normalized-loses 0.138 ( 4.75e+00 2.32e-01) 7.32e-02 ( 4.76e+00 2.82e-01) Prob-jk is known 9.57e-01 Prob-*k is known 8.00e-01 21 37 R SNcn Log height 0.079 ( 3.97e+00 3.71e-02) 3.59e-01 ( 3.98e+00 4.54e-02) 01 03 D SM fuel-type 0.071 diesel -2.31e+00 9.87e-03 9.95e-02 gas 9.49e-02 9.90e-01 9.00e-01 04 06 D SM body-style 0.065 convertible -2.03e+00 3.95e-03 3.01e-02 hardtop -5.19e-01 2.37e-02 3.98e-02 hatchback 3.77e-01 4.97e-01 3.41e-01 sedan -2.22e-01 3.74e-01 4.67e-01 wagon -1.87e-01 1.01e-01 1.22e-01 14 30 R SNcm Log peak-rpm 0.044 ( 8.56e+00 9.80e-02) 2.67e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 03 05 D SM num-of-doors 0.015 ? -5.43e-01 6.58e-03 1.13e-02 two 1.81e-01 5.20e-01 4.34e-01 four -1.58e-01 4.74e-01 5.55e-01 06 08 D SM engine-location 0.002 rear -5.43e-01 9.87e-03 1.70e-02 front 7.22e-03 9.90e-01 9.83e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.112305e+00 3.102432e-02 1.159488e+00 3.859754e-02 0.000000e+00 000027 00034 1.112305e+00 3.102432e-02 2.227762e+00 2.551096e-02 0.000000e+00 000027 00029 1.112305e+00 3.102432e-02 4.217732e+00 7.395456e-02 0.000000e+00 000027 00030 1.112305e+00 3.102432e-02 8.563767e+00 9.803089e-02 0.000000e+00 000027 00033 1.112305e+00 3.102432e-02 3.475403e+00 1.941582e-01 0.000000e+00 000027 00032 1.112305e+00 3.102432e-02 3.641247e+00 1.941582e-01 0.000000e+00 000027 00031 1.112305e+00 3.102432e-02 8.818470e+00 1.334224e-01 0.000000e+00 000028 00034 1.159488e+00 3.859754e-02 2.227762e+00 2.551096e-02 0.000000e+00 000028 00029 1.159488e+00 3.859754e-02 4.217732e+00 7.395456e-02 0.000000e+00 000028 00030 1.159488e+00 3.859754e-02 8.563767e+00 9.803089e-02 0.000000e+00 000028 00033 1.159488e+00 3.859754e-02 3.475403e+00 1.941582e-01 0.000000e+00 000028 00032 1.159488e+00 3.859754e-02 3.641247e+00 1.941582e-01 0.000000e+00 000028 00031 1.159488e+00 3.859754e-02 8.818470e+00 1.334224e-01 0.000000e+00 000034 00029 2.227762e+00 2.551096e-02 4.217732e+00 7.395456e-02 0.000000e+00 000034 00030 2.227762e+00 2.551096e-02 8.563767e+00 9.803089e-02 0.000000e+00 000034 00033 2.227762e+00 2.551096e-02 3.475403e+00 1.941582e-01 0.000000e+00 000034 00032 2.227762e+00 2.551096e-02 3.641247e+00 1.941582e-01 0.000000e+00 000034 00031 2.227762e+00 2.551096e-02 8.818470e+00 1.334224e-01 0.000000e+00 000029 00030 4.217732e+00 7.395456e-02 8.563767e+00 9.803089e-02 0.000000e+00 000029 00033 4.217732e+00 7.395456e-02 3.475403e+00 1.941582e-01 0.000000e+00 000029 00032 4.217732e+00 7.395456e-02 3.641247e+00 1.941582e-01 0.000000e+00 000029 00031 4.217732e+00 7.395456e-02 8.818470e+00 1.334224e-01 0.000000e+00 000030 00033 8.563767e+00 9.803089e-02 3.475403e+00 1.941582e-01 0.000000e+00 000030 00032 8.563767e+00 9.803089e-02 3.641247e+00 1.941582e-01 0.000000e+00 000030 00031 8.563767e+00 9.803089e-02 8.818470e+00 1.334224e-01 0.000000e+00 000033 00032 3.475403e+00 1.941582e-01 3.641247e+00 1.941582e-01 0.000000e+00 000033 00031 3.475403e+00 1.941582e-01 8.818470e+00 1.334224e-01 0.000000e+00 000032 00031 3.641247e+00 1.941582e-01 8.818470e+00 1.334224e-01 0.000000e+00 DATA_CLASS 1 #CLASS 1 - weight 46 normalized weight 0.222 relative strength 2.56e-04 ******* # class cross entropy w.r.t. global class 7.20e+00 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 19 35 R SNcn Log engine-size 1.091 ( 4.67e+00 6.51e-02) 1.93e+00 ( 4.80e+00 2.82e-01) 24 40 R SNcn Log wheel-base 1.074 ( 4.56e+00 1.39e-02) 1.93e+00 ( 4.59e+00 5.89e-02) 15 31 R SNcm Log price 0.844 ( 9.06e+00 1.65e-01) 1.74e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 00 02 D SM make 0.727 mazda -4.44e+00 9.78e-04 8.27e-02 peugot -4.00e+00 9.77e-04 5.36e-02 volvo -4.00e+00 9.77e-04 5.36e-02 bmw -3.69e+00 9.77e-04 3.91e-02 mercedes-benz -3.69e+00 9.77e-04 3.91e-02 audi -3.55e+00 9.78e-04 3.42e-02 saab -3.40e+00 9.77e-04 2.93e-02 porsche -3.22e+00 9.77e-04 2.45e-02 alfa-romero -2.72e+00 9.77e-04 1.48e-02 chevrolet -2.72e+00 9.77e-04 1.48e-02 jaguar -2.72e+00 9.77e-04 1.48e-02 renault -2.32e+00 9.77e-04 9.93e-03 mercury -1.65e+00 9.77e-04 5.08e-03 subaru 1.49e+00 2.59e-01 5.85e-02 volkswagen 1.19e+00 1.92e-01 5.85e-02 mitsubishi 8.65e-01 1.50e-01 6.33e-02 honda 7.19e-01 1.30e-01 6.33e-02 plymouth -4.20e-01 2.25e-02 3.42e-02 toyota -4.19e-01 1.02e-01 1.56e-01 nissan -3.04e-01 6.46e-02 8.76e-02 isuzu 1.35e-01 2.25e-02 1.96e-02 dodge 1.61e-03 4.40e-02 4.39e-02 20 36 R SNcn Log curb-weight 0.720 ( 7.74e+00 6.78e-02) 1.26e+00 ( 7.83e+00 1.97e-01) 22 38 R SNcn Log width 0.544 ( 4.17e+00 1.42e-02) 1.19e+00 ( 4.19e+00 3.15e-02) 23 39 R SNcn Log length 0.415 ( 5.13e+00 3.45e-02) 8.15e-01 ( 5.16e+00 7.06e-02) 05 07 D SM drive-wheels 0.302 rwd -1.85e+00 5.84e-02 3.71e-01 4wd 1.11e+00 1.37e-01 4.53e-02 fwd 3.20e-01 8.04e-01 5.84e-01 13 29 R SNcm Log horse-power 0.263 ( 4.45e+00 2.12e-01) 6.50e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 07 14 D SM engine-type 0.261 ohcv -3.03e+00 3.07e-03 6.38e-02 l -2.95e+00 3.07e-03 5.89e-02 rotor -1.88e+00 3.07e-03 2.01e-02 ohcf 1.27e+00 2.61e-01 7.35e-02 dohc -5.98e-01 3.24e-02 5.89e-02 dohcv -5.91e-01 3.07e-03 5.55e-03 ohc -3.53e-02 6.94e-01 7.19e-01 08 15 D SM num-of-cylinders 0.199 six -3.64e+00 3.07e-03 1.17e-01 five -2.87e+00 3.07e-03 5.41e-02 eight -2.10e+00 3.07e-03 2.50e-02 two -1.88e+00 3.07e-03 2.01e-02 three -5.91e-01 3.07e-03 5.55e-03 twelve -5.91e-01 3.07e-03 5.55e-03 four 2.39e-01 9.82e-01 7.73e-01 10 26 R SNcm Log normalized-loses 0.161 ( 4.69e+00 2.37e-01) 3.30e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.53e-01 Prob-*k is known 8.00e-01 17 33 R SNcn Log city-mpg 0.136 ( 3.29e+00 1.94e-01) 5.02e-01 ( 3.19e+00 2.56e-01) 09 17 D SM fuel-system 0.085 4bbl -1.73e+00 2.69e-03 1.52e-02 spdi 9.02e-01 1.09e-01 4.43e-02 mfi -7.09e-01 2.69e-03 5.46e-03 spfi -7.08e-01 2.69e-03 5.46e-03 1bbl 4.96e-01 8.87e-02 5.40e-02 mpfi -3.86e-01 3.11e-01 4.57e-01 idi 2.86e-01 1.30e-01 9.77e-02 2bbl 9.59e-02 3.53e-01 3.21e-01 16 32 R SNcn Log highway-mpg 0.084 ( 3.48e+00 1.94e-01) 4.17e-01 ( 3.40e+00 2.23e-01) 11 27 R SNcm Log bore 0.073 ( 1.19e+00 6.52e-02) 1.76e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 12 28 R SNcm Log stroke 0.067 ( 1.16e+00 1.21e-01) 1.53e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 21 37 R SNcn Log height 0.046 ( 3.98e+00 3.67e-02) 1.37e-01 ( 3.98e+00 4.54e-02) 04 06 D SM body-style 0.040 hardtop -2.23e+00 4.30e-03 3.98e-02 sedan 1.92e-01 5.66e-01 4.67e-01 hatchback -1.58e-01 2.91e-01 3.41e-01 convertible -1.54e-01 2.58e-02 3.01e-02 wagon -7.81e-02 1.13e-01 1.22e-01 18 34 R SNcn Log compression-rati 0.029 ( 2.28e+00 3.30e-01) 3.35e-02 ( 2.27e+00 2.81e-01) o 03 05 D SM num-of-doors 0.014 ? 9.29e-01 2.87e-02 1.13e-02 two -1.32e-01 3.80e-01 4.34e-01 four 6.36e-02 5.91e-01 5.55e-01 14 30 R SNcm Log peak-rpm 0.012 ( 8.55e+00 9.79e-02) 8.09e-02 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 01 03 D SM fuel-type 0.008 diesel 3.28e-01 1.38e-01 9.95e-02 gas -4.38e-02 8.62e-01 9.00e-01 06 08 D SM engine-location 0.001 rear -4.58e-01 1.08e-02 1.70e-02 front 6.33e-03 9.89e-01 9.83e-01 02 04 D SM aspiration 0.001 turbo 1.02e-01 2.02e-01 1.82e-01 std -2.43e-02 7.98e-01 8.18e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.188033e+00 6.521781e-02 1.156649e+00 1.213934e-01 0.000000e+00 000027 00034 1.188033e+00 6.521781e-02 2.278432e+00 3.296226e-01 0.000000e+00 000027 00029 1.188033e+00 6.521781e-02 4.446108e+00 2.124081e-01 0.000000e+00 000027 00030 1.188033e+00 6.521781e-02 8.545494e+00 9.794287e-02 0.000000e+00 000027 00033 1.188033e+00 6.521781e-02 3.292115e+00 1.939838e-01 0.000000e+00 000027 00032 1.188033e+00 6.521781e-02 3.482010e+00 1.939838e-01 0.000000e+00 000027 00031 1.188033e+00 6.521781e-02 9.064028e+00 1.648769e-01 0.000000e+00 000028 00034 1.156649e+00 1.213934e-01 2.278432e+00 3.296226e-01 0.000000e+00 000028 00029 1.156649e+00 1.213934e-01 4.446108e+00 2.124081e-01 0.000000e+00 000028 00030 1.156649e+00 1.213934e-01 8.545494e+00 9.794287e-02 0.000000e+00 000028 00033 1.156649e+00 1.213934e-01 3.292115e+00 1.939838e-01 0.000000e+00 000028 00032 1.156649e+00 1.213934e-01 3.482010e+00 1.939838e-01 0.000000e+00 000028 00031 1.156649e+00 1.213934e-01 9.064028e+00 1.648769e-01 0.000000e+00 000034 00029 2.278432e+00 3.296226e-01 4.446108e+00 2.124081e-01 0.000000e+00 000034 00030 2.278432e+00 3.296226e-01 8.545494e+00 9.794287e-02 0.000000e+00 000034 00033 2.278432e+00 3.296226e-01 3.292115e+00 1.939838e-01 0.000000e+00 000034 00032 2.278432e+00 3.296226e-01 3.482010e+00 1.939838e-01 0.000000e+00 000034 00031 2.278432e+00 3.296226e-01 9.064028e+00 1.648769e-01 0.000000e+00 000029 00030 4.446108e+00 2.124081e-01 8.545494e+00 9.794287e-02 0.000000e+00 000029 00033 4.446108e+00 2.124081e-01 3.292115e+00 1.939838e-01 0.000000e+00 000029 00032 4.446108e+00 2.124081e-01 3.482010e+00 1.939838e-01 0.000000e+00 000029 00031 4.446108e+00 2.124081e-01 9.064028e+00 1.648769e-01 0.000000e+00 000030 00033 8.545494e+00 9.794287e-02 3.292115e+00 1.939838e-01 0.000000e+00 000030 00032 8.545494e+00 9.794287e-02 3.482010e+00 1.939838e-01 0.000000e+00 000030 00031 8.545494e+00 9.794287e-02 9.064028e+00 1.648769e-01 0.000000e+00 000033 00032 3.292115e+00 1.939838e-01 3.482010e+00 1.939838e-01 0.000000e+00 000033 00031 3.292115e+00 1.939838e-01 9.064028e+00 1.648769e-01 0.000000e+00 000032 00031 3.482010e+00 1.939838e-01 9.064028e+00 1.648769e-01 0.000000e+00 DATA_CLASS 2 #CLASS 2 - weight 37 normalized weight 0.180 relative strength 1.32e-03 ******* # class cross entropy w.r.t. global class 9.87e+00 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 00 02 D SM make 0.989 honda -3.97e+00 1.20e-03 6.33e-02 mitsubishi -3.97e+00 1.20e-03 6.33e-02 subaru -3.89e+00 1.20e-03 5.85e-02 peugot -3.80e+00 1.20e-03 5.36e-02 dodge -3.60e+00 1.20e-03 4.39e-02 mercedes-benz -3.48e+00 1.20e-03 3.91e-02 plymouth -3.35e+00 1.20e-03 3.42e-02 porsche -3.02e+00 1.20e-03 2.45e-02 isuzu -2.80e+00 1.20e-03 1.96e-02 alfa-romero -2.51e+00 1.20e-03 1.48e-02 chevrolet -2.51e+00 1.20e-03 1.48e-02 jaguar -2.51e+00 1.20e-03 1.48e-02 renault -2.11e+00 1.20e-03 9.93e-03 mercury 1.69e+00 2.75e-02 5.08e-03 volvo 1.60e+00 2.65e-01 5.36e-02 audi 1.54e+00 1.59e-01 3.42e-02 saab 1.51e+00 1.33e-01 2.93e-02 bmw 1.40e+00 1.59e-01 3.91e-02 mazda -1.10e+00 2.76e-02 8.27e-02 volkswagen -7.54e-01 2.75e-02 5.85e-02 toyota -3.85e-01 1.06e-01 1.56e-01 nissan -8.66e-02 8.03e-02 8.76e-02 18 34 R SNcn Log compression-rati 0.932 ( 2.17e+00 7.36e-02) 1.31e+00 ( 2.27e+00 2.81e-01) o 23 39 R SNcn Log length 0.896 ( 5.22e+00 2.81e-02) 2.24e+00 ( 5.16e+00 7.06e-02) 20 36 R SNcn Log curb-weight 0.817 ( 7.97e+00 7.51e-02) 1.96e+00 ( 7.83e+00 1.97e-01) 15 31 R SNcm Log price 0.787 ( 9.75e+00 2.01e-01) 1.98e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.73e-01 Prob-*k is known 9.80e-01 09 17 D SM fuel-system 0.690 2bbl -4.58e+00 3.29e-03 3.21e-01 idi -3.39e+00 3.29e-03 9.77e-02 1bbl -2.80e+00 3.29e-03 5.40e-02 spdi -2.60e+00 3.29e-03 4.43e-02 4bbl -1.53e+00 3.29e-03 1.52e-02 mpfi 7.60e-01 9.77e-01 4.57e-01 mfi -5.05e-01 3.29e-03 5.46e-03 spfi -5.05e-01 3.29e-03 5.46e-03 13 29 R SNcm Log horse-power 0.676 ( 4.88e+00 1.75e-01) 1.67e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 24 40 R SNcn Log wheel-base 0.596 ( 4.63e+00 2.90e-02) 1.48e+00 ( 4.59e+00 5.89e-02) 19 35 R SNcn Log engine-size 0.448 ( 4.97e+00 1.50e-01) 1.12e+00 ( 4.80e+00 2.82e-01) 21 37 R SNcn Log height 0.448 ( 4.01e+00 2.44e-02) 1.12e+00 ( 3.98e+00 4.54e-02) 17 33 R SNcn Log city-mpg 0.438 ( 2.97e+00 1.94e-01) 1.14e+00 ( 3.19e+00 2.56e-01) 12 28 R SNcm Log stroke 0.349 ( 1.16e+00 5.08e-02) 2.62e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 16 32 R SNcn Log highway-mpg 0.336 ( 3.22e+00 1.94e-01) 9.19e-01 ( 3.40e+00 2.23e-01) 08 15 D SM num-of-cylinders 0.307 eight -1.89e+00 3.77e-03 2.50e-02 two -1.68e+00 3.77e-03 2.01e-02 five 1.25e+00 1.88e-01 5.41e-02 six 1.00e-00 3.18e-01 1.17e-01 four -4.79e-01 4.78e-01 7.73e-01 three -3.88e-01 3.77e-03 5.55e-03 twelve -3.88e-01 3.77e-03 5.55e-03 22 38 R SNcn Log width 0.278 ( 4.21e+00 2.34e-02) 8.62e-01 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore 0.223 ( 1.24e+00 6.31e-02) 7.06e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 07 14 D SM engine-type 0.187 ohcf -2.97e+00 3.77e-03 7.35e-02 l -2.75e+00 3.77e-03 5.89e-02 rotor -1.68e+00 3.77e-03 2.01e-02 dohc 1.00e+00 1.61e-01 5.89e-02 ohcv 5.38e-01 1.09e-01 6.38e-02 dohcv -3.88e-01 3.77e-03 5.55e-03 ohc -6.15e-03 7.15e-01 7.19e-01 04 06 D SM body-style 0.140 hardtop -2.02e+00 5.27e-03 3.98e-02 convertible -1.71e+00 5.45e-03 3.01e-02 hatchback -7.41e-01 1.62e-01 3.41e-01 sedan 3.51e-01 6.63e-01 4.67e-01 wagon 2.90e-01 1.63e-01 1.22e-01 05 07 D SM drive-wheels 0.110 fwd -5.08e-01 3.51e-01 5.84e-01 rwd 4.60e-01 5.87e-01 3.71e-01 4wd 3.04e-01 6.14e-02 4.53e-02 03 05 D SM num-of-doors 0.079 two -5.70e-01 2.45e-01 4.34e-01 four 2.96e-01 7.46e-01 5.55e-01 ? -2.54e-01 8.79e-03 1.13e-02 01 03 D SM fuel-type 0.064 diesel -2.02e+00 1.32e-02 9.95e-02 gas 9.16e-02 9.87e-01 9.00e-01 14 30 R SNcm Log peak-rpm 0.051 ( 8.57e+00 9.77e-02) 2.94e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 02 04 D SM aspiration 0.014 turbo 3.18e-01 2.50e-01 1.82e-01 std -8.71e-02 7.50e-01 8.18e-01 10 26 R SNcm Log normalized-loses 0.014 ( 4.80e+00 3.08e-01) 1.21e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 7.85e-01 Prob-*k is known 8.00e-01 06 08 D SM engine-location 0.000 rear -2.54e-01 1.32e-02 1.70e-02 front 3.87e-03 9.87e-01 9.83e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.244082e+00 6.311692e-02 1.161951e+00 5.077615e-02 0.000000e+00 000027 00034 1.244082e+00 6.311692e-02 2.171235e+00 7.364193e-02 0.000000e+00 000027 00029 1.244082e+00 6.311692e-02 4.877339e+00 1.751059e-01 0.000000e+00 000027 00030 1.244082e+00 6.311692e-02 8.566279e+00 9.769961e-02 0.000000e+00 000027 00033 1.244082e+00 6.311692e-02 2.974011e+00 1.935020e-01 0.000000e+00 000027 00032 1.244082e+00 6.311692e-02 3.223213e+00 1.935020e-01 0.000000e+00 000027 00031 1.244082e+00 6.311692e-02 9.747549e+00 2.010556e-01 0.000000e+00 000028 00034 1.161951e+00 5.077615e-02 2.171235e+00 7.364193e-02 0.000000e+00 000028 00029 1.161951e+00 5.077615e-02 4.877339e+00 1.751059e-01 0.000000e+00 000028 00030 1.161951e+00 5.077615e-02 8.566279e+00 9.769961e-02 0.000000e+00 000028 00033 1.161951e+00 5.077615e-02 2.974011e+00 1.935020e-01 0.000000e+00 000028 00032 1.161951e+00 5.077615e-02 3.223213e+00 1.935020e-01 0.000000e+00 000028 00031 1.161951e+00 5.077615e-02 9.747549e+00 2.010556e-01 0.000000e+00 000034 00029 2.171235e+00 7.364193e-02 4.877339e+00 1.751059e-01 0.000000e+00 000034 00030 2.171235e+00 7.364193e-02 8.566279e+00 9.769961e-02 0.000000e+00 000034 00033 2.171235e+00 7.364193e-02 2.974011e+00 1.935020e-01 0.000000e+00 000034 00032 2.171235e+00 7.364193e-02 3.223213e+00 1.935020e-01 0.000000e+00 000034 00031 2.171235e+00 7.364193e-02 9.747549e+00 2.010556e-01 0.000000e+00 000029 00030 4.877339e+00 1.751059e-01 8.566279e+00 9.769961e-02 0.000000e+00 000029 00033 4.877339e+00 1.751059e-01 2.974011e+00 1.935020e-01 0.000000e+00 000029 00032 4.877339e+00 1.751059e-01 3.223213e+00 1.935020e-01 0.000000e+00 000029 00031 4.877339e+00 1.751059e-01 9.747549e+00 2.010556e-01 0.000000e+00 000030 00033 8.566279e+00 9.769961e-02 2.974011e+00 1.935020e-01 0.000000e+00 000030 00032 8.566279e+00 9.769961e-02 3.223213e+00 1.935020e-01 0.000000e+00 000030 00031 8.566279e+00 9.769961e-02 9.747549e+00 2.010556e-01 0.000000e+00 000033 00032 2.974011e+00 1.935020e-01 3.223213e+00 1.935020e-01 0.000000e+00 000033 00031 2.974011e+00 1.935020e-01 9.747549e+00 2.010556e-01 0.000000e+00 000032 00031 3.223213e+00 1.935020e-01 9.747549e+00 2.010556e-01 0.000000e+00 DATA_CLASS 3 #CLASS 3 - weight 31 normalized weight 0.150 relative strength 3.85e-05 ******* # class cross entropy w.r.t. global class 9.53e+00 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 04 06 D SM body-style 0.791 sedan -4.27e+00 6.52e-03 4.67e-01 convertible 1.48e+00 1.32e-01 3.01e-02 hardtop 1.42e+00 1.64e-01 3.98e-02 wagon -1.17e+00 3.78e-02 1.22e-01 hatchback 6.60e-01 6.59e-01 3.41e-01 00 02 D SM make 0.730 honda -3.79e+00 1.43e-03 6.33e-02 subaru -3.71e+00 1.43e-03 5.85e-02 volkswagen -3.71e+00 1.43e-03 5.85e-02 peugot -3.62e+00 1.43e-03 5.36e-02 volvo -3.62e+00 1.43e-03 5.36e-02 mercedes-benz -3.31e+00 1.43e-03 3.91e-02 bmw -3.27e+00 1.48e-03 3.91e-02 audi -3.11e+00 1.53e-03 3.42e-02 chevrolet -2.33e+00 1.43e-03 1.48e-02 jaguar -2.33e+00 1.43e-03 1.48e-02 alfa-romero 1.87e+00 9.60e-02 1.48e-02 porsche 1.87e+00 1.59e-01 2.45e-02 renault 1.87e+00 6.45e-02 9.93e-03 mercury -1.26e+00 1.44e-03 5.08e-03 isuzu 5.18e-01 3.30e-02 1.96e-02 mazda 4.33e-01 1.28e-01 8.27e-02 mitsubishi 4.32e-01 9.75e-02 6.33e-02 toyota 3.08e-01 2.12e-01 1.56e-01 dodge -2.87e-01 3.30e-02 4.39e-02 saab 1.16e-01 3.30e-02 2.93e-02 nissan 9.11e-02 9.59e-02 8.76e-02 plymouth -3.68e-02 3.30e-02 3.42e-02 21 37 R SNcn Log height 0.730 ( 3.93e+00 3.14e-02) 1.61e+00 ( 3.98e+00 4.54e-02) 09 17 D SM fuel-system 0.700 2bbl -4.40e+00 3.94e-03 3.21e-01 idi -3.21e+00 3.94e-03 9.77e-02 1bbl -2.62e+00 3.94e-03 5.40e-02 mfi 1.87e+00 3.55e-02 5.46e-03 4bbl 1.87e+00 9.85e-02 1.52e-02 spfi 1.87e+00 3.55e-02 5.46e-03 spdi 1.09e+00 1.32e-01 4.43e-02 mpfi 4.08e-01 6.87e-01 4.57e-01 23 39 R SNcn Log length 0.633 ( 5.15e+00 2.41e-02) 1.26e-01 ( 5.16e+00 7.06e-02) 03 05 D SM num-of-doors 0.631 four -2.58e+00 4.20e-02 5.55e-01 two 7.82e-01 9.47e-01 4.34e-01 ? -7.48e-02 1.05e-02 1.13e-02 18 34 R SNcn Log compression-rati 0.591 ( 2.17e+00 1.07e-01) 8.67e-01 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power 0.564 ( 4.92e+00 2.64e-01) 1.27e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.37e-01 Prob-*k is known 9.90e-01 10 26 R SNcm Log normalized-loses 0.553 ( 5.04e+00 1.59e-01) 1.75e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.52e-01 Prob-*k is known 8.00e-01 20 36 R SNcn Log curb-weight 0.498 ( 7.91e+00 8.72e-02) 9.34e-01 ( 7.83e+00 1.97e-01) 24 40 R SNcn Log wheel-base 0.404 ( 4.55e+00 3.42e-02) 1.07e+00 ( 4.59e+00 5.89e-02) 17 33 R SNcn Log city-mpg 0.379 ( 2.99e+00 1.93e-01) 1.05e+00 ( 3.19e+00 2.56e-01) 11 27 R SNcm Log bore 0.329 ( 1.25e+00 9.17e-02) 5.77e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 8.73e-01 Prob-*k is known 9.80e-01 05 07 D SM drive-wheels 0.311 4wd -1.45e+00 1.06e-02 4.53e-02 fwd -9.20e-01 2.33e-01 5.84e-01 rwd 7.14e-01 7.57e-01 3.71e-01 07 14 D SM engine-type 0.285 l -2.57e+00 4.50e-03 5.89e-02 rotor 1.87e+00 1.31e-01 2.01e-02 dohcv 1.87e+00 3.60e-02 5.55e-03 ohcv 7.16e-01 1.31e-01 6.38e-02 dohc 4.09e-01 8.87e-02 5.89e-02 ohc -3.43e-01 5.10e-01 7.19e-01 ohcf 2.99e-01 9.91e-02 7.35e-02 12 28 R SNcm Log stroke 0.283 ( 1.20e+00 1.44e-01) 1.42e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 8.73e-01 Prob-*k is known 9.80e-01 08 15 D SM num-of-cylinders 0.237 five -2.46e+00 4.60e-03 5.41e-02 two 1.87e+00 1.31e-01 2.01e-02 six 6.58e-01 2.26e-01 1.17e-01 eight 3.67e-01 3.60e-02 2.50e-02 four -2.64e-01 5.93e-01 7.73e-01 three -2.08e-01 4.50e-03 5.55e-03 twelve -2.08e-01 4.50e-03 5.55e-03 15 31 R SNcm Log price 0.188 ( 9.59e+00 3.67e-01) 6.43e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.68e-01 Prob-*k is known 9.80e-01 16 32 R SNcn Log highway-mpg 0.184 ( 3.27e+00 1.93e-01) 6.64e-01 ( 3.40e+00 2.23e-01) 14 30 R SNcm Log peak-rpm 0.122 ( 8.57e+00 9.74e-02) 3.49e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.37e-01 Prob-*k is known 9.90e-01 06 08 D SM engine-location 0.118 rear 1.87e+00 1.10e-01 1.70e-02 front -9.98e-02 8.90e-01 9.83e-01 22 38 R SNcn Log width 0.106 ( 4.19e+00 2.22e-02) 1.73e-01 ( 4.19e+00 3.15e-02) 19 35 R SNcn Log engine-size 0.105 ( 4.93e+00 2.88e-01) 4.47e-01 ( 4.80e+00 2.82e-01) 01 03 D SM fuel-type 0.058 diesel -1.84e+00 1.58e-02 9.95e-02 gas 8.89e-02 9.84e-01 9.00e-01 02 04 D SM aspiration 0.002 turbo 1.26e-01 2.07e-01 1.82e-01 std -3.04e-02 7.93e-01 8.18e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.252452e+00 9.167758e-02 1.195762e+00 1.437895e-01 0.000000e+00 000027 00034 1.252452e+00 9.167758e-02 2.174490e+00 1.071232e-01 0.000000e+00 000027 00029 1.252452e+00 9.167758e-02 4.918893e+00 2.643883e-01 0.000000e+00 000027 00030 1.252452e+00 9.167758e-02 8.571619e+00 9.743957e-02 0.000000e+00 000027 00033 1.252452e+00 9.167758e-02 2.992620e+00 1.929870e-01 0.000000e+00 000027 00032 1.252452e+00 9.167758e-02 3.272883e+00 1.929870e-01 0.000000e+00 000027 00031 1.252452e+00 9.167758e-02 9.585871e+00 3.665460e-01 0.000000e+00 000028 00034 1.195762e+00 1.437895e-01 2.174490e+00 1.071232e-01 0.000000e+00 000028 00029 1.195762e+00 1.437895e-01 4.918893e+00 2.643883e-01 0.000000e+00 000028 00030 1.195762e+00 1.437895e-01 8.571619e+00 9.743957e-02 0.000000e+00 000028 00033 1.195762e+00 1.437895e-01 2.992620e+00 1.929870e-01 0.000000e+00 000028 00032 1.195762e+00 1.437895e-01 3.272883e+00 1.929870e-01 0.000000e+00 000028 00031 1.195762e+00 1.437895e-01 9.585871e+00 3.665460e-01 0.000000e+00 000034 00029 2.174490e+00 1.071232e-01 4.918893e+00 2.643883e-01 0.000000e+00 000034 00030 2.174490e+00 1.071232e-01 8.571619e+00 9.743957e-02 0.000000e+00 000034 00033 2.174490e+00 1.071232e-01 2.992620e+00 1.929870e-01 0.000000e+00 000034 00032 2.174490e+00 1.071232e-01 3.272883e+00 1.929870e-01 0.000000e+00 000034 00031 2.174490e+00 1.071232e-01 9.585871e+00 3.665460e-01 0.000000e+00 000029 00030 4.918893e+00 2.643883e-01 8.571619e+00 9.743957e-02 0.000000e+00 000029 00033 4.918893e+00 2.643883e-01 2.992620e+00 1.929870e-01 0.000000e+00 000029 00032 4.918893e+00 2.643883e-01 3.272883e+00 1.929870e-01 0.000000e+00 000029 00031 4.918893e+00 2.643883e-01 9.585871e+00 3.665460e-01 0.000000e+00 000030 00033 8.571619e+00 9.743957e-02 2.992620e+00 1.929870e-01 0.000000e+00 000030 00032 8.571619e+00 9.743957e-02 3.272883e+00 1.929870e-01 0.000000e+00 000030 00031 8.571619e+00 9.743957e-02 9.585871e+00 3.665460e-01 0.000000e+00 000033 00032 2.992620e+00 1.929870e-01 3.272883e+00 1.929870e-01 0.000000e+00 000033 00031 2.992620e+00 1.929870e-01 9.585871e+00 3.665460e-01 0.000000e+00 000032 00031 3.272883e+00 1.929870e-01 9.585871e+00 3.665460e-01 0.000000e+00 DATA_CLASS 4 #CLASS 4 - weight 18 normalized weight 0.089 relative strength 4.13e-03 ******* # class cross entropy w.r.t. global class 9.59e+00 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 12 28 R SNcm Log stroke 1.263 ( 1.24e+00 2.18e-02) 2.82e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 23 39 R SNcn Log length 0.927 ( 5.18e+00 1.84e-02) 1.22e+00 ( 5.16e+00 7.06e-02) 19 35 R SNcn Log engine-size 0.786 ( 4.79e+00 8.12e-02) 1.65e-01 ( 4.80e+00 2.82e-01) 00 02 D SM make 0.733 mitsubishi -3.27e+00 2.40e-03 6.33e-02 honda -3.23e+00 2.50e-03 6.33e-02 subaru -3.20e+00 2.37e-03 5.85e-02 peugot -3.12e+00 2.37e-03 5.36e-02 nissan -2.98e+00 4.47e-03 8.76e-02 bmw -2.80e+00 2.37e-03 3.91e-02 mercedes-benz -2.80e+00 2.37e-03 3.91e-02 saab -2.52e+00 2.37e-03 2.93e-02 porsche -2.33e+00 2.37e-03 2.45e-02 isuzu -2.11e+00 2.37e-03 1.96e-02 alfa-romero -1.83e+00 2.37e-03 1.48e-02 chevrolet -1.83e+00 2.37e-03 1.48e-02 jaguar -1.83e+00 2.37e-03 1.48e-02 mazda 1.49e+00 3.68e-01 8.27e-02 renault -1.43e+00 2.37e-03 9.93e-03 mercury -7.60e-01 2.37e-03 5.08e-03 volkswagen 6.57e-01 1.13e-01 5.85e-02 toyota 5.26e-01 2.63e-01 1.56e-01 audi 4.67e-01 5.46e-02 3.42e-02 plymouth 4.67e-01 5.46e-02 3.42e-02 dodge 2.17e-01 5.46e-02 4.39e-02 volvo 1.76e-02 5.46e-02 5.36e-02 13 29 R SNcm Log horse-power 0.679 ( 4.44e+00 1.25e-01) 1.16e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 20 36 R SNcn Log curb-weight 0.600 ( 7.82e+00 6.99e-02) 7.46e-02 ( 7.83e+00 1.97e-01) 24 40 R SNcn Log wheel-base 0.543 ( 4.62e+00 2.55e-02) 1.09e+00 ( 4.59e+00 5.89e-02) 22 38 R SNcn Log width 0.525 ( 4.20e+00 1.27e-02) 6.43e-01 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore 0.419 ( 1.19e+00 3.70e-02) 1.95e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 14 30 R SNcm Log peak-rpm 0.394 ( 8.45e+00 9.64e-02) 9.00e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 18 34 R SNcn Log compression-rati 0.375 ( 2.43e+00 4.17e-01) 3.90e-01 ( 2.27e+00 2.81e-01) o 21 37 R SNcn Log height 0.368 ( 4.01e+00 2.90e-02) 1.03e+00 ( 3.98e+00 4.54e-02) 15 31 R SNcm Log price 0.349 ( 9.33e+00 2.45e-01) 7.78e-02 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 03 05 D SM num-of-doors 0.276 ? 1.82e+00 6.96e-02 1.13e-02 two -1.27e+00 1.22e-01 4.34e-01 four 3.76e-01 8.09e-01 5.55e-01 10 26 R SNcm Log normalized-loses 0.257 ( 4.54e+00 3.06e-01) 7.40e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 7.81e-01 Prob-*k is known 8.00e-01 07 14 D SM engine-type 0.202 ohcf -2.29e+00 7.46e-03 7.35e-02 ohcv -2.15e+00 7.46e-03 6.38e-02 dohc -2.07e+00 7.46e-03 5.89e-02 l -2.07e+00 7.46e-03 5.89e-02 rotor -9.92e-01 7.46e-03 2.01e-02 dohcv 2.96e-01 7.46e-03 5.55e-03 ohc 2.84e-01 9.55e-01 7.19e-01 09 17 D SM fuel-system 0.192 1bbl -2.11e+00 6.52e-03 5.40e-02 spdi -1.92e+00 6.52e-03 4.43e-02 idi 1.02e+00 2.71e-01 9.77e-02 4bbl -8.44e-01 6.52e-03 1.52e-02 mpfi -3.50e-01 3.22e-01 4.57e-01 mfi 1.78e-01 6.52e-03 5.46e-03 spfi 1.78e-01 6.52e-03 5.46e-03 2bbl 1.53e-01 3.74e-01 3.21e-01 05 07 D SM drive-wheels 0.182 rwd -1.11e+00 1.22e-01 3.71e-01 4wd -9.57e-01 1.74e-02 4.53e-02 fwd 3.88e-01 8.61e-01 5.84e-01 17 33 R SNcn Log city-mpg 0.163 ( 3.30e+00 1.91e-01) 5.75e-01 ( 3.19e+00 2.56e-01) 01 03 D SM fuel-type 0.143 diesel 1.07e+00 2.91e-01 9.95e-02 gas -2.39e-01 7.09e-01 9.00e-01 16 32 R SNcn Log highway-mpg 0.093 ( 3.49e+00 1.91e-01) 4.41e-01 ( 3.40e+00 2.23e-01) 08 15 D SM num-of-cylinders 0.074 five -1.97e+00 7.57e-03 5.41e-02 eight -1.21e+00 7.46e-03 2.50e-02 two -9.92e-01 7.46e-03 2.01e-02 six -6.75e-01 5.97e-02 1.17e-01 three 2.96e-01 7.46e-03 5.55e-03 twelve 2.96e-01 7.46e-03 5.55e-03 four 1.56e-01 9.03e-01 7.73e-01 04 06 D SM body-style 0.044 hardtop -1.34e+00 1.04e-02 3.98e-02 convertible -1.06e+00 1.04e-02 3.01e-02 wagon 3.11e-01 1.67e-01 1.22e-01 hatchback -2.24e-01 2.72e-01 3.41e-01 sedan 1.45e-01 5.40e-01 4.67e-01 06 08 D SM engine-location 0.002 rear 4.29e-01 2.61e-02 1.70e-02 front -9.31e-03 9.74e-01 9.83e-01 02 04 D SM aspiration 0.000 turbo 2.47e-02 1.87e-01 1.82e-01 std -5.59e-03 8.13e-01 8.18e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.192302e+00 3.697407e-02 1.236598e+00 2.177110e-02 0.000000e+00 000027 00034 1.192302e+00 3.697407e-02 2.430031e+00 4.169939e-01 0.000000e+00 000027 00029 1.192302e+00 3.697407e-02 4.439024e+00 1.245579e-01 0.000000e+00 000027 00030 1.192302e+00 3.697407e-02 8.450796e+00 9.639443e-02 0.000000e+00 000027 00033 1.192302e+00 3.697407e-02 3.304640e+00 1.909170e-01 0.000000e+00 000027 00032 1.192302e+00 3.697407e-02 3.485254e+00 1.909170e-01 0.000000e+00 000027 00031 1.192302e+00 3.697407e-02 9.331057e+00 2.449372e-01 0.000000e+00 000028 00034 1.236598e+00 2.177110e-02 2.430031e+00 4.169939e-01 0.000000e+00 000028 00029 1.236598e+00 2.177110e-02 4.439024e+00 1.245579e-01 0.000000e+00 000028 00030 1.236598e+00 2.177110e-02 8.450796e+00 9.639443e-02 0.000000e+00 000028 00033 1.236598e+00 2.177110e-02 3.304640e+00 1.909170e-01 0.000000e+00 000028 00032 1.236598e+00 2.177110e-02 3.485254e+00 1.909170e-01 0.000000e+00 000028 00031 1.236598e+00 2.177110e-02 9.331057e+00 2.449372e-01 0.000000e+00 000034 00029 2.430031e+00 4.169939e-01 4.439024e+00 1.245579e-01 0.000000e+00 000034 00030 2.430031e+00 4.169939e-01 8.450796e+00 9.639443e-02 0.000000e+00 000034 00033 2.430031e+00 4.169939e-01 3.304640e+00 1.909170e-01 0.000000e+00 000034 00032 2.430031e+00 4.169939e-01 3.485254e+00 1.909170e-01 0.000000e+00 000034 00031 2.430031e+00 4.169939e-01 9.331057e+00 2.449372e-01 0.000000e+00 000029 00030 4.439024e+00 1.245579e-01 8.450796e+00 9.639443e-02 0.000000e+00 000029 00033 4.439024e+00 1.245579e-01 3.304640e+00 1.909170e-01 0.000000e+00 000029 00032 4.439024e+00 1.245579e-01 3.485254e+00 1.909170e-01 0.000000e+00 000029 00031 4.439024e+00 1.245579e-01 9.331057e+00 2.449372e-01 0.000000e+00 000030 00033 8.450796e+00 9.639443e-02 3.304640e+00 1.909170e-01 0.000000e+00 000030 00032 8.450796e+00 9.639443e-02 3.485254e+00 1.909170e-01 0.000000e+00 000030 00031 8.450796e+00 9.639443e-02 9.331057e+00 2.449372e-01 0.000000e+00 000033 00032 3.304640e+00 1.909170e-01 3.485254e+00 1.909170e-01 0.000000e+00 000033 00031 3.304640e+00 1.909170e-01 9.331057e+00 2.449372e-01 0.000000e+00 000032 00031 3.485254e+00 1.909170e-01 9.331057e+00 2.449372e-01 0.000000e+00 DATA_CLASS 5 #CLASS 5 - weight 13 normalized weight 0.064 relative strength 8.46e-05 ******* # class cross entropy w.r.t. global class 2.76e+01 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 15 31 R SNcm Log price 3.119 ( 1.04e+01 1.53e-01) 7.13e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 20 36 R SNcn Log curb-weight 2.843 ( 8.22e+00 5.49e-02) 7.22e+00 ( 7.83e+00 1.97e-01) 19 35 R SNcn Log engine-size 2.694 ( 5.44e+00 1.96e-01) 3.27e+00 ( 4.80e+00 2.82e-01) 22 38 R SNcn Log width 2.693 ( 4.26e+00 1.50e-02) 4.54e+00 ( 4.19e+00 3.15e-02) 00 02 D SM make 2.173 toyota -3.87e+00 3.24e-03 1.56e-01 nissan -3.30e+00 3.24e-03 8.76e-02 mazda -3.24e+00 3.24e-03 8.27e-02 honda -2.97e+00 3.24e-03 6.33e-02 mitsubishi -2.97e+00 3.24e-03 6.33e-02 subaru -2.89e+00 3.24e-03 5.85e-02 volkswagen -2.89e+00 3.24e-03 5.85e-02 peugot -2.81e+00 3.24e-03 5.36e-02 volvo -2.81e+00 3.24e-03 5.36e-02 jaguar 2.69e+00 2.17e-01 1.48e-02 mercedes-benz 2.69e+00 5.74e-01 3.91e-02 dodge -2.61e+00 3.24e-03 4.39e-02 audi -2.36e+00 3.24e-03 3.42e-02 plymouth -2.36e+00 3.24e-03 3.42e-02 saab -2.20e+00 3.24e-03 2.93e-02 porsche -2.02e+00 3.24e-03 2.45e-02 isuzu -1.80e+00 3.24e-03 1.96e-02 alfa-romero -1.52e+00 3.24e-03 1.48e-02 chevrolet -1.52e+00 3.24e-03 1.48e-02 bmw 1.33e+00 1.48e-01 3.91e-02 renault -1.12e+00 3.24e-03 9.93e-03 mercury -4.49e-01 3.24e-03 5.08e-03 23 39 R SNcn Log length 1.727 ( 5.28e+00 3.55e-02) 3.34e+00 ( 5.16e+00 7.06e-02) 08 15 D SM num-of-cylinders 1.682 four -4.33e+00 1.02e-02 7.73e-01 twelve 2.69e+00 8.15e-02 5.55e-03 eight 2.47e+00 2.95e-01 2.50e-02 five 1.70e+00 2.95e-01 5.41e-02 six 9.31e-01 2.97e-01 1.17e-01 two -6.80e-01 1.02e-02 2.01e-02 three 6.08e-01 1.02e-02 5.55e-03 24 40 R SNcn Log wheel-base 1.628 ( 4.70e+00 5.53e-02) 1.92e+00 ( 4.59e+00 5.89e-02) 16 32 R SNcn Log highway-mpg 1.613 ( 3.00e+00 1.89e-01) 2.10e+00 ( 3.40e+00 2.23e-01) 13 29 R SNcm Log horse-power 1.231 ( 5.08e+00 2.09e-01) 2.38e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 11 27 R SNcm Log bore 1.215 ( 1.28e+00 2.61e-02) 3.16e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 17 33 R SNcn Log city-mpg 1.142 ( 2.82e+00 1.89e-01) 1.98e+00 ( 3.19e+00 2.56e-01) 05 07 D SM drive-wheels 0.808 fwd -3.20e+00 2.38e-02 5.84e-01 rwd 9.44e-01 9.52e-01 3.71e-01 4wd -6.45e-01 2.38e-02 4.53e-02 07 14 D SM engine-type 0.532 ohcf -1.98e+00 1.02e-02 7.35e-02 l -1.76e+00 1.02e-02 5.89e-02 ohcv 1.75e+00 3.67e-01 6.38e-02 dohc 9.52e-01 1.53e-01 5.89e-02 rotor -6.80e-01 1.02e-02 2.01e-02 dohcv 6.08e-01 1.02e-02 5.55e-03 ohc -4.92e-01 4.40e-01 7.19e-01 09 17 D SM fuel-system 0.498 2bbl -3.58e+00 8.91e-03 3.21e-01 1bbl -1.80e+00 8.91e-03 5.40e-02 spdi -1.60e+00 8.91e-03 4.43e-02 idi 1.10e+00 2.94e-01 9.77e-02 4bbl -5.32e-01 8.91e-03 1.52e-02 mfi 4.90e-01 8.91e-03 5.46e-03 spfi 4.90e-01 8.91e-03 5.46e-03 mpfi 3.56e-01 6.52e-01 4.57e-01 04 06 D SM body-style 0.454 hatchback -3.17e+00 1.43e-02 3.41e-01 hardtop 1.37e+00 1.57e-01 3.98e-02 convertible 1.04e+00 8.56e-02 3.01e-02 wagon -3.58e-01 8.56e-02 1.22e-01 sedan 3.43e-01 6.58e-01 4.67e-01 18 34 R SNcn Log compression-rati 0.368 ( 2.41e+00 4.26e-01) 3.41e-01 ( 2.27e+00 2.81e-01) o 14 30 R SNcm Log peak-rpm 0.357 ( 8.45e+00 9.54e-02) 8.66e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 10 26 R SNcm Log normalized-loses 0.317 ( 4.68e+00 1.97e-01) 4.36e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 4.85e-01 Prob-*k is known 8.00e-01 12 28 R SNcm Log stroke 0.232 ( 1.24e+00 1.06e-01) 6.35e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 01 03 D SM fuel-type 0.184 diesel 1.17e+00 3.21e-01 9.95e-02 gas -2.82e-01 6.79e-01 9.00e-01 21 37 R SNcn Log height 0.067 ( 4.00e+00 5.10e-02) 2.90e-01 ( 3.98e+00 4.54e-02) 02 04 D SM aspiration 0.056 turbo 5.67e-01 3.21e-01 1.82e-01 std -1.86e-01 6.79e-01 8.18e-01 03 05 D SM num-of-doors 0.010 ? 7.41e-01 2.38e-02 1.13e-02 two -1.31e-01 3.80e-01 4.34e-01 four 7.12e-02 5.96e-01 5.55e-01 06 08 D SM engine-location 0.008 rear 7.41e-01 3.56e-02 1.70e-02 front -1.92e-02 9.64e-01 9.83e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.282190e+00 2.612966e-02 1.242660e+00 1.061657e-01 0.000000e+00 000027 00034 1.282190e+00 2.612966e-02 2.412498e+00 4.261508e-01 0.000000e+00 000027 00029 1.282190e+00 2.612966e-02 5.081445e+00 2.088601e-01 0.000000e+00 000027 00030 1.282190e+00 2.612966e-02 8.454966e+00 9.541816e-02 0.000000e+00 000027 00033 1.282190e+00 2.612966e-02 2.821324e+00 1.889834e-01 0.000000e+00 000027 00032 1.282190e+00 2.612966e-02 3.003366e+00 1.889834e-01 0.000000e+00 000027 00031 1.282190e+00 2.612966e-02 1.044028e+01 1.529128e-01 0.000000e+00 000028 00034 1.242660e+00 1.061657e-01 2.412498e+00 4.261508e-01 0.000000e+00 000028 00029 1.242660e+00 1.061657e-01 5.081445e+00 2.088601e-01 0.000000e+00 000028 00030 1.242660e+00 1.061657e-01 8.454966e+00 9.541816e-02 0.000000e+00 000028 00033 1.242660e+00 1.061657e-01 2.821324e+00 1.889834e-01 0.000000e+00 000028 00032 1.242660e+00 1.061657e-01 3.003366e+00 1.889834e-01 0.000000e+00 000028 00031 1.242660e+00 1.061657e-01 1.044028e+01 1.529128e-01 0.000000e+00 000034 00029 2.412498e+00 4.261508e-01 5.081445e+00 2.088601e-01 0.000000e+00 000034 00030 2.412498e+00 4.261508e-01 8.454966e+00 9.541816e-02 0.000000e+00 000034 00033 2.412498e+00 4.261508e-01 2.821324e+00 1.889834e-01 0.000000e+00 000034 00032 2.412498e+00 4.261508e-01 3.003366e+00 1.889834e-01 0.000000e+00 000034 00031 2.412498e+00 4.261508e-01 1.044028e+01 1.529128e-01 0.000000e+00 000029 00030 5.081445e+00 2.088601e-01 8.454966e+00 9.541816e-02 0.000000e+00 000029 00033 5.081445e+00 2.088601e-01 2.821324e+00 1.889834e-01 0.000000e+00 000029 00032 5.081445e+00 2.088601e-01 3.003366e+00 1.889834e-01 0.000000e+00 000029 00031 5.081445e+00 2.088601e-01 1.044028e+01 1.529128e-01 0.000000e+00 000030 00033 8.454966e+00 9.541816e-02 2.821324e+00 1.889834e-01 0.000000e+00 000030 00032 8.454966e+00 9.541816e-02 3.003366e+00 1.889834e-01 0.000000e+00 000030 00031 8.454966e+00 9.541816e-02 1.044028e+01 1.529128e-01 0.000000e+00 000033 00032 2.821324e+00 1.889834e-01 3.003366e+00 1.889834e-01 0.000000e+00 000033 00031 2.821324e+00 1.889834e-01 1.044028e+01 1.529128e-01 0.000000e+00 000032 00031 3.003366e+00 1.889834e-01 1.044028e+01 1.529128e-01 0.000000e+00 DATA_CLASS 6 #CLASS 6 - weight 11 normalized weight 0.054 relative strength 2.15e-01 ******* # class cross entropy w.r.t. global class 2.38e+01 ******* #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k 07 14 D SM engine-type 2.453 ohc -4.10e+00 1.19e-02 7.19e-01 l 2.76e+00 9.29e-01 5.89e-02 ohcf -1.82e+00 1.19e-02 7.35e-02 ohcv -1.68e+00 1.19e-02 6.38e-02 dohc -1.60e+00 1.19e-02 5.89e-02 dohcv 7.64e-01 1.19e-02 5.55e-03 rotor -5.24e-01 1.19e-02 2.01e-02 00 02 D SM make 2.442 toyota -3.72e+00 3.79e-03 1.56e-01 nissan -3.14e+00 3.79e-03 8.76e-02 mazda -3.08e+00 3.79e-03 8.27e-02 peugot 2.84e+00 9.20e-01 5.36e-02 honda -2.82e+00 3.79e-03 6.33e-02 mitsubishi -2.82e+00 3.79e-03 6.33e-02 subaru -2.74e+00 3.79e-03 5.85e-02 volkswagen -2.74e+00 3.79e-03 5.85e-02 volvo -2.65e+00 3.79e-03 5.36e-02 dodge -2.45e+00 3.79e-03 4.39e-02 bmw -2.33e+00 3.79e-03 3.91e-02 mercedes-benz -2.33e+00 3.79e-03 3.91e-02 audi -2.20e+00 3.79e-03 3.42e-02 plymouth -2.20e+00 3.79e-03 3.42e-02 saab -2.05e+00 3.79e-03 2.93e-02 porsche -1.87e+00 3.79e-03 2.45e-02 isuzu -1.65e+00 3.79e-03 1.96e-02 alfa-romero -1.36e+00 3.79e-03 1.48e-02 chevrolet -1.36e+00 3.79e-03 1.48e-02 jaguar -1.36e+00 3.79e-03 1.48e-02 renault -9.64e-01 3.79e-03 9.93e-03 mercury -2.93e-01 3.79e-03 5.08e-03 24 40 R SNcn Log wheel-base 2.181 ( 4.70e+00 2.61e-02) 4.25e+00 ( 4.59e+00 5.89e-02) 22 38 R SNcn Log width 2.104 ( 4.23e+00 4.78e-03) 7.84e+00 ( 4.19e+00 3.15e-02) 20 36 R SNcn Log curb-weight 1.900 ( 8.08e+00 4.09e-02) 6.12e+00 ( 7.83e+00 1.97e-01) 21 37 R SNcn Log height 1.582 ( 4.05e+00 1.58e-02) 4.00e+00 ( 3.98e+00 4.54e-02) 23 39 R SNcn Log length 1.385 ( 5.25e+00 2.92e-02) 3.28e+00 ( 5.16e+00 7.06e-02) 10 26 R SNcm Log normalized-loses 1.313 ( 5.08e+00 4.76e-02) 6.67e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.50e-01 Prob-*k is known 8.00e-01 15 31 R SNcm Log price 1.001 ( 9.64e+00 1.38e-01) 2.08e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 11 27 R SNcm Log bore 0.993 ( 1.28e+00 3.07e-02) 2.47e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 05 07 D SM drive-wheels 0.785 fwd -3.05e+00 2.78e-02 5.84e-01 rwd 9.36e-01 9.44e-01 3.71e-01 4wd -4.89e-01 2.78e-02 4.53e-02 18 34 R SNcn Log compression-rati 0.712 ( 2.51e+00 4.54e-01) 5.44e-01 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power 0.706 ( 4.60e+00 1.09e-01) 1.10e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 09 17 D SM fuel-system 0.628 2bbl -3.43e+00 1.04e-02 3.21e-01 1bbl -1.65e+00 1.04e-02 5.40e-02 idi 1.48e+00 4.27e-01 9.77e-02 spdi -1.45e+00 1.04e-02 4.43e-02 mfi 6.46e-01 1.04e-02 5.46e-03 spfi 6.46e-01 1.04e-02 5.46e-03 4bbl -3.76e-01 1.04e-02 1.52e-02 mpfi 1.11e-01 5.10e-01 4.57e-01 19 35 R SNcn Log engine-size 0.602 ( 4.90e+00 1.08e-01) 9.70e-01 ( 4.80e+00 2.82e-01) 14 30 R SNcm Log peak-rpm 0.460 ( 8.44e+00 1.02e-01) 9.19e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 03 05 D SM num-of-doors 0.451 two -2.75e+00 2.78e-02 4.34e-01 ? 8.97e-01 2.78e-02 1.13e-02 four 5.32e-01 9.44e-01 5.55e-01 04 06 D SM body-style 0.444 hatchback -3.02e+00 1.67e-02 3.41e-01 wagon 1.05e+00 3.50e-01 1.22e-01 hardtop -8.71e-01 1.67e-02 3.98e-02 convertible -5.91e-01 1.67e-02 3.01e-02 sedan 2.51e-01 6.00e-01 4.67e-01 01 03 D SM fuel-type 0.425 diesel 1.53e+00 4.58e-01 9.95e-02 gas -5.08e-01 5.42e-01 9.00e-01 12 28 R SNcm Log stroke 0.401 ( 1.14e+00 1.65e-01) 2.28e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 02 04 D SM aspiration 0.325 turbo 1.09e+00 5.42e-01 1.82e-01 std -5.79e-01 4.58e-01 8.18e-01 16 32 R SNcn Log highway-mpg 0.191 ( 3.27e+00 1.88e-01) 6.81e-01 ( 3.40e+00 2.23e-01) 17 33 R SNcn Log city-mpg 0.153 ( 3.10e+00 1.88e-01) 5.27e-01 ( 3.19e+00 2.56e-01) 08 15 D SM num-of-cylinders 0.129 six -2.29e+00 1.19e-02 1.17e-01 five -1.51e+00 1.19e-02 5.41e-02 three 7.64e-01 1.19e-02 5.55e-03 twelve 7.64e-01 1.19e-02 5.55e-03 eight -7.41e-01 1.19e-02 2.50e-02 two -5.24e-01 1.19e-02 2.01e-02 four 1.84e-01 9.29e-01 7.73e-01 06 08 D SM engine-location 0.013 rear 8.97e-01 4.17e-02 1.70e-02 front -2.54e-02 9.58e-01 9.83e-01 SIGMA CONTOURS #att_x att_y mean_x sigma_x mean_y sigma_y rotation-rad 000027 00028 1.275475e+00 3.070848e-02 1.137720e+00 1.647138e-01 0.000000e+00 000027 00034 1.275475e+00 3.070848e-02 2.514350e+00 4.538611e-01 0.000000e+00 000027 00029 1.275475e+00 3.070848e-02 4.596084e+00 1.092611e-01 0.000000e+00 000027 00030 1.275475e+00 3.070848e-02 8.443573e+00 1.022902e-01 0.000000e+00 000027 00033 1.275475e+00 3.070848e-02 3.095873e+00 1.877549e-01 0.000000e+00 000027 00032 1.275475e+00 3.070848e-02 3.273229e+00 1.877549e-01 0.000000e+00 000027 00031 1.275475e+00 3.070848e-02 9.637353e+00 1.381492e-01 0.000000e+00 000028 00034 1.137720e+00 1.647138e-01 2.514350e+00 4.538611e-01 0.000000e+00 000028 00029 1.137720e+00 1.647138e-01 4.596084e+00 1.092611e-01 0.000000e+00 000028 00030 1.137720e+00 1.647138e-01 8.443573e+00 1.022902e-01 0.000000e+00 000028 00033 1.137720e+00 1.647138e-01 3.095873e+00 1.877549e-01 0.000000e+00 000028 00032 1.137720e+00 1.647138e-01 3.273229e+00 1.877549e-01 0.000000e+00 000028 00031 1.137720e+00 1.647138e-01 9.637353e+00 1.381492e-01 0.000000e+00 000034 00029 2.514350e+00 4.538611e-01 4.596084e+00 1.092611e-01 0.000000e+00 000034 00030 2.514350e+00 4.538611e-01 8.443573e+00 1.022902e-01 0.000000e+00 000034 00033 2.514350e+00 4.538611e-01 3.095873e+00 1.877549e-01 0.000000e+00 000034 00032 2.514350e+00 4.538611e-01 3.273229e+00 1.877549e-01 0.000000e+00 000034 00031 2.514350e+00 4.538611e-01 9.637353e+00 1.381492e-01 0.000000e+00 000029 00030 4.596084e+00 1.092611e-01 8.443573e+00 1.022902e-01 0.000000e+00 000029 00033 4.596084e+00 1.092611e-01 3.095873e+00 1.877549e-01 0.000000e+00 000029 00032 4.596084e+00 1.092611e-01 3.273229e+00 1.877549e-01 0.000000e+00 000029 00031 4.596084e+00 1.092611e-01 9.637353e+00 1.381492e-01 0.000000e+00 000030 00033 8.443573e+00 1.022902e-01 3.095873e+00 1.877549e-01 0.000000e+00 000030 00032 8.443573e+00 1.022902e-01 3.273229e+00 1.877549e-01 0.000000e+00 000030 00031 8.443573e+00 1.022902e-01 9.637353e+00 1.381492e-01 0.000000e+00 000033 00032 3.095873e+00 1.877549e-01 3.273229e+00 1.877549e-01 0.000000e+00 000033 00031 3.095873e+00 1.877549e-01 9.637353e+00 1.381492e-01 0.000000e+00 000032 00031 3.273229e+00 1.877549e-01 9.637353e+00 1.381492e-01 0.000000e+00 autoclass-3.3.6.dfsg.1/data/autos/imports-85-predict.class-text-10000644000175000017500000000415611247310756022510 0ustar areare CROSS REFERENCE: CLASS => CASE NUMBER MEMBERSHIP AutoClass PREDICTION for the 11 "TEST" cases in: /home/tove/p/autoclass-c/data/autos/imports-85-predict.db2 based on the "TRAINING" classification of 205 cases in: /home/tove/p/autoclass-c/data/autos/imports-85.db2 /home/tove/p/autoclass-c/data/autos/imports-85.hd2 with log-A (approximate marginal likelihood) = -16453.535 from classification results file: /home/tove/p/autoclass-c/data/autos/imports-85.results-bin and using models: /home/tove/p/autoclass-c/data/autos/imports-85.model - index = 0 CLASS = 2 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 5 2824 audi 17450 ohc 1.00 6 2507 audi 15250 ohc 1.00 7 2844 audi 17710 ohc 1.00 8 2954 audi 18920 ohc 1.00 9 3086 audi 23875 ohc 1.00 10 3053 audi ? ohc 1.00 11 2395 bmw 16430 ohc 1.00 CLASS = 3 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 1 2548 alfa-romero 13495 dohc 1.00 2 2548 alfa-romero 16500 dohc 1.00 3 2823 alfa-romero 16500 ohcv 1.00 CLASS = 4 Case # curb-weight make price engine-type (Cls Prob) ------------------------------------------------------------------------------------------ 4 2337 audi 13950 ohc 1.00autoclass-3.3.6.dfsg.1/data/autos/imports-85.case-text-10000644000175000017500000001441611247310756020666 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 205 cases in /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 with log-A (approximate marginal likelihood) = -16453.536 from classification results file /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin and using models /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 3 1.000 47 3 0.999 93 0 1.000 2 3 1.000 48 5 1.000 94 0 0.999 3 3 1.000 49 5 1.000 95 0 1.000 4 4 0.999 50 5 1.000 96 0 1.000 5 2 1.000 51 0 1.000 97 0 1.000 6 2 0.999 52 0 1.000 98 0 0.999 7 2 1.000 53 0 1.000 99 0 1.000 8 2 1.000 54 0 1.000 100 1 0.982 9 2 1.000 55 0 1.000 101 1 0.978 10 2 0.997 56 3 1.000 102 2 1.000 11 2 0.999 57 3 1.000 103 2 1.000 12 2 0.999 58 3 1.000 104 2 1.000 13 2 1.000 59 3 1.000 105 3 1.000 14 2 1.000 60 4 0.999 106 3 1.000 15 2 1.000 61 4 1.000 107 3 0.997 16 2 0.974 62 4 1.000 108 6 1.000 17 5 0.999 63 4 1.000 109 6 1.000 18 5 1.000 64 4 1.000 110 6 1.000 19 0 1.000 65 4 1.000 111 6 1.000 20 0 1.000 66 2 1.000 112 6 1.000 21 0 1.000 67 4 1.000 113 6 1.000 22 0 1.000 68 5 1.000 114 6 1.000 23 0 1.000 69 5 1.000 115 6 1.000 24 1 1.000 70 5 1.000 116 6 1.000 25 0 1.000 71 5 1.000 117 6 1.000 26 0 1.000 72 5 1.000 118 6 1.000 27 0 1.000 73 5 1.000 119 0 1.000 28 1 1.000 74 5 1.000 120 1 1.000 29 4 1.000 75 5 1.000 121 0 1.000 30 3 1.000 76 2 0.999 122 0 1.000 31 0 1.000 77 0 1.000 123 0 0.999 32 0 1.000 78 0 1.000 124 4 1.000 33 0 1.000 79 0 1.000 125 3 1.000 34 0 1.000 80 1 1.000 126 3 1.000 35 0 1.000 81 1 0.953 127 3 1.000 36 0 0.999 82 1 0.999 128 3 1.000 37 0 0.999 83 3 1.000 129 3 1.000 38 1 1.000 84 3 1.000 130 3 1.000 39 1 1.000 85 3 1.000 131 3 1.000 40 1 1.000 86 1 0.999 132 3 1.000 41 1 1.000 87 1 0.999 133 2 0.999 42 1 0.998 88 1 1.000 134 2 1.000 43 1 1.000 89 1 1.000 135 3 1.000 44 1 1.000 90 0 1.000 136 2 1.000 45 0 1.000 91 1 1.000 137 2 1.000 46 0 1.000 92 0 1.000 138 2 1.000 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 139 1 1.000 162 0 0.978 185 1 0.999 140 1 1.000 163 0 0.756 186 1 0.992 141 1 1.000 164 0 0.991 187 1 0.986 142 1 1.000 165 0 0.993 188 1 0.925 143 1 1.000 166 1 0.995 189 1 0.985 144 1 1.000 167 3 0.621 190 1 1.000 145 1 1.000 168 3 1.000 191 1 1.000 146 1 1.000 169 3 1.000 192 2 0.998 147 1 1.000 170 3 0.999 193 4 1.000 148 1 1.000 171 3 0.999 194 4 0.999 149 1 1.000 172 3 0.999 195 2 1.000 150 1 1.000 173 3 0.993 196 2 1.000 151 0 1.000 174 4 1.000 197 2 1.000 152 0 1.000 175 4 1.000 198 2 1.000 153 0 1.000 176 4 1.000 199 2 1.000 154 0 0.999 177 4 1.000 200 2 1.000 155 0 0.939 178 4 1.000 201 2 1.000 156 1 0.999 179 2 0.982 202 2 1.000 157 0 0.998 180 2 0.983 203 2 1.000 158 0 0.998 181 2 1.000 204 4 1.000 159 1 1.000 182 2 1.000 205 2 1.000 160 1 1.000 183 1 0.999 161 0 0.999 184 1 0.999 autoclass-3.3.6.dfsg.1/data/autos/imports-85.hd20000644000175000017500000000432311247310756017304 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 26 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 discrete nominal "symboling" range 7 1 real scalar "normalized-loses" zero_point 0.0 rel_error 0.01 2 discrete nominal "make" range 22 3 discrete nominal "fuel-type" range 2 4 discrete nominal "aspiration" range 2 5 discrete nominal "num-of-doors" range 2 6 discrete nominal "body-style" range 5 7 discrete nominal "drive-wheels" range 3 8 discrete nominal "engine-location" range 2 9 real scalar "wheel-base" zero_point 0.0 rel_error 0.001 10 real scalar "length" zero_point 0.0 rel_error 0.001 11 real scalar "width" zero_point 0.0 rel_error 0.001 12 real scalar "height" zero_point 0.0 rel_error 0.001 13 real scalar "curb-weight" zero_point 0.0 rel_error 0.0002 14 discrete nominal "engine-type" range 7 15 discrete nominal "num-of-cylinders" range 7 16 real scalar "engine-size" zero_point 0.0 rel_error 0.01 17 discrete nominal "fuel-system" range 8 18 real scalar "bore" zero_point 0.0 rel_error 0.003 19 real scalar "stroke" zero_point 0.0 rel_error 0.003 20 real scalar "compression-ratio" zero_point 0.0 rel_error 0.003 21 real scalar "horse-power" zero_point 0.0 rel_error 0.01 22 real scalar "peak-rpm" zero_point 0.0 rel_error 0.02 23 real scalar "city-mpg" zero_point 0.0 rel_error 0.04 24 real scalar "highway-mpg" zero_point 0.0 rel_error 0.04 25 real scalar "price" zero_point 0.0 rel_error 0.001 autoclass-3.3.6.dfsg.1/data/autos/imports-85.case-data-10000644000175000017500000001267611247310756020621 0ustar areare # CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS #DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 # with log-A (approximate marginal likelihood) = -16453.536 # from classification results file # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin # and using models # /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 #DATA_CASE_TO_CLASS # Case # Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 3 1.000 002 3 1.000 003 3 1.000 004 4 0.999 005 2 1.000 006 2 0.999 007 2 1.000 008 2 1.000 009 2 1.000 010 2 0.997 3 0.003 011 2 0.999 3 0.001 012 2 0.999 013 2 1.000 014 2 1.000 015 2 1.000 016 2 0.974 5 0.026 017 5 0.999 018 5 1.000 019 0 1.000 020 0 1.000 021 0 1.000 022 0 1.000 023 0 1.000 024 1 1.000 025 0 1.000 026 0 1.000 027 0 1.000 028 1 1.000 029 4 1.000 030 3 1.000 031 0 1.000 032 0 1.000 033 0 1.000 034 0 1.000 035 0 1.000 036 0 0.999 037 0 0.999 038 1 1.000 039 1 1.000 040 1 1.000 041 1 1.000 042 1 0.998 4 0.002 043 1 1.000 044 1 1.000 045 0 1.000 046 0 1.000 047 3 0.999 048 5 1.000 049 5 1.000 050 5 1.000 051 0 1.000 052 0 1.000 053 0 1.000 054 0 1.000 055 0 1.000 056 3 1.000 057 3 1.000 058 3 1.000 059 3 1.000 060 4 0.999 061 4 1.000 062 4 1.000 063 4 1.000 064 4 1.000 065 4 1.000 066 2 1.000 067 4 1.000 068 5 1.000 069 5 1.000 070 5 1.000 071 5 1.000 072 5 1.000 073 5 1.000 074 5 1.000 075 5 1.000 076 2 0.999 077 0 1.000 078 0 1.000 079 0 1.000 080 1 1.000 081 1 0.953 3 0.047 082 1 0.999 083 3 1.000 084 3 1.000 085 3 1.000 086 1 0.999 087 1 0.999 088 1 1.000 089 1 1.000 090 0 1.000 091 1 1.000 092 0 1.000 093 0 1.000 094 0 0.999 095 0 1.000 096 0 1.000 097 0 1.000 098 0 0.999 099 0 1.000 100 1 0.982 4 0.018 101 1 0.978 4 0.022 102 2 1.000 103 2 1.000 104 2 1.000 105 3 1.000 106 3 1.000 107 3 0.997 2 0.003 108 6 1.000 109 6 1.000 110 6 1.000 111 6 1.000 112 6 1.000 113 6 1.000 114 6 1.000 115 6 1.000 116 6 1.000 117 6 1.000 118 6 1.000 119 0 1.000 120 1 1.000 121 0 1.000 122 0 1.000 123 0 0.999 124 4 1.000 125 3 1.000 126 3 1.000 127 3 1.000 128 3 1.000 129 3 1.000 130 3 1.000 131 3 1.000 132 3 1.000 133 2 0.999 134 2 1.000 135 3 1.000 136 2 1.000 137 2 1.000 138 2 1.000 139 1 1.000 140 1 1.000 141 1 1.000 142 1 1.000 143 1 1.000 144 1 1.000 145 1 1.000 146 1 1.000 147 1 1.000 148 1 1.000 149 1 1.000 150 1 1.000 151 0 1.000 152 0 1.000 153 0 1.000 154 0 0.999 155 0 0.939 1 0.061 156 1 0.999 157 0 0.998 1 0.002 158 0 0.998 1 0.002 159 1 1.000 160 1 1.000 161 0 0.999 162 0 0.978 1 0.022 163 0 0.756 1 0.244 164 0 0.991 1 0.009 165 0 0.993 1 0.007 166 1 0.995 3 0.005 167 3 0.621 1 0.379 168 3 1.000 169 3 1.000 170 3 0.999 171 3 0.999 172 3 0.999 173 3 0.993 2 0.007 174 4 1.000 175 4 1.000 176 4 1.000 177 4 1.000 178 4 1.000 179 2 0.982 3 0.018 180 2 0.983 3 0.017 181 2 1.000 182 2 1.000 183 1 0.999 184 1 0.999 4 0.001 185 1 0.999 186 1 0.992 4 0.008 187 1 0.986 4 0.014 188 1 0.925 4 0.075 189 1 0.985 4 0.015 190 1 1.000 191 1 1.000 192 2 0.998 4 0.002 193 4 1.000 194 4 0.999 195 2 1.000 196 2 1.000 197 2 1.000 198 2 1.000 199 2 1.000 200 2 1.000 201 2 1.000 202 2 1.000 203 2 1.000 204 4 1.000 205 2 1.000 autoclass-3.3.6.dfsg.1/data/autos/imports-85.model0000644000175000017500000000206311247310756017726 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 ;; 1 or more model definitions ;; model_index model_index 0 4 ignore 0 ;; single_normal_cm 1 18 19 21 22 25 ;; single_normal_cn 9 10 11 12 13 16 20 23 24 ;; single_multinomial default ;; this is done so that term numbers will match AC-X which has canonical model ;; type ordering, and puts single_multinomial first single_multinomial 2 3 4 5 6 7 8 14 15 17 single_normal_cm 1 18 19 21 22 25 single_normal_cn default autoclass-3.3.6.dfsg.1/data/autos/imports-85.influ-o-text-10000644000175000017500000022414611247310756021327 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 205 cases in /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 with log-A (approximate marginal likelihood) = -16453.536 from classification results file /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin and using models /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 4 tries over 1 second _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 *SAVED* -1 PROBABILITY exp(-16654.238) N_CLASSES 5 FOUND ON TRY 3 *SAVED* -2 PROBABILITY exp(-16816.657) N_CLASSES 3 FOUND ON TRY 2 PROBABILITY exp(-17041.867) N_CLASSES 2 FOUND ON TRY 1 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 7 POPULATED CLASSES (max global influence value = 3.119) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -6.98e+01 1.00e+00 50 0.242 1 -7.81e+01 2.56e-04 46 0.222 2 -7.65e+01 1.32e-03 37 0.180 3 -8.00e+01 3.85e-05 31 0.150 4 -7.53e+01 4.13e-03 18 0.089 5 -7.92e+01 8.46e-05 13 0.064 6 -7.14e+01 2.15e-01 11 0.054 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 1.65e+01 50 0.242 1 7.20e+00 46 0.222 2 9.87e+00 37 0.180 3 9.53e+00 31 0.150 4 9.59e+00 18 0.089 5 2.76e+01 13 0.064 6 2.38e+01 11 0.054 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 036 Log curb-weight 1.000 002 make 0.957 031 Log price 0.877 038 Log width 0.861 040 Log wheel-base 0.840 035 Log engine-size 0.815 039 Log length 0.777 029 Log horse-power 0.658 034 Log compression-ratio 0.563 027 Log bore 0.501 014 engine-type 0.474 017 fuel-system 0.424 037 Log height 0.380 028 Log stroke 0.363 032 Log highway-mpg 0.354 033 Log city-mpg 0.352 015 num-of-cylinders 0.325 007 drive-wheels 0.322 026 Log normalized-loses 0.315 006 body-style 0.226 005 num-of-doors 0.169 030 Log peak-rpm 0.165 003 fuel-type 0.109 004 aspiration 0.064 008 engine-location 0.017 000 symboling ----- 001 normalized-loses ----- 009 wheel-base ----- 010 length ----- 011 width ----- 012 height ----- 013 curb-weight ----- 016 engine-size ----- 018 bore ----- 019 stroke ----- 020 compression-ratio ----- 021 horse-power ----- 022 peak-rpm ----- 023 city-mpg ----- 024 highway-mpg ----- 025 price ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 50 normalized weight 0.242 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 1.65e+01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 18 34 R SNcn Log compression-rati 1.913 ( 2.23e+00 2.55e-02) 1.55e+00 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power .... 1.635 ( 4.22e+00 7.40e-02) 4.95e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 19 35 R SNcn Log engine-size .... 1.394 ( 4.52e+00 7.09e-02) 3.91e+00 ( 4.80e+00 2.82e-01) 15 31 R SNcm Log price .......... 1.374 ( 8.82e+00 1.33e-01) 3.98e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.60e-01 Prob-*k is known 9.80e-01 20 36 R SNcn Log curb-weight .... 1.362 ( 7.59e+00 6.80e-02) 3.52e+00 ( 7.83e+00 1.97e-01) 22 38 R SNcn Log width .......... 1.274 ( 4.16e+00 9.08e-03) 3.43e+00 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore ........... 1.126 ( 1.11e+00 3.10e-02) 2.81e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 09 17 D SM fuel-system ........ 0.914 mpfi ............... -5.22e+00 2.47e-03 4.57e-01 idi ................ -3.68e+00 2.47e-03 9.77e-02 spdi ............... -2.89e+00 2.47e-03 4.43e-02 4bbl ............... -1.82e+00 2.47e-03 1.52e-02 2bbl ............... 9.67e-01 8.45e-01 3.21e-01 1bbl ............... 9.57e-01 1.41e-01 5.40e-02 mfi ................ -7.94e-01 2.47e-03 5.46e-03 spfi ............... -7.94e-01 2.47e-03 5.46e-03 24 40 R SNcn Log wheel-base ..... 0.914 ( 4.54e+00 2.12e-02) 2.25e+00 ( 4.59e+00 5.89e-02) 23 39 R SNcn Log length ......... 0.812 ( 5.08e+00 4.35e-02) 1.83e+00 ( 5.16e+00 7.06e-02) 17 33 R SNcn Log city-mpg ....... 0.666 ( 3.48e+00 1.94e-01) 1.45e+00 ( 3.19e+00 2.56e-01) 16 32 R SNcn Log highway-mpg .... 0.596 ( 3.64e+00 1.94e-01) 1.24e+00 ( 3.40e+00 2.23e-01) 12 28 R SNcm Log stroke ......... 0.575 ( 1.16e+00 3.86e-02) 4.09e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 02 D SM make ............... 0.566 subaru ............. -4.18e+00 8.97e-04 5.85e-02 volkswagen ......... -4.18e+00 8.97e-04 5.85e-02 peugot ............. -4.09e+00 8.97e-04 5.36e-02 volvo .............. -4.09e+00 8.97e-04 5.36e-02 bmw ................ -3.77e+00 8.97e-04 3.91e-02 mercedes-benz ...... -3.77e+00 8.97e-04 3.91e-02 audi ............... -3.64e+00 8.97e-04 3.42e-02 saab ............... -3.49e+00 8.97e-04 2.93e-02 porsche ............ -3.31e+00 8.97e-04 2.45e-02 alfa-romero ........ -2.80e+00 8.97e-04 1.48e-02 jaguar ............. -2.80e+00 8.97e-04 1.48e-02 renault ............ -2.40e+00 8.97e-04 9.93e-03 mercury ............ -1.73e+00 8.97e-04 5.08e-03 chevrolet .......... 1.40e+00 6.01e-02 1.48e-02 plymouth ........... 8.48e-01 7.99e-02 3.42e-02 dodge .............. 8.19e-01 9.96e-02 4.39e-02 honda .............. 7.87e-01 1.39e-01 6.33e-02 isuzu .............. 7.21e-01 4.04e-02 1.96e-02 nissan ............. 7.12e-01 1.79e-01 8.76e-02 toyota ............. 3.95e-01 2.31e-01 1.56e-01 mazda .............. 1.85e-01 9.96e-02 8.27e-02 mitsubishi ......... -5.19e-02 6.01e-02 6.33e-02 05 07 D SM drive-wheels ....... 0.321 rwd ................ -2.09e+00 4.58e-02 3.71e-01 4wd ................ -5.90e-01 2.51e-02 4.53e-02 fwd ................ 4.64e-01 9.29e-01 5.84e-01 07 14 D SM engine-type ........ 0.226 ohcf ............... -3.26e+00 2.82e-03 7.35e-02 ohcv ............... -3.12e+00 2.82e-03 6.38e-02 dohc ............... -3.04e+00 2.82e-03 5.89e-02 rotor .............. -1.96e+00 2.82e-03 2.01e-02 l .................. -9.60e-01 2.26e-02 5.89e-02 dohcv .............. -6.77e-01 2.82e-03 5.55e-03 ohc ................ 2.92e-01 9.63e-01 7.19e-01 08 15 D SM num-of-cylinders ... 0.212 six ................ -3.73e+00 2.82e-03 1.17e-01 five ............... -2.95e+00 2.82e-03 5.41e-02 eight .............. -2.18e+00 2.82e-03 2.50e-02 two ................ -1.96e+00 2.82e-03 2.01e-02 three .............. 1.40e+00 2.26e-02 5.55e-03 twelve ............. -6.77e-01 2.82e-03 5.55e-03 four ............... 2.21e-01 9.63e-01 7.73e-01 02 04 D SM aspiration ......... 0.160 turbo .............. -2.91e+00 9.87e-03 1.82e-01 std ................ 1.91e-01 9.90e-01 8.18e-01 10 26 R SNcm Log normalized-loses 0.138 ( 4.75e+00 2.32e-01) 7.32e-02 ( 4.76e+00 2.82e-01) Prob-jk is known 9.57e-01 Prob-*k is known 8.00e-01 21 37 R SNcn Log height ......... 0.079 ( 3.97e+00 3.71e-02) 3.59e-01 ( 3.98e+00 4.54e-02) 01 03 D SM fuel-type .......... 0.071 diesel ............. -2.31e+00 9.87e-03 9.95e-02 gas ................ 9.49e-02 9.90e-01 9.00e-01 04 06 D SM body-style ......... 0.065 convertible ........ -2.03e+00 3.95e-03 3.01e-02 hardtop ............ -5.19e-01 2.37e-02 3.98e-02 hatchback .......... 3.77e-01 4.97e-01 3.41e-01 sedan .............. -2.22e-01 3.74e-01 4.67e-01 wagon .............. -1.87e-01 1.01e-01 1.22e-01 14 30 R SNcm Log peak-rpm ....... 0.044 ( 8.56e+00 9.80e-02) 2.67e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 03 05 D SM num-of-doors ....... 0.015 ? .................. -5.43e-01 6.58e-03 1.13e-02 two ................ 1.81e-01 5.20e-01 4.34e-01 four ............... -1.58e-01 4.74e-01 5.55e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 06 08 D SM engine-location .... 0.002 rear ............... -5.43e-01 9.87e-03 1.70e-02 front .............. 7.22e-03 9.90e-01 9.83e-01 CLASS 1 - weight 46 normalized weight 0.222 relative strength 2.56e-04 ******* class cross entropy w.r.t. global class 7.20e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 19 35 R SNcn Log engine-size .... 1.091 ( 4.67e+00 6.51e-02) 1.93e+00 ( 4.80e+00 2.82e-01) 24 40 R SNcn Log wheel-base ..... 1.074 ( 4.56e+00 1.39e-02) 1.93e+00 ( 4.59e+00 5.89e-02) 15 31 R SNcm Log price .......... 0.844 ( 9.06e+00 1.65e-01) 1.74e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 00 02 D SM make ............... 0.727 mazda .............. -4.44e+00 9.78e-04 8.27e-02 peugot ............. -4.00e+00 9.77e-04 5.36e-02 volvo .............. -4.00e+00 9.77e-04 5.36e-02 bmw ................ -3.69e+00 9.77e-04 3.91e-02 mercedes-benz ...... -3.69e+00 9.77e-04 3.91e-02 audi ............... -3.55e+00 9.78e-04 3.42e-02 saab ............... -3.40e+00 9.77e-04 2.93e-02 porsche ............ -3.22e+00 9.77e-04 2.45e-02 alfa-romero ........ -2.72e+00 9.77e-04 1.48e-02 chevrolet .......... -2.72e+00 9.77e-04 1.48e-02 jaguar ............. -2.72e+00 9.77e-04 1.48e-02 renault ............ -2.32e+00 9.77e-04 9.93e-03 mercury ............ -1.65e+00 9.77e-04 5.08e-03 subaru ............. 1.49e+00 2.59e-01 5.85e-02 volkswagen ......... 1.19e+00 1.92e-01 5.85e-02 mitsubishi ......... 8.65e-01 1.50e-01 6.33e-02 honda .............. 7.19e-01 1.30e-01 6.33e-02 plymouth ........... -4.20e-01 2.25e-02 3.42e-02 toyota ............. -4.19e-01 1.02e-01 1.56e-01 nissan ............. -3.04e-01 6.46e-02 8.76e-02 isuzu .............. 1.35e-01 2.25e-02 1.96e-02 dodge .............. 1.61e-03 4.40e-02 4.39e-02 20 36 R SNcn Log curb-weight .... 0.720 ( 7.74e+00 6.78e-02) 1.26e+00 ( 7.83e+00 1.97e-01) 22 38 R SNcn Log width .......... 0.544 ( 4.17e+00 1.42e-02) 1.19e+00 ( 4.19e+00 3.15e-02) 23 39 R SNcn Log length ......... 0.415 ( 5.13e+00 3.45e-02) 8.15e-01 ( 5.16e+00 7.06e-02) 05 07 D SM drive-wheels ....... 0.302 rwd ................ -1.85e+00 5.84e-02 3.71e-01 4wd ................ 1.11e+00 1.37e-01 4.53e-02 fwd ................ 3.20e-01 8.04e-01 5.84e-01 13 29 R SNcm Log horse-power .... 0.263 ( 4.45e+00 2.12e-01) 6.50e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 07 14 D SM engine-type ........ 0.261 ohcv ............... -3.03e+00 3.07e-03 6.38e-02 l .................. -2.95e+00 3.07e-03 5.89e-02 rotor .............. -1.88e+00 3.07e-03 2.01e-02 ohcf ............... 1.27e+00 2.61e-01 7.35e-02 dohc ............... -5.98e-01 3.24e-02 5.89e-02 dohcv .............. -5.91e-01 3.07e-03 5.55e-03 ohc ................ -3.53e-02 6.94e-01 7.19e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 08 15 D SM num-of-cylinders ... 0.199 six ................ -3.64e+00 3.07e-03 1.17e-01 five ............... -2.87e+00 3.07e-03 5.41e-02 eight .............. -2.10e+00 3.07e-03 2.50e-02 two ................ -1.88e+00 3.07e-03 2.01e-02 three .............. -5.91e-01 3.07e-03 5.55e-03 twelve ............. -5.91e-01 3.07e-03 5.55e-03 four ............... 2.39e-01 9.82e-01 7.73e-01 10 26 R SNcm Log normalized-loses 0.161 ( 4.69e+00 2.37e-01) 3.30e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.53e-01 Prob-*k is known 8.00e-01 17 33 R SNcn Log city-mpg ....... 0.136 ( 3.29e+00 1.94e-01) 5.02e-01 ( 3.19e+00 2.56e-01) 09 17 D SM fuel-system ........ 0.085 4bbl ............... -1.73e+00 2.69e-03 1.52e-02 spdi ............... 9.02e-01 1.09e-01 4.43e-02 mfi ................ -7.09e-01 2.69e-03 5.46e-03 spfi ............... -7.08e-01 2.69e-03 5.46e-03 1bbl ............... 4.96e-01 8.87e-02 5.40e-02 mpfi ............... -3.86e-01 3.11e-01 4.57e-01 idi ................ 2.86e-01 1.30e-01 9.77e-02 2bbl ............... 9.59e-02 3.53e-01 3.21e-01 16 32 R SNcn Log highway-mpg .... 0.084 ( 3.48e+00 1.94e-01) 4.17e-01 ( 3.40e+00 2.23e-01) 11 27 R SNcm Log bore ........... 0.073 ( 1.19e+00 6.52e-02) 1.76e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 12 28 R SNcm Log stroke ......... 0.067 ( 1.16e+00 1.21e-01) 1.53e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.80e-01 21 37 R SNcn Log height ......... 0.046 ( 3.98e+00 3.67e-02) 1.37e-01 ( 3.98e+00 4.54e-02) 04 06 D SM body-style ......... 0.040 hardtop ............ -2.23e+00 4.30e-03 3.98e-02 sedan .............. 1.92e-01 5.66e-01 4.67e-01 hatchback .......... -1.58e-01 2.91e-01 3.41e-01 convertible ........ -1.54e-01 2.58e-02 3.01e-02 wagon .............. -7.81e-02 1.13e-01 1.22e-01 18 34 R SNcn Log compression-rati 0.029 ( 2.28e+00 3.30e-01) 3.35e-02 ( 2.27e+00 2.81e-01) o 03 05 D SM num-of-doors ....... 0.014 ? .................. 9.29e-01 2.87e-02 1.13e-02 two ................ -1.32e-01 3.80e-01 4.34e-01 four ............... 6.36e-02 5.91e-01 5.55e-01 14 30 R SNcm Log peak-rpm ....... 0.012 ( 8.55e+00 9.79e-02) 8.09e-02 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 01 03 D SM fuel-type .......... 0.008 diesel ............. 3.28e-01 1.38e-01 9.95e-02 gas ................ -4.38e-02 8.62e-01 9.00e-01 06 08 D SM engine-location .... 0.001 rear ............... -4.58e-01 1.08e-02 1.70e-02 front .............. 6.33e-03 9.89e-01 9.83e-01 02 04 D SM aspiration ......... 0.001 turbo .............. 1.02e-01 2.02e-01 1.82e-01 std ................ -2.43e-02 7.98e-01 8.18e-01 CLASS 2 - weight 37 normalized weight 0.180 relative strength 1.32e-03 ******* class cross entropy w.r.t. global class 9.87e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 02 D SM make ............... 0.989 honda .............. -3.97e+00 1.20e-03 6.33e-02 mitsubishi ......... -3.97e+00 1.20e-03 6.33e-02 subaru ............. -3.89e+00 1.20e-03 5.85e-02 peugot ............. -3.80e+00 1.20e-03 5.36e-02 dodge .............. -3.60e+00 1.20e-03 4.39e-02 mercedes-benz ...... -3.48e+00 1.20e-03 3.91e-02 plymouth ........... -3.35e+00 1.20e-03 3.42e-02 porsche ............ -3.02e+00 1.20e-03 2.45e-02 isuzu .............. -2.80e+00 1.20e-03 1.96e-02 alfa-romero ........ -2.51e+00 1.20e-03 1.48e-02 chevrolet .......... -2.51e+00 1.20e-03 1.48e-02 jaguar ............. -2.51e+00 1.20e-03 1.48e-02 renault ............ -2.11e+00 1.20e-03 9.93e-03 mercury ............ 1.69e+00 2.75e-02 5.08e-03 volvo .............. 1.60e+00 2.65e-01 5.36e-02 audi ............... 1.54e+00 1.59e-01 3.42e-02 saab ............... 1.51e+00 1.33e-01 2.93e-02 bmw ................ 1.40e+00 1.59e-01 3.91e-02 mazda .............. -1.10e+00 2.76e-02 8.27e-02 volkswagen ......... -7.54e-01 2.75e-02 5.85e-02 toyota ............. -3.85e-01 1.06e-01 1.56e-01 nissan ............. -8.66e-02 8.03e-02 8.76e-02 18 34 R SNcn Log compression-rati 0.932 ( 2.17e+00 7.36e-02) 1.31e+00 ( 2.27e+00 2.81e-01) o 23 39 R SNcn Log length ......... 0.896 ( 5.22e+00 2.81e-02) 2.24e+00 ( 5.16e+00 7.06e-02) 20 36 R SNcn Log curb-weight .... 0.817 ( 7.97e+00 7.51e-02) 1.96e+00 ( 7.83e+00 1.97e-01) 15 31 R SNcm Log price .......... 0.787 ( 9.75e+00 2.01e-01) 1.98e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.73e-01 Prob-*k is known 9.80e-01 09 17 D SM fuel-system ........ 0.690 2bbl ............... -4.58e+00 3.29e-03 3.21e-01 idi ................ -3.39e+00 3.29e-03 9.77e-02 1bbl ............... -2.80e+00 3.29e-03 5.40e-02 spdi ............... -2.60e+00 3.29e-03 4.43e-02 4bbl ............... -1.53e+00 3.29e-03 1.52e-02 mpfi ............... 7.60e-01 9.77e-01 4.57e-01 mfi ................ -5.05e-01 3.29e-03 5.46e-03 spfi ............... -5.05e-01 3.29e-03 5.46e-03 13 29 R SNcm Log horse-power .... 0.676 ( 4.88e+00 1.75e-01) 1.67e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 24 40 R SNcn Log wheel-base ..... 0.596 ( 4.63e+00 2.90e-02) 1.48e+00 ( 4.59e+00 5.89e-02) 19 35 R SNcn Log engine-size .... 0.448 ( 4.97e+00 1.50e-01) 1.12e+00 ( 4.80e+00 2.82e-01) 21 37 R SNcn Log height ......... 0.448 ( 4.01e+00 2.44e-02) 1.12e+00 ( 3.98e+00 4.54e-02) 17 33 R SNcn Log city-mpg ....... 0.438 ( 2.97e+00 1.94e-01) 1.14e+00 ( 3.19e+00 2.56e-01) 12 28 R SNcm Log stroke ......... 0.349 ( 1.16e+00 5.08e-02) 2.62e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 16 32 R SNcn Log highway-mpg .... 0.336 ( 3.22e+00 1.94e-01) 9.19e-01 ( 3.40e+00 2.23e-01) 08 15 D SM num-of-cylinders ... 0.307 eight .............. -1.89e+00 3.77e-03 2.50e-02 two ................ -1.68e+00 3.77e-03 2.01e-02 five ............... 1.25e+00 1.88e-01 5.41e-02 six ................ 1.00e-00 3.18e-01 1.17e-01 four ............... -4.79e-01 4.78e-01 7.73e-01 three .............. -3.88e-01 3.77e-03 5.55e-03 twelve ............. -3.88e-01 3.77e-03 5.55e-03 22 38 R SNcn Log width .......... 0.278 ( 4.21e+00 2.34e-02) 8.62e-01 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore ........... 0.223 ( 1.24e+00 6.31e-02) 7.06e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 07 14 D SM engine-type ........ 0.187 ohcf ............... -2.97e+00 3.77e-03 7.35e-02 l .................. -2.75e+00 3.77e-03 5.89e-02 rotor .............. -1.68e+00 3.77e-03 2.01e-02 dohc ............... 1.00e+00 1.61e-01 5.89e-02 ohcv ............... 5.38e-01 1.09e-01 6.38e-02 dohcv .............. -3.88e-01 3.77e-03 5.55e-03 ohc ................ -6.15e-03 7.15e-01 7.19e-01 04 06 D SM body-style ......... 0.140 hardtop ............ -2.02e+00 5.27e-03 3.98e-02 convertible ........ -1.71e+00 5.45e-03 3.01e-02 hatchback .......... -7.41e-01 1.62e-01 3.41e-01 sedan .............. 3.51e-01 6.63e-01 4.67e-01 wagon .............. 2.90e-01 1.63e-01 1.22e-01 05 07 D SM drive-wheels ....... 0.110 fwd ................ -5.08e-01 3.51e-01 5.84e-01 rwd ................ 4.60e-01 5.87e-01 3.71e-01 4wd ................ 3.04e-01 6.14e-02 4.53e-02 03 05 D SM num-of-doors ....... 0.079 two ................ -5.70e-01 2.45e-01 4.34e-01 four ............... 2.96e-01 7.46e-01 5.55e-01 ? .................. -2.54e-01 8.79e-03 1.13e-02 01 03 D SM fuel-type .......... 0.064 diesel ............. -2.02e+00 1.32e-02 9.95e-02 gas ................ 9.16e-02 9.87e-01 9.00e-01 14 30 R SNcm Log peak-rpm ....... 0.051 ( 8.57e+00 9.77e-02) 2.94e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e-00 Prob-*k is known 9.90e-01 02 04 D SM aspiration ......... 0.014 turbo .............. 3.18e-01 2.50e-01 1.82e-01 std ................ -8.71e-02 7.50e-01 8.18e-01 10 26 R SNcm Log normalized-loses 0.014 ( 4.80e+00 3.08e-01) 1.21e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 7.85e-01 Prob-*k is known 8.00e-01 06 08 D SM engine-location .... 0.000 rear ............... -2.54e-01 1.32e-02 1.70e-02 front .............. 3.87e-03 9.87e-01 9.83e-01 CLASS 3 - weight 31 normalized weight 0.150 relative strength 3.85e-05 ******* class cross entropy w.r.t. global class 9.53e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 04 06 D SM body-style ......... 0.791 sedan .............. -4.27e+00 6.52e-03 4.67e-01 convertible ........ 1.48e+00 1.32e-01 3.01e-02 hardtop ............ 1.42e+00 1.64e-01 3.98e-02 wagon .............. -1.17e+00 3.78e-02 1.22e-01 hatchback .......... 6.60e-01 6.59e-01 3.41e-01 00 02 D SM make ............... 0.730 honda .............. -3.79e+00 1.43e-03 6.33e-02 subaru ............. -3.71e+00 1.43e-03 5.85e-02 volkswagen ......... -3.71e+00 1.43e-03 5.85e-02 peugot ............. -3.62e+00 1.43e-03 5.36e-02 volvo .............. -3.62e+00 1.43e-03 5.36e-02 mercedes-benz ...... -3.31e+00 1.43e-03 3.91e-02 bmw ................ -3.27e+00 1.48e-03 3.91e-02 audi ............... -3.11e+00 1.53e-03 3.42e-02 chevrolet .......... -2.33e+00 1.43e-03 1.48e-02 jaguar ............. -2.33e+00 1.43e-03 1.48e-02 alfa-romero ........ 1.87e+00 9.60e-02 1.48e-02 porsche ............ 1.87e+00 1.59e-01 2.45e-02 renault ............ 1.87e+00 6.45e-02 9.93e-03 mercury ............ -1.26e+00 1.44e-03 5.08e-03 isuzu .............. 5.18e-01 3.30e-02 1.96e-02 mazda .............. 4.33e-01 1.28e-01 8.27e-02 mitsubishi ......... 4.32e-01 9.75e-02 6.33e-02 toyota ............. 3.08e-01 2.12e-01 1.56e-01 dodge .............. -2.87e-01 3.30e-02 4.39e-02 saab ............... 1.16e-01 3.30e-02 2.93e-02 nissan ............. 9.11e-02 9.59e-02 8.76e-02 plymouth ........... -3.68e-02 3.30e-02 3.42e-02 21 37 R SNcn Log height ......... 0.730 ( 3.93e+00 3.14e-02) 1.61e+00 ( 3.98e+00 4.54e-02) 09 17 D SM fuel-system ........ 0.700 2bbl ............... -4.40e+00 3.94e-03 3.21e-01 idi ................ -3.21e+00 3.94e-03 9.77e-02 1bbl ............... -2.62e+00 3.94e-03 5.40e-02 mfi ................ 1.87e+00 3.55e-02 5.46e-03 4bbl ............... 1.87e+00 9.85e-02 1.52e-02 spfi ............... 1.87e+00 3.55e-02 5.46e-03 spdi ............... 1.09e+00 1.32e-01 4.43e-02 mpfi ............... 4.08e-01 6.87e-01 4.57e-01 23 39 R SNcn Log length ......... 0.633 ( 5.15e+00 2.41e-02) 1.26e-01 ( 5.16e+00 7.06e-02) 03 05 D SM num-of-doors ....... 0.631 four ............... -2.58e+00 4.20e-02 5.55e-01 two ................ 7.82e-01 9.47e-01 4.34e-01 ? .................. -7.48e-02 1.05e-02 1.13e-02 18 34 R SNcn Log compression-rati 0.591 ( 2.17e+00 1.07e-01) 8.67e-01 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power .... 0.564 ( 4.92e+00 2.64e-01) 1.27e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.37e-01 Prob-*k is known 9.90e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 10 26 R SNcm Log normalized-loses 0.553 ( 5.04e+00 1.59e-01) 1.75e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.52e-01 Prob-*k is known 8.00e-01 20 36 R SNcn Log curb-weight .... 0.498 ( 7.91e+00 8.72e-02) 9.34e-01 ( 7.83e+00 1.97e-01) 24 40 R SNcn Log wheel-base ..... 0.404 ( 4.55e+00 3.42e-02) 1.07e+00 ( 4.59e+00 5.89e-02) 17 33 R SNcn Log city-mpg ....... 0.379 ( 2.99e+00 1.93e-01) 1.05e+00 ( 3.19e+00 2.56e-01) 11 27 R SNcm Log bore ........... 0.329 ( 1.25e+00 9.17e-02) 5.77e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 8.73e-01 Prob-*k is known 9.80e-01 05 07 D SM drive-wheels ....... 0.311 4wd ................ -1.45e+00 1.06e-02 4.53e-02 fwd ................ -9.20e-01 2.33e-01 5.84e-01 rwd ................ 7.14e-01 7.57e-01 3.71e-01 07 14 D SM engine-type ........ 0.285 l .................. -2.57e+00 4.50e-03 5.89e-02 rotor .............. 1.87e+00 1.31e-01 2.01e-02 dohcv .............. 1.87e+00 3.60e-02 5.55e-03 ohcv ............... 7.16e-01 1.31e-01 6.38e-02 dohc ............... 4.09e-01 8.87e-02 5.89e-02 ohc ................ -3.43e-01 5.10e-01 7.19e-01 ohcf ............... 2.99e-01 9.91e-02 7.35e-02 12 28 R SNcm Log stroke ......... 0.283 ( 1.20e+00 1.44e-01) 1.42e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 8.73e-01 Prob-*k is known 9.80e-01 08 15 D SM num-of-cylinders ... 0.237 five ............... -2.46e+00 4.60e-03 5.41e-02 two ................ 1.87e+00 1.31e-01 2.01e-02 six ................ 6.58e-01 2.26e-01 1.17e-01 eight .............. 3.67e-01 3.60e-02 2.50e-02 four ............... -2.64e-01 5.93e-01 7.73e-01 three .............. -2.08e-01 4.50e-03 5.55e-03 twelve ............. -2.08e-01 4.50e-03 5.55e-03 15 31 R SNcm Log price .......... 0.188 ( 9.59e+00 3.67e-01) 6.43e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.68e-01 Prob-*k is known 9.80e-01 16 32 R SNcn Log highway-mpg .... 0.184 ( 3.27e+00 1.93e-01) 6.64e-01 ( 3.40e+00 2.23e-01) 14 30 R SNcm Log peak-rpm ....... 0.122 ( 8.57e+00 9.74e-02) 3.49e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.37e-01 Prob-*k is known 9.90e-01 06 08 D SM engine-location .... 0.118 rear ............... 1.87e+00 1.10e-01 1.70e-02 front .............. -9.98e-02 8.90e-01 9.83e-01 22 38 R SNcn Log width .......... 0.106 ( 4.19e+00 2.22e-02) 1.73e-01 ( 4.19e+00 3.15e-02) 19 35 R SNcn Log engine-size .... 0.105 ( 4.93e+00 2.88e-01) 4.47e-01 ( 4.80e+00 2.82e-01) 01 03 D SM fuel-type .......... 0.058 diesel ............. -1.84e+00 1.58e-02 9.95e-02 gas ................ 8.89e-02 9.84e-01 9.00e-01 02 04 D SM aspiration ......... 0.002 turbo .............. 1.26e-01 2.07e-01 1.82e-01 std ................ -3.04e-02 7.93e-01 8.18e-01 CLASS 4 - weight 18 normalized weight 0.089 relative strength 4.13e-03 ******* class cross entropy w.r.t. global class 9.59e+00 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 28 R SNcm Log stroke ......... 1.263 ( 1.24e+00 2.18e-02) 2.82e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 23 39 R SNcn Log length ......... 0.927 ( 5.18e+00 1.84e-02) 1.22e+00 ( 5.16e+00 7.06e-02) 19 35 R SNcn Log engine-size .... 0.786 ( 4.79e+00 8.12e-02) 1.65e-01 ( 4.80e+00 2.82e-01) 00 02 D SM make ............... 0.733 mitsubishi ......... -3.27e+00 2.40e-03 6.33e-02 honda .............. -3.23e+00 2.50e-03 6.33e-02 subaru ............. -3.20e+00 2.37e-03 5.85e-02 peugot ............. -3.12e+00 2.37e-03 5.36e-02 nissan ............. -2.98e+00 4.47e-03 8.76e-02 bmw ................ -2.80e+00 2.37e-03 3.91e-02 mercedes-benz ...... -2.80e+00 2.37e-03 3.91e-02 saab ............... -2.52e+00 2.37e-03 2.93e-02 porsche ............ -2.33e+00 2.37e-03 2.45e-02 isuzu .............. -2.11e+00 2.37e-03 1.96e-02 alfa-romero ........ -1.83e+00 2.37e-03 1.48e-02 chevrolet .......... -1.83e+00 2.37e-03 1.48e-02 jaguar ............. -1.83e+00 2.37e-03 1.48e-02 mazda .............. 1.49e+00 3.68e-01 8.27e-02 renault ............ -1.43e+00 2.37e-03 9.93e-03 mercury ............ -7.60e-01 2.37e-03 5.08e-03 volkswagen ......... 6.57e-01 1.13e-01 5.85e-02 toyota ............. 5.26e-01 2.63e-01 1.56e-01 audi ............... 4.67e-01 5.46e-02 3.42e-02 plymouth ........... 4.67e-01 5.46e-02 3.42e-02 dodge .............. 2.17e-01 5.46e-02 4.39e-02 volvo .............. 1.76e-02 5.46e-02 5.36e-02 13 29 R SNcm Log horse-power .... 0.679 ( 4.44e+00 1.25e-01) 1.16e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 20 36 R SNcn Log curb-weight .... 0.600 ( 7.82e+00 6.99e-02) 7.46e-02 ( 7.83e+00 1.97e-01) 24 40 R SNcn Log wheel-base ..... 0.543 ( 4.62e+00 2.55e-02) 1.09e+00 ( 4.59e+00 5.89e-02) 22 38 R SNcn Log width .......... 0.525 ( 4.20e+00 1.27e-02) 6.43e-01 ( 4.19e+00 3.15e-02) 11 27 R SNcm Log bore ........... 0.419 ( 1.19e+00 3.70e-02) 1.95e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 14 30 R SNcm Log peak-rpm ....... 0.394 ( 8.45e+00 9.64e-02) 9.00e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 18 34 R SNcn Log compression-rati 0.375 ( 2.43e+00 4.17e-01) 3.90e-01 ( 2.27e+00 2.81e-01) o 21 37 R SNcn Log height ......... 0.368 ( 4.01e+00 2.90e-02) 1.03e+00 ( 3.98e+00 4.54e-02) 15 31 R SNcm Log price .......... 0.349 ( 9.33e+00 2.45e-01) 7.78e-02 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 03 05 D SM num-of-doors ....... 0.276 ? .................. 1.82e+00 6.96e-02 1.13e-02 two ................ -1.27e+00 1.22e-01 4.34e-01 four ............... 3.76e-01 8.09e-01 5.55e-01 10 26 R SNcm Log normalized-loses 0.257 ( 4.54e+00 3.06e-01) 7.40e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 7.81e-01 Prob-*k is known 8.00e-01 07 14 D SM engine-type ........ 0.202 ohcf ............... -2.29e+00 7.46e-03 7.35e-02 ohcv ............... -2.15e+00 7.46e-03 6.38e-02 dohc ............... -2.07e+00 7.46e-03 5.89e-02 l .................. -2.07e+00 7.46e-03 5.89e-02 rotor .............. -9.92e-01 7.46e-03 2.01e-02 dohcv .............. 2.96e-01 7.46e-03 5.55e-03 ohc ................ 2.84e-01 9.55e-01 7.19e-01 09 17 D SM fuel-system ........ 0.192 1bbl ............... -2.11e+00 6.52e-03 5.40e-02 spdi ............... -1.92e+00 6.52e-03 4.43e-02 idi ................ 1.02e+00 2.71e-01 9.77e-02 4bbl ............... -8.44e-01 6.52e-03 1.52e-02 mpfi ............... -3.50e-01 3.22e-01 4.57e-01 mfi ................ 1.78e-01 6.52e-03 5.46e-03 spfi ............... 1.78e-01 6.52e-03 5.46e-03 2bbl ............... 1.53e-01 3.74e-01 3.21e-01 05 07 D SM drive-wheels ....... 0.182 rwd ................ -1.11e+00 1.22e-01 3.71e-01 4wd ................ -9.57e-01 1.74e-02 4.53e-02 fwd ................ 3.88e-01 8.61e-01 5.84e-01 17 33 R SNcn Log city-mpg ....... 0.163 ( 3.30e+00 1.91e-01) 5.75e-01 ( 3.19e+00 2.56e-01) 01 03 D SM fuel-type .......... 0.143 diesel ............. 1.07e+00 2.91e-01 9.95e-02 gas ................ -2.39e-01 7.09e-01 9.00e-01 16 32 R SNcn Log highway-mpg .... 0.093 ( 3.49e+00 1.91e-01) 4.41e-01 ( 3.40e+00 2.23e-01) 08 15 D SM num-of-cylinders ... 0.074 five ............... -1.97e+00 7.57e-03 5.41e-02 eight .............. -1.21e+00 7.46e-03 2.50e-02 two ................ -9.92e-01 7.46e-03 2.01e-02 six ................ -6.75e-01 5.97e-02 1.17e-01 three .............. 2.96e-01 7.46e-03 5.55e-03 twelve ............. 2.96e-01 7.46e-03 5.55e-03 four ............... 1.56e-01 9.03e-01 7.73e-01 04 06 D SM body-style ......... 0.044 hardtop ............ -1.34e+00 1.04e-02 3.98e-02 convertible ........ -1.06e+00 1.04e-02 3.01e-02 wagon .............. 3.11e-01 1.67e-01 1.22e-01 hatchback .......... -2.24e-01 2.72e-01 3.41e-01 sedan .............. 1.45e-01 5.40e-01 4.67e-01 06 08 D SM engine-location .... 0.002 rear ............... 4.29e-01 2.61e-02 1.70e-02 front .............. -9.31e-03 9.74e-01 9.83e-01 02 04 D SM aspiration ......... 0.000 turbo .............. 2.47e-02 1.87e-01 1.82e-01 std ................ -5.59e-03 8.13e-01 8.18e-01 CLASS 5 - weight 13 normalized weight 0.064 relative strength 8.46e-05 ******* class cross entropy w.r.t. global class 2.76e+01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 31 R SNcm Log price .......... 3.119 ( 1.04e+01 1.53e-01) 7.13e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 20 36 R SNcn Log curb-weight .... 2.843 ( 8.22e+00 5.49e-02) 7.22e+00 ( 7.83e+00 1.97e-01) 19 35 R SNcn Log engine-size .... 2.694 ( 5.44e+00 1.96e-01) 3.27e+00 ( 4.80e+00 2.82e-01) 22 38 R SNcn Log width .......... 2.693 ( 4.26e+00 1.50e-02) 4.54e+00 ( 4.19e+00 3.15e-02) 00 02 D SM make ............... 2.173 toyota ............. -3.87e+00 3.24e-03 1.56e-01 nissan ............. -3.30e+00 3.24e-03 8.76e-02 mazda .............. -3.24e+00 3.24e-03 8.27e-02 honda .............. -2.97e+00 3.24e-03 6.33e-02 mitsubishi ......... -2.97e+00 3.24e-03 6.33e-02 subaru ............. -2.89e+00 3.24e-03 5.85e-02 volkswagen ......... -2.89e+00 3.24e-03 5.85e-02 peugot ............. -2.81e+00 3.24e-03 5.36e-02 volvo .............. -2.81e+00 3.24e-03 5.36e-02 jaguar ............. 2.69e+00 2.17e-01 1.48e-02 mercedes-benz ...... 2.69e+00 5.74e-01 3.91e-02 dodge .............. -2.61e+00 3.24e-03 4.39e-02 audi ............... -2.36e+00 3.24e-03 3.42e-02 plymouth ........... -2.36e+00 3.24e-03 3.42e-02 saab ............... -2.20e+00 3.24e-03 2.93e-02 porsche ............ -2.02e+00 3.24e-03 2.45e-02 isuzu .............. -1.80e+00 3.24e-03 1.96e-02 alfa-romero ........ -1.52e+00 3.24e-03 1.48e-02 chevrolet .......... -1.52e+00 3.24e-03 1.48e-02 bmw ................ 1.33e+00 1.48e-01 3.91e-02 renault ............ -1.12e+00 3.24e-03 9.93e-03 mercury ............ -4.49e-01 3.24e-03 5.08e-03 23 39 R SNcn Log length ......... 1.727 ( 5.28e+00 3.55e-02) 3.34e+00 ( 5.16e+00 7.06e-02) 08 15 D SM num-of-cylinders ... 1.682 four ............... -4.33e+00 1.02e-02 7.73e-01 twelve ............. 2.69e+00 8.15e-02 5.55e-03 eight .............. 2.47e+00 2.95e-01 2.50e-02 five ............... 1.70e+00 2.95e-01 5.41e-02 six ................ 9.31e-01 2.97e-01 1.17e-01 two ................ -6.80e-01 1.02e-02 2.01e-02 three .............. 6.08e-01 1.02e-02 5.55e-03 24 40 R SNcn Log wheel-base ..... 1.628 ( 4.70e+00 5.53e-02) 1.92e+00 ( 4.59e+00 5.89e-02) 16 32 R SNcn Log highway-mpg .... 1.613 ( 3.00e+00 1.89e-01) 2.10e+00 ( 3.40e+00 2.23e-01) 13 29 R SNcm Log horse-power .... 1.231 ( 5.08e+00 2.09e-01) 2.38e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 11 27 R SNcm Log bore ........... 1.215 ( 1.28e+00 2.61e-02) 3.16e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 17 33 R SNcn Log city-mpg ....... 1.142 ( 2.82e+00 1.89e-01) 1.98e+00 ( 3.19e+00 2.56e-01) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 07 D SM drive-wheels ....... 0.808 fwd ................ -3.20e+00 2.38e-02 5.84e-01 rwd ................ 9.44e-01 9.52e-01 3.71e-01 4wd ................ -6.45e-01 2.38e-02 4.53e-02 07 14 D SM engine-type ........ 0.532 ohcf ............... -1.98e+00 1.02e-02 7.35e-02 l .................. -1.76e+00 1.02e-02 5.89e-02 ohcv ............... 1.75e+00 3.67e-01 6.38e-02 dohc ............... 9.52e-01 1.53e-01 5.89e-02 rotor .............. -6.80e-01 1.02e-02 2.01e-02 dohcv .............. 6.08e-01 1.02e-02 5.55e-03 ohc ................ -4.92e-01 4.40e-01 7.19e-01 09 17 D SM fuel-system ........ 0.498 2bbl ............... -3.58e+00 8.91e-03 3.21e-01 1bbl ............... -1.80e+00 8.91e-03 5.40e-02 spdi ............... -1.60e+00 8.91e-03 4.43e-02 idi ................ 1.10e+00 2.94e-01 9.77e-02 4bbl ............... -5.32e-01 8.91e-03 1.52e-02 mfi ................ 4.90e-01 8.91e-03 5.46e-03 spfi ............... 4.90e-01 8.91e-03 5.46e-03 mpfi ............... 3.56e-01 6.52e-01 4.57e-01 04 06 D SM body-style ......... 0.454 hatchback .......... -3.17e+00 1.43e-02 3.41e-01 hardtop ............ 1.37e+00 1.57e-01 3.98e-02 convertible ........ 1.04e+00 8.56e-02 3.01e-02 wagon .............. -3.58e-01 8.56e-02 1.22e-01 sedan .............. 3.43e-01 6.58e-01 4.67e-01 18 34 R SNcn Log compression-rati 0.368 ( 2.41e+00 4.26e-01) 3.41e-01 ( 2.27e+00 2.81e-01) o 14 30 R SNcm Log peak-rpm ....... 0.357 ( 8.45e+00 9.54e-02) 8.66e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 10 26 R SNcm Log normalized-loses 0.317 ( 4.68e+00 1.97e-01) 4.36e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 4.85e-01 Prob-*k is known 8.00e-01 12 28 R SNcm Log stroke ......... 0.232 ( 1.24e+00 1.06e-01) 6.35e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 01 03 D SM fuel-type .......... 0.184 diesel ............. 1.17e+00 3.21e-01 9.95e-02 gas ................ -2.82e-01 6.79e-01 9.00e-01 21 37 R SNcn Log height ......... 0.067 ( 4.00e+00 5.10e-02) 2.90e-01 ( 3.98e+00 4.54e-02) 02 04 D SM aspiration ......... 0.056 turbo .............. 5.67e-01 3.21e-01 1.82e-01 std ................ -1.86e-01 6.79e-01 8.18e-01 03 05 D SM num-of-doors ....... 0.010 ? .................. 7.41e-01 2.38e-02 1.13e-02 two ................ -1.31e-01 3.80e-01 4.34e-01 four ............... 7.12e-02 5.96e-01 5.55e-01 06 08 D SM engine-location .... 0.008 rear ............... 7.41e-01 3.56e-02 1.70e-02 front .............. -1.92e-02 9.64e-01 9.83e-01 CLASS 6 - weight 11 normalized weight 0.054 relative strength 2.15e-01 ******* class cross entropy w.r.t. global class 2.38e+01 ******* Model file: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 07 14 D SM engine-type ........ 2.453 ohc ................ -4.10e+00 1.19e-02 7.19e-01 l .................. 2.76e+00 9.29e-01 5.89e-02 ohcf ............... -1.82e+00 1.19e-02 7.35e-02 ohcv ............... -1.68e+00 1.19e-02 6.38e-02 dohc ............... -1.60e+00 1.19e-02 5.89e-02 dohcv .............. 7.64e-01 1.19e-02 5.55e-03 rotor .............. -5.24e-01 1.19e-02 2.01e-02 00 02 D SM make ............... 2.442 toyota ............. -3.72e+00 3.79e-03 1.56e-01 nissan ............. -3.14e+00 3.79e-03 8.76e-02 mazda .............. -3.08e+00 3.79e-03 8.27e-02 peugot ............. 2.84e+00 9.20e-01 5.36e-02 honda .............. -2.82e+00 3.79e-03 6.33e-02 mitsubishi ......... -2.82e+00 3.79e-03 6.33e-02 subaru ............. -2.74e+00 3.79e-03 5.85e-02 volkswagen ......... -2.74e+00 3.79e-03 5.85e-02 volvo .............. -2.65e+00 3.79e-03 5.36e-02 dodge .............. -2.45e+00 3.79e-03 4.39e-02 bmw ................ -2.33e+00 3.79e-03 3.91e-02 mercedes-benz ...... -2.33e+00 3.79e-03 3.91e-02 audi ............... -2.20e+00 3.79e-03 3.42e-02 plymouth ........... -2.20e+00 3.79e-03 3.42e-02 saab ............... -2.05e+00 3.79e-03 2.93e-02 porsche ............ -1.87e+00 3.79e-03 2.45e-02 isuzu .............. -1.65e+00 3.79e-03 1.96e-02 alfa-romero ........ -1.36e+00 3.79e-03 1.48e-02 chevrolet .......... -1.36e+00 3.79e-03 1.48e-02 jaguar ............. -1.36e+00 3.79e-03 1.48e-02 renault ............ -9.64e-01 3.79e-03 9.93e-03 mercury ............ -2.93e-01 3.79e-03 5.08e-03 24 40 R SNcn Log wheel-base ..... 2.181 ( 4.70e+00 2.61e-02) 4.25e+00 ( 4.59e+00 5.89e-02) 22 38 R SNcn Log width .......... 2.104 ( 4.23e+00 4.78e-03) 7.84e+00 ( 4.19e+00 3.15e-02) 20 36 R SNcn Log curb-weight .... 1.900 ( 8.08e+00 4.09e-02) 6.12e+00 ( 7.83e+00 1.97e-01) 21 37 R SNcn Log height ......... 1.582 ( 4.05e+00 1.58e-02) 4.00e+00 ( 3.98e+00 4.54e-02) 23 39 R SNcn Log length ......... 1.385 ( 5.25e+00 2.92e-02) 3.28e+00 ( 5.16e+00 7.06e-02) 10 26 R SNcm Log normalized-loses 1.313 ( 5.08e+00 4.76e-02) 6.67e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.50e-01 Prob-*k is known 8.00e-01 15 31 R SNcm Log price .......... 1.001 ( 9.64e+00 1.38e-01) 2.08e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 11 27 R SNcm Log bore ........... 0.993 ( 1.28e+00 3.07e-02) 2.47e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 07 D SM drive-wheels ....... 0.785 fwd ................ -3.05e+00 2.78e-02 5.84e-01 rwd ................ 9.36e-01 9.44e-01 3.71e-01 4wd ................ -4.89e-01 2.78e-02 4.53e-02 18 34 R SNcn Log compression-rati 0.712 ( 2.51e+00 4.54e-01) 5.44e-01 ( 2.27e+00 2.81e-01) o 13 29 R SNcm Log horse-power .... 0.706 ( 4.60e+00 1.09e-01) 1.10e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 09 17 D SM fuel-system ........ 0.628 2bbl ............... -3.43e+00 1.04e-02 3.21e-01 1bbl ............... -1.65e+00 1.04e-02 5.40e-02 idi ................ 1.48e+00 4.27e-01 9.77e-02 spdi ............... -1.45e+00 1.04e-02 4.43e-02 mfi ................ 6.46e-01 1.04e-02 5.46e-03 spfi ............... 6.46e-01 1.04e-02 5.46e-03 4bbl ............... -3.76e-01 1.04e-02 1.52e-02 mpfi ............... 1.11e-01 5.10e-01 4.57e-01 19 35 R SNcn Log engine-size .... 0.602 ( 4.90e+00 1.08e-01) 9.70e-01 ( 4.80e+00 2.82e-01) 14 30 R SNcm Log peak-rpm ....... 0.460 ( 8.44e+00 1.02e-01) 9.19e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 03 05 D SM num-of-doors ....... 0.451 two ................ -2.75e+00 2.78e-02 4.34e-01 ? .................. 8.97e-01 2.78e-02 1.13e-02 four ............... 5.32e-01 9.44e-01 5.55e-01 04 06 D SM body-style ......... 0.444 hatchback .......... -3.02e+00 1.67e-02 3.41e-01 wagon .............. 1.05e+00 3.50e-01 1.22e-01 hardtop ............ -8.71e-01 1.67e-02 3.98e-02 convertible ........ -5.91e-01 1.67e-02 3.01e-02 sedan .............. 2.51e-01 6.00e-01 4.67e-01 01 03 D SM fuel-type .......... 0.425 diesel ............. 1.53e+00 4.58e-01 9.95e-02 gas ................ -5.08e-01 5.42e-01 9.00e-01 12 28 R SNcm Log stroke ......... 0.401 ( 1.14e+00 1.65e-01) 2.28e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 02 04 D SM aspiration ......... 0.325 turbo .............. 1.09e+00 5.42e-01 1.82e-01 std ................ -5.79e-01 4.58e-01 8.18e-01 16 32 R SNcn Log highway-mpg .... 0.191 ( 3.27e+00 1.88e-01) 6.81e-01 ( 3.40e+00 2.23e-01) 17 33 R SNcn Log city-mpg ....... 0.153 ( 3.10e+00 1.88e-01) 5.27e-01 ( 3.19e+00 2.56e-01) 08 15 D SM num-of-cylinders ... 0.129 six ................ -2.29e+00 1.19e-02 1.17e-01 five ............... -1.51e+00 1.19e-02 5.41e-02 three .............. 7.64e-01 1.19e-02 5.55e-03 twelve ............. 7.64e-01 1.19e-02 5.55e-03 eight .............. -7.41e-01 1.19e-02 2.50e-02 two ................ -5.24e-01 1.19e-02 2.01e-02 four ............... 1.84e-01 9.29e-01 7.73e-01 06 08 D SM engine-location .... 0.013 rear ............... 8.97e-01 4.17e-02 1.70e-02 front .............. -2.54e-02 9.58e-01 9.83e-01 autoclass-3.3.6.dfsg.1/data/autos/imports-85.search0000644000175000017500000000111711247310756020072 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 4 1 0 6 last try reported 0 tries from best on down for n_tries 4 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 7 -1.64535360e+04 32 200 n_dups 0 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -1.66542376e+04 37 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -1.68166572e+04 16 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -1.70418666e+04 22 200 n_dups 0 start_j_list 10 15 25 -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/data/autos/imports-85.r-params0000644000175000017500000001137011247310756020351 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! core dump. If specified as false, AutoClass will not attempt to free ! storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; xref_class_report_att_list = 13, 2, 25, 14 ;; report_type = "xref_case" ;; report_type = "xref_class" ;; report_type = "influence_values" ;; num_atts_to_list = 5 ;; clsf_n_list = 2 ;; n_clsfs = 2 ;; report_mode = "data" ;; comment_data_headers_p = true sigma_contours_att_list = 18, 19, 20, 21, 22, 23, 24, 25 autoclass-3.3.6.dfsg.1/data/autos/imports-85.rlog0000644000175000017500000001771511247310756017603 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:11:37 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:11:37 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:12:49 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:12:49 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:13:35 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:13:36 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Thu Jun 7 12:15:19 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/data/autos/imports-85.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Thu Jun 7 12:15:19 2001 AUTOCLASS C (version 3.3.5unx) STARTING at Wed Mar 1 11:56:27 2006 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.influ-o-data-1 File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.case-data-1 File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.class-data-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Wed Mar 1 11:56:27 2006 AUTOCLASS C (version 3.3.5unx) STARTING at Wed Mar 1 12:03:54 2006 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(13,2,25,14); sigma_contours_att_list=(18,19,20,21,22,23,24,25) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.results-bin ADVISORY: read 4 search trials from /home/wtaylor/AC/autoclass-c/data/autos/imports-85.search File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.influ-o-text-1 File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.case-text-1 File written: /home/wtaylor/AC/autoclass-c/data/autos/imports-85.class-text-1 AUTOCLASS C (version 3.3.5unx) STOPPING at Wed Mar 1 12:03:55 2006 autoclass-3.3.6.dfsg.1/version-2-0.text0000644000175000017500000001200311247310756015573 0ustar areare AUTOCLASS C VERSION 2.0 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Added new ".s-params" parameter screen_output_p, whose default value is true. If false, no output is directed to the screen. Assuming log_file_p = true, output will be directed to the log file only. 2. autoclass-c/doc/introduction-c.text, & prediction-c.text - Added "prediction-c.text" to document the prediction mode of AutoClass C, which uses a "training" classification to predict probabilistic class membership for the cases of a "test" data file. Programming: ------------------------------ 1. autoclass-c/prog/io-results.c - In "read_class_DS_s", add debugging info to use with G_clsf_storage_log_p. 2. autoclass-c/prog/struct-class.c - In "build_class_DS", add debugging info to use with G_clsf_storage_log_p. 3. autoclass-c/prog/io-results-bin.c - In "load_class_DS_s," add debugging info to use with G_clsf_storage_log_p. 4. autoclass-c/prog/struct-data.c - In "expand_database", to handle partial databases, read G_s_params_n_data. 5. autoclass-c/prog/globals.c, globlals.h, search-control.c - Add G_s_params_n_data. Change G_ac_version to 2.0 in globals.c. 6. autoclass-c/prog/io-read-data.c, autoclass.h - In "read_data" test on n_data was off by 1. In "output_created_translations" add discrete value occurrance count. In "read_data" move "output_created_translations" call to "output_att_statistics". Add "output_att_statistics" & "output_real_att_statistics". In "create_warn_err_ds", move malloc out of declaration. 7. autoclass-c/prog/prints.c, autoclass.h - Add "sum_vector_f" for debugging. 8. autoclass-c/prog/autoclass.c - Make "main" arg list conform to ANSI C. 9. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", call "output_real_att_statistics" for each transformed attribute. 10. autoclass-c/prog/utils.c - In "randomize_list" do limit check on list index. 11. autoclass-c/prog/search-control-2.c - In all convergence functions, allocate mallocs in body of function, rather than in local variable declarations. In "get_search_DS", move malloc out of declaration. 12. autoclass-c/prog/intf-reports.c - In "xref_get_data", use n_real_att - 1, rather than i, for index to real_attribute_data; and n_discrete_att - 1 for discrete_attribute_data. Corrects garbage output when .r-params parameter "xref_class_report_att_list" contains mixed real and discrete attributes. In "xref_class_report_attributes", use %g, rather than %f for real data. In "xref_output_line_by_class", handle unknown real values. 13. autoclass-c/prog/io-read-data.c, io-results.c, io-results-bin.c, fcntlcom-ac.h - Convert binary i/o from non-standard (open/close/read/write) to ANSI (fopen/fclose/fread/fwrite). 14. autoclass-c/prog/search-control.c, search-basic.c, search-control-2.c, utils.c, globals.c, globals.h, init.c - Convert from srand/rand to srand48/lrand48 for random number generation. 15. autoclass-c/prog/predictions.c - Add this file to implement the "autoclass -predict ..." capability, which allows cases in a "test" data set to be applied to a "training" data set and have their class membership predicted. Use "prediction_p" and global "G_training_clsf" in "io-read-data.c" to force the "test" database to use the same discrete translations as the "training" database. 16. autoclass-c/load-ac; autoclass-c/prog/autoclass.c, autoclass.make, io-results.c, & autoclass.h - Changes to support item 15. 17. autoclass-c/prog/struct_data.c, struct-clsf.c, & struct-model.c - In "att_ds_equal_p", check for type = dummy. Remove "db_DS_same_source_p" and use "db_same_source_p", instead. 18. autoclass-c/prog/search-control.c - Make FILE * type local variables static, since they are passed to other functions. 19. autoclass-c/prog/autoclass.make - Compile code with "-g", rather than "-ggdb" option. 20. autoclass-c/load-ac & autoclass-c/prog/autoclass.make.sun, autoclass-c/prog/autoclass.make.sgi - Changes to support SGI IRIX version 5.2 with "cc" compiler. 21. autoclass-c/prog/io-read-data.c - In "output_warning_msgs", replaced sizeof(msg) with msg_length in first safe_sprintf call to prevent: "ERROR: vsprintf produced 80 chars (max number is 3) -- called by output_warning_msgs Program received signal SIGABRT, Aborted." ====================================================================== autoclass-3.3.6.dfsg.1/version-3-0.text0000644000175000017500000000751711247310756015612 0ustar areare AUTOCLASS C VERSION 3.0 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text, reports-c.text -- New parameter for .s-params & .r-params files: free_storage_p. The default value tells AutoClass to free the majority of its allocated storage. If specified as false, AutoClass will not attempt to free storage. 2. autoclass-c/doc/preparation-c.text - Correct typos "looses" and "scaler". 3. autoclass-c/doc/reports-c.text -- New parameter for .r-params files: report_mode. It specifies the mode of the reports to generate. The default, "text", is the current formatted text layout. The new "data" option has a parsable numerical layout -- suitable for further processing. 4. autoclass-c/sample/read.me.c, scriptc.text, imports-85c.influ-o-data-1, imports-85c.case-data-1, imports-85c.class-data-1 Updated the sample classification for report_mode = "data" reports. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.0. 2. autoclass-c/prog/autoclass.h, io-results.c, io-results-bin.c, struct-class.c, struct-clsf.c, struct-model.c Correct improper pointer casting: fprintf(stdout, "free_model(%d): %d\n", i_model, (int) model); to fprintf(stdout, "free_model(%d): %p\n", i_model, (void *) model); which generates compiler warnings on 64-bit architectures. Change prototype for list_class_storage & list_clsf_storage from int * to void **. 3. autoclass-c/prog/search-control.c, intf-reports.c - Process new params option: free_storage_p. 4. autoclass-c/prog/search-control-2.c - Correct formatted message typos "print print" and "estiamte" in PRINT_INITIAL_REPORT. 5. autoclass-c/prog/intf-reports.c - In PRE_FORMAT_ATTRIBUTES, check for num_terms > 0 prior to calling SORT_MNCN_ATTRIBUTES. 6. autoclass-c/prog/io-read-data.c - In READ_LINE, only return FALSE if no chars have been read -- allows last line with no new-line to be read correctly. 7. autoclass-c/prog/getparams.c - Correct GETPARAMS for INT_LIST: to allow "= 84, 92 " to be read as 84 & 92, rather than 84 & 84. Also allows "n_clsfs = 2 " to be read properly. 8. autoclass-c/prog/autoclass.h, intf-reports.c - Implement "report_mode" parameter. 9. autoclass-c/prog/io-read-data.c - In PROCESS_ATTRIBUTE_DEF, check for incomplete discrete and real attribute definitions. 10. autoclass-c/load-ac - Use "/bin/uname -s" to determine if host is running IRIX (SGI). 11. autoclass-c/prog/struct-class.c - In FREE_TPARM_DS, allow tparm->tppt to be UNKNOWN or IGNORE. If not matched, print advisory msg, not error msg. Do not abort. 12. autoclass-c/prog/autoclass.h, search-basic.c, model-expander-3.c, struct-class.c, struct-clsf.c, predictions.c, & search-control-2.c When creating the weights for a new class, use database->n_data for the appropriate data base, rather than model->database->n_data. In the "prediction" mode, this correctly builds the test database class weights using the size of the test database, rather than that of the training database -- which is pointed to by the model. Functions modified: SET_UP_CLSF, GET_CLASS, CLASS_MERGED_MARGINAL_FN, COPY_CLASS_DS, ADJUST_CLSF_DS_CLASSES, COPY_CLSF_DS, POP_CLASS_DS, BUILD_CLASS_DS, COPY_TO_CLASS_DS, AUTOCLASS_PREDICT, & PRINT_SEARCH_TRY. This corrects a segmentation fault which occured during storage deallocation of prediction runs. ====================================================================== autoclass-3.3.6.dfsg.1/version-2-6.text0000644000175000017500000000354411247310756015613 0ustar areare AUTOCLASS C VERSION 2.6 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.6. 2. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", correct segmentation fault which occurs when more than 50 type = real, subtype = scalar attributes are defined in the ".hd2" & ".model" files. In "log_transform", use "safe_log" to transform values -- prevent "log: SING error" error messages. 3. autoclass-c/prog/model-expander-3.c - In "check_term", since att_info can be realloc'ed in for transformed attributes, reset data_base->att_info for each time thru loop. 5. autoclass-c/prog/utils-math.c - Add "safe_log". 6. autoclass-c/prog/autoclass.h - Add function prototype for "safe_log". 7. autoclass-c/prog/model-multi-normal-cn.c - In "multi_normal_cn_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 8. autoclass-c/prog/model-single-normal-cm.c - In "build_sn_cm_priors" and "single_normal_cm_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 9. autoclass-c/prog/model-single-normal-cn.c - In "build_sn_cn_priors" and "single_normal_cn_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 10. autoclass-c/prog/search-control.c - In "autoclass_search" test for user overriding of search parameters randomize_random_p and/or start_fn_type. If done, ask for confirmation to proceed. ====================================================================== autoclass-3.3.6.dfsg.1/autoclass.TAGS0000644000175000017500000006450711247310756015402 0ustar areare prog/globals.c,1310 void ***G_plist;G_plist12,216 shortstr G_transforms[G_transforms13,233 shortstr G_transforms[NUM_TRANSFORMS], G_att_type_data[G_att_type_data13,233 int G_db_length 15,305 int G_plength 16,354 clsf_DS G_clsf_store 17,373 fxlstr G_checkpoint_file;18,402 time_t G_search_cycle_begin_time,19,429 time_t G_search_cycle_begin_time, G_last_checkpoint_written,19,429 time_t G_search_cycle_begin_time, G_last_checkpoint_written, G_min_checkpoint_period;19,429 database_DS G_input_data_base,20,515 database_DS G_input_data_base, *G_db_list G_db_list20,515 model_DS *G_model_list G_model_list21,565 int G_break_on_warnings 22,596 float G_likelihood_tolerance_ratio 23,628 unsigned int G_save_compact_p 24,674 shortstr G_ac_version 26,727 shortstr G_ac_version 28,769 FILE *G_log_file_fp G_log_file_fp30,812 char G_absolute_pathname[G_absolute_pathname31,858 int G_line_cnt_max 32,896 int G_safe_file_writing_p 36,1092 int G_safe_file_writing_p 38,1133 char G_data_file_format[G_data_file_format40,1174 int G_solaris 41,1240 double G_rand_base_normalizer;42,1327 clsf_DS G_training_clsf 43,1358 int G_prediction_p 44,1390 int G_interactive_p 45,1418 int G_num_cycles 46,1446 char G_slash 49,1537 char G_slash 51,1564 int G_clsf_storage_log_p 55,1612 int G_n_freed_classes 56,1706 prog/init.c,69 #define getcwd 6,93 void init(32,760 void init_properties(75,1904 prog/io-read-data.c,1617 void check_stop_processing(69,1545 void define_data_file_format(117,3366 void process_data_header_model_files(179,5994 void log_header(209,7085 database_DS read_database(253,9241 int check_for_non_empty(337,12414 void check_data_base(354,12717 char *output_warning_msgs(output_warning_msgs395,14419 char *output_error_msgs(output_error_msgs474,17798 void output_message_summary(513,19192 void output_messages(593,23371 void output_db_error_messages(683,27351 void read_data(729,29349 void define_attribute_definitions(845,34415 void process_attribute_definitions(865,35231 att_DS process_attribute_def(939,38131 att_DS create_att_DS(1148,48344 warn_err_DS create_warn_err_DS(1222,51115 char ***expand_att_list(expand_att_list1259,52342 int find_str_in_list(1268,52511 char **default_translation,default_translation1289,53144 char **default_translation, ***processed;processed1289,53144 void process_translation_msgs(1395,57277 char **process_translation(process_translation1411,57800 char **read_data_doit(read_data_doit1457,59405 float *translate_instance(translate_instance1490,60690 double translate_real(1529,62055 int translate_discrete(1573,63889 char **get_line_tokens(get_line_tokens1633,66503 int read_from_string(1702,69426 int read_line(1770,71429 void find_att_statistics(1806,72253 void find_real_stats(1836,73203 void store_real_stats(1897,75473 void find_discrete_stats(1929,76635 void output_att_statistics(2013,79796 void output_real_att_statistics(2050,80945 void output_created_translations(2088,82334 void check_errors_and_warnings(2132,83904 prog/io-read-model.c,690 model_DS *read_model_file(read_model_file29,892 char ***read_model_doit(read_model_doit104,3683 char ***read_lists(read_lists190,7192 char **read_list(read_list221,7938 model_DS *define_models(define_models302,10729 void generate_attribute_info(393,14372 void extend_terms_single(495,18728 void extend_terms_multi(581,22249 void extend_default_terms(668,25811 void read_model_reset(759,29591 void set_ignore_att_info(787,30511 int *get_sources_list(get_sources_list816,31684 int *get_source_list(get_source_list872,33112 int exist_intersection(909,34165 char ***canonicalize_model_group(canonicalize_model_group927,34615 void print_att_locs_and_ignore_ids(939,34827 prog/io-results.c,1258 void compress_clsf(28,803 clsf_DS expand_clsf(66,2185 void expand_clsf_models(111,3539 void expand_clsf_wts(157,5152 void save_clsf_seq(258,8516 void write_clsf_seq(333,10924 void write_clsf_DS(358,11683 void write_database_DS(404,13210 void write_att_DS(431,14224 void write_model_DS(548,19377 void write_term_DS(570,20164 void write_tparm_DS(591,20666 void write_mm_d_params(647,22716 void write_mm_s_params(679,23716 void write_mn_cn_params(696,24156 void write_sm_params(731,25340 void write_sn_cm_params(755,26176 void write_sn_cn_params(793,27860 void write_priors_DS(828,29307 void write_class_DS_s(855,30251 int make_and_validate_pathname 897,31779 int validate_results_pathname(998,35576 int validate_data_pathname(1118,40423 clsf_DS *get_clsf_seq(get_clsf_seq1214,44183 clsf_DS *read_clsf_seq(read_clsf_seq1274,46355 clsf_DS read_clsf(1339,48799 database_DS read_database_DS(1454,52999 model_DS read_model_DS(1506,54657 void read_class_DS_s(1558,56307 void read_att_DS(1641,59586 void read_tparm_DS(1821,66888 void read_mm_d_params(1892,69208 void read_mm_s_params(1927,70426 void read_mn_cn_params(1946,70999 void read_sm_params(2047,74476 void read_sn_cm_params(2096,76083 void read_sn_cn_params(2138,77483 prog/io-results-bin.c,796 void safe_fwrite(27,562 void check_load_header(62,1552 void dump_clsf_seq(74,1874 void dump_clsf_DS(105,2694 void dump_database_DS(145,3910 void dump_att_DS(164,4376 void dump_model_DS(254,8532 void dump_term_DS(268,8845 void dump_tparm_DS(287,9316 void dump_mm_d_params(328,10474 void dump_mm_s_params(362,11475 void dump_mn_cn_params(381,11939 void dump_sm_params(412,13082 void dump_class_DS_s(430,13622 clsf_DS *load_clsf_seq(load_clsf_seq457,14479 clsf_DS load_clsf(530,17224 database_DS load_database_DS(613,20345 void load_att_DS(649,21574 model_DS load_model_DS(802,28172 void load_class_DS_s(842,29320 void load_tparm_DS(913,31770 void load_mm_d_params(966,33340 void load_mm_s_params(1001,34557 void load_mn_cn_params(1021,35174 void load_sm_params(1091,37937 prog/model-expander-3.c,579 model_DS conditional_expand_model_terms(22,465 enum MODEL_TYPES model_type 33,866 model_DS expand_model_terms(56,1724 void check_model_terms(123,3881 void check_term(143,4531 void update_location_info(249,8898 void expand_model_reset(289,10484 void update_params_fn(328,11566 void arrange_model_function_terms(399,13659 double log_likelihood_fn(418,14425 double update_l_approx_fn(476,15950 double update_m_approx_fn(537,17673 int class_equivalence_fn(599,19315 double class_merged_marginal_fn(673,21707 tparm_DS *model_global_tparms(model_global_tparms754,24332 prog/matrix-utilities.c,1175 float *setf_v_v(setf_v_v23,499 float *incf_v_v(incf_v_v35,695 float *decf_v_v(decf_v_v47,891 float *incf_v_vs(incf_v_vs62,1145 float *setf_v_vs(setf_v_vs78,1460 fptr *incf_m_vvs(incf_m_vvs92,1724 double diagonal_product(112,2198 fptr *extract_diagonal_matrix(extract_diagonal_matrix126,2460 void update_means_and_covariance(152,3453 fptr *n_sm(n_sm198,4723 float *vector_root_diagonal_matrix(vector_root_diagonal_matrix213,5010 double dot_vv(232,5429 double dot_mm(250,5854 float *collect_indexed_values(collect_indexed_values264,6118 fptr *copy_to_matrix(copy_to_matrix278,6405 float *n_sv(n_sv294,6704 fptr *setf_m_ms(setf_m_ms312,6991 fptr *incf_m_ms(incf_m_ms329,7326 fptr *limit_min_diagonal_values(limit_min_diagonal_values344,7643 fptr *invert_factored_square_matrix(invert_factored_square_matrix356,7892 double determinent_f(395,8827 double star_vmv(413,9305 double trace_star_mm(435,9799 fptr *extract_rhos(extract_rhos452,10121 fptr *invert_diagonal_matrix(invert_diagonal_matrix480,10913 fptr *root_diagonal_matrix(root_diagonal_matrix500,11320 fptr *star_mm(star_mm519,11622 fptr *make_matrix(make_matrix546,12143 prog/model-single-multinomial.c,398 void sm_params_influence_fn(29,914 void single_multinomial_model_term_builder(82,3047 double single_multinomial_log_likelihood(138,4955 double single_multinomial_update_l_approx(158,5618 double single_multinomial_update_m_approx(185,6473 void single_multinomial_update_params(209,7248 int single_multinomial_class_equivalence(243,8390 void single_multinomial_class_merged_marginal(266,9204 prog/model-single-normal-cm.c,437 void sn_cm_params_influence_fn(21,442 static priors_DS build_sn_cm_priors(59,1906 void single_normal_cm_model_term_builder(114,4260 double single_normal_cm_log_likelihood(205,7994 double single_normal_cm_update_l_approx(231,8882 double single_normal_cm_update_m_approx(262,10011 void single_normal_cm_update_params(309,11852 int single_normal_cm_class_equivalence(382,14999 void single_normal_cm_class_merged_marginal(403,15673 prog/model-single-normal-cn.c,436 void sn_cn_params_influence_fn(24,527 static priors_DS build_sn_cn_priors(52,1541 void single_normal_cn_model_term_builder(107,4020 double single_normal_cn_log_likelihood(200,7955 double single_normal_cn_update_l_approx(222,8792 double single_normal_cn_update_m_approx(246,9627 void single_normal_cn_update_params(276,10700 int single_normal_cn_class_equivalence(338,13175 void single_normal_cn_class_merged_marginal(361,13986 prog/model-multi-normal-cn.c,422 void mn_cn_params_influence_fn(44,2086 tparm_DS make_mn_cn_param(98,4325 void multi_normal_cn_model_term_builder(193,7253 double multi_normal_cn_log_likelihood(269,9878 double multi_normal_cn_update_l_approx(303,11302 double multi_normal_cn_update_m_approx(326,12224 void multi_normal_cn_update_params(368,13494 int multi_normal_cn_class_equivalence(435,16307 void multi_normal_cn_class_merged_marginal(482,18060 prog/model-transforms.c,177 int find_transform(25,539 int find_singleton_transform(65,1803 int generate_singleton_transform(97,3010 att_DS log_transform(173,6218 att_DS log_odds_transform_c(310,11102 prog/model-update.c,213 void update_approximations(32,961 void update_parameters(84,3047 int delete_null_classes(112,3863 void update_wts(160,5470 int most_probable_class_for_datum_i(327,12336 void update_ln_p_x_pi_theta(355,13155 prog/search-basic.c,227 clsf_DS generate_clsf(50,2223 int random_set_clsf(113,4898 clsf_DS set_up_clsf(182,7611 void block_set_clsf(231,9727 int initialize_parameters(289,11623 class_DS *delete_class_duplicates(delete_class_duplicates329,12878 prog/search-control.c,30 int autoclass_search(49,1838 prog/search-control-2.c,1914 static float cut_where_above_table[cut_where_above_table35,1063 int *remove_too_big(remove_too_big50,1786 int too_big(70,2161 double within(85,2424 search_try_DS *safe_subseq_of_tries(safe_subseq_of_tries102,2936 void print_initial_report(126,3607 void print_report(219,8691 void print_final_report(352,14791 void print_search_try(444,19101 void empty_search_try(469,20005 int total_try_time(480,20189 search_try_DS try_variation(506,21020 int search_duration(581,24051 int converge(602,24896 int converge_search_3(663,27105 int converge_search_3a(748,30331 int converge_search_4(845,34261 int min_n_peaks(969,39295 double avg_time_till_improve(980,39509 double ln_avg_p(989,39727 double min_best_peak(997,39914 int random_j_from_ln_normal(1017,40616 double random_from_normal(1057,42201 double typical_best(1074,42670 double cut_where_above 1082,42866 double erfc_poly(1100,43327 double approx_inverse_erfc(1111,43541 double inverse_erfc 1125,43851 double interpolate(1139,44132 void upper_end_normal_fit(1176,45050 double average(1213,46085 double variance(1230,46388 double sigma(1246,46726 double avg_improve_delta_ln_p(1260,46969 double next_best_delta(1268,47163 int min_time_till_best(1280,47495 void save_search(1299,48148 void write_search_DS(1361,50105 void write_search_try_DS(1401,51601 search_DS get_search_DS(1429,52679 search_DS reconstruct_search(1476,54122 search_DS get_search_from_file(1524,55774 void get_search_try_from_file(1612,58779 int find_duplicate(1690,62067 search_try_DS *insert_new_trial(insert_new_trial1738,63481 void describe_clsf(1774,64500 void print_log 1809,65596 void apply_search_start_fn 1825,66006 int apply_search_try_fn 1852,67040 int apply_n_classes_fn 1888,68760 int validate_search_start_fn 1908,69370 int validate_search_try_fn 1931,69899 int validate_n_classes_fn 1955,70475 void describe_search(1976,70947 prog/search-converge.c,26 double base_cycle(25,580 prog/struct-class.c,457 void store_class_DS(31,930 class_DS get_class_DS(71,2098 class_DS pop_class_DS(96,2744 class_DS build_class_DS(136,3744 class_DS build_compressed_class_DS(178,4957 class_DS copy_class_DS(205,5777 class_DS copy_to_class_DS(234,6912 int class_DS_test(297,9204 tparm_DS copy_tparm_DS(347,11434 void free_class_DS(471,15833 void free_tparm_DS(529,17651 void **list_class_storage list_class_storage612,19843 double class_strength_measure(654,21149 prog/struct-clsf.c,555 void push_clsf(23,488 clsf_DS pop_clsf(47,1027 clsf_DS get_clsf_DS(74,1667 void adjust_clsf_DS_classes(110,2738 void display_step(148,4039 clsf_DS create_clsf_DS(168,4548 int clsf_DS_max_n_classes(208,5551 clsf_DS copy_clsf_DS(222,6082 int clsf_DS_test(265,7639 void store_clsf_DS_classes(310,9159 void store_clsf_DS(337,10057 float *clsf_DS_w_j(clsf_DS_w_j371,10847 void **list_clsf_storage list_clsf_storage393,11439 void free_clsf_DS(464,14020 char *clsf_att_type(clsf_att_type511,15401 void free_clsf_class_search_storage(525,15836 prog/statistics.c,33 void central_measures_x(33,1019 prog/predictions.c,73 clsf_DS autoclass_predict(21,523 int same_model_and_attributes(98,3610 prog/struct-data.c,411 database_DS find_database(21,442 int every_db_DS_same_source_p(46,1175 database_DS compress_database(66,1656 int db_DS_equal_p(82,2084 int att_DS_equal_p(102,2485 database_DS create_database(145,3822 database_DS expand_database(182,5020 int extend_database(276,8325 int db_same_source_p(318,9893 int att_info_equal(332,10255 int att_props_equivalent_p(357,10950 int att_stats_equivalent_p(382,11658 prog/struct-matrix.c,71 fptr *compute_factor(compute_factor20,422 float *solve(solve41,861 prog/struct-model.c,155 model_DS find_similar_model(23,524 int model_DS_equal_p(44,1075 model_DS expand_model(58,1411 model_DS find_model(93,2456 void free_model_DS(112,2863 prog/utils.c,1731 void to_screen_and_log_file(40,916 time_t get_universal_time 60,1402 char *format_universal_time(format_universal_time89,2010 char *format_time_duration format_time_duration105,2416 int iround 171,4573 int int_compare_less 188,4944 int int_compare_greater 198,5126 int eqstring(206,5226 float *fill(fill215,5345 void checkpoint_clsf(230,5607 int *delete_duplicates(delete_duplicates260,6535 double max_plus(304,7627 int class_duplicatesp(317,7786 int find_term(329,8006 int find_class(340,8166 int find_class_test2(351,8365 int find_database_p(365,8657 int find_model_p(379,8904 int member_int(390,9080 int find_str_in_table(401,9227 int new_random(421,9716 long lrand48(441,10363 float *randomize_list(randomize_list456,10692 int y_or_n_p(494,11492 double sigma_sq(536,12651 int char_input_test(559,13350 int char_input_test(585,13822 int percent_equal(629,15011 int prefix(642,15248 void *getf(getf656,15475 void *get(get670,15695 void add_property(690,16262 void add_to_plist 717,16916 void write_vector_float(738,17446 void write_matrix_float(758,17906 void write_matrix_integer(785,18591 void read_vector_float(814,19403 void read_matrix_float(835,19912 void read_matrix_integer(863,20633 int discard_comment_lines 890,21251 void flush_line 914,21657 int read_char_from_single_quotes 931,21955 int strcontains(956,22484 int output_int_list(973,22780 int pop_int_list(996,23326 void push_int_list(1022,23798 int member_int_list(1045,24300 int float_sort_cell_compare_gtr(1061,24647 int class_case_sort_compare_lsr(1083,25243 int att_i_sum_sort_compare_gtr(1101,25724 int float_p_p_star_compare_gtr(1125,26374 void safe_fprintf(1148,27103 void safe_sprintf(1180,27970 prog/utils-math.c,153 double log_gamma(25,549 int atoi_p 66,1996 double atof_p 95,2619 double safe_exp(127,3344 void mean_and_variance(144,3707 double safe_log(168,4290 prog/intf-reports.c,1705 int autoclass_reports(42,1222 int clsf_search_validity_check(371,16370 void influence_values_report_streams(398,17166 xref_data_DS case_class_data_sharing(465,20052 xref_data_DS case_report_streams(514,22320 xref_data_DS class_report_streams(561,24361 xref_data_DS xref_get_data(636,28124 int map_class_num_clsf_to_report(822,35880 int map_class_num_report_to_clsf(839,36317 void autoclass_xref_by_case_report(855,36763 void classification_header(909,38946 void xref_paginate_by_case(967,41645 void xref_output_page_headers(1058,45567 void autoclass_xref_by_class_report(1125,48398 void xref_paginate_by_class(1170,50222 rpt_att_string_DS *xref_class_report_attributes(xref_class_report_attributes1239,53180 void xref_paginate_by_class_hdrs(1324,56595 void xref_output_line_by_class(1368,58446 void autoclass_influence_values_report(1441,61443 void influence_values_header(1496,63799 void autoclass_class_influence_values_report(1673,73098 int populated_class_p(1804,79583 ordered_influ_vals_DS ordered_normalized_influence_values(1820,80129 void influence_values_explanation(1867,81910 void search_summary(1888,82710 void class_weights_and_strengths(1949,85081 void class_divergences(2010,87972 void text_stream_header(2066,90407 void pre_format_attributes(2117,93051 void print_attribute_header(2267,99259 int format_attribute(2314,101420 int format_discrete_attribute(2414,106097 int format_integer_attribute(2538,111991 int format_real_attribute(2573,113613 void generate_mncn_correlation_matrices 2644,116822 int attribute_model_term_number(2751,120776 void sort_mncn_attributes(2773,121379 char *filter_e_format_exponents filter_e_format_exponents2826,123287 prog/intf-extensions.c,478 clsf_DS *initialize_reports_from_results_pathname(initialize_reports_from_results_pathname33,798 clsf_DS init_clsf_for_reports(84,2639 int *get_class_weight_ordering(get_class_weight_ordering140,4507 char ***get_attribute_model_term_types(get_attribute_model_term_types176,5849 char *report_att_type(report_att_type208,7081 char *rpt_att_model_term_type(rpt_att_model_term_type225,7512 void get_models_source_info(238,7816 void get_class_model_source_info(259,8528 prog/intf-influence-values.c,115 void compute_influence_values(29,614 double influence_value(112,4373 int find_attribute_modeling_class(224,8993 prog/intf-sigma-contours.c,144 void generate_sigma_contours 32,708 int compute_sigma_contour_for_2_atts 209,8517 int class_att_loc(294,11846 float get_sigma_x_y 334,12977 prog/prints.c,696 void sum_vector_f(22,446 void print_vector_f(44,878 void print_matrix_f(65,1326 void print_matrix_i(88,1796 void print_mm_d_params(106,2125 void print_mm_s_params(126,2648 void print_mn_cn_params(132,2792 void print_sm_params(149,3252 void print_sn_cm_params(160,3615 void print_sn_cn_params(187,4601 void print_tparm_DS(203,5044 void print_priors_DS(267,6813 void print_class_DS(286,7318 void print_term_DS 323,8378 void print_real_stats_DS(339,8823 void print_discrete_stats_DS(346,9009 void print_att_DS(356,9251 void print_database_DS(387,10056 void print_model_DS(421,11163 void print_clsf_DS(460,12285 void print_search_try_DS(500,13251 void print_search_DS(524,13835 prog/getparams.c,71 void putparams(28,679 int getparams(91,2895 void defparam(275,10531 prog/autoclass.c,48 int main(41,1125 void autoclass_args 206,7892 prog/autoclass.h,5728 #define square(42,1397 #define srand48 48,1594 #define TRUE 51,1624 #define FALSE 52,1667 #define LN_SINGLE_PI 55,1790 #define ABSOLUTE_MIN_CLASS_WT 56,1850 #define MIN_CLASS_WT_FACTOR 57,1895 #define SINGLE_FLOAT_EPSILON 74,2901 #define DOUBLE_FLOAT_EPSILON 75,2954 #define LEAST_POSITIVE_SHORT_FLOAT 76,3018 #define LEAST_POSITIVE_SINGLE_FLOAT 77,3073 #define LEAST_POSITIVE_LONG_FLOAT 78,3128 #define LEAST_NEGATIVE_SINGLE_FLOAT 79,3193 #define MOST_POSITIVE_LONG_FLOAT 80,3249 #define MOST_POSITIVE_SINGLE_FLOAT 81,3313 #define MOST_NEGATIVE_SINGLE_FLOAT 82,3367 #define MOST_NEGATIVE_LONG_FLOAT 83,3422 #define INFINITY 86,3503 #define MOST_NEGATIVE_SINGLE_FLOAT_DIV_2 90,3612 #define LEAST_POSITIVE_SINGLE_LOG 91,3688 #define LEAST_POSITIVE_LONG_LOG 92,3775 #define MOST_POSITIVE_SINGLE_LOG 93,3860 #define MOST_POSITIVE_LONG_LOG 94,3959 #define LN_1_DIV_ROOT_2PI 95,4054 #define ARRAY_RANK_LIMIT 96,4106 #define STRLIMIT 97,4153 #define SEARCH_LOG_FILE_TYPE 98,4198 #define REPORT_LOG_FILE_TYPE 99,4246 #define SEARCH_FILE_TYPE 100,4295 #define RESULTS_FILE_TYPE 101,4346 #define DATA_FILE_TYPE 102,4398 #define HEADER_FILE_TYPE 103,4446 #define MODEL_FILE_TYPE 104,4494 #define FLOAT_UNKNOWN 105,4544 #define INT_UNKNOWN 106,4613 #define DISPLAY_WTS 107,4661 #define DISPLAY_PROBS 109,4817 #define DISPLAY_PARAMS 111,4979 #define SN_CM_SIGMA_SAFETY_FACTOR 113,5141 #define SN_CN_SIGMA_SAFETY_FACTOR 114,5186 #define NUM_ATT_TYPES 118,5356 #define SIZEOF_ABOVE_CUT_TABLE 119,5383 #define SIZEOF_CUT_WHERE_ABOVE_TABLE 120,5426 #define SEARCH_PARAMS_FILE_TYPE 124,5490 #define REPORTS_PARAMS_FILE_TYPE 125,5542 #define CHECKPOINT_FILE_TYPE 126,5594 #define TEMP_CHECKPOINT_FILE_TYPE 127,5643 #define INFLU_VALS_FILE_TYPE 128,5696 #define XREF_CLASS_FILE_TYPE 129,5751 #define XREF_CASE_FILE_TYPE 130,5806 #define TEMP_SEARCH_FILE_TYPE 131,5860 #define TEMP_RESULTS_FILE_TYPE 132,5914 #define RESULTS_BINARY_FILE_TYPE 133,5969 #define TEMP_RESULTS_BINARY_FILE_TYPE 134,6024 #define CHECKPOINT_BINARY_FILE_TYPE 135,6083 #define TEMP_CHECKPOINT_BINARY_FILE_TYPE 136,6136 #define PREDICT_FILE_TYPE 137,6194 #define END_OF_INT_LIST 138,6245 #define MAX_N_START_J_LIST 139,6290 #define MAX_CLASS_REPORT_ATT_LIST 140,6333 #define MAX_CLSF_N_LIST 141,6376 #define MAX_N_SIGMA_CONTOUR_LIST 142,6419 #define ALL_ATTRIBUTES 144,6516 #define SHORT_STRING_LENGTH 145,6560 #define VERY_LONG_STRING_LENGTH 151,6777 #define VERY_LONG_TOKEN_LENGTH 152,6825 #define DATA_ALLOC_INCREMENT 154,6872 #define REL_ERROR 156,6963 #define NUM_TRANSFORMS 164,7342 #define NUM_TOKENS_IN_FXLSTR 166,7426 #define WRITE_PERMISSIONS 169,7672 #define DATA_BINARY_FILE_TYPE 170,7717 #define M_PI 174,7844 #define MAXPATHLEN 181,8090 enum results_data_types185,8202 { INT_TYPE,186,8226 { INT_TYPE, CHAR_TYPE,186,8226 { INT_TYPE, CHAR_TYPE, FLOAT_TYPE,186,8226 { INT_TYPE, CHAR_TYPE, FLOAT_TYPE, DOUBLE_TYPE,186,8226 CLASS_TYPE,187,8274 TERM_TYPE,188,8291 WARN_ERR_TYPE,189,8307 REAL_STATS_TYPE,190,8326 DISCRETE_STATS_TYPE,191,8348 DUMMY_STATS_TYPE,192,8373 ATT_TYPE,193,8395 DATABASE_TYPE,194,8410 MODEL_TYPE,195,8430 CLASSIFICATION_TYPE,196,8446 CHECKPOINT_TYPE,197,8472 TPARM_TYPE198,8493 typedef float *fptr;fptr203,8540 typedef char fxlstr[fxlstr205,8630 typedef struct priors *priors_DS;priors_DS206,8661 typedef struct class *class_DS;class_DS207,8695 typedef struct term *term_DS;term_DS208,8727 typedef struct warn_err *warn_err_DS;warn_err_DS209,8757 typedef struct real_stats *real_stats_DS;real_stats_DS210,8795 typedef struct discrete_stats *discrete_stats_DS;discrete_stats_DS211,8837 typedef struct att *att_DS;att_DS212,8887 typedef struct database *database_DS;database_DS213,8915 typedef struct model *model_DS;model_DS214,8953 typedef struct classification *clsf_DS;clsf_DS215,8985 typedef struct search_try *search_try_DS;search_try_DS216,9025 typedef struct search *search_DS;search_DS217,9067 typedef char shortstr[shortstr221,9122 typedef char very_long_str[very_long_str222,9166 typedef struct checkpoint *chkpt_DS;chkpt_DS223,9219 typedef struct reports *rpt_DS;rpt_DS224,9256 typedef struct sort_cell *sort_cell_DS;sort_cell_DS225,9288 typedef struct invalid_value_errors *invalid_value_errors_DS;invalid_value_errors_DS226,9328 typedef struct incomplete_datum *incomplete_datum_DS;incomplete_datum_DS227,9390 typedef struct i_discrete *i_discrete_DS;i_discrete_DS228,9444 typedef struct i_integer *i_integer_DS;i_integer_DS229,9486 typedef struct i_real *i_real_DS;i_real_DS230,9526 typedef struct xref_data *xref_data_DS;xref_data_DS231,9560 typedef struct report_attribute_string *rpt_att_string_DS;rpt_att_string_DS232,9600 typedef struct ordered_influence_values *ordered_influ_vals_DS;ordered_influ_vals_DS233,9659 typedef struct formatted_p_p_star *formatted_p_p_star_DS;formatted_p_p_star_DS234,9723 typedef int *int_list;int_list235,9781 struct priors 249,10264 struct term 293,12223 struct warn_err 301,12500 struct real_stats 312,13061 struct discrete_stats 321,13550 struct att 332,13897 struct invalid_value_errors 352,14730 struct incomplete_datum 358,14851 struct database 364,14962 struct model 397,16901 struct checkpoint 432,18682 struct reports 444,19020 struct classification 469,20410 struct search_try 484,20747 struct search 508,21805 struct sort_cell 523,22534 struct i_discrete 529,22641 struct i_integer 537,22946 struct i_real 545,23283 struct xref_data 560,24108 struct report_attribute_string 572,24837 struct ordered_influence_values 580,25088 struct formatted_p_p_star 590,25447 prog/getparams.h,537 #define LINLIM 8,183 typedef unsigned int BOOLEAN;11,220 #define MAXPARAMS 14,258 #define PARAMNAMLEN 15,279 typedef enum {TSTRING,TSTRING20,474 typedef enum {TSTRING, TBOOL,20,474 typedef enum {TSTRING, TBOOL, TINT,20,474 typedef enum {TSTRING, TBOOL, TINT, TFLOAT,20,474 typedef enum {TSTRING, TBOOL, TINT, TFLOAT, TDOUBLE,20,474 typedef enum {TSTRING, TBOOL, TINT, TFLOAT, TDOUBLE, TINT_LIST}TINT_LIST20,474 typedef enum {TSTRING, TBOOL, TINT, TFLOAT, TDOUBLE, TINT_LIST} PARAMTYPE;20,474 } PARAM, *PARAMP;PARAMP33,844 prog/params.h,752 typedef struct new_term_params *tparm_DS;tparm_DS4,87 enum MODEL_TYPES 11,398 enum MODEL_TYPES {UNKNOWN,UNKNOWN11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D, MM_S,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D, MM_S, MN_CN,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D, MM_S, MN_CN, SM,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D, MM_S, MN_CN, SM, SN_CM,11,398 enum MODEL_TYPES {UNKNOWN, TIGNORE, MM_D, MM_S, MN_CN, SM, SN_CM, SN_CN}SN_CN11,398 struct mm_d_param 14,475 struct mm_s_param 27,1028 struct mn_cn_param 35,1314 struct sm_param 51,2174 struct sn_cm_param 61,2520 struct sn_cn_param 85,3388 struct new_term_params 108,4012 prog/fcntlcom-ac.h,171 #define _FNDELAY 26,643 #define _FAPPEND 28,712 #define _FCREAT 30,765 #define _FTRUNC 32,817 #define F_GETFL 35,889 #define F_SETFL 36,908 #define O_NDELAY 39,963 prog/minmax.h,36 #define min(2,17 #define max(3,54 prog/globals.h,0 autoclass-3.3.6.dfsg.1/prog/0000755000175000017500000000000011667631535013666 5ustar areareautoclass-3.3.6.dfsg.1/prog/globals.h0000644000175000017500000000221711247310756015455 0ustar areare /************************ global externs ***********************************/ /* 22oct94 wmt: create */ extern void ***G_plist; extern shortstr G_transforms[], G_att_type_data[]; extern int G_db_length, G_m_id, G_m_length; extern int G_plength; extern clsf_DS G_clsf_store; extern fxlstr G_checkpoint_file; extern time_t G_search_cycle_begin_time, G_last_checkpoint_written, G_min_checkpoint_period; extern database_DS G_input_data_base, *G_db_list; extern model_DS *G_model_list; extern int G_break_on_warnings; extern float G_likelihood_tolerance_ratio; extern unsigned int G_save_compact_p; extern shortstr G_ac_version; extern FILE *G_log_file_fp, *G_stream; extern char G_absolute_pathname[]; extern int G_line_cnt_max; /* only supported under unix, since it does system calls to mv and rm */ extern int G_safe_file_writing_p; extern char G_data_file_format[]; extern int G_solaris; extern double G_rand_base_normalizer; extern clsf_DS G_training_clsf; extern int G_prediction_p; extern int G_interactive_p; extern int G_num_cycles; extern char G_slash; /* for debugging */ extern int G_clsf_storage_log_p, G_n_freed_classes, G_n_create_classes_after_free; autoclass-3.3.6.dfsg.1/prog/autoclass.make.alpha.cc0000644000175000017500000000245011247310756020165 0ustar areare### AUTOCLASS C MAKE FILE FOR DEC ALPHA - bundled cc compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) ## override .s-params and .r-params parameter free_storage_p ## to not free storage, since it creates a core dump CFLAGS = $(OSFLAGS) -std1 -O2 -g3 CC = cc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/globals.c0000644000175000017500000000335111247310756015450 0ustar areare#include /* for MAXPATHLEN */ #ifndef _MSC_VER #include #endif #include "autoclass.h" /************************ GLOBALS globals ************************************* 22oct94 wmt: create */ void ***G_plist; shortstr G_transforms[NUM_TRANSFORMS], G_att_type_data[NUM_ATT_TYPES]; int G_db_length = 0, G_m_id = 0, G_m_length = 0; int G_plength = 0; clsf_DS G_clsf_store = NULL; fxlstr G_checkpoint_file; time_t G_search_cycle_begin_time, G_last_checkpoint_written, G_min_checkpoint_period; database_DS G_input_data_base, *G_db_list = NULL; model_DS *G_model_list = NULL; int G_break_on_warnings = TRUE; float G_likelihood_tolerance_ratio = 0.00001; unsigned int G_save_compact_p = FALSE; #ifdef _WIN32 shortstr G_ac_version = "3.3.6win"; #else shortstr G_ac_version = "3.3.6unx"; #endif FILE *G_log_file_fp = NULL, *G_stream = NULL; char G_absolute_pathname[MAXPATHLEN]; int G_line_cnt_max = 65; /* for reports */ /* only supported under unix, since it does system calls to mv and rm */ /* aju 980612: Implemented this rule for Win32. */ #ifdef _WIN32 int G_safe_file_writing_p = FALSE; #else int G_safe_file_writing_p = TRUE; #endif char G_data_file_format[10] = ""; /* "binary" or "ascii" */ int G_solaris = FALSE; /* used for open on Solaris 2.4 29apr95 wmt */ double G_rand_base_normalizer; clsf_DS G_training_clsf = NULL; int G_prediction_p = FALSE; int G_interactive_p = TRUE; int G_num_cycles = 0; /* handle both Unix pathnames and Windows pathnames */ #ifdef _WIN32 char G_slash = '\\'; #else char G_slash = '/'; #endif /* for debugging */ int G_clsf_storage_log_p = FALSE; /* TRUE enables print out of clsf storage activity */ int G_n_freed_classes = 0, G_n_create_classes_after_free = 0; autoclass-3.3.6.dfsg.1/prog/model-update.c0000644000175000017500000004036511247310756016413 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* UPDATE_APPROXIMATIONS 29mar95 wmt: convert caluations to double precision Updates the classification and individual class likelihood (the clsf-DS field log-A and the class_DS field log-A) and marginal likelihood (the clsf-DS field log-A and the class_DS field log-A) approximations, with respect to the weights and parameters. Note: When initializing a classification from assigned weights, call update-ln-p before calling this. */ void update_approximations( clsf_DS clsf) { int i, n_classes, n_data; double inv_n_classes, log_a, log_a_pi_theta; class_DS cl, *classes; database_DS data_base; n_classes = clsf->n_classes; inv_n_classes = -1.0 / (double) n_classes; classes = clsf->classes; data_base = clsf->database; n_data = data_base->n_data; log_a = 0.0; /* default value for single class. */ log_a_pi_theta = 0.0; /* Add the discrete class log probabilities, modeled as a single-multinomial attribute. */ if (n_classes == 1) /* Special to avoid (log-gamma 0) */ log_a += -1.0 * log_gamma( (double) n_data, FALSE); else log_a += log_gamma( (double) (n_classes - 1), FALSE) - log_gamma( (double) (n_data + n_classes - 1), FALSE) - ((double) n_classes * log_gamma( (double) (1.0 + inv_n_classes), FALSE)); /* Collect the individual class contributations to the approximate log probabilities. */ for (i=0; iw_j + 1.0 + inv_n_classes), FALSE); } /* Add the log permutation factor (log J!) to the marginal: */ log_a += log_gamma( (double) (1 + n_classes), FALSE); /*commented printf(" calc clsf->log_a_x_h= log_a + clsf->log_p_x_h_pi_theta - log_a_pi_theta\n %f %f %f\n", log_a , clsf->log_p_x_h_pi_theta , log_a_pi_theta); dbg*********/ /* Rescale the marginal posterior using the ratio of observed and approximate likelihoods: */ log_a += clsf->log_p_x_h_pi_theta - log_a_pi_theta; clsf->log_a_x_h = log_a; /*commented printf(" at end of update_approximations, clsf->log_a_x_h=%f\n", clsf->log_a_x_h); dbg*/ } /* Updates the class parameters and the clsf_DS field min-class-wt. The update-params-fn updates each class-DS field updatable-p class with respect to the updated weights. */ void update_parameters( clsf_DS clsf) { int n_classes, n_data, n_cl, collect = TRUE; class_DS *classes, cl; database_DS database; n_classes = clsf->n_classes; classes = clsf->classes; database = clsf->database; n_data = database->n_data; clsf->min_class_wt = max(ABSOLUTE_MIN_CLASS_WT, /* SN model requires w_j > 1.0 */ ((MIN_CLASS_WT_FACTOR * (float) n_data) / n_classes)); for (n_cl=0; n_clclasses; int n_classes = clsf->n_classes, j = 0, n_stored = 0; float min_wt = max(ABSOLUTE_MIN_CLASS_WT, clsf->min_class_wt); /* in the lisp opt arg min wt is handled as follows: (setf min-wt (max *absolute-min-class-wt* (or min-wt (clsf-DS-min-class-wt clsf)))) ******/ while( jw_j <= min_wt) && (cl->known_parms_p != TRUE)) { n_stored++; n_classes--; store_class_DS(cl, clsf_DS_max_n_classes(clsf)); for (i=j; in_classes = n_classes; return(n_stored); } /* UPDATE_WTS 29mar95 wmt: calculation in double 30may95 wmt: pass in two clsfs to facilitate autoclass_predict, otherwise pass in the same clsf for both args. Updates the class-DS->wts of the active classes to be the normalized class probabilities with respect to the current parameterizations. Sums and updates the class-DS->w_j. Updates and returns probability of the database relative to the model parameterizations: clsf->log-p_pi_theta. Note that the notation used here is closely modeled upon that used in Matt's math papers: read ln-p_j as "j'th element of the J-array of Log of Probability of (datum-X-sub-i and Class-sub-j GIVEN class-prob-sub-j and class-theta-sub-j)" */ void update_wts( clsf_DS training_clsf, clsf_DS test_clsf) { class_DS class, *classes = training_clsf->classes; /* Vector of classes with parameters */ class_DS *test_classes = test_clsf->classes; /* vector of classes with weights */ database_DS data_base = test_clsf->database; int i, j, significant_terms, last_j_max, j_max; int n_j = training_clsf->n_classes, /* No. of classes J */ n_i = data_base->n_data; /* No. of data i */ float **X = data_base->data, *X_i; /* Vector of data-attr values */ double *wt_j, /* Sums class probs over data */ w_j, *ln_p, /* ln-p log-likelihood of X_i in C_j */ ln_p_X_i_C_j_pi_theta_j, ln_p_X_i_C_j_pi_theta_max, *p_pi_theta, /* p Prob of C_j given X_i & params */ p_X_i_pi_theta_div_p_X_i_C_j_pi_theta_max, p_X_i_C_j_pi_theta_div_p_X_i_C_j_pi_theta_max, log_cutoff = MOST_NEGATIVE_SINGLE_FLOAT_DIV_2 + safe_log( DOUBLE_FLOAT_EPSILON), /* Log of minimum relative probability */ log_tolerance = safe_log((double) max( DOUBLE_FLOAT_EPSILON, G_likelihood_tolerance_ratio)), ln_p_X_i_pi_theta=0.0, /* ln-p log-prob of X_i given all classes */ ln_p_X_pi_theta=0.0; /* ln-p log-prob of data given all classes */ if (training_clsf->n_classes == 0) { fprintf(stderr, "ERROR: update_weights called without any classes\n"); abort(); } wt_j = (double *) malloc(n_j * sizeof(double)); /*freed @end*/ ln_p = (double *) malloc(n_j * sizeof(double)); /*freed @end*/ p_pi_theta = (double *) malloc(n_j * sizeof(double)); /*freed @end*/ for (j=0; j ln_p_X_i_C_j_pi_theta_max) { ln_p_X_i_C_j_pi_theta_max = ln_p_X_i_C_j_pi_theta_j; log_cutoff = ln_p_X_i_C_j_pi_theta_max + log_tolerance; j_max = j; } if (j == last_j_max) j=0; else if(j ==0) j=last_j_max; } /* Note: the most negative values are NOT fully computed. */ /*commented printf("\nlnp[j] for i=%d",i); for(j=0;j log_cutoff) { /* Significance test */ /* Rescale significant probs relative to p_C_j_X_i_pi_theta_j-max = 1: */ p_X_i_C_j_pi_theta_div_p_X_i_C_j_pi_theta_max = safe_exp( ln_p_X_i_C_j_pi_theta_j - ln_p_X_i_C_j_pi_theta_max); significant_terms++; p_X_i_pi_theta_div_p_X_i_C_j_pi_theta_max += p_X_i_C_j_pi_theta_div_p_X_i_C_j_pi_theta_max; p_pi_theta[j] = p_X_i_C_j_pi_theta_div_p_X_i_C_j_pi_theta_max; } /* Set insignificant probs to 0.0 */ else p_pi_theta[j] = 0.0; } } /* Compute ln-p and normalize DSp: */ ln_p_X_i_pi_theta = ln_p_X_i_C_j_pi_theta_max; if (significant_terms != 1){ ln_p_X_i_pi_theta += safe_log(p_X_i_pi_theta_div_p_X_i_C_j_pi_theta_max); j=-1; while( significant_terms > 0 ) if (p_pi_theta[++j] != 0.0) { p_pi_theta[j] /= p_X_i_pi_theta_div_p_X_i_C_j_pi_theta_max; significant_terms--; } } /* Sum the datum log-likelihood into the data-base log-likelihood: */ ln_p_X_pi_theta += ln_p_X_i_pi_theta; /* Save this datum's class probabilities as class weights & sum into wt_j: */ for (j=0; jwts[i] = p_pi_theta[j]; /* Updating class wts */ /*commented printf(" cl %d wt %d %f",j,i,test_classes[j]->wts[i]); dbg JTP*/ wt_j[j] += test_classes[j]->wts[i]; } /* fprintf(stderr, "ln_p_X_i_pi_theta = %f\n", ln_p_X_i_pi_theta); */ }/*end i*/ /* Save the classification log-likelihood and update the class w_j: */ training_clsf->log_p_x_h_pi_theta = ln_p_X_pi_theta; /*commented printf("\nclsf->log_p_x_h_pi_theta = %f",clsf->log_p_x_h_pi_theta); dbg JTP*/ for (j=0; jw_j; /* Saving the delta-wts as DSwt_j */ class->w_j = w_j; /* Update the class wts */ if (w_j == 0.0) class->log_w_j = LEAST_POSITIVE_SINGLE_LOG; else class->log_w_j = (float) safe_log((double) w_j); /*commented printf("\n class %d w_j,log_w_j=%f %f",j,class->w_j,class->log_w_j); dbg*/ } free(p_pi_theta); free(ln_p); free(wt_j); /* lisp version returns ln-p and wt_j but no call seems to use*/ } int most_probable_class_for_datum_i(int i, class_DS *classes, int n_classes) { int j,max_j; float maxval, wt_i_j; maxval = LEAST_NEGATIVE_SINGLE_FLOAT; max_j = 0; for (j=0; jwts[i]; if (maxval < wt_i_j) { maxval = wt_i_j; max_j = j; } } return(max_j); } /* UPDATE_LN_P_X_PI_THETA 29mar95 wmt: calculation in double This is a simplified version of Update-Wts which calculates ln-p_X_H_pi_theta in terms of the current class weights and parameters. Note that the notation used here is closely modeled upon that used in Matt's math papers: read DSln-p_j as 'j'th element of the J-array of Log of Probability of ( datum-X-sub-i and Class-sub-j GIVEN class-prob-sub-j and class-theta-sub-j) */ void update_ln_p_x_pi_theta( clsf_DS clsf, int no_change) { int i, j, n_i, n_j, j_max, last_j_max; float **x, *x_i; double *ln_p_x_i_c_j_pi_theta, ln_p_x_pi_theta, log_tolerance; double p_div_max, ln_p_div_max, ln_p_j; class_DS class, *classes; database_DS data_base; if (clsf->n_classes == 0){ fprintf(stderr, "ERROR: update_ln_p_x_pi_theta called without any classes\n"); abort(); } data_base = clsf->database; x = data_base->data; /* Vector of data-attr values */ n_i = data_base->n_data; /* No. of data i */ classes = clsf->classes; /* Vector of classes with parameters */ n_j = clsf->n_classes; /* No. of classes j */ /* log-likelihood of X_i in C_j */ ln_p_x_i_c_j_pi_theta = (double *) malloc( n_j * sizeof( double));/*freed @end*/ ln_p_x_pi_theta = 0.0; /* log-prob of data given all classes */ /* Log of minimum relative probability */ log_tolerance = safe_log((double) max( DOUBLE_FLOAT_EPSILON, G_likelihood_tolerance_ratio)); /* Compute the likelihood vector DSp & ln-p */ for (i=0; i. Record the maximum class likelihood ln-p_max, and its index j_max, for use in normalization. The macros Dotimes-Except-One-First and Sum-Function-Calls-List-Till-Below-Limit in the LOG-LIKELIHOOD-FN ensure that no more calculation is done than necessary to achieve log-tolerance. */ for (j=0; j ln_p_div_max) { /* Remember max raw ln-prob */ ln_p_div_max = ln_p_j; j_max = j; } if (j == last_j_max) j=0; else if(j ==0) j=last_j_max; } /* Convert the DSln-p to probabilities and sum. We avoid math problems in the division by rescaling the DSln-p_j w.r.t. ln-p_max and then filtering out the too small values with log-tolerance. */ for (j=0; j log_tolerance) /* Rescale significant probs relative to DSp_j-max = 1 and sum together as p//p_max: */ p_div_max += safe_exp( (ln_p_j - ln_p_div_max)); } /* Calculate and sum the datum log-likelihood into the data-base log-likelihood: */ ln_p_x_pi_theta += ln_p_div_max + safe_log( p_div_max); } if (no_change == FALSE){ /*commented printf(" at bottom of update_ln_p_x_pi_theta, ln_p_x_pi_theta=%f\n",ln_p_x_pi_theta); dbg*/ clsf->log_p_x_h_pi_theta = ln_p_x_pi_theta; } free( ln_p_x_i_c_j_pi_theta); } autoclass-3.3.6.dfsg.1/prog/intf-sigma-contours.c0000644000175000017500000003250211247310756017735 0ustar areare#include #include #include #include #include #ifndef _WIN32 #include #endif #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* GENERATE_SIGMA_CONTOURS call compute_sigma_contour_for_2_atts for permutations of sigma_att_list 24june97 wmt: new 20sep98 wmt: filter windows e+000 => e+00, etc with filter_e_format_exponents */ void generate_sigma_contours ( clsf_DS clsf, int clsf_class_number, int_list sigma_att_list, FILE *influence_report_fp, int comment_data_headers_p) { float mean_x = 0.0, sigma_x = 0.0, mean_y = 0.0; float sigma_y = 0.0, rotation = 0.0; int att_x = -1, att_y = -1, i, j, error_p, sigma_contours_list_len = 0; int trans_att_x = -1, trans_att_y = -1, report_class_number; int *att_err_msg_p = NULL; att_DS att_info_x, att_info_y; int term_index_x = -1, term_index_y = -1; model_DS model = clsf->classes[clsf_class_number]->model; class_DS class = clsf->classes[clsf_class_number]; term_DS *terms, term_x = NULL, term_y = NULL; char ignore_str[] = "ignore"; /* force column alignment for windows */ #ifdef _WIN32 char* format_string = "%06d %05d %+13e %+13e %+13e %+13e %+13e\n"; #else char* format_string = "%06d %05d %13e %13e %13e %13e %13e\n"; #endif fxlstr e_format_string; /* the first class to be output is report_class_number = 0 */ report_class_number = map_class_num_clsf_to_report( clsf, clsf_class_number); /* allocate space for error msg flags */ for (i=0; sigma_att_list[i] != END_OF_INT_LIST; i++) { sigma_contours_list_len++; } att_err_msg_p = (int *) malloc( (sigma_contours_list_len + 1) * sizeof( int)); for (i = 0; i < sigma_contours_list_len; i++) { att_err_msg_p[i] = FALSE; } /* 6|6|14||14||14||14||14| */ fprintf( influence_report_fp, "\nSIGMA CONTOURS \n" "%satt_x att_y mean_x sigma_x mean_y sigma_y" " rotation-rad\n", (comment_data_headers_p == TRUE) ? "#" : ""); for (i=0; sigma_att_list[i] != END_OF_INT_LIST; i++) { att_x = sigma_att_list[i]; for (j=i; sigma_att_list[j] != END_OF_INT_LIST; j++) { att_y = sigma_att_list[j]; if (att_x != att_y) { /* fprintf( stderr, "att_x %d att_y %d\n", att_x, att_y); */ error_p = FALSE; att_info_x = clsf->database->att_info[att_x]; att_info_y = clsf->database->att_info[att_y]; /* fprintf( stderr, "att_x %d msg_p %d att_y %d msg_p %d\n", att_x, att_err_msg_p[i], att_y, att_err_msg_p[j]); */ if (eqstring( att_info_x->type, "real") == FALSE) { if ((report_class_number == 0) && (att_err_msg_p[i] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour => att_n %d " "(\"%s\") \n" " is not a type real attribute.\n", att_x, att_info_x->dscrp); att_err_msg_p[i] = TRUE; } error_p = TRUE; } if (eqstring( att_info_y->type, "real") == FALSE) { if ((report_class_number == 0) && (att_err_msg_p[j] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour => att_n %d " "(\"%s\") \n" " is not a type real attribute.\n", att_y, att_info_y->dscrp); att_err_msg_p[j] = TRUE; } error_p = TRUE; } /* fprintf( stderr, "generate_sigma_contours: att_index %d att_loc_string %s\n", att_x, model->att_locs[att_x]); fprintf( stderr, "generate_sigma_contours: att_index %d att_loc_string %s\n", att_y, model->att_locs[att_y]); fprintf( stderr, "error_p %d ignore %p report_class_number %d msg %d\n", error_p, strstr( ignore_str, model->att_locs[att_x] ), report_class_number, att_err_msg_p[i]); fprintf( stderr, "error_p %d ignore %p report_class_number %d msg %d\n", error_p, strstr( ignore_str, model->att_locs[att_y] ), report_class_number, att_err_msg_p[j]); */ if (error_p == FALSE) { if (strstr( ignore_str, model->att_locs[att_x] ) != NULL) { if ((report_class_number == 0) && (att_err_msg_p[i] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour => att_n %d " "(\"%s\") \n" " has been declared ignore in the .model file.\n", att_x, att_info_x->dscrp); att_err_msg_p[i] = TRUE; } error_p = TRUE; } if (strstr( ignore_str, model->att_locs[att_y] ) != NULL) { if ((report_class_number == 0) && (att_err_msg_p[j] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour => att_n %d " "(\"%s\") \n" " has been declared ignore in the .model file.\n", att_y, att_info_y->dscrp); att_err_msg_p[j] = TRUE; } error_p = TRUE; } } if (error_p == FALSE) { term_index_x = class_att_loc( class, att_x, &trans_att_x); term_index_y = class_att_loc( class, att_y, &trans_att_y); terms = model->terms; term_x = terms[term_index_x]; term_y = terms[term_index_y]; /* fprintf( stderr, "generate_sigma_contours: term_x->type %s\n", term_x->type); fprintf( stderr, "generate_sigma_contours: term_y->type %s\n", term_y->type); */ if (strstr( term_x->type, "normal") == NULL) { if ((report_class_number == 0) && (att_err_msg_p[i] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour =>" " term_type %s \n" " is not a `normal' term for att_n %d (\"%s\") \n", term_x->type, att_x, att_info_x->dscrp); att_err_msg_p[i] = TRUE; } error_p = TRUE; } if (strstr( term_y->type, "normal") == NULL) { if ((report_class_number == 0) && (att_err_msg_p[j] == FALSE)) { fprintf( stderr, "ADVISORY: compute sigma contour =>" " term_type %s \n" " is not a `normal' term for att_n %d (\"%s\") \n", term_y->type, att_y, att_info_y->dscrp); att_err_msg_p[j] = TRUE; } error_p = TRUE; } } /* fprintf( stderr, "att_x %d trans_att_x %d term_index_x %d term_type_x %s\n", att_x, trans_att_x, term_index_x, term_x->type); fprintf( stderr, "att_y %d trans_att_y %d term_index_y %d term_type_y %s\n", att_y, trans_att_y, term_index_y, term_y->type); */ /* fprintf( stderr, "att_x %d att_y %d\n", att_x, att_y); */ if (error_p == FALSE) { compute_sigma_contour_for_2_atts ( clsf, clsf_class_number, att_x, att_y, trans_att_x, trans_att_y, term_index_x, term_index_y, &mean_x, &sigma_x, &mean_y, &sigma_y, &rotation); sprintf( e_format_string, format_string, trans_att_x, trans_att_y, mean_x, sigma_x, mean_y, sigma_y, rotation); fprintf( influence_report_fp, "%s", filter_e_format_exponents( e_format_string)); } } } } free( att_err_msg_p); } /* COMPUTE_SIGMA_CONTOUR_FOR_2_ATTS 21jun97 wmt: new 06sep97 jcs: corrected initialization of *rotation compute sigma contour for two real valued attributes. ported from Draw-2-Attributes (intf-graphics.lisp) */ int compute_sigma_contour_for_2_atts ( clsf_DS clsf, int clsf_class_number, int att_x, int att_y, int trans_att_x, int trans_att_y, int term_index_x, int term_index_y, float *mean_x, float *sigma_x, float *mean_y, float *sigma_y, float *rotation) { int n_term_list; class_DS class = clsf->classes[clsf_class_number]; model_DS model = clsf->classes[clsf_class_number]->model; term_DS *terms, term_x, term_y; i_real_DS i_real_struct = NULL; float **class_covar_x = NULL, **class_covar_y = NULL; float *term_list; float sigma_x_y, sum, diff, sigma_x_sq, sigma_y_sq, diff_sigma_x_y_term; float rotation_increment, arg; terms = model->terms; term_x = terms[term_index_x]; term_y = terms[term_index_y]; i_real_struct = (i_real_DS) class->i_values[trans_att_x]; *mean_x = i_real_struct->mean_sigma_list[0]; *sigma_x = i_real_struct->mean_sigma_list[1]; class_covar_x = i_real_struct->class_covar; i_real_struct = (i_real_DS) class->i_values[trans_att_y]; *mean_y = i_real_struct->mean_sigma_list[0]; *sigma_y = i_real_struct->mean_sigma_list[1]; class_covar_y = i_real_struct->class_covar; /* fprintf( stderr, "att_x %d, att_y %d, mean_x %e, sigma_x %e " " mean_y %e sigma_y %e rotation %e\n", att_x, att_y, *mean_x, *sigma_x, *mean_y, *sigma_y, *rotation); */ if (term_index_x == term_index_y) { /* We have a covariant pair _ so get rotation, and sigmas in rotated system */ term_list = i_real_struct->term_att_list; n_term_list = i_real_struct->n_term_att_list; sigma_x_y = get_sigma_x_y( trans_att_x, trans_att_y, class, n_term_list, term_list, class_covar_x); sigma_x_sq = *sigma_x * *sigma_x; sigma_y_sq = *sigma_y * *sigma_y; sum = sigma_x_sq + sigma_y_sq; diff = sigma_x_sq - sigma_y_sq; /* fprintf( stderr, "sigma_x_y %e sum %e diff %e \n", sigma_x_y, sum, diff); */ if (*sigma_y > *sigma_x) { *rotation = 0.5 * (float) M_PI; } else { *rotation = 0.0; } diff_sigma_x_y_term = sqrt( (diff * diff) + ( 4.0 * sigma_x_y * sigma_x_y)); *sigma_x = sqrt( 0.5 * ( sum + diff_sigma_x_y_term)); *sigma_y = sqrt( 0.5 * ( sum - diff_sigma_x_y_term)); if (percent_equal( diff, 0.0, SINGLE_FLOAT_EPSILON) == TRUE) { rotation_increment = ( (float) M_PI) / 4.0; } else { arg = ( 2.0 * sigma_x_y ) / diff; rotation_increment = (float) (0.5 * atan( (double) arg)); } *rotation = *rotation + rotation_increment; } else { /* Non-covariant pair - make certain *rotation is zero! We were retaining the value computed for the last covariant pair. */ *rotation = 0.0; } return(1); } /* CLASS_ATT_LOC return transformed index for attribute att_index sprintf(model->att_locs[old_i], "TRANSFORMED->%d", new_i); sprintf(model->att_locs[new_i], "%d", n_term); 23jun97 wmt: new 26feb98 wmt: check for real attributes defined in .hd2, but are ignored in .model; generate error to user */ int class_att_loc( class_DS class, int att_index, int *trans_att_index) { model_DS model; shortstr att_loc_string; int term_index = -1; char greater_than_char = '>'; char *str_index; model = class->model; strcpy( att_loc_string, model->att_locs[att_index]); /* fprintf( stderr, "att_index %d att_loc_string %s\n", att_index, att_loc_string); */ str_index = strchr( att_loc_string, greater_than_char); if (str_index != NULL) { /* this is a transformed attribute -- what we expected att type => real scalar */ strcpy( att_loc_string, ++str_index); sscanf( att_loc_string, "%d", trans_att_index); strcpy( att_loc_string, model->att_locs[*trans_att_index]); } else { /* att type => real location -- no transformation done */ *trans_att_index = att_index; } sscanf( att_loc_string, "%d", &term_index); /* fprintf( stderr, "att_index %d trans_att_index %d term_index %d\n", att_index, *trans_att_index, term_index); */ return( term_index); } /* GET_SIGMA_X_Y return the covariant element for att_x and att_y 23jun97 wmt: new */ float get_sigma_x_y (int att_x, int att_y, class_DS class, int n_term_list, float *term_list, float **covariance) { int covar_index_x = -1, covar_index_y = -1, i; for (i = 0; i < n_term_list; i++) { if (((int) floor( (double) term_list[i])) == att_x) { covar_index_x = i; break; } } for (i = 0; i < n_term_list; i++) { if (((int) floor( (double) term_list[i])) == att_y) { covar_index_y = i; break; } } /* fprintf( stderr, "covar_index_x %d covar_index_y %d \n", covar_index_x, covar_index_y); */ return covariance[covar_index_x][covar_index_y]; } autoclass-3.3.6.dfsg.1/prog/autoclass.make.macosx.gcc0000644000175000017500000000240411247310756020540 0ustar areare### AUTOCLASS C MAKE FILE FOR Mac OS X 10.4.x, GCC version 4.0.0, ### and libc version ???? ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) # optimize & debug - stay with IEEE compliance CFLAGS = $(OSFLAGS) -ansi -pedantic -Wall -O2 -fno-fast-math -g CC = gcc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/autoclass.make.sgi0000644000175000017500000000222111247310756017272 0ustar areare### AUTOCLASS C MAKE FILE FOR SGI IRIX 5.2 - bundled cc compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) CFLAGS = $(OSFLAGS) -ansi -pedantic -fullwarn -O2 -g3 CC = cc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/init.c0000644000175000017500000003675011247310756015001 0ustar areare#include #include #include #include #ifdef _MSC_VER #define getcwd _getcwd #include #else #include /* for MAXPATHLEN */ #include /* getcwd */ #endif #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* INIT 15dec94 wmt: discarded G_att_fnames - put its values in G_transforms 26apr95 wmt: use getcwd, rather than getwd for Solaris compatibility 29apr95 wmt: set G_solaris */ void init( void) { FILE *fp; double rand_range = 31.0, one = 1.0, two = 2.0; #ifdef _WIN32 char* slash = "\\"; #else char* slash = "/"; #endif strcpy(G_transforms[0] , "log_transform"); strcpy( G_transforms[1] , "log_odds_transform"); strcpy( G_att_type_data[0] , "dummy"); strcpy( G_att_type_data[1] , "none"); strcpy( G_att_type_data[2] , "discrete"); strcpy( G_att_type_data[3] , "real"); strcpy( G_att_type_data[4] , "real_and_error"); strcpy( G_checkpoint_file, ""); /* library function getcwd (requires sys/param.h) */ strcpy( G_absolute_pathname, ""); if (getcwd( G_absolute_pathname, MAXPATHLEN - 2) == NULL) { strcat( G_absolute_pathname, ""); strcat( G_absolute_pathname, slash); fprintf( stderr, "\nWARNING: calling getwd (current working directory) returned 0\n"); } else strcat( G_absolute_pathname, slash); /* set G_solaris, if applicable */ fp = fopen( "/usr/ucb/hostname", "r"); if (fp != NULL) { fclose( fp); G_solaris = TRUE; } G_rand_base_normalizer = pow( two, rand_range) - one; init_properties(); } void init_properties(void) { void ***t1, ***t1temp, ***t1temptemp; int *i2, *val1; char **t2, ***types; add_property("multi_multinomial_d", "modulus", "multiple"); add_property("multi_multinomial_d", "type", "multinomial"); add_property("multi_multinomial_d", "error", NULL); add_property("multi_multinomial_d", "missing", "allowed"); add_property("multi_multinomial_d", "print_string", "MM_D"); add_property("multi_multinomial_d", "params", "mm_d_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(2 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "discrete"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "nominal"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "ordered"); t1temp[1][1] = NULL; t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "circular"); t1temp[2][1] = NULL; i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_discrete"); t1[1][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("multi_multinomial_d", "att_trans_data", t1); add_property("multi_multinomial_d", "n_att_trans_data", val1); add_property("multi_multinomial_d", "single_equivalent", "single_multinomial"); i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t2 = (char **) malloc(i2[0] * sizeof(char *)); t2[0] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[0], "multi_multinomial_d"); t2[1] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[1], "multi_multinomial_s"); t2[2] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[2], "multi_multinomial_choose"); add_property("multi_multinomial_d", "multiple_equivalent", t2); add_property("multi_multinomial_d", "n_multiple_equivalent", i2); add_property("multi_multinomial_s", "modulus", "multiple"); add_property("multi_multinomial_s", "type", "multinomial"); add_property("multi_multinomial_s", "error", NULL); add_property("multi_multinomial_s", "missing", "allowed"); add_property("multi_multinomial_s", "print_string", "MM_S"); add_property("multi_multinomial_s", "params", "mm_d_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(2 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "discrete"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); /* 22oct94 wmt: 2=>3 */ t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "nominal"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "ordered"); t1temp[1][1] = NULL; t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "circular"); t1temp[2][1] = NULL; i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_discrete"); t1[1][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("multi_multinomial_s", "att_trans_data", t1); add_property("multi_multinomial_s", "n_att_trans_data", val1); add_property("multi_multinomial_s", "single_equivalent", "single_multinomial"); i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t2 = (char **) malloc(i2[0] * sizeof(char *)); t2[0] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[0], "multi_multinomial_s"); t2[1] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[1], "multi_multinomial_d"); t2[2] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[2], "multi_multinomial_choose"); add_property("multi_multinomial_s", "multiple_equivalent", t2); add_property("multi_multinomial_s", "n_multiple_equivalent", i2); add_property("multi_normal_cn", "modulus", "multiple"); add_property("multi_normal_cn", "type", "normal"); add_property("multi_normal_cn", "error", "constant"); add_property("multi_normal_cn", "missing", "no"); add_property("multi_normal_cn", "print_string", "MNcn"); add_property("multi_normal_cn", "params", "mn_cn_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(2 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "real"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "location"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "scalar"); t1temp[1][1] = (void ***) malloc(sizeof(void **)); t1temptemp = t1temp[1][1]; t1temptemp[0] = (void **) malloc(2 * sizeof(void *)); t1temptemp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][0], "transform"); t1temptemp[0][1] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][1], "log_transform"); i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_real"); t1[1][1] = i2; t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "n_scalar"); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; t1temp[2][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("multi_normal_cn", "att_trans_data", t1); add_property("multi_normal_cn", "n_att_trans_data", val1); add_property("multi_normal_cn", "single_equivalent", "single_normal_cn"); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; t2 = (char **) malloc(i2[0] * sizeof(char *)); t2[0] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[0], "multi_normal_cn"); add_property("multi_normal_cn", "multiple_equivalent", t2); add_property("multi_normal_cn", "n_multiple_equivalent", i2); add_property("single_multinomial", "modulus", "single"); add_property("single_multinomial", "type", "multinomial"); add_property("single_multinomial", "error", NULL); add_property("single_multinomial", "missing", "allowed"); add_property("single_multinomial", "print_string", "SM"); add_property("single_multinomial", "params", "sm_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(3 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "discrete"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "nominal"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "ordered"); t1temp[1][1] = NULL; t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "circular"); t1temp[2][1] = NULL; i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_discrete"); t1[1][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("single_multinomial", "att_trans_data", t1); add_property("single_multinomial", "n_att_trans_data", val1); i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t2 = (char **) malloc(i2[0] * sizeof(char *)); t2[0] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[0], "multi_multinomial_d"); t2[1] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[1], "multi_multinomial_s"); t2[2] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[2], "multi_multinomial_choose"); add_property("single_multinomial", "multiple_equivalent", t2); add_property("single_multinomial", "n_multiple_equivalent", i2); add_property("single_normal_cn", "modulus", "single"); add_property("single_normal_cn", "type", "normal"); add_property("single_normal_cn", "error", "constant"); add_property("single_normal_cn", "missing", "no"); add_property("single_normal_cn", "print_string", "SNcn"); add_property("single_normal_cn", "params", "sn_cn_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(2 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "real"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "location"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "scalar"); t1temp[1][1] = (void ***) malloc(sizeof(void **)); t1temptemp = t1temp[1][1]; t1temptemp[0] = (void **) malloc(2 * sizeof(void *)); t1temptemp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][0], "transform"); t1temptemp[0][1] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][1], "log_transform"); t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "n_scalar"); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; t1temp[2][1] = i2; i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_real"); t1[1][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("single_normal_cn", "att_trans_data", t1); add_property("single_normal_cn", "n_att_trans_data", val1); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; t2 = (char **) malloc(i2[0] * sizeof(char *)); t2[0] = (char *) malloc(STRLIMIT * sizeof(char)); strcpy(t2[0], "multi_normal_cn"); add_property("single_normal_cn", "multiple_equivalent", t2); add_property("single_normal_cn", "n_multiple_equivalent", i2); add_property("single_normal_cm", "modulus", "single"); add_property("single_normal_cm", "type", "normal"); add_property("single_normal_cm", "error", "constant"); add_property("single_normal_cm", "missing", "yes"); add_property("single_normal_cm", "print_string", "SNcm"); add_property("single_normal_cm", "params", "sn_cm_params_DS"); /* See check_attribute_types */ t1 = (void ***) malloc(2 * sizeof(void **)); t1[0] = (void **) malloc(2 * sizeof(void *)); t1[1] = (void **) malloc(2 * sizeof(void *)); t1[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[0][0], "real"); t1[0][1] = (void ***) malloc(3 * sizeof(void **)); t1temp = t1[0][1]; t1temp[0] = (void **) malloc(2 * sizeof(void *)); t1temp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[0][0], "location"); t1temp[0][1] = NULL; t1temp[1] = (void **) malloc(2 * sizeof(void *)); t1temp[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[1][0], "scalar"); t1temp[1][1] = (void ***) malloc(sizeof(void **)); t1temptemp = t1temp[1][1]; t1temptemp[0] = (void **) malloc(2 * sizeof(void *)); t1temptemp[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][0], "transform"); t1temptemp[0][1] = (char *) malloc(20 * sizeof(char)); strcpy(t1temptemp[0][1], "log_transform"); t1temp[2] = (void **) malloc(2 * sizeof(void *)); t1temp[2][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1temp[2][0], "n_scalar"); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; t1temp[2][1] = i2; i2 = (int *) malloc(sizeof(int)); i2[0] = 3; t1[1][0] = (char *) malloc(20 * sizeof(char)); strcpy(t1[1][0], "n_real"); t1[1][1] = i2; val1 = (int *) malloc(sizeof(int)); val1[0] = 2; add_property("single_normal_cm", "att_trans_data", t1); add_property("single_normal_cm", "n_att_trans_data", val1); i2 = (int *) malloc(sizeof(int)); i2[0] = 0; add_property("single_normal_cm", "multiple_equivalent", NULL); add_property("single_normal_cm", "n_multiple_equivalent", i2); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; add_property("log_transform", "n_args", i2); types = (char ***) malloc(sizeof(char **)); types[0] = (char **) malloc(3 * sizeof(char *)); types[0][0] = (char *) malloc(20 * sizeof(char)); strcpy(types[0][0], "real"); types[0][1] = (char *) malloc(20 * sizeof(char)); strcpy(types[0][1], "location"); types[0][2] = (char *) malloc(20 * sizeof(char)); strcpy(types[0][2], "scalar"); i2 = (int *) malloc(sizeof(int)); i2[0] = 1; add_property("log_transform", "types", types); add_property("log_transform", "n_types", i2); } autoclass-3.3.6.dfsg.1/prog/intf-reports.c0000644000175000017500000036441111667631470016475 0ustar areare#include #include #include #include #include #ifndef _WIN32 #include #endif #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* AUTOCLASS_REPORTS 01feb95 wmt: new - adapted from ac-x:autoclass-reports-from-results-file 26apr95 wmt: change NULL to 0 for 6th arg of defparam 26may95 wmt: add prediction capability 15oct96 wmt: compute last_classification_p to indicate when database should be freed 17mar97 wmt: added report_mode 25apr97 wmt: add prediction_p 17feb98 wmt: write screen output to log file as well 02dec98 wmt: write params to log file "-reports": generate the influence value, class cross-reference & case cross-reference reports "-predict": with test_data_file non-"", generate the class cross-reference & case cross-reference reports for test_data_file prediction */ int autoclass_reports( char *results_file_ptr, char *search_file_ptr, char *reports_params_file_ptr, char *influ_vals_file_ptr, char *xref_class_file_ptr, char *xref_case_file_ptr, char *test_data_file, char *log_file_ptr) { /* number of clsfs in the .results file for which to generate reports, starting with the first (most probable) */ int n_clsfs = 1; /* if specified, this is a zero-based index list of clsfs in the clsf sequence read from the .results file */ static int clsf_n_list[MAX_CLSF_N_LIST] = {END_OF_INT_LIST}; /* type of reports to generate: "all", "influence-values", "xref_case", or "xref_class" */ shortstr report_type = "all"; /* mode of reports to generate. "text" is formatted text layout. "data" is numerical -- suitable for further processing. */ shortstr report_mode = "text"; /* The default value does not insert # in column 1 of most report_mode = "data" header lines. If specified as true, the comment character will be inserted in most header lines */ unsigned int comment_data_headers_p = FALSE; /* if specified, the number of attributes to list in influence values report. if not overridden, all attributes will be output the output for report_mode = "text" uses these criteria to make it easier to parse with awk: remove ":"'s remove "..."'s all section headers start in column 1 secondary lines of an item can have leading blanks */ int num_atts_to_list = ALL_ATTRIBUTES; /* if specified, a list of attribute numbers (zero-based), whose values will be output in the "xref_class" report along with the case probabilities */ static int xref_class_report_att_list[MAX_CLASS_REPORT_ATT_LIST] = {END_OF_INT_LIST}; /* The default value lists each class's attributes in descending order of attribute influence value, and uses ".influ-o-text-n" as the influence values report file type. If specified as false, then each class's attributes will be listed in ascending order by attribute number. The extension of the file generated will be "influ-no-text-n". */ unsigned int order_attributes_by_influence_p = TRUE; /* The default value asks the user whether to coninue or not when data definition warnings are found. If specified as false, then AutoClass will continue, despite warnings -- the warnings will continue to be output to the terminal and the log file. */ unsigned int break_on_warnings_p = TRUE; /* The default value tells AutoClass to free the majority of its allocated storage. This is not required, and in the case of DEC Alpha's causes core dump. If specified as false, AutoClass will not attempt to free storage. */ unsigned int free_storage_p = TRUE; /* Determines how many lessor class probabilities will be printed for the case and class cross-reference reports. The default is to print the most probable class probability value and up to 4 lessor class prob- ibilities. Note this is true for both the "text" and "data" class cross-reference reports, but only true for the "data" case cross- reference report. The "text" case cross-reference report only has the most probable class probability. */ int max_num_xref_class_probs = 5; /* sigma_contours_att_list = If specified, a list of real valued attribute indices (from .hd2 file) will be to compute sigma class contour values, when generating influence values report with the data option (report_mode = "data"). If not specified, there will be no sigma class contour output. (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) */ static int sigma_contours_att_list[MAX_N_SIGMA_CONTOUR_LIST] = {END_OF_INT_LIST}; /* -------------------------------------------------------------------------*/ int r_parms_error_cnt, n_params = 0, i, clsf_n_list_from_s_params = FALSE; int num_clsf_n_list, num_clsfs_found, n_clsf, clsf_num, prediction_p = FALSE; int training_prediction_p = FALSE, last_clsf_p = FALSE; int sigma_contours_list_len = 0; search_DS search; FILE *search_file_fp, *reports_params_file_fp; static FILE *log_file_fp = NULL; PARAM params[MAXPARAMS]; clsf_DS *clsf_seq, clsf, test_clsf = NULL, training_clsf; xref_data_DS xref_data = NULL; fxlstr str, autoclass_mode; char caller[] = "autoclass_reports"; unsigned int log_file_p = TRUE; /* -------------------------------------------------------------------------*/ G_stream = stdout; params[0].paramptr = NULL; defparam( params, n_params++, "n_clsfs", TINT, &n_clsfs, 0); defparam( params, n_params++, "clsf_n_list", TINT_LIST, clsf_n_list, MAX_CLSF_N_LIST); defparam( params, n_params++, "report_type", TSTRING, report_type, SHORT_STRING_LENGTH); defparam( params, n_params++, "report_mode", TSTRING, report_mode, SHORT_STRING_LENGTH); defparam( params, n_params++, "comment_data_headers_p", TBOOL, &comment_data_headers_p, 0); defparam( params, n_params++, "num_atts_to_list", TINT, &num_atts_to_list, 0); defparam( params, n_params++, "xref_class_report_att_list", TINT_LIST, xref_class_report_att_list, MAX_CLASS_REPORT_ATT_LIST); defparam( params, n_params++, "order_attributes_by_influence_p", TBOOL, &order_attributes_by_influence_p, 0); defparam( params, n_params++, "break_on_warnings_p", TBOOL, &break_on_warnings_p, 0); defparam( params, n_params++, "free_storage_p", TBOOL, &free_storage_p, 0); defparam( params, n_params++, "max_num_xref_class_probs", TINT, &max_num_xref_class_probs, 0); defparam( params, n_params++, "sigma_contours_att_list", TINT_LIST, sigma_contours_att_list, MAX_N_SIGMA_CONTOUR_LIST); /* -------------------------------------------------- */ if (eqstring( test_data_file, "") != TRUE) prediction_p = TRUE; /* read reports params file */ fprintf( stdout, "\n\n\n### Starting Check of %s%s\n", (reports_params_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, reports_params_file_ptr); reports_params_file_fp = fopen( reports_params_file_ptr, "r"); r_parms_error_cnt = getparams( reports_params_file_fp, params); fclose( reports_params_file_fp); if ((eqstring( report_mode, "text") != TRUE) && (eqstring( report_mode , "data") != TRUE)) { fprintf( stderr, "ERROR: report_mode must be either \"text\", or " "\"data\".\n"); r_parms_error_cnt++; } if (prediction_p == TRUE) { if ((eqstring( report_type, "all") != TRUE) && (eqstring( report_type , "xref_case") != TRUE) && (eqstring( report_type , "xref_class") != TRUE)) { fprintf( stderr, "ERROR: report_type must be either \"all\", " "\"xref_case\", or \"xref_class\".\n"); r_parms_error_cnt++; } } else { if ((eqstring( report_type, "all") != TRUE) && (eqstring( report_type , "influence_values") != TRUE) && (eqstring( report_type , "xref_case") != TRUE) && (eqstring( report_type , "xref_class") != TRUE)) { fprintf( stderr, "ERROR: report_type must be either \"all\", " "\"influence_values\", \"xref_case\", or \"xref_class\".\n"); r_parms_error_cnt++; } } if ((eqstring( report_mode, "text") == TRUE) && (comment_data_headers_p == TRUE)) { fprintf( stderr, "ERROR: report_mode must be \"data\" if " "comment_data_headers_p is true.\n"); r_parms_error_cnt++; } if ( max_num_xref_class_probs < 1) { fprintf( stderr, "ERROR: max_num_xref_class_probs must be greater than 0.\n"); r_parms_error_cnt++; } fprintf(stdout, "### Ending Check of %s%s\n", (reports_params_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, reports_params_file_ptr); for (i=0; i 0) exit(1); G_break_on_warnings = break_on_warnings_p; /* end of reports params processing */ if (log_file_ptr[0] == '\0') { G_log_file_fp = NULL; log_file_p = FALSE; } else { log_file_fp = fopen( log_file_ptr, "a"); G_log_file_fp = log_file_fp; } safe_sprintf( str, sizeof( str), caller, "\n\nAUTOCLASS C (version %s) STARTING at %s \n\n", G_ac_version, format_universal_time(get_universal_time())); to_screen_and_log_file(str, G_log_file_fp, G_stream, TRUE); if (log_file_p == TRUE) { if (prediction_p == TRUE) strcpy( autoclass_mode, "-PREDICT"); else strcpy( autoclass_mode, "-REPORTS"); fprintf(log_file_fp, "AUTOCLASS %s default parameters:\n", autoclass_mode); putparams(log_file_fp, params, FALSE); } if (log_file_p == TRUE) { fprintf(log_file_fp, "USER supplied parameters which override the defaults:\n"); putparams(log_file_fp, params, TRUE); } if (clsf_n_list_from_s_params != TRUE) { for (i=(n_clsfs - 1); i>=0; i--) { /* index 0 is first on list */ push_int_list( clsf_n_list, &num_clsf_n_list, i+1, MAX_CLSF_N_LIST); } } clsf_seq = initialize_reports_from_results_pathname( results_file_ptr, clsf_n_list, &num_clsfs_found, training_prediction_p); search_file_fp = fopen( search_file_ptr, "r"); search = get_search_from_file( search_file_fp, search_file_ptr); fclose( search_file_fp); fprintf( G_stream, "\n"); for (n_clsf=0; n_clsf (clsf->database->input_n_atts - 1)) { fprintf( stderr, "ERROR: sigma_contours_att_list index %d cannot exceed %d --\n" " use indices from .hd2 file.\n", sigma_contours_att_list[i], clsf->database->input_n_atts - 1); exit(1); } sigma_contours_list_len++; } if (sigma_contours_list_len < 2) { fprintf( stderr, "ERROR: sigma_contours_att_list length must be >= 2.\n"); exit(1); } } if (prediction_p == FALSE) { /* -reports */ if ((eqstring( report_type, "all") == TRUE) || (eqstring( report_type, "influence_values") == TRUE)) influence_values_report_streams( clsf, search, num_atts_to_list, report_mode, influ_vals_file_ptr, results_file_ptr, clsf_num, test_clsf, order_attributes_by_influence_p, comment_data_headers_p, sigma_contours_att_list); if (eqstring( report_type, "all") == TRUE) xref_data = case_class_data_sharing( clsf, report_mode, report_type, xref_class_file_ptr, xref_case_file_ptr, results_file_ptr, xref_class_report_att_list, clsf_num, test_clsf, last_clsf_p, prediction_p, comment_data_headers_p, max_num_xref_class_probs); if (eqstring( report_type, "xref_case") == TRUE) { /* pass empty xref_class_report_att_list */ xref_class_report_att_list[0] = END_OF_INT_LIST; xref_data = xref_get_data( clsf, "case", xref_class_report_att_list, xref_data, last_clsf_p, prediction_p, max_num_xref_class_probs); case_report_streams( clsf, report_mode, xref_case_file_ptr, results_file_ptr, xref_data, clsf_num, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); } if (eqstring( report_type, "xref_class") == TRUE) { xref_data = xref_get_data( clsf, "class", xref_class_report_att_list, xref_data, last_clsf_p, prediction_p, max_num_xref_class_probs); class_report_streams( clsf, report_mode, xref_class_file_ptr, results_file_ptr, xref_class_report_att_list, xref_data, clsf_num, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); } } else { /* -predict */ training_clsf = clsf; test_clsf = autoclass_predict( test_data_file, training_clsf, test_clsf, log_file_fp, log_file_ptr); case_class_data_sharing( clsf, report_mode, report_type, xref_class_file_ptr, xref_case_file_ptr, results_file_ptr, xref_class_report_att_list, clsf_num, test_clsf, last_clsf_p, prediction_p, comment_data_headers_p, max_num_xref_class_probs); } /* free storage from this clsf's reports */ if (xref_data != NULL) { for (i=0; idatabase->n_data; i++) { if (xref_data[i].discrete_attribute_data != NULL) free( xref_data[i].discrete_attribute_data); if (xref_data[i].real_attribute_data != NULL) free( xref_data[i].real_attribute_data); if (xref_data[i].wt_class_pairs != NULL) free( xref_data[i].wt_class_pairs); } free ( xref_data); xref_data = NULL; } if (free_storage_p == TRUE) { free_clsf_class_search_storage( clsf, (n_clsf == (num_clsfs_found - 1)) ? search : NULL, (n_clsf == (num_clsfs_found - 1)) ? TRUE : FALSE); if (prediction_p == TRUE) free_clsf_class_search_storage( test_clsf, NULL, FALSE); } } safe_sprintf( str, sizeof( str), caller, "\n\nAUTOCLASS C (version %s) STOPPING at %s \n\n", G_ac_version, format_universal_time(get_universal_time())); to_screen_and_log_file(str, G_log_file_fp, G_stream, TRUE); if (log_file_ptr[0] != '\0') { fclose( log_file_fp); } return (0); } /* CLSF_SEARCH_VALIDITY_CHECK 06feb95 wmt: new check that clsf is in the trials of search - return position of clsf */ int clsf_search_validity_check( clsf_DS clsf, search_DS search) { double clsf_id = clsf->log_a_x_h; search_try_DS *tries = search->tries; int n_try, position = -1; for (n_try=0; n_tryn_tries; n_try++) if (percent_equal( clsf_id, tries[n_try]->ln_p, SINGLE_FLOAT_EPSILON) == TRUE) { position = n_try; break; } return (position); } /* INFLUENCE_VALUES_REPORT_STREAMS 06feb95 wmt: new 24jul95 wmt: add order_attributes_by_influence_p 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p 24jun97 wmt: add start_sigma_contours_att & stop_sigma_contours_att 28feb98 wmt: replace start/stop_sigma_contours_att with sigma_contours_att_list create streams from pathnames for influence values reports */ void influence_values_report_streams( clsf_DS clsf, search_DS search, int num_atts_to_list, shortstr report_mode, char *influ_vals_file_ptr, char *results_file_ptr, int clsf_num, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p, int_list sigma_contours_att_list) { static fxlstr influence_report_pathname; char clsf_num_string[4]; FILE *influence_report_fp = NULL; int header_information_p = FALSE, num_chars_to_trunc = 5, trunc_index; fxlstr str; char caller[] = "influence_values_report_streams"; influence_report_pathname[0] = clsf_num_string[0] = '\0'; strcpy( influence_report_pathname, influ_vals_file_ptr); /* truncate "-----.influ-text-" to "----.influ-" */ trunc_index = (int) strlen( influence_report_pathname) - num_chars_to_trunc; influence_report_pathname[trunc_index] = '\0'; if (order_attributes_by_influence_p == TRUE) strcat( influence_report_pathname, "o-"); else strcat( influence_report_pathname, "no-"); if (eqstring (report_mode, "text") == TRUE) strcat( influence_report_pathname, "text-"); else if (eqstring (report_mode, "data") == TRUE) strcat( influence_report_pathname, "data-"); sprintf( clsf_num_string, "%d", clsf_num); strcat( influence_report_pathname, clsf_num_string); if (num_atts_to_list == ALL_ATTRIBUTES) num_atts_to_list = clsf->database->n_atts; else num_atts_to_list = max( 1, min( num_atts_to_list, clsf->database->n_atts)); influence_report_fp = fopen( influence_report_pathname, "w"); autoclass_influence_values_report( clsf, search, num_atts_to_list, results_file_ptr, header_information_p, influence_report_fp, report_mode , test_clsf, order_attributes_by_influence_p, comment_data_headers_p, sigma_contours_att_list); fclose( influence_report_fp); safe_sprintf( str, sizeof( str), caller, "\nFile written: %s%s\n", (influence_report_pathname[0] == G_slash) ? "" : G_absolute_pathname, influence_report_pathname); to_screen_and_log_file(str, G_log_file_fp, G_stream, TRUE); } /* CASE_CLASS_DATA_SHARING 06feb95 wmt: new 30may95 wmt: added test_clsf for prediction 15oct96 wmt: add last_clsf_p arg 18mar97 wmt: add report_mode 25apr97 wmt: add prediction_p 14may97 wmt: add comment_data_headers_p 21jun97 wmt: add max_num_xref_class_probs 02dec98 wmt: in prediction mode, check report_type share xref data when both by case and by class reports are requested */ xref_data_DS case_class_data_sharing( clsf_DS clsf, shortstr report_mode, shortstr report_type, char *xref_class_file_ptr, char *xref_case_file_ptr, char *results_file_ptr, int_list xref_class_report_att_list, int clsf_num, clsf_DS test_clsf, int last_clsf_p, int prediction_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs) { xref_data_DS data = NULL; if ((prediction_p == FALSE) || ((prediction_p == TRUE) && ((eqstring( report_type, "all") == TRUE) || (eqstring( report_type, "xref_case") == TRUE)))) { data = xref_get_data( (test_clsf == NULL) ? clsf : test_clsf, "case", xref_class_report_att_list, data, last_clsf_p, prediction_p, max_num_xref_class_probs); case_report_streams( clsf, report_mode, xref_case_file_ptr, results_file_ptr, data, clsf_num, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); } if ((prediction_p == FALSE) || ((prediction_p == TRUE) && ((eqstring( report_type, "all") == TRUE) || (eqstring( report_type, "xref_class") == TRUE)))) { class_report_streams( clsf, report_mode, xref_class_file_ptr, results_file_ptr, xref_class_report_att_list, xref_get_data( (test_clsf == NULL) ? clsf : test_clsf, "class", xref_class_report_att_list, data, last_clsf_p, prediction_p, max_num_xref_class_probs), clsf_num, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); } return (data); } /* CASE_REPORT_STREAMS 06feb95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 21jun97 wmt: add max_num_xref_class_probs create streams from pathnames for xref by case reports */ xref_data_DS case_report_streams( clsf_DS clsf, shortstr report_mode, char *xref_case_file_ptr, char *results_file_ptr, xref_data_DS xref_data, int clsf_num, clsf_DS test_clsf, int last_clsf_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs) { static fxlstr xref_case_report_pathname; int trunc_index, num_chars_to_trunc = 5; char clsf_num_string[4]; FILE *xref_case_report_fp; fxlstr str; char caller[] = "case_report_streams"; xref_case_report_pathname[0] = clsf_num_string[0] = '\0'; strcpy( xref_case_report_pathname, xref_case_file_ptr); /* truncate "-----.case-text-" to "----.case-" */ trunc_index = (int) strlen( xref_case_report_pathname) - num_chars_to_trunc; xref_case_report_pathname[trunc_index] = '\0'; if (eqstring (report_mode, "text") == TRUE) strcat( xref_case_report_pathname, "text-"); else if (eqstring (report_mode, "data") == TRUE) strcat( xref_case_report_pathname, "data-"); sprintf( clsf_num_string, "%d", clsf_num); strcat( xref_case_report_pathname, clsf_num_string); xref_case_report_fp = fopen( xref_case_report_pathname, "w"); autoclass_xref_by_case_report( clsf, xref_case_report_fp, report_mode, xref_data, results_file_ptr, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); fclose( xref_case_report_fp); safe_sprintf( str, sizeof( str), caller, "\nFile written: %s%s\n", (xref_case_report_pathname[0] == G_slash) ? "" : G_absolute_pathname, xref_case_report_pathname); to_screen_and_log_file(str, G_log_file_fp, G_stream, TRUE); return (xref_data); } /* CLASS_REPORT_STREAMS 06feb95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 21jun97 wmt: add max_num_xref_class_probs */ xref_data_DS class_report_streams( clsf_DS clsf, shortstr report_mode, char *xref_class_file_ptr, char *results_file_ptr, int_list xref_class_report_att_list, xref_data_DS xref_data, int clsf_num, clsf_DS test_clsf, int last_clsf_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs) { static fxlstr xref_class_report_pathname; int trunc_index, num_chars_to_trunc = 5; char clsf_num_string[4]; FILE *xref_class_report_fp; fxlstr str; char caller[] = "class_report_streams"; xref_class_report_pathname[0] = clsf_num_string[0] = '\0'; strcpy( xref_class_report_pathname, xref_class_file_ptr); /* truncate "-----.class-text-" to "----.class-" */ trunc_index = (int) strlen( xref_class_report_pathname) - num_chars_to_trunc; xref_class_report_pathname[trunc_index] = '\0'; if (eqstring (report_mode, "text") == TRUE) strcat( xref_class_report_pathname, "text-"); else if (eqstring (report_mode, "data") == TRUE) strcat( xref_class_report_pathname, "data-"); sprintf( clsf_num_string, "%d", clsf_num); strcat( xref_class_report_pathname, clsf_num_string); xref_class_report_fp = fopen( xref_class_report_pathname, "w"); autoclass_xref_by_class_report( clsf, xref_class_report_fp, report_mode, xref_data, xref_class_report_att_list, results_file_ptr, test_clsf, last_clsf_p, comment_data_headers_p, max_num_xref_class_probs); fclose( xref_class_report_fp); safe_sprintf( str, sizeof( str), caller, "\nFile written: %s%s\n", (xref_class_report_pathname[0] == G_slash) ? "" : G_absolute_pathname, xref_class_report_pathname); to_screen_and_log_file(str, G_log_file_fp, G_stream, TRUE); return (xref_data); } /* XREF_GET_DATA 06feb95 wmt: new 15may95 wmt: use n_real_att-1, rather than i, for index to real_attribute_data, etc 25jun96 wmt: do not do realloc's in increments of 1 -- use DATA_ALLOC_INCREMENT 04jul96 wmt: check for malloc/realloc returning NULL 05jul96 wmt: free database allocated memory after it is transferred into report data structures 14oct96 wmt: validity check report_attributes < n_atts -- xref_class_report_att_list from .r-params file; replace free( &datum_array[att_number]) with free( &data_array[case_num]) and put it in correct location 15oct96 wmt: add last_clsf_p to indicate when database should be freed 24apr97 wmt: allocate memory for collector once for each case, rather than n_classes times. Eliminate ATTR_ALLOC_INCREMENT, and allocate once for all discrete, and once for all real report attributes, if needed, rather than invoking malloc/realloc for each report attribute. Limit probability collector to 5 values. 25apr97 wmt: add prediction_p; flag "test" cases which are not predicted in be in any of the "training" classes. Put them in class -1. 21jun97 wmt: add max_num_xref_class_probs 01nov97 wmt: allocate more storage for instance class probabilities if there are more than MAX_NUM_XREF_CLASS_PROBS, and only save for printing a maximum of MAX_NUM_XREF_CLASS_PROBS classes collects data from clsf with the ordered set of (wt class) pairs: ((h1 h2 ... h-N) (w1 c1) (w2 c2) ...). h1 = datum number, h2 ..h-N are report_attributes values */ xref_data_DS xref_get_data( clsf_DS clsf, char *type, int_list report_attributes, xref_data_DS xref_data, int last_clsf_p, int prediction_p, int max_num_xref_class_probs) { int n_classes = clsf->n_classes, n_data = clsf->database->n_data, case_num; int n_attribute_data, i, att_number = 0, n_class, n_collector; int n_discrete_att = 0, n_real_att = 0, n_atts = clsf->database->n_atts; int num_discrete_att = 0, num_real_att = 0; int xref_data_allocated = 0, collector_length = 0; float **data_array = clsf->database->data, wt, *datum_array; sort_cell_DS collector; att_DS *att_info = clsf->database->att_info; class_DS *classes = clsf->classes; shortstr *discrete_attribute_data = NULL; float *real_attribute_data = NULL; int (* class_wt_sort_func) () = float_sort_cell_compare_gtr; int (* class_case_sort_func) () = class_case_sort_compare_lsr; /* int rpt_n_class; */ /* printf( "xref_get_data: type = %s\n", type); */ /* for (i=0; report_attributes[i] != END_OF_INT_LIST; i++) { fprintf( stderr, "xref_get_data report_attributes i %d num %d\n", i, report_attributes[i]); } */ if (xref_data == NULL) { for (i=0; report_attributes[i] != END_OF_INT_LIST; i++) { att_number = report_attributes[i]; if ((att_number < 0) || (att_number >= n_atts)) { fprintf( stderr, "ERROR: .r-params file: xref_class_report_att_list index %d " "not in range: 0<->%d\n", att_number, n_atts - 1); exit(1); } if (att_info[att_number]->translations != NULL) { num_discrete_att++; } else { num_real_att++; } } for (case_num=0; case_num xref_data_allocated) { xref_data_allocated += DATA_ALLOC_INCREMENT; if (xref_data == NULL) { xref_data = (xref_data_DS) malloc( xref_data_allocated * sizeof( struct xref_data)); if (xref_data == NULL) { fprintf( stderr, "ERROR: xref_get_data(1): out of memory, malloc returned NULL!\n"); exit(1); } } else { xref_data = (xref_data_DS) realloc( xref_data, xref_data_allocated * sizeof( struct xref_data)); if (xref_data == NULL) { fprintf( stderr, "ERROR: xref_get_data(1): out of memory, realloc returned NULL!\n"); exit(1); } } } if (num_discrete_att > 0) { discrete_attribute_data = (shortstr *) malloc( num_discrete_att * sizeof( shortstr)); if (discrete_attribute_data == NULL) { fprintf( stderr, "ERROR: xref_get_data(2): out of memory, malloc returned NULL!\n"); exit(1); } } if (num_real_att > 0) { real_attribute_data = (float *) malloc( num_real_att * sizeof( float)); if (real_attribute_data == NULL) { fprintf( stderr, "ERROR: xref_get_data(3): out of memory, malloc returned NULL!\n"); exit(1); } } collector = (sort_cell_DS) malloc( max_num_xref_class_probs * sizeof( struct sort_cell)); if (collector == NULL) { fprintf( stderr, "ERROR: xref_get_data(4): out of memory, malloc returned NULL!\n"); exit(1); } xref_data[case_num].n_attribute_data = 0; xref_data[case_num].case_number = case_num + 1; for (i=0; report_attributes[i] != END_OF_INT_LIST; i++) { att_number = report_attributes[i]; xref_data[case_num].n_attribute_data++; if (att_info[att_number]->translations != NULL) { n_discrete_att++; strcpy( discrete_attribute_data[n_discrete_att - 1], att_info[att_number]->translations[(int) (datum_array[att_number])]); } else { n_real_att++; real_attribute_data[n_real_att - 1] = datum_array[att_number]; } } xref_data[case_num].discrete_attribute_data = discrete_attribute_data; xref_data[case_num].real_attribute_data = real_attribute_data; n_collector = 0; collector_length = max_num_xref_class_probs; for (n_class=0; n_classwts[case_num]; if (wt > 0.00099999) { if (n_collector >= collector_length) { /* allocate more storage for instance class probabilities */ /* fprintf( stderr, "xref_get_data: allocating more storage for instance %d\n", case_num + 1); */ collector_length += max_num_xref_class_probs; collector = (sort_cell_DS) realloc( collector, collector_length * sizeof( struct sort_cell)); } collector[n_collector].float_value = wt; collector[n_collector].int_value = map_class_num_clsf_to_report( clsf, n_class); n_collector++; /* rpt_n_class = map_class_num_clsf_to_report( clsf, n_class); if (case_num == 144) { fprintf( stderr, "xref_get_data: n_class %d rpt_n_class %d case_num %d wt %f\n", n_class, rpt_n_class, case_num + 1, wt); } */ } } if (prediction_p && (n_collector == 0)) { /* put cases which do not fall into any class - put into class 9999 */ collector[n_collector].int_value = 9999; collector[n_collector].float_value = 1.0; n_collector = 1; fprintf( stderr, "xref_get_data: case_num %d => class 9999\n", case_num); } /* sort collector by most probable class first */ qsort( (char *) collector, n_collector, sizeof( struct sort_cell), class_wt_sort_func); /* if (case_num == 144) { for (i=0; i classes */ xref_data[case_num].n_collector = min( n_collector, max_num_xref_class_probs); xref_data[case_num].wt_class_pairs = collector; if (last_clsf_p) { /* free database allocated memory after it is transferred into report data structures */ /* fprintf( stderr, "xref_get_data: free case_num %d\n", case_num); */ free( clsf->database->data[case_num]); } } } if (eqstring( type, "class") == TRUE) { /* sort xref_data by lowest class first */ qsort( (char *) xref_data, n_data, sizeof( struct xref_data), class_case_sort_func); } /* for (case_num=0; case_numreports->n_class_wt_ordering; report_n_class++) if (clsf_n_class == clsf->reports->class_wt_ordering[report_n_class]) break; return ( report_n_class); } /* MAP_CLASS_NUM_REPORT_TO_CLSF 07feb95 wmt: new map class number from report (weight ordered) to classification */ int map_class_num_report_to_clsf( clsf_DS clsf, int report_n_class) { return ( clsf->reports->class_wt_ordering[report_n_class]); } /* AUTOCLASS_XREF_BY_CASE_REPORT 07feb95 wmt: new 30may95 wmt: added test_clsf 04jul96 wmt: check for malloc/realloc returning NULL 21mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 21jun97 wmt: add max_num_xref_class_probs output cross reference by case number */ void autoclass_xref_by_case_report( clsf_DS training_clsf, FILE *xref_case_report_fp, shortstr report_mode, xref_data_DS xref_data, char *results_file_ptr, clsf_DS test_clsf, int last_clsf_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs) { int_list xref_class_report_att_list; char blank = ' '; int reports_initial_line_cnt_max = 46, prediction_initial_line_cnt_max = 42; int initial_line_cnt_max, prediction_p = 0; clsf_DS clsf; if (test_clsf == NULL) { initial_line_cnt_max = reports_initial_line_cnt_max; clsf = training_clsf; } else { initial_line_cnt_max = prediction_initial_line_cnt_max; clsf = test_clsf; } if (xref_data == NULL) { xref_class_report_att_list = (int *) malloc( sizeof( int)); if (xref_class_report_att_list == NULL) { fprintf( stderr, "ERROR: autoclass_xref_by_case_report: out of memory, malloc returned NULL!\n"); exit(1); } xref_class_report_att_list[0] = END_OF_INT_LIST; xref_data = xref_get_data( clsf, "case", xref_class_report_att_list, xref_data, last_clsf_p, prediction_p, max_num_xref_class_probs); } fprintf( xref_case_report_fp, "%s%6cCROSS REFERENCE CASE NUMBER => " "MOST PROBABLE CLASS\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); classification_header( training_clsf, results_file_ptr, xref_case_report_fp, report_mode, test_clsf, comment_data_headers_p); xref_paginate_by_case( xref_data, clsf->database->n_data, xref_case_report_fp, report_mode, initial_line_cnt_max, comment_data_headers_p); } /* CLASSIFICATION_HEADER 07feb95 wmt: new 30may95 wmt: add prediction header 21mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p output classification identification information */ void classification_header( clsf_DS clsf, char *results_file_ptr, FILE *xref_case_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int comment_data_headers_p) { char blank = ' '; if (eqstring (report_mode, "data") == TRUE) fprintf( xref_case_report_fp, "DATA_CLSF_HEADER\n"); if (test_clsf != NULL) { fprintf( xref_case_report_fp, "%s%6cAutoClass PREDICTION for the %d \"TEST\" " "cases in\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, test_clsf->database->n_data); fprintf( xref_case_report_fp, "%s%8c%s%s\n\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (test_clsf->database->data_file[0] == G_slash) ? "" : G_absolute_pathname, test_clsf->database->data_file); fprintf( xref_case_report_fp, "%s%6cbased on the \"TRAINING\" classification of %d " "cases in\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, clsf->database->n_data); } else fprintf( xref_case_report_fp, "%s%6cAutoClass CLASSIFICATION for the %d cases in\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, clsf->database->n_data); fprintf( xref_case_report_fp, "%s%8c%s%s\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (clsf->database->data_file[0] == G_slash) ? "" : G_absolute_pathname, clsf->database->data_file); fprintf( xref_case_report_fp, "%s%8c%s%s\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (clsf->database->header_file[0] == G_slash) ? "" : G_absolute_pathname, clsf->database->header_file); fprintf( xref_case_report_fp, "%s%6cwith log-A (approximate marginal likelihood) " "= %.3f\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, clsf->log_a_x_h); fprintf( xref_case_report_fp, "%s%6cfrom classification results file\n", (comment_data_headers_p == TRUE) ? "#" : "", blank); fprintf( xref_case_report_fp, "%s%8c%s%s\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (results_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, results_file_ptr); fprintf( xref_case_report_fp, "%s%6cand using models\n", (comment_data_headers_p == TRUE) ? "#" : "", blank); get_models_source_info( clsf->models, clsf->num_models, xref_case_report_fp, comment_data_headers_p); } /* XREF_PAGINATE_BY_CASE write output with headers at top of each page 07feb95 wmt: new 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p */ void xref_paginate_by_case( xref_data_DS xref_data, int n_data, FILE *xref_case_report_fp, shortstr report_mode, int initial_line_cnt_max, unsigned int comment_data_headers_p) { int line_cnt_max = initial_line_cnt_max, page_num = 0, column_1_index; int column_3_index = 0, page_1_p = TRUE, current_data_index = 0, current_n_data; int line_cnt, num_report_attribute_strings = 0, column_2_index = 0, i; struct xref_data elt1, elt2, elt3; float float_value; rpt_att_string_DS *report_attribute_strings = NULL; char blank = ' '; if (eqstring (report_mode, "data") == TRUE) fprintf( xref_case_report_fp, "%s\n%s\nDATA_CASE_TO_CLASS", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); for (current_n_data=n_data; current_n_data>0; ) { xref_output_page_headers( "case", page_1_p, num_report_attribute_strings, report_attribute_strings, xref_case_report_fp, report_mode, comment_data_headers_p); column_1_index = current_data_index; if (eqstring (report_mode, "text") == TRUE) { line_cnt_max = (int) ceil( (double) (min( (line_cnt_max * 3.0), current_n_data) / 3.0)); column_2_index = column_1_index + line_cnt_max; column_3_index = column_2_index + line_cnt_max; current_data_index = column_3_index + line_cnt_max; current_n_data -= 3 * line_cnt_max; } else { current_data_index = column_1_index + line_cnt_max; current_n_data -= line_cnt_max; } for (line_cnt=0; line_cntatt_dscrp); diff = report_att_string->dscrp_length - strlen( report_att_string->att_dscrp); blank_cnt = max( 0, diff); if (blank_cnt > 0) { sprintf( divider_format, "%%%dc", blank_cnt); fprintf( xref_report_fp, divider_format, blank); } } fprintf( xref_report_fp, " (Cls Prob)\n"); } } if (eqstring (report_mode, "text") == TRUE) fprintf( xref_report_fp, dashed_line); } } /* AUTOCLASS_XREF_BY_CLASS_REPORT 09feb95 wmt: new 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p 21jun97 wmt: add max_num_xref_class_probs output cross reference sorted by class number. report_attributes = list of attribute numbers */ void autoclass_xref_by_class_report( clsf_DS training_clsf, FILE *xref_class_report_fp, shortstr report_mode, xref_data_DS xref_data, int_list report_attributes, char *results_file_ptr, clsf_DS test_clsf, int last_clsf_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs) { char blank = ' '; int reports_initial_line_cnt = 16, prediction_initial_line_cnt = 20; int initial_line_cnt, prediction_p = 0; clsf_DS clsf; if (test_clsf == NULL) { initial_line_cnt = reports_initial_line_cnt; clsf = training_clsf; } else { initial_line_cnt = prediction_initial_line_cnt; clsf = test_clsf; } if (xref_data == NULL) xref_data = xref_get_data( clsf, "class", report_attributes, xref_data, last_clsf_p, prediction_p, max_num_xref_class_probs); fprintf( xref_class_report_fp, "%s%6cCROSS REFERENCE CLASS => " "CASE NUMBER MEMBERSHIP\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", blank, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); classification_header( training_clsf, results_file_ptr, xref_class_report_fp, report_mode, test_clsf, comment_data_headers_p); xref_paginate_by_class( clsf, xref_data, report_attributes, xref_class_report_fp, report_mode, initial_line_cnt, comment_data_headers_p); } /* XREF_PAGINATE_BY_CLASS 09feb95 wmt: new 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p write output with headers at top of each page for autoclass class sort */ void xref_paginate_by_class( clsf_DS clsf, xref_data_DS xref_data, int_list report_attributes, FILE *xref_class_report_fp, shortstr report_mode, int initial_line_cnt, unsigned int comment_data_headers_p) { int cnt = initial_line_cnt; /* initial page header */ int current_class = -1, line_cnt = G_line_cnt_max, n_data = clsf->database->n_data; int n_datum, num_wt_class_pairs, i, num_report_att_strings = 0, prob_tab = 0; shortstr *attribute_formats = NULL; rpt_att_string_DS *report_attribute_strings = NULL; struct xref_data xref_datum; sort_cell_DS wt_class_pairs; if (report_attributes[0] != END_OF_INT_LIST) { report_attribute_strings = xref_class_report_attributes( clsf, report_attributes, &attribute_formats, &prob_tab); for (i=0; report_attributes[i] != END_OF_INT_LIST; i++) ; num_report_att_strings = i; } /* elt = xref_data[n_datum]; arg1 = translations_list; arg2 = wt_class_pairs */ for (n_datum=0; n_datum line_cnt) { xref_paginate_by_class_hdrs( xref_class_report_fp, report_mode, &cnt, line_cnt, wt_class_pairs, FALSE, num_report_att_strings, report_attribute_strings, comment_data_headers_p); } xref_output_line_by_class( clsf, xref_class_report_fp, report_mode, &attribute_formats, &xref_datum, wt_class_pairs, prob_tab, report_attributes, comment_data_headers_p); if (report_attribute_strings != NULL) cnt += num_wt_class_pairs; else cnt++; } if (report_attribute_strings != NULL) { for (i=0; idatabase->att_info; int i, att_number, dscrp_length, n_trans, num_report_att_strings; *prob_tab_ptr = 6; for (i=0; report_attribute_numbers[i] != END_OF_INT_LIST; i++) ; num_report_att_strings = i; if (num_report_att_strings > 0) { report_attribute_strings = (rpt_att_string_DS *) malloc( num_report_att_strings * sizeof( rpt_att_string_DS)); if (report_attribute_strings == NULL) { fprintf( stderr, "ERROR: xref_class_report_attributes(1): out of memory, malloc returned NULL!\n"); exit(1); } } for (i=0; report_attribute_numbers[i] != END_OF_INT_LIST; i++) { report_attribute_strings[i] = (rpt_att_string_DS) malloc( sizeof( struct report_attribute_string)); if (report_attribute_strings[i] == NULL) { fprintf( stderr, "ERROR: xref_class_report_attributes(2): out of memory, malloc returned NULL!\n"); exit(1); } att_number = report_attribute_numbers[i]; att_dscrp = all_att_info[att_number]->dscrp; dscrp_length = strlen( att_dscrp); translations = all_att_info[att_number]->translations; report_attribute_strings[i]->att_number = att_number; strcpy( report_attribute_strings[i]->att_dscrp, att_dscrp); if (translations == NULL) report_attribute_strings[i]->dscrp_length = dscrp_length + 3; else { report_attribute_strings[i]->dscrp_length = dscrp_length; for (n_trans=0; n_transn_trans; n_trans++) { if ((int) strlen( translations[n_trans]) > report_attribute_strings[i]->dscrp_length) report_attribute_strings[i]->dscrp_length = strlen( translations[n_trans]); } } } for (i=0; idscrp_length; if (all_att_info[report_attribute_numbers[i]]->translations != NULL) sprintf( str, " %%-%ds", report_attribute_strings[i]->dscrp_length); else sprintf( str, " %%-%dg", report_attribute_strings[i]->dscrp_length); if ( *attribute_formats_ptr == NULL) { *attribute_formats_ptr = (shortstr *) malloc( sizeof( shortstr)); if (*attribute_formats_ptr == NULL) { fprintf( stderr, "ERROR: xref_class_report_attributes(3): out of memory, malloc returned NULL!\n"); exit(1); } } else { *attribute_formats_ptr = (shortstr *) realloc( *attribute_formats_ptr, (i + 1) * sizeof( shortstr)); if (*attribute_formats_ptr == NULL) { fprintf( stderr, "ERROR: xref_class_report_attributes(3): out of memory, realloc returned NULL!\n"); exit(1); } } strcpy( (*attribute_formats_ptr)[i], str); } return (report_attribute_strings); } /* XREF_PAGINATE_BY_CLASS_HDRS 10feb95 wmt: new 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p output class header - return cnt */ void xref_paginate_by_class_hdrs( FILE *xref_class_report_fp, shortstr report_mode, int *cnt_ptr, int line_cnt, sort_cell_DS wt_class_pairs, int init, int num_report_attribute_strings, rpt_att_string_DS *report_attribute_strings, unsigned int comment_data_headers_p) { char blank = ' '; int page_1_p = TRUE; if (eqstring (report_mode, "text") == TRUE) { if ((line_cnt - *cnt_ptr) < 10) { *cnt_ptr = 0; fprintf( xref_class_report_fp, "\f"); /* form feed */ } *cnt_ptr += 8; } if ((eqstring (report_mode, "text") == TRUE) || ((eqstring (report_mode, "data") == TRUE) && (init == TRUE))) { fprintf( xref_class_report_fp, "%s\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "data") == TRUE) fprintf( xref_class_report_fp, "DATA_CLASS %d\n", wt_class_pairs[0].int_value); fprintf( xref_class_report_fp, "%s%32c CLASS = %d %s \n%s", (comment_data_headers_p == TRUE) ? "#" : "", blank, wt_class_pairs[0].int_value, (init == TRUE) ? "" : "(continued)", (comment_data_headers_p == TRUE) ? "#" : ""); xref_output_page_headers( "class", page_1_p, num_report_attribute_strings, report_attribute_strings, xref_class_report_fp, report_mode, comment_data_headers_p); } } /* XREF_OUTPUT_LINE_BY_CLASS 10feb95 wmt: new 16may95 wmt: handle unknown real values 17mar97 wmt: add report_mode, 14may97 wmt: add comment_data_headers_p */ void xref_output_line_by_class( clsf_DS clsf, FILE *xref_class_report_fp, shortstr report_mode, shortstr **attribute_formats_ptr, xref_data_DS xref_datum_ptr, sort_cell_DS wt_class_pairs, int prob_tab, int_list report_attribute_numbers, unsigned int comment_data_headers_p) { struct xref_data xref_datum = *xref_datum_ptr; int n_discrete_att = 0, n_real_att = 0, i, print_atts_p; att_DS *att_info = clsf->database->att_info; fxlstr prob_tab_format; char blank = ' ', char_s = 's', char_g = 'g', question_mark[] = "?"; print_atts_p = (xref_datum.n_attribute_data > 0); if (print_atts_p == TRUE) { prob_tab += 4; sprintf( prob_tab_format, "\n%%%dc", prob_tab); strcat( prob_tab_format, "%2d %5.3f"); } if (eqstring (report_mode, "data") == TRUE) fprintf( xref_class_report_fp, "%03d", xref_datum.case_number); else fprintf( xref_class_report_fp, (print_atts_p == TRUE) ? "\n%6d" : "\n%11d", xref_datum.case_number); for (i=0; itranslations != NULL) { fprintf( xref_class_report_fp, (*attribute_formats_ptr)[i], xref_datum.discrete_attribute_data[n_discrete_att]); n_discrete_att++; } else { if (percent_equal( (double) xref_datum.real_attribute_data[n_real_att], FLOAT_UNKNOWN, REL_ERROR) == TRUE) { /* replace g with s in format directive */ (*attribute_formats_ptr)[i][strlen( (*attribute_formats_ptr)[i]) -1] = char_s; fprintf( xref_class_report_fp, (*attribute_formats_ptr)[i], question_mark); } else { (*attribute_formats_ptr)[i][strlen( (*attribute_formats_ptr)[i]) -1] = char_g; fprintf( xref_class_report_fp, (*attribute_formats_ptr)[i], xref_datum.real_attribute_data[n_real_att]); } n_real_att++; } } fprintf( xref_class_report_fp, "%s %5.3f%s", (print_atts_p == TRUE) ? " " : "", xref_datum.wt_class_pairs[0].float_value, (print_atts_p == TRUE) ? "" : " "); for (i=1; in_classes; report_class_number++) { autoclass_class_influence_values_report ( clsf, search, class_number_type, map_class_num_report_to_clsf( clsf, report_class_number), num_atts_to_list, header_information_p, results_file_ptr, single_class_p, influence_report_fp, report_mode, test_clsf, order_attributes_by_influence_p, comment_data_headers_p, sigma_contours_att_list); if ((eqstring (report_mode, "text") == TRUE) && (report_class_number < (clsf->n_classes - 1))) fprintf( influence_report_fp, "\f"); } } /* INFLUENCE_VALUES_HEADER 12feb95 wmt: new 15jun95 wmt: revise layout and content 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 28aug97 wmt: change fprintf( influence_report_fp, header); to fprintf( influence_report_fp, header, ""); prevent segmentation fault. output influence values header */ void influence_values_header( clsf_DS clsf, search_DS search, char *results_file_ptr, int header_information_p, FILE *influence_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int comment_data_headers_p) { int populated_classes_cnt = 0, clsf_class_number, line_cnt = 11, n_att; int max_line_cnt = G_line_cnt_max, n_atts = clsf->database->n_atts; char class_number_type[5] = "clsf"; ordered_influ_vals_DS output = NULL; fxlstr header = "\n%s num description " "I-*k \n%s", str; for (clsf_class_number=0; clsf_class_numbern_classes; clsf_class_number++) if (populated_class_p( clsf_class_number, class_number_type, clsf) == TRUE) populated_classes_cnt++; if (header_information_p == TRUE) classification_header( clsf, results_file_ptr, influence_report_fp, report_mode,test_clsf, comment_data_headers_p); output = ordered_normalized_influence_values( clsf); if (eqstring (report_mode, "text") == TRUE) influence_values_explanation( influence_report_fp); search_summary( search, influence_report_fp, report_mode, comment_data_headers_p); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\f"); fprintf( influence_report_fp, "%s\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "DATA_POP_CLASSES\n"); fprintf( influence_report_fp, "CLASSIFICATION HAS %d POPULATED CLASSES " "(max global influence value = %5.3f) \n", populated_classes_cnt, clsf->reports->max_i_value); class_weights_and_strengths( clsf, influence_report_fp, report_mode, comment_data_headers_p); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\f"); fprintf( influence_report_fp, "%s\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "data") == TRUE) fprintf ( influence_report_fp, "DATA_CLASS_DIVS\n"); fprintf( influence_report_fp, "%sCLASS DIVERGENCES\n%s", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); class_divergences( clsf, influence_report_fp, report_mode, comment_data_headers_p); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\f"); fprintf( influence_report_fp, "\n%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "DATA_NORM_INF_VALS\n"); fprintf( influence_report_fp, "%sORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE " "VALUES SUMMED OVER ALL CLASSES\n%s", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\n" " This gives a rough heuristic measure of relative influence of each\n" " attribute in differentiating the classes from the overall data set.\n" " Note that \"influence values\" are only computable with respect to the\n" " model terms. When multiple attributes are modeled by a single\n" " dependent term (e.g. multi_normal_cn), the term influence value is\n" " distributed equally over the modeled attributes.\n"); fprintf( influence_report_fp, header, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); for (n_att=0; n_att max_line_cnt) { line_cnt = 4; fprintf( influence_report_fp, "\f"); fprintf( influence_report_fp, header, ""); } } strcpy( str, ""); fprintf( influence_report_fp, "\n"); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, " "); fprintf( influence_report_fp, "%03d %-55s ", output[n_att].n_att, strncat( str, output[n_att].att_dscrp_ptr, 55)); if (output[n_att].model_term_type_ptr == NULL) fprintf( influence_report_fp, " -----"); else fprintf( influence_report_fp, "%6.3f", output[n_att].norm_att_i_sum); line_cnt++; } if (eqstring (report_mode, "text") == TRUE) { fprintf( influence_report_fp, "\f\n\nCLASS LISTINGS:\n\n" " These listings are ordered by class weight --\n" " * j is the zero-based class index,\n" " * k is the zero-based attribute index, and\n"); fprintf( influence_report_fp, " * l is the zero-based discrete attribute instance index.\n\n" " Within each class, the covariant and independent model terms are ordered\n" " by their term influence value I-jk.\n\n"); fprintf( influence_report_fp, " Covariant attributes and discrete attribute instance values are both\n" " ordered by their significance value. Significance values are computed\n" " with respect to a single class classification, using the divergence from\n" " it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the\n"); fprintf( influence_report_fp, " relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for\n" " numerical valued attributes. For the SNcm model, the value line is\n" " followed by the probabilities that the value is known, for that class\n"); fprintf( influence_report_fp, " and for the single class classification.\n\n"); fprintf( influence_report_fp, " Entries are attribute type dependent, and the corresponding headers are\n" " reproduced with each class. In these --\n" " * num/t denotes model term number,\n" " * num/a denotes attribute number,\n" " * t denotes attribute type,\n" " * mtt denotes model term type, and\n" " * I-jk denotes the term influence value for attribute k\n" " in class j. This is the cross entropy or\n" " Kullback-Leibler distance between the class and\n"); fprintf( influence_report_fp, " full database probability distributions (see\n" " interpretation-c.text).\n" " * Mean StDev\n" " -jk -jk The estimated mean and standard deviation\n" " for attribute k in class j.\n" " * |Mean-jk - The absolute difference between the\n" " Mean-*k|/ two means, scaled w.r.t. the class\n" " StDev-jk standard deviation, to get a measure\n" " of the distance between the attribute\n" " means in the class and full data.\n"); fprintf( influence_report_fp, " * Mean StDev The estimated mean and standard\n" " -*k -*k deviation for attribute k when the\n" " model is applied to the data set\n" " as a whole.\n"); fprintf( influence_report_fp, " * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01\n" " The SNcm model allows for the possibility that data\n" " values are unknown, and models this with a discrete\n" " known/unknown probability. The gaussian normal for\n" " known values is then conditional on the known\n" " probability. In this instance, we have a class\n" " where all values are known, as opposed to a database\n" " where only 99.8%% of values are known.\n"); } if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "\n"); if (output != NULL) free( output); } /* AUTOCLASS_CLASS_INFLUENCE_VALUES_REPORT 12feb95 wmt: new 20jun95 wmt: redo line_cnt computations 04jul96 wmt: check for malloc/realloc returning NULL 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 24jun97 wmt: call generate_sigma_contours; add start_sigma_contours_att & stop_sigma_contours_att 28feb98 wmt: replace start/stop_sigma_contours_att with sigma_contours_att_list 20mar98 wmt: outputing of MNcn correlation matrices after last class attribute instead of after each term is now done by a call to generate_mncn_correlation_matrices from autoclass_class_influence_values_report Generates influence values report for requested clsf class number class_number_type => :report or :clsf. class_number => report or classification class number */ void autoclass_class_influence_values_report( clsf_DS clsf, search_DS search, char *class_number_type, int class_number, int num_atts_to_list, int header_information_p, char *results_file_ptr, int single_class_p, FILE *influence_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p, int_list sigma_contours_att_list) { int clsf_class_number, line_cnt = 8, n_data = clsf->database->n_data, i; int n_atts = clsf->database->n_atts, n_att, num_term_types = 0, first_term_type = TRUE; int real_atts_header_p = FALSE, discrete_atts_header_p = FALSE, report_class_num; float class_wt; char title_line_1[2*STRLIMIT] = "", title_line_2[3*STRLIMIT] = "", *att_type; fxlstr class_model_source = "", temp; shortstr *term_types = NULL, a_term_type; char caller[] = "autoclass_class_influence_values_report"; clsf_class_number = (eqstring( class_number_type, "clsf")) ? class_number : map_class_num_report_to_clsf( clsf, class_number); /* fprintf( stderr, "autoclass_class_influence_values_report: class_number %d" " class_number_type %s clsf_class_number %d\n", class_number, class_number_type, clsf_class_number); */ class_wt = clsf->classes[clsf_class_number]->w_j; if (num_atts_to_list == ALL_ATTRIBUTES) num_atts_to_list = clsf->database->n_atts; else num_atts_to_list = max( 1, min( num_atts_to_list, clsf->database->n_atts)); report_class_num = map_class_num_clsf_to_report( clsf, clsf_class_number); if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "%s\n%s\nDATA_CLASS %d\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", report_class_num); safe_sprintf( title_line_1, sizeof( title_line_1), caller, "%sCLASS %2d - weight %3d normalized weight %5.3f relative " "strength %9.2e *******" "\n%s class cross entropy w.r.t. global class " "%9.2e *******", (comment_data_headers_p == TRUE) ? "#" : "", report_class_num, iround((double) class_wt), (class_wt / n_data), (float) safe_exp( (double) (clsf->reports->class_strength[clsf_class_number] - clsf->reports->max_class_strength)), (comment_data_headers_p == TRUE) ? "#" : "", clsf->classes[clsf_class_number]->i_sum); get_class_model_source_info( clsf->classes[clsf_class_number], class_model_source, comment_data_headers_p); safe_sprintf( title_line_2, sizeof( title_line_2), caller, " Model file: %s \n Numbers: numb/t = model term number; " "numb/a = attribute number \n Model term types (mtt): ", class_model_source); /* fprintf( stderr, "autoclass_class_influence_values_report: n_atts %d\n", n_atts); */ for (n_att=0; n_att= minimum weight (ensure that class is populated) class_number_type => "clsf" or "report" */ int populated_class_p( int class_number, char *class_number_type, clsf_DS clsf) { if (eqstring( class_number_type, "report") == TRUE) class_number = map_class_num_report_to_clsf( clsf, class_number); return ((clsf->classes[class_number]->w_j + 0.001) >= clsf->min_class_wt); } /* ORDERED_NORMALIZED_INFLUENCE_VALUES 13feb95 wmt: new 23jun95 wmt: add model_term_type_ptr to output data structure 04jul96 wmt: check for malloc/realloc returning NULL return ordered normalized influence values summed over all classes */ ordered_influ_vals_DS ordered_normalized_influence_values( clsf_DS clsf) { ordered_influ_vals_DS output = NULL; rpt_DS reports = clsf->reports; float max_i_sum, att_i_sum; int n_att, n_atts = clsf->database->n_atts, clsf_class_number = 0; int (* att_i_sum_sort_func) () = att_i_sum_sort_compare_gtr; max_i_sum = reports->att_max_i_sum; for (n_att=0; n_attatt_i_sums[n_att]; output[n_att].att_i_sum = att_i_sum; output[n_att].n_att = n_att; output[n_att].att_dscrp_ptr = clsf->database->att_info[n_att]->dscrp; output[n_att].norm_att_i_sum = (max_i_sum == 0.0) ? 0.0 : (att_i_sum / max_i_sum); /* assumes that for each attribute, all classes have the same model term type */ output[n_att].model_term_type_ptr = (char *) get( rpt_att_model_term_type( clsf, clsf_class_number, n_att), "print_string"); } qsort( (char *) output, n_atts, sizeof( struct ordered_influence_values), att_i_sum_sort_func); return (output); } /* INFLUENCE_VALUES_EXPLANATION 13feb95 wmt: new output explanation of influence value layout */ void influence_values_explanation( FILE *influence_report_fp) { fprintf( influence_report_fp, "\n\n\n\nORDER OF PRESENTATION:\n\n" " * Summary of the generating search.\n" " * Weight ordered list of the classes found & class strength heuristic.\n" " * List of class cross entropies with respect to the global class.\n" " * Ordered list of attribute influence values summed over all classes.\n" " * Class listings, ordered by class weight.\n"); } /* SEARCH_SUMMARY 13feb95 wmt: new 05may95 wmt: change search->n to search->n_tries to prevent segment violation when there are duplicates 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p output variation of print_final_report */ void search_summary( search_DS search, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { search_try_DS *tries = search->tries; int n_final_summary = search->n_final_summary, n_save = search->n_save, i; int new_line_p = FALSE; char *pad = " "; fxlstr dashes = "\n_________________________________________________________________" "____________"; if (eqstring (report_mode, "text") == TRUE) { fprintf( influence_report_fp, "\n\n\n\n\n\n"); fprintf( influence_report_fp, dashes); fprintf( influence_report_fp, dashes); } fprintf( influence_report_fp, "%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "data") == TRUE) { fprintf( influence_report_fp, "DATA_SEARCH_SUMMARY\n"); pad = ""; } fprintf( influence_report_fp, "%sSEARCH SUMMARY %d tries over %s\n", (comment_data_headers_p == TRUE) ? "#" : "", search->n, format_time_duration((time_t) search->time)); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\n _______________ "); fprintf( influence_report_fp, "%sSUMMARY OF %d BEST RESULTS", (comment_data_headers_p == TRUE) ? "#" : "", n_final_summary); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, " _________________________ ##\n"); else fprintf( influence_report_fp, "\n"); for (i=0; in_tries) { print_search_try( influence_report_fp, NULL, tries[i], (i < n_save), new_line_p, pad, FALSE); /* comment_data_headers_p); */ if (influence_report_fp != NULL) { if (i < n_save) fprintf( influence_report_fp, " -%d\n", i + 1); else fprintf( influence_report_fp, "\n"); } } } if (eqstring (report_mode, "text") == TRUE) { fprintf( influence_report_fp, "\n\n ## - report filenames suffix"); fprintf( influence_report_fp, dashes); fprintf( influence_report_fp, dashes); } } /* CLASS_WEIGHTS_AND_STRENGTHS 14feb95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 13feb98 wmt: add args to output_title fprintf for new page output class weights and strengths */ void class_weights_and_strengths( clsf_DS clsf, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { int clsf_class_number, report_class_number, n_data = clsf->database->n_data; int line_cnt = 11, max_line_cnt = G_line_cnt_max; float max_strength = clsf->reports->max_class_strength, class_wt, class_strength; fxlstr output_title = "%s\n%s Class Log of class Relative Class Normalized\n" "%s num strength class strength weight class weight\n%s"; /* influence_report_fp => stdout */ if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\n We give below a heuristic measure of class strength: the approximate\n" " geometric mean probability for instances belonging to each class,\n" " computed from the class parameters and statistics. This approximates\n" " the contribution made, by any one instance \"belonging\" to the class,\n" " to the log probability of the data set w.r.t. the classification. It\n" " thus provides a heuristic measure of how strongly each class predicts\n" " \"its\" instances.\n"); fprintf( influence_report_fp, output_title, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); for (report_class_number=0; report_class_numbern_classes; report_class_number++) { line_cnt++; if ((eqstring (report_mode, "text") == TRUE) && (line_cnt > max_line_cnt)) { fprintf( influence_report_fp, "\f"); line_cnt = 7; fprintf( influence_report_fp, output_title, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); } clsf_class_number = map_class_num_report_to_clsf( clsf, report_class_number); class_wt = clsf->classes[clsf_class_number]->w_j; class_strength = clsf->reports->class_strength[clsf_class_number]; if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "\n%02d", report_class_number); else fprintf( influence_report_fp, "\n%6d", report_class_number); fprintf( influence_report_fp, " %9.2e %9.2e %6d %6.3f", class_strength, (float) safe_exp( (double) (class_strength - max_strength)), iround( (double) class_wt), class_wt / n_data); } } /* CLASS_DIVERGENCES 22jul95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 13feb98 wmt: add args to output_title fprintf for new page output class divergences */ void class_divergences( clsf_DS clsf, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { int clsf_class_number, report_class_number, n_data = clsf->database->n_data; int line_cnt = 11, max_line_cnt = G_line_cnt_max; float class_wt, class_divergence; fxlstr output_title = "\n%s Class class cross entropy Class Normalized\n" "%s num w.r.t. global class weight class weight\n%s"; if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "\n The class divergence, or cross entropy w.r.t. the single class\n" " classification, is a measure of how strongly the class probability\n" " distribution function differs from that of the database as a whole. \n" " It is zero for identical distributions, going infinite when two\n" " discrete distributions place probability 1 on differing values of the\n" " same attribute.\n"); fprintf( influence_report_fp, output_title, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); for (report_class_number=0; report_class_numbern_classes; report_class_number++) { line_cnt++; if (eqstring (report_mode, "text") && (line_cnt > max_line_cnt)) { fprintf( influence_report_fp, "\f"); line_cnt = 7; fprintf( influence_report_fp, output_title, (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); } clsf_class_number = map_class_num_report_to_clsf( clsf, report_class_number); class_wt = clsf->classes[clsf_class_number]->w_j; class_divergence = clsf->classes[clsf_class_number]->i_sum; if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "\n%02d", report_class_number); else fprintf( influence_report_fp, "\n%6d", report_class_number); fprintf( influence_report_fp, " %9.2e %6d %6.3f", class_divergence, iround( (double) class_wt), class_wt / n_data); } } /* TEXT_STREAM_HEADER 14feb95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p output text stream header for influence values report */ void text_stream_header( int single_class_p, FILE *influence_report_fp, shortstr report_mode, int header_information_p, clsf_DS clsf, search_DS search, char *results_file_ptr, char *title_line_1, char *title_line_2, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p) { char blank = ' '; if ((eqstring (report_mode, "text") == TRUE) && (single_class_p)) fprintf( influence_report_fp, "\n\n\n%6cI N F L U E N C E V A L U E S R E P O R T \n" "%6c order attributes by influence values = %s\n" "%6c=============================================\n\n", blank, blank, (order_attributes_by_influence_p == TRUE) ? "true" : "false", blank); if (header_information_p) influence_values_header( clsf, search, results_file_ptr, header_information_p, influence_report_fp, report_mode, test_clsf, comment_data_headers_p); if (eqstring (report_mode, "text") == TRUE) fprintf( influence_report_fp, "%s\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); fprintf( influence_report_fp, title_line_1); fprintf( influence_report_fp, "\n%s\n", (comment_data_headers_p == TRUE) ? "#" : ""); if (eqstring (report_mode, "text") == TRUE) { fprintf( influence_report_fp, title_line_2); fprintf( influence_report_fp, "%s\n", (comment_data_headers_p == TRUE) ? "#" : ""); } } /* PRE_FORMAT_ATTRIBUTES 14feb95 wmt: new; combine get_attribute_header & print_attribute_header into a separate function named print_attribute_header 12jun95 wmt: sort real attributes, if they are multi_normal_cn model since they all have the same influence value. 20jun95 wmt: add real_atts_p & discrete_atts_p 24jul95 wmt: add order_attributes_by_influence_p 04jul96 wmt: check for malloc/realloc returning NULL 14oct96 wmt: Correlation matrix only printed if MNcn attribute group is last - fix it so embedded groups will also be printed 25feb97 wmt: check for num_terms > 0 prior to calling sort_mncn_attributes. 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 26feb98 wmt: properly order or not order multiple mncn model term groups outputs variable/values stored in the accumulater. the values are displayed as probabilities, that is weight/total-wt. sort the influence values of all attributes */ void pre_format_attributes( clsf_DS clsf, int clsf_class_number, int num_atts_to_list, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p) { sort_cell_DS sort_list = NULL; int (* class_wt_sort_func) () = float_sort_cell_compare_gtr; int number_of_sorted_attributes = 0, n_att, i, term_count = 0; int sort_index, n_model_term, current_model_term; i_discrete_DS discrete_iv_struct = NULL; i_integer_DS integer_iv_struct = NULL; i_real_DS real_iv_struct = NULL; char *type; model_DS model; float *mean_sigma_list; unsigned int mncn_attributes_p = FALSE; for (n_att=0; n_attdatabase->n_atts; n_att++) { type = report_att_type( clsf, clsf_class_number, n_att); if (eqstring( type, "ignore") != TRUE) { if (sort_list == NULL) { sort_list = (sort_cell_DS) malloc( sizeof( struct sort_cell)); if (sort_list == NULL) { fprintf( stderr, "ERROR: pre_format_attributes: out of memory, malloc returned NULL!\n"); exit(1); } } else { sort_list = (sort_cell_DS) realloc( sort_list, (number_of_sorted_attributes + 1) * sizeof( struct sort_cell)); if (sort_list == NULL) { fprintf( stderr, "ERROR: pre_format_attributes: out of memory, realloc returned NULL!\n"); exit(1); } } if (eqstring( type, "discrete")) { discrete_iv_struct = (i_discrete_DS) clsf->classes[clsf_class_number]->i_values[n_att]; sort_list[number_of_sorted_attributes].float_value = discrete_iv_struct->influence_value; } else if (eqstring( type, "integer")) { integer_iv_struct = (i_integer_DS) clsf->classes[clsf_class_number]->i_values[n_att]; sort_list[number_of_sorted_attributes].float_value = integer_iv_struct->influence_value; } else if (eqstring( type, "real")) { real_iv_struct = (i_real_DS) clsf->classes[clsf_class_number]->i_values[n_att]; sort_list[number_of_sorted_attributes].float_value = real_iv_struct->influence_value; } else { fprintf( stdout, "ERROR: attribute type %s not supported\n", type); abort(); } sort_list[number_of_sorted_attributes].int_value = n_att; number_of_sorted_attributes++; } } if (order_attributes_by_influence_p == TRUE) { /* sort sort_list by greatest influence value first */ qsort( (char *) sort_list, number_of_sorted_attributes, sizeof( struct sort_cell), class_wt_sort_func); } print_attribute_header( discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p); /* fprintf( stderr, "initial sort: "); for (sort_index=0; sort_indexclasses[clsf_class_number]->model; current_model_term = 0; for (sort_index=0; sort_indexclasses[clsf_class_number]->i_values[n_att]; mean_sigma_list = real_iv_struct->mean_sigma_list; sort_list[sort_index].float_value = ((float) fabs((double) (mean_sigma_list[0] - mean_sigma_list[2]))) / mean_sigma_list[1]; /* if (term_count == 0) { fprintf( stderr, "\nmncn sort atts: %d ", n_att); } else { fprintf( stderr, "%d ", n_att); } */ term_count++; } else { /* next model term is also MNcn */ if (order_attributes_by_influence_p == TRUE) { sort_mncn_attributes( sort_list, sort_index, term_count, clsf, clsf_class_number); } term_count = 1; } current_model_term = n_model_term; } else { /* do sort here if Mncn att group is embedded in list of all sorted atts and this att is not MNcn */ if (term_count > 0) if (order_attributes_by_influence_p == TRUE) { sort_mncn_attributes( sort_list, sort_index, term_count, clsf, clsf_class_number); } term_count = 0; current_model_term = 0; } } /* do sort here if Mncn att group is at end of list of all sorted atts */ if ((term_count > 0) && (order_attributes_by_influence_p == TRUE)) { sort_mncn_attributes( sort_list, sort_index, term_count, clsf, clsf_class_number); } for (i=0; i Prob 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p attribute header for text stream */ void print_attribute_header( int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { if (discrete_atts_header_p) { fprintf( influence_report_fp, "%s\n%sDISCRETE ATTRIBUTE (t = D) " " log(", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); fprintf( influence_report_fp, "\n%s numb t mtt description I-jk Value name/Index " " Prob-jkl/ Prob Prob", (comment_data_headers_p == TRUE) ? "#" : ""); fprintf( influence_report_fp, "\n%s t a " " Prob-*kl) -jkl -*kl\n", (comment_data_headers_p == TRUE) ? "#" : ""); } if (real_atts_header_p) { /* "\n%sINTEGER or REAL ATTRIBUTE (t = I or R) " */ fprintf( influence_report_fp, "%s\n%sREAL ATTRIBUTE (t = R) " " |Mean-jk -", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); fprintf( influence_report_fp, "\n%s numb t mtt description I-jk Mean StDev " " Mean-*k|/ Mean StDev", (comment_data_headers_p == TRUE) ? "#" : ""); fprintf( influence_report_fp, "\n%s t a -jk -jk " " StDev-jk -*k -*k\n%s\n", (comment_data_headers_p == TRUE) ? "#" : "", (comment_data_headers_p == TRUE) ? "#" : ""); } else fprintf( influence_report_fp, "%s\n", (comment_data_headers_p == TRUE) ? "#" : ""); } /* FORMAT_ATTRIBUTE 15feb95 wmt: new 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 14jun97 wmt: pass clsf_class_number & clsf to format_real_attribute format the listing of the requested attribute */ int format_attribute( clsf_DS clsf, int clsf_class_number, int n_att, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { static char header[60], header_continued[60]; int line_length = 20, descrp_length, n_model_term, i; shortstr temp = "", temp1 = ""; i_discrete_DS discrete_influence_values; i_integer_DS integer_influence_values; i_real_DS real_influence_values; char *type, type_letter[2], *description, model_term_type_symbol[] = " "; char *print_string; char dot = '.', caller[] = "format_attribute"; model_DS model = clsf->classes[clsf_class_number]->model; if (eqstring (report_mode, "data") == TRUE) dot = ' '; type = report_att_type( clsf, clsf_class_number, n_att); if (eqstring( type, "discrete")) strcpy( type_letter, "D"); else if (eqstring( type, "integer")) strcpy( type_letter, "I"); else if (eqstring( type, "real")) strcpy( type_letter, "R"); else { fprintf( stderr, "ERROR: type %s not handled\n", type); abort(); } description = clsf->database->att_info[n_att]->dscrp; descrp_length = strlen( description); print_string = (char *) get( rpt_att_model_term_type( clsf, clsf_class_number, n_att), "print_string"); strcpy( model_term_type_symbol, (eqstring( print_string, "ignore")) ? "__" : print_string); n_model_term = attribute_model_term_number( n_att, model); for (i=0; i< (line_length - descrp_length - 1); i++) temp1[i] = dot; temp1[i] = '\0'; strcat( strcat( strncat( temp, description, line_length), (descrp_length < line_length) ? " " : ""), temp1); safe_sprintf( header, sizeof( header), caller, "%03d %03d %s %-4s %20s", n_model_term, n_att, type_letter, model_term_type_symbol, temp); for (i=line_length; i< min( descrp_length, 2 * line_length); i++) header_continued[i - line_length] = description[i]; header_continued[i - line_length] = '\0'; if (eqstring( type, "discrete")) { discrete_influence_values = (i_discrete_DS) clsf->classes[clsf_class_number]->i_values[n_att]; line_cnt = format_discrete_attribute( n_att, clsf->database, header, header_continued, discrete_influence_values, line_length, description, line_cnt, discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p); } else if (eqstring( type, "integer")) { integer_influence_values = (i_integer_DS) clsf->classes[clsf_class_number]->i_values[n_att]; line_cnt = format_integer_attribute( header, header_continued, integer_influence_values, line_length, description, model_term_type_symbol, line_cnt, discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p); } else if (eqstring( type, "real")) { real_influence_values = (i_real_DS) clsf->classes[clsf_class_number]->i_values[n_att]; line_cnt = format_real_attribute( header, header_continued, real_influence_values, line_length, n_att, description, model_term_type_symbol, line_cnt, discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p, clsf_class_number, clsf); } return (line_cnt); } /* FORMAT_DISCRETE_ATTRIBUTE 17feb95 wmt: new 14jun95 wmt: discrete attribute instance value significance computation changed from fabs( log( local_prob / global_prob)) to local_prob * log( local_prob / global_prob) 26jun96 wmt: do not process attributes with warnings_and_errors 04jul96 wmt: check for malloc/realloc returning NULL 14oct96 wmt: correct bad test for warn_errs->single_valued_warning svw == NULL => eqstring( svw, "") 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 20sep98 wmt: filter windows e+000 => e+00, etc with filter_e_format_exponents 22jan02 wmt: prevent string overflow of discrete_string_name (strcpy => strncpy) 17aug09 wmt: put header all on one line format discrete attribute for influence values report */ int format_discrete_attribute( int n_att, database_DS d_base, char *header, char *header_continued, i_discrete_DS influence_values, int line_length, char *description, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { int index, name_length, name_max = 20, line_cnt_max = G_line_cnt_max, i; int new_lines, list_index = 0; char blank = ' ', dot = '.'; float *p_p_star_list; /* triplets of term_index, local, & global probs */ float local_prob, global_prob, att_value_influence; shortstr temp, discrete_string_name; formatted_p_p_star_DS formatted_p_p_star_list = NULL; int (* influ_influence_sort_func) () = float_p_p_star_compare_gtr; warn_err_DS warn_errs; /* |14|20|1|6|2|20|1|9|2|9|1|9| */ /* force windows to keep column alignment */ #ifdef _WIN32 char* format_string_1 = "%34s %6.3f %-20s %+9.2e %+9.2e %+9.2e\n"; char* format_string_2 = "%-20s %+9.2e %+9.2e %+9.2e\n"; #else /* char* format_string_1 = "%34s %6.3f %-20s %9.2e %9.2e %9.2e\n"; */ char* format_string_1 = "%35s %6.3f \n %20s %9.2e %9.2e %9.2e\n"; char* format_string_2 = "%-20s %9.2e %9.2e %9.2e\n"; #endif fxlstr e_format_string; static char header_prefix[60]; warn_errs = d_base->att_info[n_att]->warnings_and_errors; if (eqstring (report_mode, "data") == TRUE) dot = ' '; if ((warn_errs->num_expander_warnings == 0) && (warn_errs->num_expander_errors == 0) && (eqstring( warn_errs->single_valued_warning, ""))) { for (index=0; indexn_p_p_star_list; index += 3) { p_p_star_list = influence_values->p_p_star_list; strncpy( discrete_string_name, d_base->att_info[n_att]->translations[(int) p_p_star_list[index]], name_max + 1); /* if name_max + 1 exceeded, null terminate string */ discrete_string_name[name_max] = '\0'; name_length = strlen( discrete_string_name); local_prob = p_p_star_list[index+1]; global_prob = p_p_star_list[index+2]; att_value_influence = ((local_prob == 0.0) || (global_prob == 0.0)) ? 0.0 : (float) safe_log( (double) (local_prob / global_prob)); for (i=0; i< max( 0, (name_max - name_length - 1)); i++) temp[i] = dot; temp[i] = '\0'; strcat( strcat( discrete_string_name, (name_length < name_max) ? " " : ""), temp); if (formatted_p_p_star_list == NULL) { formatted_p_p_star_list = (formatted_p_p_star_DS) malloc( sizeof( struct formatted_p_p_star)); if (formatted_p_p_star_list == NULL) { fprintf( stderr, "ERROR: format_discrete_attribute: out of memory, malloc returned NULL!\n"); exit(1); } } else { formatted_p_p_star_list = (formatted_p_p_star_DS) realloc( formatted_p_p_star_list, (index + 1) * sizeof( struct formatted_p_p_star)); if (formatted_p_p_star_list == NULL) { fprintf( stderr, "ERROR: format_discrete_attribute: out of memory, realloc returned NULL!\n"); exit(1); } } strcpy( formatted_p_p_star_list[list_index].discrete_string_name, discrete_string_name); formatted_p_p_star_list[list_index].abs_att_value_influence = (float) fabs( (double) att_value_influence); formatted_p_p_star_list[list_index].att_value_influence = att_value_influence; formatted_p_p_star_list[list_index].local_prob = local_prob; formatted_p_p_star_list[list_index].global_prob = global_prob; list_index++; } new_lines = influence_values->n_p_p_star_list / 3; /* sort by greatest influence value first */ qsort( (char *) formatted_p_p_star_list, new_lines, sizeof( struct formatted_p_p_star), influ_influence_sort_func); line_cnt += new_lines; if ((eqstring (report_mode, "text") == TRUE) && (line_cnt > line_cnt_max)) { /* 2nd & subseq. pages */ fprintf( influence_report_fp, "\f"); line_cnt = real_atts_header_p * 4 + discrete_atts_header_p * 4 + new_lines; print_attribute_header( discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p); } /* put header all on one line */ strncpy(header_prefix, header, 14); fprintf( influence_report_fp, "%s %s\n", header_prefix, description); strcpy(header, " "); sprintf( e_format_string, format_string_1, header, influence_values->influence_value, formatted_p_p_star_list[0].discrete_string_name, formatted_p_p_star_list[0].att_value_influence, formatted_p_p_star_list[0].local_prob, formatted_p_p_star_list[0].global_prob); fprintf( influence_report_fp, "%s", filter_e_format_exponents( e_format_string)); for (i=1; i line_length)) fprintf( influence_report_fp, "%14c %-20s %6c ", blank, header_continued, blank); else fprintf( influence_report_fp, "%44c", blank); sprintf( e_format_string, format_string_2, formatted_p_p_star_list[i].discrete_string_name, formatted_p_p_star_list[i].att_value_influence, formatted_p_p_star_list[i].local_prob, formatted_p_p_star_list[i].global_prob); fprintf( influence_report_fp, "%s", filter_e_format_exponents( e_format_string)); } free( formatted_p_p_star_list); } return (line_cnt); } /* FORMAT_INTEGER_ATTRIBUTE 17feb95 wmt: new 14may97 wmt: add comment_data_headers_p */ int format_integer_attribute( char *header, char *header_continued, i_integer_DS integer_influence_values, int line_length, char *description, char *model_term_type_symbol, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p) { fprintf( stderr, "Not supported yet\n"); abort(); return (line_cnt); } /* FORMAT_REAL_ATTRIBUTE 17feb95 wmt: new 09jun95 wmt: do not output covariance matrix; use fixed decimal for correlation matrix; add (Mean-jk - Mean-*k) / StDev-jk as a significance measure 18mar97 wmt: add report_mode 14may97 wmt: add comment_data_headers_p 14jun97 wmt: pass in clsf to use in printing multiple correlation matricies 21nov97 wmt: correct correlation matrices print-out for non- contiguous attribute ranges, and print matrices once after all class attributes are listed. 20mar98 wmt: outputing of MNcn correlation matrices after last class attribute instead of after each term is now done by a call to generate_mncn_correlation_matrices from autoclass_class_influence_values_report 20sep98 wmt: filter windows e+000 => e+00, etc with filter_e_format_exponents 17aug09 wmt: put header all on one line format real attribute for influence values report */ int format_real_attribute( char *header, char *header_continued, i_real_DS influence_values, int line_length, int n_att, char *description, char *model_term_type_symbol, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p, int clsf_class_number, clsf_DS clsf) { int line_cnt_max = G_line_cnt_max, new_lines = 1; float *mean_sigma_list = influence_values->mean_sigma_list; char blank = ' '; /* |14|20|1|6|1|(|9|2|9|)|1|9|1|(|9|1|9|)| */ /* force windows to keep column alignment */ #ifdef _WIN32 char* format_string_1 = "%34s %6.3f (%+9.2e %+9.2e) %+9.2e (%+9.2e %+9.2e)\n"; char* format_string_2 = "Prob-jk is known %+9.2e Prob-*k is known %+9.2e\n"; #else /* char* format_string_1 = "%33s %6.3f (%9.2e %9.2e) %9.2e (%9.2e %9.2e)\n"; */ char* format_string_1 = "%35s %6.3f (%9.2e %9.2e) %9.2e (%9.2e %9.2e)\n"; char* format_string_2 = "Prob-jk is known %9.2e Prob-*k is known %9.2e\n"; #endif fxlstr e_format_string; static char header_prefix[60]; if (((int) strlen( description) > line_length) || eqstring( model_term_type_symbol, "SNcm")) new_lines++; if ((eqstring (report_mode, "text") == TRUE) && (line_cnt > line_cnt_max)) { /* 2nd & subseq. pages */ fprintf( influence_report_fp, "\f"); line_cnt = real_atts_header_p * 4 + discrete_atts_header_p * 4 + new_lines; print_attribute_header( discrete_atts_header_p, real_atts_header_p, influence_report_fp, report_mode, comment_data_headers_p); } else line_cnt += new_lines; /* put header all on one line */ strncpy(header_prefix, header, 14); fprintf( influence_report_fp, "%s %s\n", header_prefix, description); strcpy(header, " "); sprintf( e_format_string, format_string_1, header, influence_values->influence_value, mean_sigma_list[0], mean_sigma_list[1], ((float) fabs((double) (mean_sigma_list[0] - mean_sigma_list[2]))) / mean_sigma_list[1], mean_sigma_list[2], mean_sigma_list[3]); fprintf( influence_report_fp, "%s", filter_e_format_exponents( e_format_string)); /* if ((int) strlen( description) > line_length) */ /* fprintf( influence_report_fp, "%13c %-20s %s", blank, header_continued, */ /* (eqstring( model_term_type_symbol, "SNcm")) ? "" : "\n"); */ if (eqstring( model_term_type_symbol, "SNcm")) { if ((int) strlen( description) <= line_length) fprintf( influence_report_fp, "%37c", blank); sprintf( e_format_string, format_string_2, mean_sigma_list[4], mean_sigma_list[5]); fprintf( influence_report_fp, "%s", filter_e_format_exponents( e_format_string)); } return (line_cnt); } /* GENERATE_MNCN_CORRELATION_MATRICES 20mar98 wmt: new outputing of MNcn correlation matrices after last class attribute instead of after each term is now done by a call to generate_mncn_correlation_matrices from autoclass_class_influence_values_report */ void generate_mncn_correlation_matrices ( clsf_DS clsf, int clsf_class_number, shortstr report_mode, unsigned int comment_data_headers_p, FILE *influence_report_fp ) { int i, j, first_row, n_att; int n_term_list = 0; float *term_list = NULL, *f_list; float **covar_matrix = NULL, **correl_matrix = NULL; i_real_DS real_influence_values = NULL; int n_atts = clsf->database->n_atts; char *att_type, *model_term_type; int correl_term_list[50] = {END_OF_INT_LIST}; int num_correl_term_list, max_num_correl_term_list = 50; int *i_list, k; /* find one attr from each of the term attribute lists */ for (i = (n_atts - 1); i >= 0; i--) { att_type = report_att_type( clsf, clsf_class_number, i); model_term_type = (char *) get( rpt_att_model_term_type( clsf, clsf_class_number, i), "print_string"); if ((eqstring( att_type, "real") == TRUE) && (eqstring( model_term_type, "MNcn"))) { real_influence_values = (i_real_DS) clsf->classes[clsf_class_number]->i_values[i]; n_term_list = real_influence_values->n_term_att_list; term_list = real_influence_values->term_att_list; /* fprintf( stderr, "i %d last %d\n", i, (int) term_list[n_term_list - 1]); */ if (i == (int) term_list[n_term_list - 1]) { push_int_list( correl_term_list, &num_correl_term_list, i, max_num_correl_term_list); } } } /* fprintf( stderr, "correl_term_list: "); i_list = correl_term_list; for ( ; *i_list != END_OF_INT_LIST; i_list++) { fprintf( stderr, "%d ", *i_list); } fprintf( stderr, "\n"); */ i_list = correl_term_list; for ( ; *i_list != END_OF_INT_LIST; i_list++) { n_att = *i_list; /* fprintf( stderr, "n_att %d\n", *i_list); */ real_influence_values = (i_real_DS) clsf->classes[clsf_class_number]->i_values[n_att]; n_term_list = real_influence_values->n_term_att_list; term_list = real_influence_values->term_att_list; /* fprintf( stderr, "term_list: "); f_list = term_list; for ( k=0; k < n_term_list; k++) { fprintf( stderr, "%d ", (int) *f_list); f_list++; } fprintf( stderr, "\n"); */ if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "\nDATA_CORR_MATRIX"); fprintf( influence_report_fp, "\n%s Correlation matrix (row & column indices are " "attribute numbers)\n ", (comment_data_headers_p == TRUE) ? "#" : ""); f_list = term_list; for ( k = 0; k < n_term_list; k++) { fprintf( influence_report_fp, "%7d", (int) *f_list); f_list++; } fprintf( influence_report_fp, "\n"); covar_matrix = real_influence_values->class_covar; correl_matrix = extract_rhos( covar_matrix, n_term_list); first_row = TRUE; for (i = 0; i < n_term_list; i++) { if (first_row == TRUE) first_row = FALSE; else fprintf( influence_report_fp, "\n"); for (j = 0; j < n_term_list; j++) { /* if ((j > 0) && ((j % NUM_TOKENS_IN_FXLSTR) == 0)) fprintf( influence_report_fp, "\n%2c", blank); */ if (j == 0) { if (eqstring (report_mode, "data") == TRUE) fprintf( influence_report_fp, "%02d", (int) term_list[i]); else fprintf( influence_report_fp, " %2d", (int) term_list[i]); } fprintf( influence_report_fp, " %6.3f", correl_matrix[i][j]); } } for (k=0; kn_terms; i++) { for (j=0; jterms[i]->n_atts; j++) if (model->terms[i]->att_list[j] == ((float) n_att)) goto out; } out: return (i); } /* SORT_MNCN_ATTRIBUTES jun95 wmt: new 04jul96 wmt: check for malloc/realloc returning NULL 14oct96 wmt: type last_sorted_term_n_att as int, not float 26feb98 wmt: move making last term_att be last sorted att to pre_format_attributes sort attributes within their model term by mean significance measure */ void sort_mncn_attributes( sort_cell_DS sort_list, int sort_index, int term_count, clsf_DS clsf, int clsf_class_number) { sort_cell_DS sort_list_temp; int temp_j, j; int (* mncn_sort_func) () = float_sort_cell_compare_gtr; i_real_DS real_iv_struct; /* fprintf( stderr, "sort_mncn_attributes: sort_index %d term_count %d\n", sort_index, term_count); */ sort_list_temp = (sort_cell_DS) malloc( term_count * sizeof( struct sort_cell)); if (sort_list_temp == NULL) { fprintf( stderr, "ERROR: sort_mncn_attributes: out of memory, malloc returned NULL!\n"); exit(1); } /* fprintf( stderr, "\nmncn sorting: sort_index %d term_count %d\n", sort_index, term_count); fprintf( stderr, "sort_mncn_attributes B: "); for (j=(sort_index - term_count), temp_j=0; temp_jclasses[clsf_class_number]->i_values[sort_list[j].int_value]; } /* fprintf( stderr, "sort_mncn_attributes A: "); for (j=(sort_index - term_count), temp_j=0; temp_j e+00 & e-000 => e-00 */ char *filter_e_format_exponents ( fxlstr e_format_string ) { static char *filtered_numeric_string[STRLIMIT]; #ifdef _WIN32 fxlstr suffix_string; int char_cnt = 0, i; char *match_addr_plus, *match_addr_minus, *match_addr; /* fprintf( stderr, "e_format_string %s\n", e_format_string); */ strcpy( suffix_string, e_format_string); match_addr_plus = strstr( suffix_string, "e+0"); match_addr_minus = strstr( suffix_string, "e-0"); while ((match_addr_plus != NULL) || (match_addr_minus != NULL)) { if ((match_addr_plus != NULL) && (match_addr_minus != NULL)) { if (match_addr_plus < match_addr_minus) match_addr = match_addr_plus; else match_addr = match_addr_minus; } else if (match_addr_plus != NULL) match_addr = match_addr_plus; else match_addr = match_addr_minus; char_cnt = char_cnt + match_addr - suffix_string + 2; /* strncpy does not append NULL to string2 after copying into string1 */ for (i=0; i #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* CENTRAL_MEASURES_X 27nov94 wmt: use percent_equal for float tests 27dec94 wmt: remove INT_UNKNOWN test A variation of CENTRAL-MEASURES modified for use with Single-Normal-xxx models. Treats nil values as unknown. Returns the values: unknown-wt known-wt mean variance skewness kurtosis. This version calculates higher moments with respect to est-mean in order to obtain reasonable accuracy when the variance is very small relative to the true mean. Kurtosis has range (-1,+^N), is ~0 for Gaussian distributions, and ~-.6 for uniform ones. */ void central_measures_x( float **data, int n_data,int n_att, float *wts, double est_mean, float *unknown, float *known, float *mean, float *variance, float *skewness, float *kurtosis) { int i; float sum1, sum2, nu1, nu2, mu2, wt, value, temp, delta; *unknown = 0.0; *known = 0.0; sum1 = 0.0; sum2 = 0.0; for (i=0; i 0.0) { value = data[i][n_att]; if (!(percent_equal( (double) value, FLOAT_UNKNOWN, REL_ERROR))) { *known += wt; delta = value - (float) est_mean; temp = wt * delta; sum1 += temp; sum2 += temp * delta; } else *unknown += wt; } } if (*known == 0.0) { *mean = FLOAT_UNKNOWN; *variance = FLOAT_UNKNOWN; *skewness = FLOAT_UNKNOWN; *kurtosis = FLOAT_UNKNOWN; } else { nu1 = sum1 / *known; nu2 = sum2 / *known; mu2 = max(0.0, nu2 - (nu1 * nu1)); *mean = nu1 + (float) est_mean; *variance = mu2; *skewness = 0.0; *kurtosis = 0.0; } } autoclass-3.3.6.dfsg.1/prog/intf-extensions.c0000644000175000017500000002152411247310756017164 0ustar areare#include #include #include #include #include #ifndef _WIN32 #include #endif #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* INITIALIZE_REPORTS_FROM_RESULTS_PATHNAME 02feb95 wmt: new 17feb95 wmt: add clsf_n_list to get_clsf_seq arg list 26may95 wmt: added prediction_p Get classifications from results_file. Add reports strucuture Create global classification and compute influence values. Returns clsf list */ clsf_DS *initialize_reports_from_results_pathname( char *results_file_ptr, int_list clsf_n_list, int *num_clsfs_found_ptr, int prediction_p) { clsf_DS *clsf_list = NULL, *clsf_seq, clsf; int expand_p = TRUE, want_wts_p = TRUE, update_wts_p = TRUE; int n_best_clsfs, i_clsf, i; shortstr file_type = "results"; if (prediction_p == TRUE) expand_p = want_wts_p = update_wts_p = FALSE; clsf_seq = get_clsf_seq( results_file_ptr, expand_p, want_wts_p, update_wts_p, file_type, &n_best_clsfs, clsf_n_list); for (i=0; clsf_n_list[i] != END_OF_INT_LIST; i++) if (clsf_n_list[i] > n_best_clsfs) fprintf( stdout, "\nWARNING: requested clsf number %d not found -- max number " "is %d\n\n", clsf_n_list[i], n_best_clsfs); *num_clsfs_found_ptr = 0; for (i_clsf=0; i_clsfreports->current_results, results_file_ptr); } else free_clsf_DS( clsf_seq[i_clsf]); } return (clsf_list); } /* INIT_CLSF_FOR_REPORTS 02feb95 wmt: new 26may95 wmt: added prediction_p do expand_clsf in initialize_reports_from_results_pathname because of peculiarities of the expansion code */ clsf_DS init_clsf_for_reports( clsf_DS clsf, int prediction_p) { int n_classes; float *class_strength_list = NULL, max_strength = MOST_NEGATIVE_SINGLE_FLOAT; int i_class; class_DS *classes; n_classes = clsf->n_classes; classes = clsf->classes; if (prediction_p == FALSE) { for (i_class=0; i_class max_strength) max_strength = class_strength_list[i_class]; } } /* allocate reports struct */ clsf->reports = (rpt_DS) malloc( sizeof( struct reports)); if (prediction_p != TRUE) { clsf->reports->att_model_term_types = get_attribute_model_term_types( clsf); clsf->reports->max_class_strength = max_strength; clsf->reports->class_strength = class_strength_list; } else { clsf->reports->att_model_term_types = NULL; clsf->reports->max_class_strength = 0.0; clsf->reports->class_strength = NULL; } clsf->reports->n_class_wt_ordering = n_classes; clsf->reports->class_wt_ordering = get_class_weight_ordering( clsf); clsf->reports->datum_class_assignment = NULL; clsf->reports->att_i_sums = NULL; clsf->reports->att_max_i_sum = 0.0; clsf->reports->att_max_i_values = NULL; clsf->reports->max_i_value = 0.0; if (prediction_p != TRUE) compute_influence_values( clsf); return (clsf); } /* GET_CLASS_WEIGHT_ORDERING 03feb95 wmt: new compute ordering of classes by class membership (weight) */ int *get_class_weight_ordering( clsf_DS clsf) { int i_class, *class_weight_ordering; sort_cell_DS sort_list, temp_sort_list; int (* comp_func) () = float_sort_cell_compare_gtr; sort_list = (sort_cell_DS) malloc( clsf->n_classes * sizeof( struct sort_cell)); class_weight_ordering = (int *) malloc( clsf->n_classes * sizeof( int)); temp_sort_list = sort_list; for (i_class=0; i_classn_classes; i_class++) { temp_sort_list->float_value = clsf->classes[i_class]->w_j; temp_sort_list->int_value = i_class; /* printf ("before: order-index %d class-index %d class-weight %f\n", i_class, */ /* temp_sort_list->int_value, temp_sort_list->float_value); */ temp_sort_list++; } qsort( (char *) sort_list, clsf->n_classes, sizeof( struct sort_cell), comp_func); temp_sort_list = sort_list; for (i_class=0; i_classn_classes; i_class++) { class_weight_ordering[i_class] = temp_sort_list->int_value; /* printf ("after: order-index %d class-index %d class-weight %f\n", i_class, */ /* temp_sort_list->int_value, temp_sort_list->float_value); */ temp_sort_list++; } free( sort_list); return (class_weight_ordering); } /* GET_ATTRIBUTE_MODEL_TERM_TYPES 03feb95 wmt: new Build array of classes by attributes containing attribute model term types */ char ***get_attribute_model_term_types( clsf_DS clsf) { char ***model_term_type_array, **att_model_term_type_array; int n_classes = clsf->n_classes, n_atts = clsf->database->n_atts; int i_class, i_att, term_index, integer_p; model_DS model; model_term_type_array = (char ***) malloc( n_classes * sizeof( char **)); for (i_class=0; i_classclasses[i_class]->model; for (i_att=0; i_attatt_locs[i_att], &integer_p); att_model_term_type_array[i_att] = ( char *) malloc( sizeof( shortstr)); strcpy( att_model_term_type_array[i_att], integer_p ? model->terms[term_index]->type : "ignore"); /* printf( "i_class %d i_att %d model_term_type %s\n", i_class, i_att, */ /* att_model_term_type_array[i_att]); */ } model_term_type_array[i_class] = att_model_term_type_array; } return (model_term_type_array); } /* REPORT_ATT_TYPE 06feb95 wmt: new attribute type, with models applied, of attribute n-att in class n-class (clsf numbering), e.g. "real" */ char *report_att_type( clsf_DS clsf, int n_class, int n_att) { char * att_type; att_type = clsf_att_type( clsf, n_att); if (eqstring( rpt_att_model_term_type( clsf, n_class, n_att), "ignore") == TRUE) att_type = "ignore"; return (att_type); } /* RPT_ATT_MODEL_TERM_TYPE 06feb95 wmt: new attribute model term type stringof attribute n_att in class n_class (clsf numbering), e.g. "single_normal_cn" */ char *rpt_att_model_term_type( clsf_DS clsf, int n_class, int n_att) { return (clsf->reports->att_model_term_types[n_class][n_att]); } /* GET_MODELS_SOURCE_INFO 07feb95 wmt: new 14may97 wmt: add comment_data_headers_p Get classification model info & format into list of strings. */ void get_models_source_info( model_DS *models, int num_models, FILE *xref_case_text_fp, unsigned int comment_data_headers_p) { int i_model; char blank = ' '; for (i_model=0; i_modelmodel_file[0] == G_slash) ? "" : G_absolute_pathname, models[i_model]->model_file, models[i_model]->file_index); } /* GET_CLASS_MODEL_SOURCE_INFO 14feb95 wmt: new 14may97 wmt: add comment_data_headers_p Formats the model file pathname and index for a class */ void get_class_model_source_info( class_DS class, char *class_model_source, unsigned int comment_data_headers_p) { char *source = class->model->model_file; int index = class->model->file_index; char caller[] = "get_class_model_source_info"; safe_sprintf( class_model_source, sizeof( fxlstr), caller, "%s %s%s - index = %d", (comment_data_headers_p == TRUE) ? "#" : "", (source[0] == G_slash) ? "" : G_absolute_pathname, source, index); } autoclass-3.3.6.dfsg.1/prog/struct-model.c0000644000175000017500000000710611247310756016451 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* This searches the *model-list* for one with the same source file, file index, and database. */ model_DS find_similar_model( char *model_file, int file_index, database_DS database) { int i; model_DS model; for (i=0; imodel_file, model_file)) && (model->file_index == file_index) && (db_DS_equal_p(model->database, database))) return(model); } return(NULL); } /* MODEL_DS_EQUAL_P 18may95 wmt: Remove "db_ds_same_source_p" and use "db_same_source_p", instead. This extends the concept of model equality to compressed models. */ int model_DS_equal_p( model_DS m1, model_DS m2) { if ((m1->model_file == m2->model_file) && (m1->file_index == m2->file_index) && (db_same_source_p( m1->database, m2->database))) return(TRUE); else return(FALSE); } /* EXPAND_MODEL 23jan95 wmt: new create uncompressed model from compressed model */ model_DS expand_model( model_DS comp_model) { FILE *model_file_fp, *log_file_fp = NULL, *stream = NULL; static fxlstr model_file; int file_index, expand_p = TRUE, regenerate_p = FALSE, newlength = 0; database_DS database; model_DS model = NULL; model_file[0] = '\0'; if (comp_model->compressed_p == FALSE) return (comp_model); if (make_and_validate_pathname( "model", comp_model->model_file, &model_file, TRUE) != TRUE) exit(1); file_index = comp_model->file_index; database = expand_database( comp_model->database); if ((model = find_model( model_file, file_index, database)) != NULL) return (model); model_file_fp = fopen( model_file, "r"); if (read_model_file( model_file_fp, log_file_fp, database, regenerate_p, expand_p, stream, &newlength, model_file) != NULL) model = find_model( model_file, file_index, database); fclose( model_file_fp); return (model); } /* FIND_MODEL 23jan95 wmt: new find model in global store */ model_DS find_model( char *model_file_ptr, int file_index, database_DS database) { int i; model_DS temp; for (i=0; imodel_file, model_file_ptr) == TRUE) && (temp->file_index == file_index) && (temp->database == database)) return (temp); } return (NULL); } /* FREE_MODEL_DS 25feb95 wmt: new */ void free_model_DS( model_DS model, int i_model) { int n_prior, n_term; term_DS term; if (G_clsf_storage_log_p == TRUE) fprintf(stdout, "free_model(%d): %p\n", i_model, (void *) model); if (model->terms != NULL) { for (n_term=0; n_term < model->n_terms ; n_term++) { term = model->terms[n_term]; free ( term->att_list); free_tparm_DS( term->tparm); free( term); } free( model->terms); } if (model->att_locs != NULL) free( model->att_locs); if (model->att_ignore_ids != NULL) free( model->att_ignore_ids); if (model->priors != NULL) { for (n_prior=0; n_priornum_priors ; n_prior++) if (model->priors[n_prior] != NULL) free( model->priors[n_prior]); free( model->priors); } free( model); } autoclass-3.3.6.dfsg.1/prog/autoclass.make.solaris.cc0000644000175000017500000000240411247310756020553 0ustar areare### AUTOCLASS C MAKE FILE FOR SUN SOLARIS 5.4 -- Sun cc C compiler ### Sun cc C compiler - SC4.0 ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) # optimize CFLAGS = $(OSFLAGS) -xO4 ## debugging ## CFLAGS = $(BCFLAGS) -xO3 -g ## profiling ## CFLAGS = $(BCFLAGS) -xO4 -pg -Bstatic CC = cc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lc -lm %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ # depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/autoclass.make0000644000175000017500000000013411247310756016512 0ustar areare# for all autoclass.make.* - delete object files when no arg to load-ac clean: -rm -f *.o autoclass-3.3.6.dfsg.1/prog/autoclass.h0000644000175000017500000022704411247310756016037 0ustar areare#ifdef _MSC_VER #pragma warning (disable: 4305 4244 4113) #endif #include #include #include #include /* #include */ #include "getparams.h" /* #include "params.h" done below between typedefs and structs */ #if !defined(__SVR4) && defined(__sun) && defined(__GNUC__) /* missing gcc SunOS 4.1.3 prototypes */ extern double drand48(void); extern double erand48(unsigned short *); extern long jrand48(unsigned short *); extern void lcong48(unsigned short *); extern long lrand48(void); extern long mrand48(void); extern long nrand48(unsigned short *); extern unsigned short *seed48(unsigned short *); extern void srand48(long); #endif /* NOTE: MULTI-LINE COMMENTS ARE NOT ALLOWED */ /* the following define symbols come from GNU gcc */ /* MAXDOUBLE 1.797693134862315708e+308 */ /* MAXFLOAT ((float)3.40282346638528860e+38) */ /* MINDOUBLE 4.94065645841246544e-324 */ /* MINFLOAT ((float)1.40129846432481707e-45) */ /* LN_MAXFLOAT 8.8722839052068e+01 */ /* LN_MINFLOAT -1.03278929903432e+02 */ /* */ /* they are **not** consistent with */ /* "IEEE Standard for Binary Floating-Point Arithmetic," ANSI/IEEE */ /* Standard 754-1985, An American National Standard, August 12, 1985. */ /* as implemented in Genera 8.3 - Symbolics Lisp Machine Operating system */ /* the Genera 8.3 values are used here in the defines below. 04jan95 wmt */ #define square(x)( (x) * (x) ) /* aju 980612: min and max are already defined in MSVC Also, In win32, we use rand in place of lrand48, therefore change srand48 to srand.*/ #ifdef _MSC_VER #define srand48 srand #endif #define TRUE 1 #define FALSE 0 /* #define MAXINT 32767 */ /* #define DBG_LL 0 */ #define LN_SINGLE_PI 1.1447298858494002 #define ABSOLUTE_MIN_CLASS_WT 2.1 #define MIN_CLASS_WT_FACTOR 0.001 /* next line to globals.c to prevent CodeCenter warning 622 for */ /* max(SINGLE_FLOAT_EPSILON, LIKELIHOOD_TOLERANCE_RATIO)) */ /* #define LIKELIHOOD_TOLERANCE_RATIO 0.00001 */ /* */ /* #define SINGLE_FLOAT_EPSILON 1.1920929e-7 */ /* #define DOUBLE_FLOAT_EPSILON 2.220446049250313e-16 */ /* #define LEAST_POSITIVE_SHORT_FLOAT 1.4012985e-45 */ /* #define LEAST_POSITIVE_SINGLE_FLOAT 1.1754944e-38 */ /* #define LEAST_NEGATIVE_SINGLE_FLOAT -1.4012985e-45 */ /* #define MOST_POSITIVE_LONG_FLOAT 4.494232837155787e+307 */ /* #define MOST_POSITIVE_SINGLE_FLOAT 3.4028232e+38 */ /* #define MOST_NEGATIVE_SINGLE_FLOAT -3.4028232e38 */ /* #define MOST_NEGATIVE_SINGLE_FLOAT_DIV_2 -1.7014116e38 */ /* #define MOST_NEGATIVE_LONG_FLOAT -4.494232837155787e+307 */ /* #define INFINITY 3.4028232e+38 */ /* replace above with defines from Symbolics Genera 8.3 04jan95 wmt */ #define SINGLE_FLOAT_EPSILON 5.960465e-8 #define DOUBLE_FLOAT_EPSILON 1.1102230246255157e-16 #define LEAST_POSITIVE_SHORT_FLOAT 1.1754944e-38 #define LEAST_POSITIVE_SINGLE_FLOAT 1.1754944e-38 #define LEAST_POSITIVE_LONG_FLOAT 2.2250738585072014e-308 #define LEAST_NEGATIVE_SINGLE_FLOAT -1.1754944e-38 #define MOST_POSITIVE_LONG_FLOAT 1.7976931348623157e308 #define MOST_POSITIVE_SINGLE_FLOAT 3.4028235e38 #define MOST_NEGATIVE_SINGLE_FLOAT -3.4028235e38 #define MOST_NEGATIVE_LONG_FLOAT -1.7976931348623157e308 #ifndef MACOSX #define INFINITY 3.4028235e38 #endif /* end replaces from Symbolics Genera 8.3 */ #define MOST_NEGATIVE_SINGLE_FLOAT_DIV_2 (MOST_NEGATIVE_SINGLE_FLOAT / 2.0) #define LEAST_POSITIVE_SINGLE_LOG (log( LEAST_POSITIVE_SINGLE_FLOAT) + 0.00001) #define LEAST_POSITIVE_LONG_LOG (log( LEAST_POSITIVE_LONG_FLOAT) + 0.00001) #define MOST_POSITIVE_SINGLE_LOG ((log( MOST_POSITIVE_SINGLE_FLOAT / 2.0) + log ( 2.0)) - 0.00001) #define MOST_POSITIVE_LONG_LOG ((log( MOST_POSITIVE_LONG_FLOAT / 2.0) + log ( 2.0)) - 0.00001) #define LN_1_DIV_ROOT_2PI -0.9189385 #define ARRAY_RANK_LIMIT 65536 #define STRLIMIT 160 #define SEARCH_LOG_FILE_TYPE ".log" #define REPORT_LOG_FILE_TYPE ".rlog" #define SEARCH_FILE_TYPE ".search" #define RESULTS_FILE_TYPE ".results" #define DATA_FILE_TYPE ".db2" #define HEADER_FILE_TYPE ".hd2" #define MODEL_FILE_TYPE ".model" #define FLOAT_UNKNOWN MOST_NEGATIVE_SINGLE_FLOAT #define INT_UNKNOWN -32767 #define DISPLAY_WTS FALSE /* 5nov97 for testing jcs */ /* #define DISPLAY_WTS TRUE 5nov97 for testing jcs */ #define DISPLAY_PROBS FALSE /* 19nov97 jcs - for testing */ /* #define DISPLAY_PROBS TRUE 19nov97 jcs - for testing */ #define DISPLAY_PARAMS FALSE /* 19nov97 jcs - for testing */ /* #define DISPLAY_PARAMS TRUE 19nov97 jcs - for testing */ #define SN_CM_SIGMA_SAFETY_FACTOR 5.0 #define SN_CN_SIGMA_SAFETY_FACTOR 5.0 /* #define ATT_FLENGTH 2 15dec94 wmt */ /* #define T_LENGTH 0 15dec94 wmt */ #define NUM_ATT_TYPES 5 #define SIZEOF_ABOVE_CUT_TABLE 31 #define SIZEOF_CUT_WHERE_ABOVE_TABLE 31 /* ADDED BY WMT */ #define SEARCH_PARAMS_FILE_TYPE ".s-params" #define REPORTS_PARAMS_FILE_TYPE ".r-params" #define CHECKPOINT_FILE_TYPE ".chkpt" #define TEMP_CHECKPOINT_FILE_TYPE ".chkpt-tmp" #define INFLU_VALS_FILE_TYPE ".influ-text-" #define XREF_CLASS_FILE_TYPE ".class-text-" #define XREF_CASE_FILE_TYPE ".case-text-" #define TEMP_SEARCH_FILE_TYPE ".search-tmp" #define TEMP_RESULTS_FILE_TYPE ".results-tmp" #define RESULTS_BINARY_FILE_TYPE ".results-bin" #define TEMP_RESULTS_BINARY_FILE_TYPE ".results-tmp-bin" #define CHECKPOINT_BINARY_FILE_TYPE ".chkpt-bin" #define TEMP_CHECKPOINT_BINARY_FILE_TYPE ".chkpt-tmp-bin" #define PREDICT_FILE_TYPE ".predict" #define END_OF_INT_LIST -999 #define MAX_N_START_J_LIST 26 #define MAX_CLASS_REPORT_ATT_LIST 21 #define MAX_CLSF_N_LIST 11 #define MAX_N_SIGMA_CONTOUR_LIST 30 /* ^^: including end of list token END_OF_INT_LIST */ #define ALL_ATTRIBUTES 999 #define SHORT_STRING_LENGTH 41 /* wmt #define VERY_LONG_STRING_LENGTH 20000 #define VERY_LONG_TOKEN_LENGTH 500 */ /* ^^: for get_line_tokens, e.g. 200 real valued attributes per datum */ #define VERY_LONG_STRING_LENGTH 2000000 #define VERY_LONG_TOKEN_LENGTH 50000 #define DATA_ALLOC_INCREMENT 1000 /* ^^: used in read_data and xref_get_data */ #define REL_ERROR 0.01 /* ^^: used to test equality with percent_equal & find_duplicate */ /* data vector initialized with values UNINIT_DATA_VALUE (float_val) => -1.234560003215e-33 */ /* UNINIT_DATA_VALUE => -1.234560000000e-33 */ /* (float_val == UNINIT_DATA_VALUE) */ /* = 0 */ /* (percent_equal(float_val, UNINIT_DATA_VALUE, REL_ERROR)) */ /* = 1 */ #define NUM_TRANSFORMS 2 /* ^^: used as length of G_transforms */ #define NUM_TOKENS_IN_FXLSTR ((int) floor( (double) STRLIMIT / 15.0)) /* ^^: used in write/read_vector/matrix_float/integer so that "lines" of vectors */ /* and matricies do not exceed STRLIMIT -- 15.0 assumes format of "%.7e " */ #define WRITE_PERMISSIONS 0664 #define DATA_BINARY_FILE_TYPE ".db2-bin" /* Solaris math.h under gcc does not define M_PI - 26apr95 */ #ifndef M_PI #define M_PI 3.14159265358979323846 #endif /* On Macintosh this is not defined 12jun95 wmt */ /* MAXPATHLEN defines the longest permissable path length, */ /* including the terminating null, after expanding symbolic links. */ #ifndef MAXPATHLEN #define MAXPATHLEN 1024 #endif /* RESULTS_DATA_TYPES 13mar95 wmt: new enumerated data types for binary i/o */ enum results_data_types { INT_TYPE, CHAR_TYPE, FLOAT_TYPE, DOUBLE_TYPE, CLASS_TYPE, TERM_TYPE, WARN_ERR_TYPE, REAL_STATS_TYPE, DISCRETE_STATS_TYPE, DUMMY_STATS_TYPE, ATT_TYPE, DATABASE_TYPE, MODEL_TYPE, CLASSIFICATION_TYPE, CHECKPOINT_TYPE, TPARM_TYPE } ; /* END OF ADDED BY WMT */ typedef float *fptr; /* typedef char string[STRLIMIT]; this has been replaced by fxlstr*/ typedef char fxlstr[STRLIMIT]; typedef struct priors *priors_DS; typedef struct class *class_DS; typedef struct term *term_DS; typedef struct warn_err *warn_err_DS; typedef struct real_stats *real_stats_DS; typedef struct discrete_stats *discrete_stats_DS; typedef struct att *att_DS; typedef struct database *database_DS; typedef struct model *model_DS; typedef struct classification *clsf_DS; typedef struct search_try *search_try_DS; typedef struct search *search_DS; /* ADDED BY WMT */ typedef char shortstr[SHORT_STRING_LENGTH]; typedef char very_long_str[VERY_LONG_STRING_LENGTH]; typedef struct checkpoint *chkpt_DS; typedef struct reports *rpt_DS; typedef struct sort_cell *sort_cell_DS; typedef struct invalid_value_errors *invalid_value_errors_DS; typedef struct incomplete_datum *incomplete_datum_DS; typedef struct i_discrete *i_discrete_DS; typedef struct i_integer *i_integer_DS; typedef struct i_real *i_real_DS; typedef struct xref_data *xref_data_DS; typedef struct report_attribute_string *rpt_att_string_DS; typedef struct ordered_influence_values *ordered_influ_vals_DS; typedef struct formatted_p_p_star *formatted_p_p_star_DS; typedef int *int_list; /* int_list is terminated by END_OF_INT_LIST element */ /* END OF ADDED BY WMT */ #include "params.h" /* IMPORTANT NOTE: IF ANY CHANGES ARE MADE TO STRUCT DEFINITIONS, AND YOU WANT THOSE */ /* SLOTS TO BE SAVED IN THE .RESULTS & .SEARCH FILES, AND READ IN FOR AC-SEARCH RESTARTS */ /* OR AC-REPORTS -- YOU MUST MAKE APPROPRIATE CHANGES TO IO-RESULTS.C (WRITE/READ-<.._DS) */ /* OR SEARCH-CONTROL.C (WRITE_SEARCH_DS & WRITE_SEARCH_TRY_DS) 18nov94 wmt */ struct priors { /* used only for sn-cm and sn-cn params*/ float known_prior; /* Prior prob that values are known. */ float sigma_min; /* Min. bound on prior standard deviation. */ float sigma_max; /* Max. bound on prior standard deviation. */ float mean_mean; /* The mean of prior dist. of the attribute mean. */ float mean_sigma; /* Std Dev. of prior dist. of the attribute mean. */ float mean_var; /* Variance of prior dist. of the attribute mean. */ float minus_log_log_sigmas_ratio; float minus_log_mean_sigma; }; /* STRUCT CLASS 29mar95 wmt: log_a_w_s_h_pi_theta & log_a_w_s_h_j: float => double */ struct class { float w_j; /* Sum of weights in class. */ float log_w_j; /* Log of w_j */ float pi_j; /* Class probability parameter(~w_j/n-data). */ float log_pi_j; /* Log of class probability parameter. */ /* Log aprox-LH of Stats. WRT Hypo. & parameters. */ double log_a_w_s_h_pi_theta; double log_a_w_s_h_j; /* Log aprox-marginal-Lh of Stats. WRT class Hypo. */ int known_parms_p; /* Flag: Class parameters known & NOT TO BE UPDATED. */ /* formerly known_params_p changed spelling when changed from char 'y' to int TRUE/FALSE*/ int num_tparms; tparm_DS *tparms; int num_i_values; void **i_values; /* N-attributes vector of influence value structures. float * => void ** 06feb95 wmt*/ float i_sum; /* Sum of influence values over the attributes. */ float max_i_value; /* The maximum of the attribute influence values. */ int num_wts; /* Number of weights in the weight vector. */ float *wts; /* N-data vector of object membership probabilities. */ model_DS model; /* The class model. */ class_DS next; /* link to next class in class store for this model*/ }; struct term { shortstr type; /* One of the likelihood fn. terms in MODEL-TERM-TYPES. */ int n_atts; /* Number of attributes in this set. */ float *att_list; /* List of attributes (by number) in set. See ATT-GROUPS. */ tparm_DS tparm; }; struct warn_err { /* effects: */ shortstr unspecified_dummy_warning; /* attribute definition */ float *unused_translators_warning; /* discrete translations - not used 18jan 95 wmt */ shortstr single_valued_warning; /* model term type */ int num_expander_warnings; fxlstr *model_expander_warnings; /* model term type */ int num_expander_errors; fxlstr *model_expander_errors; /* model term type */ }; struct real_stats { /* See #'find-real-stats for constructor. */ int count; /* Number of values actually known for this attribute. */ float mx; /* Maximum value of this attribute in data. */ float mn; /* Minimum value of this attribute in data. */ float mean; /* Mean value of this attribute in data. */ float var; /* Variance of this attribute in data. */ }; struct discrete_stats { /* See #'find-discrete-stats for constructor. */ int range; /* Values will run from 0 to range inclusive, 0==unknown. */ int n_observed; int *observed; /* Accumulated number of instances of corresponding values. */ }; /* STRUCT ATT 15dec94 wmt: replace fxlstr with shortstr to save space */ struct att { shortstr type; /* One of (Att-types). */ shortstr sub_type; /* One of corresponding (Att-sub-types). */ shortstr dscrp; /* Description of attribute. */ /* One of real-stats-DS or discrete-stats-DS structures. */ real_stats_DS r_statistics; discrete_stats_DS d_statistics; int n_props; int range; float zero_point; /* type changed from int 13dec94 wmt */ int n_trans; char **translations; /* ***translations => **translations 02dec94 wmt */ float rel_error; void ***props; /* Plist of additional properties */ warn_err_DS warnings_and_errors; /* warn_err_DS structure */ float error; int missing; }; struct invalid_value_errors { /* 29nov94 wmt: new */ int n_datum; int n_att; shortstr value; }; struct incomplete_datum { /* 29nov94 wmt: new */ int n_datum; int datum_length; }; struct database { /* DJC - combination of database and compressed-database */ fxlstr data_file; /* The data file's name. */ fxlstr header_file; /* The header file's name. */ int n_data; /* The number of data, used in compressed-database */ int n_atts; /* The number of attributes, used in compressed-database */ /* Number of attributes used in the source to describe a data. */ int input_n_atts; int allo_n_atts; int compressed_p; /* Ordered N-atts vector of att-DS describing the attributes. */ att_DS *att_info; float **data; /* N-data vector of N-atts vectors, one for each object. */ int *datum_length; /* N-data vector, one for each object. 28nov94 wmt */ /* int **map; Bitmap structure for displaying data. 29nov94 */ char separator_char; /* additional data token separator (white space) */ char comment_char; /* additional data base comment character */ char unknown_token; /* additional data base unknown value token */ /* add to MISSING-VALUE-REPRESENTATIONS */ /* fxlstr data_syntax; data base syntax: vector, list, or :line 29nov94 wmt */ int num_tsp; /* attribute's whose discrete translations were supplied */ int *translations_supplied_p; int num_invalid_value_errors; /* renamed from num_ive 29nov94 wmt */ invalid_value_errors_DS *invalid_value_errors; /* type = real attributes only 29nov94 wmt */ int num_incomplete_datum; /* added 29nov94 wmt */ incomplete_datum_DS *incomplete_datum; /* type = real attributes only 29nov94 wmt */ }; /* STRUCT MODEL 23oct94 wmt: replace fxlstr with shortstr to save space 18nov94 wmt: add data_file, header file, compressed, & n_data for compressed state when database is null */ struct model { /* DJC - combination of model and compressed model */ /* 1 when model has been expanded by Expand-Model-Terms. */ shortstr id; int expanded_terms; fxlstr model_file; /* The model source file. */ int file_index; /* Index of model in model-file. */ database_DS database; /* DB to which this model applies. */ fxlstr data_file; /* The data file's name - compressed model only */ fxlstr header_file; /* The header file's name - compressed model only */ int n_data; /* number of data in data_file - compressed model only */ int compressed_p; /* TRUE if compressed - compressed model only */ int n_terms; /* Number of independent terms in a model. */ term_DS *terms; /* Vector of term-DS, of length >= N-terms. */ int n_att_locs; shortstr *att_locs; /* N-atts vector of terms attribute location indices. */ int n_att_ignore_ids; /* N-atts vector of symbols denoting source of ignore term */ shortstr *att_ignore_ids; int num_priors; priors_DS *priors; /* Model priors coresponding to DB. for sn-cm and sn-cn */ int num_class_store; class_DS class_store; /* now this is a pointer to first class available for re-use or NULL if none; was Fill-pointer vec of classes stored for reuse. */ clsf_DS global_clsf; /* A single class classification for this model. */ }; /* STRUCT CHECKPOINT 13nov94 wmt: added this functionality to save search status between multiple runs to complete one trial */ struct checkpoint { int accumulated_try_time; /* stored by checkpoint_clsf */ int current_try_j_in; /* stored by try_variation */ int current_cycle; /* stored by search try_function */ }; /* STRUCT REPORTS 20jan95 wmt: added this functionality from ac-x to save influence value calculations for reports */ struct reports { fxlstr current_results; /* pathname of clsf results file (if it exists) */ int n_class_wt_ordering; int *class_wt_ordering; /* mapping between clsf class numbering & report */ /* class numbering: map_class_num/clsf_>report */ /* map_class_num/report_>clsf */ char ***att_model_term_types; /* attribute model term types: array of length n_classes */ /* whose elements are arrays of length n_attributes */ /* which contain a list of model term type & its mnemomic */ float max_class_strength; /* max strength value for all classes */ float *class_strength; /* strength value for each class */ int *datum_class_assignment; /* vector of most probable class for each datum _ n_data long */ float *att_i_sums; /* Sum over the classes of influence values for each attribute */ float att_max_i_sum; /* Maximum of att_i_sums */ float *att_max_i_values; /* Max I-value over all classes for each attribute */ float max_i_value; /* Maximum of att_max_i_values */ }; /* STRUCT CLASSIFICATION 20jan95 wmt: eliminate n_duplicates and cycle_count move att_i_sums, att_max_i_sum, att_max_i_values and max_i_value into reports_DS 29mar95 wmt: log_p_x_h_pi_theta & log_a_x_h: float ==> double */ struct classification { /* also commnly referred to as a clsf */ double log_p_x_h_pi_theta; double log_a_x_h; database_DS database; int num_models; model_DS *models; int n_classes; class_DS *classes; float min_class_wt; rpt_DS reports; clsf_DS next; /* for clsf_store linkage*/ chkpt_DS checkpoint; }; struct search_try { int n; /* trial number. this minus 1 trials have happened before this one */ int time; /* how long this trial took internally, ignoring overhead of saving, etc. */ int j_in; /* the number of classes this trial started with */ int j_out; /* the number of classes this trial ended with */ double ln_p; /* the probability of this classification and the data */ int n_duplicates; /* a list of tries happened after this one came up with the same clsf */ search_try_DS *duplicates; clsf_DS clsf; /* the clsf this try came up with */ /* added 18feb98 wmt */ int num_cycles; /* number of cycles needed to converge */ int max_cycles; /* .s-params value of max_cycles; if num_cycles == max_cycles, trial was terminated prior to convergence */ }; /* SEARCH STATES they can be saved and the reinvoked to continue the search. need to save at least one clsf in results when save a search file. */ struct search { int n; /* the number of trials so far */ int time; /* the total time spent in previous, excluding this one */ int n_dups; /* number of times have found a duplicate clsf */ /* number of times compared two clsfs to see if they were same */ int n_dup_tries; search_try_DS last_try_reported; int n_tries; search_try_DS *tries; /* an ordered list of search tries, from best on down */ int_list start_j_list; /* current state of start_j_list - for restarts */ int n_final_summary; /* for search_summary (intf-reports.c) */ int n_save; /* for search_summary (intf-reports.c) */ }; struct sort_cell { /* 03feb95 wmt: new */ float float_value; int int_value; }; struct i_discrete { /* 06feb95 wmt: new, sm modle */ float influence_value; int n_p_p_star_list; /* number of items in p_p_star_list */ float *p_p_star_list; /* triplets of term_index, local, & global probabilities */ }; struct i_integer { /* 06feb95 wmt: new, not implemented yet */ float influence_value; int n_mean_sigma_list; /* number of items in mean_sigma_list */ float *mean_sigma_list; /* quadruplets of local_mean, local_sigma, global_mean, & global_sigma */ }; struct i_real { /* 06feb95 wmt: new, sn_cn, sn_sm, & mn_cn models */ float influence_value; int n_mean_sigma_list; /* number of items in mean_sigma_list */ float *mean_sigma_list; /* for sn_cn (4 values): local_mean, local_sigma, global_mean, & global_sigma. for sn_cm (6 values): local_mean, local_sigma, local_known_prob, global_mean, global_sigma, & global_known_prob. for mn (4 values): */ int n_term_att_list; fptr *class_covar; /* class covariance matrix - MN only */ fptr term_att_list; /* term attribute list - MN only */ }; struct xref_data { /* 07feb95 wmt: new, for class & case reports */ int class_case_sort_key; /* (n_class * num_data) + n_case */ int case_number; /* one-based data case number for this datum */ int n_attribute_data; /* number of attribute_data items, specified by xref_class_report_att_list */ shortstr *discrete_attribute_data; /* list of strings - discrete input values */ float *real_attribute_data; /* list of floats - real input values */ int n_collector; /* number of wt_class_pairs */ sort_cell_DS wt_class_pairs; /* pairs of probability weights and classes for this datum */ }; struct report_attribute_string { /* 09feb95 wmt: new, for class reports */ int att_number; shortstr att_dscrp; int dscrp_length; /* actually the max of the lengths of the attribute values */ }; struct ordered_influence_values { /* 13feb95 wmt: new, for influence_values_header for each attribute*/ float att_i_sum; /* sort key */ int n_att; char *att_dscrp_ptr; char *model_term_type_ptr; float norm_att_i_sum; /* normalized by max_i_sum */ }; struct formatted_p_p_star { /* 16feb95 wmt: for format_discrete_attribute */ shortstr discrete_string_name; float abs_att_value_influence; /* sort key -- absolute value */ float att_value_influence; float local_prob; float global_prob; }; /************** FUNCTION PROTOTYPES ****************************************/ /* system functions for which standard header location is not known */ #ifndef _WIN32 void srand48(long seedval); double drand48(); #endif long lrand48(); /* file init.c */ void init (void); void init_properties(void); /* file io-read-data.c */ void check_stop_processing( int total_error_cnt, int total_warning_cnt, FILE *log_file_fp, FILE *stream); void define_data_file_format( FILE *header_file_fp, FILE *log_file_fp, FILE *stream); void process_data_header_model_files( FILE *log_file_fp, int regenerate_p, FILE *stream, database_DS db, model_DS *models, int num_models, int *total_error_cnt, int *total_warning_cnt); void log_header( FILE *log_file_fp, FILE *stream, char *data_file_ptr, char *header_file_ptr, char *model_file_ptr, char *log_file_ptr); database_DS read_database( FILE *header_file_fp, FILE *log_file_fp, char *data_file_ptr, char *header_file_ptr, int max_data, int reread_p, FILE *stream); int check_for_non_empty( att_DS *atts, int n_atts); void check_data_base( database_DS d_base, int n_data); char *output_warning_msgs( int n_att, att_DS att, database_DS db, model_DS model); char *output_error_msgs( int n_att, att_DS att); void output_message_summary( int unspecified_dummy_warning_cnt, int ignore_model_term_warning_cnt, int unused_translators_warning_cnt, int incomplete_datum_cnt, int single_valued_warnings_cnt, int invalid_value_errors_cnt, int model_expander_warning_cnt, int model_expander_error_cnt, int *total_error_cnt, int *total_warning_cnt, FILE *log_file, FILE *stream, int output_p); void output_messages( database_DS db, model_DS *models, int num_models, FILE *log_file, FILE *stream, int *total_error_cnt, int *total_warning_cnt, char *output_msg_type_ptr); void output_db_error_messages( database_DS db, FILE *log_file, FILE *stream, int output_p); void read_data( database_DS d_base, FILE *data_file_fp, int max_data, char *data_file_ptr, FILE *log_file_fp, FILE *stream); void define_attribute_definitions( FILE *header_file_fp, char *header_file_ptr, FILE *log_file_fp, FILE *stream); void process_attribute_definitions( database_DS d_base, FILE *header_file_fp, char *header_file_ptr, FILE *log_file_fp, FILE *stream); att_DS process_attribute_def( int att_num, int *input_error, char **tokens, int num_tokens, FILE *log_file_fp, FILE *stream); att_DS create_att_DS( int att_num, int *input_error_ptr, int range, double rel_error, double error, double zero_point, char *type_ptr, char *sub_type_ptr, char *dscrp_ptr, FILE *log_file_fp, FILE *stream); warn_err_DS create_warn_err_DS(void); /* void define_discrete_translations( char ***discrete_translations, int num, database_DS data_base); */ char ***expand_att_list( char ***att_list, int num, int *nlength); int find_str_in_list( char *str, char **translations, int num); /* void process_discrete_translations( database_DS d_base, char ***value_translations, int vlength); */ void process_translation_msgs( int *translations_not_provided, int num, char *default_translation, att_DS *att_info, FILE *stream); char **process_translation( database_DS d_base, int n_att, att_DS att_dscrp, int nat, char **att_translation); char **read_data_doit( database_DS d_base, FILE *data_file, int first_read, int *instance_length_ptr, int n_comment_chars, char *comment_chars, int binary_instance_length, float **binary_instance); float *translate_instance( database_DS d_base, char **instance, int instance_length, int n_datum, FILE *log_file_fp, FILE *stream); double translate_real( database_DS d_base, int n_datum, int n_att, char *value); int translate_discrete( database_DS d_base, int n_att, att_DS attribute, char *value, FILE *log_file_fp, FILE *stream); char **get_line_tokens( FILE *stream, int separator_char, int n_comment_chars, char *comment_chars, int first_read, int *instance_length_ptr); int read_from_string( char *s1, char *s2, int string_limit, int separator_char, int n_comment_chars, char *comment_chars, int position); int read_line( char *s, int string_limit, FILE *stream); /* void read_dscrp(FILE *stream, fxlstr dscrp); does not exist 15dec94 wmt */ void find_att_statistics( database_DS d_base, FILE *log_file_fp, FILE *stream); void find_real_stats( database_DS d_base, int n_att, FILE *log_file_fp, FILE *stream); void store_real_stats( real_stats_DS statistics, att_DS att, int count, double mean, double variance, int missing, double mx, double mn); void find_discrete_stats( database_DS d_base, int n_att); void output_att_statistics( database_DS d_base, FILE *log_file_fp, FILE *stream); void output_real_att_statistics( database_DS d_base, int n_att, FILE *log_file_fp, FILE *stream); void output_created_translations( database_DS d_base, FILE *log_file_fp, FILE *stream); void check_errors_and_warnings( database_DS database, model_DS *models, int num_models); /* file io-read-model.c */ model_DS *read_model_file( FILE *model_file_fp, FILE *log_file_fp, database_DS d_base, int regenerate_p, int expand_p, FILE *stream, int *newlength, char *model_file_ptr); char ***read_model_doit( FILE *model_file_fp, int **sizes, int *num, int model_index, int first_read, FILE *log_file_fp, FILE *stream); char ***read_lists( FILE *stream, int **sizes, int *num); char **read_list( FILE *stream, int *num); model_DS *define_models(char ****model_groups, database_DS d_base, char *source, FILE *stream, int expand_p, int regenerate_p, int num_model_groups, int *newnum, int *num_groups, int **sizes, FILE *log_file_fp); void generate_attribute_info( model_DS model, char ***model_group, int i_model, int num_groups, int *sizes, database_DS d_base, FILE *log_file_fp, FILE *stream); void extend_terms_single( char *model_type, char **list, int size, model_DS model, int model_index, FILE *log_file_fp, database_DS d_base, FILE *stream); void extend_terms_multi( char *model_type, char **list, int size, model_DS model, int model_index, FILE *log_file_fp, database_DS d_base, FILE *stream); void extend_default_terms( char *model_type, int i_model, model_DS model, database_DS d_base, FILE *log_file_fp, FILE *stream); void read_model_reset( model_DS model); void set_ignore_att_info( model_DS model, database_DS d_base); int *get_sources_list( int *att_index_list, int num, att_DS *att_info, int *traced, int n_traced); int int_cmp(int x,int y); int *get_source_list( int att_index, att_DS *att_info, int *traced,int n_traced, int *n_source); int exist_intersection( int *fl1,int *fl2,int l1,int l2); char ***canonicalize_model_group( char ***model_group); void print_att_locs_and_ignore_ids( model_DS model, int model_index); /* file io-results.c */ void compress_clsf( clsf_DS clsf, model_DS dbmodel, int want_wts_p); clsf_DS expand_clsf( clsf_DS clsf, int want_wts_p, int updatewts); void expand_clsf_models( clsf_DS clsf); void expand_clsf_wts( clsf_DS clsf, float **wts_vector, int num_wts); void save_clsf_seq( clsf_DS *clsf_seq, int num, char *save_file_ptr, unsigned int save_compact_p, char *results_or_chkpt); void write_clsf_seq( clsf_DS *clsf_seq, int num, FILE *stream); void write_clsf_DS( clsf_DS clsf, FILE *stream, int clsf_num); void write_database_DS( database_DS database, FILE *stream); void write_att_DS( att_DS att_info, int n_att, FILE *stream); void write_model_DS( model_DS model, int model_num, database_DS database, FILE *stream); void write_term_DS( term_DS term, int n_term, FILE *stream); void write_tparm_DS( tparm_DS term_param, int parm_num, FILE *stream); void write_mm_d_params(struct mm_d_param *param, int n_atts, FILE *stream); void write_mm_s_params( struct mm_s_param *param, int n_atts, FILE *stream); void write_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *stream); void write_sm_params( struct sm_param *param, int n_atts, FILE *stream); void write_sn_cm_params( struct sn_cm_param *param, FILE *stream); void write_sn_cn_params( struct sn_cn_param *param, FILE *stream); void write_priors_DS( priors_DS priors, int n_priors, FILE *stream); void write_class_DS_s( class_DS *classes, int n_classes, FILE *stream); int make_and_validate_pathname ( char *type, char *file_arg, fxlstr *file_ptr, int validate_p); int validate_results_pathname( char *file_pathname, fxlstr *found_file_ptr, char *type, int exit_if_error_p, int silent_p); int validate_data_pathname( char *file_pathname, fxlstr *found_file_ptr, int exit_if_error_p, int silent_p); clsf_DS *get_clsf_seq( char *results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, char *file_type, int *n_best_clsfs_ptr, int_list expand_list); clsf_DS *read_clsf_seq( FILE *results_file_fp, char *results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, int *n_best_clsfs_ptr, int_list expand_list); clsf_DS read_clsf( FILE *results_file_fp, int expand_p, int want_wts_p, int update_wts_p, int clsf_index, clsf_DS first_clsf, int file_ac_version, int_list expand_list); database_DS read_database_DS( clsf_DS clsf, FILE *results_file_fp, int file_ac_version); model_DS read_model_DS( clsf_DS clsf, int model_index, FILE *results_file_fp, int file_ac_version); void read_class_DS_s( clsf_DS clsf, int n_classes, FILE *results_file_fp, clsf_DS first_clsf, int file_ac_version); void read_att_DS( database_DS d_base, int n_att, FILE *results_file_fp, int file_ac_version); void read_tparm_DS( tparm_DS tparm, int n_parm, FILE *results_file_fp, int file_ac_version); void read_mm_d_params(struct mm_d_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void read_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void read_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void read_sm_params( struct sm_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void read_sn_cm_params( struct sn_cm_param *param, FILE *results_file_fp, int file_ac_version); void read_sn_cn_params( struct sn_cn_param *param, FILE *results_file_fp, int file_ac_version); /* file io-results-bin.c */ void safe_write( FILE *results_fp, char *data, int data_length, int data_type, char *caller); void check_load_header( int header_type, int expected_type, char *caller); void dump_clsf_seq( clsf_DS *clsf_seq, int num, FILE *results_fp); void dump_clsf_DS( clsf_DS clsf, FILE *results_fp, int clsf_num); void dump_database_DS( database_DS database, FILE *results_fp); void dump_att_DS( att_DS att_info, int n_att, FILE *results_fp); void dump_model_DS( model_DS model, int model_num, database_DS database, FILE *results_fp); void dump_term_DS( term_DS term, int n_term, FILE *results_fp); void dump_tparm_DS( tparm_DS term_param, int parm_num, FILE *results_fp); void dump_mm_d_params(struct mm_d_param *param, int n_atts, FILE *results_fp); void dump_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_fp); void dump_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_fp); void dump_sm_params( struct sm_param *param, int n_atts, FILE *results_fp); /* void dump_sn_cm_params( struct sn_cm_param *param, FILE *results_fp); */ /* void dump_sn_cn_params( struct sn_cn_param *param, FILE *results_fp); */ void dump_class_DS_s( class_DS *classes, int n_classes, FILE *results_fp); clsf_DS *load_clsf_seq( FILE *results_file_fp, char *results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, int *n_best_clsfs_ptr, int_list expand_list); clsf_DS load_clsf( FILE *results_file_fp, int expand_p, int want_wts_p, int update_wts_p, int clsf_index, clsf_DS first_clsf, int file_ac_version, int_list expand_list); database_DS load_database_DS( clsf_DS clsf, FILE *results_file_fp, int file_ac_version); void load_att_DS( database_DS d_base, int n_att, FILE *results_file_fp, int file_ac_version); model_DS load_model_DS( clsf_DS clsf, int model_index, FILE *results_file_fp, int file_ac_version); void load_class_DS_s( clsf_DS clsf, int n_classes, FILE *results_file_fp, clsf_DS first_clsf, int file_ac_version); void load_tparm_DS( tparm_DS tparm, int n_parm, FILE *results_file_fp, int file_ac_version); void load_mm_d_params( struct mm_d_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void load_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void load_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); void load_sm_params( struct sm_param *param, int n_atts, FILE *results_file_fp, int file_ac_version); /* file matrix-utilities.c */ float *setf_v_v( float *v1, float *v2, int num); float *incf_v_v(float *v1, float *v2, int num); float *decf_v_v(float *v1, float *v2, int num); float *incf_v_vs(float *v1, float *v2, double scale, int num); float *setf_v_vs(float *v1, float *v2, double scale, int num); fptr *incf_m_vvs( fptr *m1, float *v1, float *v2, double scale, int num); double diagonal_product( fptr *m1, int num); fptr *extract_diagonal_matrix( fptr *m1, fptr *m_diagonal, int num); void update_means_and_covariance( float **data,int n_data, float *att_indices, float *wts, float *est_means, float *means, fptr *covar, float *values, int num); fptr *n_sm( double scale, fptr *m1, int num); float *vector_root_diagonal_matrix( fptr *m1, int num); double dot_vv( float *row, float *col, int num); double dot_mm( fptr *m1, fptr *m2, int num); float *collect_indexed_values( float *acc_v, float *index_list, float *values_v, int num); fptr *copy_to_matrix( fptr *from, fptr *to, int num); float *n_sv( double scale, float *vec, int num); fptr *setf_m_ms( fptr *m1, fptr *m2, double scale, int num); fptr *incf_m_ms( fptr *m1, fptr *m2, double scale, int num); fptr *limit_min_diagonal_values( fptr *m, float *mins_vec, int num); fptr *invert_factored_square_matrix( fptr *f1, fptr *m_invert, int num); double determinent_f(fptr *fs, int num); double star_vmv( fptr *m, fptr v, int num); double trace_star_mm( fptr *m1, fptr *m2, int num); fptr *extract_rhos( fptr *m, int num); fptr *invert_diagonal_matrix( fptr *m, int num); fptr *root_diagonal_matrix( fptr *m, int num); fptr *star_mm( fptr *m1, fptr *m2, int num); fptr *make_matrix( int num_rows, int num_cols); /* file model-expander-3.c */ model_DS conditional_expand_model_terms( model_DS model, int force, FILE *log_file_fp, FILE *stream); enum MODEL_TYPES model_type (shortstr str); model_DS expand_model_terms( model_DS model, FILE *log_file_fp, FILE *stream); void check_model_terms( model_DS model, FILE *log_file_fp, FILE *stream); void check_term( term_DS term, model_DS model, int n_term, FILE *log_file_fp, FILE *stream); void update_location_info( model_DS model, term_DS term, float *old_att_list); void expand_model_reset(model_DS model); void update_params_fn( class_DS class, int n_classes, database_DS data_base, int collect); void arrange_model_function_terms( model_DS model); double log_likelihood_fn( float *datum, class_DS class, double limit); double update_l_approx_fn( class_DS class); double update_m_approx_fn( class_DS class); int class_equivalence_fn( class_DS class_1, class_DS class_2, double percent_ratio, double sigma_ratio); tparm_DS *model_global_tparms( model_DS model); /* model-multi-multinomial-d.c */ /* model-multi-multinomial-s.c */ /* file model-multi-normal-cn.c */ void mn_cn_params_influence_fn( model_DS model, tparm_DS tparm, int term_index, int n_att, float *v_ptr, float *class_mean_ptr, float *class_sigma_ptr, float *global_mean_ptr, float *global_sigma_ptr, float **term_att_list_ptr, int *n_term_att_list_ptr, float ***class_covar_ptr); tparm_DS make_mn_cn_param( int n_atts); void multi_normal_cn_model_term_builder( model_DS model, term_DS term, int n_term); double multi_normal_cn_log_likelihood( tparm_DS tparm); double multi_normal_cn_update_l_approx( tparm_DS tparm); double multi_normal_cn_update_m_approx( tparm_DS tparm); void multi_normal_cn_update_params( tparm_DS tparm, int known_params_p); int multi_normal_cn_class_equivalence( tparm_DS tparm1, tparm_DS tparm2, double sigma_ratio); void multi_normal_cn_class_merged_marginal( tparm_DS tparm0, tparm_DS tparm1, tparm_DS tparm, float wt_0, float wt_1, float wt_m); /* file model-single-normal-cm.c */ void sn_cm_params_influence_fn( model_DS model, tparm_DS tparm, int term_index,int n_att, float *v, float *class_mean, float *class_sigma, float *class_known_prob, float *global_mean, float *global_sigma, float *global_known_prob); void single_normal_cm_model_term_builder( model_DS model, term_DS term, int n_term); double single_normal_cm_log_likelihood( tparm_DS tparm); double single_normal_cm_update_l_approx( tparm_DS tparm); double single_normal_cm_update_m_approx( tparm_DS tparm); void single_normal_cm_update_params( tparm_DS tparm, int known_parms_p); int single_normal_cm_class_equivalence( tparm_DS tparm1,tparm_DS tparm2, double sigma_ratio); void single_normal_cm_class_merged_marginal( tparm_DS tparm0,tparm_DS tparm1,tparm_DS tparmm); /* file model-single-normal-cn.c */ void sn_cn_params_influence_fn( model_DS model, tparm_DS tparm, int term_index, int n_att, float *v, float *class_mean,float *class_sigma, float *global_mean, float *global_sigma); void single_normal_cn_model_term_builder( model_DS model, term_DS term, int n_term); double single_normal_cn_log_likelihood( tparm_DS tparm); double single_normal_cn_update_l_approx( tparm_DS tparm); double single_normal_cn_update_m_approx( tparm_DS tparm); void single_normal_cn_update_params( tparm_DS tparm, int known_parms_p); int single_normal_cn_class_equivalence( tparm_DS tparm1,tparm_DS tparm2, double sigma_ratio); void single_normal_cn_class_merged_marginal( tparm_DS tparm0,tparm_DS tparm1, tparm_DS tparmm); /* file model-single-multinomial.c */ void sm_params_influence_fn( model_DS model, tparm_DS term_params, int term_index, int n_att, float *influence_value, float **class_div_global_att_prob_list_ptr, int *length); void single_multinomial_model_term_builder( model_DS model, term_DS term, int n_term); double single_multinomial_log_likelihood( tparm_DS tparm); double single_multinomial_update_l_approx( tparm_DS tparm); double single_multinomial_update_m_approx( tparm_DS tparm); void single_multinomial_update_params( tparm_DS tparm, int known_parms_p); int single_multinomial_class_equivalence( tparm_DS tparm1, tparm_DS tparm2, double percent_ratio); void single_multinomial_class_merged_marginal( tparm_DS tparm1, tparm_DS tparm2, tparm_DS tparmm); /* file model-transforms.c */ int find_transform( database_DS d_base, shortstr transform, int *att_list, int length, FILE *log_file_fp, FILE *stream); int find_singleton_transform( database_DS d_base, shortstr transform, int att_index, FILE *log_file_fp, FILE *stream); int generate_singleton_transform( database_DS d_base, shortstr transform, int att_index, FILE *log_file_fp, FILE *stream); att_DS log_transform( int att_index, database_DS d_base); att_DS log_odds_transform_c( int att_index, database_DS d_base); /* file model-update.c */ void update_approximations( clsf_DS clsf); void update_parameters( clsf_DS clsf); int delete_null_classes(clsf_DS clsf); void update_wts( clsf_DS training_clsf, clsf_DS test_clsf); int most_probable_class_for_datum_i( int i,class_DS *classes, int n_classes); void update_ln_p_x_pi_theta( clsf_DS clsf, int no_change); /* file search-basic.c */ clsf_DS generate_clsf( int n_classes, FILE *header_file_fp, FILE *model_file_fp, FILE *log_file_fp, FILE *stream, int reread_p, int regenerate_p, char *data_file_ptr, char *header_file_ptr, char *model_file_ptr, char *log_file_ptr, int restart_p, char *start_fn_type, unsigned int initial_cycles_p, int n_data, int start_j_list_from_s_params); int random_set_clsf( clsf_DS clsf, int n_classes, int delete_duplicates, int display_wts, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream); clsf_DS set_up_clsf( int n_classes, database_DS database, model_DS *model_set, int n_models); void block_set_clsf( clsf_DS clsf, int n_classes, int block_size, int delete_duplicates, int display_wts, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream); int initialize_parameters( clsf_DS clsf, int display_wts, int delete_duplicates, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream); class_DS *delete_class_duplicates( int *num, class_DS *classes); /* file search-control.c */ int autoclass_search( char *data_file, char *header_file, char *model_file, char *search_params_file, char *search_file, char *results_file, char *log_file); int *remove_too_big( int limit, int *list, int *num); int too_big( int limit, int *list, int num); double within( double min_val, double x, double max_val); search_try_DS *safe_subseq_of_tries( search_try_DS *seq, int begin, int end, int num, int *newnum); void print_initial_report( FILE *stream, FILE *log_file_fp, int min_report_period, time_t end_time, int max_n_tries, char *search_file_ptr, char *results_file_ptr, char *log_file_ptr, int min_save_period, int n_save); void print_report( FILE *stream, FILE *log_file_fp, search_DS search, time_t last_save, time_t last_report, int reconverge_p, char *n_classes_explain); void print_final_report( FILE *stream, FILE *log_file_fp, search_DS search, time_t begin, time_t last_save, int n_save, char *stop_reason, unsigned int results_file_p, unsigned int search_file_p, int n_final_summary, char *log_file_ptr, char *search_params_file_ptr, char *results_file_ptr, clsf_DS clsf, int reconverge_p, time_t last_report, time_t last_trial); void print_search_try( FILE *stream, FILE *log_file_fp, search_try_DS try, int saved_p, int new_line_p, char *pad, unsigned int comment_data_headers_p); void empty_search_try( search_try_DS try); int total_try_time( search_try_DS *tries, int n_tries); search_try_DS try_variation( clsf_DS clsf, int j_in, int trial_n, char *reconverge_type, char *start_fn_type, char *try_fn_type, unsigned int initial_cycles_p, time_t begin_try, double halt_range, double halt_factor, double rel_delta_range, int max_cycles, int n_average, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream); int search_duration( search_DS search, time_t now, clsf_DS clsf, time_t last_save, int reconverge_p); int converge( clsf_DS clsf, int n_average, double halt_range, double halt_factor, double delta_factor, int display_wts, int min_cycles, int max_cycles, int converge_print_p, FILE *log_file_fp, FILE *stream); int converge_search_3( clsf_DS clsf, double rel_delta_range, int display_wts, int min_cycles, int max_cycles, int n_average, int converge_print_p, FILE *log_file_fp, FILE *stream); int converge_search_3a( clsf_DS clsf, double rel_delta_range, int display_wts, int min_cycles, int max_cycles, int n_average, int converge_print_p, FILE *log_file_fp, FILE *stream); int converge_search_4( clsf_DS clsf, int display_wts, int min_cycles, int max_cycles, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream); int min_n_peaks( int n_dups, int n_dup_tries); double avg_time_till_improve( int time_so_far, int n_peaks_seen); double ln_avg_p( double ln_p_avg, double ln_p_sigma); double min_best_peak( int min_n_peak, double ln_p_avg, double ln_p_sigma); int random_j_from_ln_normal( int n_tries, search_try_DS *tries, int max_j, int explain_p, char *n_classes_explain); double random_from_normal( double mean, double sigma); double typical_best(int n_samples, double mean, double sigma); double cut_where_above( double percent); double erfc_poly( double z); double approx_inverse_erfc( double area, double z_try); double inverse_erfc( double area); double interpolate( float table[][2], int length, double key); void upper_end_normal_fit( search_try_DS *tries, int n_tries, float *ln_p_avg, float *ln_p_sigma); double average( float *list, int length); double variance( float *list, int length, double avg); double sigma( float *list, int num, double ln_p_avg); double avg_improve_delta_ln_p( int n_peaks, double ln_p_sigma); double next_best_delta( int n_samples, double sigma); int min_time_till_best( int time_so_far,int min_n_peak,int n_peaks_seen); void save_search( search_DS search, char *search_file_ptr, time_t last_save, clsf_DS clsf, int reconverge_p, int_list start_j_list, int n_final_summary, int n_save); void write_search_DS( FILE *search_file_fp, search_DS search, int_list start_j_list, int n_final_summary, int n_save); void write_search_try_DS( search_try_DS try, shortstr id, int try_num, FILE *search_file_fp ); search_DS get_search_DS(void); search_DS copy_search_wo_tries( search_DS search); search_DS reconstruct_search( FILE *search_file_fp, char *search_file_ptr, char *results_file_ptr); search_DS get_search_from_file( FILE *search_file_fp, char *search_file_ptr); void get_search_try_from_file( search_DS search, search_try_DS parent_try, int try_index, FILE *search_file_fp, char *search_file_ptr); int find_duplicate( search_try_DS try, search_try_DS *tries, int n_store, int *n_dup_tries_ptr, double rel_error, int n_tries, int restart_p); search_try_DS *insert_new_trial( search_try_DS try, search_try_DS *tries, int n_tries, int n_store, int max_n_store); void describe_clsf( clsf_DS clsf, FILE *stream, FILE *log_file_fp); void print_log (double log_number, FILE *log_file_fp, FILE *stream, int verbose_p); void apply_search_start_fn (clsf_DS clsf, char *start_fn_type, unsigned int initial_cycles_p, int j_in, FILE *log_file_fp, FILE *stream); int apply_search_try_fn (clsf_DS clsf, char *try_fn_type, double halt_range, double halt_factor, double rel_delta_range, int max_cycles, int n_average, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream); int apply_n_classes_fn ( char *n_classes_fn_type, int n_tries, search_try_DS *tries, int max_j, int explain_p, char *n_classes_explain); int validate_search_start_fn (char *start_fn_type); int validate_search_try_fn (char *try_fn_type); int validate_n_classes_fn (char *n_classes_fn_type); void describe_search( search_DS search); /* file search-converge.c */ double base_cycle( clsf_DS clsf, FILE *stream, int display_wts, int converge_cycle_p); /* file statistics.c */ void central_measures_x( float **data, int n_data, int n_att, float *wts, double est_mean, float *unknown, float *known, float *mean, float *variance, float *skewness, float *kurtosis); /* file struct-class.c */ void store_class_DS( class_DS cl, int max_n_classes); class_DS get_class_DS( model_DS model, int n_data, int want_wts_p, int check_model); class_DS pop_class_DS( model_DS model, int n_data, int want_wts_p); class_DS build_class_DS( model_DS model, int n_data, int want_wts_p); class_DS build_compressed_class_DS( model_DS comp_model); class_DS copy_class_DS(class_DS from_class, int n_data, int want_wts_p); /*JTP, classes_to_check) JTP*/ class_DS copy_to_class_DS(class_DS from_class, class_DS to_class, int n_data, int want_wts_p); int class_DS_test( class_DS cl1, class_DS cl2, double rel_error); tparm_DS copy_tparm_DS(tparm_DS old); void free_class_DS( class_DS class, char *type, clsf_DS clsf, int i_class); void free_tparm_DS( tparm_DS tparm); void **list_class_storage ( int print_p); double class_strength_measure( class_DS class); /* file struct-clsf.c */ void push_clsf( clsf_DS clsf); clsf_DS pop_clsf(void); clsf_DS get_clsf_DS( int n_classes); void adjust_clsf_DS_classes( clsf_DS clsf, int n_classes); void display_step( clsf_DS clsf, FILE *stream); clsf_DS create_clsf_DS( void); int clsf_DS_max_n_classes( clsf_DS clsf); clsf_DS copy_clsf_DS( clsf_DS cold, int want_wts_p); int clsf_DS_test( clsf_DS clsf1, clsf_DS clsf2, double rel_error); void store_clsf_DS_classes( clsf_DS clsf, class_DS *check_classes, int length); void store_clsf_DS( clsf_DS clsf, class_DS *check_classes, int length); float *clsf_DS_w_j( clsf_DS clsf); void **list_clsf_storage ( clsf_DS clsf, search_DS search, int print_p, int list_global_clsf_p); void free_clsf_DS( clsf_DS clsf); char *clsf_att_type( clsf_DS clsf, int n_att); void free_clsf_class_search_storage( clsf_DS clsf, search_DS search, int list_global_clsf_p); /* struct-data.c */ database_DS find_database( char *data_file_ptr, char *header_file_ptr, int n_data); int db_DS_same_source_p( database_DS db1, database_DS db2); int every_db_DS_same_source_p( database_DS db1, model_DS *models); database_DS compress_database( database_DS db); int db_DS_equal_p( database_DS db1, database_DS db2); int att_DS_equal_p( att_DS att1, att_DS att2); database_DS create_database( void); database_DS expand_database( database_DS comp_database); int extend_database( database_DS db, database_DS comp_db); int db_same_source_p( database_DS db, database_DS comp_db); int att_info_equal( database_DS db, database_DS comp_db); int att_props_equivalent_p( att_DS att_1, att_DS att_2); int att_stats_equivalent_p( att_DS att_1, att_DS att_2); /* file struct-matrix.c */ fptr *compute_factor( fptr *factor, int num); float *solve( fptr *fs, float *b, int num); /* file struct-model.c */ model_DS find_similar_model( char *model_file, int file_index, database_DS database); int model_DS_equal_p( model_DS m1, model_DS m2); model_DS expand_model( model_DS comp_model); model_DS find_model( char *model_file_ptr, int file_index, database_DS database); void free_model_DS( model_DS model, int i_model); /* utils.c */ void to_screen_and_log_file( fxlstr msg, FILE *log_file_fp, FILE *stream, int output_p); time_t get_universal_time(void); char *format_universal_time(time_t universal_time); char *format_time_duration (time_t delta_universal_time); int iround( double number); /* long int round_osx( double number); */ int int_compare_less (int *i_ptr, int *j_ptr); int int_compare_greater (int *i_ptr, int *j_ptr); int eqstring( char *str1, char *str2); float *fill( float *wts, double info, int num, int end); void checkpoint_clsf( clsf_DS clsf); int *delete_duplicates( int *list, int num); double max_plus( float *fl, int num); int class_duplicatesp( int n_classes, class_DS *classes); int find_term( term_DS term,term_DS *terms, int n_terms); int find_class( class_DS class, class_DS class_store); int find_class_test2( class_DS class, clsf_DS clsf, double rel_error); int find_database_p( database_DS data, database_DS *databases, int n_data); int find_model_p( model_DS model, model_DS *models, int n_models); int member_int( int val, int *list, int num); int find_str_in_table( char *str, shortstr table[], int num); int new_random( int n_data, int *used_list, int num); float *randomize_list( float *y, int n); int y_or_n_p(fxlstr str); float *reverse( float *flist, int n); double sigma_sq( int n, double sum, double sum_sq, double min_variance); int char_input_test( void); int percent_equal( double n1, double n2, double rel_error); int prefix(char *str, char *substr); void *getf( void ***list, char *property, int num); void *get( char *target,char *property); void add_property( shortstr target, shortstr pname, void *value); void add_to_plist ( att_DS att, char *target, void *value, char *type); void write_vector_float(float *vector, int n, FILE *stream); void write_matrix_float( float **vector, int m, int n, FILE *stream); void write_matrix_integer( int **vector, int m, int n, FILE *stream); void read_vector_float(float *vector, int n, FILE *stream); void read_matrix_float( float **vector, int m, int n, FILE *stream); void read_matrix_integer( int **vector, int m, int n, FILE *stream); int discard_comment_lines (FILE *stream); void flush_line (FILE *stream); int read_char_from_single_quotes (char *param_name, FILE *stream); int strcontains( char *str, int c); int output_int_list( int_list list, FILE *log_file_fp, FILE *stream); int pop_int_list( int *list, int *n_list, int *value); void push_int_list( int *list, int *n_list, int value, int max_n_list); int member_int_list( int val, int_list list); int float_sort_cell_compare_gtr( sort_cell_DS i_cell, sort_cell_DS j_cell); int class_case_sort_compare_lsr( xref_data_DS i_xref, xref_data_DS j_xref); int att_i_sum_sort_compare_gtr( ordered_influ_vals_DS i_influ_val, ordered_influ_vals_DS j_influ_val); int float_p_p_star_compare_gtr( formatted_p_p_star_DS i_formatted_p_p_star, formatted_p_p_star_DS j_formatted_p_p_star); void safe_fprintf( FILE *stream, char *caller, char *format, ...); void safe_sprintf( char *str, int str_length, char *caller, char *format, ...); /* utils-math.c */ /* put in separate file 06nov94 wmt */ double log_gamma( double x, int low_precision); int atoi_p (char *string_num, int *integer_p_ptr); double atof_p (char *string_num, int *float_p_ptr); double safe_exp( double x); void mean_and_variance( double *vector, int cnt, double *mean_ptr, double *variance_ptr); double safe_log( double x); /* getparams.c */ void putparams( FILE *fp, PARAMP pp, int only_overridden_p); int getparams( FILE *fp, PARAMP params); void defparam( PARAMP params, int nparams, char *name, PARAMTYPE type, void *ptr, int max_length); /* prints.c */ void print_vector_f(float *v, int n, char *t); void sum_vector_f( float *v, int n, char *t); void print_matrix_f( float **v, int m, int n, char *t); void print_matrix_i( int **v, int m, int n, char *t); void print_mm_d_params(struct mm_d_param p, int n); void print_mm_s_params( struct mm_s_param p, int n); void print_mn_cn_params( struct mn_cn_param p, int n); void print_sm_params( struct sm_param p, int n); void print_sn_cm_params( struct sn_cm_param p, int n); void print_sn_cn_params( struct sn_cn_param p, int n); void print_tparm_DS( tparm_DS p, char *t); void print_priors_DS( priors_DS p, char *t); void print_class_DS( class_DS p , char *t); void print_term_DS ( term_DS p, char *t); void print_real_stats_DS( real_stats_DS p, char *t); void print_discrete_stats_DS( discrete_stats_DS p, char *t); void print_att_DS( att_DS p, char *t); void print_database_DS( database_DS p, char *t); void print_model_DS( model_DS p, char *t); void print_clsf_DS( clsf_DS p, char *t); void print_search_try_DS( search_try_DS p, char *t); void print_search_DS( search_DS p, char *t); /* autoclass.c */ void autoclass_args (void); /* intf-reports.c */ int autoclass_reports( char *results_file_ptr, char *search_file_ptr, char *reports_params_file_ptr, char *influ_vals_file_ptr, char *xref_class_file_ptr, char *xref_case_file_ptr, char *test_data_file, char *log_file_ptr); int clsf_search_validity_check( clsf_DS clsf, search_DS search); void influence_values_report_streams( clsf_DS clsf, search_DS search, int num_atts_to_list, shortstr report_mode, char *influ_vals_file_ptr, char *results_file_ptr, int clsf_num, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p, int_list sigma_contours_att_list); xref_data_DS case_class_data_sharing( clsf_DS clsf, shortstr report_mode, shortstr report_type, char *xref_class_file_ptr, char *xref_case_file_ptr, char *results_file_ptr, int_list xref_class_report_att_list, int clsf_num, clsf_DS test_clsf, int last_classification_p, int prediction_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs); xref_data_DS case_report_streams( clsf_DS clsf, shortstr report_mode, char *xref_case_file_ptr, char *results_file_ptr, xref_data_DS xref_data, int clsf_num, clsf_DS test_clsf, int last_classification_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs); xref_data_DS class_report_streams( clsf_DS clsf, shortstr report_mode, char *x_class_file_ptr, char *results_file_ptr, int_list xref_class_report_att_list, xref_data_DS xref_data, int clsf_num, clsf_DS test_clsf, int last_classification_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs); xref_data_DS xref_get_data( clsf_DS clsf, char *type, int_list report_attributes, xref_data_DS xref_data, int last_classification_p, int prediction_p, int max_num_xref_class_probs); int map_class_num_clsf_to_report( clsf_DS clsf, int clsf_n_class); int map_class_num_report_to_clsf( clsf_DS clsf, int report_n_class); void autoclass_xref_by_case_report( clsf_DS clsf, FILE *xref_case_report_fp, shortstr report_mode, xref_data_DS xref_data, char *results_file_ptr, clsf_DS test_clsf, int last_classification_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs); void classification_header( clsf_DS clsf, char *results_file_ptr, FILE *xref_case_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int comment_data_headers_p); void xref_paginate_by_case( xref_data_DS xref_data, int n_data, FILE *xref_case_report_fp, shortstr report_mode, int initial_line_cnt_max, unsigned int comment_data_headers_p); void xref_output_page_headers( char *type, int page_1_p, int num_report_attribute_strings, rpt_att_string_DS *report_attribute_strings, FILE *xref_case_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); void autoclass_xref_by_class_report( clsf_DS clsf, FILE *xref_class_report_fp, shortstr report_mode, xref_data_DS xref_data, int_list report_attributes, char *results_file_ptr, clsf_DS test_clsf, int last_classification_p, unsigned int comment_data_headers_p, int max_num_xref_class_probs); void xref_paginate_by_class( clsf_DS clsf, xref_data_DS xref_data, int_list report_attributes, FILE *xref_class_report_fp, shortstr report_mode, int initial_line_cnt, unsigned int comment_data_headers_p); rpt_att_string_DS *xref_class_report_attributes( clsf_DS clsf, int_list report_attribute_numbers, shortstr **attribute_formats_ptr, int *prob_tab_ptr); void xref_paginate_by_class_hdrs( FILE *xref_class_report_fp, shortstr report_mode, int *cnt_ptr, int line_cnt, sort_cell_DS wt_class_pairs, int init, int num_report_attribute_strings, rpt_att_string_DS *report_attribute_strings, unsigned int comment_data_headers_p); void xref_output_line_by_class( clsf_DS clsf, FILE *xref_class_report_fp, shortstr report_mode, shortstr **attribute_formats_ptr, xref_data_DS xref_datum_ptr, sort_cell_DS wt_class_pairs, int prob_tab, int_list report_attribute_numbers, unsigned int comment_data_headers_p); void autoclass_influence_values_report( clsf_DS clsf, search_DS search, int num_atts_to_list, char *results_file_ptr, int header_information_p, FILE *influence_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p, int_list sigma_contours_att_list); void influence_values_header( clsf_DS clsf, search_DS search, char *results_file_ptr, int header_information_p, FILE *influence_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int comment_data_headers_p); void autoclass_class_influence_values_report( clsf_DS clsf, search_DS search, char *class_number_type, int report_class_number, int num_atts_to_list, int header_information_p, char *results_file_ptr, int single_class_p, FILE *influence_report_fp, shortstr report_mode, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p, int_list sigma_contours_att_list); int populated_class_p( int clsf_class_number, char *class_number_type, clsf_DS clsf); ordered_influ_vals_DS ordered_normalized_influence_values( clsf_DS clsf); void influence_values_explanation( FILE *influence_report_fp); void search_summary( search_DS search, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); void class_weights_and_strengths( clsf_DS clsf, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); void class_divergences( clsf_DS clsf, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); void text_stream_header( int single_class_p, FILE *influence_report_fp, shortstr report_mode, int header_information_p, clsf_DS clsf, search_DS search, char *results_file_ptr, char *title_line_1, char *title_line_2, clsf_DS test_clsf, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p); void pre_format_attributes( clsf_DS clsf, int clsf_class_number, int num_atts_to_list, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence__fp, shortstr report_mode, unsigned int order_attributes_by_influence_p, unsigned int comment_data_headers_p); void print_attribute_header( int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); int format_attribute( clsf_DS clsf, int clsf_class_number, int n_att, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); int format_discrete_attribute( int n_att, database_DS d_base, char *header, char *header_continued, i_discrete_DS influence_values, int line_length, char *description, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); int format_integer_attribute( char *header, char *header_continued, i_integer_DS influence_values, int line_length, char *description, char *model_term_type_symbol, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p); int format_real_attribute( char *header, char *header_continued, i_real_DS influence_values, int line_length, int n_att, char *description, char *model_term_type_symbol, int line_cnt, int discrete_atts_header_p, int real_atts_header_p, FILE *influence_report_fp, shortstr report_mode, unsigned int comment_data_headers_p, int clsf_class_number, clsf_DS clsf); void generate_mncn_correlation_matrices ( clsf_DS clsf, int clsf_class_number, shortstr report_mode, unsigned int comment_data_headers_p, FILE *influence_report_fp); int attribute_model_term_number( int n_att, model_DS model); void sort_mncn_attributes( sort_cell_DS sort_list, int sort_index, int term_count, clsf_DS clsf, int clsf_class_number); char *filter_e_format_exponents ( fxlstr e_format_string); /* intf-extensions.c */ clsf_DS *initialize_reports_from_results_pathname( char *results_file_ptr, int_list clsf_n_list, int *num_clsfs_found_ptr, int prediction_p); clsf_DS init_clsf_for_reports( clsf_DS clsf, int prediction_p); int *get_class_weight_ordering( clsf_DS clsf); char ***get_attribute_model_term_types( clsf_DS clsf); char *report_att_type( clsf_DS clsf, int n_class, int n_att); char *rpt_att_model_term_type( clsf_DS clsf, int n_class, int n_att); void get_models_source_info( model_DS *models, int num_models, FILE *xref_case_text_fp, unsigned int comment_data_headers_p); void get_class_model_source_info( class_DS class, char *class_model_source, unsigned int comment_data_headers_p); /* intf-influence-values.c */ void compute_influence_values( clsf_DS clsf); double influence_value( int n_class, int n_att, clsf_DS clsf, char *att_type, void **influence_struct_DS_ptr); int find_attribute_modeling_class( clsf_DS clsf, int n_class, int n_att, class_DS *class_ptr); /* predictions.c */ clsf_DS autoclass_predict( char *data_file_ptr, clsf_DS training_clsf, clsf_DS test_clsf, FILE *log_file_fp, char *log_file_ptr); int same_model_and_attributes( clsf_DS clsf1, clsf_DS clsf2); /* intf-sigma-contours.c */ void generate_sigma_contours ( clsf_DS clsf, int clsf_class_number, int_list att_list, FILE *influence_report_fp, int comment_data_headers_p); int compute_sigma_contour_for_2_atts ( clsf_DS clsf, int clsf_class_number, int att_x, int att_y, int trans_att_x, int trans_att_y, int term_index_x, int term_index_y, float *mean_x, float *sigma_x, float *mean_y, float *sigma_y, float *rotation); int class_att_loc( class_DS class, int att_index, int *trans_att_index); float get_sigma_x_y (int att_x, int att_y, class_DS class, int n_term_list, float *term_list, float **covariance); autoclass-3.3.6.dfsg.1/prog/minmax.h0000644000175000017500000000014211247310756015316 0ustar areare#ifndef _MSC_VER #define min(a,b) ( (a)<(b)?(a):(b) ) #define max(a,b) ( (a)>(b)?(a):(b) ) #endif autoclass-3.3.6.dfsg.1/prog/search-converge.c0000644000175000017500000000323311247310756017077 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* Performs a `recalculate likelihoods & update' cycle, eliminating null classes. */ /* BASE_CYCLE 30jan95 wmt: add checkpointing logic from ac-x */ double base_cycle( clsf_DS clsf, FILE *stream, int display_wts, int converge_cycle_p) { int n_stored, i; update_wts( clsf, clsf); n_stored = delete_null_classes(clsf); if ((display_wts == TRUE) && (n_stored > 0) && (stream != NULL)) fprintf(stream, "%d null classes stored from base-cycle.\n", n_stored); update_parameters(clsf); update_approximations(clsf); if ((display_wts == TRUE) && (stream != NULL)) display_step(clsf, stream); if ((eqstring( G_checkpoint_file, "") != TRUE) && (converge_cycle_p == TRUE) && (clsf->n_classes > 1) && ((get_universal_time() - G_last_checkpoint_written) > G_min_checkpoint_period)) checkpoint_clsf( clsf); if ((converge_cycle_p == TRUE) && (stream != NULL)) fprintf(stream, "."); /* show cycle */ /* fprintf(stderr, "base_cycle - log_a_x_h=%e\n", clsf->log_a_x_h); */ if (G_clsf_storage_log_p == TRUE) { fprintf( stream, "\n"); for (i=0; in_classes; i++) fprintf( stream, "%.4d ", iround( (double) clsf->classes[i]->w_j)); } return (clsf->log_a_x_h); } autoclass-3.3.6.dfsg.1/prog/semantic.cache0000644000175000017500000042177311247310756016465 0ustar areare;; Object prog/ ;; SEMANTICDB Tags save file (semanticdb-project-database-file "prog/" :tables (list (semanticdb-table "autoclass.h" :major-mode 'c++-mode :tags '(("square" function (:arguments (("" variable (:type ("x" type (:type "class") nil nil)) (reparse-symbol arg-sub-list) [1413 1415])) :type "int") nil [1406 1428])) :file "autoclass.h" :pointmax 77318 :unmatched-syntax 'nil ) (semanticdb-table "globals.c" :major-mode 'c-mode :tags 'nil :file "globals.c" :pointmax 1770 ) (semanticdb-table "search-basic.c" :major-mode 'c-mode 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"class") nil nil)) (reparse-symbol arg-sub-list) [70970 70987])) :type "void") nil [70948 71363])) :file "search-control-2.c" :pointmax 71364 ) (semanticdb-table "search-converge.c" :major-mode 'c-mode :tags '(("base_cycle" function (:arguments (("clsf" variable (:type ("clsf_DS" type (:type "class") nil nil)) (reparse-symbol arg-sub-list) [600 613]) ("" variable (:type ("FILE" type (:type "class") nil nil)) (reparse-symbol arg-sub-list) [614 620]) ("" variable (:type ("stream" type (:type "class") nil nil)) (reparse-symbol arg-sub-list) [620 627]) ("display_wts" variable (:type "int") (reparse-symbol arg-sub-list) [628 644]) ("converge_cycle_p" variable (:type "int") (reparse-symbol arg-sub-list) [645 666])) :type "double") nil [581 1691])) :file "search-converge.c" :pointmax 1692 ) (semanticdb-table "autoclass.make" :major-mode 'makefile-mode :tags 'nil :file "autoclass.make" :pointmax 93 ) ) :file "semantic.cache" :semantic-tag-version "2.0beta3" :semanticdb-version "2.0beta3" ) autoclass-3.3.6.dfsg.1/prog/struct-clsf.c0000644000175000017500000004271011247310756016300 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* PUSH_CLSF 09jan95 wmt: add G_clsf_storage_log_p printout */ void push_clsf( clsf_DS clsf) { int n_global_clsfs = 0; clsf_DS stored_clsf; clsf->next = G_clsf_store; G_clsf_store = clsf; if (G_clsf_storage_log_p == TRUE) { if (G_clsf_store != NULL) { n_global_clsfs++; stored_clsf = G_clsf_store->next; while (stored_clsf != NULL) { n_global_clsfs++; stored_clsf = stored_clsf->next; } } fprintf(stdout, "\npush_clsf(%.2d): %p\n", n_global_clsfs, (void *) clsf); } } /* POP_CLSF 09jan95 wmt: add G_clsf_storage_log_p printout */ clsf_DS pop_clsf(void) { clsf_DS temp = NULL, stored_clsf; int n_global_clsfs = 0; if (G_clsf_store != NULL) { temp = G_clsf_store; G_clsf_store = G_clsf_store->next; if (G_clsf_storage_log_p == TRUE) { if (G_clsf_store != NULL) { n_global_clsfs++; stored_clsf = G_clsf_store->next; while (stored_clsf != NULL) { n_global_clsfs++; stored_clsf = stored_clsf->next; } } fprintf(stdout, "\npop_clsf(%.2d): %p\n", n_global_clsfs, (void *) temp); } } return(temp); } /* GET_CLSF_DS 09jan95 wmt: pass clsf_DS_max_n_classes to store_class_DS */ clsf_DS get_clsf_DS( int n_classes) { int i; clsf_DS clsf; clsf = pop_clsf(); if (clsf != NULL) { if (clsf->n_classes < n_classes) { if (clsf->classes == NULL) clsf->classes = (class_DS *) malloc(n_classes * sizeof(class_DS)); else clsf->classes = (class_DS *) realloc(clsf->classes, n_classes * sizeof(class_DS)); } else for (i=n_classes; in_classes; i++) { store_class_DS(clsf->classes[i], clsf_DS_max_n_classes(clsf)); clsf->classes[i] = NULL; } /* note that could do realloc here to shrink but didnt to save time */ } else { clsf = create_clsf_DS(); clsf->classes = (class_DS *) malloc(n_classes * sizeof(class_DS)); } clsf->n_classes = n_classes; return(clsf); } /* ADJUST_CLSF_DS_CLASSES 09jan95 wmt: pass clsf_DS_max_n_classes to store_class_DS 10apr97 wmt: add database->n_data to get_class_DS call This adjusts the fill-pointer vector clsf-DS-classes, to be at least n-classes long and to contain exactly n-classes class structures. */ void adjust_clsf_DS_classes( clsf_DS clsf, int n_classes) { model_DS *models; int j, n_models, old_n_classes; class_DS cl, *classes; /* Extend classes vector as needed. */ models = clsf->models; n_models = clsf->num_models; old_n_classes = clsf->n_classes; if (old_n_classes < n_classes) { if (clsf->classes == NULL) clsf->classes = (class_DS *) malloc(n_classes * sizeof(class_DS)); else clsf->classes = (class_DS *) realloc(clsf->classes, n_classes * sizeof(class_DS)); } classes = clsf->classes; if (old_n_classes < n_classes) /* Add classes to classes vector */ for (j=old_n_classes; jdatabase->n_data, TRUE, FALSE); else if (old_n_classes > n_classes) /* Remove & store excess classes. */ for (j=n_classes; jn_classes = n_classes; } /* DISPLAY_STEP 24jan95 wmt: free wts Displays the approximate marginal probability and class weights. */ void display_step( clsf_DS clsf, FILE *stream) { int i; float *wts; wts = clsf_DS_w_j(clsf); fprintf(stream,"\ndisplaying weights for %d classes", clsf->n_classes); for (i=0; in_classes; i++) fprintf(stream, " %f", wts[i]); if (clsf->n_classes > 0) fprintf(stream, "\n"); free( wts); } /* CREATE_CLSF_DS 24oct94 wmt: added init for att_max_i_sum 13nov94 wmt: added checkpoint struct 24jan95 wmt: init temp->next 02feb95 wmt: allocate reports struct */ clsf_DS create_clsf_DS( void) { clsf_DS temp; chkpt_DS temp_chkpt; temp = (clsf_DS) malloc(sizeof(struct classification)); if (G_clsf_storage_log_p == TRUE) { fprintf(stdout, "\ncreate_clsf_DS: %p\n", (void *) temp); } temp->log_p_x_h_pi_theta = 0.0; temp->log_a_x_h = 0.0; temp->database = NULL; temp->models = NULL; temp->num_models = 0; temp->n_classes = 0; temp->classes = NULL; temp->min_class_wt = 0.0; /* allocate checkpoint struct */ temp_chkpt = (chkpt_DS) malloc( sizeof( struct checkpoint)); temp_chkpt->accumulated_try_time = 0; temp_chkpt->current_try_j_in = 0; temp_chkpt->current_cycle =0; temp->checkpoint = temp_chkpt; temp->reports = NULL; temp->next = NULL; return(temp); } /*commented JTP not called and wouldnt work int clsf_DS_n_classes(clsf) clsf_DS clsf; { return(sizeof(clsf) / sizeof(clsf_DS)); }*commented */ /* CLSF_DS_MAX_N_CLASSES 11jan95 wmt: change computation -- put in floor */ int clsf_DS_max_n_classes( clsf_DS clsf) { return (floor( (double) (((float) clsf->database->n_data) / (1.1 * clsf->min_class_wt)))); } /* COPY_CLSF_DS 09dec94 wmt: realloc 1st arg cnew->num_models => cnew->models 19jan95 wmt: copy num_wts, so it can be used to allocating storage in read_class_DS_s. revised by struct classification changes 24jan95 wmt: change want_wts_p from local variable to parameter 10apr97 wmt: add database->n_data to copy_class_DS call */ clsf_DS copy_clsf_DS( clsf_DS cold, int want_wts_p) { int i; clsf_DS cnew; cnew = get_clsf_DS(cold->n_classes); cnew->log_p_x_h_pi_theta = cold->log_p_x_h_pi_theta; cnew->log_a_x_h = cold->log_a_x_h; cnew->database = cold->database; if (cnew->num_models != cold->num_models){ if (cnew->models == NULL) cnew->models=(model_DS *) malloc(cold->num_models * sizeof(model_DS)); else cnew->models=(model_DS *) realloc(cnew->models, cold->num_models * sizeof(model_DS)); } cnew->num_models = cold->num_models; for(i=0;inum_models;i++) cnew->models[i] = cold->models[i]; cnew->n_classes = cold->n_classes; for(i=0;in_classes;i++) { cnew->classes[i] = copy_class_DS( cold->classes[i], cold->database->n_data, want_wts_p); cnew->classes[i]->num_wts = cold->classes[i]->num_wts; } /* reports do not need to be copied */ cnew->min_class_wt = cold->min_class_wt; cnew->checkpoint->accumulated_try_time = cold->checkpoint->accumulated_try_time; cnew->checkpoint->current_try_j_in = cold->checkpoint->current_try_j_in; cnew->checkpoint->current_cycle = cold->checkpoint->current_cycle; return(cnew); } /* CLSF_DS_TEST 18oct94 wmt: modified 18may95 wmt: Remove "db_ds_same_source_p" and use "db_same_source_p", instead. Here we use search to eliminate any need for a canonical ordering. The cost in search time is On^2 on the classes. */ int clsf_DS_test( clsf_DS clsf1, clsf_DS clsf2, double rel_error) { int i, found = 1; /* fprintf( stderr, "\nnew_clsf %.4f old_clsf %.4f\n", clsf1->log_a_x_h, clsf2->log_a_x_h); */ if ((db_same_source_p( clsf1->database, clsf2->database) == TRUE) && (clsf1->n_classes == clsf2->n_classes) && (percent_equal( clsf1->log_a_x_h, clsf2->log_a_x_h, rel_error) == TRUE)) { /* For each class in clsf-2, the following seeks a %= class in clsf-1: */ for (i=0; in_classes; i++) { /* fprintf( stderr, "\nnew_clsf class %d\n", i); */ if (find_class_test2( clsf2->classes[i], clsf1, rel_error) == FALSE) { found = 0; break; /* do not break for debug */ } } if (found == 1) return(TRUE); else return(FALSE); } else { /* debug for (i=0; in_classes; i++) { fprintf( stderr, "\nnew_clsf class %d\n", i); find_class_test2( clsf2->classes[i], clsf1, rel_error); } */ return(FALSE); } } /* STORE_CLSF_DS_CLASSES 09jan95 wmt: pass clsf_DS_max_n_classes to store_class_DS Classification storage management: Push all classes in 'clsf onto the appropriate class-store for recycling, and reset the 'clsf's counters. Discards class pointers duplicated in check-classes. The duplicate checking is not normally necessary, but there have been pathlogical cases where two classifications have pointers to the same class after breaks. */ void store_clsf_DS_classes( clsf_DS clsf, class_DS *check_classes, int length) { int i, n_classes; class_DS *classes; n_classes = clsf->n_classes; classes = clsf->classes; /* Clean up everything! */ for (i=0; iclasses); clsf->classes = NULL; clsf->n_classes = 0; } /* STORE_CLSF_DS 09jan95 wmt: check for clsf == temp; NULL database after calling store_clsf_DS_classes 20jan95 wmt: revised by struct classification changes This stores the classes and classification for recycling. */ void store_clsf_DS( clsf_DS clsf, class_DS *check_classes, int length) { int found; clsf_DS temp; clsf->log_p_x_h_pi_theta = 0.0; clsf->log_a_x_h = 0.0; store_clsf_DS_classes(clsf, check_classes, length); /* no dont free this, its the real database JTP free(clsf->database); */ clsf->database = NULL; /* dont free since will probably need same number next time*/ /* commented if(clsf->models != NULL) free(clsf->models); clsf->models = NULL; clsf->num_models = 0; ***** commented */ clsf->min_class_wt = 0.0; found = 0; temp = G_clsf_store; if (clsf == temp) found = 1; while (found == 0 && temp != NULL) { if (clsf == temp->next) found = 1; temp = temp->next; } if (found == 0) push_clsf(clsf); } float *clsf_DS_w_j( clsf_DS clsf) { int i, n_classes = clsf->n_classes; float *wts; class_DS *classes = clsf->classes; wts = (float *) malloc(n_classes * sizeof(float)); for (i=0; iw_j; return(wts); } /* LIST_CLSF_STORAGE 06jan95 wmt: new 18feb95 wmt: add list_global_clsf_p 05feb97 wmt: change ptr types from int to void * list the storage pointers to active clsf structures from current clsf and search structures and from global clsf store return list of clsf pointers terminated by END_OF_INT_LIST */ void **list_clsf_storage ( clsf_DS clsf, search_DS search, int print_p, int list_global_clsf_p) { void **clsf_list_ptr = NULL; int num_clsf_ptrs = 0, n_try, n_model; int n_global_clsfs = 0; clsf_DS stored_clsf, global_clsf; if (clsf == NULL) { fprintf(stderr, "\n NULL clsf passed to list_clsf_storage\n"); abort(); } if (print_p == TRUE) printf("clsf: %p\n", (void *) clsf); num_clsf_ptrs++; clsf_list_ptr = (void **) malloc( num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = (void *) clsf; if (list_global_clsf_p == TRUE) { for (n_model=0; n_modelnum_models; n_model++) { if ((global_clsf = clsf->models[n_model]->global_clsf) != NULL) { if (print_p == TRUE) printf("model global clsf: %p\n", (void *) global_clsf); num_clsf_ptrs++; clsf_list_ptr = (void **) realloc( clsf_list_ptr, num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = (void *) global_clsf; } } } if (search != NULL) { for (n_try=0; n_tryn_tries; n_try++) { if (search->tries[n_try]->clsf != NULL) { if (print_p == TRUE) printf("search_try_clsf %d: %p\n", n_try + 1, (void *) search->tries[n_try]->clsf); num_clsf_ptrs++; clsf_list_ptr = (void **) realloc( clsf_list_ptr, num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = (void *) search->tries[n_try]->clsf; } } } if (G_clsf_store != NULL) { n_global_clsfs++; if (print_p == TRUE) printf("G_clsf_store %d: %p\n", n_global_clsfs, (void *) G_clsf_store); num_clsf_ptrs++; clsf_list_ptr = (void **) realloc( clsf_list_ptr, num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = (void *) G_clsf_store; stored_clsf = G_clsf_store->next; while (stored_clsf != NULL) { n_global_clsfs++; if (print_p == TRUE) printf("G_clsf_store %d: %p\n", n_global_clsfs, (void *) stored_clsf); num_clsf_ptrs++; clsf_list_ptr = (void **) realloc( clsf_list_ptr, num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = (void *) stored_clsf; stored_clsf = stored_clsf->next; } } num_clsf_ptrs++; clsf_list_ptr = (void **) realloc( clsf_list_ptr, num_clsf_ptrs * sizeof( void *)); clsf_list_ptr[num_clsf_ptrs - 1] = NULL; return( clsf_list_ptr); } /* FREE_CLSF_DS 06jan95 wmt: new 30jun00 wmt: add pointer checks before freeing free storage for clsf and its classes and class parameters */ void free_clsf_DS( clsf_DS clsf) { int i_class, i_att, n_classes = 0, n_atts; class_DS class; if (G_clsf_storage_log_p == TRUE) fprintf(stdout, "free_clsf: %p\n", (void *) clsf); if (clsf->classes != NULL) { n_classes = clsf->n_classes; for (i_class=0; i_class < clsf->n_classes; i_class++) { class = clsf->classes[i_class]; free_class_DS( class, "clsf", clsf, i_class); } free( clsf->classes); } if (clsf->reports != NULL) { n_atts = clsf->database->n_atts; if (clsf->reports->class_strength != NULL) free( clsf->reports->class_strength); if (clsf->reports->class_wt_ordering != NULL) free( clsf->reports->class_wt_ordering); if (clsf->reports->att_model_term_types != NULL) { for (i_class=0; i_classreports->att_model_term_types[i_class][i_att]); free( clsf->reports->att_model_term_types[i_class]); } free( clsf->reports->att_model_term_types); } free( clsf->reports->att_i_sums); free( clsf->reports->att_max_i_values); free( clsf->reports); } free( clsf->checkpoint); free( clsf); } /* CLSF_ATT_TYPE 06feb95 wmt: new data type, one of G_att_type_data, of attribute n_att: this is input data type, model terms can change this to "ignore" */ char *clsf_att_type( clsf_DS clsf, int n_att) { return (clsf->database->att_info[n_att]->type); } /* FREE_CLSF_CLASS_SEARCH_STORAGE 18feb95 wmt: new 05feb97 wmt: type of ptr variables changed from int * to void ** 04may01 jcw: Now clears G_model_list and G_m_length after free. free storage allocated for classification, class, search, and search try structures. used for both search and report functions */ void free_clsf_class_search_storage( clsf_DS clsf, search_DS search, int list_global_clsf_p) { void **clsf_storage_ptrs = NULL, **class_storage_ptrs = NULL; int i, j; void **clsf_storage_ptrs_start, **class_storage_ptrs_start; clsf_DS null_clsf = NULL; search_try_DS try; if (G_clsf_storage_log_p == TRUE) printf("\nn_freed_classes = %.2d, n_create_classes_after_free = %.2d\n", G_n_freed_classes, G_n_create_classes_after_free); clsf_storage_ptrs = list_clsf_storage( clsf, search, G_clsf_storage_log_p, list_global_clsf_p); clsf_storage_ptrs_start = clsf_storage_ptrs; while (*clsf_storage_ptrs != NULL) { free_clsf_DS( (clsf_DS) *clsf_storage_ptrs); clsf_storage_ptrs++; } if (clsf_storage_ptrs != NULL) free( clsf_storage_ptrs_start); if ((list_global_clsf_p == TRUE) && (G_model_list != NULL)) { class_storage_ptrs = list_class_storage( G_clsf_storage_log_p); class_storage_ptrs_start = class_storage_ptrs; while (*class_storage_ptrs != NULL) { free_class_DS( (class_DS) *class_storage_ptrs, "model", null_clsf, 0); class_storage_ptrs++; } if (class_storage_ptrs != NULL) free( class_storage_ptrs_start); for (i=0; in_tries; i++) { try = search->tries[i]; if ((try->duplicates != NULL) && (try->n_duplicates != 0)) { for (j=0; j < try->n_duplicates; j++) { free( try->duplicates[j]); if (G_clsf_storage_log_p == TRUE) printf( "free search_try_dup: %d of %d\n", j, i); } free( try->duplicates); } free( try); if (G_clsf_storage_log_p == TRUE) printf( "free search_try: %d\n", i); } free( search->tries); free( search); if (G_clsf_storage_log_p == TRUE) printf( "free search\n"); } } autoclass-3.3.6.dfsg.1/prog/search-control-2.c0000644000175000017500000021330311247310756017107 0ustar areare#include #include #include #include #include #ifndef _MSC_VER #include #endif #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* static float above_cut_table[SIZEOF_ABOVE_CUT_TABLE][2] = {{0.0, 0.5}, {0.01, 0.496}, {0.1, 0.4602}, {0.2, 0.4207}, {0.3, 0.3821}, {0.4, 0.3446}, {0.5, 0.3085}, {0.6, 0.2742}, {0.7, 0.242}, {0.8, 0.2119}, {0.9, 0.1841}, {1.0, 0.1587}, {1.1, 0.1357}, {1.2, 0.1151}, {1.3, 0.0968}, {1.4, 0.0808}, {1.5, 0.0668}, {1.6, 0.0548}, {1.7, 0.0446}, {1.8, 0.0359}, {1.9, 0.0287}, {2.0, 0.0228}, {2.1, 0.0179}, {2.2, 0.0139}, {2.3, 0.0107}, {2.4, 0.0082}, {2.5, 0.0062}, {2.6, 0.0047}, {2.7, 0.0035}, {2.8, 0.0026}, {2.9, 0.0019}}; */ static float cut_where_above_table[SIZEOF_CUT_WHERE_ABOVE_TABLE][2] = {{0.5, 0.0}, {0.496, 0.01}, {0.4602, 0.1}, {0.4207, 0.2}, {0.3821, 0.3}, {0.3446, 0.4}, {0.3085, 0.5}, {0.2742, 0.6}, {0.242, 0.7}, {0.2119, 0.8}, {0.1841, 0.9}, {0.1587, 1.0}, {0.1357, 1.1}, {0.1151, 1.2}, {0.0968, 1.3}, {0.0808, 1.4}, {0.0668, 1.5}, {0.0548, 1.6}, {0.0446, 1.7}, {0.0359, 1.8}, {0.0287, 1.9}, {0.0228, 2.0}, {0.0179, 2.1}, {0.0139, 2.2}, {0.0107, 2.3}, {0.0082, 2.4}, {0.0062, 2.5}, {0.0047, 2.6}, {0.0035, 2.7}, {0.0026, 2.8}, {0.0019, 2.9}}; /* REMOVE_TOO_BIG 02may95 wmt: discard values equal to limit, as well Remove items from the list that are greater then or equal to the given limit. */ int *remove_too_big( int limit, int *list, int *num) { int i, size = 0, *new; new = (int *) malloc(*num * sizeof(int)); for (i=0; i<*num; i++) if (list[i] < limit) { new[size] = list[i]; size++; } *num = size; return(new); } /* TOO_BIG 10jan95 wmt: new Return TRUE if items from the list that are greater than the given limit. */ int too_big( int limit, int *list, int num) { int i, member_exceeds_limit = FALSE; for (i=0; i limit) { member_exceeds_limit = TRUE; break; } return(member_exceeds_limit); } /* pulls x to within [min,max] */ double within( double min_val, double x, double max_val) { return(max(min_val, min(max_val, x))); } /* SAFE_SUBSEQ_OF_TRIES 23nov94 wmt: renamed variables for clarity, removed +1 from n_saved = n_to_save - begin +1 won't bomb if out of range orig says if (list and end element is null ) or (end>length) then end=min(end,length) and then subseq from max(begin,0) to end returns the sub seq of seq from begin to end but not more than num and returns the count in *newnum */ search_try_DS *safe_subseq_of_tries( search_try_DS *seq, int begin, int n_to_save, int n_tries, int *n_saved) { int i; search_try_DS *new_seq = NULL; if (begin < 0) begin = 0; /* begin max(begin,0) */ if( (*n_saved = n_to_save - begin) <= 0) return NULL; if (*n_saved > n_tries) *n_saved = n_tries; new_seq = (search_try_DS *) malloc(*n_saved * sizeof(search_try_DS)); for (i=0; i<*n_saved; i++) new_seq[i] = seq[i+begin]; return(new_seq); } /* PRINT_INITIAL_REPORT modified extensively to 26oct94 wmt: add standard capability tell the user about what will happen, what will be told during search */ void print_initial_report( FILE *stream, FILE *log_file_fp, int min_report_period, time_t end_time, int max_n_tries, char *search_file_ptr, char *results_file_ptr, char *log_file_ptr, int min_save_period, int n_save) { char caller[] = "print_initial_report", *str; int str_length = 2 * sizeof( fxlstr); str = (char *) malloc( str_length); sprintf(str, "\nWELCOME TO AUTOCLASS.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " 1) Each time I have finished a new 'trial', or attempt to find a good\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " classification, I will print the number of classes that trial\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " started and ended with, such as 9->7.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " 2) If that trial results in a duplicate of a previous run, I will print\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " 'dup' first.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " 3) If that trial results in a classification better than any previous, \n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " I will print 'best' first.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, " 4) If more than%s have passed since the last report, and a new\n", format_time_duration((time_t) min_report_period)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " classification has been found which is better than any previous ones,\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " I will report on that classification and on the status of the search\n" " so far.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " 5) This report will include an estimate of the time it will take to find\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " another even better classification, and how much better that will be.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " In addition, I will estimate a lower bound on how long it might take to\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); sprintf(str, " find the very best classification, and how much better that might be.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); to_screen_and_log_file( " 6) If you are warned about too much time in overhead, " "you may want to\n", log_file_fp, stream, TRUE); to_screen_and_log_file( " change the parameters n_save, min_save_period, " "min_report_period, or\n", log_file_fp, stream, TRUE); to_screen_and_log_file( " min_checkpoint_period.\n", log_file_fp, stream, TRUE); if (G_interactive_p == FALSE) sprintf( str, " 7) Since interactive_p = false, I will continue searching\n "); else sprintf( str, " 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll\n" " go on "); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (end_time != 0) safe_sprintf( str, str_length, caller, "until %s.\n", format_universal_time(end_time)); else if (max_n_tries > 0) sprintf(str, "until I complete trial number (%d).\n", max_n_tries); else sprintf(str, "forever.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (n_save > 0) { safe_sprintf( str, str_length, caller, " 8) If needed, every%s I will save the best %d classifications\n", format_time_duration((time_t) min_save_period), n_save); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, " so far to file: \n %s%s\n", (results_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, results_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, " and a description of the search to file:\n %s%s\n", (search_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } if (log_file_fp != NULL) { safe_sprintf( str, str_length, caller, " 9) A record of this search will be printed to file:\n %s%s\n", (log_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, log_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } free( str); } /* PRINT_REPORT 13oct94 wmt: modified 26apr95 wmt: do not use NULL as value of delta_ln_p tell user about new best clsf, about search since last report, and about projections till next report */ void print_report( FILE *stream, FILE *log_file_fp, search_DS search, time_t last_save, time_t last_report, int reconverge_p, char *n_classes_explain) { int i, n_dups, n_dup_tries, time_so_far, n_peaks_seen; int n_not_reported, min_n_peak, min_best_time; time_t now, delta_time; float ln_p_avg, ln_p_sigma, ln_p, delta_ln_p, avg_best_delta_ln_p; float min_best_delta_ln_p, avg_better_ln_p, avg_better_time, time_overhead; clsf_DS clsf; search_try_DS try, *tries; fxlstr str; char caller[] = "print_report"; now = get_universal_time(); tries = search->tries; try = tries[0]; clsf = try->clsf; upper_end_normal_fit(tries, search->n_tries, &ln_p_avg, &ln_p_sigma); n_dups = search->n_dups; n_dup_tries = search->n_dup_tries; time_so_far = search_duration(search, now, clsf, last_save, reconverge_p); ln_p = (float) try->ln_p; delta_time = now - last_report; if (search->last_try_reported != NULL) delta_ln_p = ln_p - (float) search->last_try_reported->ln_p; else delta_ln_p = 0.0; n_peaks_seen = search->n_tries; n_not_reported = try->n; for (i=0; ilast_try_reported == tries[i]) n_not_reported = i; n_not_reported -= 1; min_n_peak = min_n_peaks(n_dups, n_dup_tries); avg_best_delta_ln_p = min(0.0, (float) ln_avg_p( (double) ln_p_avg, (double) ln_p_sigma)) - ln_p; min_best_delta_ln_p = max(0.0, (float) min_best_peak( min_n_peak, (double) ln_p_avg, (double) ln_p_sigma) - ln_p); avg_better_ln_p = (float) avg_improve_delta_ln_p( n_peaks_seen, (double) ln_p_sigma); avg_better_time = (float) avg_time_till_improve(time_so_far, n_peaks_seen); min_best_time = min_time_till_best(time_so_far, min_n_peak, n_peaks_seen); if (total_try_time(tries, search->n_tries) == 0) time_overhead = 0.0; else time_overhead = 1.0 - min(1.0, ((float) total_try_time(tries, search->n_tries) / (float) time_so_far)); sprintf(str, "\n\n---------------- NEW BEST CLASSIFICATION FOUND on try %d -------------\n", try->n); to_screen_and_log_file(str, log_file_fp, stream, TRUE); describe_clsf(clsf, stream, log_file_fp); if (0 < n_not_reported) { sprintf(str, "(Also found %d other better than last report.)\n", n_not_reported); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } sprintf(str, "\n----------- SEARCH STATUS as of %s -----------\n", format_universal_time(get_universal_time())); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (delta_ln_p == 0.0) { safe_sprintf(str, sizeof( str), caller, "It just took%s since beginning.\n", format_time_duration(delta_time)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } else { safe_sprintf( str, sizeof( str), caller, "It just took%s to find a classification\n ", format_time_duration(delta_time)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); print_log( (double) delta_ln_p, log_file_fp, stream, TRUE); sprintf(str, "times more probable.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } safe_sprintf( str, sizeof( str), caller, "Estimate <%s to find a classification\n ", format_time_duration((time_t) avg_better_time)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); print_log( (double) avg_better_ln_p, log_file_fp, stream, TRUE); sprintf(str, "times more probable.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "Estimate >>%s to find the very best classification,\n which may be", format_time_duration((time_t) min_best_time)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); print_log( (double) min_best_delta_ln_p, log_file_fp, stream, FALSE); sprintf(str, "to"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); print_log( (double) avg_best_delta_ln_p, log_file_fp, stream, FALSE); sprintf(str, "times more probable.\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "Have seen %d of the estimated > %d possible classifications (based on %d\n" " duplicates do far).\n", n_peaks_seen, min_n_peak, n_dups); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "Log-Normal fit to classifications probabilities has M(ean) %.1f,\n" " S(igma) %.1f\n", ln_p_avg, ln_p_sigma); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "Choosing initial n-classes %s\n", n_classes_explain); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (time_overhead > 0.10) safe_sprintf( str, sizeof( str), caller, "WARNING: %.1f %% of time so far spend doing non-try overhead tasks - \n" " should you save and/or report less?\n", (time_overhead * 100.0)); else sprintf(str, "Overhead time is %.1f %% of total search time\n", (time_overhead * 100.0)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if ((((float) n_peaks_seen / (float) min_n_peak) > 0.25) && ((n_dups + n_peaks_seen) > 10)) { safe_sprintf( str, sizeof( str), caller, "WARNING: You may be running out of peaks to find. Estimates are too \n" " optimistic\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } sprintf(str, "\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } /* PRINT_FINAL_REPORT 12oct94++ wmt: modified extensively 18feb98 wmt: print list of trials whose num_cycles reached max_cycles sum up the best 'n_final_summary' trials of the search */ void print_final_report( FILE *stream, FILE *log_file_fp, search_DS search, time_t begin, time_t last_save, int n_save, char *stop_reason, unsigned int results_file_p, unsigned int search_file_p, int n_final_summary, char *log_file_ptr, char *search_params_file_ptr, char *results_file_ptr, clsf_DS clsf, int reconverge_p, time_t last_report, time_t last_trial) { int i, new_line_p = TRUE, str_length = 2 * sizeof( fxlstr); time_t now; char caller[] = "print_final_report", *str; search_try_DS search_try; str = (char *) malloc( str_length); now = get_universal_time(); if ((last_report == begin) || (last_report != last_trial)) to_screen_and_log_file("\n\n", log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, "\nENDING SEARCH because %s at %s\n", stop_reason, format_universal_time(now)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, " after a total of %d tries over%s\n", search->n, format_time_duration((time_t) search_duration( search, now, clsf, last_save, reconverge_p))); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (0 < search->time) { safe_sprintf( str, str_length, caller, "This invocation of \"autoclass -search\" took%s\n", format_time_duration(now - begin)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } safe_sprintf( str, str_length, caller, "A log of this search is in file:\n %s%s\n", (log_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, log_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, "The search results are stored in file:\n %s%s\n", (results_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, results_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if ((search_file_p == TRUE) && (results_file_p == TRUE) && (n_save != 0)) { to_screen_and_log_file ( "This search can be restarted by having \"force_new_search_p = false\"", log_file_fp, stream, TRUE); safe_sprintf( str, str_length, caller, " in file:\n %s%s\n", (search_params_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_params_file_ptr); to_screen_and_log_file( str, log_file_fp, stream, TRUE); to_screen_and_log_file( " and reinvoking the \"autoclass -search ...\" form\n", log_file_fp, stream, TRUE); } safe_sprintf( str, str_length, caller, "\n------------------ SUMMARY OF %d BEST RESULTS ------------------\n", n_final_summary); to_screen_and_log_file(str, log_file_fp, stream, TRUE); for (i=0; in_tries; i++) print_search_try(stream, log_file_fp, search->tries[i], (i < n_save), new_line_p, "", FALSE); safe_sprintf( str, str_length, caller, "\n------------------ SUMMARY OF TRY CONVERGENCE ------------------\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); for (i=0; in_tries; i++) { search_try = search->tries[i]; safe_sprintf( str, str_length, caller, "try %4d num_cycles %4d max_cycles %4d", search_try->n, search_try->num_cycles, search_try->max_cycles); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if ((search_try->num_cycles == 0) && (search_try->max_cycles == 0)) { safe_sprintf( str, str_length, caller, " ...\n"); } else if (search_try->num_cycles == search_try->max_cycles) { safe_sprintf( str, str_length, caller, " **** NON-CONVERGENT *****\n"); } else { safe_sprintf( str, str_length, caller, " convergent\n"); } to_screen_and_log_file(str, log_file_fp, stream, TRUE); } free( str); } /* PRINT_SEARCH_TRY 14feb95 wmt: add new_line_p 15jun95 wmt: add pad 11apr97 wmt: remove ":"'s form output 14may97 wmt: add comment_data_headers_p give a one line summary of the search try */ void print_search_try( FILE *stream, FILE *log_file_fp, search_try_DS try, int saved_p, int new_line_p, char *pad, unsigned int comment_data_headers_p) { fxlstr str; char caller[] = "print_search_try"; safe_sprintf( str, sizeof( str), caller, "%s%sPROBABILITY exp(%.3f) N_CLASSES %2d FOUND ON TRY %3d", (comment_data_headers_p == TRUE) ? "#" : "", pad, try->ln_p, try->j_out, try->n); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (0 < try->n_duplicates) { sprintf(str, " DUPS %d", try->n_duplicates); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } sprintf(str, "%s%s",(saved_p) ? " *SAVED*":"", (new_line_p) ? "\n" : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } /* EMPTY_SEARCH_TRY take out the classification and store it */ void empty_search_try( search_try_DS try) { if (try->clsf != NULL) { store_clsf_DS(try->clsf, NULL, 0); try->clsf = NULL; } } /* total time spent in all tries so far */ int total_try_time( search_try_DS *tries, int n_tries) { int i, sum = 0; search_try_DS try; for (i=0; itime + ((try->duplicates != NULL) ? total_try_time(try->duplicates, try->n_duplicates) : 0.0); } return(sum); } /* TRY_VARIATION 15nov94 wmt: use apply_search_start_fn & apply_search_try_fn 27dec94 wmt: add rel_delta_range, max_cycles 08jan95 wmt: for incomplete trials, return search_try_DS with j_out = 0 30jan95 wmt: add checkpointing logic 18feb98 wmt: add num_cycles and max_cycles This randomly initialize a clsf (unless reconverge_type is not ""), then passes it to try-fn for convergence. It returns a search-try structure with a new compressed clsf and stats about the try. Clsf is usually modified by the try-fn. */ search_try_DS try_variation( clsf_DS clsf, int j_in, int trial_n, char *reconverge_type, char *start_fn_type, char *try_fn_type, unsigned int initial_cycles_p, time_t begin_try, double halt_range, double halt_factor, double rel_delta_range, int max_cycles, int n_average, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream) { clsf_DS new_clsf; search_try_DS temp; int complete_trial, want_wts_p = FALSE; shortstr str; time_t now; if (eqstring( G_checkpoint_file, "") != TRUE) { G_search_cycle_begin_time = begin_try; G_last_checkpoint_written = begin_try; if (eqstring( reconverge_type, "chkpt") == TRUE) { /* include try time from previous checkpoint sessions */ sprintf( str, "[reconverge \"chkpt\" j_in=%d] ", j_in); to_screen_and_log_file( str, log_file_fp, stream, TRUE); } else { /* init for start of checkpoint run */ clsf->checkpoint->accumulated_try_time = 0; clsf->checkpoint->current_try_j_in = j_in; clsf->checkpoint->current_cycle = 0; } } if (eqstring( reconverge_type, "results") == TRUE) { sprintf( str, "[reconverge \"results\" j_in=%d] ", j_in); to_screen_and_log_file( str, log_file_fp, stream, TRUE); } if (eqstring( reconverge_type, "") == TRUE) { sprintf( str, "[j_in=%d] ", j_in); to_screen_and_log_file( str, log_file_fp, stream, TRUE); apply_search_start_fn ( clsf, start_fn_type, initial_cycles_p, j_in, log_file_fp, stream); } G_num_cycles = 0; complete_trial = apply_search_try_fn( clsf, try_fn_type, halt_range, halt_factor, rel_delta_range, max_cycles, n_average, cs4_delta_range, sigma_beta_n_values, converge_print_p, log_file_fp, stream); temp = (struct search_try *) malloc( sizeof( struct search_try)); if (complete_trial == TRUE) { new_clsf = copy_clsf_DS( clsf, want_wts_p); compress_clsf(new_clsf, NULL, FALSE); temp->j_in = j_in; temp->clsf = new_clsf; temp->n = trial_n; temp->j_out = new_clsf->n_classes; now = get_universal_time(); if (eqstring( G_checkpoint_file, "") != TRUE) temp->time = (int) ((now - G_search_cycle_begin_time) + clsf->checkpoint->accumulated_try_time); else temp->time = (int) (now - begin_try); temp->ln_p = new_clsf->log_a_x_h; temp->n_duplicates = 0; temp->duplicates = NULL; temp->num_cycles = G_num_cycles; temp->max_cycles = max_cycles; } else temp->j_out = 0; return(temp); } /* SEARCH_DURATION 17oct94 wmt: modified 30jan95 wmt: add checkpointing time total time, including overhead, search has taken */ int search_duration( search_DS search, time_t now, clsf_DS clsf, time_t last_save, int reconverge_p) { int accumulated_try_time = 0; /* include try time from previous checkpoint sessions */ if ((eqstring( G_checkpoint_file, "") != TRUE) && (reconverge_p == TRUE)) accumulated_try_time = (int) clsf->checkpoint->accumulated_try_time; return( search->time + (int) (now - last_save) + accumulated_try_time); } /* CONVERGE 27dec94 wmt: change function type to void. free ln_p_list 18feb98 wmt: add G_num_cycles Robin's more compact version of converge-search - stoppoing criterion is the sum of the classes' log marginal probability. Hence opposite, equal changes in two classes' values have no effect on this criterion. converge_search_3 is sensitive to each classes' probability value. */ int converge( clsf_DS clsf, int n_average, double halt_range, double halt_factor, double delta_factor, int display_wts, int min_cycles, int max_cycles, int converge_print_p, FILE *log_file_fp, FILE *stream) { int n_no_change = 0, n_cycles = 0, complete_trial = TRUE; shortstr str; fxlstr long_str; int converge_cycle_p = TRUE; float ln_p, *ln_p_list; char caller[] = "converge"; ln_p_list = (float *) malloc( max_cycles * sizeof( float)); halt_range = delta_factor * halt_range; halt_factor = delta_factor * halt_factor; if (eqstring( G_checkpoint_file, "") != TRUE) n_cycles = clsf->checkpoint->current_cycle; do { ln_p = (float) base_cycle(clsf, stream, display_wts, converge_cycle_p); ln_p_list[n_cycles] = ln_p; if ((n_cycles > 0) && (max( (float) halt_range, (float) halt_factor * (- ln_p)) > (ln_p - ln_p_list[n_cycles-1]))) n_no_change++; else n_no_change = 0; n_cycles++; if (eqstring( G_checkpoint_file, "") != TRUE) clsf->checkpoint->current_cycle++; if (converge_print_p) { safe_sprintf( long_str, sizeof( long_str), caller, "\ncnt %d n_cls %d no_chng %d ln_p %.3f h_rng %.3f, h_fact*(-ln_p) %.3f", n_cycles, clsf->n_classes, n_no_change, ln_p, halt_range, halt_factor * (- ln_p)); to_screen_and_log_file( long_str, log_file_fp, stream, TRUE); } if (char_input_test() == TRUE) { complete_trial = FALSE; break; } } while (( (n_cycles < min_cycles) || (n_average > n_no_change) ) && (n_cycles < max_cycles)) ; G_num_cycles = n_cycles; if (converge_print_p) to_screen_and_log_file( "\n", log_file_fp, stream, TRUE); sprintf(str, " [c: cycles %d]", n_cycles); to_screen_and_log_file(str, log_file_fp, stream, TRUE); free(ln_p_list); return( complete_trial); } /* CONVERGE_SEARCH_3 27dec94 wmt: new function 18feb98 wmt: add G_num_cycles A class sensitive loop for driving clsf to a local minima. The loop terminates when the difference between cycles of the (class->log_a_w_s_h_j) / class->w_j vector is less than rel_delta_range for each class. */ int converge_search_3( clsf_DS clsf, double rel_delta_range, int display_wts, int min_cycles, int max_cycles, int n_average, int converge_print_p, FILE *log_file_fp, FILE *stream) { int count = 0, n_class, converge_cycle_p = TRUE, i, n_no_change = 0; int complete_trial = TRUE; shortstr str; fxlstr long_str; char caller[] = "converge_search_3"; double diff; class_DS class; double *current_class_logs, *last_class_logs, *marginal_stack, *max_logs_diff; current_class_logs = (double *) malloc( clsf->n_classes * sizeof( double)); last_class_logs = (double *) malloc( clsf->n_classes * sizeof( double)); marginal_stack = (double *) malloc( max_cycles * sizeof( double)); max_logs_diff = (double *) malloc( max_cycles * sizeof( double)); if (eqstring( G_checkpoint_file, "") != TRUE) count = clsf->checkpoint->current_cycle; if (rel_delta_range < 0.0) { fprintf(stderr, "converge_search_3 called with rel_delta_range < 0.0\n"); exit(1); } for (i=0; in_classes; n_class++) { class = clsf->classes[n_class]; current_class_logs[n_class] = class->log_a_w_s_h_j / (double) class->w_j; } while (TRUE) { count++; if (eqstring( G_checkpoint_file, "") != TRUE) clsf->checkpoint->current_cycle++; marginal_stack[count - 1] = base_cycle( clsf, stream, display_wts, converge_cycle_p); for (n_class=0; n_classn_classes; n_class++) last_class_logs[n_class] = current_class_logs[n_class]; for (n_class=0; n_classn_classes; n_class++) { class = clsf->classes[n_class]; current_class_logs[n_class] = class->log_a_w_s_h_j / (double) class->w_j; } for (n_class=0; n_classn_classes; n_class++) { diff = fabs( (current_class_logs[n_class] - last_class_logs[n_class])); if (diff > max_logs_diff[count - 1]) max_logs_diff[count - 1] = diff; } if ((count > 1) && (rel_delta_range > max_logs_diff[count - 1])) n_no_change++; else n_no_change = 0; if (converge_print_p) { safe_sprintf( long_str, sizeof( long_str), caller, "\ncnt %d, n_class %d, range %.4f, diff %.4f, ln_p %.4f, " "n_no_chng %d", count, clsf->n_classes, rel_delta_range, max_logs_diff[count - 1], marginal_stack[count - 1], n_no_change); to_screen_and_log_file( long_str, log_file_fp, stream, TRUE); } if (char_input_test() == TRUE) { complete_trial = FALSE; break; } /* quit after max-cycles, or quit if no delta exceeds rel_delta_range for 3 cycles*/ if ((count >= min_cycles) && ((n_no_change >= n_average) || (count >= max_cycles))) break; } G_num_cycles = count; if (converge_print_p) to_screen_and_log_file( "\n", log_file_fp, stream, TRUE); sprintf(str, " [cs-3: cycles %d]", count); to_screen_and_log_file(str, log_file_fp, stream, TRUE); free( current_class_logs); free( last_class_logs); free( marginal_stack); free( max_logs_diff); return( complete_trial); } /* CONVERGE_SEARCH_3A 18feb98 wmt: add G_num_cycles */ int converge_search_3a( clsf_DS clsf, double rel_delta_range, int display_wts, int min_cycles, int max_cycles, int n_average, int converge_print_p, FILE *log_file_fp, FILE *stream) { int count = 0, n_class, converge_cycle_p = TRUE, i, n_no_change = 0; int complete_trial = TRUE, halt_cnt = 0, n_max_class = 0; int num_halt_cycles = 0; /* 10 for testing */ shortstr str; fxlstr long_str; char caller[] = "converge_search_3a"; double diff, range_delta_factor = rel_delta_range / (double) clsf->database->n_data; double range_delta = 0.0; class_DS class; double *current_class_logs, *last_class_logs, *marginal_stack, *max_logs_diff; current_class_logs = (double *) malloc( clsf->n_classes * sizeof( double)); last_class_logs = (double *) malloc( clsf->n_classes * sizeof( double)); marginal_stack = (double *) malloc( max_cycles * sizeof( double)); max_logs_diff = (double *) malloc( max_cycles * sizeof( double)); if (eqstring( G_checkpoint_file, "") != TRUE) count = clsf->checkpoint->current_cycle; if (rel_delta_range < 0.0) { fprintf(stderr, "converge_search_3a called with rel_delta_range < 0.0\n"); exit(1); } for (i=0; in_classes; n_class++) { class = clsf->classes[n_class]; current_class_logs[n_class] = class->log_a_w_s_h_j / (double) class->w_j; } while (TRUE) { count++; if (eqstring( G_checkpoint_file, "") != TRUE) clsf->checkpoint->current_cycle++; marginal_stack[count - 1] = base_cycle( clsf, stream, display_wts, converge_cycle_p); for (n_class=0; n_classn_classes; n_class++) last_class_logs[n_class] = current_class_logs[n_class]; for (n_class=0; n_classn_classes; n_class++) { class = clsf->classes[n_class]; current_class_logs[n_class] = class->log_a_w_s_h_j / (double) class->w_j; } for (n_class=0; n_classn_classes; n_class++) { diff = fabs( (current_class_logs[n_class] - last_class_logs[n_class])); if (diff > max_logs_diff[count - 1]) { max_logs_diff[count - 1] = diff; n_max_class = n_class; } } if ((count > 1) && ((range_delta = range_delta_factor * (double) clsf->n_classes * fabs( current_class_logs[n_max_class])) > max_logs_diff[count - 1])) n_no_change++; else n_no_change = 0; if ((converge_print_p) && (count > 1)) { safe_sprintf( long_str, sizeof( long_str), caller, "\ncnt %d, n_cls %d, range %.4f[%.4f], diff %.4f, ln_p %.4f, " "n_no_chg %d, h %d", count, clsf->n_classes, rel_delta_range, range_delta, max_logs_diff[count - 1], marginal_stack[count - 1], n_no_change, halt_cnt); to_screen_and_log_file( long_str, log_file_fp, stream, TRUE); } if (char_input_test() == TRUE) { complete_trial = FALSE; break; } /* quit after max-cycles, or quit if no delta exceeds rel_delta_range for 3 cycles*/ if ((count >= min_cycles) && (((n_no_change >= n_average) && (++halt_cnt > num_halt_cycles)) || (count >= max_cycles))) break; } G_num_cycles = count; if (converge_print_p) to_screen_and_log_file( "\n", log_file_fp, stream, TRUE); sprintf(str, " [cs-3a: cycles %d]", count); to_screen_and_log_file(str, log_file_fp, stream, TRUE); free( current_class_logs); free( last_class_logs); free( marginal_stack); free( max_logs_diff); return( complete_trial); } /* CONVERGE_SEARCH_4 27mar95 wmt: new function 18feb98 wmt: add G_num_cycles A class sensitive loop for driving clsf to a local minima. The loop terminates when the beta (absolute slope of the best linear fit to the previous sigma_beta_n_values for each class) is less than cs4_delta_range */ int converge_search_4( clsf_DS clsf, int display_wts, int min_cycles, int max_cycles, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream) { int count = 0, n_class, converge_cycle_p = TRUE, i, sigma_beta_halt = FALSE; int complete_trial = TRUE, x_i, window_index = 0, index, n_classes_in_noise = 0; int in_noise_p, halt_cnt = 0; int num_halt_cycles = 0; /* for testing 10 */ shortstr str; fxlstr long_str; char caller[] = "converge_search_4"; double sigma_squared = 0.0, beta_squared, x_bar, y_bar, sxy, syy, dummy, x_mean = 0.0; double sxx = 0.0; double sigma_factor = 10.0 / 11.0, beta; class_DS class; double ln_p, *ordered_class_sigma_betas, **class_sigma_beta_ratios; ordered_class_sigma_betas = (double *) malloc( sigma_beta_n_values * sizeof( double)); class_sigma_beta_ratios = (double **) malloc( clsf->n_classes * sizeof( double *)); if (eqstring( G_checkpoint_file, "") != TRUE) count = clsf->checkpoint->current_cycle; for (n_class=0; n_classn_classes; n_class++) class_sigma_beta_ratios[n_class] = (double *) malloc( sigma_beta_n_values * sizeof( double)); for (i=0; icheckpoint->current_cycle++; ln_p = base_cycle( clsf, stream, display_wts, converge_cycle_p); for (n_class=0; n_classn_classes; n_class++) { class = clsf->classes[n_class]; class_sigma_beta_ratios[n_class][window_index] = class->log_a_w_s_h_j / (double) class->w_j; } window_index++; if (window_index >= sigma_beta_n_values) window_index = 0; if (count >= sigma_beta_n_values) { x_bar = sigma_beta_n_values - x_mean; n_classes_in_noise = 0; sigma_beta_halt = TRUE; for (n_class=0; n_classn_classes; n_class++) { /* put circular list into ordered list */ for (i=0; i= sigma_beta_n_values) index -= sigma_beta_n_values; ordered_class_sigma_betas[i] = class_sigma_beta_ratios[n_class][index]; } mean_and_variance( ordered_class_sigma_betas, sigma_beta_n_values, &y_bar, &dummy); sxy = syy = 0.0; for (x_i=0; x_i= sigma_beta_n_values)) { safe_sprintf( long_str, sizeof( long_str), caller, "\ncnt %d, num_cls %d, ln_p %.4f, n_in_noise %d, range %.4f" ", s_b_n_vals %d, h %d", count, clsf->n_classes, ln_p, n_classes_in_noise, cs4_delta_range, sigma_beta_n_values, halt_cnt); to_screen_and_log_file( long_str, log_file_fp, stream, TRUE); } if (char_input_test() == TRUE) { complete_trial = FALSE; break; } if ((count >= min_cycles) && (((sigma_beta_halt == TRUE) && (++halt_cnt > num_halt_cycles)) || (count >= max_cycles))) break; } G_num_cycles = count; if (converge_print_p) to_screen_and_log_file( "\n", log_file_fp, stream, TRUE); sprintf(str, " [cs-4: cycles %d]", count); to_screen_and_log_file(str, log_file_fp, stream, TRUE); for (n_class=0; n_classn_classes; n_class++) free( class_sigma_beta_ratios[n_class]); free( class_sigma_beta_ratios); free( ordered_class_sigma_betas); return( complete_trial); } /* estimates the number of local maxima in the search space */ int min_n_peaks( int n_dups,int n_dup_tries) { float val; val = (float) n_dup_tries / (float) (1.0 + n_dups); return(iround( (double) val)); } /* this will remain the avg time till find better clsf */ double avg_time_till_improve( int time_so_far, int n_peaks_seen) { return( (float) time_so_far * ((float) n_peaks_seen / (float) (n_peaks_seen + 1))); } /* a log normal distribution has a high average */ double ln_avg_p( double ln_p_avg, double ln_p_sigma) { return( ln_p_avg + ( 0.5 * (ln_p_sigma * ln_p_sigma))); } /* estimate of the best peak, given an estimated number of peaks */ double min_best_peak( int min_n_peak, double ln_p_avg, double ln_p_sigma) { return(typical_best(min_n_peak, ln_p_avg, ln_p_sigma)); } /* ----------- WAYS TO CHOOSE N-CLASSES TO TRY ------------------------------- each new try needs to start with a certain number of seed classes here are two functions to do that. if ask each to "explain", will give string about how works here fit a log-normal to the n-classes-in (or j-in) of the 6 best tries so far then pick a new j-in for the next try randomly from that distribution. */ /* RANDOM_J_FROM_LN_NORMAL 16oct94 wmt: modified fit log normal model to j (n-classes) of n-best tries so far, select a new j using that */ int random_j_from_ln_normal( int n_tries, search_try_DS *tries, int max_j, int explain_p, char *n_classes_explain) { int i, n_best_track = 10, bnum; float ln_j_avg, ln_j_sigma, *flist_ptr, min_sigma = 1.5; search_try_DS *best; char caller[] = "random_j_from_ln_normal"; best = safe_subseq_of_tries(tries, 0, n_best_track, n_tries, &bnum); flist_ptr = (float *) malloc(bnum * sizeof(float)); for (i=0; ij_in); ln_j_avg = (float) average(flist_ptr, bnum); ln_j_sigma = (float) within(safe_log((double) (1.0 + (min_sigma * (float) safe_exp((double) (-1.0 * ln_j_avg))))), sigma(flist_ptr, bnum, (double) ln_j_avg), 10.0); free(best); free(flist_ptr); if (explain_p == TRUE) { safe_sprintf( n_classes_explain, sizeof( fxlstr), caller, "randomly from a log_normal [M-S, M, M+S] = \n" " [%.1f, %.1f, %.1f]", (float) safe_exp((double) (ln_j_avg - ln_j_sigma)), (float) safe_exp((double) ln_j_avg), (float) safe_exp((double) (ln_j_avg + ln_j_sigma))); return(0); } else return (ceil( safe_exp( random_from_normal( (double) ln_j_avg, (double) ln_j_sigma)))); } /* RANDOM_FROM_NORMAL 16oct94 wmt: modified 17may95 wmt: Convert from srand/rand to srand48/lrand48 return random value picked off pseudo-normal distribution */ double random_from_normal( double mean, double sigma) { int i, n_steps = 20; double diff = 0.0, offset = 1.0; double three_over_n_steps = 3.0 / (double) n_steps; double normalizer = G_rand_base_normalizer / 2.0; for (i=0; i= length) return (-1.0); else if( key == table[i][0]) return (table[i][1]); else if( i == 0 ) return (-1.0); else return( table[i][1] - (table[i][1] - table[i-1][1]) * ((table[i][0] - key ) / (table[i][0] - table[i-1][0]))); /****** commented old code float *high, high_key, *low, low_key; while (1) { low = high; low_key = high_key; high = table[i]; high_key = high[0]; if (key == high_key) return(high[1]); if (++i >= length) return(-1.0); } *********commented */ } /* UPPER_END_NORMAL_FIT 25oct94 wmt: switched args in last call to sigma 05jan95 wmt: use floor on (n_tries / 2); correct pair-up code fit a normal to the max of pairs of list, and infer mean/sigma implies for whole */ void upper_end_normal_fit( search_try_DS *tries, int n_tries, float *ln_p_avg, float *ln_p_sigma) { int i, i_free = 0, half_length; int ordered_p = TRUE; float *ln_p, avg, sig; ln_p = (float *) malloc(n_tries * sizeof(float)); for (i=0; iln_p; if (ordered_p == TRUE) randomize_list(ln_p, n_tries); if (n_tries < 6) { *ln_p_avg = (float) average(ln_p, n_tries); *ln_p_sigma = (float) sigma(ln_p, n_tries, (double) *ln_p_avg); } else { for (i=0; i+1 ln_p[i+1]) ln_p[i_free] = ln_p[i]; else ln_p[i_free] = ln_p[i+1]; i_free++; } half_length = (int) floor( (double) (n_tries / 2)); avg = (float) average(ln_p, half_length); sig = (float) sigma(ln_p, half_length, (double) avg); *ln_p_avg = avg - (sig * (0.575 / 0.825)); /* get better numbers here! */ *ln_p_sigma = sig / 0.825; } free(ln_p); } /* AVERAGE 15dec94 wmt: add check for length == 0 */ double average( float *list, int length) { int i; float sum = 0.0; for (i=0; i 1)) for (i=0; itries; n_tries = search->n_tries; try_clsfs = (clsf_DS *) malloc(n_tries * sizeof(clsf_DS)); for (i=0; iclsf; for (i=0; iclsf = NULL; } search->time = search_duration(search, now, clsf, last_save, reconverge_p); /* write out the search */ if (G_safe_file_writing_p == TRUE) { make_and_validate_pathname( "search_tmp", search_file_ptr, &temp_search_file, FALSE); search_file_fp = fopen( temp_search_file, "w"); } else search_file_fp = fopen( search_file_ptr, "w"); write_search_DS( search_file_fp, search, start_j_list, n_final_summary, n_save); fclose( search_file_fp); if (G_safe_file_writing_p == TRUE) { if ((search_file_fp = fopen( search_file_ptr, "r")) != NULL) { fclose( search_file_fp); safe_sprintf( str, str_length, caller, "rm %s", search_file_ptr); system( str); } safe_sprintf( str, str_length, caller, "mv %s %s", temp_search_file, search_file_ptr); system( str); } /* restore clsfs */ for (i=0; iclsf = try_clsfs[i]; free( try_clsfs); free( str); } /* WRITE_SEARCH_DS 06nov94 wmt: new 25jan95 wmt: add search->start_j_list 14feb95 wmt: added n_final_summary, n_save code to write search DS to file using fprintf */ void write_search_DS( FILE *search_file_fp, search_DS search, int_list start_j_list, int n_final_summary, int n_save) { int i, n_last_try_reported; static shortstr id; char caller[] = "write_search_DS"; safe_fprintf(search_file_fp, caller, "search_DS\n"); safe_fprintf(search_file_fp, caller, "n, time, n_dups, n_dup_tries \n%d %d %d %d\n", search->n, search->time, search->n_dups, search->n_dup_tries); safe_fprintf(search_file_fp, caller, "last try reported\n"); if (search->last_try_reported != NULL) n_last_try_reported = search->last_try_reported->n; else n_last_try_reported = 0; safe_fprintf(search_file_fp, caller, "%d\n", n_last_try_reported); safe_fprintf(search_file_fp, caller, "tries from best on down for n_tries \n%d\n", search->n_tries); for (i=0; i< search->n_tries; i++) { sprintf(id, "search_try_DS %d", i); write_search_try_DS( search->tries[i], id, i, search_file_fp); } safe_fprintf(search_file_fp, caller, "start_j_list\n"); for (i=0; start_j_list[i] != END_OF_INT_LIST; i++) safe_fprintf(search_file_fp, caller, "%d\n", start_j_list[i]); safe_fprintf(search_file_fp, caller, "%d\n", END_OF_INT_LIST); safe_fprintf(search_file_fp, caller, "n_final_summary, n_save\n"); safe_fprintf(search_file_fp, caller, "%d %d\n", n_final_summary, n_save); } /* WRITE_SEARCH_TRY_DS 06nov94 wmt: new 18feb98 wmt: add num_cycles and max_cycles use fprintf to write search tries */ void write_search_try_DS( search_try_DS try, shortstr id, int try_index, FILE *search_file_fp) { int dup_index; shortstr dup_id; char caller[] = "write_search_try_DS"; if (eqstring( id, "") != TRUE) safe_fprintf(search_file_fp, caller, "%s\n", id); safe_fprintf(search_file_fp, caller, "n, time, j_in, j_out, ln_p, num_cycles, max_cycles\n" "%d %d %d %d %.8e %d %d\n", try->n, try->time, try->j_in, try->j_out, try->ln_p, try->num_cycles, try->max_cycles); if (try_index >= 0) { /* this is a not a dup try */ safe_fprintf(search_file_fp, caller, "n_dups\n%d\n", try->n_duplicates); for (dup_index=0; dup_indexn_duplicates; dup_index++) { sprintf( dup_id, "search_try_DS %d dup_try_DS %d", try_index, dup_index); write_search_try_DS( try->duplicates[dup_index], dup_id, -1, search_file_fp); } } } /* GET_SEARCH_DS 26oct94 wmt: add last_try_reported init 11jan95 wmt: add time init 25jan95 wmt: add start_j_list 16may95 wmt: move malloc out of declaration */ search_DS get_search_DS( void) { search_DS search; search = (search_DS) malloc( sizeof( struct search)); search->n = 0; search->time = 0; search->n_dups = 0; search->last_try_reported = NULL; search->n_dup_tries = 0; search->n_tries = 0; search->tries = NULL; search->start_j_list = NULL; search->n_final_summary = 0; /* for search_summary (intf-reports.c) */ search->n_save = 0; /* for search_summary (intf-reports.c) */ return (search); } /* search_DS copy_search_wo_tries( search_DS search) */ /* { */ /* search_DS temp; */ /* temp = get_search_DS(); */ /* temp->n = search->n; */ /* temp->time = search->time; */ /* temp->n_dups = search->n_dups; */ /* temp->n_dup_tries = search->n_dup_tries; */ /* temp->last_try_reported = search->last_try_reported; */ /* temp->n_tries = search->n_tries; */ /* start_j_list; copy and allocate int_list */ /* temp->n_final_summary = search->n_final_summary; */ /* temp->n_save = search->n_save; */ /* return(temp); */ /* } */ /* RECONSTRUCT_SEARCH 11jan95 wmt: replace get_object_from_file with get_search_from_file 31mar92 WMT - %= added because search file is written & loaded (with truncation) and results file is compiled (without truncation) 01may95 wmt: change type of results_file: char * => fxlstr 18may95 wmt: add results/search consistency check returns a search-state */ search_DS reconstruct_search( FILE *search_file_fp, char *search_file_ptr, char *results_file_ptr) { int i, n_best_clsfs, expand_p = TRUE, want_wts_p = TRUE, update_wts_p = FALSE; clsf_DS *best_clsfs, clsf; search_DS search = NULL; static int clsf_n_list[MAX_CLSF_N_LIST] = {END_OF_INT_LIST}; static fxlstr results_file; int exit_if_error_p = TRUE, silent_p = FALSE, n_clsf; results_file[0] = '\0'; validate_results_pathname( results_file_ptr, &results_file, "results", exit_if_error_p, silent_p); best_clsfs = get_clsf_seq( results_file, expand_p, want_wts_p, update_wts_p, "results", &n_best_clsfs, clsf_n_list); if (best_clsfs != NULL) { search = get_search_from_file( search_file_fp, search_file_ptr); if (search->n_tries < n_best_clsfs) { fprintf(stderr, "ERROR: number of search trials (%d) is less than " "the number of saved clsfs (%d)\n", search->n_tries, n_best_clsfs); exit(1); } if (search != NULL) for (i=0; itries[i]->clsf = best_clsfs[i]; } for (n_clsf=0; n_clsfn, &search->time, &search->n_dups, &search->n_dup_tries); for (i=0; i<2; i++) fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d", &n_last_try_reported); for (i=0; i<2; i++) fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d", &search->n_tries); for (try_index=0; try_indexn_tries; try_index++) get_search_try_from_file( search, NULL, try_index, search_file_fp, search_file_ptr); fgets( line, sizeof(line), search_file_fp); sscanf( line, "%s", token); if (eqstring( token, "start_j_list") != TRUE) { fprintf( stderr, "\nERROR: in \"%s\", expected \"start_j_list\", found \"%s\"\n", search_file_ptr, line); abort(); } for (i=0; istart_j_list = (int_list) malloc( (i+1) * sizeof( int)); else search->start_j_list = (int_list) realloc( search->start_j_list, (i+1) * sizeof( int)); sscanf( line, "%d", &search->start_j_list[i]); if (search->start_j_list[i] == END_OF_INT_LIST) break; } for (i=0; i<2; i++) fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d %d", &search->n_final_summary, &search->n_save); if ((i == (MAX_N_START_J_LIST - 1)) && (search->start_j_list[i] != END_OF_INT_LIST)) search->start_j_list[i] = END_OF_INT_LIST; /* use n_last_try_reported to set ptr to trial */ if (n_last_try_reported > 0) { for (i=0; in_tries; i++) if (search->tries[i]->n == n_last_try_reported) break; search->last_try_reported = search->tries[i]; } else search->last_try_reported = NULL; safe_sprintf( str, str_length, caller, "ADVISORY: read %d search trials from \n %s%s\n", search->n_tries, (search_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_file_ptr); to_screen_and_log_file( str, G_log_file_fp, G_stream, TRUE); free( str); return(search); } /* GET_SEARCH_TRY_FROM_FILE 12jan95 wmt: new 18feb98 wmt: get num_cycles and max_cycles if in search try read the .search file and allocate storage for search_try_DS */ void get_search_try_from_file( search_DS search, search_try_DS parent_try, int try_index, FILE *search_file_fp, char *search_file_ptr) { fxlstr line; int i, file_try_index, dup_file_try_index, dup_index; shortstr token, dup_token; search_try_DS search_try; fgets( line, sizeof(line), search_file_fp); if (parent_try == NULL) { sscanf( line, "%s %d", token, &file_try_index); if ((eqstring( token, "search_try_DS") != TRUE) || (try_index != file_try_index)) { fprintf( stderr, "\nERROR: in \"%s\", search_try index = %d not found\n", search_file_ptr, try_index); abort(); } if (search->tries == NULL) search->tries = (search_try_DS *) malloc( sizeof(search_try_DS)); else search->tries = (search_try_DS *) realloc( search->tries, (try_index + 1) * sizeof(search_try_DS)); search_try = (struct search_try *) malloc( sizeof( struct search_try)); search->tries[try_index] = search_try; } else { /* reading a dup try */ sscanf( line, "%s %d %s %d", token, &file_try_index, dup_token, &dup_file_try_index); if ((eqstring( dup_token, "dup_try_DS") != TRUE) || (try_index != dup_file_try_index)) { fprintf( stderr, "\nERROR: in \"%s\", dup_try_index %d not found for " "search_try index = %d\n", search_file_ptr, try_index, file_try_index); abort(); } if (parent_try->duplicates == NULL) parent_try->duplicates = (search_try_DS *) malloc( sizeof(search_try_DS)); else parent_try->duplicates = (search_try_DS *) realloc( parent_try->duplicates, (try_index + 1) * sizeof(search_try_DS)); search_try = (struct search_try *) malloc( sizeof( struct search_try)); parent_try->duplicates[try_index] = search_try; } fgets( line, sizeof(line), search_file_fp); if (strstr( line, "num_cycles") == NULL) { /* old style search try */ fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d %d %d %d %le", &search_try->n, &search_try->time, &search_try->j_in, &search_try->j_out, &search_try->ln_p); search_try->num_cycles = 0; search_try->max_cycles = 0; } else { fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d %d %d %d %le %d %d", &search_try->n, &search_try->time, &search_try->j_in, &search_try->j_out, &search_try->ln_p, &search_try->num_cycles, &search_try->max_cycles); } search_try->clsf = NULL; search_try->duplicates = NULL; if (parent_try == NULL) { for (i=0; i<2; i++) fgets( line, sizeof(line), search_file_fp); sscanf( line, "%d", &search_try->n_duplicates); for (dup_index=0; dup_indexn_duplicates; dup_index++) get_search_try_from_file( search, search_try, dup_index, search_file_fp, search_file_ptr); } else search_try->n_duplicates = 0; } /* FIND_DUPLICATE 15oct94 wmt: modified go down tries seeking a duplicate clsf. stops when tries are empty, or when past n-store on a restart */ int find_duplicate( search_try_DS try, search_try_DS *tries, int n_store, int *n_dup_tries_ptr, double rel_error, int n_tries, int restart_p) { int i, n, count = 0; clsf_DS clsf; search_try_DS old_try; *n_dup_tries_ptr = 0; if (tries == NULL) return(FALSE); clsf = try->clsf; for (i=0; iclsf != NULL) { (*n_dup_tries_ptr)++; if (clsf_DS_test(clsf, old_try->clsf, rel_error)) { n = old_try->n_duplicates ++; if (old_try->duplicates == NULL) old_try->duplicates = (search_try_DS *) malloc(old_try->n_duplicates * sizeof(search_try_DS)); else old_try->duplicates = (search_try_DS *) realloc(old_try->duplicates, old_try->n_duplicates * sizeof(search_try_DS)); old_try->duplicates[n] = try; empty_search_try(try); return(TRUE); } } else if ((restart_p == FALSE) || (count > n_store)) return(FALSE); } return(FALSE); } /* INSERT_NEW_TRIAL 27oct94 wmt: do not use tries[--i] 06jan95 wmt: n_tries = number of try; delete the least significant clsf, after ordering by ln_p add new try to list of old tries, store N_store clsfs but store tries till cows come home */ search_try_DS *insert_new_trial( search_try_DS try, search_try_DS *tries, int n_tries, int n_stored_clsf, int max_n_store) { int i, exceeded_trial_index; if (tries == NULL) tries = (search_try_DS *) malloc(sizeof(search_try_DS)); else tries = (search_try_DS *)realloc(tries, n_tries * sizeof(search_try_DS)); i = n_tries - 1; /* while( i >0 && try->ln_p > tries[i-1]->ln_p) */ /* tries[i] = tries[--i]; */ while ( i >0 && try->ln_p > tries[i-1]->ln_p) { tries[i] = tries[i-1]; i--; } tries[i] = try; /* if (n_stored_clsf >= max_n_store) JPT */ /* empty_search_try(tries[max_n_store]); */ if (n_tries > n_stored_clsf) { exceeded_trial_index = max( min( max_n_store, n_tries - 1), i); if (tries[exceeded_trial_index] != NULL) empty_search_try(tries[exceeded_trial_index]); } return(tries); } /* DESCRIBE_CLSF 14oct94 wmt: changes 26oct94 wmt: extra sizeof in malloc output class weights and marginal probability of clsf */ void describe_clsf( clsf_DS clsf, FILE *stream, FILE *log_file_fp) { int i, *temp_num_ptr; float *wts; fxlstr str; int (* comp_func) () = int_compare_greater; char caller[] = "describe_clsf"; temp_num_ptr = (int *) malloc(clsf->n_classes * sizeof(int)); sprintf( str, "It has %d CLASSES with WEIGHTS", clsf->n_classes); to_screen_and_log_file( str, log_file_fp, stream, TRUE); wts = clsf_DS_w_j( clsf); for (i=0; in_classes; i++) temp_num_ptr[i] = iround( (double) wts[i]); qsort( temp_num_ptr, clsf->n_classes, sizeof(int), comp_func); for (i=0; in_classes; i++) { sprintf( str, " %d", *(temp_num_ptr + i)); to_screen_and_log_file( str, log_file_fp, stream, TRUE); } safe_sprintf( str, sizeof( str), caller, "\nPROBABILITY of both the data and the classification = exp(%.3f)\n", clsf->log_a_x_h); to_screen_and_log_file(str, log_file_fp, stream, TRUE); free( wts); free( temp_num_ptr); } /* PRINT_LOG 14oct94 wmt: modified print log value and its non-log value */ void print_log (double log_number, FILE *log_file_fp, FILE *stream, int verbose_p) { fxlstr str; sprintf(str, " exp(%.1f) ", log_number); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (verbose_p == TRUE) { sprintf(str, "[= %.1e] ", safe_exp( log_number)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } } /* APPLY_SEARCH_START_FN 15nov94 wmt: new function */ void apply_search_start_fn (clsf_DS clsf, char *start_fn_type, unsigned int initial_cycles_p, int j_in, FILE *log_file_fp, FILE *stream) { int block_size = 0, delete_duplicates = FALSE; if (eqstring(start_fn_type, "random")) random_set_clsf(clsf, j_in, delete_duplicates, DISPLAY_WTS, initial_cycles_p, log_file_fp, stream); else if (eqstring(start_fn_type, "block")) block_set_clsf(clsf, j_in, block_size, delete_duplicates, DISPLAY_WTS, initial_cycles_p, log_file_fp, stream); else { fprintf(stderr, "\nERROR: start function type \"%s\" not handled!\n", start_fn_type); exit(1); } } /* APPLY_SEARCH_TRY_FN 15nov94 wmt: new function 10dec94 wmt: change converge halt_range from 2.5 to 0.5 to get better convergence and to agree with AutoClass X (Lisp version) 27dec94 wmt: add converge_search_3 and rel_delta_range, max_cycles 27mar95 wmt: add converge_search_4 */ int apply_search_try_fn (clsf_DS clsf, char *try_fn_type, double halt_range, double halt_factor, double rel_delta_range, int max_cycles, int n_average, double cs4_delta_range, int sigma_beta_n_values, int converge_print_p, FILE *log_file_fp, FILE *stream) { int min_cycles = 3, complete_trial; double delta_factor = 1.0; if (eqstring(try_fn_type, "converge")) complete_trial = converge( clsf, n_average, halt_range, halt_factor, delta_factor, DISPLAY_WTS, min_cycles, max_cycles, converge_print_p, log_file_fp, stream); else if (eqstring(try_fn_type, "converge_search_3")) complete_trial = converge_search_3( clsf, rel_delta_range, DISPLAY_WTS, min_cycles, max_cycles, n_average, converge_print_p, log_file_fp, stream); else if (eqstring(try_fn_type, "converge_search_4")) /* complete_trial = converge_search_3a( clsf, rel_delta_range, DISPLAY_WTS, min_cycles, max_cycles, n_average, converge_print_p, log_file_fp, stream); */ complete_trial = converge_search_4( clsf, DISPLAY_WTS, min_cycles, max_cycles, cs4_delta_range, sigma_beta_n_values, converge_print_p, log_file_fp, stream); else { fprintf(stderr, "\nERROR: try function type \"%s\" not handled!\n", try_fn_type); exit(1); } return( complete_trial); } /* APPLY_N_CLASSES_FN 14dec94 wmt: new function */ int apply_n_classes_fn ( char *n_classes_fn_type, int n_tries, search_try_DS *tries, int max_j, int explain_p, char *n_classes_explain) { int return_value; if (eqstring(n_classes_fn_type, "random_ln_normal")) return_value = random_j_from_ln_normal( n_tries, tries, max_j, explain_p, n_classes_explain); else { fprintf(stderr, "\nERROR: number of classes function type \"%s\" not handled!\n", n_classes_fn_type); exit(1); } return return_value; } /* VALIDATE_SEARCH_START_FN 10dec94 wmt: new function */ int validate_search_start_fn (char *start_fn_type) { int err_cnt = 0; if (eqstring(start_fn_type, "random")) ; else if (eqstring(start_fn_type, "block")) ; else { fprintf( stderr, "ERROR: start function type \"%s\" not handled!\n" " allowable types are \"random\" & \"block\"\n", start_fn_type); err_cnt = 1; } return( err_cnt); } /* VALIDATE_SEARCH_TRY_FN 10dec94 wmt: new function 27dec94 wmt: add converge_search_3 27mar95 wmt: add converge_search_4 */ int validate_search_try_fn (char *try_fn_type) { int err_cnt = 0; if (eqstring(try_fn_type, "converge")) ; else if (eqstring(try_fn_type, "converge_search_3")) ; else if (eqstring(try_fn_type, "converge_search_4")) ; else { fprintf( stderr, "ERROR: try function type \"%s\" not handled!\n" " allowable types are \"converge_search_3\", " "\"converge_search_4\", and \"converge\", \n", try_fn_type); err_cnt = 1; } return( err_cnt); } /* VALIDATE_N-CLASSES_FN 14dec94 wmt: new function */ int validate_n_classes_fn (char *n_classes_fn_type) { int err_cnt = 0; if (eqstring(n_classes_fn_type, "random_ln_normal")) ; else { fprintf( stderr, "ERROR: number of classes function type \"%s\" not handled!\n" " allowable types are \"random_ln_normal\" only\n", n_classes_fn_type); err_cnt = 1; } return( err_cnt); } /* DESCRIBE_SEARCH 09jan95 wmt: new print out search trials and clsf->log_a_x_h */ void describe_search( search_DS search) { search_try_DS try; int try_index; fprintf(stdout, "\n"); for (try_index=0; try_index < search->n_tries; try_index++) { try = search->tries[try_index]; fprintf(stdout, "try %.2d: n %.2d j_in %.2d j_out %.2d clsf_ln_p %.3f clsf %s\n", try_index + 1, try->n, try->j_in, try->j_out, try->ln_p, (try->clsf != NULL) ? "yes" : "no"); } } autoclass-3.3.6.dfsg.1/prog/model-single-multinomial.c0000644000175000017500000002270311247310756020736 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* SM_PARAMS_INFLUENCE_FN 06feb95 wmt: add length_ptr; realloc( 3 * => realloc( 3 * (i+1) 22jul95 wmt: replace if (global_att_prob != 0.0) with better test Compute the influence value for a Single Multinomial model term. This is the sum over value-index l of P_jkl * log( P_jkl / P_kl), where P_jkl is the the probability for value l of attribute k in class j and P_kl is the probability of value l of attribute k for a single class classification of the database. */ void sm_params_influence_fn( model_DS model, tparm_DS term_params, int term_index, int n_att, float *influence_value, float **class_div_global_att_prob_list_ptr, int *length_ptr) { struct sm_param *param; tparm_DS *p; fptr class_probs, global_probs; int i, ii; float class_att_prob, global_att_prob, *class_div_global_att_prob_list; param = &(term_params->ptype.sm); class_probs = param->val_probs; p = model_global_tparms( model); global_probs = p[term_index]->ptype.sm.val_probs; *length_ptr = 0; *influence_value = 0.0; class_div_global_att_prob_list = NULL; ii = 0; for (i=0; i < param->range; i++) { class_att_prob = class_probs[i]; global_att_prob = global_probs[i]; if (class_div_global_att_prob_list == NULL) class_div_global_att_prob_list = (float *) malloc( 3 * sizeof( float)); else class_div_global_att_prob_list = (float *) realloc( class_div_global_att_prob_list, 3 * (i+1) * sizeof( float)); class_div_global_att_prob_list[ii] = (float) i; ii++; class_div_global_att_prob_list[ii] = class_att_prob; ii++; class_div_global_att_prob_list[ii] = global_att_prob; ii++; (*length_ptr)++; if ((global_att_prob > LEAST_POSITIVE_SINGLE_FLOAT) && ((class_att_prob / global_att_prob) < MOST_POSITIVE_SINGLE_FLOAT)) *influence_value += class_att_prob * (float) log ((double) (class_att_prob / global_att_prob)); } *class_div_global_att_prob_list_ptr = class_div_global_att_prob_list; } /* SINGLE_MULTINOMIAL_MODEL_TERM_BUILDER 24oct94 wmt: initialize val_wts, val_probs, val_log_probs 21nov94 wmt: initialize all slots in tparm Funcalled from Expand-Model-Terms. This constructs parameter, prior, and intermediate results structures appropriate to a Single-Multinomial likelihood term, and places them in the model. Constructs corresponding log-likelihood and parameter update function elements and saves them on the model for later compilation. */ void single_multinomial_model_term_builder( model_DS model, term_DS term, int n_term) { int n_att, range, i; att_DS att; struct sm_param *p; tparm_DS tparm; n_att = term->att_list[0]; att = model->database->att_info[n_att]; range = att->d_statistics->range; /* Allocate data structures: */ /* Allocate parameters. */ term->tparm = tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); tparm->n_atts=range; tparm->collect=0; tparm->n_att = tparm->n_term=0; tparm->n_att_indices = tparm->n_datum = tparm->n_data = 0; tparm->wts = tparm->att_indices = tparm->datum = NULL; tparm->data = NULL; tparm->w_j = tparm->ranges = tparm->class_wt = 0.0; tparm->disc_scale = tparm->log_pi = tparm->log_att_delta = 0.0; tparm->log_delta = tparm->wt_m = tparm->log_marginal = 0.0; tparm->tppt=SM; p = &(tparm->ptype.sm) ; p->val_wts = (float *) malloc(range * sizeof(float)); for (i=0; ival_wts[i] = 0.0; p->val_probs = (float *) malloc(range * sizeof(float)); for (i=0; ival_probs[i] = 0.0; p->val_log_probs = (float *) malloc(range * sizeof(float)); for (i=0; ival_log_probs[i] = 0.0; /* Generate function elements: */ /* function elements no longer used */ p->range = range; p->inv_range =1.0/p->range; p->range_m1 = p->range - 1.0; p->range_factor = p->range_m1 / p->range; p->gamma_term = (float) log_gamma( (double) p->range_m1, FALSE) - (range * (float) log_gamma( (double) p->range_factor, FALSE) ); tparm->n_term = n_term; tparm->n_att = n_att; } /* SINGLE_MULTINOMIAL_LOG_LIKELIHOOD 20dec94 wmt: return type to double Calculates the log-probability of the datum's N-att'th attributes' value when called within the environment of Log-Likelihood-fn. */ double single_multinomial_log_likelihood( tparm_DS tparm) { int n_att = tparm->n_att; /* int n_term = tparm->n_term; */ float *datum = tparm->datum; struct sm_param *sm=&(tparm->ptype.sm); return(sm->val_log_probs[(int) datum[n_att]]); } /* SINGLE_MULTINOMIAL_UPDATE_L_APPROX 20dec94 wmt: return type to double 29mar95 wmt: calculation to double This calculates log-a_k, the approximate log likelihood of observing the weighted statistics given the class hypothesis and current parameters, when called within the environment of Update-L-Approx-Fn. See Multinomial Likelihood (eqn 21) in math paper */ double single_multinomial_update_l_approx( tparm_DS tparm) { struct sm_param *sm = &(tparm->ptype.sm); int i, range = sm->range; /* int n_term = tparm->n_term; */ float *log_probs, *wts; double log_a; wts = sm->val_wts; log_probs = sm->val_log_probs; log_a = 0.0; for (i=0; i_k, the approximate the log marginal likelihood of observing the weighted statistics given the class hypothesis alone. See Marginalized Likelihood (eqn 24) in math paper. */ double single_multinomial_update_m_approx( tparm_DS tparm) { int i; /* int n_term = tparm->n_term; */ struct sm_param *sm = &(tparm->ptype.sm); float gamma_term = sm->gamma_term, range_m1 = sm->range_m1; float range_factor = sm->range_factor, w_j = tparm->w_j; float *val_wts; double temp = 0.0; val_wts = sm->val_wts; for (i=0; irange; i++) temp += log_gamma( (double) (val_wts[i] + range_factor), FALSE); return( ((double) gamma_term) - log_gamma( (double) (w_j + range_m1), FALSE) + temp); } /* SINGLE_MULTINOMIAL_UPDATE_PARAMS 20dec94 wmt: return type to void Updates the parameters of a Single-Multinomial term when called from the environment of Update-Params-fn. */ void single_multinomial_update_params( tparm_DS tparm, int known_parms_p) { int collect = tparm->collect; int i, n_att = tparm->n_att, n_data = tparm->n_data; /* int n_term = tparm->n_term; */ struct sm_param *sm=&(tparm->ptype.sm); int n_atts = tparm->n_atts, range = sm->range; float inv_range = sm->inv_range, wt_i; float **data = tparm->data, *wts = tparm->wts, class_wt = tparm->class_wt; float disc_scale = tparm->disc_scale, *val_wts, *val_probs, *val_log_probs; val_wts = sm->val_wts; val_probs = sm->val_probs; val_log_probs = sm->val_log_probs; if (collect == TRUE) { /* Collect & regenerate class statistics */ for (i=0; i 0.0) { for (i=0; i 0.0) val_wts[(int) data[i][n_att]] += wt_i; } } } if (known_parms_p != TRUE) /* Update class parameters */ for (i=0; iptype.sm); struct sm_param *sm2 =&( tparm2->ptype.sm); int i, n_atts = tparm1->n_atts; /* int n_term = tparm1->n_term; */ float *v1=sm1->val_probs, *v2=sm2->val_probs;; for (i=0; iptype.sm); struct sm_param *sm2 = &(tparm2->ptype.sm); struct sm_param *smm = &(tparmm->ptype.sm); int i, range = sm1->range; /* int n_term = tparm1->n_term; */ float *v1=sm1->val_wts, *v2=sm2->val_wts, *vm=smm->val_wts; for (i=0; i>> */ float log_ranges; float *emp_means; /* Empherical means vector. */ fptr *emp_covar; /* Empherical covariance matrix. */ float *means; /* Means vector. */ fptr *covariance; /* Covariance matrix. */ fptr *factor; /* L/U factorization of the Covariance. */ float *values; /* Used as a temporary accumulator. */ float *temp_v; /* Used as a temporary accumulator. */ fptr *temp_m; /* Used as a temporary accumulator. */ float *min_sigma_2s; }; struct sm_param { float gamma_term; int range; float range_m1, inv_range, range_factor; fptr val_wts; /* Accumulated weight for each attribute value. */ fptr val_probs; /* Probability for each attribute value. */ fptr val_log_probs; /* Log-probs for attribute values. */ }; struct sn_cm_param { /* Single-Normal-CM model parameters. */ float known_wt; /* Accumulated weight for known values. */ float known_prob; /* Probability for known values. */ float known_log_prob; /* Log-prob for known values. */ float unknown_log_prob; /* Log-prob for unknown values. */ float weighted_mean; float weighted_var; float mean; float sigma; float log_sigma; float variance; float log_variance; float inv_variance; /* Minimum usable difference betweeen the mean and a value. */ float ll_min_diff; float skewness; float kurtosis; float prior_sigma_min_2, prior_mean_mean, prior_mean_sigma; float prior_sigmas_term, prior_sigma_max_2, prior_mean_var; float prior_known_prior; }; struct sn_cn_param { float weighted_mean; float weighted_var; float mean; float sigma; float log_sigma; float variance; float log_variance; float inv_variance; /* Minimum usable difference betweeen the mean and a value. */ float ll_min_diff; float skewness; float kurtosis; float prior_sigma_min_2, prior_mean_mean, prior_mean_sigma; float prior_sigmas_term, prior_sigma_max_2, prior_mean_var; }; /* tparm is new structure that does away with old calling mechanism and uses union for each type of term it is a combination of the oldr params and term_params */ struct new_term_params {/* pointer to this is a tparm_DS*/ int n_atts; enum MODEL_TYPES tppt; union { struct mm_d_param mm_d; struct mm_s_param mm_s; struct mn_cn_param mn_cn; struct sm_param sm; struct sn_cm_param sn_cm; struct sn_cn_param sn_cn; } ptype; int collect; int n_term, n_att, /*n_atts,*/ n_att_indices, n_datum, n_data;/*, range; */ float w_j, ranges;/* , inv_range, range_factor; */ float class_wt; float disc_scale; /* moved to sm float gamma_term; float range_m1; */ /* moved to sn_cm float prior_sigma_min_2, prior_mean_mean, prior_mean_sigma; float prior_sigmas_term, prior_sigma_max_2, prior_mean_var; float prior_known_prior; */ float log_pi; float log_att_delta; float log_delta; /* not used sncm float log_ranges; moved to mn_cn float log_gamma_term; float percent_ratio; float sigma_ratio; */ /* these are just pointers to other structs or allocated memory 27jan95 wmt */ float *wts; float *datum; float *att_indices; float **data; float wt_m; float log_marginal; }; autoclass-3.3.6.dfsg.1/prog/model-single-normal-cn.c0000644000175000017500000003460511247310756020276 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* SN_CN_PARAMS_INFLUENCE_FN 01feb95 wmt: 2.0 * global_variance (from ac-x) */ void sn_cn_params_influence_fn( model_DS model, tparm_DS tparm, int term_index, int n_att, float *v, float *class_mean, float *class_sigma, float *global_mean, float *global_sigma) { struct sn_cn_param *param; tparm_DS *p; float global_variance; param = &(tparm->ptype.sn_cn); *class_mean = param->mean; *class_sigma = param->sigma; p = model_global_tparms(model); *global_mean = p[term_index]->ptype.sn_cn.mean; *global_sigma = p[term_index]->ptype.sn_cn.sigma; global_variance = p[term_index]->ptype.sn_cn.variance; *v = (float) log ((double) (*global_sigma / *class_sigma)) + ((((square(*class_mean - *global_mean) + (param->variance - global_variance))) / 2.0) / global_variance); } /* BUILD_SN_CN_PRIORS 30jul95 wmt: change log calls to safe_log to prevent "log: SING error" error messages. Builds an SN-CN prior from the information in a fully instantiated att structure of the real type. */ static priors_DS build_sn_cn_priors( att_DS att) { int n; float range, sigma_min, sigma_max; priors_DS priors; real_stats_DS statistics = att->r_statistics;/* Attribute range information */ range = statistics->mx - statistics->mn; sigma_min = SN_CM_SIGMA_SAFETY_FACTOR * att->error; /* The max sigma for values in range. */ sigma_max = max(sigma_min, range / 2.0); if (sigma_min == sigma_max) { n = att->warnings_and_errors->num_expander_errors; att->warnings_and_errors->num_expander_errors += 1; if (n == 0) att->warnings_and_errors->model_expander_errors = (fxlstr *) malloc(sizeof(fxlstr)); else att->warnings_and_errors->model_expander_errors = (fxlstr *) realloc(att->warnings_and_errors->model_expander_errors, (n+1) * sizeof(fxlstr)); strcpy(att->warnings_and_errors->model_expander_errors[n], " single_normal_cn is faulty due to large error-to-range\n" " ratio on sigma priors.\n"); } priors = (priors_DS) malloc(sizeof(struct priors)); priors->sigma_min = sigma_min; priors->sigma_max = sigma_max; priors->known_prior = 0.0; /* not used, but needed for priors struct init */ priors->mean_mean = statistics->mean; priors->mean_sigma = max((range / 2.0), (SN_CM_SIGMA_SAFETY_FACTOR / (float) sqrt( ABSOLUTE_MIN_CLASS_WT)) * sigma_min); priors->mean_var = square(priors->mean_sigma); priors->minus_log_log_sigmas_ratio = - (float) safe_log( max( safe_log( 1.0 / (1.0 - SINGLE_FLOAT_EPSILON)), max( safe_log((double) (priors->sigma_max / priors->sigma_min)), LEAST_POSITIVE_SHORT_FLOAT))); priors->minus_log_mean_sigma = - (float) safe_log( (double) priors->mean_sigma); return(priors); } /* SINGLE_NORMAL_CN_MODEL_TERM_BUILDER 21nov94 wmt: initialize all slots in tparm 18dec94 wmt: finish "missing" error msg 30jul95 wmt: change log calls to safe_log to prevent "log: SING error" error messages. Funcalled from Expand-Model-Terms. This constructs parameter, prior, and intermediate results structures appropriate to a single-normal likelihood term, and places them in the model. Constructs corresponding log-likelihood and parameter update function elements and saves them on the model for later compilation. */ void single_normal_cn_model_term_builder( model_DS model, term_DS term, int n_term) /* model_DS model; The model-DS to which this term will contribute. */ /* term_DS term; The singleton term-DS definig this attributes use. */ /* int n_term; The term index for various model-DS substructures. */ { void ***att_trans_data; int n, n_att, n_att_trans_data; float error, log_att_delta, log_delta_div_root_2pi; database_DS data_base; att_DS att; priors_DS prior_set; tparm_DS tparm; struct sn_cn_param *sn; n_att = term->att_list[0]; /* index for this SN attribute */ data_base = model->database; att = data_base->att_info[n_att]; /* Attribute description */ error = att->error; att_trans_data = (void ***) get("single_normal_cn", "att_trans_data"); n_att_trans_data = ((int *) get("single_normal_cn", "n_att_trans_data"))[0]; if (getf(att_trans_data, att->type, n_att_trans_data) == NULL) fprintf( stderr, "Attribute %d not one allowed for single_normal_cn terms\n", n_att); if ( att->missing) { if( (n = att->warnings_and_errors->num_expander_errors ++) == 0) att->warnings_and_errors->model_expander_errors = (fxlstr *) malloc(sizeof(fxlstr)); else att->warnings_and_errors->model_expander_errors = (fxlstr *) realloc(att->warnings_and_errors->model_expander_errors, (n+1) * sizeof(fxlstr)); strcpy(att->warnings_and_errors->model_expander_errors[n], " using single_normal_cn model on attribute with missing values\n"); } if (term->n_atts != 1) fprintf( stderr, "Attribute %d using single_normal_cn model in non-singleton set\n", n_att); if (error <= 0.0) fprintf( stderr, "Attribute %d using single_normal_cn model with non-positive error\n", n_att); log_att_delta = (float) safe_log((double) error); log_delta_div_root_2pi = LN_1_DIV_ROOT_2PI + log_att_delta; /* Allocate parameters struct */ term->tparm = tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); tparm->tppt=SN_CN; tparm->n_atts = term->n_atts; tparm->n_term = n_term; tparm->n_att = n_att; tparm->n_att_indices = tparm->n_datum = tparm->n_data = 0; tparm->wts = tparm->datum = tparm->att_indices = NULL; tparm->data = NULL; tparm->w_j = tparm->ranges = tparm->class_wt = 0.0; tparm->disc_scale = 0.0; tparm->wt_m = tparm->log_marginal = 0.0; tparm->log_delta = log_delta_div_root_2pi; tparm->log_att_delta = log_att_delta; tparm->log_pi = -1.0 * (float) safe_log( M_PI); sn = &(tparm->ptype.sn_cn); /* Allocate & SET priors. */ prior_set = model->priors[n_term] = build_sn_cn_priors(att); sn->weighted_mean = sn->weighted_var = sn->mean = sn->sigma =0.0; sn->log_sigma = sn->variance = sn->log_variance = sn->inv_variance= 0.0; sn->ll_min_diff = sn->skewness = sn->kurtosis = 0.0; sn->prior_mean_mean = prior_set->mean_mean; sn->prior_mean_var = prior_set->mean_var; sn->prior_mean_sigma = prior_set->mean_sigma; sn->prior_sigmas_term = (-1.5 * (float) safe_log( 2.0)) + prior_set->minus_log_log_sigmas_ratio + (-1.0 * (float) safe_log((double) prior_set->mean_sigma)); /* Generate function elements: */ sn->prior_sigma_min_2 = square(prior_set->sigma_min); sn->prior_sigma_max_2 = square(prior_set->sigma_max); /* function elements are no longer genereated. preprocessed constands are stored in the params and calls are made with parameter lists in call*/ } /* SINGLE_NORMAL_CN_LOG_LIKELIHOOD 20dec94 wmt: return type to double When called within the environment of Log-Likelihood-fn, this calculates the probability of a Single-Normal-cn term in 'datum given 'params. */ double single_normal_cn_log_likelihood( tparm_DS tparm) { int n_att = tparm->n_att; float log_delta = tparm->log_delta, *datum = tparm->datum; struct sn_cn_param *sn = &(tparm->ptype.sn_cn); return (log_delta + (-0.5 * (sn->log_variance + (square(sn->mean - datum[n_att]) * sn->inv_variance)))); } /* SINGLE_NORMAL_CN_UPDATE_L_APPROX 20dec94 wmt: return type to double When called within the environment of Update-L-Approx-fn, this calculates the approximate log marginal likelihood log-a_k of observing the weighted statistics given the class hypothesis alone. See Single-Normal-cn-Update-M-approx-term-caller. A LOG-LINEAR APPROXIMATION IS USED IN THE REGION WHERE 0 <= w_j-known <= (* .75 *absolute-min-class-wt*) */ double single_normal_cn_update_l_approx( tparm_DS tparm) { float log_delta = tparm->log_delta, diff, t1, t2, w_j = tparm->w_j; struct sn_cn_param *sn = &(tparm->ptype.sn_cn); diff = sn->weighted_mean - sn->mean; t1 = ( fabs((double) diff) <= sqrt( LEAST_POSITIVE_SINGLE_FLOAT)) ? 0.0 : square(diff); t2 = w_j * (log_delta - sn->log_sigma) + (-0.5 * w_j * (sn->weighted_var + t1) / sn->variance); return (t2); } /* SINGLE_NORMAL_CN_UPDATE_M_APPROX 20dec94 wmt: return type to double 29mar95 wmt: calculation to double When called within the environment of Update-M-Approx-fn, this calculates the approximate log marginal likelihood log-a_k of observing the weighted statistics given the class hypothesis alone. See Single-Normal-cn-Update-M-approx-term-caller. */ double single_normal_cn_update_m_approx( tparm_DS tparm) { struct sn_cn_param *sn = &(tparm->ptype.sn_cn); float log_att_delta = tparm->log_att_delta; float prior_mean_mean = sn->prior_mean_mean; float prior_mean_sigma = sn->prior_mean_sigma, diff, t1; float prior_sigmas_term = sn->prior_sigmas_term, w_j = tparm->w_j; double t2; diff = sn->weighted_mean - prior_mean_mean; t1 = ( fabs((double) diff) <= (prior_mean_sigma * sqrt( 2.0 * LEAST_POSITIVE_SINGLE_FLOAT))) ? 0.0 : -0.5 * square( diff / prior_mean_sigma); t2 = log_gamma( (double) (0.5 * (w_j - 1.0)), FALSE) + (double) t1 + (double) (w_j * log_att_delta) + (-0.5 * (double) w_j * safe_log((double) (M_PI * w_j))) + (-0.5 * (double) (w_j - 1.0) * safe_log( (double) max( LEAST_POSITIVE_SINGLE_FLOAT, sn->weighted_var))) + (double) prior_sigmas_term; return (t2); } /* SINGLE_NORMAL_CN_UPDATE_PARAMS 27nov94 wmt: use percent_equal for float tests 20dec94 wmt: return type to void */ void single_normal_cn_update_params( tparm_DS tparm, int known_parms_p) { int n_att = tparm->n_att, n_data = tparm->n_data; struct sn_cn_param *sn=&(tparm->ptype.sn_cn); float prior_sigma_min_2 = sn->prior_sigma_min_2; float prior_sigma_max_2 = sn->prior_sigma_max_2; float prior_mean_mean = sn->prior_mean_mean; float prior_mean_var = sn->prior_mean_var; float **data = tparm->data, *wts = tparm->wts; float class_wt = tparm->class_wt, class_wt_1; float ignore1, ignore2, mean, variance, skewness, kurtosis, var_ratio; class_wt_1 = class_wt + 1; if (class_wt > 0.0) { /* Zero class-wt implies null class */ if ( tparm->collect == TRUE) { /* If not collect?, we proceed with the previous values. */ central_measures_x(data, n_data, n_att, wts, percent_equal( (double) sn->mean, FLOAT_UNKNOWN, REL_ERROR) ? (double) prior_mean_mean : (double) sn->mean, &ignore1, &ignore2, &mean, &variance, &skewness, &kurtosis); sn->weighted_mean = mean; /* Limit the weighted variance, rather than sigma, to get consistent results: */ sn->weighted_var = max(prior_sigma_min_2, min(prior_sigma_max_2, min( class_wt * SN_CN_SIGMA_SAFETY_FACTOR * prior_mean_var, variance))); sn->skewness = skewness; sn->kurtosis = kurtosis; } else { sn->weighted_mean = prior_mean_mean; sn->weighted_var = prior_mean_var; } } if (known_parms_p != TRUE) { /* Update class parameters */ var_ratio = sn->weighted_var / (class_wt_1 * prior_mean_var); sn->mean = (sn->weighted_mean * (1.0 - var_ratio)) + (prior_mean_mean * var_ratio); sn->variance = max(sn->weighted_var * (class_wt / class_wt_1), (float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT)); sn->sigma = (float) sqrt((double) sn->variance); sn->log_variance = (float) safe_log((double) sn->variance); sn->log_sigma = 0.5 * sn->log_variance; sn->inv_variance = 1.0 / sn->variance; sn->ll_min_diff = max((float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT), (sn->variance * (float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT))); } /* return(class_wt); */ } /* When called within the environment of Class-Equivalence-fn, this tests for a difference of means less than sigma=ratio times MIN of sigmas. */ int single_normal_cn_class_equivalence( tparm_DS tparm1,tparm_DS tparm2, double sigma_ratio) { struct sn_cn_param *sn1 = &(tparm1->ptype.sn_cn), *sn2 = &(tparm2->ptype.sn_cn); if (fabs((double) (sn1->mean - sn2->mean)) < (sigma_ratio * (double) min(sn1->sigma, sn2->sigma))) return(TRUE); else return(FALSE); } /* SINGLE_NORMAL_CN_CLASS_MERGED_MARGINAL 20dec94 wmt: return type to void When called within the environment of Class-Merged-Marginal-fn, this generates the sufficient statistics of Single-Normal-cn term equivalent to the weighted merging of params-0 and params-1, storing same in params-m. dont know what wt1 and wt0 and wtm are supposed to be. should snm be returned maybe and let caller get snm->... */ void single_normal_cn_class_merged_marginal( tparm_DS tparm0, tparm_DS tparm1, tparm_DS tparmm) { float wt_0=.5, wt_1=.5, wt_m=1.0; struct sn_cn_param *sn0=&(tparm0->ptype.sn_cn), *sn1=&(tparm1->ptype.sn_cn), *snm=&(tparmm->ptype.sn_cn); float prior_sigma_min_2 = sn0->prior_sigma_min_2; snm->weighted_mean = ((wt_0 * sn0->weighted_mean) + (wt_1 * sn1->weighted_mean)) / wt_m; snm->weighted_var = max(prior_sigma_min_2, (((wt_0 * (square(sn0->weighted_mean) + sn0->weighted_var)) + (wt_1 * (square(sn1->weighted_mean) + sn1->weighted_var))) / wt_m) - square(snm->weighted_mean)); /* return (snm->weighted_var); */ } autoclass-3.3.6.dfsg.1/prog/Makefile0000644000175000017500000000241411667631470015325 0ustar areare### AUTOCLASS C MAKE FILE FOR Linux version 1.2.10, GCC version 2.5.8, ### and libc version 4.6.25. ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) # optimize & debug - stay with IEEE compliance CFLAGS = $(OSFLAGS) -ansi -pedantic -Wall -O2 -fno-fast-math -g CC = gcc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/search-control.c0000644000175000017500000011077211247310756016756 0ustar areare#include #include #include #include #include #ifndef _MSC_VER #include #endif #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* AUTOCLASS_SEARCH 06oct94 -> ?? wmt: numerous modifications 07apr95 wmt: other functions moved to search-control-2.c, so that they can be compiled with -O flag. This function with -O flag has 2 errors: segmentation fault after returning from print_final-report; & trashed autoclass_search parameter model_file_ptr; under gcc 2.6.3 (see autoclass-c/prog/autoclass.make) 26apr95 wmt: Check for non-NULL "best_clsfs" prior to writing ".results[-bin]" file. Change NULL to 0 for 6th arg of defparam 17may95 wmt: Convert from srand/rand to srand48/lrand48 19dec97 wmt: if force_new_search_p is false, exit if there is no <...>.results[-bin] file. Make t the default for force_new_search_p 18feb98 wmt: add num_cycles and max_cycles to search try autoclass search main program This function is the one users should see. It has a bizillion keywords, but the simplest use is to just pass in a clsf [not any more :-) wmt] This function manages a search for good classifications, coordinating all the pieces without doing much itself. See the argument list, the documentation around that list in the code, and the initial text printed out. Note that there is no explicit testing of arguments for correct type or mutual compatability. */ int autoclass_search( char *data_file_ptr, char *header_file_ptr, char *model_file_ptr, char *search_params_file_ptr, char *search_file_ptr, char *results_file_ptr, char *log_file_ptr) { /* passed to clsf-DS-%= when deciding if a new clsf is duplicate of old REL_ERROR is 0.01 -- was 0.05 */ float rel_error = REL_ERROR; /* initially try these numbers of classes, so not to narrow the search too quickly. the state of this list is saved in the <..>.search file and used on restarts, unless an override specification of start_j_list is made in this file for the restart run. */ static int start_j_list[MAX_N_START_J_LIST] = {2, 3, 5, 7, 10, 15, 25, END_OF_INT_LIST}; /* unless 0, overrides above 2 args, and always uses this j-in */ int fixed_j = 0; /* wait at least this time since last report till report verbosely again */ int min_report_period = 30; /* the search will end this time from now if it hasn't already */ int max_duration = 0; /* if >0, search will end after this many clsf tries have been done */ int max_n_tries = 0; /* save this many clsfs to disk in results-file if 0, don't save anything (no .search & .results files) */ int n_save = 2; /* if FALSE, do not write a log file */ unsigned int log_file_p = TRUE; /* if FALSE, do not write a search file */ unsigned int search_file_p = TRUE; /* if FALSE, do not write a results file */ unsigned int results_file_p = TRUE; /* to protect against possible crash, will save to disk this often - seconds */ int min_save_period = 1800; /* don't store any more than this many clsfs internally */ int max_n_store = 10; /* was 100 06jan94 wmt */ /* print out descriptions of this many of the searches at the end */ int n_final_summary = 10; /* clsf start function: "block" or "random" */ shortstr start_fn_type = "random"; /* clsf try function: "converge_search_3", "converge_search_4" or "converge" */ shortstr try_fn_type = "converge_search_3"; /* will call this function to decide how many classes to start next try with. based on best clsfs so far. only "random_ln_normal" so far */ shortstr n_classes_fn_type = "random_ln_normal"; /* if TRUE, perform base_cycle in initialize_parameters */ unsigned int initial_cycles_p = TRUE; /* FALSE saves as ascii text, TRUE saves as machine dependent binary. */ unsigned int save_compact_p = TRUE; /* FALSE reads as ascii text, TRUE reads as machine dependent binary. */ unsigned int read_compact_p = TRUE; /* FALSE uses 1 as the seed for srand48, the pseudo-random number function TRUE uses universal time clock as the seed */ unsigned int randomize_random_p = TRUE; /* if > 0, will only read this many datum from .db2, rather than the whole file */ int n_data = 0; /* passed to try_fn_type "converge" one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between successive convergence cycles. the largest of halt_range and (halt_factor * current_log_marginal) is used. */ float halt_range = 0.5; /* passed to try_fn_type "converge" one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between successive convergence cycles. the largest of halt_range and (halt_factor * current_log_marginal) is used. */ float halt_factor = 0.0001; /* passed to try_fn_type "converge_search_3" delta for each class of log aprox-marginal-likelihood of class statistics with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the class weight (class->w_j) between successive convergence cycles. increasing this value loosens the convergence and reduces the number of cycles. decreasing this value tightens the convergence and increases the number of cycles */ float rel_delta_range = 0.0025; /* passed to try functions "converge_search_3" and "converge" number of cycles for which the convergence criterion must be satisfied for the trial to terminate. */ int n_average = 3; /* passed to try_fn_type "converge_search_4" delta for each class of log aprox-marginal-likelihood of class statistics with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the class weight (class->w_j) over sigma_beta_n_values convergence cycles. increasing this value loosens the convergence and reduces the number of cycles. decreasing this value tightens the convergence and increases the number of cycles */ float cs4_delta_range = 0.0025; /* passed to try_fn_type "converge_search_4" number of past values to use in computing sigma^2 and beta^2. */ int sigma_beta_n_values = 6; /* passed to all try functions. They will end a trial if this many cycles have been done and the convergence criteria has not been satisfied. */ int max_cycles = 200; /* if TRUE, the selected try function will print to the screen values useful in specifying non-default values for halt_range, halt_factor, rel_delta_range, n_average, sigma_beta_n_values, and cs4_delta_range. */ unsigned int converge_print_p = FALSE; /* if TRUE, will ignore any previous search results, discarding the existing .search & .results[-bin] files after confirmation by the user. if FALSE, will continue the search using the existing .search & .results[-bin] files. */ unsigned int force_new_search_p = TRUE; /* if TRUE, checkpoints of the current classification will be output every min_checkpoint_period seconds. file extension is .chkpt -- useful for very large classifications */ unsigned int checkpoint_p = FALSE; /* if checkpoint_p = TRUE, the checkpointed classification will be written this often - in seconds (= 3 hours) */ int min_checkpoint_period = 10800; /* can be either "chkpt" or "results" if "chkpt", continue convergence of the classification contained in <...>.chkpt[-bin] -- checkpoint_p must be true. if "results", continue convergence of the best classification contained in <...>.results[-bin] -- checkpoint_p must be false. */ shortstr reconverge_type = ""; /* if false, no output is directed to the screen. Assuming log_file_p = true, output will be directed to the log file only. */ unsigned int screen_output_p = TRUE; /* if false, standard input is not queried each cycle for the character q. Thus either parameter max_n_tries or max_duration must be specified, or AutoClass will run forever. */ unsigned int interactive_p = TRUE; /* The default value asks the user whether to coninue or not when data definition warnings are found. If specified as false, then AutoClass will continue, despite warnings -- the warnings will continue to be output to the terminal and the log file. */ unsigned int break_on_warnings_p = TRUE; /* The default value tells AutoClass to free the majority of its allocated storage. This is not required, and in the case of DEC Alpha's causes core dump. If specified as false, AutoClass will not attempt to free storage. */ unsigned int free_storage_p = TRUE; /* -------------------------------------------------- */ static FILE *log_file_fp = NULL, *search_file_fp = NULL; static FILE *header_file_fp = NULL, *model_file_fp = NULL; static FILE *stream, *search_params_file_fp = NULL; search_DS restart_search = NULL, search = NULL; clsf_DS clsf = NULL; int restart_p = FALSE, n_dup_tries, s_parms_error_cnt = 0; time_t begin, now, last_search_save, last_report, end_time = 0, last_results_save; time_t begin_try; int i, j_in, n_stored_clsf, dup_p, max_j, n_start_j_list, *new_start_j_list; int bclength = n_save, last_bclength = -1, list_global_clsf_p = TRUE; search_try_DS latest_try, best, *ss; static clsf_DS *best_clsfs = NULL, *last_saved_clsfs = NULL; shortstr stop_reason; char temp_str[5], caller[] = "autoclass_search"; PARAM params[MAXPARAMS]; int n_params = 0, want_wts_p = TRUE, start_j_list_from_s_params = FALSE; int expand_p = TRUE, update_wts_p = FALSE, checkpoint_clsf_cnt = 0; int results_file_found = FALSE; static int clsf_n_list[MAX_CLSF_N_LIST] = {END_OF_INT_LIST}; static fxlstr checkpoint_file, maybe_checkpoint_file, results_file, n_classes_explain; fxlstr str; int double_str_length = 2 * sizeof( fxlstr); int reread_p = FALSE, regenerate_p = FALSE, silent_p; char *double_str; unsigned int compact_p; /* -------------------------------------------------- */ stream = stdout; /* aju 980612: Cannot use as initialzer; not a constant per MSVC.*/ double_str = (char *) malloc( double_str_length); G_stream = stdout; checkpoint_file[0] = maybe_checkpoint_file[0] = results_file[0] = '\0'; params[0].paramptr = NULL; defparam( params, n_params++, "rel_error", TFLOAT, &rel_error, 0); defparam( params, n_params++, "start_j_list", TINT_LIST, start_j_list, MAX_N_START_J_LIST); defparam( params, n_params++, "n_classes_fn_type", TSTRING, n_classes_fn_type, SHORT_STRING_LENGTH); defparam( params, n_params++, "fixed_j", TINT, &fixed_j, 0); defparam( params, n_params++, "min_report_period", TINT, &min_report_period, 0); defparam( params, n_params++, "max_duration", TINT, &max_duration, 0); defparam( params, n_params++, "max_n_tries", TINT, &max_n_tries, 0); defparam( params, n_params++, "n_save", TINT, &n_save, 0); defparam( params, n_params++, "log_file_p", TBOOL, &log_file_p, 0); defparam( params, n_params++, "search_file_p", TBOOL, &search_file_p, 0); defparam( params, n_params++, "results_file_p", TBOOL, &results_file_p, 0); defparam( params, n_params++, "min_save_period", TINT, &min_save_period, 0); defparam( params, n_params++, "max_n_store", TINT, &max_n_store, 0); defparam( params, n_params++, "n_final_summary", TINT, &n_final_summary, 0); defparam( params, n_params++, "start_fn_type", TSTRING, start_fn_type, SHORT_STRING_LENGTH); defparam( params, n_params++, "try_fn_type", TSTRING, try_fn_type, SHORT_STRING_LENGTH); defparam( params, n_params++, "initial_cycles_p", TBOOL, &initial_cycles_p, 0); defparam( params, n_params++, "save_compact_p", TBOOL, &save_compact_p, 0); defparam( params, n_params++, "read_compact_p", TBOOL, &read_compact_p, 0); defparam( params, n_params++, "randomize_random_p", TBOOL, &randomize_random_p, 0); defparam( params, n_params++, "n_data", TINT, &n_data, 0); defparam( params, n_params++, "halt_range", TFLOAT, &halt_range, 0); defparam( params, n_params++, "halt_factor", TFLOAT, &halt_factor, 0); defparam( params, n_params++, "rel_delta_range", TFLOAT, &rel_delta_range, 0); defparam( params, n_params++, "n_average", TINT, &n_average, 0); defparam( params, n_params++, "cs4_delta_range", TFLOAT, &cs4_delta_range, 0); defparam( params, n_params++, "sigma_beta_n_values", TINT, &sigma_beta_n_values, 0); defparam( params, n_params++, "max_cycles", TINT, &max_cycles, 0); defparam( params, n_params++, "converge_print_p", TBOOL, &converge_print_p, 0); defparam( params, n_params++, "force_new_search_p", TBOOL, &force_new_search_p, 0); defparam( params, n_params++, "checkpoint_p", TBOOL, &checkpoint_p, 0); defparam( params, n_params++, "min_checkpoint_period", TINT, &min_checkpoint_period, 0); defparam( params, n_params++, "reconverge_type", TSTRING, &reconverge_type, SHORT_STRING_LENGTH); defparam( params, n_params++, "screen_output_p", TBOOL, &screen_output_p, 0); defparam( params, n_params++, "interactive_p", TBOOL, &interactive_p, 0); defparam( params, n_params++, "break_on_warnings_p", TBOOL, &break_on_warnings_p, 0); defparam( params, n_params++, "free_storage_p", TBOOL, &free_storage_p, 0); /* -------------------------------------------------- */ /* read search params file */ fprintf(stdout, "\n\n\n### Starting Check of %s%s\n", (search_params_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_params_file_ptr); search_params_file_fp = fopen(search_params_file_ptr, "r"); s_parms_error_cnt = getparams(search_params_file_fp, params) + validate_search_start_fn( start_fn_type) + validate_search_try_fn( try_fn_type) + validate_n_classes_fn( n_classes_fn_type); fclose(search_params_file_fp); for (i=0; start_j_list[i] != END_OF_INT_LIST; i++); n_start_j_list = i; for (i=0; i 0) exit(1); /* aju 980612: Added conditionality on interactive_p */ /* 01jul98 wmt: removed conditionality -- important that user is warned */ /* 22apr00 wmt: bring back conditionality, to prevent batch runs from going into a loop */ if ((randomize_random_p == FALSE) || (eqstring( start_fn_type, "block") == TRUE)) { if (interactive_p == TRUE) { fprintf( stderr, "\nWARNING: either start_fn_type = \"block\", or randomize_random_p\n" " = false, or both. These parameter settings are for testing\n" " *only* -- they should not be utilized for normal AutoClass\n" " runs.\n\n"); sprintf( str, "Do you want to continue {y/n} "); if (y_or_n_p( str)) fprintf( stderr, "Test run continues ...\n\n"); else exit(1); } else { fprintf( stderr, "\nERROR: either start_fn_type = \"block\", or randomize_random_p\n" " = false, or both. These parameter settings are for testing\n" " *only* -- they should not be utilized for normal AutoClass\n" " runs.\n\n"); exit(1); } } /* end of search params processing */ /* data file is not opened because it can be either binary or ascii - G_data_file_format */ if (eqstring( header_file_ptr, "") != TRUE) header_file_fp = fopen( header_file_ptr, "r"); if (eqstring( model_file_ptr, "") != TRUE) model_file_fp = fopen( model_file_ptr, "r"); if (log_file_p == TRUE) { log_file_fp = fopen( log_file_ptr, "a"); G_log_file_fp = log_file_fp; } safe_sprintf( str, sizeof( str), caller, "\n\nAUTOCLASS C (version %s) STARTING at %s \n\n", G_ac_version, format_universal_time(get_universal_time())); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (log_file_p == TRUE) { fprintf(log_file_fp, "AUTOCLASS -SEARCH default parameters:\n"); putparams(log_file_fp, params, FALSE); } if (log_file_p == TRUE) { fprintf(log_file_fp, "USER supplied parameters which override the defaults:\n"); putparams(log_file_fp, params, TRUE); } if ((eqstring( reconverge_type, "chkpt") == TRUE) && (checkpoint_p == FALSE)) { fprintf( stderr, "ERROR: if reconverge_type is \"chkpt\", checkpoint_p must " "be true, as well\n"); exit(1); } if ((eqstring( reconverge_type, "results") == TRUE) && (checkpoint_p == TRUE)) { fprintf( stderr, "ERROR: if reconverge_type is \"results\", checkpoint_p must " "be false, as well\n"); exit(1); } if (((eqstring( reconverge_type, "results") == TRUE) || (eqstring( reconverge_type, "chkpt") == TRUE)) && ( force_new_search_p == TRUE)) { fprintf( stderr, "ERROR: if reconverge_type is \"results\" or \"chkpt\", " "force_new_search_p must be false, as well\n"); exit(1); } /* set value of G_checkpoint_file */ if (checkpoint_p == TRUE) { G_checkpoint_file[0] = '\0'; G_min_checkpoint_period = min_checkpoint_period; make_and_validate_pathname( (save_compact_p) ? "checkpoint_bin" : "checkpoint", search_params_file_ptr, &G_checkpoint_file, FALSE); } if (force_new_search_p == TRUE) { compact_p = save_compact_p; silent_p = TRUE; } else { compact_p = read_compact_p; silent_p = FALSE; } /* set proper extension of results_file_ptr for validate_results_pathname */ results_file_ptr[0] = results_file[0] = '\0'; make_and_validate_pathname( (compact_p) ? "results_bin" : "results", search_params_file_ptr, &results_file, FALSE); strcpy( results_file_ptr, results_file); results_file[0] = '\0'; /* needed for validate_results_pathname */ results_file_found = validate_results_pathname( results_file_ptr, &results_file, "results", FALSE, silent_p); /* in case validate_results_pathname finds a different file than that input */ results_file_ptr[0] = '\0'; strcpy( results_file_ptr, results_file); /* aju 980612: Added conditionality on interactive_p */ /* 01jul98 wmt: removed conditionality -- important that user is warned */ /* 22apr00 wmt: bring back conditionality, to prevent batch runs from going into a loop */ if ((force_new_search_p == TRUE) && ( results_file_found == TRUE)) { if (interactive_p == TRUE) { fprintf( stderr, "\nWARNING: force_new_search_p is true and continuing will" " discard the \n search trials in:\n %s%s\n", G_absolute_pathname, results_file_ptr); sprintf( str, "Do you want to continue {y/n} "); if (! y_or_n_p( str)) exit(1); } else { fprintf( stderr, "ERROR: force_new_search_p is true and continuing will" " discard the \n search trials in:\n %s%s\n", G_absolute_pathname, results_file_ptr); exit(1); } } if ((force_new_search_p == FALSE) && ( results_file_found == FALSE)) { fprintf( stderr, "\nERROR: if force_new_search_p is false, there must " "be a <...>.results%s file\n", (read_compact_p) ? "-bin" : ""); exit(1); } if (checkpoint_p == TRUE) to_screen_and_log_file("\nWARNING: \"autoclass -search\" running in checkpointing " "mode\n\n", log_file_fp, stream, TRUE); /* for testing fprintf( stderr, "run continues ...\n"); exit(1); */ if (force_new_search_p == FALSE) { search_file_fp = fopen( search_file_ptr, "r"); restart_search = reconstruct_search( search_file_fp, search_file_ptr, results_file_ptr); fclose( search_file_fp); if (restart_search != NULL) { search = restart_search; /* if start_j_list has not been specified in the .s-params file use it's state from the .search file */ if (start_j_list_from_s_params == TRUE) { to_screen_and_log_file( "ADVISORY: start_j_list=(", log_file_fp, stream, TRUE); output_int_list( search->start_j_list, log_file_fp, stream); to_screen_and_log_file( ") has been overridden by (", log_file_fp, stream, TRUE); output_int_list( start_j_list, log_file_fp, stream); safe_sprintf( double_str, double_str_length, caller, ")\n from %s%s\n", (search_params_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_params_file_ptr); to_screen_and_log_file( double_str, log_file_fp, stream, TRUE); } else { to_screen_and_log_file( "\nADVISORY: start_j_list=(", log_file_fp, stream, TRUE); output_int_list( start_j_list, log_file_fp, stream); to_screen_and_log_file( ") has been overridden by (", log_file_fp, stream, TRUE); output_int_list( search->start_j_list, log_file_fp, stream); safe_sprintf( double_str, double_str_length, caller, ")\n from %s%s\n\n", (search_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, search_file_ptr); to_screen_and_log_file( double_str, log_file_fp, stream, TRUE); for (i=0; istart_j_list[i]; if (start_j_list[i] == END_OF_INT_LIST) break; } n_start_j_list = i; } } else search = get_search_DS(); } else search = get_search_DS(); if (0 < search->n) /* restart-p flags continuation of a search */ restart_p = TRUE; else restart_p = FALSE; if (eqstring( reconverge_type, "chkpt") == TRUE) { make_and_validate_pathname( (read_compact_p) ? "checkpoint_bin" : "checkpoint", search_params_file_ptr, &maybe_checkpoint_file, FALSE); validate_results_pathname( maybe_checkpoint_file, &checkpoint_file, "checkpoint", TRUE, FALSE); clsf = copy_clsf_DS( (get_clsf_seq( checkpoint_file, expand_p, want_wts_p, update_wts_p, "checkpoint", &checkpoint_clsf_cnt, clsf_n_list))[0], want_wts_p); } else if (search->tries != NULL) /* reconverge_type = "results", as well */ clsf = copy_clsf_DS( search->tries[0]->clsf, want_wts_p); else if (eqstring( data_file_ptr, "") != TRUE) /* or if given data-file, get clsf from that */ clsf = generate_clsf( 1, header_file_fp, model_file_fp, log_file_fp, stream, reread_p, regenerate_p, data_file_ptr, header_file_ptr, model_file_ptr, log_file_ptr, restart_p, start_fn_type, initial_cycles_p, n_data, start_j_list_from_s_params); else { fprintf(stderr, "\nERROR: Haven't been given enough info to find a classification\n"); /* fprintf(stderr, "autoclass_search needs one of these as input:\n"); */ /* fprintf(stderr, " 1) a classification\n"); */ /* fprintf(stderr, " 2) a data-file and header-file\n"); */ /* fprintf(stderr, */ /* " 3) a search-file and results-file from a previous run\n"); */ /* fprintf(stderr, " 4) a search object from a previous run\n"); */ exit(1); } /* set proper extension of results_file_ptr for print functions (save files) */ results_file_ptr[0] = results_file[0] = '\0'; make_and_validate_pathname( (save_compact_p) ? "results_bin" : "results", search_params_file_ptr, &results_file, FALSE); strcpy( results_file_ptr, results_file); expand_clsf( clsf, want_wts_p, update_wts_p); /* just in case was compressed */ if ((max_n_tries != 0) && (restart_p == TRUE)) max_n_tries = search->n + max_n_tries; max_j = clsf_DS_max_n_classes(clsf); /* n-classes must be < limit */ if (too_big( max_j, start_j_list, n_start_j_list) == TRUE) { new_start_j_list = remove_too_big(max_j, start_j_list, &n_start_j_list); for (i=0; itries == NULL) && (fixed_j == 0)) { to_screen_and_log_file ("ERROR: A new search must have at least one item in start_j_list\n", log_file_fp, stream, TRUE); exit(1); } if (eqstring( header_file_ptr, "") != TRUE) fclose( header_file_fp); if (eqstring( model_file_ptr, "") != TRUE) fclose( model_file_fp); begin = get_universal_time(); /* start the clock */ now = begin; last_search_save = begin - 1; /* must not be equal to now */ last_results_save = begin; last_report = begin; if (max_duration != 0) end_time = begin + (time_t) max_duration; /* tell the user about what will happen */ print_initial_report( stream, log_file_fp, min_report_period, end_time, max_n_tries, search_file_ptr, results_file_ptr, log_file_ptr, min_save_period, n_save); /* printf("\n###commented calls to print_model and print_clsf\n"); dbg JTP*/ /*** print_model_DS(clsf->models[0]," first model of clsf"); dbg JTP*/ /*** print_clsf_DS(clsf," after init"); dbg JTP*/ if (restart_p == TRUE) safe_sprintf( str, sizeof( str), caller, "\nRESTARTING SEARCH at %s\n\n", format_universal_time(begin)); else safe_sprintf( str, sizeof( str), caller, "\nBEGINNING SEARCH at %s\n\n", format_universal_time(begin)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (log_file_p == TRUE) fclose( log_file_fp); /*commentedprintf(" \nquitting before BIG LOOOP\n"); exit(1); dbg JTP*/ /* top of THE BIG LOOP */ while (TRUE) { search->n ++; begin_try = get_universal_time(); if (eqstring( reconverge_type, "chkpt") == TRUE) { pop_int_list( start_j_list, &n_start_j_list, &j_in); j_in = clsf->checkpoint->current_try_j_in; } else if (eqstring( reconverge_type, "results") == TRUE) j_in = clsf->n_classes; else if (fixed_j != 0) j_in = fixed_j; else if (pop_int_list( start_j_list, &n_start_j_list, &j_in) > 0) ; else j_in = (int) within( 1.0, (double) apply_n_classes_fn( n_classes_fn_type, search->n_tries, search->tries, max_j, FALSE, n_classes_explain), (double) max_j); /* DO A NEW TRIAL */ if (log_file_p == TRUE) { log_file_fp = fopen( log_file_ptr, "a"); G_log_file_fp = log_file_fp; } latest_try = try_variation(clsf, j_in, search->n, reconverge_type, start_fn_type, try_fn_type, initial_cycles_p, begin_try, (double) halt_range, (double) halt_factor, (double) rel_delta_range, max_cycles, n_average, (double) cs4_delta_range, sigma_beta_n_values, converge_print_p, log_file_fp, stream); if (latest_try->j_out != 0) { /* complete trial */ n_stored_clsf = min(max_n_store, (search->n - search->n_dups)); dup_p = find_duplicate(latest_try, search->tries, n_stored_clsf, &n_dup_tries, (double) rel_error, search->n_tries, restart_p); search->n_dup_tries += n_dup_tries; if (dup_p == TRUE) /* update counts of duplicates */ search->n_dups += 1; /* unless latest try is a duplicate, add latest try to list of tries */ else { search->n_tries ++; /* assuming store tries forever but only store clsf on tries up to n_store_clsf*/ search->tries = insert_new_trial(latest_try, search->tries, search->n_tries, n_stored_clsf, max_n_store); if (G_clsf_storage_log_p == TRUE) describe_search( search); } best = search->tries[0]; now = get_universal_time(); /* search->time += (int) latest_try->time; 12oct94 wmt: */ /* after each try, print a short report describing n-classes tried */ if (latest_try == best) sprintf(str, " %s%d->%d(%d) ", "best", latest_try->j_in, latest_try->j_out, search->n); else if (dup_p == TRUE) sprintf(str, " %s%d->%d(%d) ", "dup", latest_try->j_in, latest_try->j_out, search->n); else sprintf(str, " %d->%d(%d) ", latest_try->j_in, latest_try->j_out, search->n); to_screen_and_log_file(str, log_file_fp, stream, TRUE); /* fprintf( stderr, "\nautoclass_search: G_num_cycles %d max_cycles %d\n", G_num_cycles, max_cycles); */ if (G_num_cycles >= max_cycles) { safe_sprintf( str, sizeof( str ), caller, "\nWARNING: trial %d terminated prior to convergence" " since number of \n" " cycles reached \"max_cycles\" (%d).\n", latest_try->n, max_cycles); to_screen_and_log_file( str, G_log_file_fp, G_stream, TRUE); } /* when long enough since last long report and have found a new best clsf */ if ((search->n_tries > 2) && (best != NULL) && (now > ((time_t) min_report_period + last_report)) && ((search->last_try_reported == NULL) || (best->ln_p > search->last_try_reported->ln_p))) { if (fixed_j != 0) sprintf(n_classes_explain, "as fixed at %d", fixed_j); else if (n_start_j_list > 0) { sprintf(n_classes_explain, "off of list: ("); for (i=0; i < n_start_j_list; i++) { sprintf(temp_str, " %d", start_j_list[i]); strcat(n_classes_explain, temp_str); } strcat(n_classes_explain, " )"); } else apply_n_classes_fn( n_classes_fn_type, search->n_tries, search->tries, max_j, TRUE, n_classes_explain); print_report(stream, log_file_fp, search, last_search_save, last_report, eqstring( reconverge_type, "chkpt"), n_classes_explain); last_report = now; search->last_try_reported = best; } if (n_save >0) { ss = safe_subseq_of_tries(search->tries, 0, n_save, search->n_tries, &bclength); while (bclength > 1 && ss[bclength - 1]->clsf == NULL) bclength--; if (best_clsfs != NULL) free(best_clsfs); best_clsfs = (clsf_DS *) malloc( bclength * sizeof(clsf_DS)); for (i=0; iclsf; free(ss); if (bclength == last_bclength) for (i=0;i ((time_t) min_save_period + max( last_results_save, last_search_save))) { if ((results_file_p == TRUE) && (last_bclength != bclength)) { save_clsf_seq( best_clsfs, bclength, results_file_ptr, save_compact_p, "results"); safe_sprintf( str, sizeof( str), caller, " [saved %s/%s at %s]\n", (save_compact_p) ? RESULTS_BINARY_FILE_TYPE : RESULTS_FILE_TYPE, SEARCH_PARAMS_FILE_TYPE, format_universal_time (now)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (last_saved_clsfs != NULL) free(last_saved_clsfs); last_results_save = now; last_saved_clsfs = (clsf_DS *) malloc( bclength * sizeof(clsf_DS)); for (i=0; itime has been updated */ if (now != last_results_save) { safe_sprintf( str, sizeof( str), caller, " [saved .search at %s]\n", format_universal_time (now)); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } } } } /* reset reconverge_type, so that subsequent trials will use the start_j_list */ strcpy( reconverge_type, ""); } /* end of complete trial */ else search->n --; if (char_input_test()) /* stop if the user asks */ strcpy(stop_reason, "you asked me to"); else if ((end_time != 0) && (now > end_time)) /* or time up, */ strcpy(stop_reason, "max duration has expired"); else if ((max_n_tries != 0) && (latest_try->n == max_n_tries)) /* or # tries up */ strcpy(stop_reason, "max number of tries reached"); else strcpy(stop_reason, ""); if (eqstring(stop_reason, "") == FALSE) /* quit the try loop */ break; strcpy( reconverge_type, ""); if (log_file_p == TRUE) fclose( log_file_fp); } /* end of big loop */ print_final_report( stream, log_file_fp, search, begin, last_search_save, n_save, stop_reason, results_file_p, search_file_p, n_final_summary, log_file_ptr, search_params_file_ptr, results_file_ptr, clsf, eqstring( reconverge_type, "chkpt"), last_report, now); if ((now != last_search_save) && /* have more trials occurred since last save */ (best_clsfs != NULL)) { /* save a results file, if results changed */ if (((last_saved_clsfs == NULL) || (last_saved_clsfs != best_clsfs)) && (n_save != 0) && (results_file_p == TRUE)) save_clsf_seq( best_clsfs, bclength, results_file_ptr, save_compact_p, "results"); /* save a search-file, if supposed to */ if ((n_save != 0) && (search_file_p == TRUE)) save_search( search, search_file_ptr, last_search_save, clsf, eqstring( reconverge_type, "chkpt"), start_j_list, n_final_summary, n_save); } if (free_storage_p == TRUE) { /* database storage and model storage, including global-clsf, are not freed */ free_clsf_class_search_storage( clsf, search, list_global_clsf_p); } safe_sprintf( str, sizeof( str), caller, "\nAUTOCLASS C (version %s) STOPPING at %s \n", G_ac_version, format_universal_time(get_universal_time())); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (log_file_p == TRUE) fclose(log_file_fp); return(0); } autoclass-3.3.6.dfsg.1/prog/io-read-data.c0000644000175000017500000024516511247310756016267 0ustar areare#include #include #include #include #ifndef _MSC_VER #include #endif #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* IO-READ-DATA.C - FUNCTIONS DEFINED */ /* check_stop_processing define_data_file_format process_data_header_model_files log_header read_database check_for_non_empty check_data_base output_warning_msgs output_error_msgs output_message_summary output_messages output_db_error_messages read_data define_attribute_definitions process_attribute_definitions process_attribute_def create_att_DS create_warn_err_DS define_discrete_translations expand_att_list find_str_in_list process_discrete_translations process_translation_msgs read_data_doit translate_instance translate_real translate_discrete get_line_tokens read_from_string read_line find_att_statistics find_real_stats store_real_stats find_discrete_stats output_created_translations */ /* CHECK_STOP_PROCESSING after log file is closed, determine if run is to proceed: error msgs => (error ...) warning msgs => (y-or-n-p ..) if *break-on-warnings* is t. called by generate-clsf */ void check_stop_processing( int total_error_cnt, int total_warning_cnt, FILE *log_file_fp, FILE *stream) { fxlstr str; if (total_error_cnt > 0) { if (stream == NULL) { stream = stdout; to_screen_and_log_file("\nERRORs have occurred!", log_file_fp, stream, TRUE); } to_screen_and_log_file("\nThere is NO continuation possible\n", log_file_fp, stream, TRUE); exit(1); } else if (total_warning_cnt > 0) { /* fprintf(stderr, "\ntotal_warning_cnt is %d\n", total_warning_cnt); */ if (G_break_on_warnings == TRUE) { if (stream == NULL) { stream = stdout; to_screen_and_log_file("\nWARNINGs have occurred!", log_file_fp, stream, TRUE); } sprintf( str, "\nDo you want to EXIT - {y/n}? "); to_screen_and_log_file( str, log_file_fp, NULL, TRUE); if (y_or_n_p( str)) { to_screen_and_log_file("\nEXIT due to warning messages at user's request\n", log_file_fp, stream, TRUE); exit(1); } else to_screen_and_log_file("\n", log_file_fp, NULL, TRUE); } else to_screen_and_log_file("\nRun continues, even though warnings were found\n\n", log_file_fp, stream, TRUE); } } /* DEFINE_DATA_FILE_FORMAT 28nov94 wmt: data_syntax will be line -- discard Lisp syntaxes: :list & :vector; put descriptor (number_of_data_file_format_defs) prior to integer value; check for comment chars in first column of each line 30nov94 wmt: read characters as '?', rather than ?, in order to handle the blank char error checks & processes data file format parameters. User places this into xxxx.hd2 DATA-SYNTAX: line => 1 2 3 3 4 5 #\\return. */ void define_data_file_format( FILE *header_file_fp, FILE *log_file_fp, FILE *stream) { int i, num, num_definition_names = 4; fxlstr str, def_name_string; database_DS data_base = G_input_data_base; char caller[] = "define_data_file_format"; data_base->input_n_atts = data_base->n_atts = 0; data_base->separator_char = ' '; data_base->comment_char = ';'; data_base->unknown_token = '?'; discard_comment_lines(header_file_fp); fscanf(header_file_fp, "%s %d\n", def_name_string, &num); for (i=0; i < min(num, num_definition_names); i++) { discard_comment_lines(header_file_fp); fscanf(header_file_fp, "%s", def_name_string); if (eqstring(def_name_string, "number_of_attributes")) fscanf(header_file_fp, "%d\n", &(data_base->input_n_atts)); else if (eqstring(def_name_string, "separator_char")) data_base->separator_char = (char) read_char_from_single_quotes("separator_char", header_file_fp); else if (eqstring(def_name_string, "comment_char")) data_base->comment_char = (char) read_char_from_single_quotes("comment_char", header_file_fp); else if (eqstring(def_name_string, "unknown_token")) data_base->unknown_token = (char) read_char_from_single_quotes("unknown_token", header_file_fp); else { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: invalid data file format definition name: %s\n", def_name_string); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } } if (log_file_fp != NULL) { to_screen_and_log_file("ADVISORY[2]: data_file_format settings:\n ", log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "separator_char = '%c', comment_char = '%c', unknown_token = '%c'\n", data_base->separator_char, data_base->comment_char, data_base->unknown_token); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } if ( (data_base->n_atts = data_base->input_n_atts) <= 0) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: the number of attributes %d should be a positive integer.\n", data_base->n_atts); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } } /* PROCESS_DATA_HEADER_MODEL_FILES 30nov94 wmt: move log_header to generate_clsf 18dec94 wmt: always expand models -- do not check for errors or warnings first Reads data, header, and model files, expands model terms if no error messages, then outputs error and warning msgs */ void process_data_header_model_files( FILE *log_file_fp, int regenerate_p, FILE *stream, database_DS db, model_DS *models, int num_models, int *total_error_cnt_ptr, int *total_warning_cnt_ptr) { int i; char output_msg_type[8] = ":read"; output_messages(db, models, num_models, log_file_fp, stream, total_error_cnt_ptr, total_warning_cnt_ptr, output_msg_type); check_stop_processing(*total_error_cnt_ptr, *total_warning_cnt_ptr, log_file_fp, stream); for (i=0; (i < num_models); i++) conditional_expand_model_terms(models[i], regenerate_p, log_file_fp, stream); strcpy( output_msg_type, ":expand"); output_messages(db, models, num_models, log_file_fp, stream, total_error_cnt_ptr, total_warning_cnt_ptr, output_msg_type); to_screen_and_log_file("\n############ Input Check Concluded ##############\n", log_file_fp, stream, TRUE); } /* LOG_HEADER 12oct94 wmt: modified 30nov94 wmt: add [1], [2], [3] output appropriate header for log messages */ void log_header( FILE *log_file_fp, FILE *stream, char *data_file_ptr, char *header_file_ptr, char *model_file_ptr, char *log_file_ptr) { fxlstr str; char caller[] = "log_header"; safe_sprintf( str, sizeof( str), caller, "\n############## Starting Input Check ###############\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); if (log_file_fp != NULL) { safe_sprintf( str, sizeof( str), caller, "\nTo log file: \n %s%s\n", (log_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, log_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } safe_sprintf( str, sizeof( str), caller, "During loading of: \n [1] %s%s,\n", (data_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, data_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " [2] %s%s,\n", (header_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, header_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " [3] %s%s.\n", (model_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, model_file_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, "\n [Attribute #, value #, and datum # are zero based.]\n\n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } /* READ_DATABASE 21oct94 wmt: modified 16nov94 wmt: pass in data, header file names to be saved in database_DS 28nov94 wmt: add param log_file_fp, and add call to check_data_base 25apr95 wmt: add binary data file capability 27apr95 wmt: Solaris 2.4 fails open, unless fopen/fclose is done first 10may95 wmt: move output_created_translations call to output_att_statistics; call output_att_statistics 16may95 wmt: converted binary i/o to ANSI 20may95 wmt: added prediction_p Reads and returns the data base defined by data-file and header-file. N-data, if supplied, will overide the value in the data file. returns d-base */ database_DS read_database( FILE *header_file_fp, FILE *log_file_fp, char *data_file_ptr, char *header_file_ptr, int max_data, int reread_p, FILE *stream) { int n_att; FILE *data_file_fp = NULL; att_DS *att_info; database_DS d_base; warn_err_DS errors; if ((d_base = find_database(data_file_ptr, header_file_ptr, max_data)) != NULL) { if (reread_p == FALSE) { /* When database is already loaded we normally skip rereading it. */ to_screen_and_log_file( "Skipping a reread of the database\n", log_file_fp, stream, TRUE); } else { d_base->invalid_value_errors = NULL; d_base->num_invalid_value_errors = 0; d_base->incomplete_datum = NULL; d_base->num_incomplete_datum = 0; d_base->compressed_p = FALSE; att_info = d_base->att_info; for (n_att=0; n_attn_atts; n_att++) { errors = att_info[n_att]->warnings_and_errors; strcpy(errors->unspecified_dummy_warning, ""); strcpy(errors->single_valued_warning, ""); /******************** strcpy(errors->model_expander_warnings, ""); ******* strcpy(errors->model_expander_errors, ""); changed 6/29 JTP */ errors->unused_translators_warning = NULL; errors->model_expander_warnings = NULL; errors->num_expander_warnings = 0; errors->model_expander_errors = NULL; errors->num_expander_errors = 0; } } } else { d_base = create_database(); d_base->n_data = 0; strcpy(d_base->data_file, data_file_ptr); strcpy(d_base->header_file, header_file_ptr); d_base->compressed_p = FALSE; } G_input_data_base = d_base; define_data_file_format( header_file_fp, log_file_fp, stream); define_attribute_definitions( header_file_fp, header_file_ptr, log_file_fp, stream); /* defining discrete translations in the header file is not supported - define_discrete_translations(NULL, 0, G_input_data_base); - so just output discrete translations built in translate_discrete from the data read (after the data is read in 02dec94 wmt */ if (eqstring( G_data_file_format, "binary") == TRUE) data_file_fp = fopen( data_file_ptr, "rb"); else if (eqstring( G_data_file_format, "ascii") == TRUE) data_file_fp = fopen( data_file_ptr, "r"); else { fprintf( stderr, "ERROR: G_data_file_format \"%s\" not handled\n", G_data_file_format); exit(1); } read_data( d_base, data_file_fp, max_data, data_file_ptr, log_file_fp, stream); fclose( data_file_fp); check_data_base( d_base, d_base->n_data); if (G_prediction_p == FALSE) { find_att_statistics( d_base, log_file_fp, stream); output_att_statistics( d_base, log_file_fp, stream); } if (find_database_p( d_base, G_db_list, G_db_length) == FALSE) { G_db_length++; if (G_db_list == NULL) G_db_list = (database_DS *) malloc(G_db_length * sizeof(database_DS)); else G_db_list = (database_DS *) realloc(G_db_list, G_db_length * sizeof(database_DS)); G_db_list[G_db_length-1] = d_base; } return(d_base); } int check_for_non_empty( att_DS *atts, int n_atts) { int i; for (i=0; in_atts, n_datum, datum_length; int *datum_length_list = d_base->datum_length; int num_errors; float *datum, **data = d_base->data; incomplete_datum_DS incomp_datum; for (n_datum=0; n_datumnum_incomplete_datum++; num_errors = d_base->num_incomplete_datum; if (d_base->incomplete_datum == NULL) d_base->incomplete_datum = (incomplete_datum_DS *) malloc(num_errors * sizeof(incomplete_datum_DS)); else d_base->incomplete_datum = (incomplete_datum_DS *) realloc(d_base->incomplete_datum, (num_errors * sizeof(incomplete_datum_DS))); incomp_datum = (incomplete_datum_DS) malloc(sizeof(struct incomplete_datum)); incomp_datum->n_datum = n_datum; incomp_datum->datum_length = datum_length; d_base->incomplete_datum[num_errors - 1] = incomp_datum; } } } /* OUTPUT_WARNING_MSGS 23mar92 - WMT - single-valued functionality changed from error to warning -error slot retained for backward compatability to existing .results files 03dec94 wmt: changed unused_translators & single_valued errors 25may95 wmt: replaced sizeof(msg) with msg_length in first safe_sprintf 18feb98 wmt: for number of translators less than .hd2 range, reduce the range and output advisory, rather than outputting warning -- REMOVED 13mar98 JCS due to incompatablility with previous results files format all warning messages into returned string */ char *output_warning_msgs( int n_att, att_DS att, database_DS db, model_DS model) { char *msg, caller[] = "output_warning_msgs"; fxlstr warning_msg; shortstr *att_ignore_ids = model->att_ignore_ids; int i, msg_length = 4 * sizeof(fxlstr); warn_err_DS errors = att->warnings_and_errors; msg = (char *) malloc( msg_length); msg[0]='\0'; if (strlen(errors->unspecified_dummy_warning) != 0) safe_sprintf( msg, msg_length, caller, "WARNING[2]: attribute #%d definition has not been specified --" " type set to dummy\n", n_att); if (errors->unused_translators_warning != NULL) { safe_sprintf( warning_msg, sizeof( warning_msg), caller, "WARNING[2]: attribute #%d: \"%s\"\n", n_att, att->dscrp); strcat(msg, warning_msg); /* commented since defined translators are not implemented 03dec94 wmt */ /* length = 0; */ /* while( errors->unused_translators_warning[length] > MOST_NEGATIVE_SINGLE_FLOAT) { */ /* strncat(msg, "No occurrences observed for value %f\n", */ /* errors->unused_translators_warning[length]); */ /* length++; */ /* } */ /* safe_sprintf(warning_msg, sizeof( warning_msg), caller, */ /* "To improve sensitivity of classification, reduce range by %d\n", */ /* length); */ /* for (i=0; inum_tsp; i++) */ /* if (db->translations_supplied_p[i] == n_att) { */ /* strcat(warning_msg, */ /* "and redefine its translations to eliminate non-occurring entries\n"); */ /* break; */ /* } */ /* do this instead */ safe_sprintf( warning_msg, sizeof( warning_msg), caller, " to improve sensitivity of classification, reduce range to %d.\n", (int) errors->unused_translators_warning[0]); strcat(msg, warning_msg); } if (eqstring(att_ignore_ids[n_att], "model_term_not_specified") == TRUE) { safe_sprintf( warning_msg, sizeof( warning_msg), caller, "WARNING[3]: attribute #%d: \"%s\"\n", n_att, att->dscrp); strcat( msg, warning_msg); strcat( msg, " model term type has not been specified and is set to ignore\n"); } if (errors->model_expander_warnings != NULL) for (i=errors->num_expander_warnings-1; i>=0; i--) { safe_sprintf( warning_msg, sizeof( warning_msg), caller, "WARNING[3]: attribute #%d: \"%s\"\n", n_att, att->dscrp); strcat( msg, warning_msg); strcat( msg, errors->model_expander_warnings[i]); } if (strlen(errors->single_valued_warning) != 0) { safe_sprintf( warning_msg, sizeof( warning_msg), caller, "WARNING[3]: attribute #%d: \"%s\"\n", n_att, att->dscrp); strcat( msg, warning_msg); safe_sprintf( warning_msg, sizeof( warning_msg), caller, " has only one unique value. Change model term type to ignore.\n"); strcat( msg, warning_msg); } if ((int) strlen( msg) > (msg_length - 1)) { fprintf( stderr, "ERROR: %s produced %d chars (max number is %d)\n", caller, (int) strlen( msg), (msg_length - 1)); abort(); } return (msg); } /* OUTPUT_ERROR_MSGS 18dec94 wmt: complete error msg format all error messages into returned string */ char *output_error_msgs( int n_att, att_DS att) { char *msg, caller[] = "output_error_msgs"; fxlstr str; int i, msg_length = 2; warn_err_DS errors = att->warnings_and_errors; msg = (char *) malloc( msg_length * sizeof( char)); strcpy(msg, ""); for (i=errors->num_expander_errors-1; i>=0; i--) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: attribute #%d: \"%s\"\n", n_att, att->dscrp); msg_length = strlen( msg) + strlen( str) + strlen( errors->model_expander_errors[i]) + 1; msg = (char *) realloc( msg, msg_length * sizeof( char)); strcat( msg, str); strcat( msg, errors->model_expander_errors[i]); } if ((int) strlen( msg) > (msg_length - 1)) { fprintf( stderr, "ERROR: %s produced %d chars (max number is %d)\n", caller, (int) strlen( msg), (msg_length - 1)); abort(); } return (msg); } /* OUTPUT_MESSAGE_SUMMARY 18feb98 wmt: for number of translators less than .hd2 range, reduce the range and output advisory, rather than outputting warning -- REMOVED 13mar98 JCS due to incompatablility with previous results files output warning/error message summary and return error & warning counts 23mar92 - WMT - single-valued functionality changed from error to warning -error slot retained for backward compatability to existing .results files */ void output_message_summary( int unspecified_dummy_warning_cnt, int ignore_model_term_warning_cnt, int unused_translators_warning_cnt, int incomplete_errors_cnt, int single_valued_warnings_cnt, int invalid_value_errors_cnt, int model_expander_warning_cnt, int model_expander_error_cnt, int *total_error_cnt_ptr, int *total_warning_cnt_ptr, FILE *log_file, FILE *stream, int output_p) { fxlstr str; char caller[] = "output_message_summary"; *total_error_cnt_ptr = incomplete_errors_cnt + invalid_value_errors_cnt + model_expander_error_cnt; *total_warning_cnt_ptr = unspecified_dummy_warning_cnt + ignore_model_term_warning_cnt + unused_translators_warning_cnt + model_expander_warning_cnt + single_valued_warnings_cnt; if ((*total_error_cnt_ptr + *total_warning_cnt_ptr) > 0) { safe_sprintf( str, sizeof( str), caller, "\n******* SUMMARY OF ALL ERROR AND WARNING MESSAGES *******\n\n"); to_screen_and_log_file(str, log_file, stream, output_p); if (*total_warning_cnt_ptr > 0) { safe_sprintf( str, sizeof( str), caller, "%d WARNING message(s) occured:\n", *total_warning_cnt_ptr); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to unspecified attribute type set to dummy\n", unspecified_dummy_warning_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to excess type = discrete range(s)\n", unused_translators_warning_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to unspecified model term type set to ignore\n", ignore_model_term_warning_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to single valued attribute(s)\n", single_valued_warnings_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to model term type expansion\n", model_expander_warning_cnt); to_screen_and_log_file(str, log_file, stream, output_p); /* safe_sprintf(str, "\nAbort and change appropriate file\n"); 05dec94 wmt */ /* to_screen_and_log_file(str, log_file, stream, output_p); */ /* safe_sprintf(str, "or continue, accepting possible anomalies.\n"); */ /* to_screen_and_log_file(str, log_file, stream, output_p); */ } if (*total_error_cnt_ptr > 0) { safe_sprintf( str, sizeof( str), caller, "\n%d ERROR message(s) occured:\n", *total_error_cnt_ptr); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to incomplete datum\n", incomplete_errors_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to invalid type = real attribute value(s)\n", invalid_value_errors_cnt); to_screen_and_log_file(str, log_file, stream, output_p); safe_sprintf( str, sizeof( str), caller, " %d due to model term type expansion\n", model_expander_error_cnt); to_screen_and_log_file(str, log_file, stream, output_p); } } } /* OUTPUT_MESSAGES 02dec94 wmt: free mallocs passed in from output_warning_msgs & output_error_msgs output warning & error msgs for incomplete datum and attributes whose att-ignore-id is not 'ignore-model 23mar92 - WMT - single-valued functionality changed from error to warning -error slot retained for backward compatability to existing .results file */ void output_messages( database_DS db, model_DS *models, int num_models, FILE *log_file, FILE *stream, int *total_error_cnt_ptr, int *total_warning_cnt_ptr, char *output_msg_type_ptr) { int i, n_att, msg_header_p = FALSE, output_p = TRUE; char *warning_msgs = NULL, *error_msgs = NULL, caller[] = "output_messages"; shortstr *att_ignore_ids; att_DS att; model_DS model; warn_err_DS errors; att_DS *att_info = db->att_info; int n_atts = db->n_atts; int unused_translators_warning_cnt = 0; int unspecified_dummy_warning_cnt = 0; int ignore_model_term_warning_cnt = 0; int model_expander_warning_cnt = 0; int incomplete_datum_cnt = 0; int invalid_value_errors_cnt = 0; int single_valued_warnings_cnt = 0; int model_expander_error_cnt = 0; fxlstr str; invalid_value_errors_cnt = db->num_invalid_value_errors; incomplete_datum_cnt = db->num_incomplete_datum; if (((invalid_value_errors_cnt > 0) || (incomplete_datum_cnt > 0)) && (output_p == TRUE)) output_db_error_messages(db, log_file, stream, output_p); for (i=0; iatt_ignore_ids; for (n_att=0; n_attwarnings_and_errors; if (strlen(errors->unspecified_dummy_warning) != 0) unspecified_dummy_warning_cnt++; if (eqstring(att_ignore_ids[n_att], "model_term_not_specified")) ignore_model_term_warning_cnt++; if (errors->unused_translators_warning != NULL) unused_translators_warning_cnt++; if (errors->model_expander_warnings != NULL ) model_expander_warning_cnt +=errors->num_expander_warnings; if (strlen(errors->single_valued_warning) != 0) single_valued_warnings_cnt++; warning_msgs = output_warning_msgs(n_att, att, db, model); if (errors->model_expander_errors != NULL) model_expander_error_cnt += errors->num_expander_errors; error_msgs = output_error_msgs(n_att, att); if (((int) strlen(warning_msgs) > 0) || ((int) strlen(error_msgs) > 0)) { if ((msg_header_p == FALSE) && (output_p == TRUE)) { if (eqstring( output_msg_type_ptr, ":read")) safe_sprintf( str, sizeof( str), caller, "\n****** Error & Warning Messages from READING Model Index" " = %d ******\n\n", i); else safe_sprintf( str, sizeof( str), caller, "\n** Error & Warning Messages from READING & " "EXPANDING Model Index = %d **\n\n", i); to_screen_and_log_file(str, log_file, stream, output_p); msg_header_p = TRUE; } if ((int) strlen(warning_msgs) > 0) to_screen_and_log_file(warning_msgs, log_file, stream, output_p); if ((int) strlen(error_msgs) > 0) to_screen_and_log_file(error_msgs, log_file, stream, output_p); } free(warning_msgs); free(error_msgs); } } } if (eqstring( output_msg_type_ptr, ":read") && ((incomplete_datum_cnt + invalid_value_errors_cnt) == 0)) output_p = FALSE; output_message_summary(unspecified_dummy_warning_cnt, ignore_model_term_warning_cnt, unused_translators_warning_cnt, incomplete_datum_cnt, single_valued_warnings_cnt, invalid_value_errors_cnt, model_expander_warning_cnt, model_expander_error_cnt, total_error_cnt_ptr, total_warning_cnt_ptr, log_file, stream, output_p); } /* OUTPUT_DB_ERROR_MESSAGES 02dec94 wmt: add more detail to messages output error msgs for incomplete datum and invalid value tokens */ void output_db_error_messages( database_DS db, FILE *log_file_fp, FILE *stream, int output_p) { fxlstr str; int i, n_att; char caller[] = "output_db_error_messages"; safe_sprintf( str, sizeof( str), caller, "\n********** Error Messages from Data Base ***********\n\n"); to_screen_and_log_file(str, log_file_fp, stream, output_p); if (db->invalid_value_errors != NULL) { for (i=0; inum_invalid_value_errors; i++) { n_att = db->invalid_value_errors[i]->n_att; safe_sprintf( str, sizeof( str), caller, "ERROR[1]: in datum #%d, type = real attribute #%d: \"%s\" has\n", db->invalid_value_errors[i]->n_datum, n_att, db->att_info[n_att]->dscrp); to_screen_and_log_file(str, log_file_fp, stream, output_p); safe_sprintf( str, sizeof( str), caller, " non-number value, %s\n", db->invalid_value_errors[i]->value); to_screen_and_log_file(str, log_file_fp, stream, output_p); } } if (db->incomplete_datum != NULL) { for (i=0; inum_incomplete_datum; i++) { safe_sprintf( str, sizeof( str), caller, "ERROR[1]: datum #%d is incomplete: it has %d attributes, " "instead of %d.\n", db->incomplete_datum[i]->n_datum, db->incomplete_datum[i]->datum_length, db->n_atts); to_screen_and_log_file(str, log_file_fp, stream, output_p); } } } /* READ_DATA 15nov94 wmt: max_data check changed & add DATA_ALLOC_INCREMENT & do incremental allocation of data array 27nov94 wmt: pass instance_length to read_data_doit & translate_instance 28nov94 wmt: add datum_length, 03dec94 wmt: pass comment chars to read_data_doit 25apr95 wmt: add binary data file capability 09may95 wmt: test on (max_data - 1), rather than max_data 16may95 wmt: converted binary i/o to ANSI 13feb98 wmt: check for malloc/realloc failures */ void read_data( database_DS d_base, FILE *data_file_fp, int max_data, char *data_file_ptr, FILE *log_file_fp, FILE *stream) { int n, data_allocated = 0, instance_length = 0, *datum_length = NULL; int n_comment_chars = 3, binary_instance_length; int input_binary_instance_length, num_header_chars = 8; char **instance, db2_bin_header[10] = ""; float **data = NULL; char *str; int str_length = 2 * sizeof( fxlstr); char comment_chars[4], caller[] = "read_data"; float *binary_instance = NULL; str = (char *) malloc( str_length); binary_instance_length = d_base->n_atts * sizeof( float); if (eqstring( G_data_file_format, "binary")) { fread( &db2_bin_header, sizeof( char), num_header_chars, data_file_fp); fread( &input_binary_instance_length, sizeof( char), sizeof( float), data_file_fp); if ((eqstring( db2_bin_header, ".db2-bin") != TRUE) || (input_binary_instance_length != binary_instance_length)) { fprintf( stderr, "ERROR: %s%s, \n either \"%s\" is not the correct " "header string(\".db2-bin\") or\n" " %d is not the correct case length (%d)\n", (data_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, data_file_ptr, db2_bin_header, input_binary_instance_length, binary_instance_length); exit(1); } } /* treat blank lines are comment lines */ comment_chars[0] = d_base->comment_char; comment_chars[1] = ' '; comment_chars[2] = '\n'; comment_chars[3] = '\0'; instance = read_data_doit( d_base, data_file_fp, TRUE, &instance_length, n_comment_chars, comment_chars, binary_instance_length, &binary_instance); for (n=0; (instance != NULL) || (binary_instance != NULL); n++) { d_base->n_data += 1; if (d_base->n_data > data_allocated) { data_allocated += DATA_ALLOC_INCREMENT; if (d_base->data == NULL) { d_base->data = (fptr *) malloc(data_allocated * sizeof(float *)); if (d_base->data == NULL) { fprintf( stderr, "ERROR: read_data(1): out of memory, malloc returned NULL!\n"); exit(1); } } else { d_base->data = (fptr *) realloc(d_base->data, data_allocated * sizeof(float *)); if (d_base->data == NULL) { fprintf( stderr, "ERROR: read_data(2): out of memory, realloc returned NULL!\n"); exit(1); } } data = d_base->data; if (d_base->datum_length == NULL) { d_base->datum_length = (int *) malloc(data_allocated * sizeof(int)); if (d_base->datum_length == NULL) { fprintf( stderr, "ERROR: read_data(3): out of memory, malloc returned NULL!\n"); exit(1); } } else { d_base->datum_length = (int *) realloc(d_base->datum_length, data_allocated * sizeof(int)); if (d_base->datum_length == NULL) { fprintf( stderr, "ERROR: read_data(4): out of memory, realloc returned NULL!\n"); exit(1); } } datum_length = d_base->datum_length; } if (eqstring( G_data_file_format, "ascii")) data[(d_base->n_data-1)] = translate_instance(d_base, instance, instance_length, n, log_file_fp, stream); else data[(d_base->n_data-1)] = binary_instance; datum_length[(d_base->n_data-1)] = instance_length; instance_length = 0; /* fprintf( stderr, " read_data n %d\n", n); */ instance = read_data_doit( d_base, data_file_fp, FALSE, &instance_length, n_comment_chars, comment_chars, binary_instance_length, &binary_instance); if ((max_data != 0) && (n+1 >= max_data)) break; } if ((max_data != 0) && (n+1 < max_data)) safe_sprintf( str, str_length, caller, "\nWARNING[1]: read_data found *ONLY* %d datum in\n" " \"%s%s\"\n\n", n, (data_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, data_file_ptr); else if (n == 0) { safe_sprintf( str, str_length, caller, "\nERROR[1]: no data read by read_data from\n" " \"%s%s\"\n", (data_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, data_file_ptr); to_screen_and_log_file( str, G_log_file_fp, G_stream, TRUE); exit(1); } else safe_sprintf( str, str_length, caller, "ADVISORY[1]: %s %d datum from \n %s%s\n", (eqstring( G_data_file_format, "ascii")) ? "read" : "loaded", d_base->n_data, (data_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, data_file_ptr); to_screen_and_log_file( str, G_log_file_fp, G_stream, TRUE); free( str); } /* error checks & processes attribute definitions. User places this into xxxx.hd2 (*header-file-type*). */ void define_attribute_definitions( FILE *header_file_fp, char *header_file_ptr, FILE *log_file_fp, FILE *stream) { database_DS data_base = G_input_data_base; process_attribute_definitions( data_base, header_file_fp, header_file_ptr, log_file_fp, stream); } /* PROCESS_ATTRIBUTE_DEFINITIONS 28nov94 wmt: pass att_num to create_att_DS, and exit if input errors are found 30nov94 wmt: check for invalid att_num 12dec94 wmt: read header file using get_line_tokens, rather than scanf -- which reads past \n etc, causing all kinds of grief 28feb95 wmt: realloc att_info if n_atts > allo_n_atts checks the attribute descriptions, and sets the N-atts, & Att-info slots of the d-base data-base-DS structure. */ void process_attribute_definitions( database_DS d_base, FILE *header_file_fp, char *header_file_ptr, FILE *log_file_fp, FILE *stream) { int i, j, att_num, n_atts, first_read = FALSE, num_tokens, n_comment_chars = 5; att_DS *att_info; int input_error = FALSE, integer_p, n_atts_read = 0, str_length = 2 * sizeof( fxlstr); char *str; char **tokens = NULL, caller[] = "process_attribute_definitions"; char separator_char = ' ', comment_chars[6]; str = (char *) malloc( str_length); /* allow general comment lines anywhere in model file */ comment_chars[0] = '!'; comment_chars[1] = '#'; comment_chars[2] = ';'; comment_chars[3] = ' '; comment_chars[4] = '\n'; comment_chars[5] = '\0'; n_atts = d_base->n_atts; if (n_atts > d_base->allo_n_atts) { d_base->allo_n_atts = n_atts; d_base->att_info = (att_DS *) realloc( d_base->att_info, d_base->allo_n_atts * sizeof( att_DS)); } att_info = d_base->att_info; for (i=0; iatt_info[i] = NULL; for (i=0; iwarnings_and_errors->unspecified_dummy_warning, "true"); } } free( str); } /* PROCESS_ATTRIBUTE_DEF 15dec94 wmt: new - split off from create_att_DS 21mar97 wmt: check for incomplete discrete/real att defs read the defintion from the .hd2 file */ att_DS process_attribute_def( int att_num, int *input_error_ptr, char **tokens, int num_tokens, FILE *log_file_fp, FILE *stream) { fxlstr str; int range = 0; /* changed 6/21/JTP was INT_UNKNOWN;*/ float rel_error = 0.0, error = 0.0; /* these were -1.0 changed to 0.0 5/21/94 JTP*/ float zero_point = 0.0; int min_discrete_tokens = 6, min_real_location_tokens = 6; int min_real_scalar_tokens = 8; int min_num_tokens = 4, integer_p, float_p, index_1, index_2; char *type_ptr = NULL, *sub_type_ptr = NULL, *dscrp_ptr = NULL; char caller[] = "process_attribute_def"; if (num_tokens < min_num_tokens) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected at least %d items: \n" " \n" " read %d: %s, %s, %s, %s\n", min_num_tokens, num_tokens, tokens[0], (num_tokens >= 2) ? tokens[1] : "", (num_tokens >= 3) ? tokens[2] : "", (num_tokens >= 4) ? tokens[3] : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { type_ptr = tokens[1]; sub_type_ptr = tokens[2]; dscrp_ptr = tokens[3]; if (eqstring(type_ptr, "discrete") && eqstring(sub_type_ptr, "nominal")) { if (num_tokens < min_discrete_tokens) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected at least %d items: \n" " \n" " range \n", min_discrete_tokens); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " read %d: %s, %s, %s, %s, %s, %s\n", num_tokens, tokens[0], (num_tokens >= 2) ? tokens[1] : "", (num_tokens >= 3) ? tokens[2] : "", (num_tokens >= 4) ? tokens[3] : "", (num_tokens >= 5) ? tokens[4] : "", (num_tokens >= 6) ? tokens[5] : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { if( ! eqstring(tokens[4], "range") ) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected parameter range, got %s \n" " for attribute #%d: \"%s\"\n", tokens[4], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { range = atoi_p(tokens[5], &integer_p); if (integer_p != TRUE) { safe_sprintf(str, sizeof( str), caller, "ERROR[2]: value of parameter range read, %s, was not an integer\n" " for attribute #%d: \"%s\"\n", tokens[5], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } } } } else if (eqstring(type_ptr, "real") && eqstring(sub_type_ptr, "scalar")) { if (num_tokens < min_real_scalar_tokens) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected at least %d items: \n" " \n", min_real_scalar_tokens); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " zero_point \n" " rel_error \n"); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " read %d: %s, %s, %s, %s, %s, %s, %s, %s\n", num_tokens, tokens[0], (num_tokens >= 2) ? tokens[1] : "", (num_tokens >= 3) ? tokens[2] : "", (num_tokens >= 4) ? tokens[3] : "", (num_tokens >= 5) ? tokens[4] : "", (num_tokens >= 6) ? tokens[5] : "", (num_tokens >= 7) ? tokens[6] : "", (num_tokens >= 8) ? tokens[7] : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { if( ! eqstring(tokens[4], "zero_point") && !eqstring(tokens[6],"zero_point") ) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected parameter zero_point, got %s and %s\n" " for attribute #%d: \"%s\"\n", tokens[4], tokens[6], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else if( ! eqstring(tokens[4], "rel_error") && !eqstring(tokens[6], "rel_error") ) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected parameter rel_error, got %s and %s\n" " for attribute #%d: \"%s\"\n", tokens[4], tokens[6], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { if (eqstring(tokens[4], "zero_point")) { index_1 = 5; index_2 = 7; } else { index_1 = 7; index_2 = 5; } zero_point = (float) atof_p(tokens[index_1], &float_p); if (float_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: value of parameter zero_point read, %s, was not a float\n" " for attribute #%d: \"%s\"\n", tokens[index_1], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } rel_error = (float) atof_p(tokens[index_2], &float_p); if (float_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: value of parameter rel_error read, %s, was not a float\n" " for attribute #%d: \"%s\"\n", tokens[index_2], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } } } } else if (eqstring(type_ptr, "real") && eqstring(sub_type_ptr, "location")) { if (num_tokens < min_real_location_tokens) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected at least %d items: \n" " \n" " error \n", min_real_location_tokens); to_screen_and_log_file(str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " read %d: %s, %s, %s, %s, %s, %s\n", num_tokens, tokens[0], (num_tokens >= 2) ? tokens[1] : "", (num_tokens >= 3) ? tokens[2] : "", (num_tokens >= 4) ? tokens[3] : "", (num_tokens >= 5) ? tokens[4] : "", (num_tokens >= 6) ? tokens[5] : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { if( ! eqstring(tokens[4], "error") ) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected parameter error, got %s \n" " for attribute #%d: \"%s\"\n", tokens[4], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } else { error = atof_p(tokens[5], &float_p); if (float_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: value of parameter error read, %s, was not a float\n" " for attribute #%d: \"%s\"\n", tokens[5], att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } } } } else if (eqstring(type_ptr, "dummy") ) { if( !eqstring(sub_type_ptr, "nil") && !eqstring(sub_type_ptr,"none") ) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: expected sub_type nil or none, got %s\n" " for attribute #%d: \"%s\"\n", sub_type_ptr, att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } } else { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: unknown type/sub_type = %s/%s\n" " for attribute #%d: \"%s\"\n", type_ptr, sub_type_ptr, att_num, dscrp_ptr); to_screen_and_log_file(str, log_file_fp, stream, TRUE); *input_error_ptr = TRUE; } } return( create_att_DS( att_num, input_error_ptr, range, (double) rel_error, (double) error, (double) zero_point, type_ptr, sub_type_ptr, dscrp_ptr, log_file_fp, stream)); } /* CREATE_ATT_DS 15nov94 wmt: initialize n_props & props & missing 28nov94 wmt: pass att_num to create_att_DS, reword error msgs 12dec94 wmt: read header file using get_line_tokens, rather than scanf 15dec94 wmt: split this into read_attribute_definitions & create_att_DS 23may95 wmt: add G_prediction_p Create an att-DS from a descriptor form (index type sub-type description-string prop-list). Any translations placed on the prop-list are processed later. */ att_DS create_att_DS( int att_num, int *input_error_ptr, int range, double rel_error, double error, double zero_point, char *type_ptr, char *sub_type_ptr, char *dscrp_ptr, FILE *log_file_fp, FILE *stream) { att_DS att; int i; fxlstr str; char caller[] = "create_att_DS"; att = (att_DS) malloc(sizeof(struct att)); if (*input_error_ptr == FALSE) { strcpy(att->type, type_ptr); strcpy(att->sub_type, sub_type_ptr); if ((int) strlen( dscrp_ptr) >= SHORT_STRING_LENGTH) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: length of attribute #%d description \"%s\"\n" " is longer than %d characters\n", att_num, dscrp_ptr, SHORT_STRING_LENGTH - 1); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } strcpy(att->dscrp, dscrp_ptr); att->range = range; att->zero_point = (float) zero_point; att->translations = NULL; att->rel_error = (float) rel_error; att->error = (float) error; att->n_trans = 0; att->n_props = 0; att->props = NULL; att->missing = FALSE; if (eqstring(type_ptr, "real")) { att->d_statistics = NULL; if ((G_prediction_p == TRUE) && (G_training_clsf != NULL)) { /* force the "test" database to use the same statistics as the "training" database. */ att->r_statistics = G_training_clsf->database->att_info[att_num]->r_statistics; att->missing = G_training_clsf->database->att_info[att_num]->missing; att->range = G_training_clsf->database->att_info[att_num]->range; } else att->r_statistics = (real_stats_DS) malloc(sizeof(struct real_stats)); } else if (eqstring(type_ptr, "discrete")) { att->r_statistics = NULL; att->d_statistics = (discrete_stats_DS) malloc(sizeof(struct discrete_stats)); att->d_statistics->range = range; att->d_statistics->observed = (int *) malloc(range * sizeof(int)); att->d_statistics->n_observed = range; for (i=0; id_statistics->observed[i] = 0; /* force the "test" database to use the same discrete translations as the "training" database. */ if ((G_prediction_p == TRUE) && (G_training_clsf != NULL)) { att->n_trans = G_training_clsf->database->att_info[att_num]->n_trans; att->translations = G_training_clsf->database->att_info[att_num]->translations; } } else { att->r_statistics = NULL; att->d_statistics = NULL; } att->warnings_and_errors = create_warn_err_DS(); } return(att); } /* CREATE_WARN_ERR_DS 16may95 wmt: move malloc out of declaration */ warn_err_DS create_warn_err_DS(void) { warn_err_DS weds; weds= (warn_err_DS) malloc( sizeof( struct warn_err)); weds->model_expander_warnings = NULL; weds->num_expander_warnings = 0; weds->model_expander_errors = NULL; weds->num_expander_errors = 0; strcpy(weds->unspecified_dummy_warning, ""); strcpy(weds->single_valued_warning, ""); weds->unused_translators_warning = NULL; return(weds); } /* error checks & processes type = discrete attribute translations. void define_discrete_translations( char ***discrete_translations, int num, database_DS data_base) { int nlength; process_discrete_translations (data_base, expand_att_list(discrete_translations, num, &nlength), nlength); } */ /* This takes a list of possibly compact attribute descriptions or translations and expands it into a list of standard forms. In the standard forms, attribute indices are required to be non-negative integers or 'default. Expand-Att-List allows the use of a compact representation which substitutes lists of attribute indices for the single index. The expanded list is returned. Any duplication of indices will cause an error. */ char ***expand_att_list( char ***att_list, int num, int *nlength) { return(att_list); } /* FIND_STR_IN_LIST 02dec94 wmt: ***translations => **translations */ int find_str_in_list( char *str, char **translations, int num) { int i; for (i=0; iatt_info; */ /* default_translation = NULL; processed = NULL; for (i=0; in_atts; n_att++) { att = att_info[n_att]; if (eqstring(att->type, "discrete")) { att_translations = att->translations; if (att_translations == NULL) sprintf(temp, "%d", n_att); pos = find_str_in_lisT(temp, value_translations, vlength); att_translation = (char **) malloc(2 * sizeof(char *)); att_translation[0] = value_translations[pos][1]; att_translation[1] = NULL; range = att->range; if (att_translation != NULL) { att_translation = process_translation(d_base, n_att,att, 1, att_translation); plength++; if (processed == NULL) processed = (char ***) malloc(plength * sizeof(char **)); else processed = (char ***) realloc(processed, plength * sizeof(char **)); processed[plength-1] = (char **) malloc(2 * sizeof(char *)); processed[plength-1][0] = (char *) malloc(sizeof(fxlstr)); processed[plength-1][1] = (char *) malloc(sizeof(fxlstr)); strcpy(processed[plength-1][0], temp); strcpy(processed[plength-1][1], att_translation[0]); tplength++; if (translations_provided == NULL) translations_provided = (int *) malloc(tplength * sizeof(int)); else translations_provided = (int *) realloc(translations_provided, tplength * sizeof(int)); translations_provided[tplength - 1] = n_att; } else { tnplength++; if (translations_not_provided == NULL) translations_not_provided = (int *) malloc(tnplength * sizeof(int)); else translations_not_provided = (int *) realloc(translations_not_provided, tnplength * sizeof(int)); translations_not_provided[tnplength - 1] = n_att; att_translation = default_translation; } att->range = max(range, att->n_trans); fprintf( stream, "Reset range to %d\n", range); if ((att_translation != NULL) && (range != att->n_trans)) { sprintf(str, "ERROR[2]: attribute range %d is not equal to number of translations\n", range); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } att->n_trans += 1; if (att->translations == NULL) att->translations = (char ***) malloc(att->n_trans * sizeof(char **)); else att->translations = (char ***) realloc(att->translations, att->trans * sizeof(char **)); if (att_translation != NULL) att->translations[att->n_trans] = att_translation; else { n = att->n_trans - 1; att->translations[n] = (char **) malloc(2 * sizeof(char *)); att->translations[n][0] = (char *) malloc(sizeof(fxlstr)); sprintf(att->translations[n][0], "%d", range); att->translations[n][0] = NULL; } } } d_base->translations_supplied_p = translations_provided; if (translations_not_provided) process_translations_msgs(translations_not_provided, default_translation, att_info, stream); */ /* } */ /* output advisory msgs for translations not provided */ void process_translation_msgs( int *translations_not_provided, int num, char *default_translation, att_DS *att_info, FILE *stream) { int i; if (default_translation != NULL) fprintf(stream, "ADVISORY: the default translation will be used"); else fprintf(stream, "ADVISORY: no translations were provided"); fprintf(stream, " for these type = discrete attributes\n"); for (i=0; idscrp); } } char **process_translation( database_DS d_base, int n_att, att_DS att_dscrp, int nat, char **att_translation) { fprintf(stderr, "ERROR: process_translation called with commented code in io-read-data.c\n"); exit(1); /*************************** char *att_props[3][STRLIMIT], missing_value_token, **value_trans; char ***unknown_translators, **translator; int i, range, ulength = 0; att_props = att_dscrp->props; range = att_dscrp->range; missing_value_token = db->unknown_token; return(att_translation); for (i=0; in_atts; } else instance = get_line_tokens(data_file_fp, (int) d_base->separator_char, n_comment_chars, comment_chars, first_read, instance_length_ptr); return (instance); } /* TRANSLATE_INSTANCE 27nov94 wmt: use minimum of num_atts & instance_length; free instance read in datum, even if incomplete. After total database is read in, Check-Data-Base finds all incomplete datum at one time updated by WMT 04 Sep 91 to allow excess length datum thru */ float *translate_instance( database_DS d_base, char **instance, int instance_length, int n_datum, FILE *log_file_fp, FILE *stream) { int n_att, num_atts = d_base->n_atts, i; float *new_instance; att_DS *att_info, attribute; new_instance = (fptr) malloc(num_atts * sizeof(float)); att_info = d_base->att_info; for (n_att=0; n_att < min(num_atts, instance_length); n_att++) { attribute = att_info[n_att]; if (eqstring(attribute->type, "real")) new_instance[n_att] = (float) translate_real(d_base, n_datum, n_att, instance[n_att]); else if (eqstring(attribute->type, "discrete")) new_instance[n_att] = (float) translate_discrete(d_base, n_att, attribute, instance[n_att], log_file_fp, stream); else if (eqstring(attribute->type, "dummy") ) new_instance[n_att] = 0.0; else{ fprintf(stderr, "ERROR[2]: unknown attribute type: %s\n", attribute->type); exit(1); } } for (i=0; iunknown_token)) num = FLOAT_UNKNOWN; else { num = atof_p(value, &float_p); if (float_p != TRUE) { d_base->num_invalid_value_errors++; num_errors = d_base->num_invalid_value_errors; if (d_base->invalid_value_errors == NULL) d_base->invalid_value_errors = (invalid_value_errors_DS *) malloc(num_errors * sizeof(invalid_value_errors_DS)); else d_base->invalid_value_errors = (invalid_value_errors_DS *) realloc(d_base->invalid_value_errors, (num_errors * sizeof(invalid_value_errors_DS))); invalid_error = (invalid_value_errors_DS) malloc(sizeof(struct invalid_value_errors)); invalid_error->n_datum = n_datum; invalid_error->n_att = n_att; strcpy(invalid_error->value, value); d_base->invalid_value_errors[num_errors - 1] = invalid_error; } } return(num); } /* TRANSLATE_DISCRETE 27nov94 wmt: initialize stats->observed when length is increased 02dec94 wmt: revised building of translations: ***translations => **translations 19jan96 wmt: allocate space for translations using (strlen( value) + 1), rather than sizeof(shortstr) -- prevents corruption of translation tables. Use very_long_str to print translators. Translate string tokens of printable characters to integers. Translate unknown symbols to INT_UNKNOWN, translate other values using: a: supplied translation table, and expand translation table if necessary => advisories; or b: translation table built as needed => no advisories. */ int translate_discrete( database_DS d_base, int n_att, att_DS attribute, char *value, FILE *log_file_fp, FILE *stream) { int val, i; discrete_stats_DS stats; fxlstr long_str; very_long_str very_long_str; char caller[] = "translate_discrete"; val = find_str_in_list(value, attribute->translations, attribute->n_trans); if (val == -1) { /* not in attribute->translations yet */ val = attribute->n_trans; attribute->n_trans += 1; if (attribute->translations == NULL) attribute->translations = (char **) malloc(attribute->n_trans * sizeof(char *)); else attribute->translations = (char **) realloc(attribute->translations, attribute->n_trans * sizeof(char *)); /* attribute->translations[val] = (char *) malloc(sizeof(shortstr)); */ attribute->translations[val] = (char *) malloc( strlen( value) + 1); strcpy(attribute->translations[val], value); stats = attribute->d_statistics; if (attribute->n_trans > stats->range) { /* fprintf(stderr, "Setting range to %d\n", attribute->n_trans); 27nov94 wmt */ if (attribute->n_trans > stats->n_observed) { stats->n_observed = attribute->n_trans; stats->observed = (int *) realloc(stats->observed, stats->n_observed * sizeof(int)); for (i = stats->range; i < stats->n_observed; i++) stats->observed[i] = 0; } stats->range = attribute->n_trans; attribute->range = attribute->n_trans; /* if (member_int(n_att, */ /* d_base->translations_supplied_p, d_base->num_tsp)) 02dec94 wmt */ safe_sprintf( long_str, sizeof( long_str), caller, "ADVISORY[2]: for attribute #%d: \"%s\" range increased to %d,\n", n_att, attribute->dscrp, attribute->n_trans); to_screen_and_log_file(long_str, log_file_fp, stream, TRUE); safe_sprintf( very_long_str, sizeof( very_long_str), caller, " for value %d -- translator (%d %s).\n", val, val, attribute->translations[val]); to_screen_and_log_file(very_long_str, log_file_fp, stream, TRUE); } } return(val); } /* GET_LINE_TOKENS 21oct94 wmt: modified 23nov94 wmt: handle comment lines 27nov94 wmt: set instance_length_ptr; use very_long_str 30nov94 wmt: add comment_char functionality 28feb95 wmt: pass VERY_LONG_STRING_LENGTH to read_line 19jan96 wmt: add length checking for "form"; make it and length check for datum_string explicit */ char **get_line_tokens( FILE *stream, int separator_char, int n_comment_chars, char *comment_chars, int first_read, int *instance_length_ptr) { int position=0, length=0; char **line_tokens = NULL; char form[VERY_LONG_TOKEN_LENGTH]; char datum_string[VERY_LONG_STRING_LENGTH]; int datum_string_first_char; do { position = 0; *instance_length_ptr = 0; strcpy(form, ""); if (read_line( datum_string, VERY_LONG_STRING_LENGTH, stream) == TRUE) { /* fprintf( stderr, "datum_string1: '%s'\n", datum_string); */ while ((first_read == TRUE) && (datum_string_first_char = (int) datum_string[0]) && ((datum_string_first_char == '#') || (datum_string_first_char == '!') || (datum_string_first_char == ';') || (datum_string_first_char == ' ') || (datum_string_first_char == '\n'))) { read_line( datum_string, VERY_LONG_STRING_LENGTH, stream); /* fprintf( stderr, "datum_stringn: '%s'\n", datum_string); */ } position = read_from_string( datum_string, form, VERY_LONG_TOKEN_LENGTH, separator_char, n_comment_chars, comment_chars, position); /* fprintf( stderr, "form1: %s\n", form); */ while ((eqstring(form, "eof") == FALSE) && (eqstring(form, "comment") == FALSE)) { length++; if (line_tokens == NULL) line_tokens = (char **) malloc(length * sizeof(char *)); else line_tokens = (char **) realloc(line_tokens, length * sizeof(char *)); /* allow for end of string char */ line_tokens[length - 1] = (char *) malloc((strlen(form) + 1) * sizeof(char)); strcpy(line_tokens[length - 1], form); strcpy(form, ""); position = read_from_string(datum_string, form, VERY_LONG_TOKEN_LENGTH, separator_char, n_comment_chars, comment_chars, position); /* fprintf( stderr, "formn: %s\n", form); */ } *instance_length_ptr = length; /* fprintf( stderr, "instance_length = %d\n", length); */ if ((eqstring(form, "comment") == FALSE) && (eqstring(form, "eof") == FALSE) && (length <= 1)) { fprintf(stderr, "ERROR[1]: data is of type :vector or :list, but only :line is handled\n"); exit(1); } } } while (eqstring(form, "comment") == TRUE); /* if (length != 0) */ /* printf("1st token: %s\n", line_tokens[0]); */ return(line_tokens); } /* READ_FROM_STRING 30nov94 wmt: add comment_char functionality 03dec94 wmt: pass multiple comment chars 13dec94 wmt: allow separator_char, space, & tab to be repeated between tokens 30dec94 wmt: discard double quotes around string tokens and keep imbedded blanks 19jan96 wmt: add length checking for s2 */ int read_from_string( char *s1, char *s2, int string_limit, int separator_char, int n_comment_chars, char *comment_chars, int position) { int i = 0, str_len = strlen(s1), n_char, comment_p = FALSE, in_string_p = FALSE; char double_quote = '\"'; /* " */ if (position >= str_len) { strcpy(s2, "eof"); return(position); } if (s1[position] == EOF) { strcpy(s2, "eof"); return(position); } if (position == 0) { for (n_char=0; n_char < n_comment_chars; n_char++) { if (s1[0] == comment_chars[n_char]) { comment_p = TRUE; break; } } if (comment_p == TRUE) { strcpy(s2, "comment"); return(position); } } /* read past multiple separator chars, spaces or tabs */ while ((s1[position] == (char) separator_char) || (s1[position] == ' ') || (s1[position] == '\t')) position++; while ((in_string_p == TRUE) || ((s1[position] != ' ') && (s1[position] != '\n') && (s1[position] != '\t') && (s1[position] != EOF) && (s1[position] != (char) separator_char) && (i < str_len))) { /* printf("%d:%d:%c ", i, (int) s1[position], s1[position]); */ if (s1[position] != double_quote) { /* discard double_quotes around strings */ if (i >= (string_limit - 1)) { fprintf( stderr, "ERROR: read_from_string read a token longer than %d characters\n", string_limit-1); abort(); } s2[i] = s1[position]; i++; } if (s1[position] == double_quote) { if (i == 0) in_string_p = TRUE; else in_string_p = FALSE; } position++; } s2[i] = '\0'; if (i == 0) { strcpy(s2, "eof"); return(position); } return(position + 1); } /* READ_LINE 28feb95 wmt: pass string_limit as parameter; check for excess line length 05mar97 wmt: only return FALSE if no chars have been read -- allows last line with no new-line to be read */ int read_line( char *s, int string_limit, FILE *stream) { int c = 0, i; for (i=0; i<(string_limit-1) && ((c=fgetc(stream)) != EOF) && (c != '\n'); i++) s[i] = c; if (c == '\n') { s[i] = c; i++; } s[i] = '\0'; if ((c != '\n') && (c != EOF)) { fprintf( stderr, "ERROR: read_line read a line longer than %d characters\n", string_limit-1); abort(); } /* fprintf( stderr, "read_line: i %d string %s\n", i, s); */ if ((c == EOF) && (i == 0)) return(FALSE); else return(TRUE); } /* Checks d-base to assure the correct number of elements in each datum. return error-cnt updated by wmt 03 Sep 91 to print out bad datum 18mar92 WMT - added *print-level*, *print-length* bindings */ /* return count of supplied attribute translators not used */ void find_att_statistics( database_DS d_base, FILE *log_file_fp, FILE *stream) { int n_att; att_DS att, *att_info; att_info = d_base->att_info; for (n_att=0; n_attn_atts; n_att++) { att = att_info[n_att]; if (find_str_in_table(att->type, G_att_type_data, NUM_ATT_TYPES) > -1) { if (eqstring(att->type, "real") == TRUE) find_real_stats(d_base, n_att, log_file_fp, stream); else if (eqstring(att->type, "discrete") == TRUE) find_discrete_stats(d_base, n_att); } else { fprintf(stderr, "ERROR: (find_att_statistics) unknown attribute type: %s\n", att->type); abort(); } } } /* FIND_REAL_STATS 27nov94 wmt: use percent_equal for float tests; skip incomplete datum 15dec94 wmt: redo advisory message 27dec94 wmt: use direct variance calculation to prevent numerical problems compute means, etc for real attributes & return count of invalid values */ void find_real_stats( database_DS d_base, int n_att, FILE *log_file_fp, FILE *stream) { int n_datum, count = 0, missing = 0, percent_error; float mn = MOST_POSITIVE_SINGLE_FLOAT, mx = MOST_NEGATIVE_SINGLE_FLOAT; float val, error; double sum = 0.0, sum_sq = 0.0, mean = 0.0, variance = 0.0, double_val; double float_unknown = FLOAT_UNKNOWN, rel_error = REL_ERROR; float **data = d_base->data; att_DS att = d_base->att_info[n_att]; real_stats_DS stats = att->r_statistics; int *datum_length = d_base->datum_length; fxlstr str; char caller[] = "find_real_stats"; error = att->error; for (n_datum=0; n_datum < d_base->n_data; n_datum++) { if (n_att < datum_length[n_datum]) { val = data[n_datum][n_att]; if (percent_equal( (double) val, float_unknown, rel_error)) missing++; else { count++; mn = min(mn, val); mx = max(mx, val); sum += val; } } } if (count > 0) { mean = sum / ((double) count); for (n_datum=0; n_datum < d_base->n_data; n_datum++) { if (n_att < datum_length[n_datum]) { double_val = (double) data[n_datum][n_att]; if (! percent_equal( double_val, float_unknown, rel_error)) sum_sq += square( double_val - mean); } } variance = sum_sq / ((double) count); } if ((count == 0) || /* All values are missing */ (mx == mn)) { /* All known values are identical */ strcpy(att->warnings_and_errors->single_valued_warning, "true"); } else if ((error != 0.0) && (error > (.1 * (mx - mn)))) { percent_error = iround( (double) (100.0 * (error / (mx - mn)))); safe_sprintf( str, sizeof( str), caller, "ADVISORY[2]: attribute #%d: \"%s\", the error %f is %d%% \n" " of the range %f.\n", n_att, att->dscrp, att->error, percent_error, (mx - mn)); to_screen_and_log_file( str, log_file_fp, stream, TRUE); } store_real_stats(stats, att, count, mean, variance, missing, (double) mx, (double) mn); } /* STORE_REAL_STATS 19dec94 wmt: make parms double rather than float 27dec94 wmt: use direct variance calculation to prevent numerical problems store real stats in statistics structure */ void store_real_stats( real_stats_DS statistics, att_DS att, int count, double mean, double variance, int missing, double mx, double mn) { statistics->count = count; if (count == 0) { statistics->mx = 0.0; statistics->mn = 0.0; statistics->mean = 0.0; statistics->var = 0.0; } else { statistics->mx = (float) mx; statistics->mn = (float) mn; statistics->mean = (float) mean; statistics->var = (float) variance; } att->missing = (missing > 0)?TRUE:FALSE; } /* FIND_DISCRETE_STATS 29nov94 wmt: skip incomplete datum; put ( length < range ) in unused translators 18feb98 wmt: for number of translators less than .hd2 range, reduce the range and output advisory, rather than outputting warning -- REMOVED 13mar98 JCS due to incompatablility with previous results files check for missing values occurring and non-occurrences of specified translations -- return non-occurrence count 23mar92 - WMT - single-valued functionality changed from error to warning -error slot retained for backward compatability to existing .results files */ void find_discrete_stats( database_DS d_base, int n_att) { int i, length, n_datum, missing_value, missing_value_cnt = 0, *accumulator; int ulength=0, val; float *unused_translators = NULL; float *datum, **data = d_base->data; att_DS att = d_base->att_info[n_att]; discrete_stats_DS stats = att->d_statistics; int *datum_length = d_base->datum_length; /* fxlstr long_str; char caller[] = "find_discrete_stats"; */ missing_value = find_str_in_list("nil", att->translations, att->n_trans); accumulator = stats->observed; if (accumulator == NULL) length = 0; else length = stats->n_observed; for (i=0; in_data; n_datum++) { if (n_att < datum_length[n_datum]) { datum = data[n_datum]; val = (int) datum[n_att]; accumulator[val] ++; /* this is dealing with translated values so missing_value is the integer value to which missing values are translated*/ if ((missing_value != -1) && (val == missing_value)) /* count missing values */ missing_value_cnt++; } } att->missing = (missing_value_cnt > 0)?TRUE:FALSE; for (i=0; iwarnings_and_errors->single_valued_warning, "true"); } else if (ulength > 0) { /* Do flag this as a warning -- reducing range renders AC incompatable with results of previous versions. */ unused_translators = (float *) realloc(unused_translators, (ulength + 1) * sizeof(float)); unused_translators[ulength ] = MOST_NEGATIVE_SINGLE_FLOAT; att->warnings_and_errors->unused_translators_warning = unused_translators; /* fprintf( stderr, "find_discrete_stats: stats->range %d att->range %d" " att->n_trans %d length %d\n", stats->range, att->range, att->n_trans, length); */ /* Range reduction: seemed like a good idea. But this leaves AC unable to search, report or predict from results files made by prior AC versions. stats->range = att->n_trans; att->range = att->n_trans; att->d_statistics->range = att->n_trans; att->d_statistics->n_observed = att->n_trans; safe_sprintf( long_str, sizeof( long_str), caller, "ADVISORY[2]: for attribute #%d: \"%s\" range decreased to %d.\n", n_att, att->dscrp, att->n_trans); to_screen_and_log_file(long_str, G_log_file_fp, G_stream, TRUE); */ } else if ((length < att->range) && (ulength == 0)) { /* do this since .hd2 defined translators are not implemented 03dec94 wmt */ unused_translators = (float *) malloc(sizeof(float)); unused_translators[ulength - 1] = (float) length; } } /* OUTPUT_ATT_STATISTICS 10may95 wmt: new output real & discrete statistics from data read in. */ void output_att_statistics( database_DS d_base, FILE *log_file_fp, FILE *stream) { int n_atts = d_base->n_atts, n_att, stats_to_output_p = FALSE; /* int i, j; */ output_created_translations( d_base, log_file_fp, stream); for (n_att=0; n_attatt_info[n_att]->r_statistics != NULL) { stats_to_output_p = TRUE; break; } } if ((stats_to_output_p == TRUE) && (log_file_fp != NULL)) { to_screen_and_log_file ("ADVISORY[1]: real statistics [ min < (mean : std dev) < max ] built \n" " from input data --\n", log_file_fp, stream, TRUE); for (n_att=0; n_attdata[i][j]); } exit(1); */ } /* OUTPUT_REAL_ATT_STATISTICS 10may95 wmt: new 20may97 wmt: added check for variance exceeding infinity output real statistics for attribute n_att */ void output_real_att_statistics( database_DS d_base, int n_att, FILE *log_file_fp, FILE *stream) { att_DS att; real_stats_DS r_statistics; fxlstr str; char caller[] = "output_real_att_statistics"; if (log_file_fp != NULL) { att = d_base->att_info[n_att]; r_statistics = att->r_statistics; if (att->r_statistics != NULL) { safe_sprintf( str, sizeof( str), caller, " Attribute #%d, \"%s\":\n [", n_att, att->dscrp); to_screen_and_log_file( str, log_file_fp, stream, TRUE); safe_sprintf( str, sizeof( str), caller, " %11.4e < (%11.4e : %10.4e) < %11.4e ]\n", r_statistics->mn, r_statistics->mean, sqrt(r_statistics->var), r_statistics->mx); to_screen_and_log_file( str, log_file_fp, stream, TRUE); if (r_statistics->var > INFINITY) { fprintf( stderr, "\nERROR: (output_real_att_statistics) attribute #%d: \"%s\", \n" " the variance exceeds %e\n", n_att, att->dscrp, INFINITY); abort(); } } } } /* OUTPUT_CREATED_TRANSLATIONS 02dec94 wmt: new 10may95 wmt: add discrete value occurrance count 19jan96 wmt: increase output string length output discrete translations built from the data read in - (see translate_discrete) */ void output_created_translations( database_DS d_base, FILE *log_file_fp, FILE *stream) { int n_atts = d_base->n_atts, n_att, n_trans, translations_to_output_p = FALSE; att_DS att; char str[VERY_LONG_TOKEN_LENGTH]; char caller[] = "output_created_translations"; for (n_att=0; n_attatt_info[n_att]->n_trans > 0) { translations_to_output_p = TRUE; break; } } if ((translations_to_output_p == TRUE) && (log_file_fp != NULL)) { to_screen_and_log_file ("ADVISORY[1]: discrete translations [ (internal external):count ... ] built \n" " from input data --\n", log_file_fp, stream, TRUE); for (n_att=0; n_attatt_info[n_att]; if (att->n_trans > 0) { safe_sprintf(str, sizeof( str), caller, " Attribute #%d, \"%s\":\n [", n_att, att->dscrp); to_screen_and_log_file(str, log_file_fp, stream, TRUE); for (n_trans=0; n_trans < att->n_trans; n_trans++) { safe_sprintf( str, sizeof( str), caller, " (%d %s):%d", n_trans, att->translations[n_trans], att->d_statistics->observed[n_trans]); to_screen_and_log_file( str, log_file_fp, stream, TRUE); } to_screen_and_log_file( " ]\n", log_file_fp, stream, TRUE); } } } } /* CHECK_ERRORS_AND_WARNINGS 22jan95 wmt: new 24mar99 wmt: write warnings/errors to log file check for changed or corrupted data/header/model files */ void check_errors_and_warnings( database_DS database, model_DS *models, int num_models) { int total_error_cnt = 0, total_warning_cnt = 0; FILE *log_file_fp = NULL, *stream = NULL; shortstr output_msg_type; strcpy( output_msg_type, ":expand"); output_messages( database, models, num_models, log_file_fp, stream, &total_error_cnt, &total_warning_cnt, output_msg_type); if ((total_error_cnt + total_warning_cnt) > 0) { output_messages( database, models, num_models, G_log_file_fp, G_stream, &total_error_cnt, &total_warning_cnt, output_msg_type); check_stop_processing( total_error_cnt, total_warning_cnt, G_log_file_fp, G_stream); } } autoclass-3.3.6.dfsg.1/prog/model-expander-3.c0000644000175000017500000005756211247310756017106 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* Run-time Model Expansion Functions */ model_DS conditional_expand_model_terms( model_DS model, int force, FILE *log_file_fp, FILE *stream) { if ((force == TRUE) || (model->expanded_terms == FALSE)) return (expand_model_terms( model, log_file_fp, stream)); else return(NULL); } /* aju 980612: Prefixed enum member IGNORE with T so it would not clash with predefined Win32 type. */ enum MODEL_TYPES model_type (shortstr str) { return( (eqstring(str, "multi_multinomial_d"))? MM_D :( (eqstring(str, "multi_multinomial_s"))? MM_S :( (eqstring(str, "multi_normal_cn")) ? MN_CN :( (eqstring(str, "single_multinomial")) ? SM :( (eqstring(str, "single_normal_cm")) ? SN_CM :( (eqstring(str, "single_normal_cn")) ? SN_CN :( (eqstring(str, "ignore")) ? TIGNORE:( UNKNOWN)))))))); } /* EXPAND_MODEL_TERMS 03mar95 wmt: add call to set_ignore_att_info 23may95 wmt: added G_prediction_p Builds the working -fn's of a model from the attribute information in the terms field. A model must be re-expanded if the terms field has been modified, or if any term defining function of a xxx-fn field or any expand-xxx-fn has been added or altered. */ model_DS expand_model_terms( model_DS model, FILE *log_file_fp, FILE *stream) { int n_term; clsf_DS clsf_g; database_DS database; model_DS model_set[1]; term_DS *terms, term; int initial_cycles_p = FALSE, delete_duplicates = FALSE, n_global_classes = 1, block_size = 0; model_set[0] = model; terms = model->terms; database = model->database; expand_model_reset( model); check_model_terms ( model, log_file_fp, stream); /* insure consistent model->att_ignore_ids */ set_ignore_att_info ( model, database); /* Loop over terms placing appropriate structures in the model parameter arrays and collecting function terms as lists in the function slots: */ for (n_term=0; n_termn_terms; n_term++) { term = terms[n_term]; switch( model_type(term->type)) { case MN_CN: multi_normal_cn_model_term_builder(model, term, n_term); break; case SM: single_multinomial_model_term_builder(model, term, n_term); break; case SN_CM: single_normal_cm_model_term_builder(model, term, n_term); break; case SN_CN: single_normal_cn_model_term_builder(model, term, n_term); break; case MM_D: /****multi_multinomial_d_model_term_builder(model, term, n_term); break; */ case MM_S: /****multi_multinomial_s_model_term_builder(model, term, n_term); breaqk;***/ default: fprintf(stderr,"ERROR: unkown type in expand_model_terms: %s\n",term->type); abort(); } } arrange_model_function_terms(model); /* must proceed set-up-classification!! */ model->expanded_terms = (int) get_universal_time(); /*model_set = (model_DS *) malloc(sizeof(model_DS)); model-set experiment*/ model_set[0] = model; if (G_prediction_p == FALSE) { clsf_g = set_up_clsf(n_global_classes, database, model_set, 1); block_set_clsf(clsf_g, n_global_classes, block_size, delete_duplicates, DISPLAY_WTS, initial_cycles_p, log_file_fp, stream); model->global_clsf = clsf_g; } return(model); } /* Top level for Check-Term. */ void check_model_terms( model_DS model, FILE *log_file_fp, FILE *stream) { int n_term; term_DS *terms; terms = model->terms; for (n_term=0; n_termn_terms; n_term++) check_term(terms[n_term], model, n_term, log_file_fp, stream); } /* CHECK_TERM 28jul95 wmt: since att_info can be realloc'ed in model-transforms.c, reset ptr for each time thru loop Check-Term checks the attributes indexed in att-list for appropriate type and sub-type. It uses the att-trans-data property of the the term's type to identify the allowable types and determine if the subtype requires a transformation. */ void check_term( term_DS term, model_DS model, int n_term, FILE *log_file_fp, FILE *stream) { void ***att_trans_data = NULL, ***good_subtypes_data = NULL; void ***conditions = NULL; shortstr term_type, att_type, str, att_sub_type; int i, att_index, *source_list, *temp, nal = 0; int n_source_list, n_att_trans_data; int n_good_subtypes_data, n_conditions; float *term_att_list, *new_att_list = NULL; att_DS att, *att_info; database_DS database; fxlstr long_str; char caller[] = "check_term"; database = model->database; strcpy(term_type, term->type); /* The type of term set */ term_att_list = term->att_list; /* List of att indices used by the term */ /* Permitted att-type data (as above) */ att_trans_data = (void ***) get(term_type, "att_trans_data"); temp = (int *) get(term_type, "n_att_trans_data"); if (temp == NULL) n_att_trans_data = 0; else n_att_trans_data = *temp; /* This will list the accepted and transformed attribute indices */ for (i=0; in_atts; i++) { /* For each att-index of the Term: */ att_index = (int) term_att_list[i]; /* since att_info can be realloc'ed in model-transforms.c, reset ptr for each time thru loop */ att_info = database->att_info; att = att_info[att_index]; /* Att descriptor for this index */ strcpy(att_type, att->type); /* One of 'real, 'discrete & etc. */ good_subtypes_data = (void ***) getf(att_trans_data, att_type, n_att_trans_data); sprintf(str, "n_%s", att_type); /* this statement added 3/2/JTP*/ temp = (int *) getf(att_trans_data, str, n_att_trans_data); if (temp == NULL) n_good_subtypes_data = 0; else n_good_subtypes_data = *temp; strcpy(att_sub_type, att->sub_type); /* This attribute type is not handled by the term type. */ if (good_subtypes_data == NULL) { safe_sprintf( long_str, sizeof( long_str), caller, "ERROR[3]: model term type %s cannot handle\n" " type = %s, attribute #%d: \"%s\"\n", term_type, att_type, att_index, att->dscrp); to_screen_and_log_file(long_str, log_file_fp, stream, TRUE); exit(1); } /* Try to find and substitute the source. */ if (find_str_in_table(att_sub_type, G_transforms, NUM_TRANSFORMS) > -1) { source_list = get_source_list(att_index, att_info, NULL, 0, &n_source_list); if (n_source_list != 1) fprintf(stderr, "Multiple sources for attribute\n"); att_index = source_list[0]; strcpy(att_sub_type, att_info[att_index]->sub_type); } conditions = getf(good_subtypes_data, att_sub_type, n_good_subtypes_data); sprintf(str, "n_%s", att_sub_type); temp = getf(good_subtypes_data, str, n_good_subtypes_data); if (temp == NULL) n_conditions = 0; else n_conditions = *temp; /* Term is applicable to this subtype. */ if (n_conditions == 0) { nal++; if (new_att_list == NULL) new_att_list = (float *) malloc(nal * sizeof(float)); else new_att_list = (float *) realloc(new_att_list, nal * sizeof(float)); new_att_list[nal-1] = att_index; } /* Term is applicable to a transform. */ else if (getf(conditions, "transform", n_conditions) != NULL) { temp = (int *) malloc(sizeof(int)); temp[0] = att_index; nal++; if (new_att_list == NULL) new_att_list = (float *) malloc(nal * sizeof(float)); else new_att_list = (float *) realloc(new_att_list, nal * sizeof(float)); new_att_list[nal-1] = find_transform(database, getf(conditions, "transform", n_conditions), temp, 1, log_file_fp, stream); } /* <> */ else { safe_sprintf( long_str, sizeof( long_str), caller, "ERROR[3]: %s model terms cannot handle subtype %s of type %s attributes\n", term_type, att_sub_type, att_type); to_screen_and_log_file(long_str, log_file_fp, stream, TRUE); exit(1); } } term->n_atts = nal; term->att_list = new_att_list; update_location_info(model, term, term_att_list); } /* This extends the att-locs and att-ignore-ids to account for any change in the att-list. */ void update_location_info( model_DS model, term_DS term, float *old_att_list) { int i, n_term, new_length, old_i, new_i; float *new_att_list, mx; n_term = find_term(term, model->terms, model->n_terms); new_att_list = term->att_list; mx = (float) max_plus(new_att_list, term->n_atts); /* We have some new transformations */ if (mx >= model->n_att_locs) { /* and must make room for them: */ new_length = mx + 1.0; model->n_att_locs = new_length; model->n_att_ignore_ids = new_length; if (model->att_locs == NULL) model->att_locs = (shortstr *) malloc(new_length * sizeof(shortstr)); else model->att_locs = (shortstr *) realloc(model->att_locs, new_length * sizeof(shortstr)); if (model->att_ignore_ids == NULL) model->att_ignore_ids = (shortstr *) malloc(new_length * sizeof(shortstr)); else model->att_ignore_ids = (shortstr *) realloc(model->att_ignore_ids, new_length * sizeof(shortstr)); } new_length = term->n_atts; for (i=0; iatt_locs[old_i], "TRANSFORMED->%d", new_i); sprintf(model->att_locs[new_i], "%d", n_term); strcpy(model->att_ignore_ids[new_i], model->att_ignore_ids[old_i]); } } } /* EXPAND_MODEL_RESET 22nov94 wmt: initialize priors */ void expand_model_reset(model_DS model) { int i, n_terms = model->n_terms; class_DS cl; while( (cl=model->class_store) != NULL){ model->class_store=cl->next; free(cl); } model->num_class_store = 0; model->expanded_terms = FALSE; if( model->priors != NULL) { for (i=0; inum_priors; i++) if(model->priors[i] != NULL) free(model->priors[i]); free(model->priors); model->priors = NULL; } model->num_priors = n_terms; model->priors = (priors_DS *) malloc(n_terms * sizeof(priors_DS)); for (i=0; inum_priors; i++) model->priors[i] = NULL; if (model->global_clsf != NULL) { store_clsf_DS(model->global_clsf, NULL, 0); model->global_clsf = NULL; } } /* UPDATE_PARAMS_FN 20dec94 wmt: return type to void Updates the Likelihood parameters for class. Each element of the model-DS field update-params-terms should update the corresponding attribute set's class parameters to the MAXIMUM POSTERIOR VALUES when evaluated in the environment produced by the initial let* statement. */ void update_params_fn( class_DS class, int n_classes, database_DS data_base, int collect) { int i, num; /*, j, num, n_data, fnumber;*/ /*float **data, *wts, class_wt, class_wt_1, disc_scale;*/ /*float (*function)();*/ tparm_DS tparm; /*data = data_base->data; */ /*n_data = data_base->n_data;*/ /*params = class->params; */ /*wts = class->wts;*/ /*class_wt = class->w_j;*/ /*class_wt_1 = class_wt + 1;*/ /*disc_scale = 1.0 / (class_wt + 1.0);*/ class->pi_j = (class->w_j + (1.0 / n_classes)) / (data_base->n_data + 1.0); class->log_pi_j = (float) safe_log((double) class->pi_j); num = class->model->n_terms; for (i=0; itparms[i]; tparm->collect = collect; tparm->data = data_base->data; tparm->n_data = data_base->n_data; tparm->wts = class->wts; tparm->class_wt = class->w_j; tparm->disc_scale = 1.0 / (class->w_j + 1.0); switch(tparm->tppt) { case MN_CN: multi_normal_cn_update_params( tparm, class->known_parms_p); break; case SM: single_multinomial_update_params(tparm,class->known_parms_p); break; case SN_CM: single_normal_cm_update_params(tparm,class->known_parms_p); break; case SN_CN: single_normal_cn_update_params(tparm,class->known_parms_p); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in update_params;i,t=%d %d", i, tparm->tppt); abort(); } } /*****old for (i=0; iparms->class = class; terms[i]->parms->collect = collect; terms[i]->parms->data = data; terms[i]->parms->n_data = n_data; terms[i]->parms->params = params; terms[i]->parms->wts = wts; terms[i]->parms->class_wt = class_wt; terms[i]->parms->disc_scale = disc_scale; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); call_model_function(fnumber, terms[i]->parms); } *********/ /* return(1); */ } /* Reverses accumulated model terms to get canonical order. */ void arrange_model_function_terms( model_DS model) { /*model->log_likelihood_terms = reverse(model->log_likelihood_terms); model->update_l_approx_terms = reverse(model->update_l_approx_terms); model->update_m_approx_terms = reverse(model->update_m_approx_terms); model->update_params_terms = reverse(model->update_params_terms); model->class_equivalence_terms = reverse(model->class_equivalence_terms); model->class_merged_marginal_terms = reverse(model->class_merged_marginal_terms); */ } /* LOG_LIKELIHOOD_FN 20dec94 wmt: return type to double Computes the log-likelihood that datum belongs to class as pi_j*p_X_i_C_j_theta_j, using the individual likelihood terms given in model-DS-log-likelihood-terms. */ double log_likelihood_fn( float *datum, class_DS class, double limit) { int i = 0, num; float sum = 0.0; tparm_DS tparm; num = class->num_tparms; do { tparm=class->tparms[i]; tparm->datum=datum; switch(tparm->tppt) { case MN_CN: sum += (float) multi_normal_cn_log_likelihood(tparm); break; case SM: sum += (float) single_multinomial_log_likelihood(tparm); break; case SN_CM: sum += (float) single_normal_cm_log_likelihood(tparm); break; case SN_CN: sum += (float) single_normal_cn_log_likelihood(tparm); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in log_likelihood; parm=%d, type= %d", i, tparm->tppt); abort(); } } while ((++i < num) && (sum >= (float) limit)); /****** while ((i == 0) || (sum >= limit)) { terms[i]->parms->datum = datum; terms[i]->parms->class = class; terms[i]->parms->params = params; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); sum += call_model_function(fnumber, terms[i]->parms); i++; if (i == num) break; } ******/ return (class->log_pi_j + sum); } /* UPDATE_L_APPROX_FN 20dec94 wmt: return type to double 29mar95 wmt: calculation in double Updates the APPROXIMATE LIKELIHOOD class-DS-log-a using the update-L-approx-terms field with the current statistics and parameters. Returns class-DS-log-a. */ double update_l_approx_fn( class_DS class) { int i, num; /* j, num, fnumber;*/ float w_j; double sum = 0.0; /*float (*function)();*/ tparm_DS tparm; w_j = class->w_j; num = class->model->n_terms; for (i=0; itparms[i]; tparm->w_j = w_j; switch(tparm->tppt) { case MN_CN: sum += multi_normal_cn_update_l_approx(tparm); break; case SM: sum += single_multinomial_update_l_approx(tparm); break; case SN_CM: sum += single_normal_cm_update_l_approx(tparm); break; case SN_CN: sum += single_normal_cn_update_l_approx(tparm); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in update_l_approx; parm=%d, type=%d", i, tparm->tppt); abort(); } } /****old terms[i]->parms->w_j = w_j; terms[i]->parms->params = params; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); sum += call_model_function(fnumber, terms[i]->parms); } ******/ /*commented printf ("in update_l_approx w_j,class->log_pi_j, sum=%f %f %f\n", w_j,class->log_pi_j,sum); dbg*/ class->log_a_w_s_h_pi_theta = ((double) (w_j * class->log_pi_j)) + sum; return (class->log_a_w_s_h_pi_theta); } /* UPDATE_M_APPROX_FN 20dec94 wmt: return type to double 29mar95 wmt: calculation in double Updates the APPROXIMATE MARGINAL LIKELIHOOD log-a field using the update-M-approx-terms field and the current statistics and parameters. Note that when known-params-p, it is assumed that UPDATE-L-APPROX-FN-x was previously called. Returns the log-a of the class structure. */ double update_m_approx_fn( class_DS class) { int i,num; /* j, num, fnumber;*/ float w_j = class->w_j; double sum = 0.0; /* float (*function)();*/ tparm_DS tparm; if (class->known_parms_p == TRUE) class->log_a_w_s_h_j = class->log_a_w_s_h_pi_theta; else if (w_j <= 1.0) fprintf(stderr, "update_m_approx-fn called with w_j = %f, log_a_w_s_h_j not updated.\n", w_j); else { num = class->model->n_terms; for (i=0; itparms[i]; tparm->w_j = w_j; switch(tparm->tppt) { case MN_CN: sum += multi_normal_cn_update_m_approx(tparm); break; case SM: sum += single_multinomial_update_m_approx(tparm); break; case SN_CM: sum += single_normal_cm_update_m_approx(tparm); break; case SN_CN: sum += single_normal_cn_update_m_approx(tparm); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in update_m_approx; parm=%d, type=%d", i, tparm->tppt); abort(); } } /**** old for (i=0; iparms->w_j = w_j; terms[i]->parms->params = params; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); sum += call_model_function(fnumber, terms[i]->parms); } ******/ /*commented printf(" in update_m_approx sum=%f\n",sum); dbg*/ class->log_a_w_s_h_j = sum; } return (class->log_a_w_s_h_j); } /* CLASS_EQUIVALENCE_FN 20dec94 wmt: reply type changed from float to int */ int class_equivalence_fn( class_DS class_1, class_DS class_2, double percent_ratio, double sigma_ratio) { int i, num, ans = FALSE, reply; float w_j1 = class_1->w_j, w_j2 = class_2->w_j; /*float (*function)(); */ tparm_DS tparm1,tparm2; if ( model_DS_equal_p(class_1->model,class_2->model) == TRUE ) { num = class_1->model->n_terms; for (i=0; itparms[i]; tparm1->w_j=w_j1; tparm2=class_2->tparms[i]; tparm2->w_j=w_j2; if(tparm1->tppt != tparm2->tppt ){ fprintf(stderr, "ERROR: unequal type in class_equiv;i,s=%d %d != %d", i, tparm1->tppt, tparm2->tppt); abort(); } switch(tparm1->tppt) { case MN_CN: reply = multi_normal_cn_class_equivalence(tparm1,tparm2,sigma_ratio); break; case SM: reply = single_multinomial_class_equivalence(tparm1,tparm2,percent_ratio); break; case SN_CM: reply = single_normal_cm_class_equivalence(tparm1,tparm2,sigma_ratio); break; case SN_CN: reply = single_normal_cn_class_equivalence(tparm1,tparm2,sigma_ratio); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in class_equivalence;i,s=%d %d", i, tparm1->tppt); abort(); } /*****old terms[i]->parms->w_j = w_j1; terms[i]->parms->w_j2 = w_j2; terms[i]->parms->params = params1; terms[i]->parms->params_2 = params2; terms[i]->parms->percent_ratio = percent_ratio; terms[i]->parms->sigma_ratio = sigma_ratio; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); reply = call_model_function(fnumber, terms[i]->parms); ********/ if (reply == FALSE) { ans = FALSE; break; } else ans = TRUE; } } else ans = FALSE; return ans; } /* CLASS_MERGED_MARGINAL_FN 22oct94 wmt: pass wt_0, wt_1, wt_m to multi_normal_cn_class_merged_marginal 20dec94 wmt: return type to double 09jan95 wmt: pass clsf_DS_max_n_classes to store_class_DS 10apr97 wmt: add database->n_data to get_class_DS call this function is not called currently in this implementation - it is called under the "search merge" search strategies which are not implemented */ double class_merged_marginal_fn( clsf_DS clsf, class_DS class_0, class_DS class_1) { model_DS model; int check_model = FALSE, want_wts_p = TRUE; class_DS class_m; float wt_0, wt_1, wt_m; double log_marginal; int i, num; tparm_DS tparm0,tparm1,tparmm; if ( (model_DS_equal_p(class_0->model,class_1->model) == FALSE) || (class_0->known_parms_p == TRUE) || (class_1->known_parms_p == TRUE) ) return (0.0); else { model = class_0->model; class_m = get_class_DS(model, clsf->database->n_data, want_wts_p, check_model); wt_0 = class_0->w_j; wt_1 = class_1->w_j; wt_m = wt_0 + wt_1; if ( (wt_0 == 0.0) || (wt_1 == 0.0) ) return (0.0); else { class_m->w_j = wt_m; class_m->log_a_w_s_h_pi_theta = 0.0; class_m->log_a_w_s_h_j = 0.0; num = class_0->model->n_terms; for (i=0; itparms[i]; tparm1=class_1->tparms[i]; tparmm=class_m->tparms[i]; if(tparm0->tppt != tparm1->tppt){ fprintf(stderr, "ERROR: unequal type in class_merge;i,s=%d %d != %d", i,tparm0->tppt,tparm1->tppt); abort(); } switch(tparm1->tppt) { case MN_CN: multi_normal_cn_class_merged_marginal(tparm0, tparm1, tparmm, wt_0, wt_1, wt_m); break; case SM: single_multinomial_class_merged_marginal(tparm0,tparm1,tparmm); break; case SN_CM: single_normal_cm_class_merged_marginal(tparm0,tparm1,tparmm); break; case SN_CN: single_normal_cn_class_merged_marginal(tparm0,tparm1,tparmm); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type in class_merged_marginal;i,s=%d %d", i,tparm1->tppt); abort(); } /*****old terms[i]->parms->params = params_0; terms[i]->parms->params_2 = params_1; terms[i]->parms->params_m = params_m; terms[i]->parms->w_j = wt_0; terms[i]->parms->w_j2 = wt_1; terms[i]->parms->wt_m = wt_m; fnumber = find_string(terms[i]->type, FLOAT_FNAMES, FLOAT_FLENGTH); call_model_function(fnumber, terms[i]->parms); *******/ } update_params_fn(class_m, (clsf->n_classes)-1, clsf->database, FALSE); /*no collect*/ (void) update_l_approx_fn(class_m); log_marginal = update_m_approx_fn(class_m); store_class_DS(class_m, clsf_DS_max_n_classes(clsf)); return (log_marginal); } } } tparm_DS *model_global_tparms( model_DS model) { return(model->global_clsf->classes[0]->tparms); } autoclass-3.3.6.dfsg.1/prog/getparams.c0000644000175000017500000002567511247310756016025 0ustar areare#include #include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* PUTPARAMS 11nov94 wmt: modified from putparam -- add paramtypes TDOUBLE & TINT_LIST 11jan95 wmt: add paramptr_overridden to print parameter table when overridden-p = TRUE, only print params which have been overridden */ void putparams( FILE *fp, PARAMP pp, int only_overridden_p) { int first_param = TRUE, params_cnt = 0, *int_list_paramptr; void *paramptr_output; while (pp->paramptr !=NULL) { if ((only_overridden_p == FALSE) || ((only_overridden_p == TRUE) && pp->overridden_p)) { if (first_param == TRUE) { fprintf(fp,"%s=", pp->paramname); first_param = FALSE; } else fprintf(fp,"; %s=", pp->paramname); if ((only_overridden_p == FALSE) && (pp->overridden_p == TRUE)) paramptr_output = pp->paramptr_overridden; else paramptr_output = pp->paramptr; switch (pp->paramtype) { case TBOOL: fprintf(fp,"%s", (*(BOOLEAN *)(paramptr_output)) ? "true":"false"); break; case TSTRING: fprintf(fp,"\"%s\"", (char *) paramptr_output); break; case TINT: fprintf(fp,"%d", *((int *)(paramptr_output))); break; case TDOUBLE: fprintf(fp,"%15.12e", *((double *)(paramptr_output))); break; case TFLOAT: fprintf(fp,"%e", *((float *)(paramptr_output))); break; case TINT_LIST: int_list_paramptr = (int *) paramptr_output; fprintf( fp, "("); output_int_list( int_list_paramptr, fp, NULL); fprintf( fp, ")"); break; default: fprintf(fp," of unknown paramtype=%d",pp->paramtype); } params_cnt++; } pp++; } if ((params_cnt == 0) && (only_overridden_p == TRUE)) fprintf(fp,"None."); fprintf(fp,"\n\n"); return; } /*end putparams*/ /* GETPARAMS 11nov94 wmt: modified from getparm -- add paramtypes TDOUBLE & TINT_LIST 07dec94 wmt: add error checking of inputs & appropriate messages 11jan95 wmt: add paramptr_overridden 26apr95 wmt: test eqstring to FALSE, not NULL 15mar97 wmt: remove " " from strpbrk not-to-occur list in two places; for TINT_LIST allows "= 84, 92 " to be read as 84 & 92, rather than 84 & 84 to read all of file on fp and set values of parameters defined in parameter table pp. (initialized by defparams) any parameters not set will remain unchanged any lines not recognized will just be output */ int getparams( FILE *fp, PARAMP params) { char buff[LINLIM], *bp; PARAMP pp; int error_cnt = 0, i = 0, *int_list_paramptr, integer_p; int float_p, *int_list_paramptr_overridden; char *string_char_paramptr; shortstr input_string; while ( fgets( buff, LINLIM, fp) != NULL) { /* skip comment lines and blank lines */ if ((buff[0] == '#') || (buff[0] == '!') || (buff[0] == ';') || (buff[0] == '\n') || (buff[0] == ' ')) continue; else { bp = strtok(buff, "= \t\n"); pp = params; while ((pp->paramptr != NULL) && (eqstring( bp, pp->paramname) == FALSE)) pp++; if (pp->paramptr == NULL) { fprintf( stderr, "ERROR: undefined parameter: %s\n", buff); error_cnt++; } else { if (pp->paramtype != TINT_LIST) bp = strtok(NULL,"\n"); if (bp == NULL) { fprintf( stderr, "ERROR: no value given for: %s\n", buff); error_cnt++; continue; } /* bp still points to string with "= ", "=", " ", etc */ /* while (strpbrk(bp, "= \t") != NULL) take off leading = & \t, but not " ", since trailing blanks cause bp to be incremented to nothing. */ while (strpbrk(bp, "=\t") != NULL) bp++; switch (pp->paramtype) { case TBOOL: sscanf(bp, "%s", input_string); if ((eqstring(input_string, "true") == FALSE) && (eqstring(input_string, "false") == FALSE)) { fprintf( stderr, "ERROR: for parameter %s, \n" " neither true or false was read.\n", pp->paramname); error_cnt++; break; } pp->paramptr_overridden = (void *) malloc(sizeof(BOOLEAN)); *(BOOLEAN *)(pp->paramptr_overridden) = *(BOOLEAN *)(pp->paramptr); *(BOOLEAN *)(pp->paramptr) = (eqstring(input_string, "true") == TRUE) ?TRUE:FALSE; break; case TSTRING: string_char_paramptr = (char *) pp->paramptr; pp->paramptr_overridden = (void *) malloc( pp->max_length * sizeof( char)); strcpy( (char *) pp->paramptr_overridden, string_char_paramptr); i = 0; /* skip leading blanks */ for (; (*bp == ' '); bp++); if (*bp != '\"') { /* " - for highlit-19 */ fprintf( stderr, "ERROR: for parameter %s, first character of value " "is not a '\"'\n", pp->paramname); error_cnt++; } for (bp++; /* skip initial double quote */ ((*bp != '\0') && (*bp != '\"')); /* " - for highlit-19 */ bp++) { i++; if (i >= pp->max_length) { fprintf( stderr, "ERROR: for parameter %s, more than %d characters.\n" " were input\n", pp->paramname, pp->max_length - 1); error_cnt++; break; } sscanf(bp, "%c", string_char_paramptr); string_char_paramptr++; } *string_char_paramptr = '\0'; break; case TINT: /* fprintf( stderr, "getparams: INT bp %s\n", bp); */ pp->paramptr_overridden = (void *) malloc( sizeof( int)); *(int *) pp->paramptr_overridden = *(int *) pp->paramptr; sscanf(bp, "%s", input_string); *(int *) pp->paramptr = atoi_p(input_string, &integer_p); if (integer_p != TRUE) { fprintf( stderr, "ERROR: for parameter %s, number read, %s, \n" " was not an integer\n", pp->paramname, input_string); error_cnt++; } break; case TFLOAT: pp->paramptr_overridden = (void *) malloc( sizeof( float)); *(float *) pp->paramptr_overridden = *(float *) pp->paramptr; sscanf(bp, "%s", input_string); *(float *) pp->paramptr = (float) atof_p(input_string, &float_p); if (float_p != TRUE) { fprintf( stderr, "ERROR: for parameter %s, number read, %s, \n" " was not a float\n", pp->paramname, input_string); error_cnt++; } break; case TDOUBLE: pp->paramptr_overridden = (void *) malloc( sizeof( double)); *(double *) pp->paramptr_overridden = *(double *) pp->paramptr; sscanf(bp, "%s", input_string); *(double *) pp->paramptr = atof_p(input_string, &float_p); if (float_p != TRUE) { fprintf( stderr, "ERROR: for parameter %s, number read, %s, \n" " was not a double\n", pp->paramname, input_string); error_cnt++; } break; case TINT_LIST: /* fprintf( stderr, "getparams: bp %s\n", bp); */ pp->paramptr_overridden = (void *) malloc( pp->max_length * sizeof( int)); int_list_paramptr = (int *) pp->paramptr; int_list_paramptr_overridden = (int *) pp->paramptr_overridden; for( ; *int_list_paramptr != END_OF_INT_LIST; int_list_paramptr++) { *int_list_paramptr_overridden = *int_list_paramptr; int_list_paramptr_overridden++; } *int_list_paramptr_overridden = END_OF_INT_LIST; i = 0; int_list_paramptr = (int *) pp->paramptr; bp = strtok(NULL,",\n"); for ( ; bp != NULL; bp = strtok(NULL,",\n")) { /* fprintf( stderr, "getparams: i %d bp %s\n", i, bp); */ i++; if (i >= pp->max_length) { fprintf( stderr, "\nERROR: more than %d values input for %s", pp->max_length - 1, pp->paramname); error_cnt++; break; } /* while (strpbrk(bp, "= :\t") != NULL) */ /* take off leading = & \t, but not " ", since trailing blanks cause bp to be incremented to nothing. */ while (strpbrk(bp, "=:\t") != NULL) bp++; /* fprintf( stderr, "getparams: while: length bp %d bp `%s'\n", strlen(bp), bp); */ if ((strlen(bp) == 0) || (strlen(bp) == strspn(bp, " "))) { /* no values -- a null list */ break; } sscanf(bp, "%s", input_string); /* fprintf( stderr, "getparams: input_string %s\n", input_string); */ *int_list_paramptr = atoi_p(input_string, &integer_p); if (integer_p != TRUE) { fprintf( stderr, "ERROR: for parameter %s, number read, %s, \n" " was not an integer\n", pp->paramname, input_string); error_cnt++; break; } int_list_paramptr++; } *int_list_paramptr = END_OF_INT_LIST; break; default: fprintf(stderr, "ERROR: bad paramtype= %d for %s; parameter not set\n", pp->paramtype,buff); error_cnt++; } pp->overridden_p = TRUE; } } } return (error_cnt); } /* end getparam */ /* DEFPARAM 11nov94 wmt: modified from defparm -- add overridden_p & max_length 11jan95 wmt: add paramptr_overridden define parameter table entries */ void defparam( PARAMP params, int nparams, char *name, PARAMTYPE type, void *ptr, int max_length) { if (nparams>=MAXPARAMS) { printf(" too many params; max = %d",MAXPARAMS); abort(); } if ((int) strlen(name) >= PARAMNAMLEN) { printf(" param name too long. limit is %d",PARAMNAMLEN); abort(); } strcpy( params[nparams].paramname, name); params[nparams].paramtype = type; params[nparams].paramptr = ptr; params[nparams].paramptr_overridden = NULL; params[nparams].overridden_p = FALSE; params[nparams].max_length = max_length; /* for STRING, TINT_LIST */ params[nparams+1].paramptr = NULL; }/* end defparam */ autoclass-3.3.6.dfsg.1/prog/getparams.h0000644000175000017500000000153611247310756016020 0ustar areare#ifdef _WIN32 #include /* has select decl in Gcc-win32 b18*/ #endif /* #define TRUE 1 */ /* #define FALSE 0 */ /* allow for \n & \0 in addition to 100 characters */ #define LINLIM 102 #ifndef _MSC_VER typedef unsigned int BOOLEAN; #endif #define MAXPARAMS 40 #define PARAMNAMLEN 35 /* aju 980612: Prefixed enum members with T so they would not clash with Win32 predefined types. This affects getparams.c, intf-reports.c, and search-control.c*/ typedef enum {TSTRING, TBOOL, TINT, TFLOAT, TDOUBLE, TINT_LIST} PARAMTYPE; /* PARAMTYPE */ /* 11jan 95: add paramptr_overridden & overridden_p */ typedef struct /*parameters*/ { PARAMTYPE paramtype; char paramname[PARAMNAMLEN]; void *paramptr; void *paramptr_overridden; int overridden_p; int max_length; /* for paramtypes INT_LIST & STRING */ } PARAM, *PARAMP; autoclass-3.3.6.dfsg.1/prog/matrix-utilities.c0000644000175000017500000003020711247310756017342 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* Sets the values of vector V1 to the values of V2. */ float *setf_v_v( float *v1, float *v2, int num) { int i; for (i=0; i 0.0) { datum = data[i]; collect_indexed_values(values, att_indices, datum, num); j=num; while ( j-- > 0 && !(percent_equal( (double) values [j], FLOAT_UNKNOWN, REL_ERROR))); if (j < 0){ known += wt; decf_v_v(values, est_means, num); incf_v_vs(means, values, (double) wt, num); incf_m_vvs(covar, values, values, (double) wt, num); } else unknown += wt; } } for (i=0; i #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* FIND_TRANSFORM 15dec94 wmt: redo error msg Attempts to find the index of the transformed attributes. */ int find_transform( database_DS d_base, shortstr transform, int *att_list, int length, FILE *log_file_fp, FILE *stream) { int *temp, i, return_value; shortstr short_str; fxlstr str; char caller[] = "find_transform"; if (find_str_in_table(transform, G_transforms, NUM_TRANSFORMS) < 0) { safe_sprintf( str, sizeof( str), caller, "ERROR[2]: Attempt to find unknown transform %s on attributes:\n" " ", transform); for (i=0; iatt_info[att_index]->props, transform, d_base->att_info[att_index]->n_props); /* fprintf( stderr, "find_singleton_transform: att_index = %d, index = %d\n", att_index, (int) index); */ if (index != NULL) return(*index); else { return_value = generate_singleton_transform( d_base, transform, att_index, log_file_fp, stream); return( return_value); } } /* GENERATE_SINGLETON_TRANSFORM 15dec94 wmt: replaced G_att_fnames, ATT_FLENGTH with G_transforms, NUM_TRANSFORMS 10may95 wmt: call output_real_att_statistics 05jun95 wmt: Add G_prediction_p 21jun95 wmt: when realloc'ing d_base->att_info, initialize elements to NULL; & (++d_base->n_atts) >=, instead of (d_base->n_atts ++) > 28jul95 wmt: relloc att_info prior to storing new_att */ int generate_singleton_transform( database_DS d_base, shortstr transform, int att_index, FILE *log_file_fp, FILE *stream) { int *temp = NULL, new_index, fnumber, i; att_DS att = d_base->att_info[att_index], new_att; shortstr att_type, att_subtype; fxlstr str; char caller[] = "generate_singleton_transform"; strcpy(att_type, att->type); strcpy(att_subtype, att->sub_type); new_index = d_base->n_atts; safe_sprintf( str, sizeof( str), caller, "ADVISORY[2]: %s is being applied to attribute #%d:\n" " \"%s\" and will be stored as attribute #%d.\n", transform, att_index, att->dscrp, new_index); to_screen_and_log_file(str, log_file_fp, stream, TRUE); fnumber = find_str_in_table(transform, G_transforms, NUM_TRANSFORMS); switch(fnumber) { case 0: new_att = log_transform(att_index, d_base); break; case 1: new_att = log_odds_transform_c(att_index, d_base); break; default: fprintf(stderr,"\nERROR: (generate_singleton_transform) " "Undefined transform; %s\n", transform); exit(1); } temp = (int *) malloc(sizeof(int)); temp[0] = att_index; add_to_plist(new_att, "source", temp, "int"); add_to_plist(new_att, "source_sub_type", att->sub_type, "str"); if ((d_base->n_atts) >= d_base->allo_n_atts) { d_base->allo_n_atts *= 1.5; d_base->att_info = (att_DS *) realloc( d_base->att_info, d_base->allo_n_atts * sizeof( att_DS)); for (i=d_base->n_atts; iallo_n_atts; i++) d_base->att_info[i] = NULL; } d_base->att_info[d_base->n_atts] = new_att; d_base->n_atts++; if (eqstring( new_att->type, "real") == TRUE) { if ((G_prediction_p == TRUE) && (G_training_clsf != NULL)) { /* force the "test" database to use the same statistics as the "training" database. */ new_att->r_statistics = G_training_clsf->database->att_info[new_index]->r_statistics; new_att->missing = G_training_clsf->database->att_info[new_index]->missing; new_att->range = G_training_clsf->database->att_info[new_index]->range; } else { find_real_stats( d_base, new_index, log_file_fp, stream); output_real_att_statistics( d_base, new_index, log_file_fp, stream); } } else if (eqstring( new_att->type, "discrete") == TRUE) find_discrete_stats( d_base, new_index); temp = (int *) malloc( sizeof( int)); temp[0] = new_index; add_to_plist( att, transform, temp, "int"); return (new_index); } /* LOG_TRANSFORM 22oct94 wmt: moved new_att malloc to near top 27nov94 wmt: use percent_equal for float tests 18dec94 wmt: increment datum_length array for transformed attribute 23dec94 wmt: set unknown values to FLOAT_UNKNOWN, rather than INT_UNKNOWN 28jul95 wmt: use safe_log to transform values. Generates a log-transform for `real' attributes, which have a constant error or relative error. The constant error case can only have an approximate transformed error. Such should really be transformed to a real&error type attribute where the new relative error is sensitive to the instance value. */ att_DS log_transform( int att_index, database_DS d_base) { void ***props; int i, j, n, n_props; float value, **data = d_base->data, zero, error, new_error; float rel_error, new_rel_error = 0.0, mean, mn; att_DS att, new_att; char caller[] = "log_transform"; att = d_base->att_info[att_index]; n_props = att->n_props; props = (void ***) malloc(att->n_props * sizeof(void **)); for (i=0; in_props; i++) props[i] = att->props[i]; /****commented temp = getf(props, "zero_point", att->n_props); if (temp == NULL) zero = 0.0; else zero = (float) temp[0]; **********/ zero = att->zero_point; error = att->error; rel_error = att->rel_error; mean = att->r_statistics->mean; mn = att->r_statistics->mn; /* ---- Input checks: ---- */ if (eqstring(att->type, "real") == FALSE) { fprintf(stderr, "ERROR: Attempt to apply log_transform to non-numerical " "attribute %d of type %s\n", att_index, att->type); exit(1); } if (zero > mn) { if( (n = att->warnings_and_errors->num_expander_warnings ++) == 0) att->warnings_and_errors->model_expander_warnings = (fxlstr *) malloc(sizeof(fxlstr)); else att->warnings_and_errors->model_expander_warnings = (fxlstr *) realloc(att->warnings_and_errors->model_expander_warnings, (n+1) * sizeof(fxlstr)); safe_sprintf( att->warnings_and_errors->model_expander_warnings[n], sizeof( att->warnings_and_errors->model_expander_warnings[n]), caller, "log transform of attribute# %d using mn %f rather than %f for" " zero_point.\n Suggest decreasing attribute's rel_error.\n", att_index, mn, zero); zero = mn; /****** temp[0] = zero; add_to_plist(att, "zero_point", zero, "flt"); ***commented */ att->zero_point = mn; } /* ---- Generate the transformed properties: ---- */ /* Remove "zero_point" from props */ for (i=0; in_data; i++) { value = data[i][att_index]; d_base->data[i] = (float *) realloc(d_base->data[i], (d_base->n_atts + 1) * sizeof( float)); if (percent_equal( (double) value, FLOAT_UNKNOWN, REL_ERROR)) d_base->data[i][d_base->n_atts] = (float) FLOAT_UNKNOWN; else d_base->data[i][d_base->n_atts] = (float) safe_log( (double) (value - zero)); /* increment datum_length array for transformed attribute 18dec94 wmt */ d_base->datum_length[i] += 1; } new_att = (att_DS) malloc( sizeof( struct att)); strcpy(new_att->type, att->type); strcpy(new_att->sub_type, "log_transform"); if (eqstring(att->type, "real")) { new_att->r_statistics = (real_stats_DS) malloc(sizeof(struct real_stats)); new_att->d_statistics = NULL; } else { new_att->d_statistics = (discrete_stats_DS) malloc(sizeof(struct discrete_stats)); new_att->r_statistics = NULL; } safe_sprintf( new_att->dscrp, sizeof( new_att->dscrp), caller, "Log %s", att->dscrp); new_att->props = props; new_att->n_props = n_props; new_att->n_trans = att->n_trans; new_att->translations = att->translations; new_att->range = att->range; new_att->zero_point = att->zero_point; new_att->error = new_error; new_att->rel_error = new_rel_error; /* missing is set in store_real_stats */ new_att->warnings_and_errors = create_warn_err_DS(); return(new_att); } /* LOG_ODDS_TRANSFORM_C 20ct94 wmt: moved newe_att malloc to near top; add n_props = att->n_props; 27nov94 wmt: use percent_equal for float tests 23dec94 wmt: set unknown values to FLOAT_UNKNOWN, rather than INT_UNKNOWN Generates a log-odds-transform for `real' attributes, which have a constant error or relative error. The constant error case can only have an approximate transformed error. Such should really be transformed to a real&error type attribute where the new relative error is sensitive to the instance value. */ att_DS log_odds_transform_c( int att_index, database_DS d_base) { void ***props; int i, *temp, n_props; float value, **data = d_base->data; float mx, mn; att_DS att, new_att; char caller[] = "log_odds_transform_c"; att = d_base->att_info[att_index]; props = (void ***) malloc(att->n_props * sizeof(void **)); new_att = (att_DS) malloc(sizeof(struct att)); for (i=0; in_props; i++) /*******************************/ new_att->props[i] = att->props[i]; temp = getf(props, "min", att->n_props); if (temp == NULL) mn = 0.0; else mn = (float) temp[0]; temp = getf(props, "max", att->n_props); if (temp == NULL) mx = 1.0; else mx = (float) temp[0]; n_props = att->n_props; /* ---- Generate the new attribute values: ---- */ for (i=0; in_data; i++) { value = data[i][att_index]; d_base->data[i] = (float *) realloc(d_base->data[i], (d_base->n_atts + 1) * sizeof(float)); if (percent_equal( (double) value, (double) FLOAT_UNKNOWN, (double) REL_ERROR)) d_base->data[i][d_base->n_atts] = (float) FLOAT_UNKNOWN; else d_base->data[i][d_base->n_atts] = (float) ( safe_log((double) (value - mn)) - safe_log((double) (mx - value))); } strcpy(new_att->type, att->type); strcpy(new_att->sub_type, "log_odds_transform_c"); if (eqstring(att->type, "real")) { new_att->r_statistics = (real_stats_DS) malloc(sizeof(struct real_stats)); new_att->d_statistics = NULL; } else { new_att->d_statistics = (discrete_stats_DS) malloc(sizeof(struct discrete_stats)); new_att->r_statistics = NULL; } safe_sprintf( new_att->dscrp, sizeof( new_att->dscrp), caller, "log %s", att->dscrp); new_att->props = props; new_att->n_props = n_props; new_att->n_trans = att->n_trans; new_att->translations = att->translations; new_att->range = att->range; new_att->zero_point = att->zero_point; new_att->error = att->error; new_att->rel_error = att->rel_error; /* missing is set in store_real_stats */ new_att->warnings_and_errors = create_warn_err_DS(); return(new_att); } autoclass-3.3.6.dfsg.1/prog/model-multi-normal-cn.c0000644000175000017500000004563111247310756020150 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* MN_CN_PARAMS_INFLUENCE_FN 23feb95 wmt: new (from ac-x) Compute the influence value of Multi Normal model term with no missing values. 13 Dec 91 JCS: CURRENTLY UNDER DEVELOPMENT: This version finds the influence value for the term rather then the specified attribute. It is not clear how to do the later, since the single attribute's parameters are mixed with those of all the other attributes through the two covarience terms. An attribute is mathmatically seperable IFF we have zeros in the off diagonal elements of the corresponding row and column of both covariance matrices. This is not normally expected, particularly not for all classes of a classification. In short, a covariantly modeled attribute only rarly has any independent influence upon a class, let alone the classification. To ask what that independent influence is, is probably not a usefull question. The current solution is to return, for each attribute, the term cross entropy divided by the number of attributes modeled in the term. This will allow entorpy sums to add up correctly, but we really do need to consider returning model term information when multiple attribute terms are present. 6 May 93 JCS: Return the individual attribute mean and standard deviation w.r.t. self as is done for single normal model terms. Add the full covariance matrix as a third value . Note that since all attributes in a multiple term have common influence, they are printed together in the influence report. The covariance matrix can then follow them to show their interaction. */ void mn_cn_params_influence_fn( model_DS model, tparm_DS tparm, int term_index, int n_att, float *v_ptr, float *class_mean_ptr, float *class_sigma_ptr, float *global_mean_ptr, float *global_sigma_ptr, float **term_att_list_ptr, int *n_term_att_list_ptr, float ***class_covar_ptr) { struct mn_cn_param *param; tparm_DS *p; int i, term_att_index; float *delta_means, **global_inv_covar; param = &(tparm->ptype.mn_cn); p = model_global_tparms( model); *class_covar_ptr = param->covariance; *n_term_att_list_ptr = tparm->n_att_indices; *term_att_list_ptr = tparm->att_indices; for (i=0; i<*n_term_att_list_ptr; i++) if ((*term_att_list_ptr)[i] == n_att) break; term_att_index = i; *global_mean_ptr = p[term_index]->ptype.mn_cn.means[term_att_index]; *global_sigma_ptr = (float) sqrt( (double) p[term_index]-> ptype.mn_cn.covariance[term_att_index][term_att_index]); global_inv_covar = p[term_index]->ptype.mn_cn.temp_m; delta_means = p[term_index]->ptype.mn_cn.temp_v; invert_factored_square_matrix( p[term_index]->ptype.mn_cn.factor, global_inv_covar, *n_term_att_list_ptr); setf_v_v( delta_means, param->means, *n_term_att_list_ptr); decf_v_v( delta_means, p[term_index]->ptype.mn_cn.means, *n_term_att_list_ptr); *v_ptr = (((param->ln_root - p[term_index]->ptype.mn_cn.ln_root) + (0.5 * ( (float) star_vmv( global_inv_covar, delta_means, *n_term_att_list_ptr) + (float) trace_star_mm( *class_covar_ptr, global_inv_covar, *n_term_att_list_ptr) - *n_term_att_list_ptr))) / *n_term_att_list_ptr); *class_mean_ptr = param->means[term_att_index]; *class_sigma_ptr = (float) sqrt( (double) (*class_covar_ptr)[term_att_index][term_att_index]); } /* MAKE_MN_CN_PARAM 21nov94 wmt: initialize all slots in tparm 23dec94 wmt: initialize vectors and arrays 12nov97 jcs: sorted to reflect ordering in new_term_params definition */ tparm_DS make_mn_cn_param( int n_atts) { int i, j; tparm_DS tparm; struct mn_cn_param *mn_cn; tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); tparm->n_atts = n_atts; tparm->tppt = MN_CN; /* tparam->ptype.mn_cn substructure initialized below */ tparm->collect = FALSE; tparm->n_term = 0; tparm->n_att = 0; tparm->n_att_indices = n_atts; tparm->n_datum = 0; tparm->n_data = 0; tparm->w_j = 0.0; tparm->ranges = 0.0; tparm->class_wt = 0.0; tparm->disc_scale = 0.0; tparm->log_pi = 0.0; tparm->log_att_delta = 0.0; tparm->log_delta = 0.0; tparm->wts = NULL; /* (float *) */ tparm->datum = NULL; /* (float *) */ tparm->att_indices = NULL; /* (float *) */ tparm->data = NULL; /* (float **) */ tparm->wt_m = 0.0; tparm->log_marginal = 0.0; /* initializing the tparm->ptype.mn_cn substructure */ mn_cn = &( tparm->ptype.mn_cn); mn_cn->ln_root = 0.0; mn_cn->log_ranges = 0.0; mn_cn->emp_means = (float *) malloc(n_atts * sizeof(float)); for (i=0; iemp_means[i] = 0.0; mn_cn->emp_covar = (fptr *) malloc(n_atts * sizeof(fptr)); for (i=0; iemp_covar[i] = (float *) malloc(n_atts * sizeof(float)); for (j=0; jemp_covar[i][j] = 0.0; } mn_cn->means = (float *) malloc(n_atts * sizeof(float)); for (i=0; imeans[i] = 0.0; mn_cn->covariance = (fptr *) malloc(n_atts * sizeof(fptr)); for (i=0; icovariance[i] = (float *) malloc(n_atts * sizeof(float)); for (j=0; jcovariance[i][j] = 0.0; } mn_cn->factor = (fptr *) malloc(n_atts * sizeof(fptr)); for (i=0; ifactor[i] = (float *) malloc(n_atts * sizeof(float)); for (j=0; jfactor[i][j] = 0.0; } mn_cn->values = (float *) malloc(n_atts * sizeof(float)); for (i=0; ivalues[i] = 0.0; mn_cn->temp_v = (float *) malloc(n_atts * sizeof(float)); for (i=0; itemp_v[i] = 0.0; mn_cn->temp_m = (fptr *) malloc(n_atts * sizeof(fptr)); for (i=0; itemp_m[i] = (float *) malloc(n_atts * sizeof(float)); for (j=0; jtemp_m[i][j] = 0.0; } mn_cn->min_sigma_2s = NULL; /* (float *) set in multi_normal_cn_model_term_builder*/ return(tparm); } /* MULTI_NORMAL_CN_MODEL_TERM_BUILDER 30jul95 wmt: change log calls to safe_log to prevent "log: SING error" error messages. Funcalled from Expand-Model-Terms. This constructs parameter, prior, and intermediate results structures appropriate to a single-normal likelihood term, and places them in the model. Constructs corresponding log-likelihood and parameter update function elements and saves them on the model for later compilation. */ void multi_normal_cn_model_term_builder( model_DS model, term_DS term, int n_term) /* model_DS model; The model-DS to which this term will contribute. */ /* term_DS term; The singleton term-DS definig this attributes use. */ /* int n_term; The term index for various model-DS substructures. */ { int i, n_atts; float *errors, log_att_delta, log_ranges, log_delta_div_root_2pi_k, mn; float *att_indices; att_DS att; database_DS data_base; real_stats_DS stats; tparm_DS tparm; struct mn_cn_param *mn_cn; n_atts = term->n_atts; if (n_atts <= 1) { fprintf(stderr, "multi_normal_cn: attempt to apply to non-multiple set\n"); exit(1); } att_indices = term->att_list; /* Attribute indices for this term */ data_base = model->database; errors = (float *) malloc(n_atts * sizeof(float)); /* not freed used for min_sigma_2s */ for (i=0; iatt_info[(int) att_indices[i]]; errors[i] = att->error; } log_att_delta = 0.0; for (i=0; iatt_info[(int) att_indices[i]]; stats = att->r_statistics; log_ranges += (float) safe_log((double) (stats->mx - stats->mn)); } log_delta_div_root_2pi_k = log_att_delta + (n_atts * LN_1_DIV_ROOT_2PI); /* Allocate parameters struct. */ term->tparm = tparm = make_mn_cn_param(n_atts); mn_cn=&(tparm->ptype.mn_cn); /*******strcpy(model->priors[n_term], "implicit-prior");*/ /* JCS 2/98 Change minimum estimated sigma limiter to ~sqrt(LEAST_POSITIVE_SINGLE_FLOAT). This is still a hack, and not a satisfactory prior, but it eliminated the catastrophic cycling found with large n_atts and properly enforces the underflow constraint. mn = safe_exp( 1.0 + LEAST_POSITIVE_SINGLE_LOG / n_atts); */ mn = safe_exp( LEAST_POSITIVE_SINGLE_LOG / 2); for (i=0; in_term = n_term; tparm->att_indices = att_indices; tparm->log_delta = log_delta_div_root_2pi_k; tparm->log_att_delta = log_att_delta; tparm->n_atts = n_atts; mn_cn->log_ranges = log_ranges; mn_cn->min_sigma_2s = errors; } /* MULTI_NORMAL_CN_LOG_LIKELIHOOD 20dec94 wmt: return type to double When called within the environment of Log-Likelihood-fn, this calculates the probability of a Multi-normal-cn term over the indexed attributes. */ double multi_normal_cn_log_likelihood( tparm_DS tparm) { struct mn_cn_param *mn_cn =&(tparm->ptype.mn_cn); float *att_indices = tparm->att_indices, log_delta = tparm->log_delta; float *datum = tparm->datum, *values, *temp_v, temp; values = mn_cn->values; temp_v = mn_cn->temp_v; /* sets values to the indexed data values */ collect_indexed_values(values, att_indices, datum,tparm->n_atts); /* subtracts the means from the values */ decf_v_v(values, mn_cn->means, tparm->n_atts); /*print_vector_f(values,tparm->n_atts,"values in log_likelihood"); dbg*/ setf_v_v(temp_v, values, tparm->n_atts); /*print_vector_f(temp_v,tparm->n_atts,"temp_v in log_likelihood"); dbg*/ /*print_matrix_f(mn_cn->factor,tparm->n_atts,tparm->n_atts,"factor matrix"); dbg*/ solve(mn_cn->factor,temp_v,tparm->n_atts), /*print_vector_f(temp_v,tparm->n_atts,"temp_v after solve in log_likelihood"); dbg*/ /*printf(" dotvv =%f\n", dot_vv(values,temp_v,tparm->n_atts)); dbg*/ temp = log_delta + mn_cn->ln_root + (-0.5 * (float) dot_vv( values, temp_v, tparm->n_atts)); return (temp); } /* MULTI_NORMAL_CN_UPDATE_L_APPROX 20dec94 wmt: return type to double When called within the environment of Update-L-Approx-fn, this calculates the approximate log likelihood term (in log-a) for observing the weighted statistics given the class hypothesis and current parameters. */ double multi_normal_cn_update_l_approx( tparm_DS tparm) { struct mn_cn_param *mn_cn =&(tparm->ptype.mn_cn); float log_delta = tparm->log_delta, w_j = tparm->w_j, t1; t1 = -0.5 * (float) dot_mm(mn_cn->emp_covar, invert_factored_square_matrix(mn_cn->factor, mn_cn->temp_m, tparm->n_atts), tparm->n_atts); /* Contribution from empherical mean: zero when E = emp-mean */ t1 = (w_j * (log_delta + mn_cn->ln_root + t1)); return (t1); } /* MULTI_NORMAL_CN_UPDATE_M_APPROX 20dec94 wmt: return type to double When called within the environment of Update-M-Approx-fn, this calculates the approximate log marginal likelihood log-a_k of observing the weighted statistics given the class hypothesis alone. */ double multi_normal_cn_update_m_approx( tparm_DS tparm) { struct mn_cn_param *mn_cn = &(tparm->ptype.mn_cn); int k, n_atts = tparm->n_atts; float log_att_delta = tparm->log_att_delta, log_ranges = mn_cn->log_ranges; float w_j = tparm->w_j, h_cb, temp, wh, h1, a; fptr *g_matrix, *emp_covar; double sum; emp_covar = mn_cn->emp_covar; g_matrix = mn_cn->temp_m; h_cb = n_atts; extract_diagonal_matrix(emp_covar, g_matrix, n_atts); h1 = h_cb +1; wh = w_j + h_cb; sum = 0.0; for (k=0; kln_root + (-0.5 * n_atts * (float) safe_log( (double) (w_j - 2.0))))) + (w_j * log_att_delta) + (- log_ranges); return (temp); } /* MULTI_NORMAL_CN_UPDATE_PARAMS 20dec94 wmt: return type to void 08mar95 wmt: det to double */ void multi_normal_cn_update_params( tparm_DS tparm, int known_params_p) { struct mn_cn_param *mn_cn=&(tparm->ptype.mn_cn); int collect = tparm->collect; int n_data = tparm->n_data; float *att_indices = tparm->att_indices; float **data = tparm->data, *wts = tparm->wts, class_wt = tparm->class_wt; float *emp_means = mn_cn->emp_means, *means = mn_cn->means, *min_sigma_2s = mn_cn->min_sigma_2s, *values = mn_cn->values; fptr *emp_covar = mn_cn->emp_covar, *covar = mn_cn->covariance, *factor = mn_cn->factor, *temp_m = mn_cn->temp_m; float scale; double det; if (class_wt > 0.0) { /* Zero class-wt implies null class. */ if (collect == TRUE) { /* Collect & regenerate class statistics */ /* If not collect?, we proceed with the previous values. */ /* Updates emp-means & emp-covar, alters values: */ update_means_and_covariance(data, n_data, att_indices, wts, means, emp_means, emp_covar, values, tparm->n_atts); /* Do we want to do any limiting on the means or covariance? Following limits the covariance diagonals to square of attribute error: */ limit_min_diagonal_values(emp_covar, min_sigma_2s, tparm->n_atts); } } if (known_params_p != TRUE) { /* Update class parameters */ setf_v_v(means, emp_means,tparm->n_atts);/* Estimated value of means is the emp_means */ scale = 1.0 / (class_wt - 2.0); /*printf(" scale=%f\n",scale); dbg*/ /* print_matrix_f(emp_covar,tparm->n_atts,tparm->n_atts," in update p emp covar"); */ setf_m_ms(covar, emp_covar, (double) (class_wt * scale), tparm->n_atts); /*print_matrix_f(covar,tparm->n_atts,tparm->n_atts," in update p covar"); dbg*/ extract_diagonal_matrix(emp_covar, temp_m, tparm->n_atts); /*print_matrix_f(temp_m,tparm->n_atts,tparm->n_atts," in update p diag ele of emp covar"); dbg*/ incf_m_ms(covar,temp_m, (double) scale, tparm->n_atts); /*print_matrix_f(covar,tparm->n_atts,tparm->n_atts," in update p scaled covar"); dbg*/ copy_to_matrix(covar, factor, tparm->n_atts); /*print_matrix_f(factor,tparm->n_atts,tparm->n_atts," in update p factor"); dbg*/ compute_factor(factor, tparm->n_atts); /*print_matrix_f(factor,tparm->n_atts,tparm->n_atts," in update p factored factor "); dbg*/ det = determinent_f(factor, tparm->n_atts); mn_cn->ln_root = -0.5 * ( (det > 0.0) ? (float) safe_log( det) : LEAST_POSITIVE_SINGLE_LOG); /*printf(" det=%f ln_root=%f\n",det,mn_cn->ln_root); dbg*/ } /* return(class_wt); */ } /* MULTI_NORMAL_CN_CLASS_EQUIVALENCE 27feb95 wmt: free mallocs from vector_root_diagonal_matrix When called within the environment of class-equivalence-fn, this tests for equivalence of the parameters. */ int multi_normal_cn_class_equivalence( tparm_DS tparm1, tparm_DS tparm2, double sigma_ratio) { int c, r, n_atts = tparm1->n_atts; float *m1, *m2, *s1, *s2, *col1, *col2, sigma1, sigma2, covar1, covar2; fptr *c1, *c2; struct mn_cn_param *mn_cn1, *mn_cn2; mn_cn1 = &(tparm1->ptype.mn_cn); mn_cn2 =&( tparm2->ptype.mn_cn); m1 = mn_cn1->means; m2 = mn_cn2->means; c1 = mn_cn1->covariance; c2 = mn_cn2->covariance; s1 = vector_root_diagonal_matrix(c1, tparm1->n_atts); s2 = vector_root_diagonal_matrix(c2, tparm2->n_atts); for (c=0; c= (float) (sigma_ratio * (double) min(sigma1, sigma2))) return(FALSE); /* Comparing diagonal covariances */ if (percent_equal( (double) sigma1, (double) sigma2, (double) 1.0) == FALSE) return(FALSE); /* Comparing off diagonal covarainces */ for (r=c+1; rptype.mn_cn); mn_cn1 = &(tparm1->ptype.mn_cn); mn_cnm = &(tparmm->ptype.mn_cn); em0 = mn_cn0->emp_means; em1 = mn_cn1->emp_means; emm = mn_cnm->emp_means; ecovarm = mn_cnm->emp_covar; /* Calculate the merged empherical mean: */ setf_v_vs(emm, em0, (double) wt_0, tparm0->n_atts); incf_v_vs(emm, em1, (double) wt_1, tparm0->n_atts); n_sv( (double) (wt_m / 1.0), emm, tparm0->n_atts); /* Calculate the merged empherical covariance: W(S + m*m) = W0(S0 + m0*m0) + W1(S1 + m1*m1) */ setf_m_ms(ecovarm, mn_cn0->emp_covar, (double) wt_0, tparm0->n_atts); incf_m_ms(ecovarm, mn_cn1->emp_covar, (double) wt_1, tparm0->n_atts); incf_m_vvs(ecovarm, em0, em0, (double) wt_0, tparm0->n_atts); incf_m_vvs(ecovarm, em1, em1, (double) wt_1, tparm0->n_atts); n_sm( (double) (1.0 / wt_m), ecovarm, tparm0->n_atts); incf_m_vvs(ecovarm, emm, emm, (double) -1.0, tparm0->n_atts); /* return(wt_0); */ } autoclass-3.3.6.dfsg.1/prog/utils.c0000644000175000017500000007040411667631470015175 0ustar areare#include #include #include #include #include #include /* for char_input_test => fcntl */ /* fcntl.h not available under gcc 2.6.3 put needed flags in "fcntlcom-ac.h" & include the next 2 files */ /* #include */ #include #ifndef _MSC_VER #include #endif /* for safe_sprintf */ #include #include "autoclass.h" #include "minmax.h" #include "globals.h" #include "fcntl.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* TO_SCREEN_AND_LOG_FILE 20oct94 wmt: modified Dump a message to the screen and to the specified log file. */ void to_screen_and_log_file( char *msg, FILE *log_file_fp, FILE *stream, int output_p) { char caller[] = "to_screen_and_log_file"; if (output_p == TRUE) { if (stream != NULL) { fprintf(stream, "%s", msg); /* wmtdebug - fprintf( stderr, "%s", msg); fflush( stderr); */ } if (log_file_fp != NULL) safe_fprintf(log_file_fp, caller, "%s", msg); } } /* GET_UNIVERSAL_TIME 26apr95 wmt: remove check for NULL return value from time */ time_t get_universal_time (void) { static time_t current_time; current_time = time(¤t_time); return (current_time); } /* Ellen's version time_t get_universal_time(void) { time_t *current_time; current_time = (time_t *)malloc(sizeof(time_t)); time(current_time); printf("time = %s\n", ctime(current_time)); if (*current_time == NULL) fprintf(stderr, "Current time not available\n"); return (*current_time); } */ /* FORMAT_UNIVERSAL_TIME 01oct94 wmt: new 26apr95 wmt: NULL => '\0' call library funcion ctime (requires time.h) to format universal time */ char *format_universal_time( time_t universal_time) { char *date_time_string_ptr; date_time_string_ptr = ctime(&universal_time); /* chop off new-line */ date_time_string_ptr[strlen(date_time_string_ptr) - 1] = '\0'; return(date_time_string_ptr); } /* FORMAT_TIME_DURATION 03oct94 wmt: new format delta universal time - returns leading space, but no trailing space */ char *format_time_duration (time_t delta_universal_time) { /* static so that this var can be passed back to caller */ /* simple types (int, float) do not have to be static */ /* compound types (char *, other arrays, structs) must be static or */ /* allocated by global define or malloc */ static char time_string[50]; char temp_string[20]; int days, hours, minutes, seconds, remainder_days; int remainder_hours, delta_time; int seconds_per_minute, seconds_per_hour; int seconds_per_day; seconds_per_minute = 60; seconds_per_hour = 3600; /* 60 * 60 */ seconds_per_day = 86400; /* 24 * 60 * 60 */ delta_time = (int) delta_universal_time; days = delta_time / seconds_per_day; remainder_days = delta_time % seconds_per_day; hours = remainder_days / seconds_per_hour; remainder_hours = remainder_days % seconds_per_hour; minutes = remainder_hours / seconds_per_minute; seconds = remainder_hours % seconds_per_minute; time_string[0] = '\0'; /* reset static string to null */ if (days != 0) sprintf(time_string," %d day%s", days, (days > 1) ? "s" : ""); if (hours != 0) { sprintf(temp_string," %d hour%s", hours, (hours > 1) ? "s" : ""); strcat(time_string, temp_string); } if (minutes != 0) { sprintf(temp_string," %d minute%s", minutes, (minutes > 1) ? "s" : ""); strcat(time_string, temp_string); } if (seconds != 0) { sprintf(temp_string," %d second%s", seconds, (seconds > 1) ? "s" : ""); strcat(time_string, temp_string); } if ((days == 0) && (hours == 0) && (minutes == 0) && (seconds == 0)) sprintf(time_string," 0 seconds"); return(time_string); } /* ROUND 13oct94 wmt: new function round a float to an integer, using ceil & floor gcc ceil & floor return double int i_number, rounded_number; i_number = number; if ((number - (float) i_number) < 0.5) rounded_number = i_number; else rounded_number = i_number++; return(rounded_number); does not always produce the correct answer 06mar06 wmt: round => iround because of gcc 4.0 */ int iround (double number) { int absolute_value, rounded_value; absolute_value = (int) number; if ((number - (double) absolute_value) >= 0.5) rounded_value = (int) ceil( number); else rounded_value = (int) floor( number); return(rounded_value); } /* INT_COMPARE_LESS 14oct94 wmt: new compare function for qsort - smallest integer first */ int int_compare_less (int *i_ptr, int *j_ptr) { return(*i_ptr - *j_ptr); } /* INT_COMPARE_GREATER 14oct94 wmt: new compare function for qsort - largest integer first */ int int_compare_greater (int *i_ptr, int *j_ptr) { return(*j_ptr - *i_ptr); } /* EQSTRING */ int eqstring( char *str1, char *str2) { if (strcmp(str1, str2) == 0) return(TRUE); else return(FALSE); } float *fill( float *wts, double info, int num, int end) { int i; for (i=0; in_classes, clsf->checkpoint->current_cycle, format_universal_time( now)); clsf->checkpoint->accumulated_try_time += now - G_search_cycle_begin_time; if (eqstring( G_checkpoint_file, "") == TRUE) { fprintf( stderr, "ERROR: checkpoint_clsf called with G_checkpoint_file = " "\"\" does nothing\n"); exit(1); } else { temp = (clsf_DS *) malloc( sizeof( clsf_DS)); temp[0] = clsf; save_clsf_seq( temp, 1, G_checkpoint_file, G_save_compact_p, "chkpt"); free( temp); to_screen_and_log_file( msg_string, G_log_file_fp, G_stream, TRUE); G_last_checkpoint_written = get_universal_time(); G_search_cycle_begin_time = now; /* start cycle time after writing file */ } } int *delete_duplicates( int *list, int num) { int *new = NULL; /********** this routine appears only to be used by get_sources_list which was commented so this was too int i, j, found, length, new_length, *new; new = (int *) malloc(num * sizeof(int)); new_length = 0; for (i=0; inext; } return(FALSE); } int find_class_test2( class_DS class, clsf_DS clsf, double rel_error) { int i; for(i=0;in_classes;i++) if (class_DS_test(clsf->classes[i], class, rel_error) == TRUE) return(TRUE); return(FALSE); } /* FIND_DATABASE_P 23jan95 wmt: renamed from find_data */ int find_database_p( database_DS data, database_DS *databases, int n_data) { int i; for (i=0; i 1) { i = (int) (min( (double) lrand48( ), G_rand_base_normalizer) / normalizer); i = min( i, max_list_index); if(i != --n){ temp=y[i]; y[i]=y[n]; y[n]=temp; } } return y; } /* modified and moved to struct-clsf clsf_list_DS push_clsf(clsf, clsf_list) clsf_DS clsf; clsf_list_DS clsf_list; { clsf_list_DS temp; temp = (clsf_list_DS) malloc(sizeof(struct clsf_list)); temp->clsf = clsf; temp->next = clsf_list; return(temp); } whole routine commented here JTP */ /* Y_OR_N_P modified 11oct94 wmt ask user for yes or no answer */ int y_or_n_p( char* str) { char answer[] = " ", line[] = " "; while (1) { fprintf( stdout, "%s", str); /* scanf("%s", &answer); */ fgets( line, sizeof(line), stdin); sscanf( line, "%s", answer); if ((answer[0] != 'y') && (answer[0] != 'n')) fprintf( stdout, "Type \"y\" for yes or \"n\" for no.\n"); else break; } if (answer[0] == 'y') return(TRUE); else return(FALSE); } /* this routine commented JTP 6/29 wont work as is need to pass length. should also consier doing in place (call recursively after swapping ends?) float *reverse( float *flist) { int i, length = sizeof(flist) / sizeof(float); float *new; new = (float *) malloc(sizeof(flist)); for (i=0; i= 2.0, any two values are acceptable. */ int percent_equal( double n1, double n2, double rel_error) { double val1, val2; val1 = 0.5 * rel_error * (fabs( n1) + fabs( n2)); val2 = fabs( n1 - n2); if (val1 >= val2) return(TRUE); else return(FALSE); } int prefix(char *str,char *substr) { int i, l1 = strlen(str), l2 = strlen(substr); if (l1 < l2) return(FALSE); else for (i=0; in_props)++) == 0) att->props = (void ***) malloc(sizeof(void **)); else att->props = (void ***) realloc(att->props, att->n_props * sizeof(void **)); att->props[n] = (void **) malloc(3 * sizeof(void *)); att->props[n][0] = target; att->props[n][1] = value; att->props[n][2] = type; } /* WRITE_VECTOR_FLOAT 21nov94 wmt: new variation on print_vector_f, used for writing .results files */ void write_vector_float(float *vector, int n, FILE *stream) { int i; char caller[] = "write_vector_float"; for (i=0; i 0) && ((i % NUM_TOKENS_IN_FXLSTR) == 0)) safe_fprintf(stream, caller, "\n"); safe_fprintf(stream, caller, "%.7e ", vector[i]); } if (n > 0) safe_fprintf(stream, caller, "\n"); } /* WRITE_MATRIX_FLOAT 21nov94 wmt: new variation on print_matrix_f, used for writing .results files */ void write_matrix_float( float **vector, int m, int n, FILE *stream) { int i, j, first_row = TRUE; char caller[] = "write_matrix_float"; for (i=0; i 0) && ((j % NUM_TOKENS_IN_FXLSTR) == 0)) safe_fprintf(stream, caller, "\n"); safe_fprintf(stream, caller, "%.7e ", vector[i][j]); } } if (m > 0) safe_fprintf(stream, caller, "\n"); } /* WRITE_MATRIX_INTEGER 21nov94 wmt: new variation on print_matrix_i, used for writing .results files */ void write_matrix_integer( int **vector, int m, int n, FILE *stream) { int i, j, first_row = TRUE; char caller[] = "write_matrix_integer"; for (i=0; i 0) && ((j % NUM_TOKENS_IN_FXLSTR) == 0)) safe_fprintf(stream, caller, "\n"); safe_fprintf(stream, caller, "%d ", vector[i][j]); } } if (m > 0) safe_fprintf(stream, caller, "\n"); } /* READ_VECTOR_FLOAT 19jan95 wmt: new variation on print_vector_f, used for writing .results files. use fscanf, rather than fgets & sscanf since fscanf keeps track of file ptr, and sscanf does not keep track of string ptr. */ void read_vector_float( float *vector, int n, FILE *stream) { int i; for (i=0; i 0) && ((i % NUM_TOKENS_IN_FXLSTR) == 0)) fscanf( stream, "\n"); fscanf( stream, "%e ", &vector[i]); } if (n > 0) fscanf( stream, "\n"); } /* READ_MATRIX_FLOAT 19jan95 wmt: new variation on print_matrix_f, used for writing .results files use fscanf, rather than fgets & sscanf since fscanf keeps track of file ptr, and sscanf does not keep track of string ptr. */ void read_matrix_float( float **vector, int m, int n, FILE *stream) { int i, j, first_row = TRUE, i_file; for (i=0; i 0) && ((j % NUM_TOKENS_IN_FXLSTR) == 0)) fscanf( stream, "\n"); fscanf( stream, "%e ", &vector[i][j]); } } if (m > 0) fscanf( stream, "\n"); } /* READ_MATRIX_INTEGER 19jan95 wmt: new variation on print_matrix_i, used for writing .results files use fscanf, rather than fgets & sscanf since fscanf keeps track of file ptr, and sscanf does not keep track of string ptr. */ void read_matrix_integer( int **vector, int m, int n, FILE *stream) { int i, j, first_row = TRUE, i_file; for (i=0; i 0) && ((j % NUM_TOKENS_IN_FXLSTR) == 0)) fscanf( stream, "\n"); fscanf( stream, "%d ", &vector[i][j]); } } if (m > 0) fscanf( stream, "\n"); } /* DISCARD_COMMENT_LINES 28nov94 wmt: new check first column for '#', '!', ' ', or ';', then read to \n return EOF, if found */ int discard_comment_lines (FILE *stream) { int c; c = fgetc(stream); if (c == EOF) return(EOF); while ((c == ';') || (c == '!') || (c == '#') || (c == ' ') || (c == '\n')) { /* printf("\ndiscard "); */ if (c != '\n') flush_line(stream); c = fgetc(stream); } ungetc(c, stream); return(c); } /* FLUSH_LINE 28nov94 wmt: new read to \n or \r or EOF */ void flush_line (FILE *stream) { int c; /* printf("\nflush "); */ while (((c = fgetc(stream)) != '\n') && (c != '\r') && (c != EOF)) /* printf("%c", (char) c); */ ; } /* READ_CHAR_FROM_SINGLE_QUOTES 30nov94 wmt: new read char c from 'c', and flush rest of line */ int read_char_from_single_quotes (char *param_name, FILE *stream) { int c; c = fgetc(stream); while ((c == ' ') || (c == '\n')) c = fgetc(stream); if (c == '\'') { c = fgetc(stream); flush_line(stream); return(c); } else { fprintf(stderr, "ERROR: for %s, expected to read first ' from 'c', read %c instead!\n", param_name, (char) c); exit(1); } return(0); /* must return something */ } /* 02dec94 wmt: moved from io-read-model.c not currently used */ int strcontains( char *str, int c) { int i, length = strlen(str); for (i=0; i 0) { for (i=0; i<*n_list_ptr; i++) list[i] = list[i+1]; (*n_list_ptr)--; } else found_j_in = FALSE; return (found_j_in); } /* PUSH_INT_LIST 30jan95 wmt: new push value onto top of list */ void push_int_list( int *list, int *n_list_ptr, int value, int max_n_list) { int i; *n_list_ptr = 0; for (i=0; list[i] != END_OF_INT_LIST; i++) (*n_list_ptr)++; if (*n_list_ptr > (max_n_list - 2)) { fprintf( stderr, "ERROR: integer list of type \"int_list\" is full\n"); abort(); } for (i=*n_list_ptr; i>=0; i--) list[i+1] = list[i]; list[0] = value; (*n_list_ptr)++; } /* MEMBER_INT_LIST 02feb95 wmt: new return true if val in is list of type int_list */ int member_int_list( int val, int_list list) { int i; for (i=0; list[i] != END_OF_INT_LIST; i++) if (list[i] == val) return (TRUE); return (FALSE); } /* FLOAT_SORT_CELL_COMPARE_GTR 03feb95 wmt: new compare function for qsort - largest float first in list of struct sort_cell. when equal, sort by lowest int_value */ int float_sort_cell_compare_gtr( sort_cell_DS i_cell, sort_cell_DS j_cell) { if (i_cell->float_value < j_cell->float_value) return (1); else if (i_cell->float_value > j_cell->float_value) return (-1); else { /* equal */ if (i_cell->int_value < j_cell->int_value) return (-1); else if (i_cell->int_value > j_cell->int_value) return (1); else return (0); } } /* CLASS_CASE_SORT_COMPARE_LSR 09feb95 wmt: new compare function for qsort - lowest class first in xref_data structs keep case ordering within the class */ int class_case_sort_compare_lsr( xref_data_DS i_xref, xref_data_DS j_xref) { if (i_xref->class_case_sort_key > j_xref->class_case_sort_key) return (1); else if (i_xref->class_case_sort_key < j_xref->class_case_sort_key) return (-1); else return (0); } /* ATT_I_SUM_SORT_COMPARE_GTR 13feb95 wmt: new 10mar95 wmt: for equal att_i_sum, lower attribute number will come first compare function for ordered_normalized_influence_values */ int att_i_sum_sort_compare_gtr( ordered_influ_vals_DS i_influ_val, ordered_influ_vals_DS j_influ_val) { if (i_influ_val->att_i_sum < j_influ_val->att_i_sum) return (1); else if (i_influ_val->att_i_sum > j_influ_val->att_i_sum) return (-1); else { /* equal */ if (i_influ_val->n_att < j_influ_val->n_att) return (-1); else if (i_influ_val->n_att > j_influ_val->n_att) return (1); else return (0); } } /* FLOAT_P_P_STAR_COMPARE_GTR 16feb95 wmt: new 23jun95 wmt: sort the absolute value compare function for formatted_p_p_star_list */ int float_p_p_star_compare_gtr( formatted_p_p_star_DS i_formatted_p_p_star, formatted_p_p_star_DS j_formatted_p_p_star) { if (i_formatted_p_p_star->abs_att_value_influence < j_formatted_p_p_star->abs_att_value_influence) return (1); else if (i_formatted_p_p_star->abs_att_value_influence > j_formatted_p_p_star->abs_att_value_influence) return (-1); else return (0); } /* SAFE_FPRINTF 13mar95 wmt: new NOTE: this function is not interpreted correctly by CodeCenter, and the middle three lines must be commented out when interpreted (probably some king of library/include problem) this is a wrapper to check for error returns from fprintf */ void safe_fprintf( FILE *stream, char *caller, char *format, ...) { int return_cnt = 0; va_list arg_addr; /* fprintf( stderr, "safe_fprintf: caller %s, format \"%s\"\n", caller, format); fflush( stderr); */ va_start( arg_addr, format); return_cnt = vfprintf( stream, format, arg_addr); va_end( arg_addr); /* fprintf( stderr, "safe_fprintf: caller %s, return_cnt %d\n", caller, return_cnt); fflush( stderr); */ if (return_cnt < 0) { fprintf( stderr, "ERROR: fprintf returned %d -- called by %s\n", return_cnt, caller); abort(); } } /* SAFE_SPRINTF 02may95 wmt: new NOTE: this function is not interpreted correctly by CodeCenter, and the middle three lines must be commented out when interpreted (probably some kind of library/include problem) detect overwriting string arrays while using sprintf */ void safe_sprintf( char *str, int str_length, char *caller, char *format, ...) { int return_cnt = 0; va_list arg_addr; /* fprintf( stderr, "safe_sprintf: len %d '%s' -- called by %s\n", str_length, str, caller); */ va_start( arg_addr, format); return_cnt = (int) vsprintf( str, format, arg_addr); va_end( arg_addr); /* vsprintf always retrurns negative value - ??? */ if (return_cnt < 0) { fprintf( stderr, "ERROR: vsprintf had an error return: %d) " "-- called by %s\n", return_cnt, caller); abort(); } /* */ /* debug fprintf( stderr, "actual=%.3d, limit=%.3d, caller = %s\n", (int) strlen( str), str_length - 1, caller); */ /* if ((int) strlen( str) > (str_length - 1)) { fprintf( stderr, "ERROR: vsprintf produced %d chars (max number is %d) " "-- called by %s\n", (int) strlen( str), (str_length - 1), caller); abort(); } */ } autoclass-3.3.6.dfsg.1/prog/io-results-bin.c0000644000175000017500000011417511667631470016715 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* SAFE_FWRITE 13mar95 wmt: new 16may95 wmt: converted to ANSI i/o this is a wrapper to check for error returns from write */ void safe_fwrite( FILE *save_fp, char *data, int data_length, int data_type, char *caller) { int return_cnt, header[2]; int header_length = sizeof( header); /* wmtdebug fprintf( stderr, "safe_fwrite: save_fp %p, data_length %d, data %p, caller %s\n", save_fp, data_length, data, caller); fflush( stderr); */ if (data_type == CHAR_TYPE) data_length++; /* include '\0' */ header[0] = data_type; header[1] = data_length; return_cnt = fwrite( (char *) &header, sizeof( char), header_length, save_fp); if (return_cnt != header_length) { fprintf( stderr, "ERROR: fwrite failed -- called by %s\n", caller); abort(); } if (data_length > 0) { return_cnt = fwrite( data, sizeof( char), data_length, save_fp); if (return_cnt != data_length) { fprintf( stderr, "ERROR: write failed -- called by %s\n", caller); abort(); } } } /* CHECK_LOAD_HEADER 17mar95 wmt: new */ void check_load_header( int header_type, int expected_type, char *caller) { if (header_type != expected_type) { fprintf( stderr, "ERROR: in %s, expecting data type %d, found %d \n", caller, expected_type, header_type); abort(); } } /* DUMP_CLSF_SEQ 13mar95 wmt: support binary clsf write */ void dump_clsf_seq( clsf_DS *clsf_seq, int num, FILE *results_fp) { int i; fxlstr str; char caller[] = "dump_clsf_seq"; sprintf( str, "# ordered sequence of clsf_DS's: 0 -> %d", num - 1); safe_fwrite( results_fp, str, strlen( str), CHAR_TYPE, caller); for (i=0; ilog_a_x_h); safe_fwrite( results_fp, str, strlen( str), CHAR_TYPE, caller); } sprintf( str, "ac_version %s", G_ac_version); safe_fwrite( results_fp, str, strlen( str), CHAR_TYPE, caller); for (i=0; idatabase, results_fp); else safe_fwrite( results_fp, db_string, strlen( db_string), CHAR_TYPE, caller); for (i=0; inum_models; i++) { if (clsf_num == 0) dump_model_DS( clsf->models[i], i, clsf->database, results_fp); else { model_num_string[0] = '\0'; sprintf( model_num_string, "%s %d", model_string, i); safe_fwrite( results_fp, model_num_string, strlen( model_num_string), CHAR_TYPE, caller); } } dump_class_DS_s( clsf->classes, clsf->n_classes, results_fp); /* clsf->reports is only used for report generation - do not output */ safe_fwrite( results_fp, (char *) clsf->checkpoint, sizeof( struct checkpoint), CHECKPOINT_TYPE, caller); } /* DUMP_DATABASE_DS 15mar95 wmt: new write compressed database_DS contents in binary */ void dump_database_DS( database_DS database, FILE *results_fp) { int i; char caller[] = "dump_database_DS"; safe_fwrite( results_fp, (char *) database, sizeof( struct database), DATABASE_TYPE, caller); /* Ordered N-atts vector of att_DS describing the attributes. */ for (i=0; in_atts; i++) dump_att_DS( database->att_info[i], i, results_fp); } /* DUMP_ATT_DS 15mar95 wmt: new write att_DS contents in binary */ void dump_att_DS( att_DS att_info, int n_att, FILE *results_fp){ discrete_stats_DS discrete_stats; warn_err_DS warnings_and_errors; int i; char caller[] = "dump_att_DS"; fxlstr props_string; safe_fwrite( results_fp, (char *) att_info, sizeof( struct att), ATT_TYPE, caller); if (eqstring(att_info->type, "real")) safe_fwrite( results_fp, (char *) att_info->r_statistics, sizeof( struct real_stats), REAL_STATS_TYPE, caller); else if (eqstring(att_info->type, "discrete")) { discrete_stats = att_info->d_statistics; safe_fwrite( results_fp, (char *) discrete_stats, sizeof( struct discrete_stats), DISCRETE_STATS_TYPE, caller); safe_fwrite( results_fp, (char *) discrete_stats->observed, discrete_stats->range * sizeof( int), INT_TYPE, caller); } else if (eqstring(att_info->type, "dummy")) safe_fwrite( results_fp, props_string, 0, DUMMY_STATS_TYPE, caller); else { fprintf(stderr, "ERROR: att_info->type %s not handled\n", att_info->type); exit(1); } if (! eqstring(att_info->type, "dummy")) { for (i=0; i < att_info->n_trans; i++) safe_fwrite( results_fp, (char *) att_info->translations[i], strlen( att_info->translations[i]), CHAR_TYPE, caller); for (i=0; i < att_info->n_props; i++) { props_string[0] = '\0'; if (eqstring( (char *) att_info->props[i][2], "int") == TRUE) { sprintf( props_string, "%s %s %d", (char *) att_info->props[i][0], (char *) att_info->props[i][2], *((int *) att_info->props[i][1])); safe_fwrite( results_fp, props_string, strlen( props_string), CHAR_TYPE, caller); } else if (eqstring( (char *) att_info->props[i][2], "flt") == TRUE) { sprintf( props_string, "%s %s %f", (char *) att_info->props[i][0], (char *) att_info->props[i][2], *((float *) att_info->props[i][1])); safe_fwrite( results_fp, props_string, strlen( props_string), CHAR_TYPE, caller); } else if (eqstring( (char *) att_info->props[i][2], "str") == TRUE) { sprintf( props_string, "%s %s %s", (char *) att_info->props[i][0], (char *) att_info->props[i][2], (char *) att_info->props[i][1]); safe_fwrite( results_fp, props_string, strlen( props_string), CHAR_TYPE, caller); } else { fprintf( stderr, "property list type %s, not handled!\n", (char *) att_info->props[i][2]); abort(); } } warnings_and_errors = att_info->warnings_and_errors; safe_fwrite( results_fp, (char *) warnings_and_errors, sizeof( struct warn_err), WARN_ERR_TYPE, caller); safe_fwrite( results_fp, eqstring( warnings_and_errors->unspecified_dummy_warning, "") ? "NULL" : warnings_and_errors->unspecified_dummy_warning, eqstring( warnings_and_errors->unspecified_dummy_warning, "") ? 4 : strlen( warnings_and_errors->unspecified_dummy_warning), CHAR_TYPE, caller); safe_fwrite( results_fp, eqstring( warnings_and_errors->single_valued_warning, "") ? "NULL" : warnings_and_errors->single_valued_warning, eqstring( warnings_and_errors->single_valued_warning, "") ? 4 : strlen( warnings_and_errors->single_valued_warning), CHAR_TYPE, caller); /* float *unused_translators_warning; discrete translations not implementated */ for (i=0; i < warnings_and_errors->num_expander_warnings; i++) safe_fwrite( results_fp, warnings_and_errors->model_expander_warnings[i], strlen( warnings_and_errors->model_expander_warnings[i]), CHAR_TYPE, caller); for (i=0; i < warnings_and_errors->num_expander_errors; i++) safe_fwrite( results_fp, warnings_and_errors->model_expander_errors[i], strlen( warnings_and_errors->model_expander_errors[i]), CHAR_TYPE, caller); } } /* DUMP_MODEL_DS 15mar95 wmt: new write model_DS contents in binary - one or more models -- in compressed form */ void dump_model_DS( model_DS model, int model_num, database_DS database, FILE *results_fp) { char caller[] = "dump_model_DS"; safe_fwrite( results_fp, (char *) model, sizeof( struct model), MODEL_TYPE, caller); } /* DUMP_TERM_DS 15mar95 wmt: new write term_DS to binary file */ void dump_term_DS( term_DS term, int n_term, FILE *results_fp) { int parm_num = 0; char caller[] = "dump_term_DS"; safe_fwrite( results_fp, (char *) term, sizeof( struct term), TERM_TYPE, caller); safe_fwrite( results_fp, (char *) term->att_list, term->n_atts * sizeof( float), FLOAT_TYPE, caller); dump_tparm_DS( term->tparm, parm_num, results_fp); } /* DUMP_TPARM_DS 15mar95 wmt: new write tparm_DS (term params) to binary file */ void dump_tparm_DS( tparm_DS term_param, int parm_num, FILE *results_fp) { char caller[] = "dump_tparm_DS"; safe_fwrite( results_fp, (char *) term_param, sizeof( struct new_term_params), TPARM_TYPE, caller); switch(term_param->tppt) { case SM: dump_sm_params( &(term_param->ptype.sm), term_param->n_atts, results_fp); break; case SN_CM: /* nothing to do dump_sn_cm_params( &(term_param->ptype.sn_cm), results_fp); */ break; case SN_CN: /* nothing to do dump_sn_cn_params( &(term_param->ptype.sn_cn), results_fp); */ break; case MM_D: dump_mm_d_params( &(term_param->ptype.mm_d), term_param->n_atts, results_fp); break; case MM_S: dump_mm_s_params( &(term_param->ptype.mm_s), term_param->n_atts, results_fp); break; case MN_CN: dump_mn_cn_params( &(term_param->ptype.mn_cn), term_param->n_atts, results_fp); break; default: printf("\n dump_tparms_DS: unknown type of ENUM MODEL_TYPES =%d\n", term_param->tppt); abort(); } } /* DUMP_MM_D_PARAMS 15mar95 wmt: new write mm_d params to binary file - not converted from write_mm_d_params */ void dump_mm_d_params( struct mm_d_param *param, int n_atts, FILE *results_fp) { /* int i, m; char caller[] = "dump_mm_d_params"; */ /* safe_fprintf(stream, caller, "mm_d_params\n"); for(i=0; isizes[i]; printf("row %d, size %d\n", i, m); safe_fprintf(stream, caller, "wts\n"); write_vector_float(param->wts[i], m, stream); safe_fprintf(stream, caller, "probs\n"); write_vector_float(param->probs[i], m, stream); safe_fprintf(stream, caller, "log_probs\n"); write_vector_float(param->log_probs[i], m, stream); } safe_fprintf(stream, caller, "wts_vec\n"); write_vector_float(param->wts_vec, m, stream); safe_fprintf(stream, caller, "probs_vec\n"); write_vector_float(param->probs_vec, m, stream); safe_fprintf(stream, caller, "log_probs_vec\n"); write_vector_float(param->log_probs_vec, m, stream); */ } /* DUMP_MM_S_PARAMS 15mar95 wmt: new write mm_s_params to ascii file -- incomplete */ void dump_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_fp) { /* char caller[] = "dump_mm_s_params"; */ /* safe_fprintf(stream, caller, "mm_s_params\n"); safe_fprintf(stream, caller, "count, wt, prob, log_prob\n"); safe_fprintf(stream, caller, "%d %.7e %.7e %.7e\n", param->count, param->wt, param->prob, param->log_prob); */ } /* DUMP_MN_CN_PARAMS 15mar95 wmt: new write mn_cn_params to binary file */ void dump_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_fp) { char caller[] = "dump_mn_cn_params"; int i; safe_fwrite( results_fp, (char *) param->emp_means, n_atts * sizeof( float), FLOAT_TYPE, caller); for (i=0; iemp_covar[i], n_atts * sizeof( float), FLOAT_TYPE, caller); safe_fwrite( results_fp, (char *) param->means, n_atts * sizeof( float), FLOAT_TYPE, caller); for (i=0; icovariance[i], n_atts * sizeof( float), FLOAT_TYPE, caller); for (i=0; ifactor[i], n_atts * sizeof( float), FLOAT_TYPE, caller); safe_fwrite( results_fp, (char *) param->min_sigma_2s, n_atts * sizeof( float), FLOAT_TYPE, caller); /* values, temp_v & temp_m are temporary storage - do not save, just reinit to 0.0 */ } /* DUMP_SM_PARAMS 15mar95 wmt: new write sm_params to binary file n_atts is actually n_vals -- an overloaded slot definition */ void dump_sm_params( struct sm_param *param, int n_atts, FILE *results_fp) { char caller[] = "dump_sm_params"; safe_fwrite( results_fp, (char *) param->val_wts, n_atts * sizeof( float), FLOAT_TYPE, caller); safe_fwrite( results_fp, (char *) param->val_probs, n_atts * sizeof( float), FLOAT_TYPE, caller); safe_fwrite( results_fp, (char *) param->val_log_probs, n_atts * sizeof( float), FLOAT_TYPE, caller); } /* DUMP_CLASS_DS_S 16mar95 wmt: new write class_DS to binary file. */ void dump_class_DS_s( class_DS *classes, int n_classes, FILE *results_fp) { int i, j; char caller[] = "dump_class_DS_s"; for (i=0; i < n_classes; i++) { safe_fwrite( results_fp, (char *) classes[i], sizeof( struct class), CLASS_TYPE, caller); for (j=0; jnum_tparms; j++) dump_tparm_DS( classes[i]->tparms[j], j, results_fp); /* num_i_values, i_values, i_sum, & max_i_value used only in reports - do not output */ safe_fwrite( results_fp, (char *) &classes[i]->model->file_index, sizeof( int), INT_TYPE, caller); } } /* LOAD_CLSF_SEQ 13mar95 wmt: read binary file 16may95 wmt: converted binary i/o to ANSI 20sep98 wmt: strip of win/unx from ac_version, starting with version 3.3 27nov98 wmt: check for win/unx before stripping -- backward compatible */ clsf_DS *load_clsf_seq( FILE *results_file_fp, char * results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, int *n_best_clsfs_ptr, int_list expand_list) { int length = 0, file_ac_version, float_p, str_length = 2 * sizeof( fxlstr); int header[2], header_length = 2 * sizeof( int), token_length; clsf_DS clsf, first_clsf = NULL, *seq = NULL; shortstr token1, token2; fxlstr line; char *str; char caller[] = "load_clsf_seq"; str = (char *) malloc( str_length); do { /* read comment lines */ fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &line, sizeof( char), header[1], results_file_fp); } while (line[0] == '#'); sscanf( line, "%s %s", token1, token2); /* strip of win/unx from ac_version, if present */ if (strstr( token2, "unx") || strstr( token2, "win")) { token_length = strlen( token2); token2[token_length - 3] = '\0'; } if ((eqstring( token1, "ac_version") != TRUE) || ((file_ac_version = atof_p( token2, &float_p)) < 1.0) || (float_p != TRUE)) { fprintf( stderr, "ERROR: expecting \"ac_version n.n\", found \"%s %s\" \n", token1, token2); abort(); } while (1) { clsf = load_clsf( results_file_fp, expand_p, want_wts_p, update_wts_p, length, first_clsf, file_ac_version, expand_list); if (clsf != NULL) { if (length == 0) first_clsf = clsf; length++; if (seq == NULL) seq = (clsf_DS *) malloc( length * sizeof(clsf_DS)); else seq = (clsf_DS *) realloc(seq, length * sizeof(clsf_DS)); seq[length - 1] = clsf; } else break; } *n_best_clsfs_ptr = length; safe_sprintf( str, str_length, caller, "ADVISORY: loaded %d classification%s from \n %s%s\n", length, (length == 1) ? "" : "s", (results_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, results_file_ptr); to_screen_and_log_file( str, G_log_file_fp, G_stream, TRUE); free( str); return(seq); } /* LOAD_CLSF 16mar95 wmt: add loading capability 16may95 wmt: converted binary i/o to ANSI Intended to read a compactly represented classification as written by write_clsf_DS, and to optionally expand it to standard form. Anything else, including non-compact classifications, are returned without modification. Compact classification are identified by the presence of a list of filenames in the database field, instead of a database structure. With Expand, Wts or Update_Wts regenerates the wts vectors. update_wts also updates wts. */ clsf_DS load_clsf( FILE *results_file_fp, int expand_p, int want_wts_p, int update_wts_p, int clsf_index, clsf_DS first_clsf, int file_ac_version, int_list expand_list) { clsf_DS clsf = NULL; shortstr token1; fxlstr line; int model_index; model_DS *models; int header[2], header_length = 2 * sizeof( int); char caller[] = "load_clsf"; fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CLASSIFICATION_TYPE, caller); if (header[1] > 0) { clsf = (clsf_DS) malloc( sizeof( struct classification)); fread( clsf, sizeof( char), header[1], results_file_fp); if (first_clsf == NULL) clsf->database = load_database_DS( clsf, results_file_fp, file_ac_version); else { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( line, sizeof( char), header[1], results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "database_DS_ptr") != TRUE) { fprintf( stderr, "ERROR: expecting \"database_DS_ptr\", found \"%s\"\n", line); abort(); } clsf->database = first_clsf->database; } if (first_clsf == NULL) models = (model_DS *) malloc( clsf->num_models * sizeof( model_DS)); else models = first_clsf->models; clsf->models = models; for (model_index=0; model_indexnum_models; model_index++) { if (first_clsf == NULL) models[model_index] = load_model_DS( clsf, model_index, results_file_fp, file_ac_version); else { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( line, sizeof( char), header[1], results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "model_DS_ptr") != TRUE) { fprintf( stderr, "ERROR: expecting \"model_DS_ptr\", found \"%s\"\n", line); abort(); } } } load_class_DS_s( clsf, clsf->n_classes, results_file_fp, (first_clsf == NULL) ? clsf : first_clsf, file_ac_version); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHECKPOINT_TYPE, caller); clsf->checkpoint = (chkpt_DS) malloc( sizeof( struct checkpoint)); fread( clsf->checkpoint, sizeof( char), header[1], results_file_fp); clsf->next = NULL; if ((expand_p == TRUE) && ((expand_list[0] == END_OF_INT_LIST) || ((expand_list[0] != END_OF_INT_LIST) && (member_int_list( clsf_index+1, expand_list) == TRUE)))) { expand_clsf( clsf, want_wts_p, update_wts_p); if(first_clsf && clsf && (first_clsf->models != clsf->models)) { first_clsf->models = clsf->models; } /* fprintf( stderr, "clsf index %d expanded\n", clsf_index); */ } } return (clsf); } /* LOAD_DATABASE_DS 16mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI 21jun95 wmt: initialize realloc'ed att_info load database_DS from results file - for first clsf only */ database_DS load_database_DS( clsf_DS clsf, FILE *results_file_fp, int file_ac_version) { int n_att, i; database_DS d_base; int header[2], header_length = 2 * sizeof( int); char caller[] = "load_database_DS"; fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], DATABASE_TYPE, caller); d_base = (database_DS) malloc( sizeof( struct database)); fread( d_base, sizeof( char), header[1], results_file_fp); d_base->att_info = (att_DS *) malloc( d_base->allo_n_atts * sizeof( att_DS)); if (d_base->n_atts > d_base->allo_n_atts) { d_base->allo_n_atts = d_base->n_atts; d_base->att_info = (att_DS *) realloc( d_base->att_info, d_base->allo_n_atts * sizeof( att_DS)); for (i=0; in_atts; i++) d_base->att_info[i] = NULL; } /* Ordered N-atts vector of att_DS describing the attributes. */ for (n_att=0; n_attn_atts; n_att++) load_att_DS( d_base, n_att, results_file_fp, file_ac_version); d_base->compressed_p = TRUE; return( d_base); } /* LOAD_ATT_DS 16mar95 wmt: new load att_DS from results file and allocate storage 16may95 wmt: converted binary i/o to ANSI */ void load_att_DS( database_DS d_base, int n_att, FILE *results_file_fp, int file_ac_version) { int i, *int_value, n_props; int header[2], header_length = 2 * sizeof( int); float *float_value; char *string_value, *token_ptr, caller[] = "load_att_DS"; fxlstr line, token1, token2, token3; att_DS att; discrete_stats_DS discrete_stats; fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], ATT_TYPE, caller); att = (att_DS) malloc(sizeof(struct att)); fread( att, sizeof( char), header[1], results_file_fp); d_base->att_info[n_att] = att; n_props = att->n_props; if (eqstring(att->type, "real")) { att->r_statistics = (real_stats_DS) malloc( sizeof( struct real_stats)); att->d_statistics = NULL; fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], REAL_STATS_TYPE, caller); fread( att->r_statistics, sizeof( char), header[1], results_file_fp); } else if (eqstring(att->type, "discrete")) { att->r_statistics = NULL; att->d_statistics = (discrete_stats_DS) malloc( sizeof( struct discrete_stats)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], DISCRETE_STATS_TYPE, caller); fread( att->d_statistics, sizeof( char), header[1], results_file_fp); discrete_stats = att->d_statistics; att->d_statistics->observed = (int *) malloc( discrete_stats->range * sizeof( int)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], INT_TYPE, caller); fread( discrete_stats->observed, sizeof( char), header[1], results_file_fp); } else if (eqstring( att->type, "dummy")) { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], DUMMY_STATS_TYPE, caller); att->r_statistics = NULL; att->d_statistics = NULL; att->props = NULL; att->translations = NULL; att->warnings_and_errors = NULL; } else { fprintf(stderr, "\nERROR: att_info->type %s not handled\n", att->type); abort(); } if (! eqstring( att->type, "dummy")) { att->n_props = 0; att->props = NULL; /* att->n_props is incremented by add_to_plist */ att->translations = NULL; if (att->n_trans > 0) { att->translations = (char **) malloc( att->n_trans * sizeof( char *)); for (i=0; i < att->n_trans; i++) { att->translations[i] = (char *) malloc( sizeof( shortstr)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( att->translations[i], sizeof( char), header[1], results_file_fp); } } /* props_DS - att->n_props is incremented by add_to_plist */ if (n_props > 0) { for (i=0; i < n_props; i++) { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &line, sizeof( char), header[1], results_file_fp); sscanf( line, "%s %s %s", token1, token2, token3); token_ptr = (char *) malloc( sizeof( shortstr)); strcpy( token_ptr, token1); if (eqstring( token2, "int") == TRUE) { int_value = (int *) malloc( sizeof( int)); *int_value = atoi( token3); add_to_plist( att, token_ptr, int_value, "int"); } else if (eqstring( token2, "flt") == TRUE) { float_value = (float *) malloc( sizeof( float)); *float_value = atof( token3); add_to_plist( att, token_ptr, float_value, "flt"); } else if (eqstring( token2, "str") == TRUE) { string_value = (char *) malloc( sizeof( shortstr)); strcpy( string_value, token3); add_to_plist( att, token_ptr, string_value, "str"); } else { fprintf( stderr, "property list type %s, not handled!\n", token2); abort(); } } } att->warnings_and_errors = (warn_err_DS) malloc( sizeof( struct warn_err)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], WARN_ERR_TYPE, caller); fread( att->warnings_and_errors, sizeof( char), header[1], results_file_fp); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &token1, sizeof( char), header[1], results_file_fp); if (eqstring( token1, "NULL") == TRUE) strcpy( att->warnings_and_errors->unspecified_dummy_warning, ""); else strcpy( att->warnings_and_errors->unspecified_dummy_warning, token1); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &token2, sizeof( char), header[1], results_file_fp); if (eqstring( token2, "NULL") == TRUE) strcpy( att->warnings_and_errors->single_valued_warning, ""); else strcpy( att->warnings_and_errors->single_valued_warning, token2); /* float *unused_translators_warning; discrete translations not implementated */ if (att->warnings_and_errors->num_expander_warnings > 0) att->warnings_and_errors->model_expander_warnings = (fxlstr *) malloc( att->warnings_and_errors->num_expander_warnings * sizeof( fxlstr)); else att->warnings_and_errors->model_expander_warnings = NULL; for (i=0; i < att->warnings_and_errors->num_expander_warnings; i++) { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &att->warnings_and_errors->model_expander_warnings[i], sizeof( char), header[1], results_file_fp); } if (att->warnings_and_errors->num_expander_errors > 0) att->warnings_and_errors->model_expander_errors = (fxlstr *) malloc( att->warnings_and_errors->num_expander_errors * sizeof( fxlstr)); else att->warnings_and_errors->model_expander_errors = NULL; for (i=0; i < att->warnings_and_errors->num_expander_errors; i++) { fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CHAR_TYPE, caller); fread( &att->warnings_and_errors->model_expander_errors[i], sizeof( char), header[1], results_file_fp); } } } /* LOAD_MODEL_DS 17mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI load model_DS from results file and allocate storage - first clsf only */ model_DS load_model_DS( clsf_DS clsf, int model_index, FILE *results_file_fp, int file_ac_version) { int header[2], header_length = 2 * sizeof( int); model_DS model; char caller[] = "load_model_DS"; model = (model_DS) malloc( sizeof( struct model)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], MODEL_TYPE, caller); fread( model, sizeof( char), header[1], results_file_fp); model->compressed_p = TRUE; model->database = clsf->database; /* since this is compressed model, set everything to null */ model->expanded_terms = FALSE; model->n_terms = model->num_priors = 0; model->terms = NULL; model->priors = NULL; model->n_att_locs = 0; model->att_locs = NULL; model->n_att_ignore_ids = 0; model->att_ignore_ids = NULL; model->class_store = NULL; model->num_class_store = 0; model->global_clsf = NULL; return( model); } /* LOAD_CLASS_DS_S 17mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI 13feb98 wmt: check for malloc/realloc failures load class_DS from results file and allocate storage */ void load_class_DS_s( clsf_DS clsf, int n_classes, FILE *results_file_fp, clsf_DS first_clsf, int file_ac_version) { int i, n_parm, n_class, file_model_file_index; int header[2], header_length = 2 * sizeof( int); class_DS *classes; char caller[] = "load_class_DS_s"; classes = (class_DS *) malloc( n_classes * sizeof( class_DS)); clsf->classes = classes; for (n_class=0; n_class < n_classes; n_class++) { classes[n_class] = (class_DS) malloc( sizeof( struct class)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], CLASS_TYPE, caller); fread( classes[n_class], sizeof( char), header[1], results_file_fp); classes[n_class]->tparms = (tparm_DS *) malloc( classes[n_class]->num_tparms * sizeof( tparm_DS)); for (n_parm=0; n_parmnum_tparms; n_parm++) { classes[n_class]->tparms[n_parm] = (tparm_DS) malloc( sizeof( struct new_term_params)); load_tparm_DS( classes[n_class]->tparms[n_parm], n_parm, results_file_fp, file_ac_version); } classes[n_class]->wts = (float *) malloc( classes[n_class]->num_wts * sizeof( float)); if (classes[n_class]->wts == NULL) { fprintf( stderr, "ERROR: load_class_DS_s: out of memory, malloc returned NULL!\n"); exit(1); } for (i=0; inum_wts; i++) classes[n_class]->wts[i] = 0.0; if (G_clsf_storage_log_p == TRUE) { fprintf( stdout, "\nload_class_DS: %p, num_wts %d, wts:%p, wts-len:%d\n", (void *) classes[n_class], classes[n_class]->num_wts, (void *) classes[n_class]->wts, classes[n_class]->num_wts * (int) sizeof( float)); if (G_n_freed_classes > 0) G_n_create_classes_after_free++; } fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], INT_TYPE, caller); fread( &file_model_file_index, sizeof( char), header[1], results_file_fp); for (i=0; inum_models; i++) { if (first_clsf->models[i]->file_index == file_model_file_index) { classes[n_class]->model = first_clsf->models[i]; break; } } classes[n_class]->next = NULL; } } /* LOAD_TPARM_DS 18jan95 wmt: new 16may95 wmt: converted binary i/o to ANSI read and allocate space for tparms */ void load_tparm_DS( tparm_DS tparm, int n_parm, FILE *results_file_fp, int file_ac_version) { int header[2], header_length = 2 * sizeof( int); char caller[] = "load_tparm_DS"; fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], TPARM_TYPE, caller); fread( tparm, sizeof( char), header[1], results_file_fp); tparm->collect = 0; tparm->wts = NULL; tparm->data = NULL; tparm->att_indices = NULL; tparm->datum = NULL; switch(tparm->tppt) { case SM: load_sm_params( &(tparm->ptype.sm), tparm->n_atts, results_file_fp, file_ac_version); break; case SN_CM: /* nothing to do load_sn_cm_params( &(tparm->ptype.sn_cm), results_file_fp, file_ac_version); */ break; case SN_CN: /* nothing to do load_sn_cn_params( &(tparm->ptype.sn_cn), results_file_fp, file_ac_version); */ break; case MM_D: load_mm_d_params( &(tparm->ptype.mm_d), tparm->n_atts, results_file_fp, file_ac_version); break; case MM_S: load_mm_s_params( &(tparm->ptype.mm_s), tparm->n_atts, results_file_fp, file_ac_version); break; case MN_CN: load_mn_cn_params( &(tparm->ptype.mn_cn), tparm->n_atts, results_file_fp, file_ac_version); break; default: printf("\n load_tparms_DS: unknown type of ENUM MODEL_TYPES =%d\n", tparm->tppt); abort(); } } /* LOAD_MM_D_PARAMS 17mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI load mm_d params from ascii file */ void load_mm_d_params( struct mm_d_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { /* int i, m; */ fprintf( stderr, "load_mm_d_params not converted from dump_mm_d_params\n"); abort(); /* fprintf(results_file_p, "mm_d_params\n"); */ /* for(i=0; isizes[i]; */ /* printf("row %d, size %d\n", i, m); */ /* fprintf(results_file_p, "wts\n"); */ /* write_vector_float(param->wts[i], m, results_file_p); */ /* fprintf(results_file_p, "probs\n"); */ /* write_vector_float(param->probs[i], m, results_file_p); */ /* fprintf(results_file_p, "log_probs\n"); */ /* write_vector_float(param->log_probs[i], m, results_file_p); */ /* } */ /* fprintf(results_file_p, "wts_vec\n"); */ /* write_vector_float(param->wts_vec, m, results_file_p); */ /* fprintf(results_file_p, "probs_vec\n"); */ /* write_vector_float(param->probs_vec, m, results_file_p); */ /* fprintf(results_file_p, "log_probs_vec\n"); */ /* write_vector_float(param->log_probs_vec, m, results_file_p); */ } /* LOAD_MM_S_PARAMS 17mar95 wmt: new load mm_s_params to ascii file -- incomplete */ void load_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { fprintf( stderr, "load_mm_s_params not converted from dump_mm_s_params\n"); abort(); /* fprintf(results_file_p, "mm_s_params\n"); */ /* fprintf(results_file_p, "count, wt, prob, log_prob\n"); */ /* fprintf(results_file_p, "%d %.7e %.7e %.7e\n", param->count, param->wt, param->prob, */ /* param->log_prob); */ } /* LOAD_MN_CN_PARAMS 17mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI load mn_cn_params to ascii file, allocating storage */ void load_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { int i, j; int header[2], header_length = 2 * sizeof( int); char caller[] = "load_mn_cn_params"; param->emp_means = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->emp_means, sizeof( char), header[1], results_file_fp); param->emp_covar = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; iemp_covar[i] = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->emp_covar[i], sizeof( char), header[1], results_file_fp); } param->means = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->means, sizeof( char), header[1], results_file_fp); param->covariance = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; icovariance[i] = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->covariance[i], sizeof( char), header[1], results_file_fp); } param->factor = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; ifactor[i] = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->factor[i], sizeof( char), header[1], results_file_fp); } param->values = (float *) malloc( n_atts * sizeof( float)); for (i=0; ivalues[i] = 0.0; param->temp_v = (float *) malloc( n_atts * sizeof( float)); for (i=0; itemp_v[i] = 0.0; param->temp_m = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; itemp_m[i] = (float *) malloc( n_atts * sizeof( float)); for (j=0; jtemp_m[i][j] = 0.0; } param->min_sigma_2s = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->min_sigma_2s, sizeof( char), header[1], results_file_fp); } /* LOAD_SM_PARAMS 17mar95 wmt: new 16may95 wmt: converted binary i/o to ANSI load sm_params to ascii file, allocating storage n_atts is actually n_vals -- an overloaded slot definition */ void load_sm_params( struct sm_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { int header[2], header_length = 2 * sizeof( int); char caller[] = "load_sm_params"; param->val_wts = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->val_wts, sizeof( char), header[1], results_file_fp); param->val_probs = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->val_probs, sizeof( char), header[1], results_file_fp); param->val_log_probs = (float *) malloc( n_atts * sizeof( float)); fread( &header, sizeof( char), header_length, results_file_fp); check_load_header( header[0], FLOAT_TYPE, caller); fread( param->val_log_probs, sizeof( char), header[1], results_file_fp); } autoclass-3.3.6.dfsg.1/prog/struct-data.c0000644000175000017500000003147011247310756016263 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ database_DS find_database( char *data_file_ptr, char *header_file_ptr, int n_data) { int i; database_DS temp; /* validate_data_header_model_files(0, data_file, header_file, 0); */ for (i=0; idata_file, data_file_ptr) == TRUE) && (eqstring(temp->header_file, header_file_ptr)) && ((temp->n_data == 0) || (temp->n_data == n_data))) return(temp); } return(NULL); } /* EVERY_DB_DS_SAME_SOURCE_P 18may95 wmt: do string compares for header_file & data_file This tests if two db's (compressed or expanded) come from the same source and have the same size. It is intended to allow avoiding having to reread a database. */ int every_db_DS_same_source_p( database_DS db1, model_DS *models) { int i, length; database_DS db2; if (models == NULL) length = 0; else length = sizeof( models) / sizeof(struct model); for (i=0; idatabase; if (((eqstring( db1->header_file, db2->header_file) != TRUE) || (eqstring( db1->data_file, db2->data_file) != TRUE) || (db1->n_data != db2->n_data))) return(FALSE); } return(TRUE); } database_DS compress_database( database_DS db) { /*if (compressed_db_DS_p(db))*/ return(db); } /* DB_DS_EQUAL_P 18may95 wmt: take out: if (db1 == db2) -- always false If two databases have the same number of data and attributes, and if all the attributes are equal-p. Note that attribute transformation may cause a database and a previously compressed version of that database to fail this test. */ int db_DS_equal_p( database_DS db1, database_DS db2) { int i; if ((db1->n_data == db2->n_data) && (db1->n_atts == db2->n_atts)) { for (i=0; in_atts; i++) if (att_DS_equal_p( db1->att_info[i], db2->att_info[i]) == FALSE) return(FALSE); return(TRUE); } return(FALSE); } /* Attribute predicates: */ /* ATT_DS_EQUAL_P 18may95 wmt: check for type = dummy */ int att_DS_equal_p( att_DS att1,att_DS att2) { int i, num; if (eqstring(att1->type, att2->type) == FALSE) return(FALSE); if (eqstring(att1->sub_type, att2->sub_type) == FALSE) return(FALSE); if (eqstring(att1->dscrp, att2->dscrp) == FALSE) return(FALSE); if (att1->n_props != att2->n_props) return(FALSE); if (eqstring(att1->type, "discrete") == TRUE) { if (att1->d_statistics->range != att2->d_statistics->range) return(FALSE); num = att1->d_statistics->n_observed; if (num != att2->d_statistics->n_observed) return(FALSE); for (i=0; id_statistics->observed[i] != att2->d_statistics->observed[i]) return(FALSE); } else if (eqstring(att1->type, "real") == TRUE) { if (att1->r_statistics->count != att2->r_statistics->count) return(FALSE); if (att1->r_statistics->mx != att2->r_statistics->mx) return(FALSE); if (att1->r_statistics->mn != att2->r_statistics->mn) return(FALSE); if (att1->r_statistics->mean != att2->r_statistics->mean) return(FALSE); if (att1->r_statistics->var != att2->r_statistics->var) return(FALSE); } return(TRUE); } /* CREATE_DATABASE 15nov94 wmt: initialize data and n_data 28nov94 wmt: initialize datum_length 21jun95 wmt: initialize att_info[] */ database_DS create_database(void) { database_DS temp; int i; temp = (database_DS) malloc(sizeof(struct database)); strcpy(temp->data_file, ""); strcpy(temp->header_file, ""); temp->data = NULL; temp->datum_length = NULL; temp->n_data = 0; temp->n_atts = 0; temp->allo_n_atts = 50; /* arbitrary start; bump by 1.5 when needed*/ temp->att_info = (att_DS *) malloc( temp->allo_n_atts * sizeof(att_DS)); for (i=0; iallo_n_atts; i++) temp->att_info[i] = NULL; temp->translations_supplied_p = NULL; temp->num_tsp = 0; temp->invalid_value_errors = NULL; temp->num_invalid_value_errors = 0; temp->incomplete_datum = NULL; temp->num_incomplete_datum = 0; return(temp); } /* EXPAND_DATABASE 22jan95 wmt: new 25apr95 wmt: allow binary data files 09may95 wmt: to handle partial databases, read G_s_params_n_data 27apr97 wmt: make error msg more informative 24oct97 wmt: use comp_database->n_data rather than G_s_params_n_data, since G_s_params_n_data does not do the right thing when expand_database is called from intf-reports.c if not found in global store, read data from disk */ database_DS expand_database( database_DS comp_database) { static fxlstr data_file, header_file; int valid_file_p = TRUE, reread_p = FALSE, num_models = 0; int n_att, n_prop, i; att_DS att; database_DS database; model_DS *models = NULL; FILE *header_file_fp, *log_file_fp = NULL; int exit_if_error_p = FALSE, silent_p = FALSE; data_file[0] = header_file[0] = '\0'; if (comp_database->compressed_p == FALSE) return (comp_database); if (validate_data_pathname( comp_database->data_file, &data_file, exit_if_error_p, silent_p) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname( "header", comp_database->header_file, &header_file, TRUE) != TRUE) valid_file_p = FALSE; if (valid_file_p == FALSE) exit(1); if ((database = find_database( data_file, header_file, comp_database->n_data)) != NULL) { return (database); } header_file_fp = fopen( header_file, "r"); /* pass stderr as stream arg to allow error msgs out */ database = read_database( header_file_fp, log_file_fp, data_file, header_file, comp_database->n_data, reread_p, stderr); fclose( header_file_fp); check_errors_and_warnings( database, models, num_models); /* Now check to see if comp_database has been expanded relative to database if ((database->n_atts != comp_database->n_atts) || (att_info_equal( database, comp_database) != TRUE)) extend_database( database, comp_database); */ if (db_same_source_p( database, comp_database) != TRUE) { fprintf( stderr, "ERROR: expand_database has db and comp_db from differing sources\n" " check: data_file_path, header_file_path, or n_data\n"); exit(1); } /* Check for equality of the input attributes, which preceed the transformed attributes, if any. */ if (att_info_equal( database, comp_database) != TRUE) { fprintf( stderr, "ERROR: expand_database found unmatched common attributes defs in " "<.results[-bin] file> and %s\n", header_file); exit(1); } /* free storage of compressed database */ for (n_att=0; n_attn_atts; n_att++) { att = comp_database->att_info[n_att]; if (att->n_props > 0) { for (n_prop=0; n_prop< att->n_props; n_prop++) { free( att->props[n_prop][0]); free( att->props[n_prop][1]); /* free( att->props[n_prop][2]); not freeable, points to static strings */ free( att->props[n_prop]); } free( att->props); } if (eqstring( att->type, "real")) free( att->r_statistics); else if (eqstring( att->type, "discrete")) free( att->d_statistics); if (att->n_trans > 0) { for (i=0; i< att->n_trans; i++) free( att->translations[i]); free( att->translations); } if (att->warnings_and_errors != NULL) free( att->warnings_and_errors); free( att); } free( comp_database->att_info); free( comp_database); return (database); } /* EXTEND_DATABASE 27feb95 wmt: new, from ac-x 06mar95 wmt: could not get this to work This extends the attributes of db to match the att_info of comp_db, which is presumed to have origionated from the same source via transformations. */ int extend_database( database_DS db, database_DS comp_db) { int db_match = TRUE, att_index, *att_list; shortstr transform; FILE *log_file_fp = NULL, *stream = NULL; att_DS att_i; if (db_same_source_p( db, comp_db) != TRUE) { fprintf( stderr, "ERROR: extend_database has db and comp_db from differing sources\n"); exit(1); } /* Check for equality of the input attributes, which preceed the transformed attributes. */ if (att_info_equal( db, comp_db) != TRUE) { fprintf( stderr, "ERROR: extend_database found unmatched common attributes in " "db and comp_db"); exit(1); } /* The problem is now to determine what transforms were made to comp_db, and in what order and then to reproduce these transformations on db */ for (att_index=db->n_atts; att_indexn_atts; att_index++) { att_i = comp_db->att_info[att_index]; strcpy( transform, att_i->sub_type); att_list = (int *) malloc( sizeof( int)); att_list[0] = *((int *) getf( att_i->props, "source", att_i->n_props)); find_transform( db, transform, att_list, 1, log_file_fp, stream); } if ((db->n_atts != comp_db->n_atts) || (att_info_equal( db, comp_db) != TRUE)) { fprintf( stderr, "ERROR: extend_database failed to produce corresponding attributes"); exit(1); } return( db_match); } /* DB_SAME_SOURCE_P 27feb95 wmt: new, from ac-x This tests if two db's (compressed or expanded) come from the same source and have the same size. It is intended to allow avoiding having to reread a database. */ int db_same_source_p( database_DS db, database_DS comp_db) { return (eqstring( db->header_file, comp_db->header_file) && eqstring( db->data_file, comp_db->data_file) && (db->n_data == comp_db->n_data)); } /* ATT_INFO_EQUAL 27feb95 wmt: new Checks the attribute type, subtype, relevant properties, description & statistics */ int att_info_equal( database_DS db1, database_DS db2) { int equal_p = TRUE, n_att; att_DS att_1, att_2; for (n_att=0; n_attn_atts, db2->n_atts); n_att++) { att_1 = db1->att_info[n_att]; att_2 = db2->att_info[n_att]; if ((att_props_equivalent_p( att_1, att_2) != TRUE) || (eqstring( att_1->dscrp, att_2->dscrp) != TRUE) || (att_stats_equivalent_p( att_1, att_2) != TRUE)) { equal_p = FALSE; break; } } return (equal_p); } /* ATT_PROPS_EQUIVALENT_P 01mar95 wmt: new Checks for for equality of those properties relevant to the attributes type & subtype, using *Att-type-data* to identify the relevant properties */ int att_props_equivalent_p( att_DS att_1, att_DS att_2) { int equal_p = TRUE, i; if ((eqstring( att_1->type, att_2->type) != TRUE) || (eqstring( att_1->sub_type, att_2->sub_type) != TRUE)) equal_p = FALSE; else { for (i=0; in_props, att_2->n_props); i++) if (((att_1->props[i][0] != att_2->props[i][0]) != TRUE) || /* target */ ((att_1->props[i][1] != att_2->props[i][1]) != TRUE) || /* value */ ((att_1->props[i][2] != att_2->props[i][2]) != TRUE)) { /* type */ equal_p = FALSE; break; } } return (equal_p); } /* ATT_STATS_EQUIVALENT_P 01mar95 wmt: new test for equality in attribute definition components */ int att_stats_equivalent_p( att_DS att_1, att_DS att_2) { int equal_p = TRUE, i; if (eqstring( att_1->type, "real")) { if (((att_1->r_statistics->count == att_2->r_statistics->count) != TRUE) || (percent_equal( (double) att_1->r_statistics->mx, (double) att_2->r_statistics->mx, REL_ERROR) != TRUE) || (percent_equal( (double) att_1->r_statistics->mn, (double) att_2->r_statistics->mn, REL_ERROR) != TRUE) || (percent_equal( (double) att_1->r_statistics->mean, (double) att_2->r_statistics->mean, REL_ERROR) != TRUE) || (percent_equal( (double) att_1->r_statistics->var, (double) att_2->r_statistics->var, REL_ERROR) != TRUE)) equal_p = FALSE; } else if (eqstring( att_1->type, "discrete")) { if (((att_1->d_statistics->range == att_2->d_statistics->range) != TRUE) || ((att_1->d_statistics->n_observed == att_2->d_statistics->n_observed) != TRUE)) equal_p = FALSE; for (i=0; id_statistics->n_observed, att_2->d_statistics->n_observed); i++) if ((att_1->d_statistics->observed[i] == att_2->d_statistics->observed[i]) != TRUE) { equal_p = FALSE; break; } } else if (eqstring( att_1->type, "dummy")) ; else { fprintf( stderr, "ERROR: att_type %s not handled\n", att_1->type); abort(); } return ( equal_p); } autoclass-3.3.6.dfsg.1/prog/autoclass.make.hp.cc0000644000175000017500000000216311247310756017510 0ustar areare### AUTOCLASS C MAKE FILE FOR HP-UX - bundled cc compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) CFLAGS = $(OSFLAGS) -Aa -O CC = cc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ ## depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/struct-matrix.c0000644000175000017500000000214111247310756016647 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ fptr *compute_factor( fptr *a, int n) /* does LU factorization of nxn matrix a in place.*/ { int i,j,k; for (k=0;k=0;i--) { for(j=i+1;j #include #include #include #include #ifndef _WIN32 #include #endif #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* COMPUTE_INFLUENCE_VALUES 03feb95 wmt: new create & fill arrays of classification attribute influence values */ void compute_influence_values( clsf_DS clsf) { int n_atts = clsf->database->n_atts, n_classes = clsf->n_classes, n_class, n_att; class_DS class; rpt_DS reports = clsf->reports; float curr_influence_value, class_influence_value_max, influence_sum, global_influence_value_max = 0.0, *influence_sums, *all_classes_influence_value_max; char *att_type; void *influence_struct_DS = NULL, **attribute_array; influence_sums = (float *) malloc( n_atts * sizeof( float)); all_classes_influence_value_max = (float *) malloc( n_atts * sizeof( float)); for (n_att=0; n_attclasses[n_class]; if (class->w_j >= clsf->min_class_wt) { attribute_array = (void **) malloc( n_atts * sizeof( void *)); influence_sum = 0.0; class_influence_value_max = 0.0; for (n_att=0; n_att all_classes_influence_value_max[n_att]) all_classes_influence_value_max[n_att] = curr_influence_value; if (curr_influence_value > class_influence_value_max) class_influence_value_max = curr_influence_value; if (curr_influence_value > global_influence_value_max) global_influence_value_max = curr_influence_value; } class->max_i_value = class_influence_value_max; class->i_values = attribute_array; class->i_sum = influence_sum; /* printf("n_class %d, class_influence_value_max %f, influence_sum %f\n", n_class, class_influence_value_max, influence_sum); */ } } reports->max_i_value = global_influence_value_max; reports->att_max_i_values = all_classes_influence_value_max; reports->att_i_sums = influence_sums; reports->att_max_i_sum = 0.0; for (n_att=0; n_att reports->att_max_i_sum) reports->att_max_i_sum = influence_sums[n_att]; /* printf( "max_i_value %f, att_max_i_sum %f\n", reports->max_i_value, reports->att_max_i_sum); for (n_att=0; n_attatt_max_i_values[n_att], reports->att_i_sums[n_att]); */ } /* INFLUENCE_VALUE 06feb95 wmt: new 27apr97 wmt: do not process attribute values which have null translations. this occurs when user supplies excessive an excessive range value in .hd2, and ignores warning to correct it. Compute influence value for real or discrete attribute */ double influence_value( int n_class, int n_att, clsf_DS clsf, char *att_type, void **influence_struct_DS_ptr) { class_DS class = NULL; int term_index, n_att_prob_list, n_term_att_list = 0; tparm_DS term_params; float influence_value, *class_div_global_att_prob_list = NULL; float class_mean, class_sigma, class_known_prob, global_mean, global_sigma; float global_known_prob, *term_att_list = NULL, **class_covar = NULL; i_discrete_DS i_discrete_struct; i_real_DS i_real_struct; att_DS att; /* int i; */ term_index = find_attribute_modeling_class( clsf, n_class, n_att, &class); if (class == NULL) { *influence_struct_DS_ptr = NULL; return (0.0); } else { term_params = class->tparms[term_index]; switch(term_params->tppt) { case MN_CN: mn_cn_params_influence_fn( class->model, term_params, term_index, n_att, &influence_value, &class_mean, &class_sigma, &global_mean, &global_sigma, &term_att_list, &n_term_att_list, &class_covar); break; case SM: sm_params_influence_fn( class->model, term_params, term_index, n_att, &influence_value, &class_div_global_att_prob_list, &n_att_prob_list); break; case SN_CM: sn_cm_params_influence_fn( class->model, term_params, term_index, n_att, &influence_value, &class_mean, &class_sigma, &class_known_prob, &global_mean, &global_sigma, &global_known_prob); break; case SN_CN: sn_cn_params_influence_fn( class->model, term_params, term_index, n_att, &influence_value, &class_mean, &class_sigma, &global_mean, &global_sigma); break; case MM_D: case MM_S: default: fprintf(stderr, "ERROR: unknown type of ENUM MODEL_TYPES in influence_value: %d\n", term_params->tppt); abort(); } /* Use the magnitude of the cross entropy value */ influence_value = (float) fabs( (double) influence_value); if (eqstring( att_type, "discrete") == TRUE) { i_discrete_struct = (i_discrete_DS) malloc( sizeof( struct i_discrete)); i_discrete_struct->influence_value = influence_value; att = clsf->database->att_info[n_att]; if (n_att_prob_list > att->n_trans) n_att_prob_list = att->n_trans; i_discrete_struct->n_p_p_star_list = 3 * n_att_prob_list; i_discrete_struct->p_p_star_list = class_div_global_att_prob_list; *influence_struct_DS_ptr = (void *) i_discrete_struct; /* printf( "n_class %d, n_att %d, influence_value %f n_att_prob_list %d\n", n_class, n_att, influence_value, n_att_prob_list); for (i=0;iinfluence_value = influence_value; i_real_struct->mean_sigma_list = (float *) malloc( ((term_params->tppt == SN_CM) ? 6 : 4) * sizeof( float)); i_real_struct->n_mean_sigma_list = (term_params->tppt == SN_CM) ? 6 : 4; i_real_struct->mean_sigma_list[0] = class_mean; i_real_struct->mean_sigma_list[1] = class_sigma; i_real_struct->mean_sigma_list[2] = global_mean; i_real_struct->mean_sigma_list[3] = global_sigma; if (term_params->tppt == SN_CM) { i_real_struct->mean_sigma_list[4] = class_known_prob; i_real_struct->mean_sigma_list[5] = global_known_prob; } i_real_struct->n_term_att_list = n_term_att_list; i_real_struct->class_covar = class_covar; i_real_struct->term_att_list = term_att_list; *influence_struct_DS_ptr = (void *) i_real_struct; } else { fprintf( stderr, "influence_value called with unknown attribute_type: %s\n", att_type); abort(); } } return (influence_value); } /* FIND_ATTRIBUTE_MODELING_CLASS 06feb95 wmt: new attribute model is in the class */ int find_attribute_modeling_class( clsf_DS clsf, int n_class, int n_att, class_DS *class_ptr) { class_DS class; class = clsf->classes[n_class]; *class_ptr = class; return ( atoi( class->model->att_locs[n_att])); } autoclass-3.3.6.dfsg.1/prog/autoclass.c0000644000175000017500000002107011247310756016021 0ustar areare#include #include #include #include #include #ifndef _MSC_VER #include #endif #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* MAIN 06oct94 wmt: initial revisions 24jan95 wmt: revised arguments 18may95 wmt: added "-predict" mode 02dec98 wmt: initialize log file for "-predict" mode main entry for module autoclass search, reports, and prediction # show args % autoclass # autoclass search % autoclass -search <..>.db2[-bin] <..>.hd2 <..>.model <..>.s-params # autoclass reports % autoclass -reports <..>.results[-bin] <..>.search <..>.r-params # autoclass prediction % autoclass -predict .db2 .results[-bin] .search .r-params */ int main( int argc, char *argv[]) { char *data_file_arg_ptr, *header_file_arg_ptr, *model_file_arg_ptr, *search_params_file_arg_ptr, *reports_params_file_arg_ptr, *ac_option, *results_file_arg_ptr, *search_file_arg_ptr; static fxlstr data_file, header_file, model_file, search_params_file, search_file, results_file, log_file, reports_params_file; static fxlstr influ_vals_file, xref_class_file, xref_case_file, test_data_file; int valid_file_p = TRUE, num_search_args = 6, num_reports_args = 5; int exit_if_error_p = FALSE, silent_p = FALSE, num_predict_args = 6; data_file[0] = header_file[0] = model_file[0] = search_params_file[0] = '\0'; search_file[0] = results_file[0] = log_file[0] = '\0'; reports_params_file[0] = influ_vals_file[0] = xref_class_file[0] = '\0'; xref_case_file[0] = test_data_file[0] = '\0'; init(); switch (argc) { case 1 : fprintf( stdout, "\n\nAUTOCLASS C (version %s)\n", G_ac_version); autoclass_args(); /* show arg options */ exit(0); break; case 5 : case 6 : ac_option = argv[1]; if ((eqstring( ac_option, "-search") != TRUE) && (eqstring( ac_option, "-reports") != TRUE) && (eqstring( ac_option, "-predict") != TRUE)) { fprintf( stderr, "ERROR: the second argument must be \"-search\"," " \"-reports\", or \"-predict\"\n"); exit(1); } break; default : fprintf (stderr, "\nERROR: invalid number of arguments for \"autoclass\" \n"); autoclass_args(); exit(1); break; } if (eqstring( ac_option, "-search") == TRUE) { if (argc != num_search_args) { fprintf (stderr, "\nERROR: invalid number of arguments for \"autoclass -search\" \n"); autoclass_args(); exit(1); } data_file_arg_ptr = argv[2]; header_file_arg_ptr = argv[3]; model_file_arg_ptr = argv[4]; search_params_file_arg_ptr = argv[5]; if (validate_data_pathname( data_file_arg_ptr, &data_file, exit_if_error_p, silent_p) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname("header", header_file_arg_ptr, &header_file, TRUE) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname("model", model_file_arg_ptr, &model_file, TRUE) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname("search params", search_params_file_arg_ptr, &search_params_file, TRUE) != TRUE) valid_file_p = FALSE; if (valid_file_p == FALSE) { autoclass_args(); exit(1); } /* make .log, .search & .results pathnames, using search_params_file_arg_ptr for path */ make_and_validate_pathname( "log", search_params_file_arg_ptr, &log_file, FALSE); make_and_validate_pathname( "search", search_params_file_arg_ptr, &search_file, FALSE); make_and_validate_pathname( "results", search_params_file_arg_ptr, &results_file, FALSE); autoclass_search( data_file, header_file, model_file, search_params_file, search_file, results_file, log_file); } else if (eqstring( ac_option, "-reports") == TRUE) { if (argc != num_reports_args) { fprintf (stderr, "\nERROR: invalid number of arguments for \"autoclass -reports\" \n"); autoclass_args(); exit(1); } results_file_arg_ptr = argv[2]; search_file_arg_ptr = argv[3]; reports_params_file_arg_ptr = argv[4]; if (validate_results_pathname( results_file_arg_ptr, &results_file, "results", exit_if_error_p, silent_p) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname( "search", search_file_arg_ptr, &search_file, TRUE) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname( "reports params", reports_params_file_arg_ptr, &reports_params_file, TRUE) != TRUE) valid_file_p = FALSE; if (valid_file_p == FALSE) { autoclass_args(); exit(1); } /* make .influ-textn, .class-text-n & .case-text-n pathnames, using reports_params_file_arg_ptr for path */ make_and_validate_pathname("influ_vals", reports_params_file_arg_ptr, &influ_vals_file, FALSE); make_and_validate_pathname("xref_class", reports_params_file_arg_ptr, &xref_class_file, FALSE); make_and_validate_pathname("xref_case", reports_params_file_arg_ptr, &xref_case_file, FALSE); make_and_validate_pathname("rlog", reports_params_file_arg_ptr, &log_file, FALSE); autoclass_reports( results_file, search_file, reports_params_file, influ_vals_file, xref_class_file, xref_case_file, test_data_file, log_file); } else { /* -predict */ if (argc != num_predict_args) { fprintf (stderr, "\nERROR: invalid number of arguments for \"autoclass -predict\" \n"); autoclass_args(); exit(1); } data_file_arg_ptr = argv[2]; results_file_arg_ptr = argv[3]; search_file_arg_ptr = argv[4]; reports_params_file_arg_ptr = argv[5]; if (validate_data_pathname( data_file_arg_ptr, &test_data_file, exit_if_error_p, silent_p) != TRUE) valid_file_p = FALSE; if (validate_results_pathname( results_file_arg_ptr, &results_file, "results", exit_if_error_p, silent_p) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname( "search", search_file_arg_ptr, &search_file, TRUE) != TRUE) valid_file_p = FALSE; if (make_and_validate_pathname( "reports params", reports_params_file_arg_ptr, &reports_params_file, TRUE) != TRUE) valid_file_p = FALSE; if (valid_file_p == FALSE) { autoclass_args(); exit(1); } /* make .class-text-n & .case-text-n pathnames, using data_file_arg_ptr for path */ make_and_validate_pathname("xref_class", data_file_arg_ptr, &xref_class_file, FALSE); make_and_validate_pathname("xref_case", data_file_arg_ptr, &xref_case_file, FALSE); make_and_validate_pathname("log", reports_params_file_arg_ptr, &log_file, FALSE); autoclass_reports( results_file, search_file, reports_params_file, influ_vals_file, xref_class_file, xref_case_file, test_data_file, log_file); } return(0); } /* AUTOCLASS_ARGS 10oct94 wmt: new 24jan95 wmt: name changed from autoclass_search_args 18may95 wmt: added "-predict" mode output argument options to autoclass */ void autoclass_args () { #ifdef _WIN32 char operate[] = "> Autoclass.exe"; #else char operate[] = "> autoclass"; #endif fprintf (stderr, "\n AutoClass Search: " "\n %s -search <.db2[-bin] file path> <.hd2 file path>" "\n <.model file path> <.s-params file path> \n", operate); fprintf (stderr, "\n AutoClass Reports: " "\n %s -reports <.results[-bin] file path> " "<.search file path> " "\n <.r-params file path> \n", operate); fprintf (stderr, "\n AutoClass Prediction: " "\n %s -predict " "\n " "\n " " \n\n", operate); } autoclass-3.3.6.dfsg.1/prog/utils-math.c0000644000175000017500000001064411247310756016117 0ustar areare#include #include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* LOG_GAMMA 03nov94 wmt: move into separate file 19dec94 wmt: return double rather than float */ double log_gamma( double x, int low_precision) { if (x > 3.0) { if (low_precision == TRUE) return(((x - 0.5) * (safe_log(x))) + (-1.0 * x) + 0.9189385332046727 + /* log(sqrt(2*pi)) */ (0.08333333333333333 / x) + /* (1/12) / x */ /* -(1/360 / x^3) */ (-1.0 * (0.002777777777777778 / (x*x*x)))); else return(((x - 0.5) * (safe_log(x))) + (-1.0 * x) + 0.9189385332046727 + /* log(sqrt(2*pi)) */ (0.08333333333333333 / x) + /* (1/12) / x */ /* -(1/360 / x^3) */ (-1.0 * (0.002777777777777778 / (x*x*x))) + (0.00007936507936507937 / (x*x*x*x*x)) + /* (1/1260 / x^5) */ /* -(1/1680 / x^7) */ (0.00005952380952380953 / pow( x, 7))); } if ((x == 1.0) || (x == 2.0)) return(0.0); if (x > 0.0) return(log_gamma( 3.0 + x, low_precision ) - safe_log((double) (x * (1.0 + x) * (2.0 + x)))); fprintf( stderr, "Attempted to take log_gamma %20.15f\n", x); return 0.0; /*this is not any good but must return something*/ } /* ATOI_P 01dec94 wmt: new convert string to integer - set integer_p FALSE is not an integer since: > atoi("1@3"); => (int) 1 and > atoi("103"); => (int) 103 all characters in the token must be validated */ int atoi_p (char *string_num, int *integer_p_ptr) { int num = 0, i, string_length, a_char; string_length = strlen(string_num); *integer_p_ptr = TRUE; for (i=0; i= '0') && (a_char <= '9')) continue; if ((a_char == '+') || (a_char == '-')) continue; *integer_p_ptr = FALSE; break; } if (string_length == 0) *integer_p_ptr = FALSE; if (*integer_p_ptr == TRUE) num = atoi(string_num); return(num); } /* ATOF_P 01dec94 wmt: new convert string to float - set float_p FALSE if not an float */ double atof_p (char *string_num, int *float_p_ptr) { double num = 0.0; int a_char, string_length, i; string_length = strlen(string_num); *float_p_ptr = TRUE; for (i=0; i= '0') && (a_char <= '9')) continue; if ((a_char == '+') || (a_char == '-') || (a_char == '.') || (a_char == 'e') || (a_char == 'E')) continue; *float_p_ptr = FALSE; break; } if (string_length == 0) *float_p_ptr = FALSE; if (*float_p_ptr == TRUE) num = atof(string_num); return(num); } /* SAFE_EXP 14feb95 wmt: new Satisfies attempts to take an out-of-bounds exponent, by providing limiting values. */ double safe_exp( double x) { if (x >= MOST_POSITIVE_LONG_LOG) return (MOST_POSITIVE_LONG_FLOAT); else if (x <= LEAST_POSITIVE_LONG_LOG) return (0.0); else return (exp( x)); } /* MEAN_AND_VARIANCE 27mar95 wmt: new compute mean and variance of a vector of doubles, using direct variance calculation to prevent numerical problems */ void mean_and_variance( double *vector, int cnt, double *mean_ptr, double *variance_ptr) { double sum = 0.0, sum_sq = 0.0, double_mean; int i; for (i=0; i= MOST_POSITIVE_SINGLE_FLOAT) return (MOST_POSITIVE_SINGLE_LOG); else if (x <= LEAST_POSITIVE_SINGLE_FLOAT) return( LEAST_POSITIVE_SINGLE_LOG ); else return (log( x)); } autoclass-3.3.6.dfsg.1/prog/autoclass.make.tc0000644000175000017500000000243111247310756017121 0ustar areare### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### # CFLAGS = -ansi -g # debugging info -g needed by TestCenter CFLAGS = -ansi -g -O # optimize # CFLAGS = -ansi # /source/kronos/src0/CenterLine.Eval/CenterLine/bin/proof \ # CC = /source/kronos/src0/CenterLine/bin/clcc # CodeCenter C compiler # will not work unless CenterLine libraries and include files are used CC = gcc # GNU C compiler DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass.tc: $(OBJS) /usr/local2/CenterLine/bin/proof \ $(CC) $(CFLAGS) -o autoclass.tc $(OBJS) -lm -lc %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/search-basic.c0000644000175000017500000003174011247310756016354 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* GENERATE_CLSF 15nov94 wmt: pass n_data = 0, rather than 300 to read_database; use apply_search_start_fn 25apr95 wmt: add binary data file capability 20may95 wmt: added G_prediction_p Generates and returns a classification from data-base and model(s). Checks for data-base and model(s) already available in *db-list* and *model-list* and if found uses them, otherwise it generates data-base and model(s) by combining information from data-file, header-file, and model-file. Optionally initializes the classification using the start-fn. N-CLASSES is the initial value for the initialized classification. DATA-FILE is fully qualified pathname (file type forced to *data-file-type*). HEADER-FILE can be omitted (gets the same file name as data-file), be a file name (gets the same root as data-file), or be a fully qualified pathname. In all cases, the file type is forced to *header-file-type*. MODEL-FILE has same behavior as header-file. The file type is forced to *model-file-type*. LOG-FILE-P can be nil (no log file produced). If t the file type is forced to be *log-file-type*. OUTPUT-FILES-DEFAULT (names the log file) can be omitted (gets the same root as data-file and file-name of && , be a file name (gets the same root as data-file), or be a fully qualified pathname. (REREAD_P T) forces a re-read of data-file, and (REGENERATE_P T) forces a re-generation of the model(s) even if they are found in *db-list* and *model-list*. CLSF-INIT-FUN specifies the classification initialization function. (DISPLAY-WTS T) will display class weights produced by the initialization. *package* is bound for interns by read. */ clsf_DS generate_clsf( int n_classes, FILE *header_file_fp, FILE *model_file_fp, FILE *log_file_fp, FILE *stream, int reread_p, int regenerate_p, char *data_file_ptr, char *header_file_ptr, char *model_file_ptr, char *log_file_ptr, int restart_p, char *start_fn_type, unsigned int initial_cycles_p, int max_data, int start_j_list_from_s_params) { int total_error_cnt, total_warning_cnt, num_models = 0; clsf_DS clsf; database_DS db; model_DS *models; int expand_p = FALSE; /* read_database & read_model_file are done here because jtp designed it this way, moving them to process_data_header_model_files breaks the program - wmt */ log_header(log_file_fp, stream, data_file_ptr, header_file_ptr, model_file_ptr, log_file_ptr); db = read_database( header_file_fp, log_file_fp, data_file_ptr, header_file_ptr, max_data, reread_p, stream); models = read_model_file(model_file_fp, log_file_fp, db, regenerate_p, expand_p, stream, &num_models, model_file_ptr); process_data_header_model_files( log_file_fp, regenerate_p, stream, db, models, num_models, &total_error_cnt, &total_warning_cnt); if ((G_prediction_p == FALSE) && (start_j_list_from_s_params == FALSE) && (restart_p == FALSE) && (db->n_data > 1000)) { to_screen_and_log_file("\nWARNING: the default start_j_list may not find the correct\n" " number of classes in your data set!\n", log_file_fp, stream, TRUE); total_warning_cnt++; } check_stop_processing( total_error_cnt, total_warning_cnt, log_file_fp, stderr); if ((G_prediction_p == TRUE) && (G_training_clsf != NULL)) models = G_training_clsf->models; clsf = set_up_clsf( n_classes, db, models, num_models); if ((restart_p == FALSE) && (G_prediction_p == FALSE)) apply_search_start_fn (clsf, start_fn_type, initial_cycles_p, n_classes, log_file_fp, stream); return(clsf); } /* RANDOM_SET_CLSF 10nov94 wmt: initialize n_used_list 05jan95 wmt: upgrade calculations to ac-x code 17may95 wmt: Convert from srand/rand to srand48/lrand48 This resets the classification 'clsf to size n-classes and uses choose-random-prototype to randomly initializes any added classes, or all classes with 'random-start. Classes are initialized by choosing one class as a prototype and adding 1% from two others for variance. The function modifies the classification and returns the number of classes and the marginal probability. */ int random_set_clsf( clsf_DS clsf, int n_classes, int delete_duplicates, int display_wts, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream) { int n, i, n_data, index_0, *used_list, n_used_list = 0, num_atts = 0, n_others; int index_1, *used_cls_list, n_used_cls_list = 0, m; class_DS *classes, class; float proto_wt, wt_0, wt_1; n_classes = max(1, min((int) ceil((double) n_classes), clsf_DS_max_n_classes(clsf))); adjust_clsf_DS_classes(clsf, n_classes); n_data = clsf->database->n_data; for (i=0; idatabase->n_atts; i++) if (eqstring( clsf->database->att_info[i]->type, "dummy") != TRUE) num_atts++; classes = clsf->classes; proto_wt = (float) n_data / n_classes; wt_0 = 0.95 * proto_wt; n_others = min( num_atts, (int) ceil( (double) (n_data / 2))); wt_1 = (0.05 * proto_wt) / n_others; used_list = (int *) malloc( n_classes * sizeof(int)); used_cls_list = (int *) malloc( (n_others + 1) * sizeof(int)); for (i=0; iwts = fill( class->wts, 0.0, class->num_wts, n_data); class->wts[index_0] = wt_0; for (m=0; mwts[index_1] = wt_1; } /* printf("\n"); */ /* for (i=0; inum_wts; i++) */ /* printf("%f ", class->wts[i]); */ class->w_j = max( proto_wt, clsf->min_class_wt); } free(used_list); free(used_cls_list); return(initialize_parameters(clsf, display_wts, delete_duplicates, initial_cycles_p, log_file_fp, stream)); } /* SET_UP_CLSF Sets up a classification-DS of n-classes, using the model-set and database. If the model-set contains two or more model-DS, the classes are evenly distributed between them. The classification is not initialized. Use one of the *start-fn-list* functions. 10apr97 wmt: add database->n_data to get_class_DS & copy_class_DS calls */ clsf_DS set_up_clsf( int n_classes, database_DS database, model_DS *model_set, int n_models) { int i, n_class; class_DS *classes; clsf_DS clsf; int force_p = FALSE, want_wts_p = TRUE, check_model_p = TRUE; FILE *log_file_p = NULL; for (i=0; iclasses; if (every_db_DS_same_source_p(database, model_set) == FALSE) { fprintf(stderr, "ERROR: Model has database other than the current database.\n"); abort(); } /* Get a set of prototype classes */ for (n_class=0; n_classn_data, want_wts_p, check_model_p); /* Fill remaining class slots. */ for (n_class=n_models; n_classn_data, want_wts_p); clsf->database = database; clsf->num_models = n_models; clsf->models = model_set; clsf->min_class_wt = max(ABSOLUTE_MIN_CLASS_WT, (MIN_CLASS_WT_FACTOR * (float) database->n_data) / (float) n_classes); return(clsf); } /* BLOCK_SET_CLSF 15dec94 wmt: added params FILE *log_file_fp, FILE *stream 05jan95 wmt: upgrade to ac-x code 22mar95 wmt: ceil -> floor for n_classes & block_size; only full blocks Initalize a classification by dividing the database into sequential blocks, assigning each block to a class, and updating the class parameters. Blocks are are specified by giving n-classes or block-size (which overides n-classes when specified). This is useful for checking the `correct' classification in artifical databases formed of blocks of data generated from some model such as those implimented through #'gen-formatted-data. The function modifies the classification and returns the number of classes and the marginal probability. */ void block_set_clsf( clsf_DS clsf, int n_classes, int block_size, int delete_duplicates, int display_wts, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream) { int i, n_class, n_data, base, limit, num_wts, count; float *wts; class_DS class, *classes; n_data = clsf->database->n_data; base = 0; limit = 0; if (block_size != 0) { block_size = max(1, min(block_size, n_data)); n_classes = (int) floor((double) ((float) n_data / (float) block_size)); } else if (n_classes != 0) { n_classes = max(1, min(n_classes, clsf_DS_max_n_classes( clsf))); block_size = (int) floor((double) ((float) n_data / (float) n_classes)); } else { n_classes = clsf->n_classes; block_size = (int) floor((double) ((float) n_data / (float) n_classes)); } adjust_clsf_DS_classes(clsf, n_classes); classes = clsf->classes; for (n_class = 0; n_class < n_classes; n_class++) { class = classes[n_class]; num_wts = class->num_wts; wts = class->wts; count = 0; limit = min( limit + block_size, n_data); for (i=0; i= limit)) wts[i] = 0.0; else { wts[i] = 1.0; count++; } class->w_j = max( clsf->min_class_wt, (float) count); base += count; } initialize_parameters(clsf, display_wts, delete_duplicates, initial_cycles_p, log_file_fp, stream); } /* INITIALIZE_PARAMETERS 15dec94 wmt: add params FILE *log_file_fp, FILE *stream, exit for "Too many classes for available data" This is to be applied to a classification which has just had its wts reset. It updates the parameters and does two base-cycle's to assure that the weights and parameters are in approximate agreement. With delete-duplicates, it then eliminates any obvious duplicates. */ int initialize_parameters( clsf_DS clsf, int display_wts, int delete_duplicates, unsigned int initial_cycles_p, FILE *log_file_fp, FILE *stream) { int converge_cycle_p = FALSE; if (clsf->n_classes > clsf_DS_max_n_classes(clsf)) { to_screen_and_log_file("ERROR: too many classes for available data!!!\n", log_file_fp, stream, TRUE); exit(1); } clsf->log_p_x_h_pi_theta = 0.0; update_parameters(clsf); update_ln_p_x_pi_theta(clsf, FALSE); update_approximations(clsf); if (display_wts == TRUE) display_step( clsf, stream); if (initial_cycles_p == TRUE) { base_cycle(clsf, stream, display_wts, converge_cycle_p); if (delete_duplicates == TRUE) while (class_duplicatesp(clsf->n_classes, clsf->classes) == TRUE) { if (stream != NULL) fprintf( stream, "Before class_duplicatesp, n_classes is %d\n", clsf->n_classes); clsf->classes = delete_class_duplicates(&(clsf->n_classes), clsf->classes); if (stream != NULL) fprintf( stream, "After class_duplicatesp, n_classes is %d\n", clsf->n_classes); base_cycle(clsf, stream, display_wts, converge_cycle_p); } } return(clsf->n_classes); } class_DS *delete_class_duplicates( int *num, class_DS *classes) { int i, j, k, newlength = *num; for (i=0; i #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* READ_MODEL_FILE 02dec94 wmt: pass in model_file_ptr 04dec94 wmt: read model file in line format, rather than list format This reads a list of model groups from a .model file, and hands it on to Define-Models for processing (with the model-file as its source). :Expand_P t means to call #'Expand-Model-Terms, creating a useable model. :Regenerate_P t means to regenerate any previous versions of these models that are found in *Model-List*. */ model_DS *read_model_file( FILE *model_file_fp, FILE *log_file_fp, database_DS d_base, int regenerate_p, int expand_p, FILE *stream, int *newlength, char *model_file_ptr) { char ****model_groups = NULL, ***model_group; int length = 0, *size, **sizes = NULL, *num_groups = NULL, num, i, j, k; model_DS *models; int first_read = TRUE, str_length = 2 * sizeof( fxlstr); char caller[] = "read_model_file", *str; str = (char *) malloc( str_length); length = 0; while ((model_group = read_model_doit(model_file_fp, &size, &num, length, first_read, log_file_fp, stream)) != NULL) { length++; if (model_groups == NULL) { model_groups = (char ****) malloc(length * sizeof(char ***)); sizes = (int **) malloc(length * sizeof(int *)); num_groups = (int *) malloc(length * sizeof(int)); } else { model_groups = (char ****) realloc(model_groups, length * sizeof(char ***)); sizes = (int **) realloc(sizes, length * sizeof(int *)); num_groups = (int *) realloc(num_groups, length * sizeof(int)); } model_groups[length - 1] = model_group; sizes[length - 1] = size; num_groups[length - 1] = num; } models = define_models(model_groups, d_base, model_file_ptr, stream, expand_p, regenerate_p, length, newlength, num_groups, sizes, log_file_fp); safe_sprintf( str, str_length, caller, "ADVISORY[3]: read %d model def%s from \n %s%s\n", length, (length > 1) ? "s" : "", (model_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, model_file_ptr); to_screen_and_log_file( str, log_file_fp, stream, TRUE); for (i=0; i " ", \n read %d: %s %s %s %s\n", size, list[0], (size >= 2) ? list[1] : "", (size >= 3) ? list[2] : "", (size >= 4) ? "<...>" : ""); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } if (eqstring(list[0], "model_index") != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: expected model_index, read %s\n", list[0]); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } model_index_read = atoi_p(list[1], &integer_p); if (integer_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: model index read, %s, was not an integer\n", list[1]); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } if (model_index_read != model_index) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: expected model index %d, read %d\n", model_index, model_index_read); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } num_model_def_lines = atoi_p(list[2], &integer_p); if (integer_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: num model definition lines read, %s, was not an integer\n", list[2]); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } for (i=0; i 0) { *num += 1; if (big_list == NULL) { big_list = (char ***) malloc(*num * sizeof(char **)); *sizes = (int *) malloc(*num * sizeof(int)); } else { big_list = (char ***) realloc(big_list, *num * sizeof(char **)); *sizes = (int *) realloc(*sizes, *num * sizeof(int)); } big_list[*num - 1] = list; (*sizes)[*num - 1] = size - 1; } } if (*num < num_model_def_lines) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: expected %d model definition lines, only read %d\n", num_model_def_lines, *num); to_screen_and_log_file( str, log_file_fp, stream, TRUE); exit(1); } } return(big_list); } char ***read_lists( FILE *stream, int **sizes, int *num) { int c, size; char **list, ***big_list = NULL; *num = 0; while ( ((c = fgetc(stream)) != EOF) && (c !='(' )); if (c == EOF) return(NULL); while (( list = read_list(stream, &size) ) != NULL) { *num += 1; if (big_list == NULL) { big_list = (char ***) malloc(*num * sizeof(char **)); *sizes = (int *) malloc(*num * sizeof(int)); } else { big_list = (char ***) realloc(big_list, *num * sizeof(char **)); *sizes = (int *) realloc(*sizes, *num * sizeof(int)); } big_list[*num - 1] = list; (*sizes)[*num - 1] = size - 1; } while ( ((c = fgetc(stream)) != EOF) && ( c !=')' ) ); return(big_list); } char **read_list( FILE *stream, int *num) /* modified to store left and right parens in list if reads a list containing list so typically returnx xxxmodel n1 n2 n3 or xxxmodel ( n1 n2 ... ) ( m1 m2 m3...) */ { int i, c; char temp[255], /* arbitrarilyh chose 255 but no check done*/ **list = NULL; int needright=1; *num = 0; /* read down to start of this list or return NULL if hit EOF first */ while ( (c=fgetc(stream)) != EOF && c != '(' ); if (c == EOF) return(NULL); list = (char **) malloc(sizeof(char *)); do { fscanf(stream, "%s", temp); if(temp[0] == '(' ){/* list contains list*/ needright++; list = (char **) realloc(list, ++(*num) * sizeof(char *)); list[*num - 1] = (char *) malloc(2 * sizeof(char)); strcpy(list[*num - 1], "("); if ((int) strlen(temp) > 1) /* has number too*/ for (i=0;temp[i] != '\0';i++)temp[i]=temp[i+1];/*squeeze the ( */ else fscanf(stream, "%s", temp); /* all we had was paren so get next */ } for(i=0;temp[i] != '\0'; i++) if(temp[i] == ')' ){ temp[i]='\0'; /* overlay )*/ if(needright-- > 1 ){ /* need to output this right paren */ if(i != 0){ /* but came in with no whitespace by number*/ list = (char **) realloc(list, ++(*num) * sizeof(char *)); list[*num - 1] = (char *) malloc((strlen(temp)+1) * sizeof(char)); strcpy(list[*num - 1], temp); } strcpy(temp, ")" ); } break; } if(temp[0] != '\0'){ list = (char **) realloc(list, ++(*num )* sizeof(char *)); list[*num - 1] = (char *) malloc((strlen(temp)+1) * sizeof(char)); strcpy(list[*num - 1], temp); } } while (needright); while( (c=fgetc(stream)) != EOF && c != '\n' && c != ')' ); return(list); } /* DEFINE_MODELS 22oct94 wmt: use n_atts, init att_locs to "", init model->terms, model->priors, model->global_clsf 17nov94 wmt: FILE *source => char *source 22nov94 wmt: init att_ignore_ids to "" This generates a set of models from a model-group list. A model-group is a compact representation of an AutoClass likelihood model which specifies what model terms are to be applied to which attributes. If there is a model in *model-list* which corresponds to a model group, that will be used and optionally regenerated from the model group. Otherwise a new model is created. The d-base must be a database-DS. The :source must be some unique identifier. When called from Read-Model-File the model file pathname is used. :Expand_P t means to call #'Expand-Model-Terms, creating a useable model. :Regenerate_P t means to regenerate an existing model using the new model-group. */ model_DS *define_models( char ****model_groups, database_DS d_base, char *source, FILE *stream, int expand_p, int regenerate_p, int num_model_groups, int *newnum, int *num_groups, int **sizes, FILE *log_file_fp) { int i_model, i_att, index = 0, length = 0, n_atts = d_base->n_atts; char ***model_group, caller[] = "define_models"; model_DS model, *models; fxlstr str; models = NULL; for (i_model=0; i_modelid, sizeof( model->id), caller, "MODEL-%d", G_m_id++); model->expanded_terms = FALSE; /* Ensures GENERATE-ATTRIBUTE-INFO call */ model->n_terms = model->num_priors = 0; model->terms = NULL; model->priors = NULL; strcpy(model->model_file, source); model->file_index = index; model->n_att_locs = n_atts; model->att_locs = (shortstr *) malloc(n_atts * sizeof(shortstr)); for (i_att=0; i_attatt_locs[i_att][0] = '\0'; model->n_att_ignore_ids = n_atts; model->att_ignore_ids = (shortstr *) malloc(n_atts * sizeof(shortstr)); for (i_att=0; i_attatt_ignore_ids[i_att][0] = '\0'; model->database = d_base; /* JTP model->class_store = (class_DS *) malloc(20 * sizeof(class_DS));*/ model->class_store = NULL; /* added for new class_store scheme*/ model->num_class_store = 0; model->global_clsf = NULL; /* compressed model params */ strcpy( model->data_file, ""); strcpy( model->header_file, ""); model->n_data = 0; model->compressed_p = FALSE; generate_attribute_info(model, model_group, i_model, num_groups[i_model], sizes[i_model], d_base, log_file_fp, stream); } if (expand_p == TRUE) conditional_expand_model_terms(model, regenerate_p, log_file_fp, stream); if (find_model_p( model, G_model_list, G_m_length) == FALSE) { if (G_model_list == NULL) G_model_list = (model_DS *) malloc(++G_m_length * sizeof(model_DS)); else G_model_list = (model_DS *) realloc(G_model_list, ++G_m_length * sizeof(model_DS)); G_model_list[G_m_length - 1] = model; } if (models == NULL) models = (model_DS *) malloc(++length * sizeof(model_DS)); else models = (model_DS *) realloc(models, ++length * sizeof(model_DS)); models[length - 1] = model; index++; } if (models == NULL) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: No models read from model source: %s\n", source); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } *newnum = length; return(models); } /* GENERATE_ATTRIBUTE_INFO 05nov94 wmt: last ignore attribute not processed: for (j=0; j for (j=1; j<=sizes[i]; j++) and add check for out of bounds attribute number 06dec94 wmt: pass model index to extend_terms Set up N-terms, terms, att-locs, att-ignore, att-indepn, and att-groups of model. */ void generate_attribute_info( model_DS model, char ***model_group, int i_model, int num_groups, int *sizes, database_DS d_base, FILE *log_file_fp, FILE *stream) { shortstr default_set_type; int i_group, j, n_att, integer_p; shortstr set_type; fxlstr str; char caller[] = "generate_attribute_info"; default_set_type[0] = '\0'; /* Make space for non-ignored groups. */ for (i_group=0; i_group < num_groups; i_group++) { strcpy(set_type, model_group[i_group][0]); if (eqstring(model_group[i_group][1], "default") == FALSE) { if (model_type(set_type) == UNKNOWN){ n_att = atoi_p(model_group[i_group][1], &integer_p); safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, \n" " is not handled\n", i_model, set_type); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (prefix(set_type, "ignore") == TRUE) { for (j=1; j<=sizes[i_group]; j++) { n_att = atoi_p(model_group[i_group][j], &integer_p); if (integer_p != TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = ignore, " "attribute \n number read, %s, was not an integer\n", i_model, model_group[i_group][j]); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (n_att >= model->n_att_locs) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, %d is an invalid model term type = " "ignore \n attribute number, must be less than %d\n", i_model, n_att, model->n_att_locs); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(model->att_locs[n_att], "") == FALSE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = ignore, \n" " attempt to re-use attribute %d\n", i_model, n_att); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else { strcpy(model->att_locs[n_att], "ignore"); strcpy(model->att_ignore_ids[n_att], "ignore_model"); } } } else if (prefix(set_type, "single") == TRUE) { extend_terms_single(set_type, model_group[i_group], sizes[i_group], model, i_model, log_file_fp, d_base, stream); } else if (prefix(set_type, "multi") == TRUE) { extend_terms_multi(set_type, model_group[i_group], sizes[i_group], model, i_model, log_file_fp, d_base, stream); } else { fprintf(stderr, "ERROR: No method for generating attribute sets for set_type %s\n", set_type); exit(1); } } /* Default: check for previous default and otherwise set up default. */ else { if (eqstring(default_set_type, "") == FALSE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, default model term type, %s, \n" " specified twice.\n", i_model, set_type); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } strcpy(default_set_type, set_type); } } if (default_set_type[0] != '\0') extend_default_terms(default_set_type, i_model, model, d_base, log_file_fp, stream); for (n_att=0; n_attn_att_locs; n_att++) if (eqstring(model->att_locs[n_att], "") == TRUE) { set_ignore_att_info(model, d_base); break; } } /* EXTEND_TERMS_SINGLE 04nov94 wmt: added test for out of bounds attribute value read from model file. 06dec94 wmt: pass model index to extend_terms 23dec94 wmt: renamed from extend_terms 28feb95 wmt: do not allocate tparm - it is done by appropriate model term builder For each set in set-list, build a term and add it to model->terms. a set is a single attribute number */ void extend_terms_single( char *model_type, char **list, int size, model_DS model, int model_index, FILE *log_file_fp, database_DS d_base, FILE *stream) { shortstr *model_att_locs = model->att_locs; int i, n_att, num, integer_p; int begi = 0, endi = size, j, doinglist = FALSE; fxlstr str; att_DS *att_info = d_base->att_info; char caller[] = "extend_terms_single"; /* Check and store set indices in att-loc array */ for (i=0; i= model->n_att_locs) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, \n" " %d is an invalid attribute number, must be less than %d\n", model_index, model_type, n_att, model->n_att_locs); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(model_att_locs[n_att], "") == FALSE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, \n" " attempt to re-use attribute %d\n", model_index, model_type, n_att); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(att_info[n_att]->type, "dummy") == FALSE) safe_sprintf( model_att_locs[n_att], sizeof( model_att_locs[n_att]), caller, "%d", model->n_terms); } num = model->n_terms ++; if (model->terms == NULL) model->terms = (term_DS *) malloc(model->n_terms * sizeof(term_DS)); else model->terms = (term_DS *) realloc(model->terms, model->n_terms * sizeof(term_DS)); model->terms[num] = (term_DS) malloc(sizeof(struct term)); strcpy(model->terms[num]->type, model_type); /* model->terms[num]->tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); */ model->terms[num]->n_atts = endi-begi; model->terms[num]->att_list = (float *) malloc((model->terms[num]->n_atts) * sizeof(float)); j = 0; while (begi++ < endi) { /* these token have already been read by atoi_p, so atof_p is not needed 07dec94 wmt */ model->terms[num]->att_list[j++] = (float) atof(list[begi]); } } /*endif for not doing a list*/ } } /* EXTEND_TERMS_MULTI 23dec94 wmt: derived from extend_terms to handle multi models 28feb95 wmt: do not allocate tparm - it is done by appropriate model term builder For set-list, build a term and add it to model->terms. a set-list is two or more attribute numbers */ void extend_terms_multi( char *model_type, char **list, int size, model_DS model, int model_index, FILE *log_file_fp, database_DS d_base, FILE *stream) { shortstr *model_att_locs = model->att_locs; int n_att, num, integer_p; int begi, endi, j; fxlstr str; att_DS *att_info = d_base->att_info; char caller[] = "extend_terms_multi"; /* Check and store set indices in att-loc array */ /* for (i=0; i= model->n_att_locs) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, \n" " %d is an invalid attribute number, must be less than %d\n", model_index, model_type, n_att, model->n_att_locs); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(model_att_locs[n_att], "") == FALSE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, \n" " attempt to re-use attribute %d\n", model_index, model_type, n_att); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(att_info[n_att]->type, "dummy") == FALSE) safe_sprintf( model_att_locs[n_att], sizeof( model_att_locs[n_att]), caller, "%d", model->n_terms); } num = model->n_terms ++; if (model->terms == NULL) model->terms = (term_DS *) malloc(model->n_terms * sizeof(term_DS)); else model->terms = (term_DS *) realloc(model->terms, model->n_terms * sizeof(term_DS)); model->terms[num] = (term_DS) malloc(sizeof(struct term)); strcpy(model->terms[num]->type, model_type); /* model->terms[num]->tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); */ model->terms[num]->n_atts = endi - begi; model->terms[num]->att_list = (float *) malloc((model->terms[num]->n_atts) * sizeof(float)); j = 0; while (begi++ < endi) { /* these token have already been read by atoi_p, so atof_p is not needed 07dec94 wmt */ model->terms[num]->att_list[j++] = (float) atof(list[begi]); } } /* EXTEND_DEFAULT_TERMS 05nov94 wmt: added test for invalid attribute number read from model file 06dec94 wmt: pass model index to default_extend_terms 28feb95 wmt: do not allocate tparm - it is done by appropriate model term builder For each non 'dummy attribute not already set , build an term-DS and add it to (model-DS-terms model) for model-type /= 'ignore. */ void extend_default_terms( char *model_type, int i_model, model_DS model, database_DS d_base, FILE *log_file_fp, FILE *stream) { shortstr *model_att_locs = model->att_locs; int i, j, n_att, nad = 0, nsl = 0, *atts_defaulted = NULL, **set_list = NULL; int num; att_DS *att_info = d_base->att_info; fxlstr str; char caller[] = "extend_default_terms"; if (eqstring(model_type, "ignore") == TRUE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, ignore is not a valid default " "model term type\n", i_model); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } for (n_att=0; n_attn_att_locs; n_att++) { if ((eqstring(model_att_locs[n_att], "") == TRUE) && (eqstring(att_info[n_att]->type, "dummy") == FALSE)) { nad++; if (atts_defaulted == NULL) atts_defaulted = (int *) malloc(nad * sizeof(int)); else atts_defaulted = (int *) realloc(atts_defaulted, nad * sizeof(int)); atts_defaulted[nad-1] = n_att; nsl++; if (set_list == NULL) set_list = (int **) malloc(nsl * sizeof(int *)); else set_list = (int **) realloc(set_list, nsl * sizeof(int *)); set_list[nsl-1] = (int *) malloc(sizeof(int)); set_list[nsl-1][0] = n_att; } } for (i=nsl-1; i>=0; i--) { /* for (i=0; i= model->n_att_locs) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, %d " "is an invalid \n attribute number, must be less than %d\n", i_model, model_type, n_att, model->n_att_locs); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else if (eqstring(model_att_locs[n_att], "") == FALSE) { safe_sprintf( str, sizeof( str), caller, "ERROR[3]: for model index = %d, model term type = %s, " "attempt to \n re-use attribute %d\n", i_model, model_type, n_att); to_screen_and_log_file(str, log_file_fp, stream, TRUE); exit(1); } else safe_sprintf( model_att_locs[n_att], sizeof( model_att_locs[n_att]), caller, "%d", model->n_terms); } model->n_terms += 1; num = model->n_terms - 1; if (model->terms == NULL) model->terms = (term_DS *) malloc(model->n_terms * sizeof(term_DS)); else model->terms = (term_DS *) realloc(model->terms, model->n_terms * sizeof(term_DS)); model->terms[num] = (term_DS) malloc(sizeof(struct term)); strcpy(model->terms[num]->type, model_type); model->terms[num]->n_atts = 1; model->terms[num]->att_list = (float *) malloc(sizeof(float)); model->terms[num]->att_list[0] = set_list[i][0]; /* model->terms[num]->tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); */ } if (atts_defaulted != NULL) { safe_sprintf( str, sizeof( str), caller, "ADVISORY[3]: the default model term type, %s, \n" " will be used for these attributes:\n", model_type); to_screen_and_log_file(str, log_file_fp, stream, TRUE); for (i=0; idscrp); to_screen_and_log_file(str, log_file_fp, stream, TRUE); } } } /* This resets the model just sufficiently that Generate-Attribute-Info will work. */ void read_model_reset( model_DS model) { int n_atts; model->expanded_terms = FALSE; n_atts = model->database->input_n_atts; model->att_locs = NULL; model->n_att_locs = 0; model->att_ignore_ids = NULL; model->n_att_ignore_ids = 0; /* commented JTP free(model->class_store); */ model->class_store = NULL; /* this means old ones lost but this routine not called*/ model->num_class_store = 0; store_clsf_DS(model->global_clsf, NULL, 0); /* dont free since is in store JTP free(model->global_clsf); */ model->global_clsf = NULL; } /* SET_IGNORE_ATT_INFO 04nov94 wmt: test for "", not NULL on unspecified_dummy_warning 02mar95 wmt: find_str_in_table test is >=, rather than > replaces nil in model-DS-att-locs with "ignore". Fills att_ignore_ids with "transformed-attribute-ignored", "att_type_not_specified", "att_type_is_dummy", or "model_term_not_specified" */ void set_ignore_att_info( model_DS model, database_DS d_base) { int i; shortstr *locs = model->att_locs, *att_ignore_ids = model->att_ignore_ids; for (i=0; in_att_locs; i++) if (eqstring(locs[i], "") == TRUE) { strcpy(locs[i], "ignore"); if (find_str_in_table(d_base->att_info[i]->sub_type, G_transforms, NUM_TRANSFORMS) >= 0) strcpy(att_ignore_ids[i], "transformed-attribute-ignored"); else if (eqstring( d_base->att_info[i]->warnings_and_errors->unspecified_dummy_warning, "") == FALSE) strcpy(att_ignore_ids[i], "att_type_not_specified"); else if (eqstring(d_base->att_info[i]->type, "dummy") == TRUE) strcpy(att_ignore_ids[i], "att_type_is_dummy"); else strcpy(att_ignore_ids[i], "model_term_not_specified"); } /* fprintf( stderr, "set_ignore_att_info\n"); print_att_locs_and_ignore_ids( model, model->file_index); */ } /* this back traces the effect of translations, finding the source attribute indices. Returns the list of ultimate source attribute indices corresponding to those in 'att-index-list. JCS 6/90 */ int *get_sources_list( int *att_index_list, int num, att_DS *att_info, int *traced, int n_traced) { int *list = NULL; /**** only call to this was recursive one so commented since couldnt really figure out what was being altered, and what was geing done in place. in LISP is called by (defun Model-Group-List (model &key (use-sources t)) int i, j, *temp, *list, n_temp, n_list; int (*cmp)() = int_cmp; for (i=0; iprops, "source", att_info[att_index]->n_props); temp = (int *) getf(att_info[att_index]->props, "n_source", att_info[att_index]->n_props); *n_source = *temp; if (source == NULL) { source = (int *) malloc(sizeof(int)); source[0] = att_index; return(source); } else if (*n_source == 1) { if (member_int(source[0], traced, n_traced) == TRUE) { fprintf(stderr, "ERROR: get_source_list found circular reference\n"); exit(1); } else return(source); } else { if (exist_intersection(traced, source, n_traced, *n_source)) { fprintf(stderr, "ERROR: get_source_list found circularity of attribute source references\n"); exit(1); } else get_sources_list(source, *n_source, att_info, traced, n_traced); return(source); } } /* EXIST_INTERSECTION 21oct94 wmt: = changed to == */ int exist_intersection( int *fl1,int *fl2,int l1,int l2) { int i, j; for (i=0; in_att_locs; n_att++) { fprintf( stderr, "%d=%d:%s\n", model_index, n_att, model->att_locs[n_att]); fprintf( stderr, "%d=%d:%s\n", model_index, n_att, model->att_ignore_ids[n_att]); } } autoclass-3.3.6.dfsg.1/prog/struct-class.c0000644000175000017500000005142211247310756016456 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* STORE_CLASS_DS 09jan95 wmt: only store clsf_DS_max_n_classes(clsf) classes, free any excess Class storage Management: Given the size of a class, and the number that will be needed on an extensive search, it is usefull to directly manage the storage of temporarily un-needed classes. Note however that this tends to counteract the compaction normally performed by the garbage collector. These routines DO NOT attempt to update or maintain the clsf-DS-classes. */ void store_class_DS( class_DS cl, int max_n_classes) { model_DS cl_model; clsf_DS null_clsf = NULL; cl_model = cl->model; if (cl_model->num_class_store >= max_n_classes) free_class_DS( cl, "clsf", null_clsf, 0); else if (find_class(cl, cl_model->class_store) == FALSE) { /* multiple reference check */ cl->w_j = 0; cl->log_w_j = 0; cl->pi_j = 1.0; cl->log_pi_j = 0.0; cl->log_a_w_s_h_pi_theta = 0.0; cl->log_a_w_s_h_j = 0.0; cl->known_parms_p = FALSE; cl->i_values = NULL; cl->num_i_values = 0; cl->i_sum = 0.0; cl->max_i_value = 0.0; /* wts no longer zeroed here since may not even need and done in pop in case have to alloc.*/ cl->next=cl_model->class_store; cl_model->class_store = cl; cl_model->num_class_store += 1; if (G_clsf_storage_log_p == TRUE) fprintf(stdout, "\nstore_class_DS(%.2d [max=%d]): %p\n", cl_model->num_class_store, max_n_classes, (void *) cl); } } /* Get a class either from the model's class-store or by construction. 10apr97 wmt: add database->n_data to get_class_DS, pop_class_DS, & build_class_DS args */ class_DS get_class_DS( model_DS model, int n_data, int want_wts_p, int check_model) { FILE *log_file_fp = NULL; class_DS temp; if ( model == NULL ) { fprintf(stderr,"ERROR: get_class called with NULL model\n"); abort(); } if (check_model == TRUE) conditional_expand_model_terms(model, FALSE, log_file_fp, G_stream); if ((temp = pop_class_DS(model, n_data, want_wts_p)) != NULL) return(temp); return(build_class_DS(model, n_data, want_wts_p)); } /* Pops a class off the model's class-store. This may produce NULL. 10apr97 wmt: add n_data arg, instead of using model->database->n_data */ class_DS pop_class_DS( model_DS model, int n_data, int want_wts_p) { int i; class_DS cl; if ( (cl = model->class_store) == NULL) return(NULL); model->num_class_store -= 1; model->class_store = cl->next; if (want_wts_p == TRUE) { if( cl->wts == NULL ) cl->wts = (float *) malloc( n_data * sizeof(float)); else if( cl->num_wts != n_data) cl->wts = (float *) realloc(cl->wts, n_data * sizeof(float)); cl->num_wts = n_data; for (i=0; iwts[i] = 0.0; } else { if ( cl->wts != NULL) free(cl->wts); cl->wts = NULL; cl->num_wts = 0; } if (G_clsf_storage_log_p == TRUE) fprintf(stdout, "\npop_class_DS(%.2d): %p\n", model->num_class_store, (void *) cl); return(cl); } /* BUILD_CLASS_DS 24oct94 wmt: init known_parms_p 10apr97 wmt: add n_data to args; use it rather than model->database->n_data Build a class instantiating a fully expanded model. */ class_DS build_class_DS( model_DS model, int n_data, int want_wts_p) { int i, num; class_DS temp; temp = (class_DS) malloc(sizeof(struct class)); temp->w_j = temp->log_w_j = temp->log_pi_j = 0.0; temp->pi_j = 1.0; temp->log_a_w_s_h_pi_theta = temp->log_a_w_s_h_j = 0.0; temp->known_parms_p = FALSE; temp->model = model; temp->num_tparms = num = model->n_terms; temp->tparms = (tparm_DS *) malloc(num * sizeof(tparm_DS)); for (i=0; itparms[i] = copy_tparm_DS(model->terms[i]->tparm); temp->num_i_values = 0; temp->i_values = NULL; temp->i_sum = temp->max_i_value = 0.0; if (want_wts_p == TRUE) { temp->num_wts = n_data; temp->wts = (float *) malloc(temp->num_wts * sizeof(float)); for (i=0; iwts[i] = 0.0; } else { temp->wts = NULL; temp->num_wts = 0; } temp->next = NULL; if (G_clsf_storage_log_p == TRUE) { fprintf( stdout, "\nbuild_class_DS: %p, num_wts %d, wts:%p, wts-len:%d\n", (void *) temp, temp->num_wts, (void *) temp->wts, temp->num_wts * (int) sizeof( float)); if (G_n_freed_classes > 0) G_n_create_classes_after_free++; } return(temp); } class_DS build_compressed_class_DS( model_DS comp_model) { class_DS temp; temp = (class_DS) malloc(sizeof(struct class)); temp->w_j = temp->log_w_j = temp->log_pi_j = 0.0; temp->pi_j = 1.0; temp->log_a_w_s_h_pi_theta = temp->log_a_w_s_h_j = 0.0; temp->model = comp_model; temp->num_tparms = 0; temp->tparms = NULL; temp->num_i_values = 0; temp->i_values = NULL; temp->num_wts = 0; temp->wts = NULL; return(temp); } /* Copies a class-DS so as to ensure there is no sub-structure sharing. With wts==0, this produces a class-DS with compressed (nil) wts. See #'compress-clsf. Normally the class-store is used to recycle classes. 10apr97 wmt: add database->n_data to get_class_DS call; add n_data arg; add database->n_data to copy_to_class_DS call */ class_DS copy_class_DS(class_DS from_class, int n_data, int want_wts_p) /*JTP, classes_to_check) JTP*/ { class_DS to_class; model_DS model; model = from_class->model; to_class = get_class_DS(model, n_data, want_wts_p, TRUE); /* commented if (classes_to_check != NULL && (to_class == from_class) || (find_class(to_class, classes_to_check) != FALSE)) fprintf(stderr, "Redundant class\n"); *****commented JTP*/ return(copy_to_class_DS( from_class, to_class, n_data, want_wts_p)); } /* COPY_TO_CLASS_DS 20jan95 wmt: copy num_wts, if want_wts_p is true 08feb95 wmt: do not copy i_values; error if non NULL 25feb95 wmt: if tparms already allocated, free them. 10apr97 wmt: add n_data to args; use rather than model->database->n_data This completely replaces the structured contents of to-class with full depth copies of those of from-class, except that when wts==0 the wts are not copied. Only the structural framework of to-class is retained, substructures being replaced. This would be better for some kind of recursive replace on params and i-values. */ class_DS copy_to_class_DS( class_DS from_class, class_DS to_class, int n_data, int want_wts_p) { int i, num, n_tparm; tparm_DS *tparms; model_DS model; to_class->model = model = from_class->model; to_class->w_j = from_class->w_j; to_class->log_w_j = from_class->w_j; to_class->pi_j = from_class->pi_j; to_class->log_pi_j = from_class->log_pi_j; to_class->log_a_w_s_h_pi_theta = from_class->log_a_w_s_h_pi_theta; to_class->log_a_w_s_h_j = from_class->log_a_w_s_h_j; to_class->known_parms_p = from_class->known_parms_p; if (to_class->tparms != NULL) { for (n_tparm=0; n_tparm < to_class->num_tparms ; n_tparm++) { free_tparm_DS( to_class->tparms[n_tparm]); } free( to_class->tparms); } to_class->num_tparms = num = from_class->num_tparms; tparms = from_class->tparms; to_class->tparms = (tparm_DS *) malloc(num * sizeof(tparm_DS)); for (i=0; itparms[i] = copy_tparm_DS(tparms[i]); } to_class->num_i_values = from_class->num_i_values; to_class->i_values = from_class->i_values; to_class->i_sum = from_class->i_sum; to_class->max_i_value = from_class->max_i_value; if (from_class->i_values != NULL) { fprintf( stderr, "ERROR: from_class->i_values is non NULL\n"); abort(); } /* A compressed class has wts==0 */ /* get class should have allocated wts and zeroed or cleaned up if compressed*/ if ( want_wts_p == TRUE && from_class->num_wts == n_data ) { if( to_class->wts == NULL || from_class->wts == NULL ) { fprintf(stderr,"ERROR: copy class wt allocation error 1"); abort(); } for(i=0; iwts[i] = from_class->wts[i]; to_class->num_wts = from_class->num_wts; } else if( to_class->wts != NULL || to_class->num_wts > 0 ) { fprintf(stderr,"ERROR: copy class wt allocation error 2"); abort(); } return(to_class); } /* CLASS_DS_TEST 18oct94 wmt: modified 20dec94 wmt: added call to class_equivalence_fn Determines if two classes have exactly the same parameterization. */ int class_DS_test( class_DS cl1, class_DS cl2, double rel_error) { /* int i, cnt = 0; debug */ /* fprintf( stderr, "w_j %d log_a_w_s_h_pi_theta %d log_a_w_s_h_j %d " "class_equivalence_fn %d\n", (percent_equal( (double) cl1->w_j, (double) cl2->w_j, rel_error) == TRUE), (percent_equal( cl1->log_a_w_s_h_pi_theta, cl2->log_a_w_s_h_pi_theta, rel_error) == TRUE), (percent_equal( cl1->log_a_w_s_h_j, cl2->log_a_w_s_h_j, rel_error) == TRUE), (class_equivalence_fn( cl1, cl2, rel_error, rel_error) == TRUE)); fprintf( stderr, "clsf1=w_j %f log_a_w_s_h_pi_theta %f log_a_w_s_h_j %f\n", cl1->w_j, cl1->log_a_w_s_h_pi_theta, cl1->log_a_w_s_h_j); fprintf( stderr, "clsf2=w_j %f log_a_w_s_h_pi_theta %f log_a_w_s_h_j %f\n", cl2->w_j, cl2->log_a_w_s_h_pi_theta, cl2->log_a_w_s_h_j); */ /* for this to work change update_wts_p to TRUE in reconstruct_search so that the wts vectors will be expanded */ /* for (i=0; inum_wts; i++) { if (percent_equal( (double) cl1->wts[i], (double) cl2->wts[i], (double) 0.1) == TRUE) cnt++; */ /* else fprintf( stderr, " %d:%.4f/%.4f", i, cl1->wts[i], cl2->wts[i]); */ /* } fprintf( stderr, "num_wts %d, cnt %d\n", cl1->num_wts, cnt); */ /* centerline_stop(); */ if ((model_DS_equal_p(cl1->model, cl2->model) == TRUE) && (cl1->known_parms_p == cl2->known_parms_p) && (percent_equal( (double) cl1->w_j, (double) cl2->w_j, rel_error) == TRUE) && (percent_equal( cl1->log_a_w_s_h_pi_theta, cl2->log_a_w_s_h_pi_theta, rel_error) == TRUE) && (percent_equal( cl1->log_a_w_s_h_j, cl2->log_a_w_s_h_j, rel_error) == TRUE) && (class_equivalence_fn( cl1, cl2, rel_error, rel_error) == TRUE)) return(TRUE); else return(FALSE); } /* COPY_TPARM_DS 26jan95 wmt: for sn_cn, ensure all slots are copied routine was originally converted twice. the other one was in model-multi-multinomial-d.c. It was deleted after comparing to this one and finding no difference except spacing. JTP */ tparm_DS copy_tparm_DS( tparm_DS old) { int i, j, n_atts; struct mm_d_param *o1, *n1; struct mm_s_param *o2, *n2; struct mn_cn_param *o3, *n3; struct sm_param *o4, *n4; struct sn_cm_param *o5, *n5; struct sn_cn_param *o6, *n6; tparm_DS new; if(old == NULL) return (NULL); /*jtp added just for safety*/ n_atts = old->n_atts; new = (tparm_DS) malloc(sizeof(struct new_term_params)); *new = *old; /* note that there are vectors and other things that may need correcting after this mass copy*/ if (new->tppt == MM_D) { o1 = &(old->ptype.mm_d); n1 =&( new->ptype.mm_d); n1->sizes = (int *) malloc(n_atts * sizeof(int)); n1->wts = (fptr *) malloc(n_atts * sizeof(fptr)); n1->probs = (fptr *) malloc(n_atts * sizeof(fptr)); n1->log_probs = (fptr *) malloc(n_atts * sizeof(fptr)); n1->wts_vec = (float *) malloc(n_atts * sizeof(float)); n1->probs_vec = (float *) malloc(n_atts * sizeof(float)); n1->log_probs_vec = (float *) malloc(n_atts * sizeof(float)); for (i=0; isizes[i] = o1->sizes[i]; n1->wts[i] = (float *) malloc(o1->sizes[i] * sizeof(float)); n1->probs[i] = (float *) malloc(o1->sizes[i] * sizeof(float)); n1->log_probs[i] = (float *) malloc(o1->sizes[i] * sizeof(float)); n1->wts_vec[i] = o1->wts_vec[i]; n1->probs_vec[i] = o1->wts_vec[i]; n1->log_probs_vec[i] = o1->log_probs_vec[i]; for (j=0; jsizes[i]; j++) { n1->wts[i][j] = o1->wts[i][j]; n1->probs[i][j] = o1->probs[i][j]; n1->log_probs[i][j] = o1->log_probs[i][j]; } } } else if (new->tppt == MM_S ) { o2 = &(old->ptype.mm_s); n2 = &(new->ptype.mm_s); n2->wt = o2->wt; n2->prob = o2->prob; n2->log_prob = o2->log_prob; } else if (new->tppt == MN_CN ) { o3 = &(old->ptype.mn_cn); n3 =&( new->ptype.mn_cn); n3->emp_means = (fptr) malloc(n_atts * sizeof(float)); n3->emp_covar = (fptr *) malloc(n_atts * sizeof(fptr)); n3->means = (fptr) malloc(n_atts * sizeof(float)); n3->covariance = (fptr *) malloc(n_atts * sizeof(fptr)); n3->factor = (fptr *) malloc(n_atts * sizeof(fptr)); n3->ln_root = o3->ln_root; n3->values = (fptr) malloc(n_atts * sizeof(float)); n3->temp_v = (fptr) malloc(n_atts * sizeof(float)); n3->temp_m = (fptr *) malloc(n_atts * sizeof(fptr)); n3->min_sigma_2s = (float *)malloc(n_atts * sizeof(float)); for (i=0; iemp_means[i] = o3->emp_means[i]; n3->means[i] = o3->means[i]; n3->values[i] = o3->values[i]; n3->temp_v[i] = o3->temp_v[i]; n3->min_sigma_2s[i] = o3->min_sigma_2s[i]; n3->emp_covar[i] = (fptr) malloc(n_atts * sizeof(float)); n3->covariance[i] = (fptr) malloc(n_atts * sizeof(float)); n3->factor[i] = (fptr) malloc(n_atts * sizeof(float)); n3->temp_m[i] = (fptr) malloc(n_atts * sizeof(float)); for (j=0; jemp_covar[i][j] = o3->emp_covar[i][j]; n3->covariance[i][j] = o3->covariance[i][j]; n3->factor[i][j] = o3->factor[i][j]; n3->temp_m[i][j] = o3->temp_m[i][j]; } } } else if (new->tppt == SM ) { o4 = &(old->ptype.sm); n4 = &(new->ptype.sm); *n4=*o4; /*get all scalars then replace pointers below*/ n4->val_wts = (fptr) malloc(n_atts * sizeof(float)); n4->val_probs = (fptr) malloc(n_atts * sizeof(float)); n4->val_log_probs = (fptr) malloc(n_atts * sizeof(float)); for (i=0; ival_wts[i] = o4->val_wts[i]; n4->val_probs[i] = o4->val_probs[i]; n4->val_log_probs[i] = o4->val_log_probs[i]; } } else if (new->tppt == SN_CM ) { o5 = &(old->ptype.sn_cm); n5 = &(new->ptype.sn_cm); *n5=*o5; /*get all scalars then replace pointers below*/ /* no vectors or arrays to copy */ } else if (new->tppt == SN_CN ) { o6 = &(old->ptype.sn_cn); n6 = &(new->ptype.sn_cn); *n6 = *o6; /*get all scalars then replace pointers below*/ /* no vectors or arrays to copy */ } else { fprintf(stderr, "ERROR: unknown enum MODEL_TYPES in copy_tparm=%d", new->tppt); abort(); } return(new); } /* FREE_CLASS_DS 07jan95 wmt: new free storage for class and its parameters */ void free_class_DS( class_DS class, char *type, clsf_DS clsf, int n_class) { int n_tparm, n_att; tparm_DS tparm; char *att_type; i_discrete_DS i_discrete_struct; i_real_DS i_real_struct; if (G_clsf_storage_log_p == TRUE) fprintf(stdout, "free_class(%s): %p\n", type, (void *) class); G_n_freed_classes++; for (n_tparm=0; n_tparm < class->num_tparms ; n_tparm++) { tparm = class->tparms[n_tparm]; free_tparm_DS( tparm); } free( class->tparms); if (class->i_values != NULL) { for (n_att=0; n_attdatabase->n_atts; n_att++) { att_type = report_att_type( clsf, n_class, n_att); if (eqstring( att_type, "discrete") == TRUE) { i_discrete_struct = (i_discrete_DS) class->i_values[n_att]; free( i_discrete_struct->p_p_star_list); free( i_discrete_struct); } if (eqstring( att_type, "integer") == TRUE) { fprintf( stderr, "att_type integer not supported\n"); abort(); } if (eqstring( att_type, "real") == TRUE) { i_real_struct = (i_real_DS) class->i_values[n_att]; free( i_real_struct->mean_sigma_list); free( i_real_struct); /* term_att_list & class_covar are pointers to other storage, not malloc's */ } } free( class->i_values); } /* wts are freed by compress_clsf called by try_variation, but for situations where they are not */ if (class->wts != NULL) free( class->wts); free( class); } /* FREE_TPARM_DS 06jan94 wmt: new -- adapted from copy_tparm_DS 24mar97 wmt: allow tparm->tppt to be UNKNOWN, TIGNORE print advisory msg, not error msg & abort aju 980612: Prefixed enum member IGNORE with T so it would not clash with predefined Win32 type. free components of tparm_DS */ void free_tparm_DS( tparm_DS tparm) { int i, n_atts; struct mm_d_param *tparm1; struct mm_s_param *tparm2; struct mn_cn_param *tparm3; struct sm_param *tparm4; struct sn_cm_param *tparm5; struct sn_cn_param *tparm6; if(tparm == NULL) return; n_atts = tparm->n_atts; if (tparm->tppt == MM_D) { tparm1 =&( tparm->ptype.mm_d); free( tparm1->sizes); free( tparm1->wts); free( tparm1->probs); free( tparm1->log_probs); free( tparm1->wts_vec); free( tparm1->probs_vec); free( tparm1->log_probs_vec); for (i=0; iwts[i]); free( tparm1->probs[i]); free( tparm1->log_probs[i]); } } else if (tparm->tppt == MM_S ) { tparm2 = &(tparm->ptype.mm_s); } else if (tparm->tppt == MN_CN ) { tparm3 =&( tparm->ptype.mn_cn); free( tparm3->emp_means); free( tparm3->means); free( tparm3->values); free( tparm3->temp_v); free( tparm3->min_sigma_2s); for (i=0; iemp_covar[i]); free( tparm3->covariance[i]); free( tparm3->factor[i]); free( tparm3->temp_m[i]); } free( tparm3->emp_covar); free( tparm3->covariance); free( tparm3->factor); free( tparm3->temp_m); } else if (tparm->tppt == SM ) { tparm4 = &(tparm->ptype.sm); free( tparm4->val_wts); free( tparm4->val_probs); free( tparm4->val_log_probs); } else if (tparm->tppt == SN_CM ) { tparm5 = &(tparm->ptype.sn_cm); } else if (tparm->tppt == SN_CN ) { tparm6 = &(tparm->ptype.sn_cn); } else if ((tparm->tppt == UNKNOWN ) || (tparm->tppt == TIGNORE )) { /* nothing to free */ } else { fprintf(stderr, "ADVISORY: unknown enum MODEL_TYPES in free_tparm_DS = %d\n", tparm->tppt); /* abort(); */ } /* fprintf(stderr, "ADVISORY: enum MODEL_TYPES in free_tparm_DS = %d\n", tparm->tppt); */ free( tparm); } /* LIST_CLASS_STORAGE 07jan95 wmt: new 05feb97 wmt: use list of void *, rather than list of int list the storage pointers to class structures which are stored in models return list of clsf pointers terminated by END_OF_INT_LIST */ void **list_class_storage ( int print_p) { void **class_list_ptr = NULL; int num_class_ptrs = 0, n_model, n_class; class_DS class; model_DS model; if (G_model_list == NULL) { fprintf(stderr, "\nG_model_list is NULL\n"); abort(); } for (n_model=0; n_modelclass_store; for (n_class=0; n_class < model->num_class_store; n_class++) { num_class_ptrs++; if (print_p == TRUE) printf("model-%d class_store %d: %p\n", n_model, num_class_ptrs, (void *) class); if (class_list_ptr == NULL) class_list_ptr = (void **) malloc( num_class_ptrs * sizeof(void *)); else class_list_ptr = (void **) realloc( class_list_ptr, num_class_ptrs * sizeof(void *)); class_list_ptr[num_class_ptrs - 1] = (void *) class; class = class->next; } } num_class_ptrs++; if (class_list_ptr == NULL) class_list_ptr = (void **) malloc( num_class_ptrs * sizeof(void *)); else class_list_ptr = (void **) realloc( class_list_ptr, num_class_ptrs * sizeof(void *)); class_list_ptr[num_class_ptrs - 1] = NULL; return( class_list_ptr); } /* CLASS_STRENGTH_MEASURE 03feb95 wmt: new hueristic measure of class strength: class information density */ double class_strength_measure( class_DS class) { return ( class->log_a_w_s_h_pi_theta / ((double) class->w_j)); } autoclass-3.3.6.dfsg.1/prog/autoclass.make.sunos.acc0000644000175000017500000000256111247310756020413 0ustar areare### AUTOCLASS C MAKE FILE FOR SUN OS 4.1.3 -- Sun acc C compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) BCFLAGS = $(OSFLAGS) -Xc -vc -I/usr/local2/lang/SC3.0.1/include/cc_413_U1 # optimize CFLAGS = $(BCFLAGS) -O4 ## debugging ## CFLAGS = $(BCFLAGS) -O3 -g ## profiling ## CFLAGS = $(BCFLAGS) -O4 -pg -Bstatic LDFLAGS = -L/usr/local2/lang/lib -L/usr/local2/lang/SC3.0.1/lib CC = acc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) $(LDFLAGS) %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ # depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/predictions.c0000644000175000017500000001101711247310756016346 0ustar areare#include #include #include #include #include #ifndef _MSC_VER #include #endif #include "autoclass.h" #include "globals.h" /* AUTOCLASS_PREDICT 18may95 wmt: adapted from ac-x::predict-class 10apr97 wmt: add database->n_data to copy_class_DS call 30jun00 wmt: allocate separate storage for test_clsf->reports->class_wt_ordering use an autoclass "training" classification to predict class membership of cases in a "test" data base. */ clsf_DS autoclass_predict( char *data_file_ptr, clsf_DS training_clsf, clsf_DS test_clsf, FILE *log_file_fp, char *log_file_ptr) { FILE *stream = stdout; FILE *header_file_fp = NULL, *model_file_fp = NULL; char *header_file_ptr, *model_file_ptr; shortstr start_fn_type = "block"; int want_wts_p = TRUE, n_classes = training_clsf->n_classes; int num_classes = 1, reread_p = FALSE, regenerate_p = FALSE, n_class; int restart_p = FALSE, initial_cycles_p = FALSE, n_data = 0; int start_j_list_from_s_params = FALSE; /* ------------------------------------------------------------*/ G_training_clsf = training_clsf; G_prediction_p = TRUE; if (test_clsf != NULL) { /* do not print out input checking again */ stream = NULL; G_stream = NULL; log_file_fp = NULL; log_file_ptr = NULL; } /* get test database */ header_file_ptr = training_clsf->database->header_file; model_file_ptr = training_clsf->models[0]->model_file; if (eqstring( header_file_ptr, "") != TRUE) header_file_fp = fopen( header_file_ptr, "r"); if (eqstring( model_file_ptr, "") != TRUE) model_file_fp = fopen( model_file_ptr, "r"); test_clsf = generate_clsf( num_classes, header_file_fp, model_file_fp, log_file_fp, stream, reread_p, regenerate_p, data_file_ptr, header_file_ptr, model_file_ptr, log_file_ptr, restart_p, start_fn_type, initial_cycles_p, n_data, start_j_list_from_s_params); if (header_file_fp != NULL) fclose( header_file_fp); if (model_file_fp != NULL) fclose( model_file_fp); init_clsf_for_reports( test_clsf, G_prediction_p); if (test_clsf != NULL) G_stream = stdout; /* use weight ordering from training clsf */ test_clsf->reports->n_class_wt_ordering = training_clsf->reports->n_class_wt_ordering; /* test_clsf->reports->class_wt_ordering = training_clsf->reports->class_wt_ordering; */ /* allocate separate storage */ test_clsf->reports->class_wt_ordering = get_class_weight_ordering( training_clsf); /* create training classes in test_clsf in order to store the predicted weights */ test_clsf->classes = (class_DS *) realloc( test_clsf->classes, n_classes * sizeof( class_DS)); test_clsf->n_classes = n_classes; for (n_class=num_classes; n_classclasses[n_class] = copy_class_DS( test_clsf->classes[0], test_clsf->database->n_data, want_wts_p); if (same_model_and_attributes( test_clsf, training_clsf) == FALSE) { fprintf( stdout, "ERROR: training classification & test data have different " "models and/or different attributes \n"); exit (1); } update_wts( training_clsf, test_clsf); return (test_clsf); } /* SAME_MODEL_AND_ATTRIBUTES 20may95 wmt: new check if two clsfs have the same model and attributes -- used by autoclass_predict */ int same_model_and_attributes( clsf_DS clsf1, clsf_DS clsf2) { int i; model_DS model1, model2; database_DS db1, db2; att_DS att1, att2; if ((clsf1->num_models != 1) || (clsf2->num_models != 1)) { fprintf( stderr, "ERROR: -predict assumes only one model\n"); exit (1); } model1 = clsf1->models[0]; model2 = clsf2->models[0]; db1 = clsf1->database; db2 = clsf2->database; if ((eqstring( model1->model_file, model2->model_file)) && (model1->file_index == model2->file_index) && (db1->n_atts == db2->n_atts)) { for (i=0; in_atts; i++) { att1 = db1->att_info[i]; att2 = db2->att_info[i]; if (eqstring( att1->type, att2->type) == FALSE) return(FALSE); if (eqstring( att1->sub_type, att2->sub_type) == FALSE) return(FALSE); if (eqstring( att1->dscrp, att2->dscrp) == FALSE) return(FALSE); if (att1->n_props != att2->n_props) return(FALSE); } return(TRUE); } else return(FALSE); } autoclass-3.3.6.dfsg.1/prog/io-results.c0000644000175000017500000023133011247310756016133 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* With non-nill dbmodel this replaces the database and model structures referenced in the clsf-DS and class-DS levels with the corresponding compressed structures. With non-nill wts, it substitutes nil's for the class-DS-w_j. The origional values are returned as multiple values for possible restoration in With-Compressed-Clsf and With-Compressed-Clsf-Seq. */ void compress_clsf( clsf_DS clsf, model_DS dbmodel, int want_wts_p) { int i; class_DS *classes; database_DS database; model_DS *models; database = clsf->database; models = clsf->models; classes = clsf->classes; /* wts vector not returned so why save? JTP wts_vector = (float **) malloc(clsf->n_classes * sizeof(float *)); for (i=0; in_classes; i++) wts_vector[i] = classes[i]->wts; ********commented */ /* DJC - Removed code that checks for previous compression */ if (want_wts_p == FALSE) for (i=0; in_classes; i++){ if(classes[i]->wts != NULL) free(classes[i]->wts); classes[i]->wts = NULL; } } /* EXPAND_CLSF 22jan95 wmt: rework code according to ac-x version Restores a previously compressed classification to near origional form. With 'wts or 'update-wts and other keys nil, this does a full expansion ('wts) or expansion and update ('update-wts). With both nil, the class-DS-wts are not expanded. The other keys provide a mechanism for restoring a classification to its previous condition in with-compressed-clsf and with-compressed-clsf-seq by providing preferred choices for the expansion. Database & models may sequences of compressed structures, and wts-vector may be a sequence of nils, allowing restoration to a (partially) compressed state. */ clsf_DS expand_clsf( clsf_DS clsf, int want_wts_p, int update_wts_p) { int i_model, i_class, num_wts = 0, n_terms, i_term; float **wts_vector = NULL; tparm_DS tparm; /* If needed, restore the database and model pointers: */ if (clsf->database->compressed_p == TRUE) { clsf->database = expand_database( clsf->database); /* reset database ptrs */ for (i_model=0; i_modelnum_models; i_model++) clsf->models[i_model]->database = clsf->database; expand_clsf_models( clsf); check_errors_and_warnings( clsf->database, clsf->models, clsf->num_models); } /* reset class tparms att_indices ptrs to expanded model struct */ for (i_class=0; i_classn_classes; i_class++) { n_terms = clsf->classes[i_class]->model->n_terms; for (i_term=0; i_termclasses[i_class]->tparms[i_term]; tparm->att_indices = clsf->classes[i_class]->model->terms[i_term]->att_list; } } /* If needed, restore the class weights vectors: */ if ((want_wts_p == TRUE) || (update_wts_p == TRUE)) expand_clsf_wts( clsf, wts_vector, num_wts); if ((update_wts_p == TRUE) && (wts_vector == NULL)) update_wts( clsf, clsf); return(clsf); } /* EXPAND_CLSF_MODELS 23jan95 wmt: new expand models, reading model file and expanding models, if necessary */ void expand_clsf_models( clsf_DS clsf) { int i_model, i_class, force = FALSE; model_DS *models, *comp_models; FILE *log_file_fp = NULL, *stream = NULL; comp_models = clsf->models; models = (model_DS *) malloc( clsf->num_models * sizeof( model_DS)); for (i_model=0; i_modelnum_models; i_model++) models[i_model] = expand_model( comp_models[i_model]); clsf->models = models; for (i_model=0; (i_model < clsf->num_models); i_model++) conditional_expand_model_terms( clsf->models[i_model], force, log_file_fp, stream); /* reset model ptrs */ for (i_class=0; i_classn_classes; i_class++) { for (i_model=0; i_modelnum_models; i_model++) { if (clsf->classes[i_class]->model->file_index == clsf->models[i_model]->file_index) { clsf->classes[i_class]->model = clsf->models[i_model]; break; } } } /* free comp models */ for (i_model=0; i_modelnum_models; i_model++) free_model_DS( comp_models[i_model], i_model); free( comp_models); } /* EXPAND_CLSF_WTS 13feb98 wmt: check for malloc/realloc failures Normally called from Expand-clsf. This first checks to see if the classification weights need expansion. It then checks to see if wts_vector is provided and suitable, optionally using it. Otherwise wts vectors of n_data length are generated and filled with zeros. Note that a n_classes wts_vector of nils is accepted as a special case, allowing use of the with-compressed-clsf-seq macro with classes having previously compressed weights. */ void expand_clsf_wts( clsf_DS clsf, float **wts_vector, int num_wts) { int i, j, n_data, found, n_classes; float *cl_wts, sum; class_DS *classes = clsf->classes; n_classes=clsf->n_classes; n_data = clsf->database->n_data; /* printf("\n###in expand_clsf_wts with ndata,numwts=%d %d\n",n_data,num_wts); jtpDBG */ found=0; i=0; while(found==0 && iwts; if( cl_wts==NULL) found = 1; else{ sum = 0.0; for (j=0; jnum_wts; j++) sum += cl_wts[j]; if ((classes[i]->num_wts != n_data) || (classes[i]->w_j != sum)) found = 1; }/*end if else*/ i++; } if (found == 0) { /* Check if class-DS-wts exist and have correct totals. */ found = 1; if (num_wts == n_classes && wts_vector != NULL) { /* Check wts lengths and sums against classes */ for (i=0; iw_j != sum))) found = 0; ***************** commented JTP */ /* above section recoded just let bad sum catch bad length*/ for (j=0; jw_j != sum)) found = 0; } if (found == 0) { /* Use the given wts. */ for (i=0; iwts != NULL) free(classes[i]->wts); /* note that wts_vector is used as is not copied */ classes[i]->wts = wts_vector[i]; } } else for (i=0; iwts == NULL) { classes[i]->wts = (float *) malloc(n_data * sizeof(float)); if (classes[i]->wts == NULL) { fprintf( stderr, "ERROR: expand_clsf_wts(1): out of memory, malloc returned NULL!\n"); exit(1); } } for (j=0;jwts[j]=0.0; } } else for (i=0; iwts == NULL) { classes[i]->wts = (float *) malloc(n_data * sizeof(float)); if (classes[i]->wts == NULL) { fprintf( stderr, "ERROR: expand_clsf_wts(2): out of memory, malloc returned NULL!\n"); exit(1); } } for (j=0;jwts[j]=0.0; } } } /* SAVE_CLSF_SEQ 12nov94 wmt: activated this function 18feb95 wmt: write file as temporary and rename after completion (G_safe_file_writing_p should be true only for unix systems, since this does system calls for mv and rm shell commands) 13mar95 wmt: add binary file capability 27apr95 wmt: Solaris 2.4 fails open, unless fopen/fclose is done first 02may95 wmt: double size of str - prevent "Premature end of file reading symbol table" error. 16may95 wmt: converted binary i/o to ANSI This saves a sequence of classifications to a file. */ void save_clsf_seq( clsf_DS *clsf_seq, int num, char *save_file_ptr, unsigned int save_compact_p, char *results_or_chkpt) { FILE *save_file_fp = NULL; static fxlstr temp_save_file, save_file; shortstr ext_type, temp_ext_type; int str_length = 2 * sizeof( fxlstr); char caller[] = "save_clsf_seq", *str; str = (char *) malloc( str_length); if (eqstring( results_or_chkpt, "results")) { if (save_compact_p == TRUE) { strcpy( ext_type, "results_bin"); strcpy( temp_ext_type, "results_tmp_bin"); } else { strcpy( ext_type, "results"); strcpy( temp_ext_type, "results_tmp"); } } else if (eqstring( results_or_chkpt, "chkpt")) { if (save_compact_p == TRUE) { strcpy( ext_type, "checkpoint_bin"); strcpy( temp_ext_type, "checkpoint_tmp_bin"); } else { strcpy( ext_type, "checkpoint"); strcpy( temp_ext_type, "checkpoint_tmp"); } } else { fprintf( stderr, "ERROR: save file extension type %s not handled\n", results_or_chkpt); abort(); } temp_save_file[0] = '\0'; save_file[0] = '\0'; make_and_validate_pathname( ext_type, save_file_ptr, &save_file, FALSE); if (G_safe_file_writing_p == TRUE) { make_and_validate_pathname( temp_ext_type, save_file_ptr, &temp_save_file, FALSE); save_file_fp = fopen( temp_save_file, (save_compact_p) ? "wb" : "w"); } else save_file_fp = fopen( save_file, (save_compact_p) ? "wb" : "w"); if (save_compact_p) dump_clsf_seq( clsf_seq, num, save_file_fp); else write_clsf_seq( clsf_seq, num, save_file_fp); fclose( save_file_fp); if (G_safe_file_writing_p == TRUE) { if ((save_file_fp = fopen( save_file, "r")) != NULL) { fclose( save_file_fp); safe_sprintf( str, str_length, caller, "rm %s", save_file); system( str); } safe_sprintf( str, str_length, caller, "mv %s %s", temp_save_file, save_file); system( str); } free( str); } /* WRITE_CLSF_SEQ 18nov94 wmt: output clsf header prior to outputting the clsfs Performs Write-Clsf on a sequence of classifications. Since we must do a dynamic traversal of the clsf nested structures in order to write them out, we will do the "compress-clsf/compress-database/ compress-models" functions as a part of the writing -- thus no compress-clsf, write-clsfs, and then expand clsf (wmt). */ void write_clsf_seq( clsf_DS *clsf_seq, int num, FILE *stream) { int i; char caller[] = "write_clsf_seq"; safe_fprintf( stream, caller, "# ordered sequence of clsf_DS's: 0 -> %d\n", num - 1); for (i=0; ilog_a_x_h); safe_fprintf( stream, caller, "ac_version %s\n", G_ac_version); for (i=0; ilog_p_x_h_pi_theta, clsf->log_a_x_h); if (clsf_num == 0) write_database_DS( clsf->database, stream); else safe_fprintf(stream, caller, "database_DS_ptr\n"); safe_fprintf(stream, caller, "num_models\n"); safe_fprintf(stream, caller, "%d\n", clsf->num_models); for (i=0; inum_models; i++) { if (clsf_num == 0) write_model_DS( clsf->models[i], i, clsf->database, stream); else safe_fprintf(stream, caller, "model_DS_ptr %d\n", i); } safe_fprintf(stream, caller, "n_classes\n"); safe_fprintf(stream, caller, "%d\n", clsf->n_classes); write_class_DS_s( clsf->classes, clsf->n_classes, stream); safe_fprintf(stream, caller, "min_class_wt\n"); safe_fprintf(stream, caller, "%.7f\n", clsf->min_class_wt); /* clsf->reports is only used for report generation - do not output */ safe_fprintf(stream, caller, "chkpt_DS\n"); safe_fprintf(stream, caller, "accumulated_try_time, current_try_j_in, current_cycle\n"); safe_fprintf(stream, caller, "%d %d %d\n", clsf->checkpoint->accumulated_try_time, clsf->checkpoint->current_try_j_in, clsf->checkpoint->current_cycle); } /* WRITE_DATABASE_DS 13nov94 wmt: new write compressed database_DS contents in ascii */ void write_database_DS( database_DS database, FILE *stream) { int i; char caller[] = "write_database_DS"; safe_fprintf(stream, caller, "database_DS\n"); safe_fprintf(stream, caller, "data_file, header_file\n"); safe_fprintf(stream, caller, "%s %s\n", database->data_file, database->header_file); safe_fprintf(stream, caller, "n_data, n_atts, input_n_atts\n"); safe_fprintf(stream, caller, "%d %d %d\n", database->n_data, database->n_atts, database->input_n_atts); /* Ordered N-atts vector of att_DS describing the attributes. */ for (i=0; in_atts; i++) write_att_DS( database->att_info[i], i, stream); } /* WRITE_ATT_DS 13may02 wmt: for warnings_and_errors->num_expander_warnings and warnings_and_errors->num_expander_errors, write char by char, removing \n characters. Make sure that msg is only 1 line, since that is how it is read in. 13nov94 wmt: new write att_DS contents in ascii */ void write_att_DS( att_DS att_info, int n_att, FILE *stream) { real_stats_DS real_stats; discrete_stats_DS discrete_stats; warn_err_DS warnings_and_errors; fxlstr line; int i, j; char caller[] = "write_att_DS"; safe_fprintf(stream, caller, "att_DS %d\n", n_att); safe_fprintf(stream, caller, "type, subtype, dscrp\n"); safe_fprintf(stream, caller, "%s %s \"%s\" \n", att_info->type, att_info->sub_type, att_info->dscrp); if (eqstring(att_info->type, "real")) { real_stats = att_info->r_statistics; safe_fprintf(stream, caller, "real_stats_DS\n"); safe_fprintf(stream, caller, "count, max, min, mean, var\n"); safe_fprintf(stream, caller, "%d %.7e %.7e %.7e %.7e\n", real_stats->count, real_stats->mx, real_stats->mn, real_stats->mean, real_stats->var); } else if (eqstring(att_info->type, "discrete")) { discrete_stats = att_info->d_statistics; safe_fprintf(stream, caller, "discrete_stats_DS\n"); safe_fprintf(stream, caller, "range, n_observed\n"); safe_fprintf(stream, caller, "%d %d\n", discrete_stats->range, discrete_stats->n_observed); for(i=0; i < discrete_stats->range; i++) safe_fprintf(stream, caller, "%d %d\n", i, discrete_stats->observed[i]); } else if (eqstring(att_info->type, "dummy")) { safe_fprintf(stream, caller, "dummy_stats_DS\n"); safe_fprintf(stream, caller, "%d\n", NULL); } else { fprintf(stderr, "\nERROR: att_info->type %s not handled\n", att_info->type); exit(1); } if (! eqstring(att_info->type, "dummy")) { safe_fprintf(stream, caller, "n_props, range, zero_point, n_trans\n"); safe_fprintf(stream, caller, "%d %d %f %d\n", att_info->n_props, att_info->range, att_info->zero_point, att_info->n_trans); safe_fprintf(stream, caller, "translations_DS\n"); if (att_info->translations != NULL) for (i=0; i < att_info->n_trans; i++) safe_fprintf(stream, caller, "%d %s\n", i, att_info->translations[i]); else safe_fprintf(stream, caller, "%d\n", NULL); safe_fprintf(stream, caller, "props_DS\n"); if (att_info->props != NULL) for (i=0; i < att_info->n_props; i++) { if (eqstring( (char *) att_info->props[i][2], "int") == TRUE) safe_fprintf(stream, caller, "%s %s %d\n", (char *) att_info->props[i][0], (char *) att_info->props[i][2], *((int *) att_info->props[i][1])); else if (eqstring( (char *) att_info->props[i][2], "flt") == TRUE) safe_fprintf(stream, caller, "%s %s %f\n", (char *) att_info->props[i][0], (char *) att_info->props[i][2], *((float *) att_info->props[i][1])); else if (eqstring( (char *) att_info->props[i][2], "str") == TRUE) safe_fprintf(stream, caller, "%s %s %s\n", (char *) att_info->props[i][0], (char *) att_info->props[i][2], (char *) att_info->props[i][1]); else { fprintf( stderr, "ERROR: property list type %s, not handled!\n", (char *) att_info->props[i][2]); abort(); } } else safe_fprintf(stream, caller, "%d\n", NULL); warnings_and_errors = att_info->warnings_and_errors; safe_fprintf(stream, caller, "warn_err_DS\n"); safe_fprintf(stream, caller, "unspecified_dummy_warning, single_valued_warning, " "num_expander_warnings, num_expander_errors\n"); safe_fprintf(stream, caller, "%s %s %d %d\n", eqstring(warnings_and_errors->unspecified_dummy_warning, "") ? "NULL" : warnings_and_errors->unspecified_dummy_warning, eqstring(warnings_and_errors->single_valued_warning, "") ? "NULL" : warnings_and_errors->single_valued_warning, warnings_and_errors->num_expander_warnings, warnings_and_errors->num_expander_errors); /* float *unused_translators_warning; discrete translations not implementated */ for (i=0; i < warnings_and_errors->num_expander_warnings; i++) { /* safe_fprintf(stream, caller, "%s\n", warnings_and_errors->model_expander_warnings[i]); write char by char, removing \n characters */ strcpy( line, warnings_and_errors->model_expander_warnings[i]); for (j=0; j < strlen( line); j++) { if (line[j] != '\n') putc( line[j], stream); } putc( '\n', stream); } for (i=0; i < warnings_and_errors->num_expander_errors; i++) { /* safe_fprintf(stream, caller, "%s\n", warnings_and_errors->model_expander_errors[i]); write char by char, removing \n characters */ strcpy( line, warnings_and_errors->model_expander_errors[i]); for (j=0; j < strlen( line); j++) { if (line[j] != '\n') putc( line[j], stream); } putc( '\n', stream); } safe_fprintf(stream, caller, "rel_error, error, missing\n"); safe_fprintf(stream, caller, "%.7e %.7e %d\n", att_info->rel_error, att_info->error, att_info->missing); } } /* WRITE_MODEL_DS 17nov94 wmt: new write model_DS contents in ascii - one or more models -- in compressed form */ void write_model_DS( model_DS model, int model_num, database_DS database, FILE *stream) { char caller[] = "write_model_DS"; safe_fprintf(stream, caller, "model_DS %d\n", model_num); safe_fprintf(stream, caller, "id, file_index\n"); safe_fprintf(stream, caller, "%s %d\n", model->id, model->file_index); safe_fprintf(stream, caller, "model_file\n"); safe_fprintf(stream, caller, "%s \n", model->model_file); /* output the data_file, header file, and n-data as the compressed database_DS */ safe_fprintf(stream, caller, "data_file, header_file, n_data\n"); safe_fprintf(stream, caller, "%s %s %d\n", database->data_file, database->header_file, database->n_data); } /* WRITE_TERM_DS 18nov94 wmt: new write term_DS to ascii file */ void write_term_DS( term_DS term, int n_term, FILE *stream) { int parm_num = 0; char caller[] = "write_term_DS"; safe_fprintf(stream, caller, "term_DS %d\n", n_term); safe_fprintf(stream, caller, "n_atts, type\n"); safe_fprintf(stream, caller, "%d %s\n", term->n_atts, term->type); write_vector_float( term->att_list, term->n_atts, stream); write_tparm_DS( term->tparm, parm_num, stream); } /* WRITE_TPARM_DS 18nov94 wmt: new write tparm_DS (term params) to ascii file */ void write_tparm_DS( tparm_DS term_param, int parm_num, FILE *stream) { char caller[] = "write_tparm_DS"; safe_fprintf(stream, caller, "tparm_DS %d\n", parm_num); safe_fprintf(stream, caller, "n_atts, tppt(type)\n"); safe_fprintf(stream, caller, "%d %d ", term_param->n_atts, term_param->tppt); switch(term_param->tppt) { case SM: write_sm_params( &(term_param->ptype.sm), term_param->n_atts, stream); break; case SN_CM: write_sn_cm_params( &(term_param->ptype.sn_cm), stream); break; case SN_CN: write_sn_cn_params( &(term_param->ptype.sn_cn), stream); break; case MM_D: write_mm_d_params( &(term_param->ptype.mm_d), term_param->n_atts, stream); break; case MM_S: write_mm_s_params( &(term_param->ptype.mm_s), term_param->n_atts, stream); break; case MN_CN: write_mn_cn_params( &(term_param->ptype.mn_cn), term_param->n_atts, stream); break; default: printf("\n write_tparms_DS: unknown type of enum MODEL_TYPES=%d\n", term_param->tppt); abort(); } safe_fprintf(stream, caller, "n_term, n_att, n_att_indices, n_datum, n_data\n"); safe_fprintf(stream, caller, "%d %d %d %d %d\n", term_param->n_term, term_param->n_att, term_param->n_att_indices, term_param->n_datum, term_param->n_data); safe_fprintf(stream, caller, "w_j, ranges, class_wt, disc_scale\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e\n", term_param->w_j, term_param->ranges, term_param->class_wt, term_param->disc_scale); safe_fprintf(stream, caller, "log_pi, log_att_delta, log_delta, wt_m, log_marginal\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e %.7e\n", term_param->log_pi, term_param->log_att_delta, term_param->log_delta, term_param->wt_m, term_param->log_marginal); /* term_param->wts, term_param->datum, term_param->att_indices, term_param->data are just ptrs to other structs -- no need to output */ } /* WRITE_MM_D_PARAMS 21nov94 wmt: new write mm_d params to ascii file */ void write_mm_d_params( struct mm_d_param *param, int n_atts, FILE *stream) { int i, m = 0; char caller[] = "write_mm_d_params"; safe_fprintf(stream, caller, "mm_d_params\n"); for(i=0; isizes[i]; printf("row %d, size %d\n", i, m); safe_fprintf(stream, caller, "wts\n"); write_vector_float(param->wts[i], m, stream); safe_fprintf(stream, caller, "probs\n"); write_vector_float(param->probs[i], m, stream); safe_fprintf(stream, caller, "log_probs\n"); write_vector_float(param->log_probs[i], m, stream); } safe_fprintf(stream, caller, "wts_vec\n"); write_vector_float(param->wts_vec, m, stream); safe_fprintf(stream, caller, "probs_vec\n"); write_vector_float(param->probs_vec, m, stream); safe_fprintf(stream, caller, "log_probs_vec\n"); write_vector_float(param->log_probs_vec, m, stream); } /* WRITE_MM_S_PARAMS 21nov94 wmt: new write mm_s_params to ascii file -- incomplete */ void write_mm_s_params( struct mm_s_param *param, int n_atts, FILE *stream) { char caller[] = "write_mm_s_params"; safe_fprintf(stream, caller, "mm_s_params\n"); safe_fprintf(stream, caller, "count, wt, prob, log_prob\n"); safe_fprintf(stream, caller, "%d %.7e %.7e %.7e\n", param->count, param->wt, param->prob, param->log_prob); } /* WRITE_MN_CN_PARAMS 21nov94 wmt: new write mn_cn_params to ascii file */ void write_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *stream) { char caller[] = "write_mn_cn_params"; safe_fprintf(stream, caller, "mn_cn_params\n"); safe_fprintf(stream, caller, "ln_root log_ranges\n"); safe_fprintf(stream, caller, "%.7e %.7e\n", param->ln_root, param->log_ranges); safe_fprintf(stream, caller, "emp_means\n"); write_vector_float(param->emp_means, n_atts, stream); safe_fprintf(stream, caller, "emp_covar\n"); write_matrix_float(param->emp_covar, n_atts, n_atts, stream); safe_fprintf(stream, caller, "means\n"); write_vector_float(param->means, n_atts, stream); safe_fprintf(stream, caller, "covariance\n"); write_matrix_float(param->covariance, n_atts, n_atts, stream); safe_fprintf(stream, caller, "factor\n"); write_matrix_float(param->factor, n_atts, n_atts, stream); /* values, temp_v & temp_m are temporary storage - do not save, just reinit to 0.0 */ safe_fprintf(stream, caller, "min_sigma_2s\n"); write_vector_float(param->min_sigma_2s, n_atts, stream); } /* WRITE_SM_PARAMS 21nov94 wmt: new write sm_params to ascii file n_atts is actually n_vals -- an overloaded slot definition */ void write_sm_params( struct sm_param *param, int n_atts, FILE *stream) { char caller[] = "write_sm_params"; safe_fprintf(stream, caller, "sm_params\n"); safe_fprintf(stream, caller, "gamma_term, range, range_m1, inv_range, " "range_factor\n"); safe_fprintf(stream, caller, "%.7e %d %.7e %.7e %.7e \n", param->gamma_term, param->range, param->range_m1, param->inv_range, param->range_factor); safe_fprintf(stream, caller, "val_wts\n"); write_vector_float(param->val_wts, n_atts, stream); safe_fprintf(stream, caller, "val_probs\n"); write_vector_float(param->val_probs, n_atts, stream); safe_fprintf(stream, caller, "val_log_probs\n"); write_vector_float(param->val_log_probs, n_atts, stream); } /* WRITE_SN_CM_PARAMS 21nov94 wmt: new write sn_cm_params to ascii file */ void write_sn_cm_params( struct sn_cm_param *param, FILE *stream) { char caller[] = "write_sn_cm_params"; safe_fprintf(stream, caller, "sn_cm_params\n"); safe_fprintf(stream, caller, "known_wt, known_prob, known_log_prob, unknown_log_prob\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e\n", param->known_wt, param->known_prob, param->known_log_prob, param->unknown_log_prob); safe_fprintf(stream, caller, "weighted_mean, weighted_var, mean\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", param->weighted_mean, param->weighted_var, param->mean); safe_fprintf(stream, caller, "sigma, log_sigma, variance, log_variance, inv_variance\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e %.7e\n", param->sigma, param->log_sigma, param->variance, param->log_variance, param->inv_variance); safe_fprintf(stream, caller, "ll_min_diff, skewness, kurtosis\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", param->ll_min_diff, param->skewness, param->kurtosis); safe_fprintf(stream, caller, "prior_sigma_min_2, prior_mean_mean, prior_mean_sigma\n"); safe_fprintf(stream, caller, " %.7e %.7e %.7e\n", param->prior_sigma_min_2, param->prior_mean_mean, param->prior_mean_sigma); safe_fprintf(stream, caller, "prior_sigmas_term, prior_sigma_max_2, prior_mean_var, " "prior_known_prior\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e\n", param->prior_sigmas_term, param->prior_sigma_max_2, param->prior_mean_var, param->prior_known_prior); } /* WRITE_SN_CN_PARAMS 21nov94 wmt: new write sn_cn_params to ascii file */ void write_sn_cn_params( struct sn_cn_param *param, FILE *stream) { char caller[] = "write_sn_cn_params"; safe_fprintf(stream, caller, "sn_cn_params\n"); safe_fprintf(stream, caller, "weighted_mean, weighted_var, mean\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", param->weighted_mean, param->weighted_var, param->mean); safe_fprintf(stream, caller, "sigma, log_sigma, variance, log_variance, inv_variance\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e %.7e %.7e\n", param->sigma, param->log_sigma, param->variance, param->log_variance, param->inv_variance); safe_fprintf(stream, caller, "ll_min_diff, skewness, kurtosis\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", param->ll_min_diff, param->skewness, param->kurtosis); safe_fprintf(stream, caller, "prior_sigma_min_2, prior_mean_mean, prior_mean_sigma\n"); safe_fprintf(stream, caller, " %.7e %.7e %.7e\n", param->prior_sigma_min_2, param->prior_mean_mean, param->prior_mean_sigma); safe_fprintf(stream, caller, "prior_sigmas_term, prior_sigma_max_2, prior_mean_var, " "prior_known_prior\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", param->prior_sigmas_term, param->prior_sigma_max_2, param->prior_mean_var); } /* WRITE_PRIORS_DS 18nov94 wmt: new write priors_DS to ascii file. only sn-cn & sn-cm models are non-NULL not used */ void write_priors_DS(priors_DS priors, int n_prior, FILE *stream) { char caller[] = "write_priors_DS"; safe_fprintf(stream, caller, "priors_DS %d\n", n_prior); if (priors != NULL) { safe_fprintf(stream, caller, "known_prior, sigma_min, sigma_max\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", priors->known_prior, priors->sigma_min, priors->sigma_max); safe_fprintf(stream, caller, "mean_mean, mean_sigma, mean_var\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", priors->mean_mean, priors->mean_sigma, priors->mean_var); safe_fprintf(stream, caller, "minus_log_log_sigmas_ratio, minus_log_mean_sigma\n"); safe_fprintf(stream, caller, "%.7e %.7e\n", priors->minus_log_log_sigmas_ratio, priors->minus_log_mean_sigma); } else safe_fprintf(stream, caller, "NULL\n"); } /* WRITE_CLASS_DS_S 22nov94 wmt: new write class_DS to ascii file. */ void write_class_DS_s( class_DS *classes, int n_classes, FILE *stream) { int i, j; char caller[] = "write_class_DS_s"; for (i=0; i < n_classes; i++) { safe_fprintf(stream, caller, "class_DS %d\n", i); safe_fprintf(stream, caller, "w_j, pi_j\n"); safe_fprintf(stream, caller, "%.7e %.7e\n", classes[i]->w_j, classes[i]->pi_j); safe_fprintf(stream, caller, "log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j\n"); safe_fprintf(stream, caller, "%.7e %.7e %.7e\n", classes[i]->log_pi_j, classes[i]->log_a_w_s_h_pi_theta, classes[i]->log_a_w_s_h_j); safe_fprintf(stream, caller, "known_parms_p, num_tparms\n"); safe_fprintf(stream, caller, "%d %d\n", classes[i]->known_parms_p, classes[i]->num_tparms); for (j=0; jnum_tparms; j++) write_tparm_DS( classes[i]->tparms[j], j, stream); /* num_i_values, i_values, i_sum, & max_i_value used only in reports - do not output */ safe_fprintf(stream, caller, "num_wts\n%d\n", classes[i]->num_wts); safe_fprintf(stream, caller, "model_DS_ptr %d\n", classes[i]->model->file_index); } } /* MAKE_AND_VALIDATE_PATHNAME 06oct94 wmt 01may95 wmt: change type of file_ptr: char * => fxlstr * 18may95 wmt: added "-predict" mode 13jun95 wmt: only do fclose, if fopen returns non-NULL 07sep95 wmt: check for presence of '.' 13feb98 wmt: change strchr to strrchr to handle `../ac.xxx' create pathname with forced extension and optionally validate its existence */ int make_and_validate_pathname (char *type, char *file_arg, fxlstr *file_ptr, int validate_p) { FILE *file_fp; char *file_arg_ext, *file_ext; char dot_char = '.'; int file_valid_p = TRUE; if (eqstring(type, "data")) file_ext = DATA_FILE_TYPE; else if (eqstring(type, "header")) file_ext = HEADER_FILE_TYPE; else if (eqstring(type, "model")) file_ext = MODEL_FILE_TYPE; else if (eqstring(type, "search params")) file_ext = SEARCH_PARAMS_FILE_TYPE; else if (eqstring(type, "reports params")) file_ext = REPORTS_PARAMS_FILE_TYPE; else if (eqstring(type, "search")) file_ext = SEARCH_FILE_TYPE; else if (eqstring(type, "search_tmp")) file_ext = TEMP_SEARCH_FILE_TYPE; else if (eqstring(type, "results")) file_ext = RESULTS_FILE_TYPE; else if (eqstring(type, "results_tmp")) file_ext = TEMP_RESULTS_FILE_TYPE; else if (eqstring(type, "results_bin")) file_ext = RESULTS_BINARY_FILE_TYPE; else if (eqstring(type, "results_tmp_bin")) file_ext = TEMP_RESULTS_BINARY_FILE_TYPE; else if (eqstring(type, "log")) file_ext = SEARCH_LOG_FILE_TYPE; else if (eqstring(type, "rlog")) file_ext = REPORT_LOG_FILE_TYPE; else if (eqstring(type, "checkpoint")) file_ext = CHECKPOINT_FILE_TYPE; else if (eqstring(type, "checkpoint_tmp")) file_ext = TEMP_CHECKPOINT_FILE_TYPE; else if (eqstring(type, "checkpoint_bin")) file_ext = CHECKPOINT_BINARY_FILE_TYPE; else if (eqstring(type, "checkpoint_tmp_bin")) file_ext = TEMP_CHECKPOINT_BINARY_FILE_TYPE; else if (eqstring(type, "influ_vals")) file_ext = INFLU_VALS_FILE_TYPE; else if (eqstring(type, "xref_class")) file_ext = XREF_CLASS_FILE_TYPE; else if (eqstring(type, "xref_case")) file_ext = XREF_CASE_FILE_TYPE; else if (eqstring(type, "predict")) file_ext = PREDICT_FILE_TYPE; else { fprintf(stderr, "ERROR: file type: %s not handled! \n", type); exit(1); } if ((int) strlen( file_arg) > (STRLIMIT - 1)) { fprintf ( stderr, "ERROR: pathname %s is greater than %d chars -- see autoclass.h \n", file_arg, (STRLIMIT - 1)); exit(1); } file_arg_ext = strrchr( file_arg, dot_char); if (file_arg_ext == NULL) { fprintf (stderr, "ERROR: pathname %s does not contain '.' character \n", file_arg); file_valid_p = FALSE; } else { strncat( *file_ptr, file_arg, strlen( file_arg) - strlen( file_arg_ext)); strncat( *file_ptr, file_ext, strlen( file_ext)); /* printf("file %s\n", *file_ptr); */ if (validate_p == TRUE) { file_fp = fopen( *file_ptr, "r"); if (file_fp == NULL) { fprintf( stderr, "ERROR: %s file: %s%s not found! \n", type, ((*file_ptr)[0] == G_slash) ? "" : G_absolute_pathname, *file_ptr); file_valid_p = FALSE; } else fclose(file_fp); } } return(file_valid_p); } /* VALIDATE_RESULTS_PATHNAME 17mar95 wmt: new 27apr95 wmt: Solaris 2.4 fails open, unless fopen/fclose is done first 01may95 wmt: change type of file_ptr: char * => fxlstr * 16may95 wmt: converted binary i/o to ANSI 13jun95 wmt: only do fclose, if fopen returns non-NULL 06sep95 wmt: prefer the user supplied file extension, and only attempt to open ".result-bin", and then ".results", if no extension or an invalid extension is supplied. 25jun96 wmt: use binary_file, rather than file, were it is intended 18apr97 wmt: handle checkpoint files similarly to results files => determine if they are ascii or binary, rather than assuming they are binary. 13feb98 wmt: change strchr to strrchr to handle `../ac.results' check for either binary or ascii "results" or "checkpoint" files */ int validate_results_pathname( char *file_pathname, fxlstr *found_file_ptr, char *type, int exit_if_error_p, int silent_p) { FILE *file_fp, *binary_file_fp; char *file_arg_ext; static fxlstr file, binary_file; char dot_char = '.', user_extension[10] = ""; int file_valid_p = FALSE, file_arg_ext_length; file[0] = binary_file[0] = '\0'; if ((int) strlen( file_pathname) > (STRLIMIT - 1)) { fprintf (stderr, "ERROR: pathname %s is greater than %d chars -- see autoclass.h \n", file_pathname, (STRLIMIT - 1)); exit(1); } file_arg_ext = strrchr( file_pathname, dot_char); if (file_arg_ext == NULL) { fprintf (stderr, "ERROR: results file pathname %s does not contain '.' character \n", file_pathname); if (exit_if_error_p == TRUE) exit(1); else return (file_valid_p); } file_arg_ext_length = (int) strlen( file_arg_ext); if (eqstring( type, "results")) { if (strstr( file_pathname, RESULTS_FILE_TYPE) && (file_arg_ext_length == (int) strlen( RESULTS_FILE_TYPE))) { strcpy( file, file_pathname); strcpy (user_extension, "ascii"); } else if (strstr( file_pathname, RESULTS_BINARY_FILE_TYPE) && (file_arg_ext_length == (int) strlen( RESULTS_BINARY_FILE_TYPE))) { strcpy( binary_file, file_pathname); strcpy (user_extension, "binary"); } } else if (eqstring( type, "checkpoint")) { if (strstr( file_pathname, CHECKPOINT_FILE_TYPE) && (file_arg_ext_length == (int) strlen( CHECKPOINT_FILE_TYPE))) { strcpy( file, file_pathname); strcpy (user_extension, "ascii"); } else if (strstr( file_pathname, CHECKPOINT_BINARY_FILE_TYPE) && (file_arg_ext_length == (int) strlen( CHECKPOINT_BINARY_FILE_TYPE))) { strcpy( binary_file, file_pathname); strcpy (user_extension, "binary"); } } else { fprintf( stderr, "ERROR: type %s not handled by validate_results_pathname\n", type); abort(); } if (eqstring( user_extension, "")) { strncat( file, file_pathname, strlen( file_pathname) - file_arg_ext_length); strncat( binary_file, file_pathname, strlen( file_pathname) - file_arg_ext_length); if (eqstring( type, "results")) { strncat( file, RESULTS_FILE_TYPE, strlen( RESULTS_FILE_TYPE)); strncat( binary_file, RESULTS_BINARY_FILE_TYPE, strlen( RESULTS_BINARY_FILE_TYPE)); } else if (eqstring( type, "checkpoint")) { strncat( file, CHECKPOINT_FILE_TYPE, strlen( CHECKPOINT_FILE_TYPE)); strncat( binary_file, CHECKPOINT_BINARY_FILE_TYPE, strlen( CHECKPOINT_BINARY_FILE_TYPE)); } } /* printf("file %s\n", file); */ /* printf("binary-file %s\n", binary_file); */ if ((eqstring( user_extension, "")) || (eqstring( user_extension, "binary"))) { binary_file_fp = fopen( binary_file, "rb"); if (binary_file_fp != NULL) { file_valid_p = TRUE; strcpy( *found_file_ptr, binary_file); fclose( binary_file_fp); } } if ((file_valid_p == FALSE) && ((eqstring( user_extension, "")) || (eqstring( user_extension, "ascii")))) { file_fp = fopen( file, "r"); if (file_fp != NULL) { file_valid_p = TRUE; strcpy( *found_file_ptr, file); fclose( file_fp); } } if ((silent_p != TRUE) && (file_valid_p == FALSE)) { if (eqstring( user_extension, "")) { fprintf(stderr, "ERROR: neither \n %s%s, \n nor \n " "%s%s \n were found! \n", (binary_file[0] == G_slash) ? "" : G_absolute_pathname, binary_file, (file[0] == G_slash) ? "" : G_absolute_pathname, file); } else if (eqstring( user_extension, "ascii")) { fprintf(stderr, "ERROR: %s%s, \n was not found! \n", (file[0] == G_slash) ? "" : G_absolute_pathname, file); } else { fprintf(stderr, "ERROR: %s%s, \n was not found! \n", (binary_file[0] == G_slash) ? "" : G_absolute_pathname, binary_file); } if (exit_if_error_p == TRUE) exit(1); } return (file_valid_p); } /* VALIDATE_DATA_PATHNAME 24apr95 wmt: new 27apr95 wmt: Solaris 2.4 fails open, unless fopen/fclose is done first 01may95 wmt: change type of file_ptr: char * => fxlstr * 16may95 wmt: converted binary i/o to ANSI 13jun95 wmt: only do fclose, if fopen returns non-NULL 06sep95 wmt: prefer the user supplied file extension, and only attempt to open ".db2", and then ".db2-bin", if no extension or an invalid extension is supplied. 25jun96 wmt: use binary_file, rather than file, were it is intended 13feb98 wmt: change strchr to strrchr to handle `../ac.db2' check for either binary or ascii "data" files */ int validate_data_pathname( char *file_pathname, fxlstr *found_file_ptr, int exit_if_error_p, int silent_p) { FILE *file_fp, *binary_file_fp; static fxlstr file, binary_file; char dot_char = '.', user_extension[10] = "", *file_arg_ext; int file_valid_p = FALSE, file_arg_ext_length; file[0] = binary_file[0] = '\0'; if ((int) strlen( file_pathname) > (STRLIMIT - 1)) { fprintf (stderr, "ERROR: data file pathname %s is greater than %d chars -- " "see autoclass.h \n", file_pathname, (STRLIMIT - 1)); exit(1); } file_arg_ext = strrchr( file_pathname, dot_char); if (file_arg_ext == NULL) { fprintf (stderr, "ERROR: data file pathname %s does not contain '.' character \n", file_pathname); if (exit_if_error_p == TRUE) exit(1); else return (file_valid_p); } file_arg_ext_length = (int) strlen( file_arg_ext); if (strstr( file_pathname, DATA_FILE_TYPE) && (file_arg_ext_length == (int) strlen( DATA_FILE_TYPE))) { strcpy( file, file_pathname); strcpy (user_extension, "ascii"); } else if (strstr( file_pathname, DATA_BINARY_FILE_TYPE) && (file_arg_ext_length == (int) strlen( DATA_BINARY_FILE_TYPE))) { strcpy( binary_file, file_pathname); strcpy (user_extension, "binary"); } else { strncat( file, file_pathname, strlen( file_pathname) - file_arg_ext_length); strncat( binary_file, file_pathname, strlen( file_pathname) - file_arg_ext_length); strncat( file, DATA_FILE_TYPE, strlen( DATA_FILE_TYPE)); strncat( binary_file, DATA_BINARY_FILE_TYPE, strlen( DATA_BINARY_FILE_TYPE)); } /* printf("file %s\n", file); */ /* printf("binary-file %s\n", binary_file); */ if ((eqstring( user_extension, "")) || (eqstring( user_extension, "ascii"))) { file_fp = fopen( file, "r"); if (file_fp != NULL) { file_valid_p = TRUE; strcpy( G_data_file_format, "ascii"); strcpy( *found_file_ptr, file); fclose( file_fp); } } if ((file_valid_p == FALSE) && ((eqstring( user_extension, "")) || (eqstring( user_extension, "binary")))) { binary_file_fp = fopen( binary_file, "rb"); if (binary_file_fp != NULL) { file_valid_p = TRUE; strcpy( G_data_file_format, "binary"); strcpy( *found_file_ptr, binary_file); fclose( binary_file_fp); } } if ((silent_p != TRUE) && (file_valid_p == FALSE)) { if (eqstring( user_extension, "")) { fprintf(stderr, "ERROR: neither \n %s%s, \n nor \n " "%s%s \n were found! \n", (binary_file[0] == G_slash) ? "" : G_absolute_pathname, binary_file, (file[0] == G_slash) ? "" : G_absolute_pathname, file); } else if (eqstring( user_extension, "ascii")) { fprintf(stderr, "ERROR: %s%s, \n was not found! \n", (file[0] == G_slash) ? "" : G_absolute_pathname, file); } else { fprintf(stderr, "ERROR: %s%s, \n was not found! \n", (binary_file[0] == G_slash) ? "" : G_absolute_pathname, binary_file); } if (exit_if_error_p == TRUE) exit(1); } return (file_valid_p); } /* GET_CLSF_SEQ 13jan95 wmt: modified, add save_file_ptr, and n_best_clsfs_ptr 17feb95 wmt: add expand_list 27apr95 wmt: Solaris 2.4 fails open, unless fopen/fclose is done first 16may95 wmt: converted binary i/o to ANSI 07sep95 wmt: simplify the test for "ascii" or "binary" results file format -- also more portable. 18apr97 wmt: handle checkpoint files properly 13feb98 wmt: change strchr to strrchr to handle `../ac.results-bin' This tries to read save-file. */ clsf_DS *get_clsf_seq( char *results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, char *file_type, int *n_best_clsfs_ptr, int_list expand_list) { FILE *results_file_fp; int dot_char = '.'; char *file_ext_addr; clsf_DS *clsf_seq; file_ext_addr = strrchr( results_file_ptr, dot_char); if (eqstring( file_type, "results")) { if (strstr( results_file_ptr, RESULTS_BINARY_FILE_TYPE) && ((int) strlen( file_ext_addr) == (int) strlen( RESULTS_BINARY_FILE_TYPE))) { results_file_fp = fopen( results_file_ptr, "rb"); clsf_seq = load_clsf_seq( results_file_fp, results_file_ptr, expand_p, want_wts_p, update_wts_p, n_best_clsfs_ptr, expand_list); } else { results_file_fp = fopen( results_file_ptr, "r"); clsf_seq = read_clsf_seq( results_file_fp, results_file_ptr, expand_p, want_wts_p, update_wts_p, n_best_clsfs_ptr, expand_list); } } else if (eqstring( file_type, "checkpoint")) { if (strstr( results_file_ptr, CHECKPOINT_BINARY_FILE_TYPE) && ((int) strlen( file_ext_addr) == (int) strlen( CHECKPOINT_BINARY_FILE_TYPE))) { results_file_fp = fopen( results_file_ptr, "rb"); clsf_seq = load_clsf_seq( results_file_fp, results_file_ptr, expand_p, want_wts_p, update_wts_p, n_best_clsfs_ptr, expand_list); } else { results_file_fp = fopen( results_file_ptr, "r"); clsf_seq = read_clsf_seq( results_file_fp, results_file_ptr, expand_p, want_wts_p, update_wts_p, n_best_clsfs_ptr, expand_list); } } else { fprintf( stderr, "ERROR: file_type %s not handled by get_clsf_seq\n", file_type); abort(); } fclose( results_file_fp); return (clsf_seq); } /* READ_CLSF_SEQ 09dec 94 wmt: correct malloc/relloc 13jan95 wmt: add n_best_clsfs_ptr 17feb95 wmt: add expand_list 20sep98 wmt: strip of win/unx from ac_version, starting with version 3.3 27nov98 wmt: check for win/unx before stripping -- backward compatible Performs a sequence of Read_Clsf. */ clsf_DS *read_clsf_seq( FILE *results_file_fp, char *results_file_ptr, int expand_p, int want_wts_p, int update_wts_p, int *n_best_clsfs_ptr, int_list expand_list) { int length = 0, file_ac_version, float_p, token_length; clsf_DS clsf, first_clsf = NULL, *seq = NULL; shortstr token1, token2; fxlstr line; char caller[] = "read_clsf_seq"; do { /* read comment lines */ fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %s", token1, token2); } while (eqstring( token1, "#")); /* strip of win/unx from ac_version, if present */ if (strstr( token2, "unx") || strstr( token2, "win")) { token_length = strlen( token2); token2[token_length - 3] = '\0'; } if ((eqstring( token1, "ac_version") != TRUE) || ((file_ac_version = atof_p( token2, &float_p)) < 1.0) || (float_p != TRUE)) { fprintf( stderr, "ERROR: expecting \"ac_version n.n\", found \"%s\" \n", line); abort(); } while (1) { clsf = read_clsf( results_file_fp, expand_p, want_wts_p, update_wts_p, length, first_clsf, file_ac_version, expand_list); if (clsf != NULL) { if (length == 0) first_clsf = clsf; length++; if (seq == NULL) seq = (clsf_DS *) malloc( length * sizeof(clsf_DS)); else seq = (clsf_DS *) realloc(seq, length * sizeof(clsf_DS)); seq[length - 1] = clsf; } else break; } *n_best_clsfs_ptr = length; safe_sprintf( line, sizeof( line), caller, "ADVISORY: read %d classifications from \n %s%s\n", length, (results_file_ptr[0] == G_slash) ? "" : G_absolute_pathname, results_file_ptr); to_screen_and_log_file( line, G_log_file_fp, G_stream, TRUE); return(seq); } /* READ_CLSF 13jan95 wmt: add reading capability 17feb95 wmt: add expand_list for use by initialize_reports_from_results_pathname Intended to read a compactly represented classification as written by write_clsf_DS, and to optionally expand it to standard form. Anything else, including non-compact classifications, are returned without modification. Compact classification are identified by the presence of a list of filenames in the database field, instead of a database structure. With Expand, Wts or Update_Wts regenerates the wts vectors. update_wts also updates wts. */ clsf_DS read_clsf( FILE *results_file_fp, int expand_p, int want_wts_p, int update_wts_p, int clsf_index, clsf_DS first_clsf, int file_ac_version, int_list expand_list) { clsf_DS clsf = NULL; shortstr token1, token2; fxlstr line; int integer_p, file_clsf_index, i, model_index; model_DS *models; if (fgets( line, sizeof(line), results_file_fp) != NULL) { sscanf( line, "%s %s", token1, token2); if ((eqstring( token1, "clsf_DS") == TRUE) && ((file_clsf_index = atoi_p( token2, &integer_p)) == clsf_index) && (integer_p == TRUE)) { clsf = create_clsf_DS(); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%le %le\n", &clsf->log_p_x_h_pi_theta, &clsf->log_a_x_h); if (first_clsf == NULL) clsf->database = read_database_DS( clsf, results_file_fp, file_ac_version); else { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "database_DS_ptr") != TRUE) { fprintf( stderr, "ERROR: expecting \"database_DS_ptr\", found \"%s\"\n", line); abort(); } clsf->database = first_clsf->database; } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "num_models") != TRUE) { fprintf( stderr, "ERROR: expecting \"num_models\", found \"%s\" \n", line); abort(); } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d", &clsf->num_models); if (first_clsf == NULL) models = (model_DS *) malloc( clsf->num_models * sizeof( model_DS)); else models = first_clsf->models; clsf->models = models; for (model_index=0; model_indexnum_models; model_index++) { if (first_clsf == NULL) models[model_index] = read_model_DS( clsf, model_index, results_file_fp, file_ac_version); else { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "model_DS_ptr") != TRUE) { fprintf( stderr, "ERROR: expecting \"model_DS_ptr\", found \"%s\"\n", line); abort(); } } } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "n_classes") != TRUE) { fprintf( stderr, "ERROR: expecting \"n_classes\", found \"%s\" \n", line); abort(); } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d", &clsf->n_classes); read_class_DS_s( clsf, clsf->n_classes, results_file_fp, (first_clsf == NULL) ? clsf : first_clsf, file_ac_version); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%f", &clsf->min_class_wt); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "chkpt_DS") != TRUE) { fprintf( stderr, "ERROR: expecting \"chkpt_DS\", found \"%s\" \n", line); abort(); } for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d %d\n", &clsf->checkpoint->accumulated_try_time, &clsf->checkpoint->current_try_j_in, &clsf->checkpoint->current_cycle); clsf->next = NULL; } else { fprintf( stderr, "ERROR: expecting clsf_DS index %d, found \"%s\" \n", clsf_index, line); abort(); } if ((expand_p == TRUE) && ((expand_list[0] == END_OF_INT_LIST) || ((expand_list[0] != END_OF_INT_LIST) && (member_int_list( clsf_index+1, expand_list) == TRUE)))) { expand_clsf( clsf, want_wts_p, update_wts_p); /* fprintf( stderr, "clsf index %d expanded\n", clsf_index); */ } } return (clsf); } /* READ_DATABASE_DS 17jan95 wmt: new 06mar95 wmt: expand att_info, if needed 21jun95 wmt: initialize realloc'ed att_info read database_DS from results file - for first clsf only */ database_DS read_database_DS( clsf_DS clsf, FILE *results_file_fp, int file_ac_version) { int i, n_data, n_atts, input_n_atts, n_att; fxlstr line, data_file, header_file; shortstr token; database_DS d_base; fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s\n", token); if (eqstring( token, "database_DS") != TRUE) { fprintf( stderr, "ERROR: expecting \"database_DS\", found \"%s\"\n", line); abort(); } for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %s", data_file, header_file); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d %d", &n_data, &n_atts, &input_n_atts); d_base = create_database(); d_base->n_data = 0; strcpy(d_base->data_file, data_file); strcpy(d_base->header_file, header_file); d_base->n_data = n_data; d_base->n_atts = n_atts; d_base->input_n_atts = input_n_atts; if (n_atts > d_base->allo_n_atts) { d_base->allo_n_atts = n_atts; d_base->att_info = (att_DS *) realloc( d_base->att_info, d_base->allo_n_atts * sizeof( att_DS)); for (i=0; iatt_info[i] = NULL; } /* Ordered N-atts vector of att_DS describing the attributes. */ for (n_att=0; n_attn_atts; n_att++) read_att_DS( d_base, n_att, results_file_fp, file_ac_version); d_base->compressed_p = TRUE; d_base->separator_char = ' '; d_base->comment_char = ';'; d_base->unknown_token = '?'; return( d_base); } /* READ_MODEL_DS 17jan95 wmt: new read model_DS from results file and allocate storage - first clsf only */ model_DS read_model_DS( clsf_DS clsf, int model_index, FILE *results_file_fp, int file_ac_version) { int i, file_model_index; shortstr token1; model_DS model; fxlstr line; fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", token1, &file_model_index); if ((eqstring( token1, "model_DS") != TRUE) || (file_model_index != model_index)) { fprintf( stderr, "ERROR: expecting \"model_DS\" and model_index = %d, found \"%s\"\n", model_index, line); abort(); } model = (model_DS) malloc( sizeof( struct model)); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", model->id, &model->file_index); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", model->model_file); /* the data_file, header file, n_data are the compressed database_DS */ for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %s %d", model->data_file, model->header_file, &model->n_data); model->compressed_p = TRUE; model->database = clsf->database; /* since this is compressed model, set everthing to null */ model->expanded_terms = FALSE; model->n_terms = model->num_priors = 0; model->terms = NULL; model->priors = NULL; model->n_att_locs = 0; model->att_locs = NULL; model->n_att_ignore_ids = 0; model->att_ignore_ids = NULL; model->class_store = NULL; model->num_class_store = 0; model->global_clsf = NULL; return( model); } /* READ_CLASS_DS_S 17jan95 wmt: new read class_DS from results file and allocate storage */ void read_class_DS_s( clsf_DS clsf, int n_classes, FILE *results_file_fp, clsf_DS first_clsf, int file_ac_version) { int i, n_parm, n_class, file_n_class, file_model_file_index; shortstr token1; fxlstr line; class_DS *classes; classes = (class_DS *) malloc( n_classes * sizeof( class_DS)); clsf->classes = classes; for (n_class=0; n_class < n_classes; n_class++) { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", token1, &file_n_class); if ((eqstring( token1, "class_DS") != TRUE) || (file_n_class != n_class)) { fprintf( stderr, "ERROR: expecting \"class_DS\" and n_class = %d, found \"%s\"\n", n_class, line); abort(); } classes[n_class] = (class_DS) malloc( sizeof( struct class)); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e", &classes[n_class]->w_j, &classes[n_class]->pi_j); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %le %le", &classes[n_class]->log_pi_j, &classes[n_class]->log_a_w_s_h_pi_theta, &classes[n_class]->log_a_w_s_h_j); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d", &classes[n_class]->known_parms_p, &classes[n_class]->num_tparms); classes[n_class]->tparms = (tparm_DS *) malloc( classes[n_class]->num_tparms * sizeof( tparm_DS)); for (n_parm=0; n_parmnum_tparms; n_parm++) { classes[n_class]->tparms[n_parm] = (tparm_DS) malloc( sizeof( struct new_term_params)); read_tparm_DS( classes[n_class]->tparms[n_parm], n_parm, results_file_fp, file_ac_version); } classes[n_class]->num_i_values = 0; classes[n_class]->i_values = NULL; classes[n_class]->i_sum = 0.0; classes[n_class]->max_i_value = 0.0; for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d", &classes[n_class]->num_wts); classes[n_class]->wts = (float *) malloc( classes[n_class]->num_wts * sizeof( float)); for (i=0; inum_wts; i++) classes[n_class]->wts[i] = 0.0; if (G_clsf_storage_log_p == TRUE) { fprintf( stdout, "\nread_class_DS_s: %p, num_wts %d, wts:%p, wts-len:%d\n", (void *) classes[n_class], classes[n_class]->num_wts, (void *) classes[n_class]->wts, classes[n_class]->num_wts * (int) sizeof( float)); if (G_n_freed_classes > 0) G_n_create_classes_after_free++; } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", token1, &file_model_file_index); if (eqstring( token1, "model_DS_ptr") != TRUE) { fprintf( stderr, "ERROR: expecting \"model_DS_ptr\" and file_index, found \"%s\"\n", line); abort(); } for (i=0; inum_models; i++) { if (first_clsf->models[i]->file_index == file_model_file_index) { classes[n_class]->model = first_clsf->models[i]; break; } } classes[n_class]->next = NULL; } } /* READ_ATT_DS 17jan95 wmt: new 24jan02 wmt: do not assume that translations length < shortstr read att_DS from results file and allocate storage */ void read_att_DS( database_DS d_base, int n_att, FILE *results_file_fp, int file_ac_version) { int i, file_n_att, index, *int_value, n_comment_chars = 0, token_list_length; int int_token, expected_token_list_length = 3, n_props; float *float_value; char *string_value, **token_list = NULL, comment_chars[1], separator_char = ' '; char *token_ptr; fxlstr line, token1, token2, token3; att_DS att; real_stats_DS real_stats; discrete_stats_DS discrete_stats; comment_chars[0] = '\0'; fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", token1, &file_n_att); if ((eqstring( token1, "att_DS") != TRUE) || (file_n_att != n_att)) { fprintf( stderr, "ERROR: expecting \"att_DS\" and n_att = %d, found \"%s\"\n", n_att, line); abort(); } att = (att_DS) malloc(sizeof(struct att)); d_base->att_info[n_att] = att; fgets( line, sizeof(line), results_file_fp); /* use get_line_tokens because of imbedded blanks in att->dscrp */ token_list = get_line_tokens( results_file_fp, (int) separator_char, n_comment_chars, comment_chars, FALSE, &token_list_length); if (token_list_length != expected_token_list_length) abort(); strcpy( att->type, token_list[0]); strcpy( att->sub_type, token_list[1]); strcpy( att->dscrp, token_list[2]); for (i=0; itype, "real")) { att->r_statistics = (real_stats_DS) malloc(sizeof(struct real_stats)); att->d_statistics = NULL; real_stats = att->r_statistics; for (i=0; i<3; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %e %e %e %e", &real_stats->count, &real_stats->mx, &real_stats->mn, &real_stats->mean, &real_stats->var); } else if (eqstring(att->type, "discrete")) { att->r_statistics = NULL; att->d_statistics = (discrete_stats_DS) malloc(sizeof(struct discrete_stats)); discrete_stats = att->d_statistics; for (i=0; i<3; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line,"%d %d", &discrete_stats->range, &discrete_stats->n_observed); att->d_statistics->observed = (int *) malloc(discrete_stats->range * sizeof(int)); for(i=0; i < discrete_stats->range; i++) { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d\n", &index, &discrete_stats->observed[i]); if (index != i) { fprintf( stderr, "ERROR: expecting observed[%d], found \"%s\"\n", i, line); abort(); } } } else if (eqstring( att->type, "dummy")) { att->r_statistics = NULL; att->d_statistics = NULL; att->props = NULL; att->n_props = 0; att->translations = NULL; att->n_trans = 0; att->range = att->missing = 0; att->error = att->rel_error = att->zero_point = 0.0; att->warnings_and_errors = NULL; for (i=0; i<2; i++) fgets( line, sizeof( line), results_file_fp); } else { fprintf(stderr, "\nERROR: att_info->type %s not handled\n", att->type); abort(); } if (! eqstring( att->type, "dummy")) { for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d %f %d\n", &n_props, &att->range, &att->zero_point, &att->n_trans); att->n_props = 0; /* att->n_props is incremented by add_to_plist */ fgets( line, sizeof(line), results_file_fp); /* translations_DS */ if (att->n_trans > 0) { att->translations = (char **) malloc( att->n_trans * sizeof(char *)); for (i=0; i < att->n_trans; i++) { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %s", &int_token, token2); att->translations[i] = (char *) malloc( strlen( token2) + 1); strcpy( att->translations[i], token2); } } else { att->translations = NULL; fgets( line, sizeof(line), results_file_fp); /* read the zero */ } fgets( line, sizeof(line), results_file_fp); /* props_DS */ if (n_props > 0) { for (i=0; i < n_props; i++) { fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %s %s", token1, token2, token3); token_ptr = (char *) malloc( strlen( token1) + 1); strcpy( token_ptr, token1); if (eqstring( token2, "int") == TRUE) { int_value = (int *) malloc( sizeof( int)); *int_value = atoi( token3); add_to_plist( att, token_ptr, int_value, "int"); } else if (eqstring( token2, "flt") == TRUE) { float_value = (float *) malloc( sizeof( float)); *float_value = atof( token3); add_to_plist( att, token_ptr, float_value, "flt"); } else if (eqstring( token2, "str") == TRUE) { string_value = (char *) malloc( strlen( token3) + 1); strcpy( string_value, token3); add_to_plist( att, token_ptr, string_value, "str"); } else { fprintf( stderr, "property list type %s, not handled!\n", token2); abort(); } } } else { att->props = NULL; fgets( line, sizeof(line), results_file_fp); /* read the zero */ } att->warnings_and_errors = create_warn_err_DS(); fgets( line, sizeof(line), results_file_fp); /* warn_err_DS */ for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %s %d %d", token1, token2, &att->warnings_and_errors->num_expander_warnings, &att->warnings_and_errors->num_expander_errors); if (eqstring( token1, "NULL") == TRUE) strcpy( att->warnings_and_errors->unspecified_dummy_warning, ""); else strcpy( att->warnings_and_errors->unspecified_dummy_warning, token1); if (eqstring( token2, "NULL") == TRUE) strcpy( att->warnings_and_errors->single_valued_warning, ""); else strcpy( att->warnings_and_errors->single_valued_warning, token2); /* float *unused_translators_warning; discrete translations not implementated */ if (att->warnings_and_errors->num_expander_warnings > 0) att->warnings_and_errors->model_expander_warnings = (fxlstr *) malloc( att->warnings_and_errors->num_expander_warnings * sizeof( fxlstr)); else att->warnings_and_errors->model_expander_warnings = NULL; for (i=0; i < att->warnings_and_errors->num_expander_warnings; i++) { fgets( line, sizeof(line), results_file_fp); strcpy( att->warnings_and_errors->model_expander_warnings[i], line); } if (att->warnings_and_errors->num_expander_errors > 0) att->warnings_and_errors->model_expander_errors = (fxlstr *) malloc( att->warnings_and_errors->num_expander_errors * sizeof( fxlstr)); else att->warnings_and_errors->model_expander_errors = NULL; for (i=0; i < att->warnings_and_errors->num_expander_errors; i++) { fgets( line, sizeof(line), results_file_fp); strcpy( att->warnings_and_errors->model_expander_errors[i], line); } for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %d", &att->rel_error, &att->error, &att->missing); } } /* READ_TPARM_DS 18jan95 wmt: new read and allocate space for tparms */ void read_tparm_DS( tparm_DS tparm, int n_parm, FILE *results_file_fp, int file_ac_version) { int i, file_n_parm, tppt; fxlstr line; shortstr token1; fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s %d", token1, &file_n_parm); if ((eqstring( token1, "tparm_DS") != TRUE) || (file_n_parm != n_parm)) { fprintf( stderr, "ERROR: expecting \"tparm_DS\" and n_parm = %d, found \"%s\"\n", n_parm, line); abort(); } for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d", &tparm->n_atts, &tppt); tparm->tppt = (enum MODEL_TYPES) tppt; tparm->collect = 0; tparm->wts = NULL; tparm->data = NULL; tparm->att_indices = NULL; tparm->datum = NULL; switch(tparm->tppt) { case SM: read_sm_params( &(tparm->ptype.sm), tparm->n_atts, results_file_fp, file_ac_version); break; case SN_CM: read_sn_cm_params( &(tparm->ptype.sn_cm), results_file_fp, file_ac_version); break; case SN_CN: read_sn_cn_params( &(tparm->ptype.sn_cn), results_file_fp, file_ac_version); break; case MM_D: read_mm_d_params( &(tparm->ptype.mm_d), tparm->n_atts, results_file_fp, file_ac_version); break; case MM_S: read_mm_s_params( &(tparm->ptype.mm_s), tparm->n_atts, results_file_fp, file_ac_version); break; case MN_CN: read_mn_cn_params( &(tparm->ptype.mn_cn), tparm->n_atts, results_file_fp, file_ac_version); break; default: printf("\n read_tparms_DS: unknown type of ENUM MODEL_TYPES =%d\n", tparm->tppt); abort(); } for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%d %d %d %d %d", &tparm->n_term, &tparm->n_att, &tparm->n_att_indices, &tparm->n_datum, &tparm->n_data); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e", &tparm->w_j, &tparm->ranges, &tparm->class_wt, &tparm->disc_scale); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e %e", &tparm->log_pi, &tparm->log_att_delta, &tparm->log_delta, &tparm->wt_m, &tparm->log_marginal); } /* READ_MM_D_PARAMS 19jan95 wmt: new read mm_d params from ascii file */ void read_mm_d_params( struct mm_d_param *param, int n_atts, FILE *results_file_p, int file_ac_version) { /* int i, m; */ fprintf( stderr, "read_mm_d_params not converted from write_mm_d_params\n"); abort(); /* fprintf(results_file_p, "mm_d_params\n"); */ /* for(i=0; isizes[i]; */ /* printf("row %d, size %d\n", i, m); */ /* fprintf(results_file_p, "wts\n"); */ /* write_vector_float(param->wts[i], m, results_file_p); */ /* fprintf(results_file_p, "probs\n"); */ /* write_vector_float(param->probs[i], m, results_file_p); */ /* fprintf(results_file_p, "log_probs\n"); */ /* write_vector_float(param->log_probs[i], m, results_file_p); */ /* } */ /* fprintf(results_file_p, "wts_vec\n"); */ /* write_vector_float(param->wts_vec, m, results_file_p); */ /* fprintf(results_file_p, "probs_vec\n"); */ /* write_vector_float(param->probs_vec, m, results_file_p); */ /* fprintf(results_file_p, "log_probs_vec\n"); */ /* write_vector_float(param->log_probs_vec, m, results_file_p); */ } /* READ_MM_S_PARAMS 19jan95 wmt: new write mm_s_params to ascii file -- incomplete */ void read_mm_s_params( struct mm_s_param *param, int n_atts, FILE *results_file_p, int file_ac_version) { fprintf( stderr, "read_mm_s_params not converted from write_mm_s_params\n"); abort(); /* fprintf(results_file_p, "mm_s_params\n"); */ /* fprintf(results_file_p, "count, wt, prob, log_prob\n"); */ /* fprintf(results_file_p, "%d %.7e %.7e %.7e\n", param->count, param->wt, param->prob, */ /* param->log_prob); */ } /* READ_MN_CN_PARAMS 19jan95 wmt: new write mn_cn_params to ascii file, allocating storage */ void read_mn_cn_params( struct mn_cn_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { int i, j; fxlstr line; shortstr token1; for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e", ¶m->ln_root, ¶m->log_ranges); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "emp_means") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"emp_means\", read \"%s\"\n", line); abort(); } param->emp_means = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->emp_means, n_atts, results_file_fp); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "emp_covar") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"emp_covar\", read \"%s\"\n", line); abort(); } param->emp_covar = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; iemp_covar[i] = (float *) malloc( n_atts * sizeof( float)); read_matrix_float( param->emp_covar, n_atts, n_atts, results_file_fp); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "means") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"means\", read \"%s\"\n", line); abort(); } param->means = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->means, n_atts, results_file_fp); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "covariance") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"covariance\", read \"%s\"\n", line); abort(); } param->covariance = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; icovariance[i] = (float *) malloc( n_atts * sizeof( float)); read_matrix_float(param->covariance, n_atts, n_atts, results_file_fp); fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "factor") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"factor\", read \"%s\"\n", line); abort(); } param->factor = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; ifactor[i] = (float *) malloc( n_atts * sizeof( float)); read_matrix_float(param->factor, n_atts, n_atts, results_file_fp); param->values = (float *) malloc( n_atts * sizeof( float)); for (i=0; ivalues[i] = 0.0; param->temp_v = (float *) malloc( n_atts * sizeof( float)); for (i=0; itemp_v[i] = 0.0; param->temp_m = (fptr *) malloc( n_atts * sizeof( fptr)); for (i=0; itemp_m[i] = (float *) malloc( n_atts * sizeof( float)); for (j=0; jtemp_m[i][j] = 0.0; } fgets( line, sizeof(line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "min_sigma_2s") != TRUE) { fprintf( stderr, "read_mn_cn_params expected \"min_sigma_2s\", read \"%s\"\n", line); abort(); } param->min_sigma_2s = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->min_sigma_2s, n_atts, results_file_fp); } /* READ_SM_PARAMS 19jan95 wmt: new write sm_params to ascii file, allocating storage n_atts is actually n_vals -- an overloaded slot definition */ void read_sm_params( struct sm_param *param, int n_atts, FILE *results_file_fp, int file_ac_version) { int i; fxlstr line; shortstr token1; for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %d %e %e %e", ¶m->gamma_term, ¶m->range, ¶m->range_m1, ¶m->inv_range, ¶m->range_factor); fgets( line, sizeof( line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "val_wts") != TRUE) { fprintf( stderr, "read_sm_params expected \"val_wts\", read \"%s\"\n", line); abort(); } param->val_wts = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->val_wts, n_atts, results_file_fp); fgets( line, sizeof( line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "val_probs") != TRUE) { fprintf( stderr, "read_sm_params expected \"val_probs\", read \"%s\"\n", line); abort(); } param->val_probs = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->val_probs, n_atts, results_file_fp); fgets( line, sizeof( line), results_file_fp); sscanf( line, "%s", token1); if (eqstring( token1, "val_log_probs") != TRUE) { fprintf( stderr, "read_sm_params expected \"val_log_probs\", read \"%s\"\n", line); abort(); } param->val_log_probs = (float *) malloc( n_atts * sizeof( float)); read_vector_float( param->val_log_probs, n_atts, results_file_fp); } /* READ_SN_CM_PARAMS 19jan95 wmt: new write sn_cm_params to ascii file, allocating storage */ void read_sn_cm_params( struct sn_cm_param *param, FILE *results_file_fp, int file_ac_version) { int i; fxlstr line; for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e", ¶m->known_wt, ¶m->known_prob, ¶m->known_log_prob, ¶m->unknown_log_prob); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e", ¶m->weighted_mean, ¶m->weighted_var, ¶m->mean); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e %e", ¶m->sigma, ¶m->log_sigma, ¶m->variance, ¶m->log_variance, ¶m->inv_variance); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e\n", ¶m->ll_min_diff, ¶m->skewness, ¶m->kurtosis); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, " %e %e %e", ¶m->prior_sigma_min_2, ¶m->prior_mean_mean, ¶m->prior_mean_sigma); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e", ¶m->prior_sigmas_term, ¶m->prior_sigma_max_2, ¶m->prior_mean_var, ¶m->prior_known_prior); } /* READ_SN_CN_PARAMS 19jan95 wmt: new write sn_cn_params to ascii file */ void read_sn_cn_params( struct sn_cn_param *param, FILE *results_file_fp, int file_ac_version) { int i; fxlstr line; for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e", ¶m->weighted_mean, ¶m->weighted_var, ¶m->mean); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e %e %e", ¶m->sigma, ¶m->log_sigma, ¶m->variance, ¶m->log_variance, ¶m->inv_variance); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e", ¶m->ll_min_diff, ¶m->skewness, ¶m->kurtosis); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, " %e %e %e", ¶m->prior_sigma_min_2, ¶m->prior_mean_mean, ¶m->prior_mean_sigma); for (i=0; i<2; i++) fgets( line, sizeof(line), results_file_fp); sscanf( line, "%e %e %e", ¶m->prior_sigmas_term, ¶m->prior_sigma_max_2, ¶m->prior_mean_var); } autoclass-3.3.6.dfsg.1/prog/autoclass.make.solaris.gcc0000644000175000017500000000270211247310756020723 0ustar areare### AUTOCLASS C MAKE FILE FOR GNU C Compiler -- SUN SOLARIS 2.4 (5.4) ### using gcc version 2.6.3 C compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) # optimize - stay with IEEE compliance BCFLAGS = $(OSFLAGS) -ansi -pedantic -Wall -O2 -fno-fast-math # CFLAGS = $(BCFLAGS) # with debugging CFLAGS = $(BCFLAGS) -g # with profiling (gprof) # CFLAGS = $(BCFLAGS) -pg # CodeCenter C compiler # will not work unless CenterLine libraries and include files are used CC = gcc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) -lc -lm %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ # depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/model-single-normal-cm.c0000644000175000017500000004040611247310756020271 0ustar areare#include #include #include #include #include "autoclass.h" #include "minmax.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ void sn_cm_params_influence_fn( model_DS model, tparm_DS tparm, int term_index,int n_att, float *v, float *class_mean, float *class_sigma, float *class_known_prob, float *global_mean, float *global_sigma, float *global_known_prob) { struct sn_cm_param *param; tparm_DS *p; float class_variance, global_variance, v1, v2, v3; param = &(tparm->ptype.sn_cm); *class_mean = param->mean; *class_sigma = param->sigma; *class_known_prob = param->known_prob; class_variance = param->variance; p = model_global_tparms(model); *global_mean = p[term_index]->ptype.sn_cm.mean; *global_sigma = p[term_index]->ptype.sn_cm.sigma; *global_known_prob = p[term_index]->ptype.sn_cm.known_prob; global_variance = p[term_index]->ptype.sn_cm.variance; v1 = *class_known_prob * (param->known_log_prob - p[term_index]->ptype.sn_cm.known_log_prob); v2 = (1.0 - *class_known_prob) * (param->unknown_log_prob - p[term_index]->ptype.sn_cm.unknown_log_prob); v3 = *class_known_prob * ((float) log ((double) (*global_sigma / *class_sigma)) + (((square(*class_mean - *global_mean) + (class_variance - global_variance)) / 2.0) / global_variance)); *v = v1 + v2 + v3; } /* BUILD_SN_CM_PRIORS 30jul95 wmt: change log calls to safe_log to prevent "log: SING error" error messages. Builds an SN-CM prior from the information in a fully instantiated att structure of the real type. */ static priors_DS build_sn_cm_priors( database_DS data_base, att_DS att) { int n; float range, sigma_min, sigma_max; priors_DS priors; real_stats_DS statistics = att->r_statistics;/* Attribute range information */ range = statistics->mx - statistics->mn; sigma_min = SN_CM_SIGMA_SAFETY_FACTOR * att->error; /* The max sigma for values in range. */ sigma_max = max(sigma_min, range / 2.0); if (sigma_min == sigma_max) { if ( (n = att->warnings_and_errors->num_expander_errors ++) == 0) att->warnings_and_errors->model_expander_errors = (fxlstr *) malloc(sizeof(fxlstr)); else att->warnings_and_errors->model_expander_errors = (fxlstr *) realloc(att->warnings_and_errors->model_expander_errors, (n+1) * sizeof(fxlstr)); strcpy(att->warnings_and_errors->model_expander_errors[n], " single_normal_cm is faulty due to large error-to-range\n" " ratio on sigma priors.\n"); } priors = (priors_DS) malloc(sizeof(struct priors)); priors->known_prior = (0.5 + statistics->count) / (1.0 + data_base->n_data); priors->sigma_min = sigma_min; priors->sigma_max = sigma_max; priors->mean_mean = statistics->mean; priors->mean_sigma = max((range / 2.0), (SN_CM_SIGMA_SAFETY_FACTOR / (float) sqrt( ABSOLUTE_MIN_CLASS_WT)) * sigma_min); priors->mean_var = square(priors->mean_sigma); priors->minus_log_log_sigmas_ratio = - (float) safe_log( max( safe_log( 1.0 / (1.0 - SINGLE_FLOAT_EPSILON)), max( safe_log( (double) (priors->sigma_max / priors->sigma_min)), LEAST_POSITIVE_SHORT_FLOAT))); priors->minus_log_mean_sigma = - (float) safe_log( (double) priors->mean_sigma); return(priors); } /* SINGLE_NORMAL_CM_MODEL_TERM_BUILDER 21nov94 wmt: initialize all slots in tparm 30jul95 wmt: change log calls to safe_log to prevent "log: SING error" error messages. Funcalled from Expand-Model-Terms. This constructs parameter, prior, and intermediate results structures appropriate to a single-normal likelihood term, and places them in the model. Constructs corresponding log-likelihood and parameter update function elements and saves them on the model for later compilation. */ void single_normal_cm_model_term_builder( model_DS model, term_DS term, int n_term) { void ***att_trans_data; int n, n_att, n_att_trans_data; float log_att_delta, log_delta_div_root_2pi, error; att_DS att; database_DS data_base; priors_DS prior_set; tparm_DS tparm; struct sn_cm_param *sn; n_att = term->att_list[0]; data_base = model->database; att = data_base->att_info[n_att]; error = att->error; att_trans_data = (void ***) get("single_normal_cm", "att_trans_data"); n_att_trans_data = ((int *) get("single_normal_cm", "n_att_trans_data"))[0]; if (getf(att_trans_data, att->type, n_att_trans_data) == NULL) fprintf( stderr, "Attribute %d: \"%s\" not one of those allowed for single_normal_cm terms.\n", n_att, att->dscrp); if ( att->missing == FALSE) { if( (n = att->warnings_and_errors->num_expander_warnings ++) == 0) att->warnings_and_errors->model_expander_warnings = (fxlstr *) malloc(sizeof(fxlstr)); else att->warnings_and_errors->model_expander_warnings = (fxlstr *) realloc(att->warnings_and_errors->model_expander_warnings, (n+1) * sizeof(fxlstr)); strcpy(att->warnings_and_errors->model_expander_warnings[n], " using single_normal_cm model on att which has NO missing values\n"); } if (term->n_atts != 1) fprintf( stderr, "Attribute %d: \"%s\": attempting to use single_normal_cm model in a \n" " non-singleton attribute set\n", n_att, att->dscrp); if (error <= 0.0) fprintf( stderr, "Attribute %d: \"%s\", attempting to use single_normal_cm model " "with non-positive error value %f.\n", n_att, att->dscrp, error); log_att_delta = (float) safe_log((double) error); log_delta_div_root_2pi = LN_1_DIV_ROOT_2PI + log_att_delta; /* Allocate parameters struct. */ term->tparm = tparm = (tparm_DS) malloc(sizeof(struct new_term_params)); tparm->tppt=SN_CM; tparm->n_atts = term->n_atts; tparm->n_term = n_term; tparm->n_att = n_att; tparm->n_att_indices = tparm->n_datum = tparm->n_data = 0; tparm->wts = tparm->datum = tparm->att_indices = NULL; tparm->data = NULL; tparm->w_j = tparm->ranges = tparm->class_wt = 0.0; tparm->disc_scale = 0.0; tparm->wt_m = tparm->log_marginal = 0.0; tparm->log_delta = log_delta_div_root_2pi; tparm->log_att_delta = log_att_delta; tparm->log_pi = (float) (-1.0 * safe_log( M_PI)); sn = &(tparm->ptype.sn_cm); sn->known_wt = sn->known_prob = sn->known_log_prob = sn->unknown_log_prob = 0.0; sn->weighted_mean = sn->weighted_var = sn->mean = sn->sigma = 0.0; sn->log_sigma = sn->variance = sn->log_variance = sn->inv_variance = 0.0; sn->ll_min_diff = sn->skewness = sn->kurtosis = 0.0; /* Allocate & SET priors. */ prior_set = model->priors[n_term] = build_sn_cm_priors(data_base, att); sn->prior_mean_mean = prior_set->mean_mean; sn->prior_mean_var = prior_set->mean_var; sn->prior_mean_sigma = prior_set->mean_sigma; sn->prior_sigmas_term = (-1.5 * (float) safe_log( 2.0)) + prior_set->minus_log_log_sigmas_ratio + (-1.0 * (float) safe_log((double) prior_set->mean_sigma)); sn->prior_sigma_min_2 = square(prior_set->sigma_min); sn->prior_sigma_max_2 = square(prior_set->sigma_max); sn->prior_known_prior = prior_set->known_prior; /*done above temp->parms->prior_sigma_min_2 = square(prior_set->sigma_min);*/ } /* SINGLE_NORMAL_CM_LOG_LIKELIHOOD 27nov94 wmt: use percent_equal for float tests 20dec94 wmt: return type to double 23dec94 wmt: check unknown values with FLOAT_UNKNOWN, rather than INT_UNKNOWN */ double single_normal_cm_log_likelihood( tparm_DS tparm) { int n_att = tparm->n_att; struct sn_cm_param *sn = &(tparm->ptype.sn_cm); float log_delta = tparm->log_delta, *datum = tparm->datum, value, diff, temp; value = datum[n_att]; if( percent_equal( (double) value, FLOAT_UNKNOWN, REL_ERROR)) return(sn->unknown_log_prob); diff = sn->mean - value; if ((float) fabs((double) diff) <= sn->ll_min_diff) temp = 0.0; else temp = square(diff) * sn->inv_variance; return (sn->known_log_prob + log_delta + (-0.5 * (temp + sn->log_variance))); } /* SINGLE_NORMAL_CM_UPDATE_L_APPROX 20dec94 wmt: return type to double When called within the environment of Update-L-Approx-fn, this calculates the approximate log likelihood log-a_k of observing the weighted statistics given the class hypothesis and current parameters. */ double single_normal_cm_update_l_approx( tparm_DS tparm) { struct sn_cm_param *sn=&(tparm->ptype.sn_cm); float w_j_known, log_delta = tparm->log_delta, diff, t1, t2; float w_j = tparm->w_j; w_j_known = sn->known_wt; diff = sn->weighted_mean - sn->mean; if (fabs((double) diff) <= sqrt( LEAST_POSITIVE_SINGLE_FLOAT)) t1 = 0.0; else t1 = square( diff); t1 = -0.5 * w_j_known * ((sn->weighted_var + t1) / sn->variance); t2 = ((w_j - w_j_known) * sn->unknown_log_prob) + (w_j_known * sn->known_log_prob) + (w_j_known * (log_delta - (float) safe_log((double) sn->sigma))) + t1; return (t2); } /* SINGLE_NORMAL_CM_UPDATE_M_APPROX 20dec94 wmt: return type to double 29mar95 wmt: calculation to double When called within the environment of Update-M-Approx-fn, this calculates the approximate log marginal likelihood log-a_k of observing the weighted statistics given the class hypothesis alone. See Single-Normal-cn-Update-M-approx-term-caller. A LOG-LINEAR APPROXIMATION IS USED IN THE REGION WHERE 0 <= w_j-known <= (* .75 *absolute-min-class-wt*) */ double single_normal_cm_update_m_approx( tparm_DS tparm) { struct sn_cm_param *sn=&(tparm->ptype.sn_cm); float log_att_delta = tparm->log_att_delta; float prior_mean_mean = sn->prior_mean_mean; float prior_sigmas_term = sn->prior_sigmas_term, diff, t1, t2; float prior_mean_sigma = sn->prior_mean_sigma; float log_pi = tparm->log_pi, w_j = tparm->w_j, w_j_known, t_w_j_known; double temp; w_j_known = sn->known_wt; t_w_j_known = max(w_j_known, 0.75 * ABSOLUTE_MIN_CLASS_WT); diff = sn->weighted_mean - prior_mean_mean; if ((float) fabs((double) diff) <= (prior_mean_sigma * (float) sqrt( 2.0 * LEAST_POSITIVE_SINGLE_FLOAT))) t1 = 0.0; else t1 = -0.5 * square(diff / prior_mean_sigma); if (w_j_known == t_w_j_known) t2 = 1.0; else t2 = w_j_known / t_w_j_known; temp = (double) log_pi + (-1.0 * (log_gamma( (double) (w_j + 1.0), FALSE))) + log_gamma( (double) (0.5 + (w_j - t_w_j_known)), FALSE) + log_gamma( (double) (0.5 + t_w_j_known), FALSE) + log_gamma( (double) (0.5 * (t_w_j_known - 1.0)), FALSE) + (double) t1 + (double) (t_w_j_known * log_att_delta) + (-0.5 * (double) t_w_j_known * safe_log( M_PI * (double) t_w_j_known)) + (-0.5 * (double) (t_w_j_known - 1.0) * safe_log((double) max( LEAST_POSITIVE_SINGLE_FLOAT, sn->weighted_var))) + prior_sigmas_term; return( ((double) t2) * temp); } /* SINGLE_NORMAL_CM_UPDATE_PARAMS 27nov94 wmt: use percent_equal for float tests 20dec94 wmt: return type to void When called within the environment of Update-Params-fn, this updates the param-set of a Single-Normal-cn term. See Single-Normal-cn-Update-Params-term-caller. Revised 12Feb90 JCS Use of double precision for weighted calculations will triple the runtime. */ void single_normal_cm_update_params( tparm_DS tparm, int known_parms_p) { int n_att = tparm->n_att, n_data = tparm->n_data; struct sn_cm_param *sn_cm=&(tparm->ptype.sn_cm); float prior_sigma_min_2 = sn_cm->prior_sigma_min_2; float prior_sigma_max_2 = sn_cm->prior_sigma_max_2; float prior_mean_mean = sn_cm->prior_mean_mean; float prior_mean_var = sn_cm->prior_mean_var; float prior_known_prior = sn_cm->prior_known_prior; float **data = tparm->data, *wts = tparm->wts, prob_known, var_ratio; float class_wt = tparm->class_wt, class_wt_1; float ignore1, known, mean, variance, skewness, kurtosis; class_wt_1 = class_wt + 1; if (class_wt > 0.0) { /* Zero class-wt implies null class */ /* Update the class statistics from class-DS-wts & database */ /* If not collect?, we proceed with the previous values. */ if (tparm->collect == TRUE) { central_measures_x(data, n_data, n_att, wts, percent_equal( (double) sn_cm->mean, FLOAT_UNKNOWN, REL_ERROR) ? (double) prior_mean_mean : (double) sn_cm->mean, &ignore1, &known, &mean, &variance, &skewness, &kurtosis); if (known == 0.0) { sn_cm->known_wt = 0.0; sn_cm->weighted_mean = prior_mean_mean; sn_cm->weighted_var = prior_mean_var; } else { sn_cm->known_wt = known; /* commented fprintf(stderr, "Setting known_wt to known %f\n", known);*/ sn_cm->weighted_mean = mean; sn_cm->weighted_var = max(prior_sigma_min_2, min(prior_sigma_max_2, variance)); sn_cm->skewness = skewness; sn_cm->kurtosis = kurtosis; } } } else { sn_cm->known_wt = 0.0; sn_cm->weighted_mean = prior_mean_mean; sn_cm->weighted_var = prior_mean_var; } if (known_parms_p != TRUE) { prob_known = max(LEAST_POSITIVE_SINGLE_FLOAT, (sn_cm->known_wt + prior_known_prior) / class_wt_1); sn_cm->known_prob = prob_known; sn_cm->known_log_prob = (float) safe_log((double) prob_known); sn_cm->unknown_log_prob = (float) safe_log((double) max( LEAST_POSITIVE_SINGLE_FLOAT, 1.0 - prob_known)); var_ratio = sn_cm->weighted_var / (class_wt_1 * prior_mean_var); sn_cm->mean = (sn_cm->weighted_mean * (1.0 - var_ratio)) + (prior_mean_mean * var_ratio); sn_cm->variance = max(sn_cm->weighted_var * (class_wt / class_wt_1), (float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT)); sn_cm->sigma = (float) sqrt((double) sn_cm->variance); sn_cm->log_variance = (float) safe_log((double) sn_cm->variance); sn_cm->log_sigma = 0.5 * sn_cm->log_variance; sn_cm->inv_variance = 1.0 / sn_cm->variance; sn_cm->ll_min_diff = max((float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT), (sn_cm->variance * (float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT))); } /* return(class_wt); */ } /* When called within the environment of Class-Equivalence-fn, this tests for a difference of means less than sigma=ratio times MIN of sigmas. */ int single_normal_cm_class_equivalence( tparm_DS tparm1,tparm_DS tparm2, double sigma_ratio) { struct sn_cm_param *sn1 = &(tparm1->ptype.sn_cm); struct sn_cm_param *sn2 = &(tparm2->ptype.sn_cm); if (fabs((double) (sn1->mean - sn2->mean)) < (sigma_ratio * (double) min(sn1->sigma, sn2->sigma))) return(TRUE); else return(FALSE); } /* SINGLE_NORMAL_CM_CLASS_MERGED_MARGINAL 20dec94 wmt: return type to void When called within the environment of Class-Merged-Marginal-fn, this generates the sufficient statistics of Single-Normal-cn term equivalent to the weighted merging of params-0 and params-1, storing same in params-m. */ void single_normal_cm_class_merged_marginal( tparm_DS tparm0,tparm_DS tparm1,tparm_DS tparmm) { struct sn_cm_param *sn0=&(tparm0->ptype.sn_cm); struct sn_cm_param *sn1=&(tparm1->ptype.sn_cm); struct sn_cm_param *snm=&(tparmm->ptype.sn_cm); float prior_sigma_min_2 = sn0->prior_sigma_min_2; float kwt0, kwt1, kwtm; kwt0 = sn0->known_wt; kwt1 = sn1->known_wt; kwtm = kwt0 + kwt1; snm->known_wt = kwtm; fprintf(stderr, "Set known_wt to %f\n", kwtm); if (kwtm != 0.0) { snm->weighted_mean = ((kwt0 * sn0->weighted_mean) + (kwt1 * sn1->weighted_mean)) / kwtm; snm->weighted_var = max(prior_sigma_min_2, (((kwt0 * (square(sn0->weighted_mean) + sn0->weighted_var)) + (kwt1 * (square(sn1->weighted_mean) + sn1->weighted_var))) / kwtm) - square(snm->weighted_mean)); /* return (snm->weighted_var); **************/ } /* else return (FLOAT_UNKNOWN); ****************** nil? */ } autoclass-3.3.6.dfsg.1/prog/autoclass.make.sunos.gcc0000644000175000017500000000312011247310756020411 0ustar areare### AUTOCLASS C MAKE FILE FOR GNU C Compiler -- SUN OS 4.1.3 ### using gcc version 2.6.3 C compiler ### WHEN ADDING FILES HERE, ALSO ADD THEM TO LOAD-AC ### ## THE FIRST CHARACTER OF EACH commandList must be tab # targetList: dependencyList # commandList ## evaluate (setq-default indent-tabs-mode t) # optimize - stay with IEEE compliance BCFLAGS = $(OSFLAGS) -ansi -pedantic -O2 -Wall -fno-fast-math \ -I/usr/local2/GNU/lib/gcc-lib/sparc-sun-sunos4.1.3/2.6.3/include # CFLAGS = $(BCFLAGS) # with debugging CFLAGS = $(BCFLAGS) -g # with profiling (gprof) # CFLAGS = $(BCFLAGS) -pg LDFLAGS = -L/usr/local2/GNU/lib/gcc-lib/sparc-sun-sunos4.1.3/2.6.3 # CodeCenter C compiler # will not work unless CenterLine libraries and include files are used CC = gcc DEPEND = SRCS = globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c OBJS = $(SRCS:.c=.o) autoclass: $(OBJS) $(CC) $(CFLAGS) -o autoclass $(OBJS) $(LDFLAGS) -lm %.o : %.c $(CC) $(CFLAGS) -c $< -o $@ # depend: $(SRCS) # IF YOU PUT ANYTHING HERE IT WILL GO AWAY autoclass-3.3.6.dfsg.1/prog/prints.c0000644000175000017500000003433311247310756015350 0ustar areare#include #include #include #include #include "autoclass.h" #include "globals.h" /* SUPRESS CODECENTER WARNING MESSSAGES */ /* empty body for 'while' statement */ /*SUPPRESS 570*/ /* formal parameter '<---->' was not used */ /*SUPPRESS 761*/ /* automatic variable '<---->' was not used */ /*SUPPRESS 762*/ /* automatic variable '<---->' was set but not used */ /*SUPPRESS 765*/ /* 01jan95 wmt: new */ void sum_vector_f( float *v, int n, char *t) { int i; float float_sum = 0.0; double double_sum = 0.0; if(v==NULL) { printf(" \npointer passed is null for %s\n",t); return; } printf("\nvector %s, n=%d\n", t, n); for (i=0; i vector_print_limit) { printf("\n\n****limiting n=%d to %d\n", n, vector_print_limit); n = vector_print_limit; } for(i=0;in_atts, p->tppt); switch(p->tppt) { case SM: print_sm_params( p->ptype.sm,p->n_atts); break; case SN_CM: print_sn_cm_params( p->ptype.sn_cm,p->n_atts); break; case SN_CN: print_sn_cn_params( p->ptype.sn_cn,p->n_atts); break; case MM_D: print_mm_d_params( p->ptype.mm_d,p->n_atts); break; case MM_S: print_mm_s_params( p->ptype.mm_s,p->n_atts); break; case MN_CN: print_mn_cn_params( p->ptype.mn_cn,p->n_atts); break; default: printf(" \n\n\n in print_tparms_DS UNKNOWN TYPE=%d\n", p->tppt); } printf(" collect %d\n",p->collect); printf(" n_term, n_att, n_att_indices, n_datum, n_data\n"); printf(" %d %d %d %d %d\n", p->n_term, p->n_att, p->n_att_indices, p->n_datum, p->n_data); printf(" w_j, ranges= %g %g\n", p->w_j, p->ranges); printf(" class_wt,disc_scale\n"); printf(" %g %g\n", p->class_wt, p->disc_scale); printf(" log_pi, log_att_delta, log_delta\n"); /* , log_ranges, log_gamma_term ); */ printf(" %g %g %g\n", p->log_pi, p->log_att_delta, p->log_delta); /*, p->log_ranges, p->log_gamma_term);*/ /********* printf(" percent_ratio, sigma_ratio %g %g \n", p->percent_ratio, p->sigma_ratio ); *********/ printf(" wt_m, log_marginal %g %g\n", p->wt_m,p->log_marginal); print_vector_f( p->wts, p->n_datum, "wts"); print_vector_f( p->datum, p->n_datum, "datum"); print_vector_f( p->att_indices, p->n_att_indices, "att_indices"); print_matrix_f( p->data, p->n_data, p->n_att," data"); } void print_priors_DS( priors_DS p, char *t) { if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf("\n\n priors %s\n",t); printf(" known_prior, sigma_min, sigma_max %g %g %g \n", p->known_prior,p->sigma_min,p->sigma_max); printf(" mean_mean, mean_sigma, mean_var %g %g %g \n", p->mean_mean,p->mean_sigma,p->mean_var); printf(" minus_log_log_sigmas_ratio, minus_log_mean_sigma %g %g\n", p->minus_log_log_sigmas_ratio,p->minus_log_mean_sigma); } void print_class_DS( class_DS p , char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" \n\nclass %s\n",t); printf(" w_j, pi_j %g %g\n", p->w_j, p->pi_j); printf(" log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j %g %g %g\n", p->log_pi_j, p->log_a_w_s_h_pi_theta, p->log_a_w_s_h_j); printf(" known_parms_p, num_tparms %d %d\n", p->known_parms_p, p->num_tparms); for (i=0;inum_tparms;i++) print_tparm_DS(p->tparms[i]," from class"); /* print_vector_f(p->i_values,p->num_i_values," i_values"); */ printf(" void **i_values; N-attributes vector of influence value structures.\n"); printf(" i_sum,max_i_value %g %g \n",p->i_sum, p->max_i_value); print_vector_f(p->wts,p->num_wts,"wt vector"); /* *wts; N-data vector of object membership probabilities. */ printf("skipping call to print_model that is in print_class\n"); /*commented print_model_DS(p->model, "model from class"); *****/ printf(" next pointer is%sNULL\n", (NULL==p->next)?" ":" NOT"); } void print_term_DS ( term_DS p, char *t) { if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } /* One of the likelihood fn. terms in MODEL-TERM-TYPES. */ printf("\n\n term %s, n_atts,type=%d %s\n",t,p->n_atts,p->type); print_vector_f(p->att_list,p->n_atts,"att_list from term"); /*float *att_list; List of attributes (by number) in set. See ATT-GROUPS. */ print_tparm_DS(p->tparm,"from term"); } void print_real_stats_DS( real_stats_DS p, char *t) { printf(" real stats from %s\n",t); printf(" count,max,min,mean,var %d %5g %g %g %g\n", p->count,p->mx,p->mn,p->mean,p->var); } void print_discrete_stats_DS( discrete_stats_DS p, char *t) { int i; printf(" discrete stats from %s\n",t); printf(" range,n_observed %d %d \n", p->range,p->n_observed); for(i=0;i<=p->range;i++)printf(" %d %d \n",i,p->observed[i]); } void print_att_DS( att_DS p, char *t) { if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" att %s, \n type,subtype,descrp=%s %s \"%s\" \n", t,p->type, p->sub_type, p->dscrp); if(eqstring(p->type,"real")) print_real_stats_DS(p->r_statistics," rstats from att"); else print_discrete_stats_DS(p->d_statistics," dstats from att"); printf(" n_props,range,zero_point,n_trans %d %d %f %d\n", p->n_props, p->range, p->zero_point, p->n_trans); printf(" translations triple pointer is%sNULL\n", (NULL==p->translations)?" ":" NOT "); printf(" props triple pointer is%sNULL\n", (NULL==p->props)?" ":" NOT "); printf(" not printing warings and errors\n"); printf(" rel_error, error, missing %g %g %d\n", p->rel_error, p->error, p->missing); } void print_database_DS( database_DS p, char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" not prinitng file pointers for data and header in db %s\n",t); printf(" n_data,n_atts,input_n_atts,compressed_p %d %d %d %d \n", p->n_data, p->n_atts, p->input_n_atts, p->compressed_p); /* Ordered N-atts vector of att_DS describing the attributes. */ for(i=0;in_atts;i++) { printf("\n%d th ",i); print_att_DS(p->att_info[i]," info from database"); } print_matrix_f(p->data,p->n_data,p->n_atts," data"); printf(" separator_char,comment_char,unknown_token %c %c %c\n", p->separator_char,p->comment_char,p->unknown_token); printf("num_tsp %d, translations_supplied_p is %sNULL\n", p->num_tsp,(NULL==p->translations_supplied_p)?" ":"NOT "); printf("num_invalid_value_errors %d, invalid_value_errors is %sNULL\n", p->num_invalid_value_errors,(NULL==p->invalid_value_errors)?" ":"NOT "); printf("num_incomplete_datum %d, incomplete_datum is %sNULL\n", p->num_incomplete_datum, (NULL==p->incomplete_datum)?" ":"NOT "); } void print_model_DS( model_DS p, char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf("\n\n model %s; id =%s, expanded_terms univ time=%d\n", t, p->id, p->expanded_terms); printf(" model file pointer not printed; file index =%d\n", p->file_index); print_database_DS(p->database, "database in model"); printf("\n\nthis model contains %d terms\n",p->n_terms); for(i=0;in_terms;i++) print_term_DS(p->terms[i]," ith term in model"); printf(" n_att_locs=%d\n", p->n_att_locs); for(i=0;in_att_locs;i++) printf("%d %s\n",i,p->att_locs[i]); printf(" n_att_ignore_ids=%d\n", p->n_att_ignore_ids); for(i=0;in_att_ignore_ids;i++) printf("%d %s\n",i,p->att_ignore_ids[i]); printf(" num_priors=%d\n", p->num_priors); for(i=0;i< p->num_priors;i++) print_priors_DS(p->priors[i],"priors"); printf(" num_class_store=%d; class_store is%sNULL\n", p->num_class_store,(NULL==p->class_store)?" ":" NOT "); printf(" not printing global clsf from model\n"); /*commented print_clsf_DS(p->global_clsf," global clsf from model"); *****/ } void print_clsf_DS( clsf_DS p, char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" clsf %s\n",t); printf("log_p_x_h_pi_theta, log_a_x_h %g %g\n", p->log_p_x_h_pi_theta, p->log_a_x_h); printf(" database pointer is%sNULL\n", (NULL==p->next)?" ":" NOT "); printf(" num_models=%d\n", p->num_models); printf(" skipping 1 call for each to print_model in clsf\n"); /****** commented for(i=0;i< p->num_models;i++) print_model_DS(p->models[i]," ith clsf model"); ****/ printf(" n_classes=%d\n", p->n_classes); printf(" class pointer is%sNULL\n", (NULL==p->classes)?" ":" NOT "); for(i=0;i< p->n_classes;i++) print_class_DS(p->classes[i],"ith clsf class"); printf("min_class_wt %g\n", p->min_class_wt ); printf(" clsf reports pointer is%sNULL\n", (NULL==p->reports)?" ":" NOT "); printf(" clsf_store next pointer is%sNULL\n", (NULL==p->next)?" ":" NOT "); } void print_search_try_DS( search_try_DS p, char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" search try %s",t); printf(" n,time,j_in,j_out,ln_p, %d %d %d %d %g\n", p->n,p->time,p->j_in,p->j_out,p->ln_p); printf(" number of duplicates=%d\n",p->n_duplicates); for(i=0;in_duplicates;i++) print_search_try_DS(p->duplicates[i],"duplicate tries"); /* a list of tries happened after this one came up with the same clsf */ print_clsf_DS( p->clsf,"clsf from try"); /* the clsf this try came up with */ } void print_search_DS( search_DS p, char *t) { int i; if(p==NULL) { printf("\npointer passed is null for %s\n",t); return; } printf(" search struct %s\n",t); printf(" n, time, n_dups, n_dup_tries %d %d %d %d\n", p->n, p->time, p->n_dups, p->n_dup_tries); print_search_try_DS(p->last_try_reported," last try reported"); printf(" tries from best on down for n_tries =%d\n",p->n_tries); for (i=0;i< p->n_tries;i++) print_search_try_DS(p->tries[i],"best to worst try"); printf(" start_j_list: "); for (i=0; p->start_j_list[i] != END_OF_INT_LIST; i++) printf("%d, ", p->start_j_list[i]); printf("\n n_final_summary, n_save %d %d\n", p->n_final_summary, p->n_save); } autoclass-3.3.6.dfsg.1/prog/fcntlcom-ac.h0000644000175000017500000000205111247310756016214 0ustar areare/* Copyright (c) 1988 AT&T */ /* All Rights Reserved */ /* THIS IS UNPUBLISHED PROPRIETARY SOURCE CODE OF AT&T */ /* The copyright notice above does not evidence any */ /* actual or intended publication of such source code. */ /* does not exist in SunOS 4.1.3 GNU installation */ /* & do not exist in Solaris GNU installation */ /* hard code flags from fcntlcom.h & fcntl.h */ /* 27apr95 wmt */ #ifndef NO_FCNTL_H #ifndef _MSC_VER #include #endif #include #else /* * Rewack the FXXXXX values as _FXXXX so that _POSIX_SOURCE works. */ /* non blocking I/O (4.2 style) */ #define _FNDELAY 0x0004 /* append (writes guaranteed at the end) */ #define _FAPPEND 0x0008 /* open with file create */ #define _FCREAT 0x0200 /* open with truncation */ #define _FTRUNC 0x0400 /* fcntl(2) requests - get & set file flags */ #define F_GETFL 3 #define F_SETFL 4 /* Non-blocking I/O (4.2 style) */ #define O_NDELAY _FNDELAY extern int fcntl(); extern int _filbuf (); #endif /* NO_FCNTL_H */ autoclass-3.3.6.dfsg.1/load-ac0000755000175000017500000000632511247310756014144 0ustar areare#!/bin/csh -f # make the C version of AutoClass # with -tags arg, emacs tags files will be generated cd prog set makeflags = "" set etags_flag = -f set sunos_solaris = "" if ( "`/bin/uname -s`" == "IRIX64" ) then # SGI IRIX 6.4 set makeflags = "OSFLAGS=-D_SVR4_SOURCE" make $makeflags -f autoclass.make.sgi else if ( "`/bin/uname -s`" == "IRIX" ) then # SGI IRIX set makeflags = "OSFLAGS=-D_SVR4_SOURCE" make $makeflags -f autoclass.make.sgi else if (-x /bin/uname && x`/bin/uname` == "xLinux") then # LINUX 1.2.10 set makeflags = "OSFLAGS=-D_POSIX_SOURCE" make $makeflags -f autoclass.make.linux.gcc else if ( "`/bin/uname -s`" == "HP-UX" ) then # HP UX set makeflags = "OSFLAGS=-D_HPUX_SOURCE" make $makeflags -f autoclass.make.hp.cc else if ( "`/bin/uname -s`" == "OSF1" ) then # DEC ALPHA set makeflags = "OSFLAGS=" make $makeflags -f autoclass.make.alpha.cc else if ( "`/bin/uname -s`" == "FreeBSD" ) then # Free BSD set makeflags = "OSFLAGS=" make $makeflags -f autoclass.make.freebsd.gcc else if ( "`/bin/uname -s`" == "SunOS" ) then # SUN-OS OR SOLARIS if (-f /usr/ucb/hostid) then # SOLARIS set sunos_solaris = "solaris" echo -n "Which compiler, GNU(gcc) or Solaris(cc)? - {gcc|cc}: " set input = $< if (("$input" != "gcc") && ("$input" != "cc")) then echo "Must be 'gcc', or 'cc'" exit(1) endif set etags_flag = -o else # SUN-OS set sunos_solaris = "sunos" echo -n "Which compiler, GNU(gcc) or SunOS(acc)? - {gcc|acc}: " set input = $< if (("$input" != "gcc") && ("$input" != "acc")) then echo "Must be 'gcc', or 'acc'" exit(1) endif endif if ("$input" == "gcc") then if ("$sunos_solaris" == "sunos") then make "OSFLAGS=-DNO_FCNTL_H -D__USE_FIXED_PROTOTYPES__" -f autoclass.make.sunos.gcc else make "OSFLAGS=-DNO_FCNTL_H -D__STDC__ -D__EXTENSIONS__" -f autoclass.make.solaris.gcc endif else if ("$input" == "acc") then make $makeflags -f autoclass.make.sunos.acc else if ("$input" == "cc") then make $makeflags -f autoclass.make.solaris.cc else echo "Must be 'gcc', 'cc', or 'acc'" exit(1) endif else echo "Unrecognized Unix operating system" exit endif mv autoclass ../autoclass ## if ($1 != "") then # does not work for SGI IRIX 5.2 csh or Linux 1.3.2 if ("X$1" != "X") then # compute emacs tags for autoclass etags $etags_flag ../autoclass.TAGS \ globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c \ autoclass.h getparams.h globals.h params.h fcntlcom-ac.h minmax.h else # remove .o files (for distribution) make -f autoclass.make endif # back up to parent directory cd .. autoclass-3.3.6.dfsg.1/version-3-3-4.text0000644000175000017500000000510111247310756015741 0ustar areare AUTOCLASS C VERSION 3.3.4 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/sample files were regenerated because of the SAFE_LOG change (item 6. below). Only very minor changes occurred. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.4. 2. autoclass-c/prog/predictions.c - In autoclass_predict, allocate separate storage for test_clsf->reports->class_wt_ordering to prevent segmentation violation on Linux platforms when running in predict mode. 3. autoclass-c/prog/autoclass.h, minmax.h - Macros min() and max() have been moved to a new file: minmax.h. Added `#include "minmax.h"' to the following files: intf-reports.c io-read-data.c matrix-utilities.c model-multi-normal-cn.c model-single-normal-cm.c model-single-normal-cn.c model-update.c search-basic.c search-control-2.c search-control.c statistics.c struct-data.c utils.c Removed the prototypes for build_sn_cm_priors() and build_sn_cn_priors(). These functions are used only in the .c files that contain them, so are now static functions. Changed the prototype for log_gamma(), for reasons explained below. 4. autoclass-c/prog/getparams.c - Corrected argument to sizeof() on line 142. 5. autoclass-c/prog/struct-clsf.c - Zero global pointer and counter variables after deleting the structures to which they refer. 6. autoclass-c/prog/utils-math.c - Before, the function safe_log() returned 0.0 when its argument was less than or equal to LEAST_POSITIVE_SINGLE_FLOAT. This is clearly wrong. Log(x) approaches -infinity (not 0) as x approaches 0. The fix is to have safe_log() return LEAST_POSITIVE_SINGLE_LOG for x near 0. 7. autoclass-c/prog/search-control-2.c - In variance, check for lists of length less than 2, and return 0. Items 3 - 7 were submitted by Jack Wathey . 8. autoclass-c/prog/intf-reports.c - Correct FORMAT_DISCRETE_ATTRIBUTE to prevent string overrun and segmentation violations when single multinomial values exceed 20 characters, while running in report mode. 9. autoclass-c/prog/io-results.c - Correct READ_ATT_DS to prevent string overrun and segmentation violations when single multinomial values exceed 40 characters, while running in report mode. autoclass-3.3.6.dfsg.1/version-2-7.text0000644000175000017500000000235011247310756015606 0ustar areare AUTOCLASS C VERSION 2.7 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add documentation for search parameter "interactive_p". This will allow AutoClass to be run as a background task, since it will not be querying standard input for the "quit" character. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.7. Add "G_interactive_p". 2. autoclass-c/prog/globals.h - Add "G_interactive_p". 3. autoclass-c/prog/utils.c - In "char_input_test", test for "G_interactive_p" -- if false, do not do the test. 4. autoclass-c/prog/search-control.c - In "autoclass_search", process "interactive_p" from the search parameters file, and output advisory message if set to false. 5. autoclass-c/prog/search-control-2.c - In "print_initial_report", notify user that "typing q to quit" is not functional when "interactive_p" = false. ====================================================================== autoclass-3.3.6.dfsg.1/version-2-8.text0000644000175000017500000001110511247310756015605 0ustar areare AUTOCLASS C VERSION 2.8 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add new search parameter "read_compact_p", which directs AutoClass to read the "results" and "checkpoint" files in either binary format -- ".results-bin"/".chkpt-bin" (read_compact_p = true); or ascii format -- ".results"/".chkpt" (read_compact_p = false). The default is read_compact_p = true. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.8. 2. autoclass-c/prog/io-results.c - In "validate_data_pathname", prefer the user supplied file extension, and only attempt to open ".db2", and then ".db2-bin", if no extension (/name.) or an invalid extension is supplied. Check for presence of '.' in pathname. In "validate_results_pathname" prefer the user supplied file extension, and only attempt to open ".result-bin", and then ".results", if no extension (/name.) or an invalid extension is supplied. Check for presence of '.' in pathname. In "make_and_validate_pathname" check for presence of '.' in pathname. In "get_clsf_seq" simplify the test for "ascii" or "binary" results file format -- also more portable. 3. autoclass-c/prog/search-control.c - In "autoclass_search" use make_and_validate_pathname and search parameter "save_compact_p" to determine file extension of "results" file prior to calling validate_results_pathname. Add "read_compact_p" search parameter for use in reading "results" and "checkpoint" files. Make short search trial printout more portable. 4. autoclass-c/load-ac; autoclass-c/prog/autoclass.make.* Define make files with -I and -L parameters for SunOS 4.1.3 and change naming convention: .sun. => .sunos. or .solaris. Specifically the files are now -- autoclass.make.solaris.cc, autoclass.make.solaris.gcc, autoclass.make.sunos.acc, and autoclass.make.sunos.gcc 5. autoclass-c/prog/io-read-data.c, autoclass.h - In "translate_discrete", allocate space for translations using (strlen( value) + 1), rather than sizeof(shortstr) -- prevents corruption of discrete data translation tables when translations are longer than (SHORT_STRING_LENGTH - 1) = 40 characters. In "get_line_tokens" and "read_from_string", add length checking for "form"; make it and length check for "datum_string" explicit. Increase output string length in "output_created_translations". 6. autoclass-c/prog/io-read-data.c, autoclass.h - Increase from 3000 to 20000 the value of VERY_LONG_STRING_LENGTH to handle very large datum lines. 7. autoclass-c/prog/io-results.c - In VALIDATE_RESULTS_PATHNAME and VALIDATE_DATA_PATHNAME, use binary_file, rather than file, were it is intended. 8. autoclass-c/prog/intf-reports.c, io-read-data.c, autoclass.h - Increase DATA_ALLOC_INCREMENT from 100 to 1000 for reading very large datasets. Add DATA_ALLOC_INCREMENT logic of READ_DATA to XREF_GET_DATA. This will prevent segmentation faults encountered when reading very large .db2 files into the reports processing function of AutoClass. 9. autoclass-c/prog/autoclass.make.solaris.cc, autoclass.make.solaris.gcc, autoclass.make.sunos.acc, and autoclass.make.sunos.gcc - Comment out "depend: $(SRCS)", so that all source files are not compiled even when only one file changes. 10. autoclass-c/prog/intf-reports.c - In FORMAT_DISCRETE_ATTRIBUTE, do not process attributes with warning or error messages -- this prevents segmentation faults. In XREF_GET_DATA, free database allocated memory after it is transferred into report data structures. This reduces the amount of memory required when generating reports for very large data bases, and prevents running out of memory. In all functions calling malloc/realloc for dynamic memory allocation, checks have been added to notify the user if memory is exhausted. 11. autoclass-c/load-ac & autoclass-c/prog/autoclass.make.hp.cc - Port the "make" file for HP-UX operating system using the bundled "cc" compiler. ====================================================================== autoclass-3.3.6.dfsg.1/version-3-3-1.text0000644000175000017500000000152211247310756015741 0ustar areare AUTOCLASS C VERSION 3.3.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.1. 2. autoclass-c/prog/io-results.c, io-results-bin.c - In READ_CLSF_SEQ and LOAD_CLSF_SEQ, check for win/unx suffix in ac_version before stripping it off. This corrects an incompatibility with .results[-bin] files written by AutoClass C versions prior to version 3.3. The error, for .results-bin files, looks like this: ERROR: expecting "ac_version n.n", found "ac_version "; Abort. ====================================================================== autoclass-3.3.6.dfsg.1/version-3-2-2.text0000644000175000017500000000156011247310756015743 0ustar areare AUTOCLASS C VERSION 3.2.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - clarify the usage of the RECONVERGE_TYPE parameter. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.2.2. 2. autoclass-c/data/uci-dbs-readme.text - Replaced out-of-data information with current Web pointer. 3. autoclass-c/data/tests.c & autoclass-c/data/glass/ report files Version 3.2 contained changes to the multi-normal-cn model which changed slightly the results of the non-random test cases. 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X|$$!uej$^D$@BD$?E$<$0G_ EGED$ D$D$\G$U\$1D$ D$[$֥QD$ D$@[E$.D$%D$ D$Z!ËE$D$D$ D$XE$D$D$ D$ZE$1D$蝲D$ D$`YE$3D$zD$XD$`YD$ZD$ ZD$@[D$[$\莊M1]u}]aáED$HD$$d>D$ 4$xE_ wD$D$ D$3D$`YG$~\$1D$ D$Z$1蚱zD$ D$ Xt$$qyqD$ D$Wt$$@D$ D$Vt$$ D$ D$@Vt$$1D$ D$Xt$$D$XD$UD$@VD$VD$ WD$ XD$Z$`YD$ `$TED$>D$$h?D$ ED$>D$$(?D$ U]Ít&'UWVS^}E)E}Ut+1ƍED$E D$E$9}u߃[^_]Ë$ÐUS@@t Ћu[]US[Y[multiplemodulusmulti_multinomial_dallowedmissingMM_Dprint_stringmm_d_params_DSn_att_trans_datasingle_multinomialsingle_equivalentn_multiple_equivalentmulti_multinomial_sMM_Smulti_normal_cnconstantnoMNcnmn_cn_params_DSsingle_normal_cnSMsm_params_DSSNcnsn_cn_params_DSsingle_normal_cmyesSNcmsn_cm_params_DSn_argslog_transformn_types/usr/ucb/hostname WARNING: calling getwd (current working directory) returned 0 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #%d, "%s": [ ********** Error Messages from Data Base *********** ERROR[1]: in datum #%d, type = real attribute #%d: "%s" has non-number value, %s ERROR[1]: datum #%d is incomplete: it has %d attributes, instead of %d. ******* SUMMARY OF ALL ERROR AND WARNING MESSAGES ******* %d WARNING message(s) occured: %d due to unspecified attribute type set to dummy %d due to excess type = discrete range(s) %d due to unspecified model term type set to ignore %d due to single valued attribute(s) %d due to model term type expansion %d ERROR message(s) occured: %d due to invalid type = real attribute value(s) ############## Starting Input Check ############### During loading of: [1] %s%s, [Attribute #, value #, and datum # are zero based.] %11.4e < (%11.4e : %10.4e) < %11.4e ] ERROR: (output_real_att_statistics) attribute #%d: "%s", the variance exceeds %e ADVISORY[1]: real statistics [ min < (mean : std dev) < max ] built from input data -- ADVISORY[2]: attribute #%d: "%s", the error %f is %d%% of the range %f. ERROR: (find_att_statistics) unknown attribute type: %s ERROR: read_line read a line longer than %d characters ERROR: read_from_string read a token longer than %d characters ADVISORY[2]: for attribute #%d: "%s" range increased to %d, for value %d -- translator (%d %s). ADVISORY: the default translation will be usedADVISORY: no translations were provided for these type = discrete attributes ERROR[1]: data is of type :vector or :list, but only :line is handled ERROR: process_translation called with commented code in io-read-data.c ERROR[2]: length of attribute #%d description "%s" is longer than %d characters ERROR[2]: unknown attribute type: %s ERROR: %s%s, either "%s" is not the correct header string(".db2-bin") or %d is not the correct case length (%d) ERROR: read_data(1): out of memory, malloc returned NULL! ERROR: read_data(2): out of memory, realloc returned NULL! ERROR: read_data(3): out of memory, malloc returned NULL! ERROR: read_data(4): out of memory, realloc returned NULL! WARNING[1]: read_data found *ONLY* %d datum in "%s%s" ERROR[1]: no data read by read_data from "%s%s" ADVISORY[1]: %s %d datum from %s%s ERROR[2]: expected at least %d items: read %d: %s, %s, %s, %s ERROR[2]: expected at least %d items: range read %d: %s, %s, %s, %s, %s, %s ERROR[2]: expected parameter range, got %s for attribute #%d: "%s" ERROR[2]: value of parameter range read, %s, was not an integer for attribute #%d: "%s" ERROR[2]: expected at least %d items: zero_point rel_error read %d: %s, %s, %s, %s, %s, %s, %s, %s ERROR[2]: expected parameter zero_point, got %s and %s for attribute #%d: "%s" ERROR[2]: expected parameter rel_error, got %s and %s for attribute #%d: "%s" ERROR[2]: value of parameter zero_point read, %s, was not a float for attribute #%d: "%s" ERROR[2]: value of parameter rel_error read, %s, was not a float for attribute #%d: "%s" ERROR[2]: expected at least %d items: error ERROR[2]: expected parameter error, got %s for attribute #%d: "%s" ERROR[2]: value of parameter error read, %s, was not a float for attribute #%d: "%s" ERROR[2]: expected sub_type nil or none, got %s for attribute #%d: "%s" ERROR[2]: unknown type/sub_type = %s/%s for attribute #%d: "%s" ERROR[2]: expecting integer attribute index %d, read %s ADVISORY[2]: read %d attribute defs from %s%s ERROR[2]: attribute #%d: "%s" ERROR: %s produced %d chars (max number is %d) WARNING[2]: attribute #%d definition has not been specified -- type set to dummy WARNING[2]: attribute #%d: "%s" to improve sensitivity of classification, reduce range to %d. WARNING[3]: attribute #%d: "%s" model term type has not been specified and is set to ignore has only one unique value. Change model term type to ignore. ****** Error & Warning Messages from READING Model Index = %d ****** ** Error & Warning Messages from READING & EXPANDING Model Index = %d ** ERROR[2]: invalid data file format definition name: %s ADVISORY[2]: data_file_format settings: separator_char = '%c', comment_char = '%c', unknown_token = '%c' ERROR[2]: the number of attributes %d should be a positive integer. Skipping a reread of the database ERROR: G_data_file_format "%s" not handled There is NO continuation possible EXIT due to warning messages at user's request Run continues, even though warnings were found ############ Input Check Concluded ############## (%d %s):%d ] output_created_translationsoutput_db_error_messages %d due to incomplete datum output_message_summary To log file: %s%s [2] %s%s, [3] %s%s. log_headeroutput_real_att_statisticsfind_real_statsnilrealtranslate_discrete #%d "%s"eofcommentcreate_att_DSdummybinary.db2-binasciiloadedread_datanominalscalarzero_pointrel_errorlocationnoneprocess_attribute_defunspecified_attributeprocess_attribute_definitionsoutput_error_msgsmodel_term_not_specifiedoutput_warning_msgsignore_model:readoutput_messagesnumber_of_attributesseparator_charcomment_charunknown_tokendefine_data_file_formatrb ERRORs have occurred! WARNINGs have occurred!MG?MB:read%d=%d:%s n_sourceignore #%d: "%s" extend_default_termsextend_terms_multiextend_terms_singledefaultgenerate_attribute_infoMODEL-%ddefine_models<...>model_indexread_model_doitread_model_file## all code commented in get_sources_list ERROR: get_source_list found circular reference ERROR: get_source_list found circularity of attribute source references ERROR[3]: for model index = %d, ignore is not a valid default model term type ERROR[3]: for model index = %d, model term type = %s, %d is an invalid attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = %s, attempt to re-use attribute %d ADVISORY[3]: the default model term type, %s, will be used for these attributes: ERROR[3]: for model index = %d, model term type = %s, attribute number read, %s, was not an integer ERROR[3]: for model index = %d, model term type = %s, %d is an invalid attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = %s, attempt to re-use attribute %d ERROR[3]: for model index = %d, model term type = %s, is not handled ERROR[3]: for model index = %d, model term type = ignore, attribute number read, %s, was not an integer ERROR[3]: for model index = %d, %d is an invalid model term type = ignore attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = ignore, attempt to re-use attribute %d ERROR: No method for generating attribute sets for set_type %s ERROR[3]: for model index = %d, default model term type, %s, specified twice. ERROR[3]: No models read from model source: %s ERROR[3]: expected 3 items: model_index , read %d: %s %s %s %s ERROR[3]: expected model_index, read %s ERROR[3]: model index read, %s, was not an integer ERROR[3]: expected model index %d, read %d ERROR[3]: num model definition lines read, %s, was not an integer ERROR[3]: expected %d model definition lines, only read %d ADVISORY[3]: read %d model def%s from %s%s ERROR[3]: No models read from "%s%s" %e %e %e %e %e%e %e %e %s %s %d%e %d %e %e %eval_wtsval_probsval_log_probsemp_meansemp_covarcovariancemin_sigma_2s%d %d %d %d %dclass_DS%e %le %lemodel_DS_ptr%d %e %e %e %e%d %d %f %d %d %s%s %s %sfltstr%s %s %d %dNULL%e %e %d.hd2.modelsearch params.s-paramsreports params.r-params.searchsearch_tmp.search-tmp.resultsresults_tmp.results-tmpresults_bin.results-binresults_tmp_bin.results-tmp-bin.log.rlogcheckpoint.chkptcheckpoint_tmp.chkpt-tmpcheckpoint_bin.chkpt-bincheckpoint_tmp_bin.chkpt-tmp-bininflu_vals.influ-text-xref_class.class-text-xref_case.case-text-.predict.db2priors_DS %d write_priors_DSsn_cn_params %.7e %.7e %.7e %.7e %.7e write_sn_cn_paramssn_cm_params write_sn_cm_paramsmm_s_params count, wt, prob, log_prob %d %.7e %.7e %.7e write_mm_s_paramsmodel_DS %d id, file_index model_file %s %s %s %d write_model_DSsm_params %.7e %d %.7e %.7e %.7e val_wts val_probs val_log_probs write_sm_paramsmm_d_params row %d, size %d wts_vec log_probs_vec write_mm_d_paramsmn_cn_params ln_root log_ranges emp_means emp_covar covariance factor min_sigma_2s write_mn_cn_paramstparm_DS %d n_atts, tppt(type) %d %d write_tparm_DSclass_DS %d w_j, pi_j known_parms_p, num_tparms num_wts %d model_DS_ptr %d write_class_DS_sterm_DS %d n_atts, type %d %s write_term_DSatt_DS %d type, subtype, dscrp %s %s "%s" real_stats_DS count, max, min, mean, var %d %.7e %.7e %.7e %.7e discrete_stats_DS range, n_observed dummy_stats_DS translations_DS props_DS %s %s %f %s %s %s warn_err_DS %s %s %d %d rel_error, error, missing %.7e %.7e %d write_att_DSdatabase_DS data_file, header_file n_data, n_atts, input_n_atts write_database_DSclsf_DS %d database_DS_ptr num_models n_classes min_class_wt %.7f chkpt_DS write_clsf_DSac_version %s write_clsf_seqwbwrm %smv %s %ssave_clsf_seq%le %le database_DS_ptrnum_modelsn_classeschkpt_DSunxwinac_versionread_clsf_seqERROR: expecting "model_DS" and model_index = %d, found "%s" read_sm_params expected "val_wts", read "%s" read_sm_params expected "val_probs", read "%s" read_sm_params expected "val_log_probs", read "%s" read_mn_cn_params expected "emp_means", read "%s" read_mn_cn_params expected "emp_covar", read "%s" read_mn_cn_params expected "means", read "%s" read_mn_cn_params expected "covariance", read "%s" read_mn_cn_params expected "factor", read "%s" read_mn_cn_params expected "min_sigma_2s", read "%s" read_mm_s_params not converted from write_mm_s_params read_mm_d_params not converted from write_mm_d_params ERROR: expecting "tparm_DS" and n_parm = %d, found "%s" read_tparms_DS: unknown type of ENUM MODEL_TYPES =%d ERROR: expecting "class_DS" and n_class = %d, found "%s" read_class_DS_s: %p, num_wts %d, wts:%p, wts-len:%d ERROR: expecting "model_DS_ptr" and file_index, found "%s" ERROR: expecting "att_DS" and n_att = %d, found "%s" ERROR: expecting observed[%d], found "%s" ERROR: att_info->type %s not handled property list type %s, not handled! ERROR: expecting "database_DS", found "%s" ERROR: expand_clsf_wts(1): out of memory, malloc returned NULL! ERROR: expand_clsf_wts(2): out of memory, malloc returned NULL! ERROR: file type: %s not handled! ERROR: pathname %s is greater than %d chars -- see autoclass.h ERROR: pathname %s does not contain '.' character ERROR: %s file: %s%s not found! ERROR: data file pathname %s is greater than %d chars -- see autoclass.h ERROR: data file pathname %s does not contain '.' character ERROR: neither %s%s, nor %s%s were found! ERROR: %s%s, was not found! ERROR: results file pathname %s does not contain '.' character ERROR: type %s not handled by validate_results_pathname known_prior, sigma_min, sigma_max mean_mean, mean_sigma, mean_var minus_log_log_sigmas_ratio, minus_log_mean_sigma weighted_mean, weighted_var, mean sigma, log_sigma, variance, log_variance, inv_variance ll_min_diff, skewness, kurtosis prior_sigma_min_2, prior_mean_mean, prior_mean_sigma prior_sigmas_term, prior_sigma_max_2, prior_mean_var, prior_known_prior known_wt, known_prob, known_log_prob, unknown_log_prob data_file, header_file, n_data gamma_term, range, range_m1, inv_range, range_factor write_tparms_DS: unknown type of enum MODEL_TYPES=%d n_term, n_att, n_att_indices, n_datum, n_data w_j, ranges, class_wt, disc_scale log_pi, log_att_delta, log_delta, wt_m, log_marginal log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j n_props, range, zero_point, n_trans ERROR: property list type %s, not handled! unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors log_p_x_h_pi_theta, log_a_x_h accumulated_try_time, current_try_j_in, current_cycle # ordered sequence of clsf_DS's: 0 -> %d # clsf_DS %d: log_a_x_h = %.7e ERROR: save file extension type %s not handled ERROR: expecting "database_DS_ptr", found "%s" ERROR: expecting "num_models", found "%s" ERROR: expecting "model_DS_ptr", found "%s" ERROR: expecting "n_classes", found "%s" ERROR: expecting "chkpt_DS", found "%s" ERROR: expecting clsf_DS index %d, found "%s" ERROR: expecting "ac_version n.n", found "%s" ADVISORY: read %d classifications from %s%s ERROR: file_type %s not handled by get_clsf_seq 6&6&W&''''(`U`UuUVVVVVload_mm_s_params not converted from dump_mm_s_params load_mm_d_params not converted from dump_mm_d_params ERROR: in %s, expecting data type %d, found %d load_tparms_DS: unknown type of ENUM MODEL_TYPES =%d ERROR: load_class_DS_s: out of memory, malloc returned NULL! load_class_DS: %p, num_wts %d, wts:%p, wts-len:%d ERROR: expecting "ac_version n.n", found "%s %s" ADVISORY: loaded %d classification%s from %s%s ERROR: fwrite failed -- called by %s ERROR: write failed -- called by %s dump_tparms_DS: unknown type of ENUM MODEL_TYPES =%d ERROR: att_info->type %s not handled # ordered sequence of clsf_DS's: 0 -> %d# clsf_DS %d: log_a_x_h = %.7eload_sm_paramsload_mn_cn_paramsload_tparm_DSload_class_DS_sload_model_DSload_att_DSload_database_DSload_clsfload_clsf_seqdump_sm_paramsdump_mn_cn_paramsdump_tparm_DSdump_class_DS_sdump_term_DSdump_model_DS%s %s %fdump_att_DSdump_database_DSdump_clsf_DSac_version %sdump_clsf_seq|| }|C}(}}}ERROR: unequal type in class_equiv;i,s=%d %d != %dERROR: unknown type in class_equivalence;i,s=%d %dupdate_m_approx-fn called with w_j = %f, log_a_w_s_h_j not updated. ERROR: unknown type in update_m_approx; parm=%d, type=%dERROR: unknown type in update_l_approx; parm=%d, type=%dERROR: unknown type in log_likelihood; parm=%d, type= %dERROR: unknown type in update_params;i,t=%d %dERROR: unequal type in class_merge;i,s=%d %d != %dERROR: unknown type in class_merged_marginal;i,s=%d %dERROR[3]: model term type %s cannot handle type = %s, attribute #%d: "%s" Multiple sources for attribute ERROR[3]: %s model terms cannot handle subtype %s of type %s attributes ERROR: unkown type in expand_model_terms: %s TRANSFORMED->%dn_%scheck_term: 8Set known_wt to %f ??@43>I435 @Uw<433333?@-DT! @%!g?qE @??Attribute %d: "%s" not one of those allowed for single_normal_cm terms. using single_normal_cm model on att which has NO missing values Attribute %d: "%s": attempting to use single_normal_cm model in a non-singleton attribute set Attribute %d: "%s", attempting to use single_normal_cm model with non-positive error value %f. single_normal_cm is faulty due to large error-to-range ratio on sigma priors. !pnHP?%!g@?multi_normal_cn: attempt to apply to non-multiple set minmaxlog %slog_odds_transform_cLog %ssource_sub_typegenerate_singleton_transformfind_transformERROR: Attempt to apply log_transform to non-numerical attribute %d of type %s log transform of attribute# %d using mn %f rather than %f for zero_point. Suggest decreasing attribute's rel_error. ERROR: Attribute %d has no error property ADVISORY[2]: %s is being applied to attribute #%d: "%s" and will be stored as attribute #%d. ERROR: (generate_singleton_transform) Undefined transform; %s find_singleton_transform: att_index = %d, index = %d ERROR[2]: Attempt to find unknown transform %s on attributes: ERROR: Currently unable to deal with multiple argument transforms: %s ?ff@ERROR: update_ln_p_x_pi_theta called without any classes ERROR: update_weights called without any classes %d(%d) dup %d->%d(%d) as fixed at %d [saved %s/%s at %s] [saved .search at %s] autoclass_search ### Starting Check of %s%s ERROR: reconverge_type must be either "chkpt", or "results". ADVISORY: interactive_p = false, the quit character q/Q will not be recognized. I hope you have specified max_n_tries or max_duration! ADVISORY: screen_output_p = false, no more screen output for this search. I hope you have specified max_n_tries or max_duration ! WARNING: either start_fn_type = "block", or randomize_random_p = false, or both. These parameter settings are for testing *only* -- they should not be utilized for normal AutoClass runs. ERROR: either start_fn_type = "block", or randomize_random_p = false, or both. These parameter settings are for testing *only* -- they should not be utilized for normal AutoClass runs. AUTOCLASS C (version %s) STARTING at %s AUTOCLASS -SEARCH default parameters: USER supplied parameters which override the defaults: ERROR: if reconverge_type is "chkpt", checkpoint_p must be true, as well ERROR: if reconverge_type is "results", checkpoint_p must be false, as well ERROR: if reconverge_type is "results" or "chkpt", force_new_search_p must be false, as well WARNING: force_new_search_p is true and continuing will discard the search trials in: %s%s ERROR: force_new_search_p is true and continuing will discard the search trials in: %s%s ERROR: if force_new_search_p is false, there must be a <...>.results%s file WARNING: "autoclass -search" running in checkpointing mode ERROR: Haven't been given enough info to find a classification ADVISORY: start_j_list has been modified to: (ERROR: A new search must have at least one item in start_j_list WARNING: trial %d terminated prior to convergence since number of cycles reached "max_cycles" (%d). AUTOCLASS C (version %s) STOPPING at %s randomconverge_search_3random_ln_normal@@p?>M6dMffffff?jﴑ[?Hr?~?]tE?random_ln_normalconvergeconverge_search_3converge_search_4random exp(%.1f) [= %.1e] DUPS %d *SAVED*%s%sprint_search_trydescribe_clsf%s %d %s %ddup_try_DSnum_cycles%d %d %d %d %le%d %d %d %d %le %d %dget_search_from_filen_dups %d write_search_try_DSsearch_DS last try reported search_try_DS %dstart_j_list n_final_summary, n_save write_search_DSsave_searchrandom_j_from_ln_normal [cs-4: cycles %d] [cs-3a: cycles %d]converge_search_3a [cs-3: cycles %d] [c: cycles %d][reconverge "chkpt" j_in=%d] [j_in=%d] in file: %s%s ... convergent print_final_reportprint_report min_checkpoint_period. until %s. print_initial_reporttry %.2d: n %.2d j_in %.2d j_out %.2d clsf_ln_p %.3f clsf %s ERROR: number of classes function type "%s" not handled! allowable types are "random_ln_normal" only ERROR: try function type "%s" not handled! allowable types are "converge_search_3", "converge_search_4", and "converge", ERROR: start function type "%s" not handled! allowable types are "random" & "block" ERROR: start function type "%s" not handled! %s%sPROBABILITY exp(%.3f) N_CLASSES %2d FOUND ON TRY %3dIt has %d CLASSES with WEIGHTS PROBABILITY of both the data and the classification = exp(%.3f) ERROR: in "%s", search_try index = %d not found ERROR: in "%s", dup_try_index %d not found for search_try index = %d ERROR: in "%s", expected "start_j_list", found "%s" ADVISORY: read %d search trials from %s%s ERROR: "%s" is not a valid search file ERROR: number of search trials (%d) is less than the number of saved clsfs (%d) ERROR: .results[-bin] file and .search file not from the same run ERROR: average called for a zero length list! n, time, j_in, j_out, ln_p, num_cycles, max_cycles %d %d %d %d %.8e %d %d search_try_DS %d dup_try_DS %dn, time, n_dups, n_dup_tries %d %d %d %d tries from best on down for n_tries %d randomly from a log_normal [M-S, M, M+S] = [%.1f, %.1f, %.1f] ERROR: number of classes function type "%s" not handled! n_cls %d, s^2/b^2 %.4f, beta %.4f, range %.4f, in_n %d cnt %d, num_cls %d, ln_p %.4f, n_in_noise %d, range %.4f, s_b_n_vals %d, h %dconverge_search_3a called with rel_delta_range < 0.0 cnt %d, n_cls %d, range %.4f[%.4f], diff %.4f, ln_p %.4f, n_no_chg %d, h %dconverge_search_3 called with rel_delta_range < 0.0 cnt %d, n_class %d, range %.4f, diff %.4f, ln_p %.4f, n_no_chng %d cnt %d n_cls %d no_chng %d ln_p %.3f h_rng %.3f, h_fact*(-ln_p) %.3f ERROR: try function type "%s" not handled! [reconverge "results" j_in=%d] ENDING SEARCH because %s at %s after a total of %d tries over%s This invocation of "autoclass -search" took%s A log of this search is in file: %s%s The search results are stored in file: %s%s This search can be restarted by having "force_new_search_p = false" and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF %d BEST RESULTS ------------------ ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try %4d num_cycles %4d max_cycles %4d **** NON-CONVERGENT ***** ---------------- NEW BEST CLASSIFICATION FOUND on try %d ------------- (Also found %d other better than last report.) ----------- SEARCH STATUS as of %s ----------- It just took%s since beginning. It just took%s to find a classification Estimate <%s to find a classification Estimate >>%s to find the very best classification, which may beHave seen %d of the estimated > %d possible classifications (based on %d duplicates do far). Log-Normal fit to classifications probabilities has M(ean) %.1f, S(igma) %.1f Choosing initial n-classes %s WARNING: %.1f %% of time so far spend doing non-try overhead tasks - should you save and/or report less? Overhead time is %.1f %% of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial 2) If that trial results in a duplicate of a previous run, I will print 3) If that trial results in a classification better than any previous, 4) If more than%s have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or 7) Since interactive_p = false, I will continue searching 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (%d). 8) If needed, every%s I will save the best %d classifications so far to file: %s%s and a description of the search to file: %s%s 9) A record of this search will be printed to file: %s%s %d null classes stored from base-cycle. %.4d G_model_list is NULL model-%d class_store %d: %p pop_class_DS(%.2d): %p free_class(%s): %p ADVISORY: unknown enum MODEL_TYPES in free_tparm_DS = %d ERROR: unknown enum MODEL_TYPES in copy_tparm=%dERROR: from_class->i_values is non NULL ERROR: copy class wt allocation error 1ERROR: copy class wt allocation error 2 build_class_DS: %p, num_wts %d, wts:%p, wts-len:%d att_type integer not supported ERROR: get_class called with NULL model store_class_DS(%.2d [max=%d]): %p free_clsf: %p pop_clsf(%.2d): %p push_clsf(%.2d): %p create_clsf_DS: %p model global clsf: %p search_try_clsf %d: %p G_clsf_store %d: %p free search_try: %d free search NULL clsf passed to list_clsf_storage n_freed_classes = %.2d, n_create_classes_after_free = %.2d free search_try_dup: %d of %d displaying weights for %d classes?ERROR: -predict assumes only one model ERROR: training classification & test data have different models and/or different attributes blockERROR: att_type %s not handled ERROR: extend_database has db and comp_db from differing sources ERROR: extend_database found unmatched common attributes in db and comp_dbERROR: extend_database failed to produce corresponding attributesERROR: expand_database has db and comp_db from differing sources check: data_file_path, header_file_path, or n_data ERROR: expand_database found unmatched common attributes defs in <.results[-bin] file> and %s free_model(%d): %p ERROR: vsprintf had an error return: %d) -- called by %s ERROR: fprintf returned %d -- called by %s ERROR: integer list of type "int_list" is full all code commented in delete_duplicates ERROR: for %s, expected to read first ' from 'c', read %c instead! Type "y" for yes or "n" for no. WARNING: new_random: unable to find an unused number [checkpt clsf (j=%d, cycle=%d) at %s] ERROR: checkpoint_clsf called with G_checkpoint_file = "" does nothing write_matrix_integer%.7e write_matrix_floatwrite_vector_float%e to_screen_and_log_file, %d day%s %d hour%s %d minute%s %d second%sdg?UUUUUU?llfHg?팛&5?@Attempted to take log_gamma %20.15f ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. ERROR: sort_mncn_attributes: out of memory, malloc returned NULL! %s %sDISCRETE ATTRIBUTE (t = D) log( %s numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob %s t a Prob-*kl) -jkl -*kl %s %sREAL ATTRIBUTE (t = R) |Mean-jk - %s numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev %s t a -jk -jk StDev-jk -*k -*k %s %s Correlation matrix (row & column indices are attribute numbers) %sCase# Class Prob Case # Class Prob Case # Class Prob %sCase# Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) %s %sCase # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) ------------------------------------------------------------------------------------------ %35s %6.3f (%9.2e %9.2e) %9.2e (%9.2e %9.2e) Prob-jk is known %9.2e Prob-*k is known %9.2e ERROR: format_discrete_attribute: out of memory, malloc returned NULL! ERROR: format_discrete_attribute: out of memory, realloc returned NULL! %35s %6.3f %20s %9.2e %9.2e %9.2e ERROR: ordered_normalized_influence_values : out of memory, malloc returned NULL! ERROR: ordered_normalized_influence_values : out of memory, realloc returned NULL! ERROR: xref_class_report_attributes(1): out of memory, malloc returned NULL! ERROR: xref_class_report_attributes(2): out of memory, malloc returned NULL! ERROR: xref_class_report_attributes(3): out of memory, malloc returned NULL! ERROR: xref_class_report_attributes(3): out of memory, realloc returned NULL! ERROR: .r-params file: xref_class_report_att_list index %d not in range: 0<->%d ERROR: xref_get_data(1): out of memory, malloc returned NULL! ERROR: xref_get_data(1): out of memory, realloc returned NULL! ERROR: xref_get_data(2): out of memory, malloc returned NULL! ERROR: xref_get_data(3): out of memory, malloc returned NULL! ERROR: xref_get_data(4): out of memory, malloc returned NULL! xref_get_data: case_num %d => class 9999 ERROR: pre_format_attributes: out of memory, malloc returned NULL! ERROR: pre_format_attributes: out of memory, realloc returned NULL! ERROR: attribute type %s not supported The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. %9.2e %6d %6.3f We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. %9.2e %9.2e %6d %6.3f%sSEARCH SUMMARY %d tries over %s _________________________ ## ## - report filenames suffix%s%6cAutoClass PREDICTION for the %d "TEST" cases in %s%6cbased on the "TRAINING" classification of %d cases in %s%6cAutoClass CLASSIFICATION for the %d cases in %s%6cwith log-A (approximate marginal likelihood) = %.3f %s%6cfrom classification results file CLASSIFICATION HAS %d POPULATED CLASSES (max global influence value = %5.3f) %sORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES %s This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8%% of values are known. %6cI N F L U E N C E V A L U E S R E P O R T %6c order attributes by influence values = %s %6c============================================= %sCLASS %2d - weight %3d normalized weight %5.3f relative strength %9.2e ******* %s class cross entropy w.r.t. global class %9.2e ******* Model file: %s Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): ERROR: autoclass_class_influence_values_report: out of memory, malloc returned NULL! ERROR: autoclass_class_influence_values_report: out of memory, realloc returned NULL! autoclass_class_influence_values_report%s%6cCROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP %s %s ERROR: autoclass_xref_by_case_report: out of memory, malloc returned NULL! %s%6cCROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS %s %s influence_values_report_streamsorder_attributes_by_influence_pERROR: report_mode must be either "text", or "data". ERROR: report_type must be either "all", "xref_case", or "xref_class". ERROR: report_type must be either "all", "influence_values", "xref_case", or "xref_class". ERROR: report_mode must be "data" if comment_data_headers_p is true. ERROR: max_num_xref_class_probs must be greater than 0. AUTOCLASS %s default parameters: ERROR: sigma_contours_att_list index %d cannot exceed %d -- use indices from .hd2 file. ERROR: sigma_contours_att_list length must be >= 2. AUTOCLASS C (version %s) STOPPING at %s DATA_CORR_MATRIX%7d %2d %6.3ftext %sCase # %s (Cls Prob) %s %s %s (continued)%s%32c CLASS = %d %s %s%37cNot supported yet %14c %-20s %6c %44c%-20s %9.2e %9.2e %9.2e %%-%ds %%-%dgERROR: type %s not handled __%03d %03d %s %-4s %20sformat_attribute %02d %6d DATA_SEARCH_SUMMARY _______________ %sSUMMARY OF %d BEST RESULTS -%d %%%dc%03d %11d%s %5.3f%s%c%4d %5.3f ?%s %s DATA_CASE_TO_CLASS%03d %11d %11d %4d %5.3f %11d %4d %5.3fDATA_CLSF_HEADER %s%8c%s%s %s%8c%s%s %s%6cand using models DATA_POP_CLASSES DATA_CLASS_DIVS %sCLASS DIVERGENCES %sDATA_NORM_INF_VALS %03d %-55s -----truefalse%s %s DATA_CLASS %d %s(%s %s) File written: %s%s class_report_streamscase_report_streamsalln_clsfsclsf_n_listreport_typereport_modecomment_data_headers_pnum_atts_to_listxref_class_report_att_listmax_num_xref_class_probssigma_contours_att_listinfluence_valuesautoclass_reports8*CbP?+? %s Class class cross entropy Class Normalized %s num w.r.t. global class weight class weight %s%s %s Class Log of class Relative Class Normalized %s num strength class strength weight class weight %s _____________________________________________________________________________ %s num description I-*k %sall%s %s%s - index = %dget_class_model_source_info%s%8c%s%s - index = %d WARNING: requested clsf number %d not found -- max number is %d resultsERROR: unknown type of ENUM MODEL_TYPES in influence_value: %d influence_value called with unknown attribute_type: %s ERROR: attribute type %s not handled @I? SIGMA CONTOURS %satt_x att_y mean_x sigma_x mean_y sigma_y rotation-rad ADVISORY: compute sigma contour => att_n %d ("%s") is not a type real attribute. ADVISORY: compute sigma contour => att_n %d ("%s") has been declared ignore in the .model file. ADVISORY: compute sigma contour => term_type %s is not a `normal' term for att_n %d ("%s") %06d %05d %13e %13e %13e %13e %13e discrete stats from %s range,n_observed %d %d real stats from %s priors %s vector %s, n=%d float: %f, double %12.10f %g %g %g %g %g prior_known_prior %g rstats from att dstats from att NOT matrix %s, m,n=%d %d row %d matrix %s, m,n=%d %d . . . skipping to %d %g %d th info from database data ****limiting n=%d to %d %g %d %g %g %g ln_root %g log_ranges %g emp meanstemp_vtemp_m row %d,size=%d wts_veclog_probs_vec collect %d %d %d %d %d %d w_j, ranges= %g %g class_wt,disc_scale wt_m, log_marginal %g %g datumatt_indicesatt_list from termdatabase in model ith term in model n_att_locs=%d n_att_ignore_ids=%d num_priors=%d priors class %s w_j, pi_j %g %g from class i_sum,max_i_value %g %g wt vector NOT next pointer is%sNULL clsf %s database pointer is%sNULL num_models=%d n_classes=%d class pointer is%sNULL ith clsf classmin_class_wt %g search try %s number of duplicates=%d duplicate triesclsf from try search struct %s last try reportedbest to worst try start_j_list: %d, count,max,min,mean,var %d %5g %g %g %g pointer passed is null for %s known_prior, sigma_min, sigma_max %g %g %g mean_mean, mean_sigma, mean_var %g %g %g minus_log_log_sigmas_ratio, minus_log_mean_sigma %g %g mm_s_params count,wt,prob %d %g %g %g pointer passed is null for %s weighted_mean, weighted_var, meansigma, log_sigma, variance, log_variance, inv_variance ll_min_diff, skewness, kurtosis %g %g %g known_wt, known_prob, known_log_prob, unknown_log_prob weighted_mean, weighted_var, mean %g %g %g sigma, log_sigma, variance, log_variance, inv_variance prior_sigma_min_2, prior_mean_mean, prior_mean_sigma prior_sigmas_term, prior_sigma_max_2, prior_mean_var att %s, type,subtype,descrp=%s %s "%s" n_props,range,zero_point,n_trans %d %d %f %d translations triple pointer is%sNULL props triple pointer is%sNULL not printing warings and errors rel_error, error, missing %g %g %d not prinitng file pointers for data and header in db %s n_data,n_atts,input_n_atts,compressed_p %d %d %d %d separator_char,comment_char,unknown_token %c %c %c num_tsp %d, translations_supplied_p is %sNULL num_invalid_value_errors %d, invalid_value_errors is %sNULL num_incomplete_datum %d, incomplete_datum is %sNULL sm_param gamma_term,range,range_m1,inv_range,range_factor tparmDS %s;n_atts=%d type=%d in print_tparms_DS UNKNOWN TYPE=%d n_term, n_att, n_att_indices, n_datum, n_data log_pi, log_att_delta, log_delta term %s, n_atts,type=%d %s model %s; id =%s, expanded_terms univ time=%d model file pointer not printed; file index =%d this model contains %d terms num_class_store=%d; class_store is%sNULL not printing global clsf from model log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j %g %g %g known_parms_p, num_tparms %d %d void **i_values; N-attributes vector of influence value structures.skipping call to print_model that is in print_classlog_p_x_h_pi_theta, log_a_x_h %g %g skipping 1 call for each to print_model in clsf clsf reports pointer is%sNULL clsf_store next pointer is%sNULL n,time,j_in,j_out,ln_p, %d %d %d %d %g n, time, n_dups, n_dup_tries %d %d %d %d tries from best on down for n_tries =%d n_final_summary, n_save %d %d ^p^pq'rRrrrs too many params; max = %d; %s=%15.12e of unknown paramtype=%dNone.= = %c=: param name too long. limit is %dERROR: undefined parameter: %s ERROR: no value given for: %s ERROR: for parameter %s, neither true or false was read. ERROR: for parameter %s, first character of value is not a '"' ERROR: for parameter %s, more than %d characters. were input ERROR: for parameter %s, number read, %s, was not an integer ERROR: for parameter %s, number read, %s, was not a float ERROR: for parameter %s, number read, %s, was not a double ERROR: more than %d values input for %sERROR: bad paramtype= %d for %s; parameter not set *|F|j||||"$~ AutoClass Search: %s -search <.db2[-bin] file path> <.hd2 file path> <.model file path> <.s-params file path> AutoClass Reports: %s -reports <.results[-bin] file path> <.search file path> <.r-params file path> AutoClass Prediction: %s -predict ERROR: the second argument must be "-search", "-reports", or "-predict" ERROR: invalid number of arguments for "autoclass" ERROR: invalid number of arguments for "autoclass -search" ERROR: invalid number of arguments for "autoclass -reports" ERROR: invalid number of arguments for "autoclass -predict" > autoclass AUTOCLASS C (version %s) -search-reports-predicta  oHx  @ o0oo@Ɗ֊&6FVfvƋ֋&6FVfvƌ֌&6FVfvƍ֍&6FVfv'73.3.6unxA/ ?> #=e>L>>>io>>>?c>?w>333?PX>L?<>fff?A">? >̌?=??=ff?xz=33?pΈ=?u`=?}6=? =ff?C<33?Ǻ<@#D.L#F*/#GA 3#H<#LlE#TsF#XG#\zH#`I%#dGKZ#h M#l 4  # # rZ#    'CD' 7 5BH@%  # U# # # # #&##9Y?L w_j # # # # #! #&Z# Z#$#(/ Z#,3 #0C#4#8Z#< wts#@> #D!#H>2 8& #~'Z#,RW(#09) #4IOl. #/#,0 #01Z#\G 2#`3Z#d4#hW,0P9Z# mx:# mn;#s<# var=#X7=w 7.BZ#< CZ#> D #ԂattM #N #)O #RQ#| R,#SZ#7.TZ#z U#HVZ#|W#)X#Y#4Z>#[#M\Z#fՌc|Pm# n#FoZ#~pZ#*rZ#ysZ#etZ#v# w#+x #ez# {#|#Z# #7Z#;#%Z#)#F* >L id #Z#,#0fZ#c#P# #FZ#eZ#Z## Z#+#Z##Z#@%#Z##_* #.;5 ;  4##c#Z## ;Z#};# #$SS #(* #, #0   (   S   Z# Z#Z#^ d  S#Z# # #P#T# ##?#S##d+ 1 k ;4waZ#EbZ#.?c #v |  )wgZ#+hZ#Z    ~mZ# &n # vv_# yZ#` zZ#d EzZ#h {Z#l w{Z#p F{Z#tw_j|#x |#| }# |~#  # A# #wts# 4# # # # d #F d  USMAGG  m #wts# s# o# # # #H 0PZ#wt# `# \ # ',# t$# %# &# P'# ^'(# { )# !2*# !+# ,# -#$ .#(# 3 , 4# 7.5Z# . 6# Z6# 6# 7# |8# k9#dX= ># $?# V@# TA# jB# sC# sD# E# F# EG#$ H#( I#, )K#0 L#4 M#8 AO#< O#@ O#D  P#H |P#L P#P Q#T_5DU jV# sW# sX# Y# Z# E[# \# ]# )_# `#$ a#( Ac#, c#0 c#4  d#8 |d#< d#@Xo%pF-q rsmst#ud w k  7q ]  ] F  ( 0@^ n  (M X@]ZE ZE!ZEZES* En$^ ^^^`FE EZ B$BO0Ei @B[E E ^2 ZlB&ZpB ' (tB7 )ZEy*^+* F,ZF-ZB8.ZF 3B7Z FR 8ZF8ZF@G`,\ c0 g  int:;a_  o { .xw .C Z##`## j# ## ## #$E#(#,x#0 !#4#Z#8 'Z#<)z#@ ->#D.L#F*/#GA 3#H<#LlE#TsF#XG#\zH#`I%#dGKZ#h M#l 4  # # rZ#  ~  'D  Zx L L`St1M#n M#,c M#vi2N7 Nt2OSO) G !`Gfp"< #one#two# 'q u  - (M  _T7 Zy06bP,mc0 g  int:;a_  o { .xw .C Z##`## j# ## ## #$E#(#,x#0 !#4#Z#8 'Z#<)z#@ ->#D.L#F*/#GA 3#H<#LlE#TsF#XG#\zH#`I%#dGKZ#h M#l   4  # # rZ#    'C D! 1 5<B@%  # U# # # # #&##9Y?L w_j # # # # #! #&Z# Z#$#(/ Z#,3 #0C#4#8Z#< wts #@> #D!#H82 8& #~'Z#,RW( #09) #4CIl. #/ #,0 #01Z#\G 2#`3Z#d4#hW&0P9Z# mx:# mn;#s<# var=#X17q 7.BZ#< CZ#> D #|{attM #N #)O #RQ#| R&#SZ#7.TZ#z U#HVZ#|W#)X#Y#4Z8#[#M\Z#fՆc|Pm# n#FoZ#~pZ#*rZ#ysZ#etZ#v# w#+x #ez# {#|#Z# #7Z#;#%Z#)#F$ >L id #Z#,#0fZ#c{#P# #FZ#eZ#Z## Z#+#Z##Z#@%#Z##_$ #.;/ 5  4##c{#Z## ;Z#};# #$Sk #($ #, #0   ({   + 1 k   Z# Z#Z#v | 8 S#Z# #  #P#T # # #?#S ##dC I  ;4waZ#EbZ#.?c #   )wgZ#+hZ#Z  " ~mZ# &n"# vvw# yZ#` zZ#d EzZ#h {Z#l w{Z#p F{Z#tw_j|#x |#| }# |~#  # A# #wts # 4 #  # # # d #^ d  USMAGG  m #wts# s# o#  #  #  #H 0PZ#wt# `# \ # ',# t$# %# & # P'# ^'( # { )# !2*# !+ # , # -#$ . #(; 3 , 4# 7.5Z# . 6# Z6# 6# 7# |8# k9#|X= ># $?# V@# TA# jB# sC# sD# E# F# EG#$ H#( I#, )K#0 L#4 M#8 AO#< O#@ O#D  P#H |P#L P#P Q#Tw5DU jV# sW# sX# Y# Z# E[# \# ]# )_# `#$ a#( Ac#, c#0 c#4  d#8 |d#< d#@Xo%p^-q rsmst;u|  q8   1i RZP~VQ~QZ4iSZSQ qRW numZ|  L= l iattiq0PjZsj5EjUMjZmxjumnkm)({]( (!~*Zu| E*ZA H*Zb!h*Zatt+qu"str, | -P #O + *db{] /Z"str~iZ? 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H0+call_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__dtor_idx.5790completed.5788__do_global_dtors_auxframe_dummy__CTOR_END____FRAME_END____JCR_END____do_global_ctors_auxglobals.cinit.cio-read-data.cio-read-model.cio-results.cbinary_file.5489file.5488binary_file.5420file.5419save_file.4871temp_save_file.4870io-results-bin.cmodel-expander-3.cmatrix-utilities.cmodel-single-multinomial.cmodel-single-normal-cm.cmodel-single-normal-cn.cmodel-multi-normal-cn.cmodel-transforms.cmodel-update.csearch-basic.csearch-control.cstream.4736results_file.4780maybe_checkpoint_file.4779checkpoint_file.4778start_j_list.4696search_params_file_fp.4737header_file_fp.4734model_file_fp.4735log_file_fp.4732search_file_fp.4733best_clsfs.4764n_classes_explain.4781last_saved_clsfs.4765clsf_n_list.4777search-control-2.cresults_file.5948clsf_n_list.5947id.5878temp_search_file.5836cut_where_above_tablesearch-converge.cstruct-class.cstruct-clsf.cstatistics.cpredictions.cstruct-data.cheader_file.4816data_file.4815struct-matrix.cstruct-model.cmodel_file.4703utils.ctime_string.5199current_time.5183utils-math.cintf-reports.cfiltered_numeric_string.6652header_prefix.6434header_prefix.6290header.6179header_continued.6180xref_class_report_pathname.4971xref_case_report_pathname.4938influence_report_pathname.4874clsf_n_list.4697xref_class_report_att_list.4702sigma_contours_att_list.4707log_file_fp.4723intf-extensions.cintf-influence-values.cintf-sigma-contours.cprints.cgetparams.cautoclass.csearch_params_file.4701model_file.4700header_file.4699data_file.4698log_file.4704results_file.4703search_file.4702xref_class_file.4707influ_vals_file.4706reports_params_file.4705test_data_file.4709xref_case_file.4708__preinit_array_start__fini_array_end_GLOBAL_OFFSET_TABLE___preinit_array_end__fini_array_start__init_array_end__init_array_start_DYNAMICdata_startdecf_v_vfputs@@GLIBC_2.0G_plistabort@@GLIBC_2.0delete_class_duplicatessingle_normal_cm_class_merged_marginalexpand_clsfG_db_listnext_best_deltaupdate_wtsfind_attribute_modeling_classread_char_from_single_quoteslog_gammasprintf@@GLIBC_2.0sm_params_influence_fnlog_odds_transform_cget_class_DSformat_integer_attributecopy_to_matrixtrace_star_mmsn_cm_params_influence_fnwrite_sm_paramswrite_database_DSdefine_attribute_definitionsexpand_modelsearch_summaryupdate_ln_p_x_pi_thetasrand48@@GLIBC_2.0check_load_headerread_model_filetranslate_realload_clsf_seq__libc_csu_finisingle_multinomial_class_equivalenceincf_m_vvsqsort@@GLIBC_2.0class_duplicatespvalidate_data_pathnameread_model_doitinterpolatefind_discrete_statsinit_clsf_for_reportsfree_model_DSupper_end_normal_fit_startget_class_weight_orderingsingle_normal_cm_class_equivalenceprocess_attribute_definitionsload_att_DSload_mm_s_paramsclass_case_sort_compare_lsrclass_merged_marginal_fnvector_root_diagonal_matrixstore_clsf_DSvariancefind_similar_modelmember_int_listconverge_search_4pop_clsflimit_min_diagonal_valuesread_sn_cn_paramsarrange_model_function_termsto_screen_and_log_fileinfluence_valueread_model_DSdisplay_stepsqrtf@@GLIBC_2.0log_headerset_up_clsfget_sources_listG_line_cnt_maxget_clsf_seq__gmon_start__list_clsf_storage_Jv_RegisterClassescompute_factor_fp_hwrealloc@@GLIBC_2.0write_clsf_DSstore_class_DSavg_improve_delta_ln_pG_clsf_storeread_from_stringadd_propertyconverge_search_3afind_model_pvsprintf@@GLIBC_2.0update_location_infodump_att_DScheck_termwrite_clsf_seqexpand_att_listblock_set_clsffloat_sort_cell_compare_gtrprocess_attribute_defevery_db_DS_same_source_pdiscard_comment_linessum_vector_fread_matrix_integerread_data_doitstrchr@@GLIBC_2.0print_att_locs_and_ignore_idsprint_model_DS_finisafe_sprintfsetf_m_msattribute_model_term_numberload_mm_d_paramsgenerate_attribute_infosystem@@GLIBC_2.0strncpy@@GLIBC_2.0putchar@@GLIBC_2.0sigmaadd_to_plistln_avg_pnew_randomcase_report_streamsstore_clsf_DS_classesexpand_model_termspow@@GLIBC_2.0load_tparm_DSformat_discrete_attributeprint_vector_fmin_best_peakfgets@@GLIBC_2.0search_durationmemset@@GLIBC_2.0find_transformfind_singleton_transformcompress_clsfprint_sm_paramsprint_search_try_DS__strtol_internal@@GLIBC_2.0dump_term_DSprint_matrix_ibuild_compressed_class_DSG_data_file_formatwrite_vector_floatordered_normalized_influence_valuesrandom_set_clsfvalidate_search_start_fnclsf_DS_testclsf_DS_max_n_classes__libc_start_main@@GLIBC_2.0G_db_lengthclass_equivalence_fn_IO_getc@@GLIBC_2.0output_real_att_statisticsfloor@@GLIBC_2.0create_databasegetfsingle_normal_cn_update_m_approxextract_rhosG_att_type_datastrrchr@@GLIBC_2.0G_likelihood_tolerance_ratioincf_v_vwrite_matrix_floatdump_model_DSprint_class_DSupdate_params_fnupdate_approximationsinsert_new_trialfree_clsf_DSprint_tparm_DSint_compare_lessG_safe_file_writing_pcompute_influence_valuesmax_plusread_model_resetwrite_mm_d_paramsupdate_m_approx_fnpush_int_listread_vector_floatprint_priors_DSwrite_class_DS_sexist_intersectionG_model_listinit_propertiesexpand_databasesingle_multinomial_log_likelihoodexp@@GLIBC_2.0ungetc@@GLIBC_2.0single_normal_cm_model_term_build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0ustar areare AUTOCLASS C VERSION 3.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/data/tests.c - Reconfigure parameter values for the checkpointing test case. 2. autoclass-c/data/glass/glassc-chkpt.s-params - Include checkpoint test param settings from tests.c 3. autoclass-c/data/autos/* - Add input data files for last **non**-random trial test of autoclass-c/data/tests.c 4. autoclass-c/doc/prediction-c.text - Add text concerning handling of "test" cases which are not predicted to be in any of the "training" classes. 5. autoclass-c/doc/reports-c.text - Add new reports param: comment_data_headers_p, which prefixes the "#" comment character to all lines except the minimum for selective parsing. Add new reports param: max_num_xref_class_probs, which determines how many lessor class probabilities will be printed for the case and class cross-reference reports. The default value is 5. Add new report params: start_sigma_contours_att & stop_sigma_contours_att. This adds the capability to compute sigma class contour values for specified pairs of real valued attributes, when generating the influence values report with the data option (report_mode = "data"). See section "Generating Sigma Contour Values". Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.1. 2. autoclass-c/prog/io-results.c - In VALIDATE_RESULTS_PATHNAME, handle checkpoint files similarly to results files: determine if they are ascii or binary, rather than assuming they are binary. This was only a problem when .s-params parameters save_compact_p = false, and read_compact_p = false. In GET_CLSF_SEQ, handle checkpoint files similarly to results files. This fix now allows checkpoint files to be loaded for reconvergence. 3. autoclass-c/prog/intf-reports.c, autoclass.h - In XREF_GET_DATA, allocate memory for collector once for each case, rather than n_classes times. This fix now permits reports to be generated for data sets of 100,000 cases and more, without causing a segmentation fault. Eliminate ATTR_ALLOC_INCREMENT, and allocate once for all discrete, and once for all real report attributes, if needed, rather than invoking malloc/realloc for each report attribute. 4. autoclass-c/prog/intf-reports.c, autoclass.h - In AUTOCLASS_REPORTS, pass prediction_p to CASE_CLASS_DATA_SHARING, so that XREF_GET_DATA can flag "test" cases which are not predicted in be in any of the "training" classes. Put them in class -1. This is only functional for "autoclass -predict ..." runs. The following message will appear in the screen output for each case that is not a member of any of the "training" classes: xref_get_data: case_num xxx => class 9999 Class 9999 members will appear in the "case" and "class" cross- reference reports. 5. autoclass-c/prog/intf-influence-values.c - In INFLUENCE_VALUE, do not process attribute values which have null translations. This occurs when the user supplies an excessive range value in .hd2, and ignores the warning to correct it. This prevents a segmentation fault. 6. autoclass-c/prog/struct-data.c - In EXPAND_DATABASE, make error msg more informative. 7. autoclass-c/prog/autoclass.h, intf-reports.c, intf-extenstions.c, search-control-2.c - Implement new reports param "comment_data_headers_p", which prefixes the "#" comment character to all lines except the minimum for selective parsing. 8. autoclass-c/prog/io-read-data.c - In OUTPUT_REAL_ATT_STATISTICS, add error check for attribute variance exceeding infinity. This situation is caused by "out-liers" with very large deviations from the other attribute values, and usually means that these attribute values are erroneous. AutoClass C can not proceed in this situation. 9. autoclass-c/prog/intf-reports.c - In the influence values report for multi_normal_cn models, when there are more than one covariant normal correlation matrix, print all of them for each class, not just the one for the least significant attribute of the current class. Changes to FORMAT_ATTRIBUTE & FORMAT_REAL_ATTRIBUTE. 10. autoclass-c/prog/intf-reports.c - In the case cross-reference report (report_type = "xref_case") generated with the data option (report_mode = "data"), other class probabilities are now printed, if their values are greater than or equal to 0.001, and there are not more than (MAX_NUM_XREF_CLASS_PROBS - 1) of them. Changes to XREF_PAGINATE_BY_CASE, & XREF_OUTPUT_PAGE_HEADERS. 11. autoclass-c/prog/intf-reports.c - In the case and class cross-reference reports, the print out of probabilities has increased by one significant digit (0.04 => 0.041); and the minimum value printed is now 0.001, rather than 0.01. The maximum number of lessor probabilities printed out is (MAX_NUM_XREF_CLASS_PROBS - 1). Changes to XREF_PAGINATE_BY_CASE, & XREF_OUTPUT_LINE_BY_CLASS. 12. autoclass-c/prog/intf-reports.c - Add new report parameter MAX_NUM_XREF_CLASS_PROBS, which determines how many lessor class probability values will be printed in the case and class cross-reference reports. 13. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.*, autoclass-c/prog/autoclass.h, intf-sigma-contours.c, intf-reports.c - Add capability to compute sigma class contour values for specified pairs of real valued attributes, when generating the influence values report with the data option (report_mode = "data"). Add new report params start_sigma_contours_att & stop_sigma_contours_att. ====================================================================== autoclass-3.3.6.dfsg.1/version-1-5.text0000644000175000017500000000730311247310756015606 0ustar areare AUTOCLASS C VERSION 1.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass/doc/introduction-c.text, kdd-95.ps, tr-fia-90-12-7-01.ps - Postscript papers are now included as postscript, instead of uuencoded postscript. 2. autoclass/doc/preparation-c.text - Added binary data file input option. Programming: ------------------------------ 1. autoclass-c/prog/autoclass.c - In "main", call "validate_data_pathname" to allow either .db2 ("ascii") or .db2-bin ("binary") data file extensions. The identifying header of a .db2-bin file is - ".db2-bin" - char[8] - 32-bit integer with byte-length of each data case. The data cases follow in binary "float" format -- 32 bit fields. 2. autoclass-c/prog/io-results.c - Add "validate_data_pathname". 3. autoclass-c/prog/autoclass.h - Function prototype definition change/addition. Add DATA_BINARY_FILE_TYPE. Change character array variables of length MAX_PATHNAME_LENGTH (81) to variables of type fxlstr (length 160) to handle very long file pathnames. #define M_PI if not defined -- needed under Solaris. Use pow rather than exp2, since exp2 not available under Solaris gcc 2.6.3. 4. autoclass-c/prog/io-read-data.c - In "read_database" change NULL to FALSE, so that int/int rather than int/ptr comparison is made. Detected by Solaris GNU gcc. "read_database", "read_data" and "read_database_doit" modified to handle binary data files. 5. autoclass-c/prog/globals.h, globals.c - Add G_data_file_format. 6. autoclass-c/prog/search-control.c - In "autoclass-search" do not open/close ".db2" data file. Check for non-NULL "best_clsfs" prior to writing ".results[-bin]" file. 7. autoclass-c/prog/struct-data.c - In "expand_database", call "validate_data_pathname" to allow either .db2 ("ascii") or .db2-bin ("binary") data file extensions. 8. autoclass-c/prog/search-basic.c - Modified "generate_clsf"'s call to "read_database". 9. autoclass-c/prog/utils.c, io-read-data.c, io-results-bin.c & io-results.c - Since the include file is not available in the Solaris GNU gcc implementation, hard code them in "fcntlcom-ac.h". Solaris 2.4 fails open, unless fopen/fclose is done first. 10. autoclass-c/load-ac - Add "fcntlcom-ac.h". Use "clean" make target. 11. autoclass-c/prog/search-control-2.c - In "print_report", do not use NULL as value of delta_ln_p. In "print_final_report", corrected the overwriting of a string array in cases where long pathnames are used. 12. autoclass-c/prog/utils.c, intf-reports.c, search-control.c, & getparams.c - Correct compiler warnings found by Solaris gcc version 2.6.3. 13. autoclass-c/prog/init.c - In "init", use getcwd, rather than getwd for Solaris compatibility. 14. autoclass-c/prog/autoclass.make - Include "clean" target. Add compiler options "-pedantic -Wall". 15. autoclass-c/prog/utils.c - Add "safe_sprintf", and use it in other programs in lieu of "sprintf" to detect string overwrites. Corrected string overwrite which caused abort and the message "Premature end of file reading symbol table". 16. autoclass-c/prog/intf-reports.c - In "search_summary" change search->n to search->n_tries to prevent segment violation when there are duplicates. ====================================================================== autoclass-3.3.6.dfsg.1/version-3-3-6.text0000644000175000017500000000211511247310756015745 0ustar areare AUTOCLASS C VERSION 3.3.6 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.6. 2. autoclass-c/prog/utils.c - Comment out the last error check in safe_sprintf to prevent errors of this type: "ERROR: vsprintf produced 41 chars (max number is 40) -- called by log_transform Abort". 3. autoclass-c/prog/intf-reports.c - Reworked the placement of '#' characters for the 'comment_data_headers_p = true' report setting. Attribute names for both real and discrete attributes can now be of arbitrary length and remain on the same line, i.e. they will not be split onto two lines. 4. autoclass-c/prog/io-read-data.c - Print standard deviation, rather than variance, for input data summary of real attributes in the .log file. (output_att_statistics & output_real_att_statistics)autoclass-3.3.6.dfsg.1/load-ac-macosx0000644000175000017500000000244011247310756015423 0ustar areare#!/bin/csh -f # make the C version of AutoClass # with -tags arg, emacs tags files will be generated cd prog set makeflags = "OSFLAGS=-DMACOSX" set etags_flag = -f set sunos_solaris = "" if ( "`/usr/bin/uname -s`" == "Darwin" ) then # APPLE MAC OS X make $makeflags -f autoclass.make.macosx.gcc else echo "Unrecognized operating system" exit endif mv autoclass ../autoclass if ("X$1" != "X") then # compute emacs tags for autoclass etags $etags_flag ../autoclass.TAGS \ globals.c init.c io-read-data.c io-read-model.c io-results.c \ io-results-bin.c model-expander-3.c matrix-utilities.c \ model-single-multinomial.c model-single-normal-cm.c \ model-single-normal-cn.c model-multi-normal-cn.c \ model-transforms.c model-update.c search-basic.c \ search-control.c search-control-2.c \ search-converge.c struct-class.c struct-clsf.c \ statistics.c predictions.c \ struct-data.c struct-matrix.c struct-model.c \ utils.c utils-math.c \ intf-reports.c intf-extensions.c intf-influence-values.c \ intf-sigma-contours.c \ prints.c getparams.c autoclass.c \ autoclass.h getparams.h globals.h params.h fcntlcom-ac.h minmax.h else # remove .o files (for distribution) make -f autoclass.make endif # back up to parent directory cd .. autoclass-3.3.6.dfsg.1/doc/0000755000175000017500000000000011667632240013456 5ustar areareautoclass-3.3.6.dfsg.1/doc/interpretation-c.text0000644000175000017500000002712311247310756017657 0ustar areareINTERPRETATION OF AUTOCLASS RESULTS ------------------------------------ 1.0 What Have You Got? 2.0 Assumptions 3.0 Influence Report 4.0 Cross Entropy 5.0 Attribute Influence Values 6.0 Class And Case Reports 7.0 Comparing Influence Report Class Weights And Class/Case Report Assignments 8.0 Alternative Classifications 9.0 What Next? 1.0 WHAT HAVE YOU GOT? ---------------------- Now you have run AutoClass on your data set -- what have you got? Typically, the AutoClass search procedure finds many classifications, but only saves the few best. These are now available for inspection and interpretation. The most important indicator of the relative merits of these alternative classifications is Log total posterior probability value. Note that since the probability lies between 1 and 0, the corresponding Log probability is negative and ranges from 0 to negative infinity. The difference between these Log probability values raised to the power e gives the relative probability of the alternatives classifications. So a difference of, say 100, implies one classification is e^100 ~= 10^43 more likely than the other. However, these numbers can be very misleading, since they give the relative probability of alternative classifications under the AutoClass ***assumptions***. 2.0 ASSUMPTIONS --------------- Specifically, the most important AutoClass assumptions are the use of normal models for real variables, and the assumption of independence of attributes within a class. Since these assumptions are often violated in practice, the difference in posterior probability of alternative classifications can be partly due to one classification being closer to satisfying the assumptions than another, rather than to a real difference in classification quality. Another source of uncertainty about the utility of Log probability values is that they do not take into account any specific prior knowledge the user may have about the domain. This means that it is often worth looking at alternative classifications to see if you can interpret them, but it is worth starting from the most probable first. Note that if the Log probability value is much greater than that for the one class case, it is saying that there is overwhelming evidence for ***some*** structure in the data, and part of this structure has been captured by the AutoClass classification. 3.0 INFLUENCE REPORT -------------------- So you have now picked a classification you want to examine, based on its Log probability value; how do you examine it? The first thing to do is to generate an "influence" report on the classification using the report generation facilities documented in "autoclass-c/doc/reports-c.text". An influence report is designed to summarize the important information buried in the AutoClass data structures. The first part of this report gives the heuristic class "strengths". Class "strength" is here defined as the geometric mean probability that any instance "belonging to" class, would have been generated from the class probability model. It thus provides a heuristic measure of how strongly each class predicts "its" instances. The second part is a listing of the overall "influence" of each of the attributes used in the classification. These give a rough heuristic measure of the relative importance of each attribute in the classification. Attribute "influence values" are a class probability weighted average of the "influence" of each attribute in the classes, as described below. The next part of the report is a summary description of each of the classes. The classes are arbitrarily numbered from 0 up to n, in order of descending class weight. A class weight of say 34.1 means that the weighted sum of membership probabilities for class is 34.1. Note that a class weight of 34 does not necessarily mean that 34 cases belong to that class, since many cases may have only partial membership in that class. Within each class, attributes or attribute sets are ordered by the "influence" of their model term. 4.0 CROSS ENTROPY ----------------- A commonly used measure of the divergence between two probability distributions is the cross entropy: the sum over all possible values x, of P(x|c...)*log[P(x|c...)/P(x|g...)], where c... and g... define the distributions. It ranges from zero, for identical distributions, to infinite for distributions placing probability 1 on differing values of an attribute. With conditionally independent terms in the probability distributions, the cross entropy can be factored to a sum over these terms. These factors provide a measure of the corresponding modeled attribute's influence in differentiating the two distributions. We define the modeled term's "influence" on a class to be the cross entropy term for the class distribution w.r.t. the global class distribution of the single class classification. "Influence" is thus a measure of how strongly the model term helps differentiate the class from the whole data set. With independently modeled attributes, the influence can legitimately be ascribed to the attribute itself. With correlated or covariant attributes sets, the cross entropy factor is a function of the entire set, and we distribute the influence value equally over the modeled attributes. 5.0 ATTRIBUTE INFLUENCE VALUES ------------------------------ In the "influence" report on each class, the attribute parameters for that class are given in order of highest influence value for the model term attribute sets. Only the first few attribute sets usually have significant influence values. If an influence value drops below about 20% of the highest value, then it is probably not significant, but all attribute sets are listed for completeness. In addition to the influence value for each attribute set, the values of the attribute set parameters in that class are given along with the corresponding "global" values. The global values are computed directly from the data independent of the classification. For example, if the class mean of attribute "temperature" is 90 with standard deviation of 2.5, but the global mean is 68 with a standard deviation of 16.3, then this class has selected out cases with much higher than average temperature, and a rather small spread in this high range. Similarly, for discrete attribute sets, the probability of each outcome in that class is given, along with the corresponding global probability -- ordered by its significance: the absolute value of (log { / }). The sign of the significance value shows the direction of change from the global class. This information gives an overview of how each class differs from the average for all the data, in order of the most significant differences. 6.0 CLASS AND CASE REPORTS -------------------------- Having gained a description of the classes from the "influence" report, you may want to follow-up to see which classes your favorite cases ended up in. Conversely, you may want to see which cases belong to a particular class. For this kind of cross-reference information two complementary reports can be generated. These are more fully documented in "reports-c.text". The "class" report, lists all the cases which have significant membership in each class and the degree to which each such case belongs to that class. Cases whose class membership is less than 90% in the current class have their other class membership listed as well. The cases within a class are ordered in increasing case number. The alternative "cases" report states which class (or classes) a case belongs to, and the membership probability in the most probable class. These two reports allow you to find which cases belong to which classes or the other way around. If nearly every case has close to 99% membership in a single class, then it means that the classes are well separated, while a high degree of cross-membership indicates that the classes are heavily overlapped. Highly overlapped classes are an indication that the idea of classification is breaking down and that groups of mutually highly overlapped classes, a kind of meta class, is probably a better way of understanding the data. 7.0 COMPARING INFLUENCE REPORT CLASS WEIGHTS AND CLASS/CASE REPORT ASSIGNMENTS ------------------------------------------------------------------------------ The class weight given as the class probability parameter, is essentially the sum over all data instances, of the normalized probability that the instance is a member of the class. It is probably an error on our part that we format this number as an integer in the report, rather than emphasizing its real nature. You will find the actual real value recorded as the w_j parameter in the class_DS structures on any .results[-bin] file. The .case and .class reports give probabilities that cases are members of classes. Any assignment of cases to classes requires some decision rule. The maximum probability assignment rule is often implicitly assumed, but it cannot be expected that the resulting partition sizes will equal the class weights unless nearly all class membership probabilities are effectively one or zero. With non-1/0 membership probabilities, matching the class weights requires summing the probabilities. In addition, there is the question of completeness of the EM (expectation maximization) convergence. EM alternates between estimating class parameters and estimating class membership probabilities. These estimates converge on each other, but never actually meet. AutoClass implements several convergence algorithms with alternate stopping criteria using appropriate parameters in the .s-params file. Proper setting of these parameters, to get reasonably complete and efficient convergence may require experimentation. 8.0 ALTERNATIVE CLASSIFICATIONS ------------------------------- In summary, the various reports that can be generated give you a way of viewing the current classification. It is usually a good idea to look at alternative classifications even though they do not have the minimum Log probability values. These other classifications usually have classes that correspond closely to strong classes in other classifications, but can differ in the weak classes. The "strength" of a class within a classification can usually be judged by how dramatically the highest influence value attributes in the class differ from the corresponding global attributes. If none of the classifications seem quite satisfactory, it is always possible to run AutoClass again to generate new classifications. 9.0 WHAT NEXT? -------------- Finally, the question of what to do after you have found an insightful classification arises. Usually, classification is a preliminary data analysis step for examining a set of cases (things, examples, etc.) to see if they can be grouped so that members of the group are "similar" to each other. AutoClass gives such a grouping without the user having to define a similarity measure. The built-in "similarity" measure is the mutual predictiveness of the cases. The next step is to try to "explain" why some objects are more like others than those in a different group. Usually, domain knowledge suggests an answer. For example, a classification of people based on income, buying habits, location, age, etc., may reveal particular social classes that were not obvious before the classification analysis. To obtain further information about such classes, further information, such as number of cars, what TV shows are watched, etc., would reveal even more information. Longitudinal studies would give information about how social classes arise and what influences their attitudes -- all of which is going way beyond the initial classification. autoclass-3.3.6.dfsg.1/doc/prediction-c.text0000644000175000017500000000604411247310756016747 0ustar areare PREDICTION USING CLASSIFICATIONS Classifications can be used to predict class membership for new cases. So in addition to possibly giving you some insight into the structure behind your data, you can now use Autoclass directly to make predictions, and compare Autoclass to other learning systems. This technique for predicting class probabilities is applicable to all attributes, regardless of data type/sub_type or likelihood model term type. In the event that the class membership of a data case does not exceed 0.0099999 for any of the "training" classes, the following message will appear in the screen output for each case: xref_get_data: case_num xxx => class 9999 Class 9999 members will appear in the "case" and "class" cross-reference reports with a class membership of 1.0. Cautionary Points: The usual way of using Autoclass is to put all of your data in a data_file, describe that data with model and header files, and run "autoclass -search". Now, instead of one data_file you will have two, a training_data_file and a test_data_file. It is most important that both databases have the same AutoClass internal representation. Should this not be true, AutoClass will exit, or possibly in in some situations, crash. The prediction mode is designed to hopefully direct the user into conforming to this requirement. Preparation: Prediction requires having a training classification and a test database. The training classification is generated by the running of "autoclass -search" on the training data_file ("data/soybean/soyc.db2"), for example: % autoclass -search data/soybean/soyc.db2 data/soybean/soyc.hd2 data/soybean/soyc.model data/soybean/soyc.s-params This will produce "soyc.results-bin" and "soyc.search". Then create a "reports" parameter file, such as "soyc.r-params" (see "reports-c.text"), and run AutoClass in "reports" mode, such as: % autoclass -reports data/soybean/soyc.results-bin data/soybean/soyc.search data/soybean/soyc.r-params This will generate class and case cross-reference files, and an influence values file. The file names are based on the ".r-params" file name: data/soybean/soyc.class-text-1 data/soybean/soyc.case-text-1 data/soybean/soyc.influ-text-1 These will describe the classes found in the training_data_file. Now this classification can be used to predict the probabilistic class membership of the test_data_file cases ("data/soybean/soyc-predict.db2") in the training_data_file classes. % autoclass -predict data/soybean/soyc-predict.db2 data/soybean/soyc.results-bin data/soybean/soyc.search data/soybean/soyc.r-params This will generate class and case cross-reference files for the test_data_file cases predicting their probabilistic class memberships in the training_data_file classes. The file names are based on the ".db2" file name: data/soybean/soyc-predict.class-text-1 data/soybean/soyc-predict.case-text-1 -------------------------------------------------------------------------------- autoclass-3.3.6.dfsg.1/doc/introduction-c.text0000644000175000017500000000412011247310756017321 0ustar areareWelcome to the AutoClass C Documentation! The documentation is divided into a basic core that most users will need to at least skim through, and various other supporting documentation. BASIC DOCUMENTATION: preparation-c.text How to prepare data for use by AutoClass search-c.text How to run AutoClass to find classifications. reports-c.text How to examine the classification in various ways. interpretation-c.text How to interpret AutoClass results. checkpoint-c.text Protocols for running a checkpointed search. prediction-c.text Use classifications to predict class membership for new cases. SUPPORTING DOCUMENTATION: classes-c.text What classification is all about, for beginners. models-c.text Brief descriptions of the model term implementations. kdd-95.ps Postscript file containing: P. Cheeseman, J. Stutz, "Bayesian Classification (AutoClass): Theory and Results", in "Advances in Knowledge Discovery and Data Mining", Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy Uthurusamy, Eds. The AAAI Press, Menlo Park, expected fall 1995. % ghostview kdd-95.ps or % lpr kdd-95.ps tr-fia-90-12-7-01.ps Postscript file containing: R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification Theory", Technical Report FIA-90-12-7-01, NASA Ames Research Center, Artificial Intelligence Branch, May 1991 (the figures are not included, since they were inserted by "cut-and-paste" methods into the original "camera-ready" copy -- this will be corrected in the future) % ghostview tr-fia-90-12-7-01.ps or % lpr tr-fia-90-12-7-01.ps autoclass-3.3.6.dfsg.1/doc/reports-c.text0000644000175000017500000002625211247310756016310 0ustar areare Contents -------- Generating Reports Generating Sigma Contour Values Reports Params File Keywords Changing Filenames In A Saved Results File ------------------------------------------------------------------------------------ GENERATING REPORTS ------------------------------------------------------------------------------------ You are provided three standard reports generated from "results" files by invoking: % autoclass -reports <.results[-bin] file path> <.search file path> <.r-params file path> The standard reports are 1) attribute influence values: presents the relative influence or significance of the data's attributes both globally (averaged over all classes), and locally (specifically for each class). A heuristic for relative class strength is also listed; 2) cross-reference by case (datum) number: lists the primary class probability for each datum, ordered by case number. When report_mode = "data", additional lesser class probabilities (greater than or equal to 0.001) are listed for each datum; 3) cross-reference by class number: for each class the primary class probability and any lesser class probabilities (greater than or equal to 0.001) are listed for each datum in the class, ordered by case number. It is also possible to list, for each datum, the values of attributes, which you select. The attribute influence values report attempts to provide relative measures of the "influence" of the data attributes on the classes found by the classification. The normalized class strengths, the normalized attribute influence values summed over all classes, and the individual influence values (I[jkl]) are all only relative measures and should be interpreted with more meaning than rank ordering, but not like anything approaching absolute values. The reports are output to files whose names and pathnames are taken from the ".r-params" file pathname. The report file types (extensions) are: influence values report: "influ-o-text-n" or "influ-no-text-n" cross-reference by case: "case-text-n" cross-reference by class: "class-text-n" or, if report_mode is overridden to "data": influence values report: "influ-o-data-n" or "influ-no-data-n" cross-reference by case: "case-data-n" cross-reference by class: "class-data-n" were n is the classification number from the "results" file. The first or best classification is numbered 1, the next best 2, etc. The default is to generate reports only for the best classification in the "results" file. You can produce reports for other saved classifications by using report params keywords n_clsfs and clsf_n_list. The "influ-o-text-n" file type is the default (order_attributes_by_influence_p = true), and lists each class's attributes in descending order of attribute influence value. If the value of order_attributes_by_influence_p is overridden to be false in the <...>.r-params file, then each class's attributes will be listed in ascending order by attribute number. The extension of the file generated will be "influ-no-text-n". This method of listing facilitates the visual comparison of attribute values between classes. See sample reports in directory ....autoclass-c/sample/: "imports-85.influ-o-text-1" "imports-85.case-text-1" "imports-85.class-text-1" which were generated by the form: % autoclass -reports sample/imports-85c.results-bin sample/imports-85c.search sample/imports-85c.r-params with xref_class_report_att_list = 2, 5, 6 in the ".r-params" file. Logging messages will be written to a ".rlog" file, a separate file from that used to log messages during search runs (".log"). ------------------------------------------------------------------------------- GENERATING SIGMA CONTOUR VALUES ------------------------------------------------------------------------------- The AutoClass C reports provide the capability to compute sigma class contour values for specified pairs of real valued attributes, when generating the influence values report with the data option (report_mode = "data"). Note that sigma class contours are not generated from discrete type attributes. The sigma contours are the two dimensional equivalent of n-sigma error bars in one dimension. Specifically, for two independent attributes the n-sigma contour is defined as the ellipse where ((x - xMean) / xSigma)^2 + ((y - yMean) / ySigma)^2 == n With covariant attributes, the n-sigma contours are defined identically, in the rotated coordinate system of the distribution's principle axes. Thus independent attributes give ellipses oriented parallel with the attribute axes, while the axes of sigma contours of covariant attributes are rotated about the center determined by the means. In either case the sigma contour represents a line where the class probability is constant, irrespective of any other class probabilities. With three or more attributes the n-sigma contours become k-dimensional ellipsoidal surfaces. This code takes advantage of the fact that the parallel projection of an n-dimensional ellipsoid, onto any 2-dim plane, is an ellipse. In this simplified case of projecting the single sigma ellipsoid onto the coordinate planes, it is also true that the 2-dim covariances of this ellipse are equal to the corresponding elements of the n-dim ellipsoid's covariances. The Eigen-system of the 2-dim covariance then gives the variances w.r.t. the principal axes of the ellipse, and the rotation that aligns it with the data. This represents the best way to display a distribution in the marginal plane. ------------------------------------------------------------------------------- REPORTS PARAMS FILE KEYWORDS ------------------------------------------------------------------------------- # PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to continue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! core dump. If specified as false, AutoClass will not attempt to free ! storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; ------------------------------------------------------------------------------- CHANGING FILENAMES IN A SAVED RESULTS FILE ------------------------------------------------------------------------------- AutoClass caches the data, header, and model file pathnames in the saved classification structure in the ascii ".results" file. If the ".results" and ".search" files are moved to a different directory location, the search cannot be successfully restarted if you use absolute pathnames. Thus it is advantageous to run invoke AutoClass in a parent directory of the data, header, and model files, so that relative pathnames can be used. The pathnames cached will then be relative, and the files can be moved to a different host or file system and restarted. ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- autoclass-3.3.6.dfsg.1/doc/models-c.text0000644000175000017500000001107011247310756016065 0ustar areare AUTOCLASS MODEL DESCRIPTIONS 1.0 Introduction 1.1 Single Multinomial Model 1.2 Single Normal CN Model 1.3 Single Normal CM Model 1.4 Multi Normal CN Model 1.0 Introduction The following text sections are brief overviews of each model's implementation. 1.1 Single Multinomial Model This implements a single multinomial likelihood model term for symbolic or integer attributes which is conditionally independent of other attributes given the class. The likelihood is defined to be the probability of the particular attribute value for the class. It is assumed here that the data values have been transformed from native format to a zero based and contiguous series of integers, presumably in the data input module. Any missing values must be represented by one of these integer values. The likelihood parameters are a vector of probabilities for each possible value of each attribute. Priors are implicit, and implemented in the code. 1.2 Single Normal CN Model This term models a single real valued attribute with a conditionally independent Gaussian normal distribution. This model assumes that there are no missing values and that the measurement error is both constant and small relative to the model variance. The probability of any particular observation is then the integral of the posterior distribution from (X - error) to (X + error). The model parameters are a mean and variance. The sufficient statistics are the weighted mean, variance, and (constant) geometric mean error. The parameter prior distributations are a normal for the mean and a log uniform for the root variance (sigma). The prior distribution parameters are heuristicly approximated from the database statistics. This model is directly applicable to real location attributes. It is indirectly applicable to real scaler (bounded below) attributes, using a log- transform of the attribute. It is also applicable to bounded (above and below) real attributes with a log-odds transform, but this is not yet available. It may be applied to integer attributes where they are considered to measured points on a continuous distribution. Such attributes must have been described as reals. 1.3 Single Normal CM Model This term models a single real valued attribute as a class conditionally independent binary probability of actually observing a value, with a Gaussian normal probability for those values actually observed. Thus the failure to observe a value for the attribute is considered to be a real possibility of the measurement process and is modeled as such. The probability due to the attribute in any particular observation is then the discrete probability of having found a value multiplied (when a value is found) by the integral of the normal distribution from (X - error) to (X + error). This model assumes that the measurement error is constant and is small relative to the data variance. The model parameters are the binary probability of observing a value, a mean and a variance. The sufficient statistics are the fraction of observations, and the weighted mean, variance, and (constant) geometric mean error. The parameter prior distributations are a simple conjunct for the binary, a normal for the mean and a log uniform for the root variance (sigma). The prior distribution parameters are heuristicly approximated from the database statistics. This model is directly applicable to real location attributes. It is indirectly applicable to real scaler (bounded below) attributes, using a log- transform of the attribute. It is also applicable to bounded (above and below) real attributes with a log-odds-transform, but this is not yet available. It may be applied to integer attributes where values are considered to be measured points on a continuous distribution. Such attributes must have been described as reals. 1.4 Multi Normal CN Model This implements a likelihood term representing a set of real valued attributes, each with constant measurement error and without missing values, which are conditionally independent of other attributes given the class. This term is defined as a normal covariant distribution, parameterized by a means vector and covariance matrix. The current priors implementation assumes simple conjunct priors taken from the global empirical values. Note that one objective in writing this has been to avoid allocation of temporary vectors and matrices to store intermediate results. Thus the term parameter structure contains several temp structures that are used in much the manner of a Fortran common structure. autoclass-3.3.6.dfsg.1/doc/classes-c.text0000644000175000017500000004001011247310756016233 0ustar areare This file discusses what classification is, what a user should expect to achieve (with AutoClass), and how to use the results. Why Classification: A classification is usually thought of as a partitioning of a set of things into subsets in which the members are "more similar" to each other than to non-members. The concept of "more similar" is open to numerous interpretations. Whenever we say `this thing is a Fubar' we are assigning the thing to the class designated by the name 'Fubar'. Classification is basic to the act of identification, and provides one of the simplest forms of prediction. In saying `all (or most) fubar are snafu' we expect that any thing classified as fubar is likely to exhibit the property of snafu. Often the only rational justification for a statement like `all (or most) fubar are snafu' is a statistical association or causal hypothesis found by study of the snafu property in things classified as fubar. What to classify: The members of almost any set of things that can be described in a regular manner can be classified, however, finding an appropriate representation may be a problem. In the AutoClass work we currently limit our analysis to things that can be described by a set of features or properties, without referring to other things. This allows us to represent our things by a data vector corresponding to a fixed attribute set. The attributes are names of measurable or distinguishable properties of the things. The data values corresponding to each attribute are thus limited to be either numbers or the elements of a fixed set of attribute-specific symbols. AutoClass cannot express relationships between things because such relationships are not a property of the thing itself. In particular, AutoClass cannot express time relationships such as "before" or "after". Nor can it account for any sequential ordering of the data. Despite these limitations, AutoClass is applicable to the many problems where such relations do not exist, can be ignored or transformed into appropriate attributes. For instance, temporal ordering data can be re-expressed as "time of (year, week, day)" or "time elapsed since ...". A similar problem occurs with `subset' type attributes whose potential values are subsets of a fixed set of possible values. These can be transformed into a set of binary attributes corresponding to each of the possible values. The number of thing to be classified can be large or small. The number of classes AutoClass can be expected to find is typically much smaller than the number of data. What is a classification: Classification is typically described as a partitioning of the set of things. This is the way humans typically approach classification. Given the question `what is it?', our response is to say `it is a fubar' or `it is a baz'. Partitioning is also appealing when we classify in order to predict, particularly when we want to predict some property of a thing. However, partitioning implies sharp boundaries: either logical subsets of discrete attributes or subranges of measured attributes. We may be limited in our ability to distinguish discrete attribute values, and we are always limited in our ability to measure continuous attribute values. Thus a partitioning classifier may find some things lying "near" the class boundaries for which unequivocal class assignment is not possible and may not be desirable. Our solution to this problem is to redefine what is meant by classification. In the AutoClass approach, class membership is expressed probabilistically rather than as logical assignment. Thus every one of our things is considered to have a probability that it belongs to each of the possible classes. These class membership probabilities must sum to 1 for each thing, because each thing must belong to **some** class, even though there is insufficient information to say **which** class. This form of "fuzzy" classification produces classes that are closer to everyday concepts of "cats", "cars" and "crazy people" than partitioning does. There are no boundaries and no class assignments, only the membership probabilities. When every single thing has a probability of more than .99 in its most probable class, the classes are well separated and we have well defined measures of class similarity and dissimilarity. If few things have a probability of more than .5 in any class, it means that the classes are heavily overlapped. In this case the combination of the classes into larger structures is usually more meaningful than considering each class separately. In either case we have a measure of how well our classification fits the data and individual data fit the classes. No simple partitioning can provide such information. Class Models: We achieve probabilistic classification by redefining the classes in terms of parameterized probability distributions, such as normal (Gaussian) or beta distributions. We choose distributions that mimic the processes that are suspected of having produced the observed data. We limit our choice to those that have a probability value (discrete attributes) or probability density (real valued attributes) throughout the attribute space. A thing's class probabilities are then computed directly from the parameterized class distributions. Thus the classes provide statistical models of thing descriptions. The class parameters may be thought of as indirectly specifying regions in attribute space where each class's probability dominates all others. These regions correspond approximately to the subsets of the attribute space that a partitioning classifier would use. In AutoClass we refer to the functional form of our probability distributions as the class model. A **class** is defined as a particular set of parameter values and their associated model. A **classification** is defined as a set of classes and the probabilities of each class. The classification problem is to first choose an appropriate class model (or set of alternate models), then to search out good classifications based on these models, and finally to rate the relative quality of the alternative classifications. As an example of such modeling, consider the case of an attribute such as "height". If we expect that the measured height of a thing depends only on its class, we can model the probable value in terms of a characteristic height. We know there will some measurement error and we usually expect to find some intrinsic variation in the heights of a class of things. Our model must allow for such variation. Also, height is a scalar quantity bounded below by zero. Experience has shown that such scalar quantities typically have a normal distribution in Log space (which is not bounded by zero). Therefore we choose a log normal distribution for our model. The corresponding model parameters are the mean log height and standard deviation of log height. Given specific parameter values, we can calculate the probability that any particular observation would result from measurement of the height of any thing known to be a member of the class. Given multiple classes, we can calculate the relative probability that a particular height would come from each class. There is no generally accepted way to rate the relative quality of alternate classifications. The method of setting up models and searching out sets of descriptive classes has been the subject of statistical research for decades. The type of model that we have described, that gives the probability of the data conditioned on the hypothesized model and parameters: P(X|H,p); is known as a likelihood function. Maximum Likelihood Estimation (MLE) deals with finding the set of models and parameters that maximizes this probability. In some quarters this is the method of choice for classification. Unfortunately, MLE fails to provide a convincing way to compare alternate classifications that differ in class models or the number of classes. MLE increases with both model complexity and number of classes (until the class number equals the number of things). Clearly this contradicts our intuition that moderately simple classifications are more desirable than very complex ones. What we would really like to have is the probability of the hypothesized model given the data, P(H|X). Alternatively, we can find the **relative** probabilities of models given the data; i.e. P(H1|X)/P(H2|X) = P(H1,X)/P(H2,X). Then we can directly compare alternate models, in this case models with different numbers of classes. The value of P(H,X) is obtained from P(H,X) = P(X|H)P(H), where P(X|H) is obtained by integration over the parameters in P(X,p|H) = P(X|p,H)P(p|H). So we need two additional probabilities for each hypothesized model: the model probability P(H) and the conditional parameter probability distribution P(p|H). The mathematics for doing this integration in the current AutoClass models are described in the referenced papers listed in the "read-me.text" file. Results from AutoClass The result of an AutoClass run is one or more of the best classifications found. A classification effectively consists of the class model(s) and a set of classes, each with the class probability and parameters. Classifications are rated in terms of the log of the relative marginal probability of the hypothesized model given the data. Those with log marginals that differ by less than 5 are are considered to be nearly equally probable. This is because any other model that gives better classifications will probably give far better classifications. Note that the log marginal is strongly dependent on the data, and thus comparisons can only be made between classifications made on a single database. Using Classification results: The ultimate reason for doing classification is to increase understanding of the domain or to improve predictions compared to unclassified data. Given a classification and a partial observation, one can always use the classification to make a statistical estimate of the unobserved attribute values. One can also use a classification as the departure point for constructing new models, or theories specific to the particular domain, based on the insight provided by the classification and the user's domain knowledge. When classification is done by partitioning, prediction can only be done by selecting the classes compatible with a partial observation and then listing the allowed values or ranges of the unknown attributes. With statistical classification, the usual procedure is to select the class that most probably generates the partial observation and then use that class's distributions for the unknowns. This is generally satisfactory only if the probability of the chosen class is far greater than any other. The result is quite doubtful when the thing has relatively high probability of belonging to more than one class, and especially so when alternative classes predict very different values for the unknowns. We prefer to use the class probabilities of the thing to construct a weighted sum of the predictions of all the classes. If the resulting attribute distribution is broad and flat we know that the partial observation does not contain sufficient relevant information to predict this attribute. If the distribution has a sharp single peak we can predict the attribute value with confidence, knowing that either one class is nearly certain or that a choice among the most probable classes would be of little influence. A multiply peaked distribution gives us a set of weighted choices. We can still make a prediction, but now the knowledge of the alternatives and their relative probabilities may prevent overconfidence. The prediction subsystem (not currently implemented) provides just such a capability for the limited case of discrete attributes modeled with an independent multinomial distribution. Extensions to dependent discrete attributes and to real valued attributes are under consideration. Efficient representation of probability distributions for real values is a special problem. Until the later have been implemented, we provide a function for predicting the class probabilities of new cases. Refinement of Statistical Models: The models currently used in AutoClass are quite simple. There are both independent and covariant versions of the multinomial model for discrete attributes and the Gaussian normal model for real valued attributes. There are two minor refinements. For discrete attributes and independent reals, we model missing attribute values as a discrete value of the attribute: `failure to observe'. This acknowledges that we are classifying the results of observations, not the things observed, and must model what we have rather than what we might have obtained. The second refinement is to allow translations of continuous valued attributes. The simple normal model implicitly assumes that the attribute values represent noisy measurements of a class point value that could lie anywhere between plus and minus infinity. The log normal and log odds normal translations extend this model to classify point values that are bounded below, and bounded above and below. These translations apply equally to both independent and covariant models. The simplest way to refine a model is to eliminate consideration of attributes that are not contributing anything to the classification. The obvious case of a discrete attribute where every observation has the same value is flagged by AutoClass. A close inspection of the influence value reports will usually show several attributes that have negligible influence on all classes. In the absence of strong prior reasons for their retention, these can be ignored. In AutoClass this is done by specifying the `ignore' model term for any attribute. One will usually find that this increases the posterior probabilities and sharpens the probabilities of both the class assignments and the remaining attributes. But ignoring attributes introduces a problem in comparing the resulting classifications. In ignoring some attributes, we are effectively working with a smaller database. This alone will increase the probability of our classifications. The solution is to normalize the classification probability relative to the quantity of data used for the classification. The simplest way is to use the ratio of the probability to the number of active attributes. One should also factor in the number of cases when comparing classifications over databases of differing case numbers. The next step is to allow for dependence of attributes within the class. The multi-xxx model terms provide a limited capability for this, allowing covariance of discrete or real valued attributes, but not together. However this version of AutoClass has no capability for searching the space of possible covariances. Such search must be done manually by running with alternate model files and comparing the resulting best classifications. There are two points to keep in mind. A covariant attribute model requires more parameters than would be needed to model the same attributes independently. Thus the covariant model is apriori less probable than the independent model. This is another example of the tradeoff between descriptivness and the cost of description. It will rarely be advantageous to simply group all attributes into a single covariant likelihood term. The real advantage of covariant models appears when the covariant groups are carefully chosen with regard to everything known about the attributes. The second point involves running time. There is an increase in both the cycle time and the number of cycles required to generate a classification with covariant attributes. And this time increase is roughly proportional to the maximum covariant group size. So the use of large covariant attribute groups entails large classification time penalties. It may be advantageous to do a principle components analysis on the data set, and to use the new independent variables as input to AutoClass. This will tend to remove the major covariances that might be present. Minor covariances, such as those limited to specific classes, will still need to explicitly modeled.autoclass-3.3.6.dfsg.1/doc/checkpoint-c.text0000644000175000017500000000545511247310756016743 0ustar areare Checkpointing: With very large databases there is a significant probability of a system crash during any one classification try. Under such circumstances it is advisable to take the time to checkpoint the calculations for possible restart. Checkpointing is initiated by specifying the CHECKPOINT_P keyword parameter value as true in the ".s-params" file. This causes the inner convergence step, to save a copy of the classification onto the checkpoint file each time the classification is updated, providing a certain period of time has elapsed. The file extension is ".chkpt[-bin]". Each time a AutoClass completes a cycle, a "." is output to the screen to provide you with information to be used in setting the MIN_CHECKPOINT_PERIOD value (default 10800 seconds or 3 hours). There is obviously a trade-off between frequency of checkpointing and the probability that your machine may crash, since the repetitive writing of the checkpoint file will slow the search process. Restarting AutoClass Search: To recover the classification and continue the search after rebooting and reloading AutoClass, specify RECONVERGE_TYPE = "chkpt" in the ".s-params" file (specify FORCE_NEW_SEARCH_P as false). AutoClass will reload the appropriate database and models, provided there has been no change in their filenames since the time they were loaded for the checkpointed classification run. The ".s-params" file contains any non- default arguments that were provided to the original call. In the beginning of a search, before START_J_LIST has been emptied, it will be necessary to trim the original list to what would have remained in the crashed search. This can be determined by looking at the ".log" file to determine what values were already used. If the START_J_LIST has been emptied, then an empty START_J_LIST should be specified in the ".s-params" file. This is done either by start_j_list = or start_j_list = -9999 Here is an a set of scripts to demonstrate check-pointing: % autoclass -search data/glass/glassc.db2 data/glass/glass-3c.hd2 \ data/glass/glass-mnc.model data/glass/glassc-chkpt.s-params Run 1) ## glassc-chkpt.s-params max_n_tries = 2 force_new_search_p = true ## -------------------- ;; run to completion Run 2) ## glassc-chkpt.s-params force_new_search_p = false max_n_tries = 10 checkpoint_p = true min_checkpoint_period = 2 ## -------------------- ;; after 1 checkpoint, ctrl-C to simulate cpu crash Run 3) ## glassc-chkpt.s-params force_new_search_p = false max_n_tries = 1 checkpoint_p = true min_checkpoint_period = 1 reconverge_type = "chkpt" ## -------------------- ;; checkpointed trial should finish -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- autoclass-3.3.6.dfsg.1/doc/preparation-c.text0000644000175000017500000006446111247310756017142 0ustar areare PREPARING DATA FOR AUTOCLASS 1.0 Introduction 1.1 Applicable Types of Data 1.1.1 Real Scalar Data: Error and Rel-error 1.2 Probability Models 1.2.1 SINGLE_NORMAL_CN/CM and MULTI_NORMAL_CN Models 1.3 Input Files 1.3.1 Data File 1.3.1.1 Handling Missing Values 1.3.2 Header File 1.3.2.1 Header File Example 1.3.3 Model File 1.3.3.1 Model File Example 1.4 Checking Input Files Footnotes 1.0 Introduction This documentation file is directed at anyone who will be preparing data sets for AutoClass C. It requires no statistics or Artificial Intelligence background. 1.1 Applicable Types of Data AutoClass is applicable to observations of things that can be described by a set of features or properties, without referring to other things. This allows us to represent the observations by a data vector corresponding to a fixed attribute set. Attributes are names of measurable or distinguishable properties of the things observed. The data values corresponding to each attribute are thus limited to be either numbers or the elements of a fixed set of attribute specific symbols. With numeric data, a measurement error is assumed and must be provided with the attribute description. AutoClass cannot express relationships between things because such relationships are not a property of the thing itself. Nor can AutoClass deal with properties expressed as sets of values. However the current models do allow for missing or unknown values. The program itself imposes no specific limit on the number of data, but databases having more than 10^5 values (cases * attributes) may require excessive search time. Note that there are techniques for re-expressing some data types in forms acceptable to AutoClass. If a set valued property is limited to subsets of a small set of symbols, one can re-express the property as a set of binary attributes, one for each of the possible symbols. Temporal ordering data can be expressed as "time of (year, week, day)" or "time elapsed since ...". And one can always indicate that a relation has been observed, even if the related thing cannot be named. A simple example of the later is the transformation of `married-to' to `married?'. 1.1.1 Real Scalar Data: ERROR and REL_ERROR (see footnote #1) AutoClass and its documentation were written with the idea that it would be applied to "direct" measurements of instance properties. In such cases, multiple measurement of single instances will soon establish the limit beyond which increasing measurement "precision" is simply noise. The classic example results from attempting to use a digital VOM meter with hand held probes on an oxidized contact: when set at the appropriate range, the last few digits will vary almost randomly. In such cases it is relatively easy to establish an average error appropriate for the reported measurements. It is the range of digits over which measurement noise dominates the measured value. Thus with measurement error the fundamental question is which digits are due to the measured property and which to measurement noise. Truncation error will often dominate measurement error. Here the classical example is human age: measurable to within a few minutes, easily computable to within a few days, yet generally reported in years. The reported value has been truncated to much less than its potential precision. Thus the error in that reported value is on the order of half the least difference of the representation. Truncation error can arise from a variety of causes. Its presence should be suspected whenever measurements of intrinsically continuous properties are reported as integers or limited precision floating point numbers. Lacking any specific information bearing on the magnitude of measurement or truncation errors, we adopt the default of assuming the reported data to be truncated at the measurement error. Thus we adopt a default error of 1/2 the least difference of the representation. This is often 1/2 the least significant reported digit. But beware of cases where the least difference between values is greater than the least digit. Things get more difficult with "indirect" attribute values computed as functions over one or more measurements. In principle one can carry any known errors (or equivalently, precision) through the function to determine the value's error. In practice this is rarely done - conventional math routines assume that floating point numbers are integer sums of a limited range of integer powers of two. Any unspecified digits are assumed to be zero. Mathematica's (Wolfram Research) implementation of Arbitrary-Precision Numbers makes no such assumptions about unspecified digits. Its results are truncated at the first digit that could be affected by an unspecified digit in any input. Thus it returns no more precision that is justified by the inputs and mathematical manipulations. It can be most educational to see how quickly one loses precision in relatively simple calculations, and how such loss is affected by different forms of mathematically identical calculations. It is not difficult to devise calculations that start with high precision inputs and return negative precision results: values which are entirely meaningless. We strongly recommend the use of such a tool for investigating the effects of any data manipulations used to generate AutoClass inputs. Lacking access to Mathematica or an equivalent, one should certainly investigate the effects of varying data values over their error range to gauge the effect on the resulting functional values. Sometime this can be done symbolically. More often it will require a numerical investigation. Of course, such investigations assume that one knows error range or precision of the function inputs. Lacking definite information on this point, one can use the default truncation value. The fundamental question in all of this is: "To what extent do you believe the numbers that are to be given to AutoClass?" AutoClass will run quite happily with whatever it is given. It is up to the user to decide what is meaningful and what is not. "Real scalar" is our term for singly bounded real values, typically bounded below at zero. Classical examples are the height and length of an object, neither of which can be negative. The corresponding counter examples would be elevation and location, both measured with respect to some arbitrary zero and capable of going negative. For scalar reals we use the Log-normal model which has zero probability density at and below zero. This is currently implemented by taking the logs of the data values and applying the Gaussian Normal model. The current Normal model requires a constant error term to set the bounds of integration. It turns out that a constant error in the logarithm of a value is equivalent to a relative error in the original value. That is, the error in the value should be proportional to the value, rather than being itself a constant. And REL_ERROR is just the ratio of the error to the value. If your knowledge of the data generating process is sufficient to specify such a ratio, just give it as the value of REL_ERROR. Otherwise give your estimate of the constant error as ERROR, and AutoClass will compute the ratio of this to the average data value and use this as REL_ERROR. 1.2 Probability Models The SINGLE_MULTINOMIAL, SINGLE_NORMAL_CM, and SINGLE_NORMAL_CN probability models assume that the attributes are conditionally independent given the class. Thus within each class the probability that an instance of the class would have a particular value of any attribute depended only on the class and was independent of all other attribute values. The MULTI_NORMAL_CN covariant model expresses mutual dependences within the class. The probability that the class will produce any particular instance is then the product of any independent and covariant probability terms. We use covariant or independent multinomial model terms for discrete attributes of nominal, ordered, and circular subtypes (all are currently handled identically). These model terms allow any number of values for an attribute, including unknown values. We use Gaussian normal model terms for real numerical attributes, or any representing measurements. There are actually two independent versions, one of which allows for the possibility of unknown values. The covariant normal model term requires that all attribute values be known for every case. There is also an `ignore' model term for attributes which are not to be considered in generating the classification. 1.2.1 SINGLE_NORMAL_CN/CM and MULTI_NORMAL_CN Models The SINGLE_NORMAL_CN/CM and MULTI_NORMAL_CN models were originally written for use with real valued attributes of the location subtype. Such attributes are unbounded - their values can potentially range to +/- infinity. A scalar real valued attribute is singly bounded. Its values are constrained by prior information to lie to one side of a zero point, typically 0.0, and have no values lying in the `negative' region. Thus Normal models that assign non-zero probability density to the `negative' region are less than optimally informative. Note that we say `less than optimal' rather than `incorrect'. The standard Normal model will generally do a good job of classifying scalar reals, and will do an excellent job with scalar reals that are clustered well away from the zero point. But we can do better, especially when the values cluster close to the zero point. The better model is the Log-Normal, obtained by substituting ln(x-zero) for x in the Normal model. This model assigns zero probability density to x <= zero. The peak probability density value, at x=e^(mu-sigma^2), can be arbitrarily close to the zero point while quite independent of the distribution's variance of s^2 = e^(2*mu + sigma^2)*(e^(sigma^2) - 1). Yet for small sigma, say sigma < .1, the Log-Normal is visually indistinguishable from a Normal. In the current AutoClass C we obtain the Log-Normal model by transforming the attribute values to ln(x-zero) and applying the appropriate xxx-Normal-yy model. The key for obtaining this variation is the specification of the real subtype as `scalar', with appropriate ZERO_POINT and REL_ERROR values. When AutoClass C is instructed to apply a Normal model to such an attribute, it automatically performs the transformation, effectively applying the equivalent Log-Normal model. The specification of a real valued attribute's subtype is thus a specification of the type of Normal model to be used on that attribute. The MULTI_NORMAL_CN model implements a multi-dimensional normal distribution over a group of attributes that have real continuous values, with no values missing. It is the model of choice for such attributes when they are thought to have correlated values. When such correlations are present, the classifications obtained using the MULTI_NORMAL_CN model will generally be more probable than those obtained with the SINGLE_NORMAL_CN model, because they better describe the data distributions. But one should not apply it indiscriminately. Lacking strong prior evidence for correlations, but suspecting them, one needs to try all reasonable combinations of attributes and compare the probability of the resulting classifications. As an example, consider a database of instance vectors describing physical objects that have intrinsic size and shape, neither of which is recorded. Then one expects that the recorded attributes length, width, and depth, will vary linearly with size, and have differing ratios with respect to shape. Given a sufficient number of instances of each shape, the MULTI_NORMAL_CN model applied to length, width, and depth, should pick out the shape classes in terms of the attribute correlations. The SINGLE_NORMAL_CN model might pick out the shapes, but it would tend to divide each shape into classes of similar size, and to merge similar sizes of differing shapes into common classes. 1.3 Input Files An AutoClass data set resides in two files. There is a a header file (file type "hd2") that describes the specific data format and attribute definitions. The actual data values are in a data file (file type "db2"). We use two files to allow editing of data descriptions without having to deal with the entire data set. This makes it easy to experiment with different descriptions of the database without having to reproduce the data set. Internally, an AutoClass database structure is identified by its header and data files, and the number of data loaded. A classification of a data set is made with respect to a model which specifies the form of the probability distribution function for classes in that data set. Normally the model structure is defined in a model file (file type "model"), containing one or more models. Internally, a model is defined relative to a particular database. Thus it is identified by the corresponding database, the model's model file and its sequential position in the file. 1.3.1 Data File The data file contains a sequence of data objects (datum or case) terminated by the end of the file. The number of values for each data object must be equal to the number of attributes defined in the header file. There is an implied "new-line" ('\n') after each data object. Data objects must be groups of tokens delimited by "new-line". Attributes are typed as REAL, DISCRETE, or DUMMY. Real attribute values are numbers, either integer or floating point. Discrete attribute values can be strings, symbols, or integers. A dummy attribute value can be any of these types. Dummy's are read in but otherwise ignored -- they will be set to zeros in the the internal database. Thus the actual values will not be available for use in report output. To have these attribute values available, use either type REAL or type DISCRETE, and define their model type as IGNORE in the .model file. Missing values for any attribute type may be represented by either '?', or other token specified in the header file. All are translated to a special unique value after being read, so this symbol is effectively reserved for unknown/missing values. Example: white 38.991306 0.54248405 2 2 1 red 25.254923 0.5010235 9 2 1 yellow 32.407973 ? 8 2 1 all_white 28.953982 0.5267696 0 1 1 The data file can optionally be input in binary format. This is useful for very large data files in order to reduce disk space and time for reading the file. The user must create the binary file to conform to the following: - the file name extension must be ".db2-bin", rather than ".db2". - the file begins with a 12-byte header - char[8] = ".db2-bin", - 32-bit integer with byte-length of each data case. - the data cases follow in binary "float" format -- 32 bit fields. Real valued data, and discrete integer data converted to floating point format are accommodated. Discrete character data (e.g. "white", in above example) would have to be assigned integer values, and converted to floating point format. Note: DOS derived data files that are to be used in a Unix environment should first be processed with dos2unix, to remove carriage returns (^M) from the lines. We have observed a case where such carriage returns were read as part of a discrete data value, passed through AutoClass, and printed in the xxx.influ report, where they destroyed the data formatting. Should this occur, xxx.influ data formatting can still be restored with dos2unix. 1.3.1.1 Handling Missing Values Since we were designing AutoClass to work with arbitrary data sets, we could make no universally valid assumptions about the mechanisms that generate any missing data the system might encounter. Lacking specifics, we could choose no basis for "correcting" missing data. Thus we were forced to deal with the data, and only the data, independent of any information about the data's origins. This is the great disadvantage of any general purpose classifier: You either make assumptions that seem good for the current application, but will be absurd in others, or you ignore the background information that justifies such assumptions. We took the latter course, treating missing values as valid data. Thus our classifications are actually over the convolution of original subjects through the data collection process, and our results may be dominated by either. When no missing values are present, one expects the results to be dominated by the subject characteristics. With large proportion of missing values, the subjects are much obscured by the data collection process, and one must expect that any patterns found in the data may be due to the collection process rather than the subjects. Only strong prior knowledge about the collection process can justify attempting to deconvolve the data. Note that if one regards a classifier as classifying subjects, rather than data on subjects, then missing data is merely the most obvious example of erroneous data, which presents a far larger and more intractable problem. The assumption, common under this viewpoint, that only the missing data are in error, is clearly absurd. AutoClass deals only with the existing record. Attempting to classify what *should* have been recorded, requires a far more sophisticated system that is carefully tuned to the collection process. 1.3.2 Header File The header file specifies the data file format, and the definitions of the data attributes. The header file functional specifications consists of two parts -- the data set format definition specifications, and the attribute descriptors (; in column 1 identifies a comment): ;; num_db2_format_defs value (number of format def lines that follow), ;; range of n is 1 -> 5 num_db2_format_defs n ;; number_of_attributes token and value required number_of_attributes ;; following are optional - default values are specified separator_char ' ' comment_char ';' unknown_token '?' separator_char ',' ;; attribute descriptors ;; Each attribute descriptor is a line of: Attribute index (zero based, beginning in column 1) Attribute type. See below. Attribute subtype. See below Attribute description: symbol (no embedded blanks) or string; <= 40 characters Specific property and value pairs. See below. Currently available combinations: type subtype property type(s) ---- -------- --------------- dummy none/nil -- discrete nominal range real location error real scalar zero_point rel_error An example is given below in section 1.3.2.1. The ERROR property should represent your best estimate of the average error expected in the measurement and recording of that real attribute. Lacking better information, the error can be taken as 1/2 the minimum possible difference between measured values. It can be argued that real values are often truncated, so that smaller errors may be justified, particularly for generated data. But AutoClass only sees the recorded values. So it needs the error in the recorded values, rather than the actual measurement error. Setting this error much smaller than the minimum expressible difference implies the possibility of values that cannot be expressed in the data. Worse, it implies that two identical values must represent measurements that were much closer than they might actually have been. This leads to over-fitting of the classification. The REL_ERROR property is used for SCALAR reals when the error is proportional to the measured value. The ERROR property is not supported. AutoClass uses the error as a lower bound on the width of the normal distribution. So small error estimates tend to give narrower peaks and to increase both the number of classes and the classification probability. Broad error estimates tend to limit the number of classes. The scalar ZERO_POINT property is the smallest value that the measurement process could have produced. This is often 0.0, or less by some error range. Similarly, the bounded real's min and max properties are exclusive bounds on the attributes generating process. For a calculated percentage these would be 0-e and 100+e, where e is an error value. The discrete attribute's range is the number of possible values the attribute can take on. This range must include unknown as a value when such values occur. 1.3.2.1 Header File Example !#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 7 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 dummy nil "True class, range = 1 - 3" 1 real location "X location, m. in range of 25.0 - 40.0" error .25 2 real location "Y location, m. in range of 0.5 - 0.7" error .05 3 real scalar "Weight, kg. in range of 5.0 - 10.0" zero_point 0.0 rel_error .001 4 discrete nominal "Truth value, range = 1 - 2" range 2 5 discrete nominal "Color of foobar, 10 values" range 10 6 discrete nominal Spectral_color_group range 6 1.3.3 Model File The model file contains data describing the model(s) that will be used for the classification. Each model is specified by one or more model group definition lines. Each model group line associates zero-based attribute indices with a model term type. Each model group line consists of: A model term type (one of single_multinomial, single_normal_cm, single_normal_cn, multi_normal_cn, or ignore). One or more attribute indices (attribute set list), or the symbol default. Notes: - At least one model definition is required (model_index token). - There may be multiple entries in a model for any model term type. - An attribute index must not appear more than once in a model list. - ignore is not a valid default model term type. - Model term types currently consists of: single_multinomial - models discrete attributes as multinomials, with missing values. single_normal_cn - models real valued attributes as normals; no missing values. single_normal_cm - models real valued attributes with missing values. multi_normal_cn - is a covariant normal model without missing values. ignore - allows the model to ignore one or more attributes. - See the documentation in models-c.text for further information about specific model terms. - single_normal_cn/cm and multi_normal_cn modeled data, whose subtype is scalar (value distribution is away from 0.0, and is thus not a "normal" distribution) will be log transformed and modeled with the log-normal model. For data whose subtype is location (value distribution is around 0.0), no transform is done, and the normal model is used. 1.3.3.1 Model File Example The tokens "model_index n m" must appear on the first non-comment line, and precede the model term definition lines. "n" is the zero-based model index, typically 0 where there is only one model -- the majority of search situations. "m" is the number of model term definition lines that follow. Note that single model terms may have one or more zero-based attribute indices on each line. Multi model term set lists are two or more zero-based attribute indices per line. !#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ;; 1 or more model definitions ;; model_index model_index 0 7 ignore 0 single_normal_cn 3 single_normal_cn 17 18 21 multi_normal_cn 1 2 multi_normal_cn 8 9 10 multi_normal_cn 11 12 13 single_multinomial default 1.4 Checking Input Files AutoClass, when invoked in the "search" mode will check the validity of the set of data, header, model, and search parameter files. Errors will stop the search from starting, and warnings will ask the user whether to continue. A history of the error and warning messages is saved, by default, in the log file. The AutoClass search form is: % autoclass -search
All files must be specified as fully qualified relative or absolute pathnames. File name extensions (file types) for all files are forced to canonical values required by the AutoClass program: data file ("ascii") db2 data file ("binary") db2-bin header file hd2 model file model search params file s-params The search parameter definitions are discussed in search-c.text, as well as contained as comments in all .s-params files: for example, autoclass-c/sample/imports-85.s-params. The log file will be named . If LOG_FILE_P (search params file parameter) is false, then no log file is generated. The log file is created such that multiple sessions of AUTOCLASS -SEARCH <...> will result in only one log file. The file extension of the log file is forced to "log". N_DATA (search params file parameter), if supplied, allows the reading of less than the full data file. This is useful when the data file is very large and you are just interested in validating the header and model file contents. All advisory, warning, and error messages are output to the screen, and to the log file, providing that the LOG_FILE_P argument is true (the default). Advisory messages are output to provide information which is not crucial to the continuance of the run. Warning messages contain information which may affect the quality of the run. The default condition is to stop the run when one or more warning messages are generated, and ask the user whether to proceed. Error messages are fatal, and the run state will be terminated. Footnotes: #1) REL_ERROR is in upper-case to distinguish it from the surrounding text. It represents the lower-case keyword rel_error which is how it is used in the .hd2 file. This is true of other upper-case words or phrases which occur in this text. autoclass-3.3.6.dfsg.1/doc/search-c.text0000644000175000017500000010270011247310756016050 0ustar areareSEARCHING FOR GOOD CLASSIFICATIONS 1.0 What Results Are 2.0 What Results Mean 3.0 How It Works 4.0 When To Stop 5.0 What Gets Returned 6.0 How To Get Started 7.0 Status Reports 8.0 Search Variations 9.0 How Many Classes? 10.0 Do I Have Enough Memory and Disk Space? 11.0 Just How Slow Is It? 12.0 Changing Filenames in a Saved Classification File 13.0 Those Parameters Again -- With Annotations 14.0 How to get AutoClass C to Produce Repeatable Results Now that you have succeeded in describing your data with a header file and model file that passes the AUTOCLASS -SEARCH <...> input checks, you will have entered the search domain where Autoclass classifies your data. (At last!) The main function to use in finding a good classification of your data is AUTOCLASS -SEARCH, and using it will take most of the computation time. Searches are invoked with: % autoclass -search <.db2 file path> <.hd2 file path> <.model file path> <.s-params file path> The sample-run (autoclass-c/sample/) that comes with Autoclass shows some sample searches, and browsing these is probably the fastest way to get familiar with how to do searches. The test data sets located under autoclass-c/data/ will show you some other header (.hd2), model (.model), and search params (.s-params) file setups. The remainder of this section describes how to do searches in somewhat more detail. The capitalized tokens below are generally search params file parameters. 1.0 WHAT RESULTS ARE Autoclass is looking for the best classification(s) of the data it can find. A classification is composed of: 1) a set of classes, each of which is described by a set of class parameters, which specify how the class is distributed along the various attributes. For example, "height normally distributed with mean 4.67 ft and standard deviation .32 ft", 2) a set of class weights, describing what percentage of cases are likely to be in each class. 3) a probabilistic assignment of cases in the data to these classes. I.e. for each case, the relative probability that it is a member of each class. As a strictly Bayesian system (accept no substitutes!), the quality measure Autoclass uses is the total probability that, had you known nothing about your data or its domain, you would have found this set of data generated by this underlying model. This includes the prior probability that the "world" would have chosen this number of classes, this set of relative class weights, and this set of parameters for each class, and the likelihood that such a set of classes would have generated this set of values for the attributes in the data cases. These probabilities are typically very small, in the range of e^-30000, and so are usually expressed in exponential notation. 2.0 WHAT RESULTS MEAN It is important to remember that all of these probabilities are GIVEN that the real model is in the model family that Autoclass has restricted its attention to. If Autoclass is looking for Gaussian classes and the real classes are Poisson, then the fact that Autoclass found 5 Gaussian classes may not say much about how many Poisson classes there really are. The relative probability between different classifications found can be very large, like e^1000, so the very best classification found is usually overwhelmingly more probable than the rest (and overwhelmingly less probable than any better classifications as yet undiscovered). If Autoclass should manage to find two classifications that are within about exp(5-10) of each other (i.e. within 100 to 10,000 times more probable) then you should consider them to be about equally probable, as our computation is usually not more accurate than this (and sometimes much less). 3.0 HOW IT WORKS Autoclass repeatedly creates a random classification and then tries to massage this into a high probability classification though local changes, until it converges to some "local maximum". It then remembers what it found and starts over again, continuing until you tell it to stop. Each effort is called a "try", and the computed probability is intended to cover the whole volume in parameter space around this maximum, rather than just the peak. The standard approach to massaging is to 1) Compute the probabilistic class memberships of cases using the class parameters and the implied relative likelihoods. 2) Using the new class members, compute class statistics (like mean) and revise the class parameters. and repeat till they stop changing. There are three available convergence algorithms: "converge_search_3" (the default), "converge_search_4" and "converge". Their specification is controlled by search params file parameter TRY_FN_TYPE. 4.0 WHEN TO STOP You can tell AUTOCLASS -SEARCH to stop by: 1) giving a MAX_DURATION (in seconds) argument at the beginning; 2) giving a MAX_N_TRIES (an integer) argument at the beginning; or 3) by typing a "q" and after you have seen enough tries. The MAX_DURATION and MAX_N_TRIES arguments are useful if you desire to run AUTOCLASS -SEARCH in batch mode. If you are restarting AUTOCLASS -SEARCH from a previous search, the value of MAX_N_TRIES you provide, for instance 3, will tell the program to compute 3 more tries in addition to however many it has already done. The same incremental behavior is exhibited by MAX_DURATION. Deciding when to stop is a judgment call and it's up to you. Since the search includes a random component, there's always the chance that if you let it keep going it will find something better. So you need to trade off how much better it might be with how long it might take to find it. The search status reports that are printed when a new best classification is found are intended to provide you information to help you make this tradeoff. One clear sign that you should probably stop is if most of the classifications found are duplicates of previous ones (flagged by "dup" as they are found). This should only happen for very small sets of data or when fixing a very small number of classes, like two. Our experience is that for moderately large to extremely large data sets (~200 to ~10,000 datum), it is necessary to run AutoClass for at least 50 trials. 5.0 WHAT GETS RETURNED Just before returning, AUTOCLASS -SEARCH will give short descriptions of the best classifications found. How many will be described can be controlled with N_FINAL_SUMMARY. By default AUTOCLASS -SEARCH will write out a number of files, both at the end and periodically during the search (in case your system crashes before it finishes). These files will all have the same name (taken from the search params pathname [.s-params]), and differ only in their file extensions. If your search runs are very long and there is a possibility that your machine may crash, you can have intermediate "results" files written out. These can be used to restart your search run with minimum loss of search effort. See the documentation file checkpoint-c.text. A ".log" file will hold a listing of most of what was printed to the screen during the run, unless you set LOG_FILE_P to false to say you want no such foolishness. Unless RESULTS_FILE_P is false, a binary ".results-bin" file (the default) or an ASCII ".results" text file, will hold the best classifications that were returned, and unless SEARCH_FILE_P is false, a ".search" file will hold the record of the search tries. SAVE_COMPACT_P controls whether the "results" files are saved as binary or ASCII text. If the C global variable "G_safe_file_writing_p" is defined as TRUE in "autoclass-c/prog/globals.c", the names of "results" files (those that contain the saved classifications) are modified internally to account for redundant file writing. If the search params file name is "my_saved_clsfs" you will see the following "results" file names (ignoring directories and pathnames for this example) SAVE_COMPACT_P = true -- "my_saved_clsfs.results-bin" - completely written file "my_saved_clsfs.results-tmp-bin" - partially written file, renamed when complete SAVE_COMPACT_P = false -- "my_saved_clsfs.results" - completely written file "my_saved_clsfs.results-tmp" - partially written file, renamed when complete If check pointing is being done, these additional names will appear SAVE_COMPACT_P = true -- "my_saved_clsfs.chkpt-bin" - completely written checkpoint file "my_saved_clsfs.chkpt-tmp-bin" - partially written checkpoint file, renamed when complete SAVE_COMPACT_P = false -- "my_saved_clsfs.chkpt" - completely written checkpoint file "my_saved_clsfs.chkpt-tmp" - partially written checkpoint file, renamed when complete 6.0 HOW TO GET STARTED The way to invoke AUTOCLASS -SEARCH is: % autoclass -search <.db2 file path> <.hd2 file path> <.model file path> <.s-params file path> To restart a previous search, specify that FORCE_NEW_SEARCH_P has the value false in the search params file, since its default is true. Specifying false tells AUTOCLASS -SEARCH to try to find a previous compatible search (<...>.results[-bin] & <...>.search) to continue from, and will restart using it if found. To force a new search instead of restarting an old one, give the parameter FORCE_NEW_SEARCH_P the value of true, or use the default. If there is an existing search (<...>.results[-bin] & <...>.search), the user will be asked to confirm continuation since continuation will discard the existing search. If a previous search is continued, the message "RESTARTING SEARCH" will be given instead of the usual "BEGINNING SEARCH". It is generally better to continue a previous search than to start a new one, unless you are trying a significantly different search method, in which case statistics from the previous search may mislead the current one. 7.0 STATUS REPORTS A running commentary on the search will be printed to the screen and to the log file (unless LOG_FILE_P is false). Note that the ".log" file will contain a listing of all default search params values, and the values of all params that are overridden. After each try a very short report (only a few characters long) is given. After each new best classification, a longer report is given, but no more often than MIN_REPORT_PERIOD (default is 30 seconds). 8.0 SEARCH VARIATIONS AUTOCLASS -SEARCH by default uses a certain standard search method or try function (TRY_FN_TYPE = "converge_search_3"). Two others are also available: "converge_search_4" and "converge"). They are provided in case your problem is one that may happen to benefit from them. In general the default method will result in finding better classifications at the expense of a longer search time. The default was chosen so as to be robust, giving even performance across many problems. The alternatives to the default may do better on some problems, but may do substantially worse on others. "converge_search_3" uses an absolute stopping criterion (REL_DELTA_RANGE, default value of 0.0025) which tests the variation of each class of the delta of the log approximate-marginal-likelihood of the class statistics with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the class weight (class->w_j) between successive convergence cycles. Increasing this value loosens the convergence and reduces the number of cycles. Decreasing this value tightens the convergence and increases the number of cycles. N_AVERAGE (default value of 3) specifies how many successive cycles must meet the stopping criterion before the trial terminates. "converge_search_4" uses an absolute stopping criterion (CS4_DELTA_RANGE, default value of 0.0025) which tests the variation of each class of the slope for each class of log approximate-marginal-likelihood of the class statistics with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the class weight (class->w_j) over SIGMA_BETA_N_VALUES (default value 6) convergence cycles. Increasing the value of CS4_DELTA_RANGE loosens the convergence and reduces the number of cycles. Decreasing this value tightens the convergence and increases the number of cycles. Computationally, this try function is more expensive than "converge_search_3", but may prove useful if the computational "noise" is significant compared to the variations in the computed values. Key calculations are done in double precision floating point, and for the largest data base we have tested so far ( 5,420 cases of 93 attributes), computational noise has not been a problem, although the value of MAX_CYCLES needed to be increased to 400. "converge" uses one of two absolute stopping criterion which test the variation of the classification (clsf) log_marginal (clsf->log_a_x_h) delta between successive convergence cycles. The largest of HALT_RANGE (default value 0.5) and HALT_FACTOR * current_clsf_log_marginal) is used (default value of HALT_FACTOR is 0.0001). Increasing these values loosens the convergence and reduces the number of cycles. Decreasing these values tightens the convergence and increases the number of cycles. N_AVERAGE (default value of 3) specifies how many cycles must meet the stopping criteria before the trial terminates. This is a very approximate stopping criterion, but will give you some feel for the kind of classifications to expect. It would be useful for "exploratory" searches of a data base. The purpose of RECONVERGE_TYPE = "chkpt" is to complete an interrupted classification by continuing from its last checkpoint. The purpose of RECONVERGE_TYPE = "results" is to attempt further refinement of the best completed classification using a different value of TRY_FN_TYPE ("converge_search_3", "converge_search_4", "converge"). If MAX_N_TRIES is greater than 1, then in each case, after the reconvergence has completed, AutoClass will perform further search trials based on the parameter values in the <...>.s-params file. With the use of RECONVERGE_TYPE ( default value ""), you may apply more than one try function to a classification. Say you generate several exploratory trials using TRY_FN_TYPE = "converge", and quit the search saving .search and .results[-bin] files. Then you can begin another search with TRY_FN_TYPE = "converge_search_3", RECONVERGE_TYPE = "results", and MAX_N_TRIES = 1. This will result in the further convergence of the best classification generated with TRY_FN_TYPE = "converge", with TRY_FN_TYPE = "converge_search_3". When AutoClass completes this search try, you will have an additional refined classification. A good way to verify that any of the alternate TRY_FUN_TYPE are generating a well converged classification is to run AutoClass in prediction mode on the same data used for generating the classification. Then generate and compare the corresponding case or class cross reference files for the original classification and the prediction. Small differences between these files are to be expected, while large differences indicate incomplete convergence. Differences between such file pairs should, on average and modulo class deletions, decrease monotonically with further convergence. The standard way to create a random classification to begin a try is with the default value of "random" for START_FN_TYPE. At this point there are no alternatives. Specifying "block" for START_FN_TYPE produces repeatable non-random searches. That is how the <..>.s-params files in the autoclass-c/data/.. sub-directories are specified. This is how development testing is done. MAX_CYCLES controls the maximum number of convergence cycles that will be performed in any one trial by the convergence functions. Its default value is 200. The screen output shows a period (".") for each cycle completed. If your search trials run for 200 cycles, then either your data base is very complex (increase the value), or the TRY_FN_TYPE is not adequate for situation (try another of the available ones, and use CONVERGE_PRINT_P to get more information on what is going on). Specifying CONVERGE_PRINT_P to be true will generate a brief print-out for each cycle which will provide information so that you can modify the default values of REL_DELTA_RANGE & N_AVERAGE for "converge_search_3"; CS4_DELTA_RANGE & SIGMA_BETA_N_VALUES for "converge_search_4"; and HALT_RANGE, HALT_FACTOR, and N_AVERAGE for "converge". Their default values are given in the <..>.s-params files in the autoclass-c/data/.. sub-directories. 9.0 HOW MANY CLASSES? Each new try begins with a certain number of classes and may end up with a smaller number, as some classes may drop out of the convergence. In general, you want to begin the try with some number of classes that previous tries have indicated look promising, and you want to be sure you are fishing around elsewhere in case you missed something before. N_CLASSES_FN_TYPE = "random_ln_normal" is the default way to make this choice. It fits a log normal to the number of classes (usually called "j" for short) of the 10 best classifications found so far, and randomly selects from that. There is currently no alternative. To start the game off, the default is to go down START_J_LIST for the first few tries, and then switch to N_CLASSES_FN_TYPE. If you believe that the probable number of classes in your data base is say 75, then instead of using the default value of START_J_LIST (2, 3, 5, 7, 10, 15, 25), specify something like 50, 60, 70, 80, 90, 100. If one wants to always look for, say, three classes, one can use FIXED_J and override the above. Search status reports will describe what the current method for choosing j is. 10.0 DO I HAVE ENOUGH MEMORY AND DISK SPACE? Internally, the storage requirements in the current system are of order n_classes_per_clsf * (n_data + n_stored_clsfs * n_attributes * n_attribute_values). This depends on the number of cases, the number of attributes, the values per attribute (use 2 if a real value), and the number of classifications stored away for comparison to see if others are duplicates -- controlled by MAX_N_STORE (default value = 10). The search process does not itself consume significant memory, but storage of the results may do so. AutoClass C is configured to handle a maximum of 999 attributes. If you attempt to run with more than that you will get array bound violations. In that case, change these configuration parameters in prog/autoclass.h and recompile AutoClass C: #define ALL_ATTRIBUTES 999 #define VERY_LONG_STRING_LENGTH 20000 #define VERY_LONG_TOKEN_LENGTH 500 For example, these values will handle several thousand attributes: #define ALL_ATTRIBUTES 9999 #define VERY_LONG_STRING_LENGTH 50000 #define VERY_LONG_TOKEN_LENGTH 50000 Disk space taken up by the "log" file will of course depend on the duration of the search. N_SAVE (default value = 2) determines how many best classifications are saved into the ".results[-bin]" file. SAVE_COMPACT_P controls whether the "results" and "checkpoint" files are saved as binary. Binary files are faster and more compact, but are not portable. The default value of SAVE_COMPACT_P is true, which causes binary files to be written. If the time taken to save the "results" files is a problem, consider increasing MIN_SAVE_PERIOD (default value = 1800 seconds or 30 minutes). Files are saved to disk this often if there is anything different to report. 11.0 JUST HOW SLOW IS IT? Compute time is of order n_data * n_attributes * n_classes * n_tries * converge_cycles_per_try. The major uncertainties in this are the number of basic back and forth cycles till convergence in each try, and of course the number of tries. The number of cycles per trial is typically 10-100 for TRY_FN_TYPE "converge", and 10-200+ for "converge_search_3" and "converge_search-4". The maximum number is specified by MAX_N_TRIES (default value = 200). The number of trials is up to you and your available computing resources. The running time of very large data sets will be quite uncertain. We advise that a few small scale test runs be made on your system to determine a baseline. Specify N_DATA to limit how many data vectors are read. Given a very large quantity of data, AutoClass may find its most probable classifications at upwards of a hundred classes, and this will require that START_J_LIST be specified appropriately (See section 9.0 HOW MANY CLASSES?). If you are quite certain that you only want a few classes, you can force AutoClass to search with a fixed number of classes specified by FIXED_J. You will then need to run separate searches with each different fixed number of classes. 12.0 CHANGING FILENAMES IN A SAVED CLASSIFICATION FILE AutoClass caches the data, header, and model file pathnames in the saved classification structure of the binary (".results-bin") or ASCII (".results") "results" files. If the "results" and "search" files are moved to a different directory location, the search cannot be successfully restarted if you have used absolute pathnames. Thus it is advantageous to run invoke AutoClass in a parent directory of the data, header, and model files, so that relative pathnames can be used. Since the pathnames cached will then be relative, the files can be moved to a different host or file system and restarted -- providing the same relative pathname hierarchy exists. However, since the ".results" file is ASCII text, those pathnames could be changed with a text editor (SAVE_COMPACT_P must be specified as false). 13.0 THOSE PARAMETERS AGAIN -- WITH ANNOTATIONS # PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! Specifies the relative difference measure used by clsf-DS-%=, when ! deciding if a new clsf is a duplicate of an old one. # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! Initially try these numbers of classes, so as not to narrow the ! search too quickly. The state of this list is saved in the ! <..>.search file and used on restarts, unless an override ! specification of start_j_list is made in the .s-params file for the ! restart run. This list should bracket your expected number of ! classes, and by a wide margin! ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! Once start_j_list is exhausted, AutoClass will call this function to ! decide how many classes to start with on the next try, based on the ! 10 best classifications found so far. ! Currently only "random_ln_normal" is available. # fixed_j = 0 ! When fixed_j > 0, overrides start_j_list and n_classes_fn_type, and ! AutoClass will always use this value for the initial number of classes. # min_report_period = 30 ! Wait at least this time (in seconds) since last report until ! reporting verbosely again. ! Should be set longer than the expected run time when checking for ! repeatability of results. For repeatable results, also see ! force_new_search_p, start_fn_type and randomize_random_p. # NOTE: At least one of "interactive_p", "max_duration", and "max_n_tries" must be active. Otherwise AutoClass will run indefinitly. See below. # interactive_p = true ! When false, allows run to continue until otherwise halted. ! When true, standard input is queried on each cycle for the quit ! character "q", which, when detected, triggers an immediate halt. # max_duration = 0 ! When = 0, allows run to continue until otherwise halted. ! When > 0, specifies the maximum number of seconds to run. # max_n_tries = 0 ! When = 0, allows run to continue until otherwise halted. ! When > 0, specifies the maximum number of tries to make. # n_save = 2 ! Save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files). # log_file_p = true ! If false, do not write a log file. # search_file_p = true ! If false, do not write a search file. # results_file_p = true ! If false, do not write a results file. # min_save_period = 1800 ! CPU crash protection. This specifies the maximum time, in seconds, ! that AutoClass will run before it saves the current results to disk. ! The default time is 30 minutes. # max_n_store = 10 ! Specifies the maximum number of classifications stored internally. # n_final_summary = 10 ! Specifies the number of trials to be printed out after search ends. # start_fn_type = "random" ! One of {"random", "block"}. This specifies the type of class ! initialization. For normal search, use "random", which randomly ! selects instances to be initial class means, and adds appropriate ! variances. For testing with repeatable search, use "block", which ! partitions the database into successive blocks of near equal size. ! For repeatable results, also see force_new_search_p, ! min_report_period, and randomize_random_p. # try_fn_type = "converge_search_3" ! One of {"converge_search_3", "converge_search_4", "converge"}. ! These specify alternate search stopping criteria. ! "converge" mearly tests the rate of change of the log_marginal ! classification probability (clsf->log_a_x_h), without checking ! rate of change of individual classes(see halt_range and ! halt_factor). ! "converge_search_3" and "converge_search_4" each monitor the ratio ! class->log_a_w_s_h_j/class->w_j for all classes, and continue ! convergence until all pass the quiescence criteria for n_average ! cycles. "converge_search_3" tests differences between successive ! convergence cycles (see "rel_delta_range"). This provides a ! reasonable, general purpose stopping criteria. ! "converge_search_4" averages the ratio over "sigma_beta_n_values" ! cycles (see "cs4_delta_range"). This is preferred when ! "converge_search_3 produces many similar classes. # initial_cycles_p = true ! If true, perform base_cycle in initialize_parameters. ! false is used only for testing. # save_compact_p = true ! true saves classifications as machine dependent binary ! (.results-bin & .chkpt-bin). ! false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary ! (.results-bin & .chkpt-bin). ! false reads as ascii text (.results & .chkpt). # randomize_random_p = true ! false seeds lrand48, the pseudo-random number function with 1 ! to give repeatable test cases. true uses universal time clock ! as the seed, giving semi-random searches. ! For repeatable results, also see force_new_search_p, ! min_report_period and start_fn_type. # n_data = 0 ! With n_data = 0, the entire database is read from .db2. ! With n_data > 0, only this number of data are read. # halt_range = 0.5 ! Passed to try_fn_type "converge". With the "converge" ! try_fn_type, convergence is halted when the larger of halt_range ! and (halt_factor * current_log_marginal) exceeds the difference ! between successive cycle values of the classification log_marginal ! (clsf->log_a_x_h). Decreasing this value may tighten the ! convergence and increase the number of cycles. # halt_factor = 0.0001 ! Passed to try_fn_type "converge". With the "converge" ! try_fn_type, convergence is halted when the larger of halt_range ! and (halt_factor * current_log_marginal) exceeds the difference ! between successive cycle values of the classification log_marginal ! (clsf->log_a_x_h). Decreasing this value may tighten the ! convergence and increase the number of cycles. # rel_delta_range = 0.0025 ! Passed to try function "converge_search_3", which monitors the ! ratio of log approx-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) ! divided by the class weight (class->w_j), for each class. ! "converge_search_3" halts convergence when the difference between ! cycles, of this ratio, for every class, has been exceeded by ! "rel_delta_range" for "n_average" cycles. Decreasing ! "rel_delta_range" tightens the convergence and increases the ! number of cycles. # cs4_delta_range = 0.0025 ! Passed to try function "converge_search_4", which monitors the ! ratio of (class->log_a_w_s_h_j)/(class->w_j), for each class, ! averaged over "sigma_beta_n_values" convergence cycles. ! "converge_search_4" halts convergence when the maximum difference ! in average values of this ratio falls below "cs4_delta_range". ! Decreasing "cs4_delta_range" tightens the convergence and ! increases the number of cycles. # n_average = 3 ! Passed to try functions "converge_search_3" and "converge". ! The number of cycles for which the convergence criterion ! must be satisfied for the trial to terminate. # sigma_beta_n_values = 6 ! Passed to try_fn_type "converge_search_4". The number of past ! values to use in computing sigma^2 (noise) and beta^2 (signal). # max_cycles = 200 ! This is the maximum number of cycles permitted for any one convergence ! of a classification, regardless of any other stopping criteria. This ! is very dependent upon your database and choice of model and ! convergence parameters, but should be about twice the average number ! of cycles reported in the screen dump and .log file # converge_print_p = false ! If true, the selected try function will print to the screen values ! useful in specifying non-default values for halt_range, halt_factor, ! rel_delta_range, n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! If true, checkpoints of the current classification will be written ! every "min_checkpoint_period" seconds, with file extension ! .chkpt[-bin]. This is only useful for very large classifications # min_checkpoint_period = 10800 ! If checkpoint_p = true, the checkpointed classification will be ! written this often - in seconds (default = 3 hours) # reconverge_type = "" ! Can be either "chkpt" or "results". If "checkpoint_p" = true and ! "reconverge_type" = "chkpt", then continue convergence of the ! classification contained in <...>.chkpt[-bin]. If "checkpoint_p " ! = false and "reconverge_type" = "results", continue convergence of ! the best classification contained in <...>.results[-bin]. # screen_output_p = true ! If false, no output is directed to the screen. Assuming ! log_file_p = true, output will be directed to the log file only. # break_on_warnings_p = true ! The default value asks the user whether or not to continue, when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal and the log file. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! core dump. If specified as false, AutoClass will not attempt to free ! storage. #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; 14.0 HOW TO GET AUTOCLASS C TO PRODUCE REPEATABLE RESULTS In some situations, repeatable classifications are required: comparing basic AutoClass C integrity on different platforms, porting AutoClass C to a new platform, etc. In order to accomplish this two things are necessary: 1) the same random number generator must be used, and 2) the search parameters must be specified properly. Random Number Generator This implementation of AutoClass C uses the Unix srand48/lrand48 random number generator which generates pseudo-random numbers using the well-known linear congruential algorithm and 48-bit integer arithmetic. lrand48() returns non- negative long integers uniformly distributed over the interval [0, 2**31]. Search Parameters The following .s-params file parameters should be specified: force_new_search_p = true start_fn_type "block" randomize_random_p = false ;; specify the number of trials you wish to run max_n_tries = 50 ;; specify a time greater than duration of run min_report_period = 30000 Note that no current best classification reports will be produced. Only a final classification summary will be output. autoclass-3.3.6.dfsg.1/version-2-5.text0000644000175000017500000000675611247310756015622 0ustar areare AUTOCLASS C VERSION 2.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/reports-c.text - Minor typographical changes. Added new report generation parameter: order_attributes_by_influence_p. Its default value is true. The file extension of the influence values report has been changed from ".influ-text-1" to ".influ-o-text-1" when order_attributes_by_influence_p = true, and to ".influ-no-text-1" when order_attributes_by_influence_p = false. 2. autoclass-c/doc/interpretation-c.text - Minor changes to the text. 3. autoclass-c/sample/imports-85c.influ-o-text-1 Influence values report has been significantly revised and reformatted. 4. autoclass-c/doc/search-c.text - Corrected definition of fixed_j. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.5. 2. autoclass-c/prog/intf-reports.c, utils.c - Formatting change to "format_real_attribute" for multiple multivariate attribute groups. Remove covariance matrix output and reformat the correlation matrix output to fixed decimal point notation. For the influence values report, sort real valued attributes of the same model group by the first significance value, if that group is multi_normal_cn. For discrete attributes: relabel the headers "Prob", rather than "Mean"; and correct the instance value significance computation to be "local_prob * log( local_prob / global_prob)". 3. autoclass-c/prog/autoclass.h - Add #ifndef for MAXPATHLEN. 4. autoclass-c/prog/io-results.c - In "validate_data_pathname", "validate_results_pathname", & "make_and_validate_pathname", only do fclose, if fopen returns non-NULL. 5. autoclass-c/prog/search-control-2.c - Add "pad" argument to "print_search_try". 6. autoclass-c/prog/intf-extensions.c - Formatting change to "get_models_source_info". 7. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.sun.gcc, autoclass.make.sun.acc, autoclass.make.sun.cc, autoclass.make.sgi - (remove autoclass.make.sun) Add SunOS/Solaris C compiler support. 8. autoclass-c/prog/io-results.c, io-read-model.c, io-read-data.c, utils.c, intf-reports.c, getparmas.c - Cast return values of "strlen" to int. 9. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", correct segmentation fault which occurs when more than 25 type = real, subtype = scalar attributes are defined in the ".hd2" & ".model" files. 10. autoclass-c/prog/struct-data.c, io-results.c, io-results-bin.c - Properly initialize att_info array when it exceeds preallocated size. 11. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.linux.gcc, autoclass.make.sun.*, autoclass.make.sgi, fcntlcom-ac.h - Thanks to Andrew Lewycky , added mods for port to Linux version 1.2.10, GCC version 2.5.8, libc version 4.6.25. 12. autoclass-c/prog/model-single-multitnomial.c - In "sm_params_influence_fn", add check for out-of-bounds arguments to the log function to prevent "log domain" errors. ====================================================================== autoclass-3.3.6.dfsg.1/read-me.text0000644000175000017500000006171411247310756015141 0ustar areare GENERAL INFORMATION FOR AUTOCLASS C ------------------------------------------------------------------------------- CONTENTS: What Is Autoclass What Is Autoclass III What Is Autoclass X What Is Autoclass C Update History Compatibility & Porting Considerations Limitations Building The Autoclass C System Use Of The Autoclass C System Theoretical Questions Technical Questions Implementation Questions References WHAT IS AUTOCLASS: AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features: - determines the number of classes automatically; - can use mixed discrete and real valued data; - can handle missing values; - processing time is roughly linear in the amount of the data; - cases have probabilistic class membership; - allows correlation between attributes within a class; - generates reports describing the classes found; and - predicts "test" case class memberships from a "training" classification. Inputs consist of a database of attribute vectors (cases), either real or discrete valued, and a class model. Default class models are provided. AutoClass finds the set of classes that is maximally probable with respect to the data and model. The output is a set of class descriptions, and partial membership of the cases in the classes. For more details see "Bayesian Classification (AutoClass): Theory and Results" (kdd-95.ps in ~/autoclass-c/doc/), "Bayesian Classification Theory" (tr-fia-90-12-7-01.ps in ~/autoclass-c/doc/). A list of references is included below. WHAT IS AUTOCLASS III: AutoClass III, programmed in Common Lisp, is the official released implementation of AutoClass available from COSMIC (NASA's software distribution agency): COSMIC University of Georgia 382 East Broad Street Athens, GA 30602 USA voice: (706) 542-3265 fax: (706) 542-4807 telex: 41- 190 UGA IRC ATHENS e-mail: cosmic@@uga.bitnet or service@@cossack.cosmic.uga.edu Request "AutoClass III - Automatic Class Discovery from Data (ARC-13180)". WHAT IS AUTOCLASS X: AutoClass X is an experimental extension to AutoClass III, available only domestically, by means of a non-disclosure agreement. It implements hierarchical classification where attributes are associated with appropriate levels of the class hierarchy. The search methodology is currently in development. It is implemented in Common Lisp. Contact Will Taylor (taylor@ptolemy.arc.nasa.gov). WHAT IS AUTOCLASS C: AutoClass C is a publicly available implementation of AutoClass III, with some improvements from AutoClass X, done in the C language. It was programmed by Dr. Diane Cook (cook@centauri.uta.edu) and Joseph Potts (potts@cse.uta.edu) of the University of Texas at Arlington. Will Taylor (taylor@ptolemy.arc.nasa.gov) "productized" the software through extensive testing, addition of sample data bases, and re-working the user documentation. Significant new features of the C implementation are: - it is about 10-20 times faster than the Lisp implementations: AutoClass III & AutoClass X; - it uses double precision floating point for its "inner loop" weight calculations, producing a higher "signal-to-noise" ratio than the Lisp versions, and thus more precise convergences for very large data sets (adding double precision to the Lisp versions would slow them down even more). It provides four models: single_multinomial - discrete attribute multinomial model, including missing values. single_normal - real valued attribute model with without missing values; sub-types: location and scalar. single_normal_missing - real valued attribute model with missing values; sub-types: location and scalar. multi_normal - real valued covariant normal model without missing values. Additional models were done in Lisp for AutoClass X, and may be implemented in C at some later time. These models are: single_multinomial_ignore - discrete attribute multinomial model, ignoring missing values. single_poisson - models low value count (integer) attributes as Poisson distributions. multi_multinomial_dense - a dense covariant multinomial model. multi_multinomial_sparse - a sparse covariant multinomial model. The C implementation also does not provide single_multinomial model value translations, and canonical model group/attribute ordering. UPDATE HISTORY: Version: 1.0 15 Apr 95 initial version of AutoClass C Version: 1.5 08 May 95 ported to Sun Solaris 2.4; corrected string overwrite problems; compilation of file search-control.c is now optimized; & added binary data file input option. (See "autoclass-c/version-1-5.text") Version: 2.0 08 Jun 95 ported to SGI IRIX version 5.2; converted binary i/o from non-standard (open/close/ read/write) to ANSI (fopen/fclose/fread/fwrite); converted from srand/rand to srand48/lrand48 for random number generation; added prediction capability which uses a "training" classification to predict probabilistic class membership for the cases of a "test" data file; added new ".s-params" parameter "screen_output_p"; added output of real and discrete attribute statistics when data base is initially read; corrected garbage output when ".r-params" parameter "xref_class_report_att_list" contains mixed real and discrete attributes; corrected the handling of unknown real values in reports output; and corrected an error in function "output_warning_msgs" which caused an abort condition. (See "autoclass-c/version-2-0.text") Version: 2.5 28 Jul 95 Influence values report has been significantly revised and reformatted; add SunOS/Solaris C compiler support; correct segmentation fault which occurs when more than 25 type = real, subtype = scalar attributes are defined; correct "LOG domain" errors in generation of influence values for model "single_multinomial"; and added mods for port to Linux operating system using gcc compiler. (See "autoclass-c/version-2-5.text") Version: 2.6 02 Aug 95 Correct segmentation fault which occurs when more than 50 type = real, subtype = scalar attributes are defined; add function safe_log to prevent "log: SING error" error messages; and require user to confirm search runs using test settings for .s-params file parameters: start_fn_type and randomize_random_p. (See "autoclass-c/version-2-6.text") Version: 2.7 16 Aug 95 Add search parameter to allow AutoClass to be run as a background task. (See "autoclass-c/version-2-7.text") Version: 2.8 03 Sep 96 Add search parameter "read_compact_p", which directs AutoClass to read the "results" and "checkpoint" files in either binary format or ascii format; redefine make files with -I and -L parameters for SunOS 4.1.3; change make file naming conventions; prevent corruption of discrete data translation tables when translations are longer than 40 characters; increase from 3000 to 20000 the value of VERY_LONG_STRING_LENGTH to handle very large datum lines; increase DATA_ALLOC_INCREMENT from 100 to 1000 for reading very large datasets; add DATA_ALLOC_INCREMENT logic of READ_DATA to XREF_GET_DATA -- this will prevent segmentation faults encountered when reading very large .db2 files into the reports processing function of AutoClass; in FORMAT_DISCRETE_ATTRIBUTE, do not process attributes with warning or error messages -- this prevents segmentation faults; in XREF_GET_DATA, free database allocated memory after it is transferred into report data structures --this reduces the amount of memory required when generating reports for very large data bases, and prevents running out of memory; in all functions calling malloc/realloc for dynamic memory allocation, checks have been added to notify the user if memory is exhausted; and port the "make" file for HP-UX operating system using the bundled "cc" compiler. (See "autoclass-c/version-2-8.text") Version: 2.9 21 Oct 96 Correct bugs which occur when generating reports of discrete type data -- these were introduced in version 2.8. Added new parameter for both ".s-params" & ".r-params" files: break_on_warnings_p. (See "autoclass-c/version-2-9.text") Version: 3.0 15 Apr 97 New parameter for .r-params files: report_mode -- "text" (current report output) or "data" (parsable format for further processing); correct minor bugs; improve input checking for .hd2 file; correct segmentation fault which occurred in prediction runs when the size of the "test" database was larger than that of the "training" database; and new parameter for .s-params & .r-params files: free_storage_p. (See "autoclass-c/version-3-0.text") Version: 3.1 04 Jul 97 New parameters for .r-params files: comment_data_headers_p, max_num_xref_class_probs, start_sigma_contours_att, & stop_sigma_contours_att. Allow checkpoint files to be loaded for reconvergence. Allow reports to be generated for data sets of 100,000 cases and more, without causing a segmentation fault. For "-predict" runs, handle "test" cases which are not predicted in be in any of the "training" classes. When there is more than one covariant normal correlation matrix, print all of them. In the case cross-reference report (report_type = "xref_case") generated with the data option (report_mode = "data"), other class probabilities are now printed. In the case and class cross- reference reports, the print out of probabilities has increased by one significant digit (0.04 => 0.041), and the minimum value printed is now 0.001, rather than 0.01. Add capability to compute sigma class contour values for specified pairs of real valued attributes. (See "autoclass-c/version-3-1.text") Version: 3.2 13 Apr 98 Changed the behavior of search parameter force_new_search_p; amplified some documentation sections; corrected several segmentation faults in reports generation; corrected several errors in sigma contours output; correct problem with cross-reference reports class assignment when there are more than five marginal probabilities; change layout of influence values report to print matrices after all class attributes are listed; warn user when default start_j_list may not find the correct number of classes in data set; warn user of search trials which do not converge and print convergence summary at the end of each run; the multi-normal model was corrected to prevent oscillation in the expectation maximization calculations; and allow non-contiguous groups of attributes to be specified for sigma contours calculations. (See "autoclass-c/version-3-2.text") Version: 3.2.1 04 Jun 98 Minor documentation changes. (See "autoclass-c/version-3-2-1.text") Version: 3.2.2 02 Jul 98 Minor documentation changes. (See "autoclass-c/version-3-2-2.text") Version: 3.3 23 Sep 98 Integrated source port of version 3.2.2 to Windows NT/95. Update sample AutoClass C run files contained in autoclass-c/sample. (See "autoclass-c/version-3-3.text") Version: 3.3.1 30 Nov 98 Correct incompatibility with .results[-bin] files written by AutoClass C versions prior to version 3.3. (See "autoclass-c/version-3-3-1.text") Version: 3.3.2 13 Sep 99 In all situations warning and error messages are now written to the log file. (See "autoclass-c/version-3-3-2.text") Version: 3.3.3 01 May 00 Add Dec Alpha support; correct Dec Alpha crashes when attampting to free memory at the end of search runs; conditionalize two warning tests to fail in batch mode; and separate log files are now written for "-search" (.log) and "-reports" (.rlog). (See "autoclass-c/version-3-3-3.text") Version: 3.3.4 24 Jan 02 Correct bugs in -predict and -report modes; correct "safe_log" function for range near 0; and minor code cleanup. (See "autoclass-c/version-3-3-4.text") Version: 3.3.5 07 Mar 07 Add FreeBSD and MacOSX support; correct minor bugs. (See "autoclass-c/version-3-3-5.text") Version: 3.3.6 01 Sep 09 Improvements to reports for 'report_mode = "data"' and 'comment_data_headers_p = true'. (See "autoclass-c/version-3-3-6.text") COMPATIBILITY & PORTING CONSIDERATIONS: AutoClass C was written in ANSI C using the GNU gcc compiler version 2.6.3 running on a SunSparc under SunOS 4.1.3. It has also been ported to and tested on: - SunSparc under Solaris 2.6 using GCC version 2.95.2; - SunSparc under Solaris 2.4 using SPARCompiler C version 3.00; - SunSparc under SunOS 4.1.3 using SPARCompiler C version 3.00; - SGI Indigo under IRIX 5.2 using the bundled cc compiler; - Redhat Linux version 6.1, GCC version 2.95.2; - HP9000/735 & HP9000/C110 under HPUX 10.10 using the bundled cc compiler; - Windows NT/95 using the Microsoft Visual C++ 5.0 compiler. Considerations for porting to other platforms, operating systems, and compilers: - int & float types must be at least 32 bit words - floating point arithmetic must be IEEE standard - values.h constant #defines are not consistent with IEEE standard -- used Symbolics Genera 8.3 values in autoclass.h - globals.c, io-results.c, & search-control-2.c: G_safe_file_writing_p = TRUE; only supported under Unix, since it does system calls to mv (rename file) and rm (delete file). - utils.c: char_input_test -- which implements the typing of 'q' and to quit the search -- uses Unix system call fcntl, and file fcntlcom-ac.h; get_universal_time -- uses Unix system call time. - init.c: init -- uses Unix system call getcwd (get current working directory); sets "normalizer" value for random number generator library function "srand48". - search-control.c, search-basic.c, search-control-2.c, & utils.c: Use C library functions srand48/lrand48 for random number generation. LIMITATIONS: AutoClass C is limited by memory requirements that are roughly in proportion to the number of data, times the number of attributes (the data space); plus the number of classes, times number of modeled attributes (the model space); plus a fixed program space. Thus there should be no limit on the number of attributes beyond the program addressable memory, but there are definite tradeoffs with respect to the model space, and performance degradations as paging requirements increase. For very large data sets, you may well find that even if you can handle the data, the processing time is excessive. If that is the case, it may be worthwhile to try class generation on random subsets of the data set. This should pick out the major classes, although it will miss small ones that are only vaguely represented in the random subsets. You can then switch to prediction mode to classify the entire data set. BUILDING THE AUTOCLASS C SYSTEM -- UNIX PLATFORMS Assuming that "." is not in $PATH -- % cd ~/autoclass-c # or equivalent % chmod u+x load-ac # if you have not already done so % load-ac { Which compiler, GNU(gcc) or SunOS(acc)? - {gcc|acc}: } { Which compiler, GNU(gcc) or Solaris(cc)? - {gcc|cc}: } { no prompt if SGI or Linux } % ./autoclass-c/autoclass # show autoclass options AutoClass Search: % ./autoclass -search <.db2[-bin] file path> <.hd2 file path> <.model file path> <.s-params file path> AutoClass Reports: % ./autoclass -reports <.results[-bin] file path> <.search file path> <.r-params file path> AutoClass Prediction: % ./autoclass -predict BUILDING THE AUTOCLASS C SYSTEM -- MAC OSX PLATFORMS Assuming that "." is not in $PATH -- % cd ~/autoclass-c # or equivalent % chmod u+x load-ac-macosx # if you have not already done so % load-ac-macosx % ./autoclass-c/autoclass # show autoclass options AutoClass Search: % ./autoclass -search <.db2[-bin] file path> <.hd2 file path> <.model file path> <.s-params file path> AutoClass Reports: % ./autoclass -reports <.results[-bin] file path> <.search file path> <.r-params file path> AutoClass Prediction: % ./autoclass -predict BUILDING THE AUTOCLASS C SYSTEM -- WINDOWS PLATFORMS Use Mirosoft Visual C++ 5.0 Developer Studio to build Autoclass.exe File->Open Workspace: f:\autoclass-c-win\prog\AutoclassC.dsw Build->Build Autoclass.exe f:\autoclass-c-win> copy prog\Debug\Autoclass.exe . f:\autoclass-c-win> Autoclass.exe # show autoclass options AutoClass Search: f:\autoclass-c-win> Autoclass.exe -search <.db2[-bin] file path> <.hd2 file path> <.model file path> <.s-params file path> AutoClass Reports: f:\autoclass-c-win> Autoclass.exe -reports <.results[-bin] file path> <.search file path> <.r-params file path> AutoClass Prediction: f:\autoclass-c-win> Autoclass.exe -predict USE OF THE AUTOCLASS C SYSTEM -- UNIX & MAC OSX PLATFORMS Assuming that "." is not in $PATH -- To use Autoclass, first you need data (your ".db2" file), then you need to describe it to AutoClass (your ".hd2" & ".model" files), and also tell AutoClass what parameter values to use for the search (your ".s-params" file) and for the report generation (your ".r-params" file). Next, you generate classification results from your data using % cd ~/autoclass-c % ./autoclass-c/autoclass -search data/glass/glassc.db2 data/glass/glass-3c.hd2 data/glass/glass-mnc.model data/glass/glassc.s-params and you produce reports with % ./autoclass-c/autoclass -reports data/glass/glassc.results-bin data/glass/glassc.search data/glass/glassc.r-params and, optionally, use this classification for prediction of test cases % ./autoclass-c/autoclass -predict data/glass/glassc-predict.db2 data/glass/glassc.results-bin data/glass/glassc.search data/glass/glassc.r-params See autoclass-c/doc/introduction-c.text for detailed documentation of the AutoClass C system. A database with sample classification run output is provided in ~/autoclass-c/sample/. Test databases, with .db2, .hd2, .model, .s-params, and .r-params files for each of the model term types, are provided in: ~/autoclass-c/data/autos/ ~/autoclass-c/data/3-dim/ ~/autoclass-c/data/glass/ ~/autoclass-c/data/rna/ ~/autoclass-c/data/soybean/ Test summary output for these databases is provided in: ~/autoclass-c/data/tests.c Note that the parameters specified in the .s-params files for the test data bases specify repeatable, non-random classification runs. For proper random classifications of your data sets, remove these "override" parameters in your .s-params files. USE OF THE AUTOCLASS C SYSTEM -- WINDOWS PLATFORMS To use Autoclass, first you need data (your ".db2" file), then you need to describe it to AutoClass (your ".hd2" & ".model" files), and also tell AutoClass what parameter values to use for the search (your ".s-params" file) and for the report generation (your ".r-params" file). Next, you generate classification results from your data using > cd f:\autoclass-c-win # for example f:\autoclass-c-win> Autoclass.exe -search data\glass\glassc.db2 data\glass\glass-3c.hd2 data\glass\glass-mnc.model data\glass\glassc.s-params and you produce reports with f:\autoclass-c-win> Autoclass.exe -reports data\glass\glassc.results-bin data\glass\glassc.search data\glass\glassc.r-params and, optionally, use this classification for prediction of test cases f:\autoclass-c-win> Autoclass.exe -predict data\glass\glassc-predict.db2 data\glass\glassc.results-bin data\glass\glassc.search data\glass\glassc.r-params See autoclass-c-win\doc\introduction-c.text for detailed documentation of the AutoClass C system. A database with sample classification run output is provided in f:\autoclass-c-win\sample\. Test databases, with .db2, .hd2, .model, .s-params, and .r-params files for each of the model term types, are provided in: f:\autoclass-c-win\data\autos\ f:\autoclass-c-win\data\3-dim\ f:\autoclass-c-win\data\glass\ f:\autoclass-c-win\data\rna\ f:\autoclass-c-win\data\soybean\ Test summary output for these databases is provided in: f:\autoclass-c-win\data\tests.c Note that the parameters specified in the .s-params files for the test data bases specify repeatable, non-random classification runs. For proper random classifications of your data sets, remove these "override" parameters in your .s-params files. TECHNICAL QUESTIONS: Contact John Stutz (stutz@ptolemy.arc.nasa.gov) if you have questions concerning the applicability of AutoClass to your data analysis situation. IMPLEMENTATION QUESTIONS: Contact Will Taylor (taylor@ptolemy.arc.nasa.gov) if you have questions concerning the implementation, installation, and running of AutoClass C, including "bugs" and features you may add to the existing code. REFERENCES: P. Cheeseman, et al. "Autoclass: A Bayesian Classification System", Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, MI. June 12-14 1988. Morgan Kaufmann, San Francisco, 1988, pp. 54-64, P. Cheeseman, et al. "Bayesian Classification", Proceedings of the Seventh National Conference of Artificial Intelligence (AAAI-88), St. Paul, MN. August 22-26, 1988. Morgan Kaufmann, San Francisco, 1988, pp. 607-611. J. Goebel, et al. "A Bayesian Classification of the IRAS LRS Atlas", Astron. Astrophys. 222, L5-L8 (1989). P. Cheeseman, et al. "Automatic Classification of Spectra from the Infrared Astronomical Satellite (IRAS)", NASA Reference Publication 1217 (1989) P. Cheeseman, "On Finding the Most Probable Model", Computational Models of Discovery and Theory Formation, ed. by Jeff Shrager and Pat Langley. Morgan Kaufmann, San Francisco, 1990, pp. 73-96. R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification Theory", Technical Report FIA-90-12-7-01, NASA Ames Research Center, Artificial Intelligence Branch, May 1991 R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification with Correlation and Inheritance", Proceedings of 12th International Joint Conference on Artificial Intelligence, Sydney, Australia. August 24-30, 1991. Morgan Kaufmann Publishers, San Francisco, 1991, pp.692-698. B. Kanefsky, J. Stutz, P. Cheeseman, "An Automatic Classification of a Landsat/TM Image from Kansas (FIFE)", Technical Report FIA-91-26, NASA Ames Research Center, Artificial Intelligence Branch, September 1991. B. Kanefsky, J. Stutz, P. Cheeseman, W. Taylor, "An Improved Automatic Classification of a Landsat/TM Image from Kansas (FIFE)", Technical Report FIA-94-01, NASA Ames Research Center, Artificial Intelligence Branch, January 1994. J. Stutz, P. Cheeseman, "AutoClass - a Bayesian Approach to Classification", in "Maximum Entropy and Bayesian Methods, Cambridge 1994", John Skilling & Subuiso Sibisi Eds. Kluwer Academic Publishers, Dordrecht, 1995. P. Cheeseman, J. Stutz, "Bayesian Classification (AutoClass): Theory and Results", in Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy Uthurusamy, Eds. The AAAI Press, Menlo Park, expected fall 1995. autoclass-3.3.6.dfsg.1/version-3-3.text0000644000175000017500000000756211247310756015615 0ustar areare AUTOCLASS C VERSION 3.3 NOTES ====================================================================== ====================================================================== NOTE: This version is an integrated source port of version 3.2.2 to Windows NT/95. There are no new capabilities or bug fixes over and above version 3.2.2. Windows Compatibility Changes: ----------------------------------- Thanks to Autumn , we now have an integrated source release of AutoClass C for Unix platforms and Windows platforms (requires Microsoft Visual C++ 5.0). Due to the Unix "line feed" and the Windows "carriage return/line feed" incompatibility, there are two distributions, one for Unix-based platforms, and one for Windows platforms. Summary of source changes: autoclass-c/prog/*.c - Using _MSC_VER in preprocessor forms, remove the include of Unix-specific headers, and add the Win32 equivalent. autoclass-c/prog/autoclass.h - Use rand in place of lrand48, therefore change srand48 to srand. autoclass-c/prog/getparams.c, getparams.h, intf-reports.c, search-control.c Prefixed enum members with T so they would not clash with predefined types. autoclass-c/prog/init.c getcwd is called _getcwd in MSVC. autoclass-c/prog/model-expander-3.c, params.h, struct-class.c - Prefixed enum member IGNORE with T so it would not clash with predefined type. autoclass-c/prog/search-control.c - Made two queries conditional on interactive_p; moved init of stream with stdout -- MSVC doesn't think it's a constant. autoclass-c/prog/utils.c - Created an lrand48 for win32; created a char_input_test() that works with win32. autoclass-c/prog/fcntlcom-ac.h - Adapt for MSVC. Documentation: ------------------------------ 1. Update sample AutoClass C run files contained in autoclass-c/sample. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3unx or 3.3win, depending on which platform AutoClass C is compiled. 2. autoclass-c/prog/globals.c, globals.h, intf-extensions.c, intf-reports.c, search-control-2.c, search-control.c, io-results-bin.c, io-results.c, io-read-model.c, & io-read-data.c - Add G_slash, which is "/" for Unix, and "\" for Windows. Change all occurrences of local variable "slash" to "G_slash". This will allow AutoClass C to handle both Unix and Windows relative and absolute pathnames properly. 3. autoclass-c/prog/init.c - In INIT, append either "/" or "\" to G_absolute_pathname. 4. autoclass-c/prog/intf-sigma-contours.c - In GENERATE_SIGMA_CONTOURS use %+13e for Windows instead of %13e in order to maintain column alignment for positive and negative values. 5. autoclass-c/prog/intf-reports.c - For Windows, use %+9.2e rather than %9.2 in FORMAT_REAL_ATTRIBUTE and FORMAT_DISCRETE_ATTRIBUTE, in order to maintain column alignment for positive and negative values. Call FILTER_E_FORMAT_EXPONENTS to filter Windows peculiar %e format output of e+000 => e+00, and e-000 => e-00. 6. autoclass-c/prog/intf-sigma-contours.c - For Windows, use %+13e rather than %13 in GENERATE_SIGMA_CONTOURS, in order to maintain column alignment for positive and negative values. Call FILTER_E_FORMAT_EXPONENTS to filter Windows peculiar %e format output of e+000 => e+00, etc, and e-000 => e-00. 7. autoclass-c/prog/io-results.c, io-results-bin.c - In READ_CLSF_SEQ and LOAD_CLSF_SEQ, truncate unx/win from the version prior to checking for numeric content. 8. autoclass-c/prog/autoclass.c - In AUTOCLASS_ARGS, display "autoclass" or "Autoclass.exe" depending on platform. ====================================================================== autoclass-3.3.6.dfsg.1/version-3-3-2.text0000644000175000017500000000447311247310756015752 0ustar areare AUTOCLASS C VERSION 3.3.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add a paragraph to section 8.0 SEARCH VARIATIONS discussing how running AutoClass in prediction mode can indicate whether you currently have a well converged classification. Add a paragraph to section 11.0 JUST HOW SLOW IS IT? discussing how to deal with very large data sets. 2. autoclass-c/doc/preparation-c.text - Add section 1.3.1.1 HANDLING MISSING VALUES, which discusses AutoClass C's approach to dealing with missing values in the input data set. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.2. 2. autoclass-c/prog/autoclass.h, intf-reports.c - In AUTOCLASS_REPORTS, write the default and overridden parameters from the .r-params file to the log file. Add error checking for report_type. In CASE_CLASS_DATA_SHARING, correct problem where "-predict" mode ignores report_type options of "xref_case" and "xref_class". 3. autoclass-c/prog/autoclass.c - In MAIN, initialize log file for "-predict" mode. Prior to this change, there were no log entries for "-predict" mode runs. 4. autoclass-c/prog/io-read-data.c - In CHECK_ERRORS_AND_WARNINGS, write warnings and errors to log file. In the situations of restarting a classification search, running a classification report, or running a classification prediction, warning and error messages which have until now gone only to the screen, will now go into the log file. 5. autoclass-c/prog/intf-reports.c, predictions.c - In AUTOCLASS_REPORTS, pass log file arguments to AUTOCLASS_PREDICT, so that errors and warnings generated in processing the test classification will be written to the log file. 6. autoclass-c/prog/model-transforms.c - In LOG_TRANSFORM, add "Suggest decreasing attribute's rel_error." to error message: "log transform of attribute# 5 using mn -120.398972 rather than 0.000000 for zero_point." ====================================================================== autoclass-3.3.6.dfsg.1/version-2-9.text0000644000175000017500000000553411247310756015617 0ustar areare AUTOCLASS C VERSION 2.9 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text, reports-c.text - Added new parameter for both ".s-params" & ".r-params" files: break_on_warnings_p. The default value asks the user whether to continue or not when data definition warnings are found. If specified as false, then AutoClass will continue, despite warnings -- the warning will continue to be output to the terminal and, in the case of the "-search" option, to the log file. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.9. 2. autoclass-c/prog/autoclass.make.solaris.cc - Correct compiler options for SC4.1 cc compiler 3. autoclass-c/prog/intf-reports.c - In FORMAT_DISCRETE_ATTRIBUTE, correct bad test for warn_errs->single_valued_warning. This prevented "discrete" type attributes from being displayed in the influence values report, because a segmentation fault occurred. This problem was introduced in version 2.8. 4. autoclass-c/prog/autoclass.h - In STRUCT I_REAL, type "last_sorted_term_n_att" as int, not float. 5. autoclass-c/prog/intf-reports.c - In SORT_MNCN_ATTRIBUTES, type "last_sorted_term_n_att" as int, not float. In PRE_FORMAT_ATTRIBUTES, revise logic for computing/printing the correlation matrix. Items 4. & 5 correct the problem of the correlation matrix for attribute type multi_multinomial_cn is only printed if it is last in the sorted list of attributes. 6. autoclass-c/prog/struct-data.c - In EXPAND_DATABASE, make cosmetic change to an error message. 7. autoclass-c/prog/intf-reports.c - In XREF_GET_DATA, validity check that .r-params file values of xref_class_report_att_list are in the range 0 - (number of attributes - 1). Prevents segmentation fault. Also in XREF_GET_DATA, correct syntax and location of memory free command. Prevents segmentation fault when processing discrete type data. This problem was introduced in version 2.8. 8. autoclass-c/prog/getparams.h - Increased MAXPARAMS to 40. 9. autoclass-c/prog/search-control.c, intf-reports.c - Added code to parse the new parameter: break_on_warnings_p. 10. autoclass-c/prog/autoclass.h, intf-reports.c - Compute last_clsf_p and pass to XREF_GET_DATA, so that freeing of data will not be done until all clsfs have been processed. To take advantage of the memory reduction, only process one clsf and a time -- this applies only to very large data sets. ====================================================================== autoclass-3.3.6.dfsg.1/changelog0000644000175000017500000016536611667631470014610 0ustar areare AUTOCLASS C VERSION 3.3.6 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.6. 2. autoclass-c/prog/utils.c - Comment out the last error check in safe_sprintf to prevent errors of this type: "ERROR: vsprintf produced 41 chars (max number is 40) -- called by log_transform Abort". 3. autoclass-c/prog/intf-reports.c - Reworked the placement of '#' characters for the 'comment_data_headers_p = true' report setting. Attribute names for both real and discrete attributes can now be of arbitrary length and remain on the same line, i.e. they will not be split onto two lines. 4. autoclass-c/prog/io-read-data.c - Print standard deviation, rather than variance, for input data summary of real attributes in the .log file. (output_att_statistics & output_real_att_statistics) AUTOCLASS C VERSION 3.3.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.5. 2. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.freebsd.gcc - Add support for the FreeBSD unix variant operating system. 3. autoclass-c/prog/model-multi-normal-cn.c - Change all calls to log, with safe_log, which checks for a zero argument. Certain real valued data set values caused a double precision underflow (< e-308) which resulted in 0.0. 4. autoclass-c/prog/intf-reports.c, utils-math.c, search-control-2.c, model-update.c, model-transforms.c, model-single-normal-cm.c, model-single-normal-cn.c, model-single-multinomial.c, model-expander-3.c - Make the change in item 3. to all files referencing log. 5. autoclass-c/sample/read.me.c - Correct file name typo: scriptc.lisp => scriptc.text 6. autoclass-c/load-ac - To prevent bad default .cshrc files from crashing the build, change "#!/bin/csh" to "#!/bin/csh -f". 7. autoclass-c/prog/io-results.c - write_att_DS modified to output warnings_and_errors->num_expander_warnings and warnings_and_errors->num_expander_errors strings with embedded carriage returns removed. This corrects a problem which occurs when the user's data generates warning messages during input checking, which the user ignores, and the user has specified save_compact_p = false and read_compact_p = false in their .s-params file. When they attempt to create reports, "autoclass -reports ..." breaks with an unexpected data error. 8. autoclass-c/load-ac-macosx, autoclass-c/prog/autoclass.make.macosx.gcc - Add support for the Macintosh OSX 10.4 operating system utilizing gcc 4.0. (OSFLAGS=-DMACOSX) 9. autoclass-c/prog/utils.c, autoclass-c/prog/autoclass.h - Routine "int round(double)" replaced by "int iround(double)". References to "round" were changed to "iround" in all affected routines. 10. autoclass-c/prog/autoclass.h - For MacOSX, do not define INFINITY here -- conflicts with OSX math library. AUTOCLASS C VERSION 3.3.4 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/sample files were regenerated because of the SAFE_LOG change (item 6. below). Only very minor changes occurred. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.4. 2. autoclass-c/prog/predictions.c - In autoclass_predict, allocate separate storage for test_clsf->reports->class_wt_ordering to prevent segmentation violation on Linux platforms when running in predict mode. 3. autoclass-c/prog/autoclass.h, minmax.h - Macros min() and max() have been moved to a new file: minmax.h. Added `#include "minmax.h"' to the following files: intf-reports.c io-read-data.c matrix-utilities.c model-multi-normal-cn.c model-single-normal-cm.c model-single-normal-cn.c model-update.c search-basic.c search-control-2.c search-control.c statistics.c struct-data.c utils.c Removed the prototypes for build_sn_cm_priors() and build_sn_cn_priors(). These functions are used only in the .c files that contain them, so are now static functions. Changed the prototype for log_gamma(), for reasons explained below. 4. autoclass-c/prog/getparams.c - Corrected argument to sizeof() on line 142. 5. autoclass-c/prog/struct-clsf.c - Zero global pointer and counter variables after deleting the structures to which they refer. 6. autoclass-c/prog/utils-math.c - Before, the function safe_log() returned 0.0 when its argument was less than or equal to LEAST_POSITIVE_SINGLE_FLOAT. This is clearly wrong. Log(x) approaches -infinity (not 0) as x approaches 0. The fix is to have safe_log() return LEAST_POSITIVE_SINGLE_LOG for x near 0. 7. autoclass-c/prog/search-control-2.c - In variance, check for lists of length less than 2, and return 0. Items 3 - 7 were submitted by Jack Wathey . 8. autoclass-c/prog/intf-reports.c - Correct FORMAT_DISCRETE_ATTRIBUTE to prevent string overrun and segmentation violations when single multinomial values exceed 20 characters, while running in report mode. 9. autoclass-c/prog/io-results.c - Correct READ_ATT_DS to prevent string overrun and segmentation violations when single multinomial values exceed 40 characters, while running in report mode. AUTOCLASS C VERSION 3.3.3 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/reports-c.text - document that report log messages will go into a ".rlog" file, rather than the ".log" which is used during search runs. Also minor typos corrected. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.3. 2. autoclass-c/prog/init.c, intf-reports.c, intf-sigma-contours.c - Sun Solaris CC compiler breaks when #ifdef, etc preprocessor directives do not start in column 1. All preprocessor directives now start in column 1. 3. autoclass-c/prog/autoclass.make.alpha.cc, autoclass-c/load-ac - A Makefile for the Dec Alpha (OSF1 v4.0) has been added. 4. autoclass-c/prog/prints.c - Modified PRINT_VECTOR_F to eliminate compiler warning. 5. autoclass-c/prog/search-control.c - Conditionalize two warning tests to fail in batch mode (.s-params parameter interactive_p = false), rather than attempt to ask the user whether to proceed. 6. autoclass-c/prog/autoclass.h, autoclass.c, io-results.c - To make it convenient to generate reports while the search is still running, so you can decide whether or not to stop the search, but not have the search log file be overwritten with the report log file, the report log file will now be written to a file with the extension ".rlog". The search output will continue to be directed to a file with the extension ".log". 7. autoclass-c/prog/getparams.c, init.c, io-read-model.c, struct-class.c - Change sizeof(int) to sizeof(void *), so that 64-bit architectures will be handled properly. This corrects the core dump which occurs on Dec Alpha platforms at the end of each search or reports run, when AutoClass C attempts to free allocated storage. AUTOCLASS C VERSION 3.3.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add a paragraph to section 8.0 SEARCH VARIATIONS discussing how running AutoClass in prediction mode can indicate whether you currently have a well converged classification. Add a paragraph to section 11.0 JUST HOW SLOW IS IT? discussing how to deal with very large data sets. 2. autoclass-c/doc/preparation-c.text - Add section 1.3.1.1 HANDLING MISSING VALUES, which discusses AutoClass C's approach to dealing with missing values in the input data set. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.2. 2. autoclass-c/prog/autoclass.h, intf-reports.c - In AUTOCLASS_REPORTS, write the default and overridden parameters from the .r-params file to the log file. Add error checking for report_type. In CASE_CLASS_DATA_SHARING, correct problem where "-predict" mode ignores report_type options of "xref_case" and "xref_class". 3. autoclass-c/prog/autoclass.c - In MAIN, initialize log file for "-predict" mode. Prior to this change, there were no log entries for "-predict" mode runs. 4. autoclass-c/prog/io-read-data.c - In CHECK_ERRORS_AND_WARNINGS, write warnings and errors to log file. In the situations of restarting a classification search, running a classification report, or running a classification prediction, warning and error messages which have until now gone only to the screen, will now go into the log file. 5. autoclass-c/prog/intf-reports.c, predictions.c - In AUTOCLASS_REPORTS, pass log file arguments to AUTOCLASS_PREDICT, so that errors and warnings generated in processing the test classification will be written to the log file. 6. autoclass-c/prog/model-transforms.c - In LOG_TRANSFORM, add "Suggest decreasing attribute's rel_error." to error message: "log transform of attribute# 5 using mn -120.398972 rather than 0.000000 for zero_point." ====================================================================== AUTOCLASS C VERSION 3.3.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.1. 2. autoclass-c/prog/io-results.c, io-results-bin.c - In READ_CLSF_SEQ and LOAD_CLSF_SEQ, check for win/unx suffix in ac_version before stripping it off. This corrects an incompatibility with .results[-bin] files written by AutoClass C versions prior to version 3.3. The error, for .results-bin files, looks like this: ERROR: expecting "ac_version n.n", found "ac_version "; Abort. ====================================================================== AUTOCLASS C VERSION 3.3 NOTES ====================================================================== ====================================================================== NOTE: This version is an integrated source port of version 3.2.2 to Windows NT/95. There are no new capabilities or bug fixes over and above version 3.2.2. Windows Compatibility Changes: ----------------------------------- Thanks to Autumn , we now have an integrated source release of AutoClass C for Unix platforms and Windows platforms (requires Microsoft Visual C++ 5.0). Due to the Unix "line feed" and the Windows "carriage return/line feed" incompatibility, there are two distributions, one for Unix-based platforms, and one for Windows platforms. Summary of source changes: autoclass-c/prog/*.c - Using _MSC_VER in preprocessor forms, remove the include of Unix-specific headers, and add the Win32 equivalent. autoclass-c/prog/autoclass.h - Use rand in place of lrand48, therefore change srand48 to srand. autoclass-c/prog/getparams.c, getparams.h, intf-reports.c, search-control.c Prefixed enum members with T so they would not clash with predefined types. autoclass-c/prog/init.c getcwd is called _getcwd in MSVC. autoclass-c/prog/model-expander-3.c, params.h, struct-class.c - Prefixed enum member IGNORE with T so it would not clash with predefined type. autoclass-c/prog/search-control.c - Made two queries conditional on interactive_p; moved init of stream with stdout -- MSVC doesn't think it's a constant. autoclass-c/prog/utils.c - Created an lrand48 for win32; created a char_input_test() that works with win32. autoclass-c/prog/fcntlcom-ac.h - Adapt for MSVC. Documentation: ------------------------------ 1. Update sample AutoClass C run files contained in autoclass-c/sample. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3unx or 3.3win, depending on which platform AutoClass C is compiled. 2. autoclass-c/prog/globals.c, globals.h, intf-extensions.c, intf-reports.c, search-control-2.c, search-control.c, io-results-bin.c, io-results.c, io-read-model.c, & io-read-data.c - Add G_slash, which is "/" for Unix, and "\" for Windows. Change all occurrences of local variable "slash" to "G_slash". This will allow AutoClass C to handle both Unix and Windows relative and absolute pathnames properly. 3. autoclass-c/prog/init.c - In INIT, append either "/" or "\" to G_absolute_pathname. 4. autoclass-c/prog/intf-sigma-contours.c - In GENERATE_SIGMA_CONTOURS use %+13e for Windows instead of %13e in order to maintain column alignment for positive and negative values. 5. autoclass-c/prog/intf-reports.c - For Windows, use %+9.2e rather than %9.2 in FORMAT_REAL_ATTRIBUTE and FORMAT_DISCRETE_ATTRIBUTE, in order to maintain column alignment for positive and negative values. Call FILTER_E_FORMAT_EXPONENTS to filter Windows peculiar %e format output of e+000 => e+00, and e-000 => e-00. 6. autoclass-c/prog/intf-sigma-contours.c - For Windows, use %+13e rather than %13 in GENERATE_SIGMA_CONTOURS, in order to maintain column alignment for positive and negative values. Call FILTER_E_FORMAT_EXPONENTS to filter Windows peculiar %e format output of e+000 => e+00, etc, and e-000 => e-00. 7. autoclass-c/prog/io-results.c, io-results-bin.c - In READ_CLSF_SEQ and LOAD_CLSF_SEQ, truncate unx/win from the version prior to checking for numeric content. 8. autoclass-c/prog/autoclass.c - In AUTOCLASS_ARGS, display "autoclass" or "Autoclass.exe" depending on platform. ====================================================================== AUTOCLASS C VERSION 3.2.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - clarify the usage of the RECONVERGE_TYPE parameter. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.2.2. 2. autoclass-c/data/uci-dbs-readme.text - Replaced out-of-data information with current Web pointer. 3. autoclass-c/data/tests.c & autoclass-c/data/glass/ report files Version 3.2 contained changes to the multi-normal-cn model which changed slightly the results of the non-random test cases. They have been updated. ====================================================================== AUTOCLASS C VERSION 3.2.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/checkpoint-c.text - bring up to date the usage of force_new_search_p in the examples. Programming: ------------------------------ 1. autoclass-c/prog/globals.c, globals.h, search-control.c, intf-reports.c, autoclass.c, io-results.c, io-results-bin.c - Update "G_ac_version" to 3.2.1, and change type from float to string. 2. autoclass-c/prog/autoclass.h, io-read-data.c - Comment out unused functions: DEFINE_DISCRETE_TRANSLATIONS, and PROCESS_DISCRETE_TRANSLATIONS. Unused #defines MAXINT and DBG_LL commented out. ====================================================================== AUTOCLASS C VERSION 3.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Added a new section: 14.0 How to get AutoClass C to Produce Repeatable Results. Added information about running AutoClass C with more than 1000 attributes in sections: 10.0 Do I Have Enough Memory and Disk Space? Changed the behavior of search parameter force_new_search_p in order to prevent search trials from being inadvertently lost: if TRUE, will ignore any previous search results, discarding the existing .search & .results[-bin] files after confirmation by the user; if FALSE, will continue the search using the existing .search & .results[-bin] files. The default value of force_new_search_p is now true. 2. autoclass-c/doc/interpretation-c.text - Added section headings and a new section entitled: Comparing Influence Report Class Weights And Class/Case Report Assignments 3. autoclass-c/doc/preparation-c.text - Added more to section: 1.2.1 SINGLE_NORMAL_CN/CM and MULTI_NORMAL_CN Models 4. autoclass-c/doc/reports-c.text - Improved the last pargraph of Generating Sigma Contour Values. Replace parameters start_sigma_contours_att and stop_sigma_contours_att with sigma_contours_att_list, to allow non-contiguous groups of attributes to be specified. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.2. 2. autoclass-c/prog/intf-reports.c - In INFLUENCE_VALUES_HEADER, change `fprintf( influence_report_fp, header);' to `fprintf( influence_report_fp, header, "");', and in CLASS_WEIGHTS_AND_STRENGTHS and CLASS_DIVERGENCES add args to output_title fprintf for new page -- this prevents segmentation faults, when the number of attributes exceeds one page, while in report_mode = "text". 3. autoclass-c/prog/intf-sigma-contours.c - In COMPUTE_SIGMA_CONTOUR_FOR_2_ATTS, corrected initialization of *rotation. This corrects erroneous values of the contour's rotation. 4. autoclass-c/prog/struct-class.c - Correct compiler warning "struct-class.c:239: warning: unused variable `database'". 5. autoclass-c/prog/struct-data.c, globals.h, globals.c, search-control.c - In EXPAND_DATABASE, use comp_database->n_data rather than G_s_params_n_data, since G_s_params_n_data does not do the right thing when expand_database is called during report generation (it reads the whole file, not just n_data cases). Remove references to G_s_params_n_data from the 2nd to 4th files. 6. autoclass-c/prog/intf-reports.c - In XREF_GET_DATA, allocate more storage for instance class probabilities if there are more than MAX_NUM_XREF_CLASS_PROBS, and only save for printing a maximum of MAX_NUM_XREF_CLASS_PROBS classes. IMPORTANT NOTE: This bug fix means that for any previous reports generated by AutoClass C, any data base instance which has five class probability entries in the class cross-reference report, and 1.0 minus the sum of the five probabilities is greater than the largest of them, is in the WRONG CLASS! Re-run the reports with this version! 7. autoclass-c/prog/autoclass.c - Print the AutoClass C version when the user invokes AutoClass with no arguments: % autoclass 8. autoclass-c/load-ac - Specified define flags for SunOS gcc and Solaris gcc compilations to prevent compiler warnings. Added IRIX 6.4 compatibility. 9. autoclass-c/prog/autoclass.h - For gcc under SunOS, include function prototypes for *rand48 functions, to prevent compiler warnings. 10. autoclass-c/prog/intf-reports.c - Add descriptive text for each influence value class parameter for reports with parameter report_mode = "text". 11. autoclass-c/prog/autoclass.make.solaris.cc - Corrected optimization flag. 12. autoclass-c/prog/intf-reports.c - In FORMAT_REAL_ATTRIBUTE, correct correlation matrices print-out for non-contiguous model term attributes, and print matrices only once, after all class attributes are listed. 13. autoclass-c/prog/search-control.c - In AUTOCLASS_SEARCH, if force_new_search_p is false, exit if there is no <...>.results[-bin] file. Make TRUE the default for force_new_search_p. 14. autoclass-c/prog/intf-reports.c - In PRINT_ATTRIBUTE_HEADER, remove references to INTEGER attribute type. 15. autoclass-c/prog/getparams.c - In GETPARAMS, correct logic so that missing "line feed" on last line of the file will be read properly, rather than getting: ERROR: line read exceeds 100 characters: <.....>. In GETPARAMS, correct logic so that an empty integer list (e.g. start_j_list =) may be entered in the .s-params file. This is needed for a restart search situation when it is necessay to peel off as many classes from the start_j_list as were already done by the previous run. If all of the start_j_list was done already, then an empty list is required. 16. autoclass-c/prog/io-read-data.c, io-results.c, io-results-bin.c - In READ_DATA, EXPAND_CLSF_WTS, and LOAD_CLASS_DS_S add checks for "out of memory" returns from malloc and realloc. 17. autoclass-c/prog/io-results.c - In MAKE_AND_VALIDATE_PATHNAME, VALIDATE_RESULTS_PATHNAME, VALIDATE_DATA_PATHNAME, and GET_CLSF_SEQ change strchr to strrchr to handle `../filename.extension' 18. autoclass-c/prog/autoclass.h, predictions.c, search-basic.c, & search-control.c - Notify the user with a warning messasge and an option to exit from an initial classification run, if the data set size is greater than 1000. The messasge is "WARNING: the default start_j_list may not find the correct number of classes in your data set!". 19. autoclass-c/prog/autoclass.h, autoclass.c, & intf-reports.c - Write -reports option screen output to log file. 20. autoclass-c/prog/io-read-data.c - In FIND_DISCRETE_STATS, when the number of discrete value translators is less than attribute definition range, reduce the range and output an advisory, rather than outputting warning message and asking the user whether to proceed or not. The above change was REMOVED, since it caused an incompatablility with previous results files: "ERROR: expand_database found unmatched common attributes defs in <.results[-bin] file> and ........ 21. autoclass-c/prog/global.h, global.c, search-control-2.c, & search-control.c - Warn user of search trials which do not converge, which means that their number of try cycles reached the value of the "max_cycles" search parameter. Do this by printing a warning message after the trial completes. Also after the "SUMMARY OF n BEST RESULTS" at the conclusion of each run, print "SUMMARY OF TRY CONVERGENCE" for the n best results. 22. autoclass-c/prog/model-multi-normal-cn.c - It was recently brought to our attention that the multi-normal model, with more than about 10 attributes and several thousand instances, would consistently run to the the max_duration or max_n_tries limit, regardless of how large those limits were. Suitably instrumented experiments showed that EM (expectation maximization) was actually oscillating. The problem was traced to a conceptual error in the underflow limiting code that constrains the estimation of empirical standard deviations. This has been corrected. However users should be alert for, and report, any further problems of this nature. 23. autoclass-c/prog/autoclass.h, intf-reports.c - For MNcn attributes, do not sort them within their model term when order_attributes_by_influence_p = false. The outputing of MNcn correlation matrices after last class attribute, instead of after each term, is now done by a call to GENERATE_MNCN_CORRELATION_MATRICES from AUTOCLASS_CLASS_INFLUENCE_VALUES_REPORT. 24. autoclass-c/prog/intf-reports.c, intf-sigma-contours.c - Replace report parameters start_sigma_contours_att and stop_sigma_contours_att with sigma_contours_att_list, to allow non-contiguous groups of attributes to be specified. Check for attribute indices of reports parameter sigma_contours_att_list which are declared "ignore" by the .model file. Prevents segmentation fault. Correct erroneous rotations for non-covariant pairs of attributes modeled in two different covariant normal terms (the rotations in these cases should be 0.0). 25. autoclass-c/prog/intf-reports.c - Previously when specifying report_type = "xref_case" or report_type = "xref_class" along with n_clsfs > 1 or clsf_n_list with more than 1 list element, the .case-text-n or .class-text-n data would be identical. Sometimes segmentation faults would occur. This has been corrected. This was not a problem for report_type = "all" (the default). Also when using the default for report_type ("all"), previously the memory allocated for each classification's cross reference was not deallocated after each classification was processed. It is now properly deallocated. ====================================================================== AUTOCLASS C VERSION 3.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/data/tests.c - Reconfigure parameter values for the checkpointing test case. 2. autoclass-c/data/glass/glassc-chkpt.s-params - Include checkpoint test param settings from tests.c 3. autoclass-c/data/autos/* - Add input data files for last **non**-random trial test of autoclass-c/data/tests.c 4. autoclass-c/doc/prediction-c.text - Add text concerning handling of "test" cases which are not predicted to be in any of the "training" classes. 5. autoclass-c/doc/reports-c.text - Add new reports param: comment_data_headers_p, which prefixes the "#" comment character to all lines except the minimum for selective parsing. Add new reports param: max_num_xref_class_probs, which determines how many lessor class probabilities will be printed for the case and class cross-reference reports. The default value is 5. Add new report params: start_sigma_contours_att & stop_sigma_contours_att. This adds the capability to compute sigma class contour values for specified pairs of real valued attributes, when generating the influence values report with the data option (report_mode = "data"). See section "Generating Sigma Contour Values". Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.1. 2. autoclass-c/prog/io-results.c - In VALIDATE_RESULTS_PATHNAME, handle checkpoint files similarly to results files: determine if they are ascii or binary, rather than assuming they are binary. This was only a problem when .s-params parameters save_compact_p = false, and read_compact_p = false. In GET_CLSF_SEQ, handle checkpoint files similarly to results files. This fix now allows checkpoint files to be loaded for reconvergence. 3. autoclass-c/prog/intf-reports.c, autoclass.h - In XREF_GET_DATA, allocate memory for collector once for each case, rather than n_classes times. This fix now permits reports to be generated for data sets of 100,000 cases and more, without causing a segmentation fault. Eliminate ATTR_ALLOC_INCREMENT, and allocate once for all discrete, and once for all real report attributes, if needed, rather than invoking malloc/realloc for each report attribute. 4. autoclass-c/prog/intf-reports.c, autoclass.h - In AUTOCLASS_REPORTS, pass prediction_p to CASE_CLASS_DATA_SHARING, so that XREF_GET_DATA can flag "test" cases which are not predicted in be in any of the "training" classes. Put them in class -1. This is only functional for "autoclass -predict ..." runs. The following message will appear in the screen output for each case that is not a member of any of the "training" classes: xref_get_data: case_num xxx => class 9999 Class 9999 members will appear in the "case" and "class" cross- reference reports. 5. autoclass-c/prog/intf-influence-values.c - In INFLUENCE_VALUE, do not process attribute values which have null translations. This occurs when the user supplies an excessive range value in .hd2, and ignores the warning to correct it. This prevents a segmentation fault. 6. autoclass-c/prog/struct-data.c - In EXPAND_DATABASE, make error msg more informative. 7. autoclass-c/prog/autoclass.h, intf-reports.c, intf-extenstions.c, search-control-2.c - Implement new reports param "comment_data_headers_p", which prefixes the "#" comment character to all lines except the minimum for selective parsing. 8. autoclass-c/prog/io-read-data.c - In OUTPUT_REAL_ATT_STATISTICS, add error check for attribute variance exceeding infinity. This situation is caused by "out-liers" with very large deviations from the other attribute values, and usually means that these attribute values are erroneous. AutoClass C can not proceed in this situation. 9. autoclass-c/prog/intf-reports.c - In the influence values report for multi_normal_cn models, when there are more than one covariant normal correlation matrix, print all of them for each class, not just the one for the least significant attribute of the current class. Changes to FORMAT_ATTRIBUTE & FORMAT_REAL_ATTRIBUTE. 10. autoclass-c/prog/intf-reports.c - In the case cross-reference report (report_type = "xref_case") generated with the data option (report_mode = "data"), other class probabilities are now printed, if their values are greater than or equal to 0.001, and there are not more than (MAX_NUM_XREF_CLASS_PROBS - 1) of them. Changes to XREF_PAGINATE_BY_CASE, & XREF_OUTPUT_PAGE_HEADERS. 11. autoclass-c/prog/intf-reports.c - In the case and class cross-reference reports, the print out of probabilities has increased by one significant digit (0.04 => 0.041); and the minimum value printed is now 0.001, rather than 0.01. The maximum number of lessor probabilities printed out is (MAX_NUM_XREF_CLASS_PROBS - 1). Changes to XREF_PAGINATE_BY_CASE, & XREF_OUTPUT_LINE_BY_CLASS. 12. autoclass-c/prog/intf-reports.c - Add new report parameter MAX_NUM_XREF_CLASS_PROBS, which determines how many lessor class probability values will be printed in the case and class cross-reference reports. 13. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.*, autoclass-c/prog/autoclass.h, intf-sigma-contours.c, intf-reports.c - Add capability to compute sigma class contour values for specified pairs of real valued attributes, when generating the influence values report with the data option (report_mode = "data"). Add new report params start_sigma_contours_att & stop_sigma_contours_att. ====================================================================== AUTOCLASS C VERSION 3.0 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text, reports-c.text -- New parameter for .s-params & .r-params files: free_storage_p. The default value tells AutoClass to free the majority of its allocated storage. If specified as false, AutoClass will not attempt to free storage. 2. autoclass-c/doc/preparation-c.text - Correct typos "looses" and "scaler". 3. autoclass-c/doc/reports-c.text -- New parameter for .r-params files: report_mode. It specifies the mode of the reports to generate. The default, "text", is the current formatted text layout. The new "data" option has a parsable numerical layout -- suitable for further processing. 4. autoclass-c/sample/read.me.c, scriptc.text, imports-85c.influ-o-data-1, imports-85c.case-data-1, imports-85c.class-data-1 Updated the sample classification for report_mode = "data" reports. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.0. 2. autoclass-c/prog/autoclass.h, io-results.c, io-results-bin.c, struct-class.c, struct-clsf.c, struct-model.c Correct improper pointer casting: fprintf(stdout, "free_model(%d): %d\n", i_model, (int) model); to fprintf(stdout, "free_model(%d): %p\n", i_model, (void *) model); which generates compiler warnings on 64-bit architectures. Change prototype for list_class_storage & list_clsf_storage from int * to void **. 3. autoclass-c/prog/search-control.c, intf-reports.c - Process new params option: free_storage_p. 4. autoclass-c/prog/search-control-2.c - Correct formatted message typos "print print" and "estiamte" in PRINT_INITIAL_REPORT. 5. autoclass-c/prog/intf-reports.c - In PRE_FORMAT_ATTRIBUTES, check for num_terms > 0 prior to calling SORT_MNCN_ATTRIBUTES. 6. autoclass-c/prog/io-read-data.c - In READ_LINE, only return FALSE if no chars have been read -- allows last line with no new-line to be read correctly. 7. autoclass-c/prog/getparams.c - Correct GETPARAMS for INT_LIST: to allow "= 84, 92 " to be read as 84 & 92, rather than 84 & 84. Also allows "n_clsfs = 2 " to be read properly. 8. autoclass-c/prog/autoclass.h, intf-reports.c - Implement "report_mode" parameter. 9. autoclass-c/prog/io-read-data.c - In PROCESS_ATTRIBUTE_DEF, check for incomplete discrete and real attribute definitions. 10. autoclass-c/load-ac - Use "/bin/uname -s" to determine if host is running IRIX (SGI). 11. autoclass-c/prog/struct-class.c - In FREE_TPARM_DS, allow tparm->tppt to be UNKNOWN or IGNORE. If not matched, print advisory msg, not error msg. Do not abort. 12. autoclass-c/prog/autoclass.h, search-basic.c, model-expander-3.c, struct-class.c, struct-clsf.c, predictions.c, & search-control-2.c When creating the weights for a new class, use database->n_data for the appropriate data base, rather than model->database->n_data. In the "prediction" mode, this correctly builds the test database class weights using the size of the test database, rather than that of the training database -- which is pointed to by the model. Functions modified: SET_UP_CLSF, GET_CLASS, CLASS_MERGED_MARGINAL_FN, COPY_CLASS_DS, ADJUST_CLSF_DS_CLASSES, COPY_CLSF_DS, POP_CLASS_DS, BUILD_CLASS_DS, COPY_TO_CLASS_DS, AUTOCLASS_PREDICT, & PRINT_SEARCH_TRY. This corrects a segmentation fault which occured during storage deallocation of prediction runs. ====================================================================== AUTOCLASS C VERSION 2.9 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text, reports-c.text - Added new parameter for both ".s-params" & ".r-params" files: break_on_warnings_p. The default value asks the user whether to continue or not when data definition warnings are found. If specified as false, then AutoClass will continue, despite warnings -- the warning will continue to be output to the terminal and, in the case of the "-search" option, to the log file. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.9. 2. autoclass-c/prog/autoclass.make.solaris.cc - Correct compiler options for SC4.1 cc compiler 3. autoclass-c/prog/intf-reports.c - In FORMAT_DISCRETE_ATTRIBUTE, correct bad test for warn_errs->single_valued_warning. This prevented "discrete" type attributes from being displayed in the influence values report, because a segmentation fault occurred. This problem was introduced in version 2.8. 4. autoclass-c/prog/autoclass.h - In STRUCT I_REAL, type "last_sorted_term_n_att" as int, not float. 5. autoclass-c/prog/intf-reports.c - In SORT_MNCN_ATTRIBUTES, type "last_sorted_term_n_att" as int, not float. In PRE_FORMAT_ATTRIBUTES, revise logic for computing/printing the correlation matrix. Items 4. & 5 correct the problem of the correlation matrix for attribute type multi_multinomial_cn is only printed if it is last in the sorted list of attributes. 6. autoclass-c/prog/struct-data.c - In EXPAND_DATABASE, make cosmetic change to an error message. 7. autoclass-c/prog/intf-reports.c - In XREF_GET_DATA, validity check that .r-params file values of xref_class_report_att_list are in the range 0 - (number of attributes - 1). Prevents segmentation fault. Also in XREF_GET_DATA, correct syntax and location of memory free command. Prevents segmentation fault when processing discrete type data. This problem was introduced in version 2.8. 8. autoclass-c/prog/getparams.h - Increased MAXPARAMS to 40. 9. autoclass-c/prog/search-control.c, intf-reports.c - Added code to parse the new parameter: break_on_warnings_p. 10. autoclass-c/prog/autoclass.h, intf-reports.c - Compute last_clsf_p and pass to XREF_GET_DATA, so that freeing of data will not be done until all clsfs have been processed. To take advantage of the memory reduction, only process one clsf and a time -- this applies only to very large data sets. ====================================================================== AUTOCLASS C VERSION 2.8 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add new search parameter "read_compact_p", which directs AutoClass to read the "results" and "checkpoint" files in either binary format -- ".results-bin"/".chkpt-bin" (read_compact_p = true); or ascii format -- ".results"/".chkpt" (read_compact_p = false). The default is read_compact_p = true. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.8. 2. autoclass-c/prog/io-results.c - In "validate_data_pathname", prefer the user supplied file extension, and only attempt to open ".db2", and then ".db2-bin", if no extension (/name.) or an invalid extension is supplied. Check for presence of '.' in pathname. In "validate_results_pathname" prefer the user supplied file extension, and only attempt to open ".result-bin", and then ".results", if no extension (/name.) or an invalid extension is supplied. Check for presence of '.' in pathname. In "make_and_validate_pathname" check for presence of '.' in pathname. In "get_clsf_seq" simplify the test for "ascii" or "binary" results file format -- also more portable. 3. autoclass-c/prog/search-control.c - In "autoclass_search" use make_and_validate_pathname and search parameter "save_compact_p" to determine file extension of "results" file prior to calling validate_results_pathname. Add "read_compact_p" search parameter for use in reading "results" and "checkpoint" files. Make short search trial printout more portable. 4. autoclass-c/load-ac; autoclass-c/prog/autoclass.make.* Define make files with -I and -L parameters for SunOS 4.1.3 and change naming convention: .sun. => .sunos. or .solaris. Specifically the files are now -- autoclass.make.solaris.cc, autoclass.make.solaris.gcc, autoclass.make.sunos.acc, and autoclass.make.sunos.gcc 5. autoclass-c/prog/io-read-data.c, autoclass.h - In "translate_discrete", allocate space for translations using (strlen( value) + 1), rather than sizeof(shortstr) -- prevents corruption of discrete data translation tables when translations are longer than (SHORT_STRING_LENGTH - 1) = 40 characters. In "get_line_tokens" and "read_from_string", add length checking for "form"; make it and length check for "datum_string" explicit. Increase output string length in "output_created_translations". 6. autoclass-c/prog/io-read-data.c, autoclass.h - Increase from 3000 to 20000 the value of VERY_LONG_STRING_LENGTH to handle very large datum lines. 7. autoclass-c/prog/io-results.c - In VALIDATE_RESULTS_PATHNAME and VALIDATE_DATA_PATHNAME, use binary_file, rather than file, were it is intended. 8. autoclass-c/prog/intf-reports.c, io-read-data.c, autoclass.h - Increase DATA_ALLOC_INCREMENT from 100 to 1000 for reading very large datasets. Add DATA_ALLOC_INCREMENT logic of READ_DATA to XREF_GET_DATA. This will prevent segmentation faults encountered when reading very large .db2 files into the reports processing function of AutoClass. 9. autoclass-c/prog/autoclass.make.solaris.cc, autoclass.make.solaris.gcc, autoclass.make.sunos.acc, and autoclass.make.sunos.gcc - Comment out "depend: $(SRCS)", so that all source files are not compiled even when only one file changes. 10. autoclass-c/prog/intf-reports.c - In FORMAT_DISCRETE_ATTRIBUTE, do not process attributes with warning or error messages -- this prevents segmentation faults. In XREF_GET_DATA, free database allocated memory after it is transferred into report data structures. This reduces the amount of memory required when generating reports for very large data bases, and prevents running out of memory. In all functions calling malloc/realloc for dynamic memory allocation, checks have been added to notify the user if memory is exhausted. 11. autoclass-c/load-ac & autoclass-c/prog/autoclass.make.hp.cc - Port the "make" file for HP-UX operating system using the bundled "cc" compiler. ====================================================================== AUTOCLASS C VERSION 2.7 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Add documentation for search parameter "interactive_p". This will allow AutoClass to be run as a background task, since it will not be querying standard input for the "quit" character. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.7. Add "G_interactive_p". 2. autoclass-c/prog/globals.h - Add "G_interactive_p". 3. autoclass-c/prog/utils.c - In "char_input_test", test for "G_interactive_p" -- if false, do not do the test. 4. autoclass-c/prog/search-control.c - In "autoclass_search", process "interactive_p" from the search parameters file, and output advisory message if set to false. 5. autoclass-c/prog/search-control-2.c - In "print_initial_report", notify user that "typing q to quit" is not functional when "interactive_p" = false. ====================================================================== AUTOCLASS C VERSION 2.6 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.6. 2. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", correct segmentation fault which occurs when more than 50 type = real, subtype = scalar attributes are defined in the ".hd2" & ".model" files. In "log_transform", use "safe_log" to transform values -- prevent "log: SING error" error messages. 3. autoclass-c/prog/model-expander-3.c - In "check_term", since att_info can be realloc'ed in for transformed attributes, reset data_base->att_info for each time thru loop. 5. autoclass-c/prog/utils-math.c - Add "safe_log". 6. autoclass-c/prog/autoclass.h - Add function prototype for "safe_log". 7. autoclass-c/prog/model-multi-normal-cn.c - In "multi_normal_cn_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 8. autoclass-c/prog/model-single-normal-cm.c - In "build_sn_cm_priors" and "single_normal_cm_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 9. autoclass-c/prog/model-single-normal-cn.c - In "build_sn_cn_priors" and "single_normal_cn_model_term_builder" change log calls to safe_log to prevent "log: SING error" error messages. 10. autoclass-c/prog/search-control.c - In "autoclass_search" test for user overriding of search parameters randomize_random_p and/or start_fn_type. If done, ask for confirmation to proceed. ====================================================================== AUTOCLASS C VERSION 2.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/reports-c.text - Minor typographical changes. Added new report generation parameter: order_attributes_by_influence_p. Its default value is true. The file extension of the influence values report has been changed from ".influ-text-1" to ".influ-o-text-1" when order_attributes_by_influence_p = true, and to ".influ-no-text-1" when order_attributes_by_influence_p = false. 2. autoclass-c/doc/interpretation-c.text - Minor changes to the text. 3. autoclass-c/sample/imports-85c.influ-o-text-1 Influence values report has been significantly revised and reformatted. 4. autoclass-c/doc/search-c.text - Corrected definition of fixed_j. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 2.5. 2. autoclass-c/prog/intf-reports.c, utils.c - Formatting change to "format_real_attribute" for multiple multivariate attribute groups. Remove covariance matrix output and reformat the correlation matrix output to fixed decimal point notation. For the influence values report, sort real valued attributes of the same model group by the first significance value, if that group is multi_normal_cn. For discrete attributes: relabel the headers "Prob", rather than "Mean"; and correct the instance value significance computation to be "local_prob * log( local_prob / global_prob)". 3. autoclass-c/prog/autoclass.h - Add #ifndef for MAXPATHLEN. 4. autoclass-c/prog/io-results.c - In "validate_data_pathname", "validate_results_pathname", & "make_and_validate_pathname", only do fclose, if fopen returns non-NULL. 5. autoclass-c/prog/search-control-2.c - Add "pad" argument to "print_search_try". 6. autoclass-c/prog/intf-extensions.c - Formatting change to "get_models_source_info". 7. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.sun.gcc, autoclass.make.sun.acc, autoclass.make.sun.cc, autoclass.make.sgi - (remove autoclass.make.sun) Add SunOS/Solaris C compiler support. 8. autoclass-c/prog/io-results.c, io-read-model.c, io-read-data.c, utils.c, intf-reports.c, getparmas.c - Cast return values of "strlen" to int. 9. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", correct segmentation fault which occurs when more than 25 type = real, subtype = scalar attributes are defined in the ".hd2" & ".model" files. 10. autoclass-c/prog/struct-data.c, io-results.c, io-results-bin.c - Properly initialize att_info array when it exceeds preallocated size. 11. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.linux.gcc, autoclass.make.sun.*, autoclass.make.sgi, fcntlcom-ac.h - Thanks to Andrew Lewycky , added mods for port to Linux version 1.2.10, GCC version 2.5.8, libc version 4.6.25. 12. autoclass-c/prog/model-single-multitnomial.c - In "sm_params_influence_fn", add check for out-of-bounds arguments to the log function to prevent "log domain" errors. ====================================================================== AUTOCLASS C VERSION 2.0 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Added new ".s-params" parameter screen_output_p, whose default value is true. If false, no output is directed to the screen. Assuming log_file_p = true, output will be directed to the log file only. 2. autoclass-c/doc/introduction-c.text, & prediction-c.text - Added "prediction-c.text" to document the prediction mode of AutoClass C, which uses a "training" classification to predict probabilistic class membership for the cases of a "test" data file. Programming: ------------------------------ 1. autoclass-c/prog/io-results.c - In "read_class_DS_s", add debugging info to use with G_clsf_storage_log_p. 2. autoclass-c/prog/struct-class.c - In "build_class_DS", add debugging info to use with G_clsf_storage_log_p. 3. autoclass-c/prog/io-results-bin.c - In "load_class_DS_s," add debugging info to use with G_clsf_storage_log_p. 4. autoclass-c/prog/struct-data.c - In "expand_database", to handle partial databases, read G_s_params_n_data. 5. autoclass-c/prog/globals.c, globlals.h, search-control.c - Add G_s_params_n_data. Change G_ac_version to 2.0 in globals.c. 6. autoclass-c/prog/io-read-data.c, autoclass.h - In "read_data" test on n_data was off by 1. In "output_created_translations" add discrete value occurrance count. In "read_data" move "output_created_translations" call to "output_att_statistics". Add "output_att_statistics" & "output_real_att_statistics". In "create_warn_err_ds", move malloc out of declaration. 7. autoclass-c/prog/prints.c, autoclass.h - Add "sum_vector_f" for debugging. 8. autoclass-c/prog/autoclass.c - Make "main" arg list conform to ANSI C. 9. autoclass-c/prog/model-transforms.c - In "generate_singleton_transform", call "output_real_att_statistics" for each transformed attribute. 10. autoclass-c/prog/utils.c - In "randomize_list" do limit check on list index. 11. autoclass-c/prog/search-control-2.c - In all convergence functions, allocate mallocs in body of function, rather than in local variable declarations. In "get_search_DS", move malloc out of declaration. 12. autoclass-c/prog/intf-reports.c - In "xref_get_data", use n_real_att - 1, rather than i, for index to real_attribute_data; and n_discrete_att - 1 for discrete_attribute_data. Corrects garbage output when .r-params parameter "xref_class_report_att_list" contains mixed real and discrete attributes. In "xref_class_report_attributes", use %g, rather than %f for real data. In "xref_output_line_by_class", handle unknown real values. 13. autoclass-c/prog/io-read-data.c, io-results.c, io-results-bin.c, fcntlcom-ac.h - Convert binary i/o from non-standard (open/close/read/write) to ANSI (fopen/fclose/fread/fwrite). 14. autoclass-c/prog/search-control.c, search-basic.c, search-control-2.c, utils.c, globals.c, globals.h, init.c - Convert from srand/rand to srand48/lrand48 for random number generation. 15. autoclass-c/prog/predictions.c - Add this file to implement the "autoclass -predict ..." capability, which allows cases in a "test" data set to be applied to a "training" data set and have their class membership predicted. Use "prediction_p" and global "G_training_clsf" in "io-read-data.c" to force the "test" database to use the same discrete translations as the "training" database. 16. autoclass-c/load-ac; autoclass-c/prog/autoclass.c, autoclass.make, io-results.c, & autoclass.h - Changes to support item 15. 17. autoclass-c/prog/struct_data.c, struct-clsf.c, & struct-model.c - In "att_ds_equal_p", check for type = dummy. Remove "db_DS_same_source_p" and use "db_same_source_p", instead. 18. autoclass-c/prog/search-control.c - Make FILE * type local variables static, since they are passed to other functions. 19. autoclass-c/prog/autoclass.make - Compile code with "-g", rather than "-ggdb" option. 20. autoclass-c/load-ac & autoclass-c/prog/autoclass.make.sun, autoclass-c/prog/autoclass.make.sgi - Changes to support SGI IRIX version 5.2 with "cc" compiler. 21. autoclass-c/prog/io-read-data.c - In "output_warning_msgs", replaced sizeof(msg) with msg_length in first safe_sprintf call to prevent: "ERROR: vsprintf produced 80 chars (max number is 3) -- called by output_warning_msgs Program received signal SIGABRT, Aborted." ====================================================================== AUTOCLASS C VERSION 1.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass/doc/introduction-c.text, kdd-95.ps, tr-fia-90-12-7-01.ps - Postscript papers are now included as postscript, instead of uuencoded postscript. 2. autoclass/doc/preparation-c.text - Added binary data file input option. Programming: ------------------------------ 1. autoclass-c/prog/autoclass.c - In "main", call "validate_data_pathname" to allow either .db2 ("ascii") or .db2-bin ("binary") data file extensions. The identifying header of a .db2-bin file is - ".db2-bin" - char[8] - 32-bit integer with byte-length of each data case. The data cases follow in binary "float" format -- 32 bit fields. 2. autoclass-c/prog/io-results.c - Add "validate_data_pathname". 3. autoclass-c/prog/autoclass.h - Function prototype definition change/addition. Add DATA_BINARY_FILE_TYPE. Change character array variables of length MAX_PATHNAME_LENGTH (81) to variables of type fxlstr (length 160) to handle very long file pathnames. #define M_PI if not defined -- needed under Solaris. Use pow rather than exp2, since exp2 not available under Solaris gcc 2.6.3. 4. autoclass-c/prog/io-read-data.c - In "read_database" change NULL to FALSE, so that int/int rather than int/ptr comparison is made. Detected by Solaris GNU gcc. "read_database", "read_data" and "read_database_doit" modified to handle binary data files. 5. autoclass-c/prog/globals.h, globals.c - Add G_data_file_format. 6. autoclass-c/prog/search-control.c - In "autoclass-search" do not open/close ".db2" data file. Check for non-NULL "best_clsfs" prior to writing ".results[-bin]" file. 7. autoclass-c/prog/struct-data.c - In "expand_database", call "validate_data_pathname" to allow either .db2 ("ascii") or .db2-bin ("binary") data file extensions. 8. autoclass-c/prog/search-basic.c - Modified "generate_clsf"'s call to "read_database". 9. autoclass-c/prog/utils.c, io-read-data.c, io-results-bin.c & io-results.c - Since the include file is not available in the Solaris GNU gcc implementation, hard code them in "fcntlcom-ac.h". Solaris 2.4 fails open, unless fopen/fclose is done first. 10. autoclass-c/load-ac - Add "fcntlcom-ac.h". Use "clean" make target. 11. autoclass-c/prog/search-control-2.c - In "print_report", do not use NULL as value of delta_ln_p. In "print_final_report", corrected the overwriting of a string array in cases where long pathnames are used. 12. autoclass-c/prog/utils.c, intf-reports.c, search-control.c, & getparams.c - Correct compiler warnings found by Solaris gcc version 2.6.3. 13. autoclass-c/prog/init.c - In "init", use getcwd, rather than getwd for Solaris compatibility. 14. autoclass-c/prog/autoclass.make - Include "clean" target. Add compiler options "-pedantic -Wall". 15. autoclass-c/prog/utils.c - Add "safe_sprintf", and use it in other programs in lieu of "sprintf" to detect string overwrites. Corrected string overwrite which caused abort and the message "Premature end of file reading symbol table". 16. autoclass-c/prog/intf-reports.c - In "search_summary" change search->n to search->n_tries to prevent segment violation when there are duplicates. ====================================================================== autoclass-3.3.6.dfsg.1/sample/0000755000175000017500000000000011667631535014200 5ustar areareautoclass-3.3.6.dfsg.1/sample/imports-85c.model0000644000175000017500000000150211247310756017303 0ustar areare!#; AutoClass C model file -- extension .model !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 ;; 1 or more model definitions ;; model_index model_index 0 4 ignore 0 single_normal_cm 1 18 19 21 22 25 single_normal_cn 9 10 11 12 13 16 20 23 24 single_multinomial default autoclass-3.3.6.dfsg.1/sample/screenc.text0000644000175000017500000014056011247310756016527 0ustar arearengorongoro.wtaylor 511> ./autoclass -search sample/imports-85c.db2 sample/imports-85c.hd2 sample/imports-85c.model sample/imports-85c.s-params > & sample/screenc3.text ### Starting Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params ### Ending Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:08:23 2001 ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (12). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 10:08:23 2001 [j_in=2] ............. [cs-3: cycles 13] best2->2(1) [j_in=3] ........... [cs-3: cycles 11] best3->3(2) [j_in=5] ............................... [cs-3: cycles 31] best5->5(3) [j_in=7] ........................... [cs-3: cycles 27] best7->7(4) [j_in=10] .................................................. [cs-3: cycles 50] 10->10(5) [j_in=15] .................. [cs-3: cycles 18] 15->14(6) [j_in=25] ........ [cs-3: cycles 8] 25->21(7) [j_in=1] .... [cs-3: cycles 4] 1->1(8) [j_in=13] ........................... [cs-3: cycles 27] 13->13(9) [j_in=15] ................... [cs-3: cycles 19] best15->15(10) [j_in=10] ............. [cs-3: cycles 13] 10->10(11) [j_in=6] ........................................... [cs-3: cycles 43] 6->6(12) ENDING SEARCH because max number of tries reached at Mon Jun 11 10:08:26 2001 after a total of 12 tries over 4 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16410.967) N_CLASSES 15 FOUND ON TRY 10 *SAVED* PROBABILITY exp(-16437.588) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16476.523) N_CLASSES 5 FOUND ON TRY 3 PROBABILITY exp(-16512.283) N_CLASSES 10 FOUND ON TRY 5 PROBABILITY exp(-16514.296) N_CLASSES 6 FOUND ON TRY 12 PROBABILITY exp(-16549.938) N_CLASSES 21 FOUND ON TRY 7 PROBABILITY exp(-16583.874) N_CLASSES 10 FOUND ON TRY 11 PROBABILITY exp(-16639.164) N_CLASSES 14 FOUND ON TRY 6 PROBABILITY exp(-16641.226) N_CLASSES 13 FOUND ON TRY 9 PROBABILITY exp(-16844.588) N_CLASSES 3 FOUND ON TRY 2 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 10 num_cycles 19 max_cycles 200 convergent try 4 num_cycles 27 max_cycles 200 convergent try 3 num_cycles 31 max_cycles 200 convergent try 5 num_cycles 50 max_cycles 200 convergent try 12 num_cycles 43 max_cycles 200 convergent try 7 num_cycles 8 max_cycles 200 convergent try 11 num_cycles 13 max_cycles 200 convergent try 6 num_cycles 18 max_cycles 200 convergent try 9 num_cycles 27 max_cycles 200 convergent try 2 num_cycles 11 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:08:26 2001 ngorongoro.wtaylor 512> ./autoclass -search sample/imports-85c.db2 sample/imports-85c.hd2 sample/imports-85c.model sample/imports-85c.s-params > & sample/screenc3.text ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ### Starting Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params ### Ending Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:08:49 2001 ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 12 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search ADVISORY: start_j_list=(2,3,5,7,10,15,25) has been overridden by () from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (22). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log RESTARTING SEARCH at Mon Jun 11 10:08:49 2001 [j_in=7] ........................................ [cs-3: cycles 40] 7->7(13) [j_in=9] ............................. [cs-3: cycles 29] 9->9(14) [j_in=7] ................................................................................................ [cs-3: cycles 96] 7->7(15) [j_in=4] ............................. [cs-3: cycles 29] 4->4(16) [j_in=3] ................................. [cs-3: cycles 33] 3->3(17) [j_in=8] ...................................... [cs-3: cycles 38] 8->8(18) [j_in=9] ............................... [cs-3: cycles 31] best9->9(19) [j_in=12] ................ [cs-3: cycles 16] 12->11(20) [j_in=11] ........... [cs-3: cycles 11] best11->10(21) [j_in=8] .............................. [cs-3: cycles 30] 8->8(22) ENDING SEARCH because max number of tries reached at Mon Jun 11 10:08:53 2001 after a total of 22 tries over 9 seconds This invocation of "autoclass -search" took 4 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16347.467) N_CLASSES 10 FOUND ON TRY 21 *SAVED* PROBABILITY exp(-16376.557) N_CLASSES 9 FOUND ON TRY 19 *SAVED* PROBABILITY exp(-16384.448) N_CLASSES 8 FOUND ON TRY 22 PROBABILITY exp(-16410.967) N_CLASSES 15 FOUND ON TRY 10 PROBABILITY exp(-16411.990) N_CLASSES 11 FOUND ON TRY 20 PROBABILITY exp(-16437.588) N_CLASSES 7 FOUND ON TRY 4 PROBABILITY exp(-16467.782) N_CLASSES 9 FOUND ON TRY 14 PROBABILITY exp(-16476.523) N_CLASSES 5 FOUND ON TRY 3 PROBABILITY exp(-16494.730) N_CLASSES 8 FOUND ON TRY 18 PROBABILITY exp(-16505.551) N_CLASSES 7 FOUND ON TRY 15 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 21 num_cycles 11 max_cycles 200 convergent try 19 num_cycles 31 max_cycles 200 convergent try 22 num_cycles 30 max_cycles 200 convergent try 10 num_cycles 19 max_cycles 200 convergent try 20 num_cycles 16 max_cycles 200 convergent try 4 num_cycles 27 max_cycles 200 convergent try 14 num_cycles 29 max_cycles 200 convergent try 3 num_cycles 31 max_cycles 200 convergent try 18 num_cycles 38 max_cycles 200 convergent try 15 num_cycles 96 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:08:53 2001 ngorongoro.wtaylor 513> ./autoclass -search sample/imports-85c.db2 sample/imports-85c.hd2 sample/imports-85c.model sample/imports-85c.s-params > & sample/screenc3.text ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ### Starting Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params ### Ending Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:13:51 2001 ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 22 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search ADVISORY: start_j_list=(2,3,5,7,10,15,25) has been overridden by () from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until Mon Jun 11 10:15:51 2001. 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log RESTARTING SEARCH at Mon Jun 11 10:13:51 2001 [j_in=9] ............... [cs-3: cycles 15] 9->9(23) [j_in=11] ........................ [cs-3: cycles 24] 11->10(24) [j_in=12] ............ [cs-3: cycles 12] 12->12(25) [j_in=17] ............ [cs-3: cycles 12] 17->16(26) [j_in=7] ................ [cs-3: cycles 16] 7->7(27) [j_in=13] .......... [cs-3: cycles 10] 13->13(28) [j_in=13] .......... [cs-3: cycles 10] 13->13(29) [j_in=13] ............. [cs-3: cycles 13] 13->13(30) [j_in=8] ............... [cs-3: cycles 15] 8->8(31) [j_in=10] ......................... [cs-3: cycles 25] 10->10(32) [j_in=18] ............................ [cs-3: cycles 28] best18->17(33) [j_in=8] ................ [cs-3: cycles 16] 8->8(34) [j_in=12] .................. [cs-3: cycles 18] 12->12(35) [j_in=15] ...................... [cs-3: cycles 22] 15->15(36) [j_in=14] ............. [cs-3: cycles 13] 14->13(37) [j_in=15] .............. [cs-3: cycles 14] 15->14(38) [j_in=15] .............. [cs-3: cycles 14] dup15->14(39) [j_in=15] .............. [cs-3: cycles 14] dup15->14(40) [j_in=15] ................................... [cs-3: cycles 35] 15->14(41) [j_in=16] ................................... [cs-3: cycles 35] dup16->14(42) [j_in=11] ...................................... [cs-3: cycles 38] 11->10(43) [j_in=8] ................ [cs-3: cycles 16] 8->8(44) [j_in=15] .............. [cs-3: cycles 14] 15->13(45) [j_in=10] .............................. [cs-3: cycles 30] 10->10(46) [j_in=17] ........... [cs-3: cycles 11] 17->17(47) [j_in=13] ....................... [cs-3: cycles 23] 13->13(48) [j_in=14] ....................... [cs-3: cycles 23] 14->14(49) [j_in=12] ............................... [cs-3: cycles 31] 12->12(50) [j_in=11] ........................ [cs-3: cycles 24] 11->11(51) [j_in=19] .......................... [cs-3: cycles 26] 19->18(52) [j_in=18] .............. [cs-3: cycles 14] 18->17(53) [j_in=15] ............................... [cs-3: cycles 31] 15->15(54) [j_in=20] ................ [cs-3: cycles 16] 20->20(55) [j_in=15] .................. [cs-3: cycles 18] 15->15(56) [j_in=15] .................. [cs-3: cycles 18] dup15->15(57) [j_in=15] .............. [cs-3: cycles 14] 15->14(58) [j_in=20] ............... [cs-3: cycles 15] 20->18(59) [j_in=14] ............... [cs-3: cycles 15] 14->13(60) [j_in=14] ........................................... [cs-3: cycles 43] 14->14(61) [j_in=15] .......................... [cs-3: cycles 26] 15->14(62) [j_in=17] ............. [cs-3: cycles 13] 17->16(63) [j_in=14] ................ [cs-3: cycles 16] 14->14(64) [j_in=16] .............. [cs-3: cycles 14] 16->16(65) [j_in=22] ........... [cs-3: cycles 11] 22->20(66) [j_in=20] ............... [cs-3: cycles 15] 20->19(67) [j_in=14] ................. [cs-3: cycles 17] 14->14(68) [j_in=13] ................. [cs-3: cycles 17] 13->13(69) [j_in=15] ....... [cs-3: cycles 7] best15->15(70) [j_in=14] ...................................... [cs-3: cycles 38] 14->14(71) [j_in=14] ...................................... [cs-3: cycles 38] 14->14(72) [j_in=14] ................ [cs-3: cycles 16] best14->13(73) [j_in=13] ................... [cs-3: cycles 19] 13->12(74) [j_in=17] ................ [cs-3: cycles 16] best17->15(75) [j_in=13] ...................................... [cs-3: cycles 38] best13->12(76) [j_in=17] ........... [cs-3: cycles 11] 17->16(77) [j_in=13] ...................................... [cs-3: cycles 38] dup13->12(78) [j_in=17] ............. [cs-3: cycles 13] 17->15(79) [j_in=15] ............. [cs-3: cycles 13] 15->14(80) [j_in=19] ............. [cs-3: cycles 13] 19->17(81) [j_in=17] ......... [cs-3: cycles 9] 17->15(82) [j_in=15] ................ [cs-3: cycles 16] 15->14(83) [j_in=14] ........... [cs-3: cycles 11] 14->14(84) [j_in=17] ......... [cs-3: cycles 9] dup17->15(85) [j_in=15] ....... [cs-3: cycles 7] 15->15(86) [j_in=15] ....... [cs-3: cycles 7] 15->15(87) [j_in=15] ....... [cs-3: cycles 7] 15->15(88) [j_in=15] ....... [cs-3: cycles 7] 15->15(89) [j_in=15] ....... [cs-3: cycles 7] 15->15(90) [j_in=15] ....... [cs-3: cycles 7] 15->15(91) [j_in=15] ....... [cs-3: cycles 7] 15->15(92) [j_in=15] ............. [cs-3: cycles 13] 15->15(93) [j_in=11] ...................... [cs-3: cycles 22] 11->11(94) [j_in=15] ............. [cs-3: cycles 13] 15->15(95) [j_in=11] ...................... [cs-3: cycles 22] 11->11(96) [j_in=15] ............. [cs-3: cycles 13] 15->15(97) [j_in=14] .................................. [cs-3: cycles 34] 14->14(98) [j_in=16] ............. [cs-3: cycles 13] 16->16(99) [j_in=15] ......... [cs-3: cycles 9] 15->15(100) [j_in=15] ......... [cs-3: cycles 9] 15->15(101) [j_in=15] ......... [cs-3: cycles 9] 15->15(102) [j_in=15] ......... [cs-3: cycles 9] 15->15(103) [j_in=15] ......... [cs-3: cycles 9] 15->15(104) [j_in=15] ....................... [cs-3: cycles 23] 15->15(105) [j_in=13] ................... [cs-3: cycles 19] 13->13(106) [j_in=11] .................. [cs-3: cycles 18] 11->11(107) [j_in=17] ........................... [cs-3: cycles 27] 17->17(108) [j_in=15] ................. [cs-3: cycles 17] 15->15(109) [j_in=15] ................. [cs-3: cycles 17] 15->15(110) [j_in=15] ............ [cs-3: cycles 12] 15->15(111) [j_in=18] ................ [cs-3: cycles 16] 18->18(112) [j_in=15] ............ [cs-3: cycles 12] 15->15(113) [j_in=18] ................ [cs-3: cycles 16] 18->18(114) [j_in=15] ................. [cs-3: cycles 17] 15->14(115) [j_in=15] ................. [cs-3: cycles 17] 15->14(116) [j_in=15] ................. [cs-3: cycles 17] 15->14(117) [j_in=15] ......................... [cs-3: cycles 25] 15->15(118) [j_in=15] ......................... [cs-3: cycles 25] 15->15(119) [j_in=15] ......................... [cs-3: cycles 25] 15->15(120) [j_in=15] ............ [cs-3: cycles 12] 15->15(121) [j_in=17] ............ [cs-3: cycles 12] 17->16(122) [j_in=16] ............ [cs-3: cycles 12] 16->16(123) [j_in=16] ............ [cs-3: cycles 12] 16->16(124) [j_in=15] ........... [cs-3: cycles 11] 15->15(125) [j_in=14] ............... [cs-3: cycles 15] 14->14(126) [j_in=15] ........... [cs-3: cycles 11] 15->15(127) [j_in=14] ............... [cs-3: cycles 15] 14->14(128) ---------------- NEW BEST CLASSIFICATION FOUND on try 76 ------------- It has 12 CLASSES with WEIGHTS 30 24 24 22 21 20 17 13 12 9 7 6 PROBABILITY of both the data and the classification = exp(-16211.601) (Also found 75 other better than last report.) ----------- SEARCH STATUS as of Mon Jun 11 10:14:22 2001 ----------- It just took 31 seconds since beginning. Estimate < 40 seconds to find a classification exp(22.5) [= 5.9e+09] times more probable. Estimate >> 7 seconds to find the very best classification, which may be exp(0.0) to exp(3800.8) times more probable. Have seen 122 of the estimated > 144 possible classifications (based on 6 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16460.8, S(igma) 90.0 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [12.5, 14.6, 16.9] Overhead time is 7.3 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=16] ............................. [cs-3: cycles 29] 16->16(129) [j_in=17] ............................... [cs-3: cycles 31] 17->17(130) [j_in=14] ............................. [cs-3: cycles 29] 14->14(131) [j_in=21] .............. [cs-3: cycles 14] 21->21(132) [j_in=17] ............. [cs-3: cycles 13] 17->17(133) [j_in=15] ................... [cs-3: cycles 19] 15->15(134) [j_in=16] .................... [cs-3: cycles 20] 16->16(135) [j_in=14] ................... [cs-3: cycles 19] 14->14(136) [j_in=16] ...................... [cs-3: cycles 22] 16->16(137) [j_in=19] ................. [cs-3: cycles 17] 19->19(138) [j_in=14] ........... [cs-3: cycles 11] 14->14(139) [j_in=15] ............ [cs-3: cycles 12] 15->15(140) [j_in=14] ........... [cs-3: cycles 11] dup14->14(141) [j_in=15] ............ [cs-3: cycles 12] dup15->15(142) [j_in=14] ........... [cs-3: cycles 11] dup14->14(143) [j_in=15] ............................. [cs-3: cycles 29] 15->15(144) [j_in=18] ............ [cs-3: cycles 12] 18->18(145) [j_in=13] ........... [cs-3: cycles 11] 13->13(146) [j_in=12] ............. [cs-3: cycles 13] 12->11(147) [j_in=17] ................. [cs-3: cycles 17] 17->16(148) [j_in=15] .................... [cs-3: cycles 20] 15->14(149) [j_in=16] .................. [cs-3: cycles 18] 16->15(150) [j_in=20] ............. [cs-3: cycles 13] 20->17(151) [j_in=16] .................... [cs-3: cycles 20] 16->15(152) [j_in=16] .................... [cs-3: cycles 20] dup16->15(153) [j_in=16] .............. [cs-3: cycles 14] 16->15(154) [j_in=17] .............. [cs-3: cycles 14] 17->16(155) [j_in=15] .............. [cs-3: cycles 14] 15->14(156) [j_in=15] .............. [cs-3: cycles 14] 15->14(157) [j_in=15] ......... [cs-3: cycles 9] 15->15(158) [j_in=14] .................... [cs-3: cycles 20] 14->14(159) [j_in=16] ......... [cs-3: cycles 9] 16->16(160) [j_in=15] ......... [cs-3: cycles 9] 15->15(161) [j_in=14] .......................... [cs-3: cycles 26] 14->12(162) [j_in=15] ...................... [cs-3: cycles 22] 15->13(163) [j_in=12] ................... [cs-3: cycles 19] 12->11(164) [j_in=14] ......... [cs-3: cycles 9] 14->14(165) [j_in=16] ............... [cs-3: cycles 15] 16->16(166) [j_in=15] .............. [cs-3: cycles 14] 15->15(167) [j_in=18] .............. [cs-3: cycles 14] 18->18(168) [j_in=13] .............. [cs-3: cycles 14] 13->12(169) [j_in=13] .............. [cs-3: cycles 14] 13->12(170) [j_in=13] .............. [cs-3: cycles 14] 13->12(171) [j_in=13] .............. [cs-3: cycles 14] 13->12(172) [j_in=13] .............. [cs-3: cycles 14] 13->12(173) [j_in=13] ....................... [cs-3: cycles 23] 13->13(174) [j_in=13] ....................... [cs-3: cycles 23] 13->13(175) [j_in=13] ....................... [cs-3: cycles 23] 13->13(176) [j_in=13] ............................................... [cs-3: cycles 47] 13->12(177) [j_in=15] ................... [cs-3: cycles 19] 15->14(178) [j_in=17] .............. [cs-3: cycles 14] 17->16(179) [j_in=16] ................. [cs-3: cycles 17] 16->15(180) [j_in=14] ...................... [cs-3: cycles 22] 14->13(181) [j_in=16] ............ [cs-3: cycles 12] 16->16(182) [j_in=13] ......................................... [cs-3: cycles 41] 13->13(183) [j_in=15] ............ [cs-3: cycles 12] 15->15(184) [j_in=14] ............ [cs-3: cycles 12] 14->14(185) [j_in=15] .................... [cs-3: cycles 20] 15->15(186) [j_in=16] ..................... [cs-3: cycles 21] 16->16(187) [j_in=16] ............. [cs-3: cycles 13] 16->16(188) [j_in=15] ............. [cs-3: cycles 13] 15->15(189) [j_in=13] ........................ [cs-3: cycles 24] 13->13(190) [j_in=15] ......... [cs-3: cycles 9] 15->15(191) [j_in=17] ......... [cs-3: cycles 9] 17->17(192) [j_in=16] ......... [cs-3: cycles 9] 16->16(193) [j_in=15] ......... [cs-3: cycles 9] 15->15(194) [j_in=17] ......... [cs-3: cycles 9] 17->17(195) [j_in=16] ......... [cs-3: cycles 9] dup16->16(196) [j_in=15] .................... [cs-3: cycles 20] 15->15(197) [j_in=15] .................... [cs-3: cycles 20] 15->15(198) [j_in=15] .................... [cs-3: cycles 20] 15->15(199) [j_in=15] ........................................... [cs-3: cycles 43] 15->15(200) [j_in=15] ........................................... [cs-3: cycles 43] 15->15(201) [j_in=15] .................... [cs-3: cycles 20] 15->15(202) [j_in=17] ........... [cs-3: cycles 11] 17->16(203) [j_in=15] ............. [cs-3: cycles 13] 15->15(204) [j_in=17] ............. [cs-3: cycles 13] 17->17(205) [j_in=16] ............. [cs-3: cycles 13] 16->16(206) [j_in=16] ............. [cs-3: cycles 13] 16->16(207) [j_in=16] ............ [cs-3: cycles 12] 16->16(208) [j_in=15] ............ [cs-3: cycles 12] 15->15(209) [j_in=15] ............ [cs-3: cycles 12] dup15->15(210) [j_in=15] ............ [cs-3: cycles 12] dup15->15(211) [j_in=15] ............... [cs-3: cycles 15] 15->13(212) [j_in=14] ............... [cs-3: cycles 15] 14->14(213) [j_in=14] ............... [cs-3: cycles 15] 14->14(214) [j_in=14] ............... [cs-3: cycles 15] 14->14(215) [j_in=14] ..................... [cs-3: cycles 21] 14->13(216) [j_in=16] .......................... [cs-3: cycles 26] 16->15(217) [j_in=15] .......................... [cs-3: cycles 26] 15->14(218) [j_in=15] ................................. [cs-3: cycles 33] 15->15(219) [j_in=16] .............. [cs-3: cycles 14] 16->16(220) [j_in=14] .................. [cs-3: cycles 18] 14->14(221) [j_in=13] ........................ [cs-3: cycles 24] 13->13(222) [j_in=16] ............. [cs-3: cycles 13] 16->16(223) [j_in=15] ............ [cs-3: cycles 12] 15->15(224) [j_in=14] .............. [cs-3: cycles 14] 14->14(225) [j_in=13] ............... [cs-3: cycles 15] 13->13(226) [j_in=17] ............. [cs-3: cycles 13] 17->16(227) [j_in=16] ............. [cs-3: cycles 13] 16->16(228) [j_in=15] ....................... [cs-3: cycles 23] 15->15(229) [j_in=15] ....................... [cs-3: cycles 23] 15->15(230) [j_in=15] .............. [cs-3: cycles 14] 15->14(231) [j_in=17] ..................... [cs-3: cycles 21] 17->16(232) [j_in=12] ................... [cs-3: cycles 19] 12->12(233) [j_in=15] .............. [cs-3: cycles 14] 15->14(234) [j_in=17] .................... [cs-3: cycles 20] 17->16(235) [j_in=14] .............. [cs-3: cycles 14] 14->14(236) [j_in=17] .................... [cs-3: cycles 20] 17->16(237) [j_in=14] .......... [cs-3: cycles 10] 14->14(238) [j_in=15] ............ [cs-3: cycles 12] 15->15(239) [j_in=16] ............. [cs-3: cycles 13] 16->16(240) [j_in=15] ............ [cs-3: cycles 12] 15->15(241) [j_in=16] ............................. [cs-3: cycles 29] 16->16(242) [j_in=16] ............................. [cs-3: cycles 29] 16->16(243) [j_in=16] ......................... [cs-3: cycles 25] 16->16(244) [j_in=14] ................ [cs-3: cycles 16] 14->14(245) [j_in=19] ........................ [cs-3: cycles 24] 19->19(246) [j_in=15] ........................ [cs-3: cycles 24] 15->14(247) [j_in=15] ........................ [cs-3: cycles 24] 15->14(248) [j_in=15] ..................... [cs-3: cycles 21] 15->14(249) [j_in=16] .................... [cs-3: cycles 20] 16->15(250) [j_in=15] ..................... [cs-3: cycles 21] 15->14(251) [j_in=16] ......... [cs-3: cycles 9] 16->16(252) [j_in=17] .......... [cs-3: cycles 10] 17->17(253) [j_in=19] ............... [cs-3: cycles 15] 19->19(254) [j_in=16] ......... [cs-3: cycles 9] 16->16(255) [j_in=17] ....................... [cs-3: cycles 23] 17->16(256) [j_in=19] ............... [cs-3: cycles 15] 19->18(257) [j_in=16] ...................... [cs-3: cycles 22] 16->15(258) [j_in=18] ........ [cs-3: cycles 8] 18->16(259) [j_in=18] ........ [cs-3: cycles 8] 18->16(260) [j_in=18] ........ [cs-3: cycles 8] 18->16(261) [j_in=18] ........ [cs-3: cycles 8] 18->16(262) [j_in=18] ........ [cs-3: cycles 8] 18->16(263) [j_in=18] ........... [cs-3: cycles 11] 18->18(264) [j_in=16] ...................... [cs-3: cycles 22] 16->16(265) [j_in=19] .................... [cs-3: cycles 20] 19->19(266) [j_in=15] ............................... [cs-3: cycles 31] 15->15(267) [j_in=16] ............. [cs-3: cycles 13] 16->16(268) [j_in=17] ................... [cs-3: cycles 19] 17->17(269) [j_in=14] .................... [cs-3: cycles 20] 14->14(270) [j_in=17] ................................................ [cs-3: cycles 48] 17->17(271) [j_in=18] ......... [cs-3: cycles 9] 18->18(272) [j_in=18] ......... [cs-3: cycles 9] 18->18(273) [j_in=18] ......... [cs-3: cycles 9] 18->18(274) [j_in=18] ............. [cs-3: cycles 13] 18->17(275) [j_in=16] ..................... [cs-3: cycles 21] 16->16(276) [j_in=18] ............. [cs-3: cycles 13] 18->17(277) [j_in=16] ..................... [cs-3: cycles 21] 16->16(278) [j_in=18] .................... [cs-3: cycles 20] 18->18(279) [j_in=14] ............ [cs-3: cycles 12] 14->14(280) [j_in=15] ................... [cs-3: cycles 19] 15->15(281) [j_in=14] .............................. [cs-3: cycles 30] 14->14(282) [j_in=15] .............................. [cs-3: cycles 30] 15->15(283) [j_in=16] .................. [cs-3: cycles 18] 16->16(284) [j_in=16] .................. [cs-3: cycles 18] 16->16(285) [j_in=16] .................. [cs-3: cycles 18] 16->16(286) [j_in=16] ................. [cs-3: cycles 17] 16->16(287) [j_in=15] ................. [cs-3: cycles 17] 15->15(288) [j_in=17] .............. [cs-3: cycles 14] 17->17(289) [j_in=12] ................. [cs-3: cycles 17] 12->11(290) [j_in=15] ............. [cs-3: cycles 13] 15->14(291) [j_in=15] ............. [cs-3: cycles 13] 15->14(292) [j_in=15] ............. [cs-3: cycles 13] 15->14(293) [j_in=15] ........................... [cs-3: cycles 27] 15->15(294) [j_in=16] ........................... [cs-3: cycles 27] 16->15(295) [j_in=15] ......... [cs-3: cycles 9] 15->15(296) [j_in=18] ..................... [cs-3: cycles 21] 18->18(297) [j_in=14] ............ [cs-3: cycles 12] 14->14(298) [j_in=12] ............. [cs-3: cycles 13] 12->12(299) [j_in=17] ............. [cs-3: cycles 13] 17->17(300) [j_in=19] ............... [cs-3: cycles 15] 19->19(301) [j_in=14] ............................ [cs-3: cycles 28] 14->14(302) [j_in=14] .................. [cs-3: cycles 18] 14->14(303) [j_in=15] ...................... [cs-3: cycles 22] 15->15(304) [j_in=16] ...................... [cs-3: cycles 22] 16->16(305) [j_in=15] ............... [cs-3: cycles 15] 15->15(306) [j_in=18] .......................... [cs-3: cycles 26] 18->17(307) [j_in=16] ......... [cs-3: cycles 9] 16->16(308) [j_in=19] ......................................... [cs-3: cycles 41] 19->18(309) [j_in=19] ......................................... [cs-3: cycles 41] 19->18(310) [j_in=19] ............. [cs-3: cycles 13] 19->18(311) [j_in=17] ......... [cs-3: cycles 9] 17->17(312) [j_in=16] ...................... [cs-3: cycles 22] 16->16(313) [j_in=16] ...................... [cs-3: cycles 22] 16->16(314) [j_in=16] ...................... [cs-3: cycles 22] 16->16(315) [j_in=16] ................. [cs-3: cycles 17] 16->15(316) [j_in=16] ................. [cs-3: cycles 17] 16->15(317) [j_in=16] ................ [cs-3: cycles 16] 16->16(318) [j_in=16] ................ [cs-3: cycles 16] 16->16(319) [j_in=16] ................ [cs-3: cycles 16] 16->16(320) [j_in=16] ................ [cs-3: cycles 16] 16->16(321) [j_in=16] .............. [cs-3: cycles 14] 16->15(322) [j_in=16] .............. [cs-3: cycles 14] 16->15(323) [j_in=16] .............. [cs-3: cycles 14] 16->15(324) [j_in=16] .............. [cs-3: cycles 14] 16->15(325) [j_in=16] .......... [cs-3: cycles 10] 16->15(326) [j_in=16] .......... [cs-3: cycles 10] 16->15(327) [j_in=16] .......... [cs-3: cycles 10] 16->15(328) [j_in=16] .......... [cs-3: cycles 10] 16->15(329) [j_in=16] .............. [cs-3: cycles 14] 16->16(330) [j_in=16] .............. [cs-3: cycles 14] 16->16(331) [j_in=16] .............. [cs-3: cycles 14] 16->16(332) [j_in=16] .............. [cs-3: cycles 14] 16->16(333) [j_in=16] ....................... [cs-3: cycles 23] 16->14(334) [j_in=16] ....................... [cs-3: cycles 23] 16->14(335) [j_in=16] ....................... [cs-3: cycles 23] 16->14(336) [j_in=16] ................................. [cs-3: cycles 33] 16->14(337) [j_in=13] ..................... [cs-3: cycles 21] 13->12(338) [j_in=14] ................................ [cs-3: cycles 32] 14->14(339) [j_in=18] ............. [cs-3: cycles 13] 18->18(340) [j_in=18] ............. [cs-3: cycles 13] 18->18(341) [j_in=18] ............ [cs-3: cycles 12] 18->18(342) [j_in=17] .................. [cs-3: cycles 18] 17->17(343) [j_in=16] .................. [cs-3: cycles 18] 16->16(344) [j_in=13] ............ [cs-3: cycles 12] 13->13(345) [j_in=13] ............ [cs-3: cycles 12] 13->13(346) [j_in=13] ............ [cs-3: cycles 12] 13->13(347) [j_in=13] ............ [cs-3: cycles 12] 13->13(348) [j_in=13] ............ [cs-3: cycles 12] 13->13(349) [j_in=13] ....................... [cs-3: cycles 23] 13->13(350) [j_in=14] ................ [cs-3: cycles 16] 14->13(351) [j_in=14] ................ [cs-3: cycles 16] dup14->13(352) [j_in=14] ................ [cs-3: cycles 16] dup14->13(353) [j_in=14] ............................ [cs-3: cycles 28] 14->14(354) [j_in=17] ................. [cs-3: cycles 17] 17->17(355) [j_in=15] .................... [cs-3: cycles 20] 15->15(356) [j_in=15] .................... [cs-3: cycles 20] 15->15(357) [j_in=15] .................... [cs-3: cycles 20] 15->15(358) [j_in=15] ............. [cs-3: cycles 13] 15->15(359) [j_in=16] ................. [cs-3: cycles 17] 16->16(360) [j_in=17] ............. [cs-3: cycles 13] 17->17(361) [j_in=16] ................. [cs-3: cycles 17] 16->16(362) [j_in=17] ................ [cs-3: cycles 16] 17->17(363) [j_in=15] ............ [cs-3: cycles 12] 15->15(364) [j_in=16] .................. [cs-3: cycles 18] 16->16(365) [j_in=16] ............... [cs-3: cycles 15] 16->15(366) [j_in=14] ............... [cs-3: cycles 15] 14->13(367) [j_in=16] ............... [cs-3: cycles 15] 16->15(368) [j_in=14] ............... [cs-3: cycles 15] 14->13(369) [j_in=16] .............................................. [cs-3: cycles 46] 16->15(370) [j_in=16] .............................................. [cs-3: cycles 46] 16->15(371) [j_in=16] .......... [cs-3: cycles 10] 16->15(372) [j_in=17] .......... [cs-3: cycles 10] 17->16(373) [j_in=14] .............. [cs-3: cycles 14] 14->13(374) [j_in=16] ........... [cs-3: cycles 11] 16->16(375) [j_in=16] ........... [cs-3: cycles 11] 16->16(376) [j_in=16] ........... [cs-3: cycles 11] 16->16(377) [j_in=16] ........... [cs-3: cycles 11] 16->16(378) [j_in=16] ............. [cs-3: cycles 13] 16->15(379) [j_in=19] ............ [cs-3: cycles 12] 19->18(380) [j_in=14] ............. [cs-3: cycles 13] 14->13(381) [j_in=13] .................... [cs-3: cycles 20] 13->12(382) [j_in=15] ......... [cs-3: cycles 9] 15->15(383) [j_in=17] ......... [cs-3: cycles 9] 17->17(384) [j_in=15] ......... [cs-3: cycles 9] 15->15(385) [j_in=17] ......... [cs-3: cycles 9] 17->17(386) [j_in=15] ......... [cs-3: cycles 9] 15->15(387) [j_in=17] .................. [cs-3: cycles 18] 17->16(388) [j_in=14] ........... [cs-3: cycles 11] 14->14(389) [j_in=18] .................. [cs-3: cycles 18] 18->17(390) [j_in=14] ........... [cs-3: cycles 11] 14->14(391) [j_in=18] ............... [cs-3: cycles 15] 18->16(392) [j_in=14] ............ [cs-3: cycles 12] 14->14(393) [j_in=16] ................ [cs-3: cycles 16] 16->15(394) [j_in=19] ......... [cs-3: cycles 9] 19->17(395) [j_in=14] ............. [cs-3: cycles 13] 14->14(396) [j_in=19] ......................... [cs-3: cycles 25] 19->19(397) [j_in=16] ............. [cs-3: cycles 13] 16->16(398) [j_in=14] ...................... [cs-3: cycles 22] 14->14(399) [j_in=17] ........... [cs-3: cycles 11] 17->17(400) [j_in=15] ........... [cs-3: cycles 11] 15->15(401) [j_in=13] ...................... [cs-3: cycles 22] 13->13(402) [j_in=17] ..................... [cs-3: cycles 21] 17->17(403) [j_in=16] .................. [cs-3: cycles 18] 16->16(404) [j_in=13] ....................... [cs-3: cycles 23] 13->12(405) [j_in=15] ................ [cs-3: cycles 16] 15->14(406) [j_in=14] ............ [cs-3: cycles 12] 14->13(407) [j_in=16] ............ [cs-3: cycles 12] 16->15(408) [j_in=14] ...................... [cs-3: cycles 22] 14->14(409) [j_in=15] .............. [cs-3: cycles 14] 15->15(410) [j_in=19] .............. [cs-3: cycles 14] 19->19(411) [j_in=14] ...................... [cs-3: cycles 22] 14->14(412) [j_in=15] ........... [cs-3: cycles 11] 15->15(413) [j_in=18] ..................... [cs-3: cycles 21] 18->18(414) [j_in=13] ........... [cs-3: cycles 11] 13->13(415) [j_in=16] ............... [cs-3: cycles 15] 16->15(416) [j_in=15] ............... [cs-3: cycles 15] 15->15(417) [j_in=17] ............... [cs-3: cycles 15] 17->16(418) [j_in=13] ............................................ [cs-3: cycles 44] 13->13(419) [j_in=16] .......................... [cs-3: cycles 26] 16->16(420) ENDING SEARCH because max duration has expired at Mon Jun 11 10:15:52 2001 after a total of 420 tries over 2 minutes 11 seconds This invocation of "autoclass -search" took 2 minutes 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16211.601) N_CLASSES 12 FOUND ON TRY 76 DUPS 1 *SAVED* PROBABILITY exp(-16216.971) N_CLASSES 13 FOUND ON TRY 351 DUPS 2 *SAVED* PROBABILITY exp(-16241.062) N_CLASSES 14 FOUND ON TRY 245 PROBABILITY exp(-16245.009) N_CLASSES 16 FOUND ON TRY 135 PROBABILITY exp(-16247.711) N_CLASSES 16 FOUND ON TRY 193 DUPS 1 PROBABILITY exp(-16247.786) N_CLASSES 13 FOUND ON TRY 183 PROBABILITY exp(-16248.708) N_CLASSES 16 FOUND ON TRY 208 PROBABILITY exp(-16254.312) N_CLASSES 15 FOUND ON TRY 75 PROBABILITY exp(-16254.821) N_CLASSES 17 FOUND ON TRY 133 PROBABILITY exp(-16255.435) N_CLASSES 15 FOUND ON TRY 258 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 76 num_cycles 38 max_cycles 200 convergent try 351 num_cycles 16 max_cycles 200 convergent try 245 num_cycles 16 max_cycles 200 convergent try 135 num_cycles 20 max_cycles 200 convergent try 193 num_cycles 9 max_cycles 200 convergent try 183 num_cycles 41 max_cycles 200 convergent try 208 num_cycles 12 max_cycles 200 convergent try 75 num_cycles 16 max_cycles 200 convergent try 133 num_cycles 13 max_cycles 200 convergent try 258 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:15:52 2001 ngorongoro.wtaylor 514> ./autoclass -reports sample/imports-85c.results-bin sample/imports-85c.search sample/imports-85c.r-params ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ### Starting Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.r-params ### Ending Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.r-params AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:29:32 2001 ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 405 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:29:32 2001 ngorongoro.wtaylor 515> ./autoclass -reports sample/imports-85c.results-bin sample/imports-85c.search sample/imports-85c.r-params ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ### Starting Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.r-params ### Ending Check of /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.r-params AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:30:46 2001 ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 405 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:30:46 2001 autoclass-3.3.6.dfsg.1/sample/imports-85c.influ-o-text-10000644000175000017500000046741611247310756020720 0ustar areare I N F L U E N C E V A L U E S R E P O R T order attributes by influence values = true ============================================= AutoClass CLASSIFICATION for the 205 cases in /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 with log-A (approximate marginal likelihood) = -16230.401 from classification results file /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin and using models /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ _____________________________________________________________________________ SEARCH SUMMARY 250 tries over 1 minute 22 seconds _______________ SUMMARY OF 10 BEST RESULTS _________________________ ## PROBABILITY exp(-16230.401) N_CLASSES 15 FOUND ON TRY 213 *SAVED* -1 PROBABILITY exp(-16245.405) N_CLASSES 16 FOUND ON TRY 240 *SAVED* -2 PROBABILITY exp(-16258.217) N_CLASSES 14 FOUND ON TRY 248 PROBABILITY exp(-16272.233) N_CLASSES 9 FOUND ON TRY 212 PROBABILITY exp(-16282.909) N_CLASSES 16 FOUND ON TRY 42 PROBABILITY exp(-16285.766) N_CLASSES 16 FOUND ON TRY 83 DUPS 3 PROBABILITY exp(-16289.486) N_CLASSES 13 FOUND ON TRY 99 PROBABILITY exp(-16292.727) N_CLASSES 19 FOUND ON TRY 113 PROBABILITY exp(-16295.621) N_CLASSES 12 FOUND ON TRY 51 PROBABILITY exp(-16297.324) N_CLASSES 15 FOUND ON TRY 184 ## - report filenames suffix _____________________________________________________________________________ _____________________________________________________________________________ CLASSIFICATION HAS 15 POPULATED CLASSES (max global influence value = 6.920) We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. Class Log of class Relative Class Normalized num strength class strength weight class weight 0 -7.01e+01 1.20e-04 31 0.151 1 -7.29e+01 7.55e-06 22 0.107 2 -6.32e+01 1.29e-01 18 0.088 3 -6.11e+01 1.00e+00 16 0.078 4 -7.31e+01 6.14e-06 15 0.073 5 -8.07e+01 3.00e-09 14 0.068 6 -7.51e+01 8.62e-07 14 0.068 7 -6.86e+01 5.30e-04 13 0.063 8 -7.61e+01 2.98e-07 11 0.054 9 -7.43e+01 1.86e-06 10 0.049 10 -7.10e+01 5.15e-05 10 0.049 11 -7.13e+01 3.58e-05 9 0.044 12 -7.04e+01 9.06e-05 9 0.044 13 -7.16e+01 2.67e-05 8 0.039 14 -6.21e+01 3.52e-01 5 0.024 CLASS DIVERGENCES The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. Class class cross entropy Class Normalized num w.r.t. global class weight class weight 0 1.15e+01 31 0.151 1 1.44e+01 22 0.107 2 2.06e+01 18 0.088 3 2.67e+01 16 0.078 4 1.51e+01 15 0.073 5 2.54e+01 14 0.068 6 1.42e+01 14 0.068 7 1.66e+01 13 0.063 8 1.06e+01 11 0.054 9 1.76e+01 10 0.049 10 2.25e+01 10 0.049 11 2.22e+01 9 0.044 12 2.56e+01 9 0.044 13 1.72e+01 8 0.039 14 3.92e+01 5 0.024 ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. num description I-*k 038 Log compression-ratio 1.000 033 Log length 0.661 036 Log curb-weight 0.649 002 make 0.643 034 Log width 0.569 029 Log horse-power 0.564 037 Log engine-size 0.555 031 Log price 0.537 032 Log wheel-base 0.520 028 Log stroke 0.493 027 Log bore 0.482 017 fuel-system 0.440 035 Log height 0.361 026 Log normalized-loses 0.325 014 engine-type 0.276 039 Log city-mpg 0.250 040 Log highway-mpg 0.212 015 num-of-cylinders 0.198 007 drive-wheels 0.197 006 body-style 0.195 030 Log peak-rpm 0.181 003 fuel-type 0.145 005 num-of-doors 0.139 004 aspiration 0.081 008 engine-location 0.032 000 symboling ----- 001 normalized-loses ----- 009 wheel-base ----- 010 length ----- 011 width ----- 012 height ----- 013 curb-weight ----- 016 engine-size ----- 018 bore ----- 019 stroke ----- 020 compression-ratio ----- 021 horse-power ----- 022 peak-rpm ----- 023 city-mpg ----- 024 highway-mpg ----- 025 price ----- CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8% of values are known. CLASS 0 - weight 31 normalized weight 0.151 relative strength 1.20e-04 ******* class cross entropy w.r.t. global class 1.15e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.398 ( 1.24e+00 1.99e-02) 3.46e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.260 ( 7.77e+00 3.59e-02) 1.57e+00 ( 7.83e+00 1.97e-01) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.223 ( 4.75e+00 5.19e-02) 9.45e-01 ( 4.80e+00 2.82e-01) 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.110 ( 2.16e+00 6.17e-02) 1.71e+00 ( 2.27e+00 2.81e-01) o 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.894 ( 5.16e+00 1.81e-02) 1.71e-01 ( 5.16e+00 7.06e-02) 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.853 ( 9.16e+00 1.47e-01) 1.27e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.850 ( 4.52e+00 9.52e-02) 6.76e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e+00 Prob-*k is known 9.90e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.670 ( 1.18e+00 2.81e-02) 6.19e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 24 02 D SM make ............... 0.560 subaru ............. -3.72e+00 1.42e-03 5.85e-02 peugot ............. -3.63e+00 1.42e-03 5.36e-02 volvo .............. -3.63e+00 1.42e-03 5.36e-02 bmw ................ -3.31e+00 1.42e-03 3.91e-02 mercedes-benz ...... -3.31e+00 1.42e-03 3.91e-02 saab ............... -3.03e+00 1.42e-03 2.93e-02 porsche ............ -2.85e+00 1.42e-03 2.45e-02 isuzu .............. -2.63e+00 1.42e-03 1.96e-02 alfa-romero ........ -2.34e+00 1.42e-03 1.48e-02 chevrolet .......... -2.34e+00 1.42e-03 1.48e-02 jaguar ............. -2.34e+00 1.42e-03 1.48e-02 renault ............ -1.94e+00 1.42e-03 9.93e-03 mercury ............ -1.27e+00 1.42e-03 5.08e-03 mitsubishi ......... 1.09e+00 1.89e-01 6.33e-02 honda .............. 1.09e+00 1.89e-01 6.33e-02 volkswagen ......... 9.89e-01 1.57e-01 5.85e-02 mazda .............. 6.45e-01 1.58e-01 8.27e-02 nissan ............. -3.15e-01 6.40e-02 8.76e-02 dodge .............. -2.95e-01 3.27e-02 4.39e-02 toyota ............. -2.07e-01 1.26e-01 1.56e-01 audi ............... -4.56e-02 3.27e-02 3.42e-02 plymouth ........... -4.53e-02 3.27e-02 3.42e-02 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.500 ( 4.59e+00 2.35e-02) 8.52e-02 ( 4.59e+00 5.89e-02) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.488 ( 4.18e+00 1.28e-02) 2.82e-01 ( 4.19e+00 3.15e-02) 19 07 D SM drive-wheels ....... 0.453 rwd ................ -3.57e+00 1.04e-02 3.71e-01 4wd ................ -1.47e+00 1.04e-02 4.53e-02 fwd ................ 5.17e-01 9.79e-01 5.84e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 17 14 D SM engine-type ........ 0.239 ohcf ............... -2.80e+00 4.47e-03 7.35e-02 ohcv ............... -2.66e+00 4.47e-03 6.38e-02 dohc ............... -2.58e+00 4.47e-03 5.89e-02 l .................. -2.58e+00 4.47e-03 5.89e-02 rotor .............. -1.50e+00 4.47e-03 2.01e-02 ohc ................ 3.03e-01 9.73e-01 7.19e-01 dohcv .............. -2.17e-01 4.47e-03 5.55e-03 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.213 ( 4.64e+00 2.53e-01) 4.71e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.63e-01 Prob-*k is known 8.00e-01 16 15 D SM num-of-cylinders ... 0.183 six ................ -3.27e+00 4.47e-03 1.17e-01 five ............... -2.49e+00 4.47e-03 5.41e-02 eight .............. -1.72e+00 4.47e-03 2.50e-02 two ................ -1.50e+00 4.47e-03 2.01e-02 four ............... 2.31e-01 9.73e-01 7.73e-01 three .............. -2.17e-01 4.47e-03 5.55e-03 twelve ............. -2.17e-01 4.47e-03 5.55e-03 15 17 D SM fuel-system ........ 0.175 idi ................ -3.22e+00 3.91e-03 9.77e-02 4bbl ............... -1.36e+00 3.91e-03 1.52e-02 1bbl ............... 8.70e-01 1.29e-01 5.40e-02 spdi ............... 7.91e-01 9.77e-02 4.43e-02 mfi ................ -3.35e-01 3.91e-03 5.46e-03 spfi ............... -3.35e-01 3.91e-03 5.46e-03 mpfi ............... -2.74e-01 3.47e-01 4.57e-01 2bbl ............... 2.46e-01 4.10e-01 3.21e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.087 ( 3.25e+00 1.93e-01) 2.73e-01 ( 3.19e+00 2.56e-01) 21 05 D SM num-of-doors ....... 0.065 two ................ -5.09e-01 2.61e-01 4.34e-01 four ............... 2.73e-01 7.29e-01 5.55e-01 ? .................. -8.33e-02 1.04e-02 1.13e-02 23 03 D SM fuel-type .......... 0.059 diesel ............. -1.85e+00 1.56e-02 9.95e-02 gas ................ 8.91e-02 9.84e-01 9.00e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.057 ( 3.46e+00 1.93e-01) 3.18e-01 ( 3.40e+00 2.23e-01) 20 06 D SM body-style ......... 0.049 hardtop ............ -1.85e+00 6.25e-03 3.98e-02 convertible ........ -1.57e+00 6.25e-03 3.01e-02 wagon .............. -2.06e-01 9.96e-02 1.22e-01 sedan .............. 1.98e-01 5.69e-01 4.67e-01 hatchback .......... -6.63e-02 3.19e-01 3.41e-01 22 04 D SM aspiration ......... 0.044 turbo .............. -8.45e-01 7.82e-02 1.82e-01 std ................ 1.20e-01 9.22e-01 8.18e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.020 ( 3.99e+00 4.18e-02) 1.78e-01 ( 3.98e+00 4.54e-02) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.009 ( 8.54e+00 9.75e-02) 3.30e-02 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e+00 Prob-*k is known 9.90e-01 18 08 D SM engine-location .... 0.000 rear ............... -8.33e-02 1.56e-02 1.70e-02 front .............. 1.38e-03 9.84e-01 9.83e-01 CLASS 1 - weight 22 normalized weight 0.107 relative strength 7.55e-06 ******* class cross entropy w.r.t. global class 1.44e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 2.083 ( 5.23e+00 9.76e-03) 7.89e+00 ( 5.16e+00 7.06e-02) 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.390 ( 8.01e+00 4.78e-02) 3.88e+00 ( 7.83e+00 1.97e-01) 24 02 D SM make ............... 1.218 mazda .............. -3.73e+00 1.98e-03 8.27e-02 honda .............. -3.47e+00 1.98e-03 6.33e-02 mitsubishi ......... -3.47e+00 1.98e-03 6.33e-02 subaru ............. -3.39e+00 1.98e-03 5.85e-02 volkswagen ......... -3.39e+00 1.98e-03 5.85e-02 dodge .............. -3.10e+00 1.98e-03 4.39e-02 mercedes-benz ...... -2.98e+00 1.98e-03 3.91e-02 audi ............... -2.85e+00 1.98e-03 3.42e-02 plymouth ........... -2.85e+00 1.98e-03 3.42e-02 porsche ............ -2.52e+00 1.98e-03 2.45e-02 isuzu .............. -2.30e+00 1.98e-03 1.96e-02 volvo .............. 2.10e+00 4.37e-01 5.36e-02 alfa-romero ........ -2.01e+00 1.98e-03 1.48e-02 chevrolet .......... -2.01e+00 1.98e-03 1.48e-02 jaguar ............. -2.01e+00 1.98e-03 1.48e-02 saab ............... 1.79e+00 1.76e-01 2.93e-02 renault ............ -1.61e+00 1.98e-03 9.93e-03 mercury ............ -9.43e-01 1.98e-03 5.08e-03 bmw ................ 8.18e-01 8.85e-02 3.91e-02 toyota ............. -5.59e-01 8.89e-02 1.56e-01 nissan ............. 4.13e-01 1.32e-01 8.76e-02 peugot ............. -1.64e-01 4.55e-02 5.36e-02 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 1.042 ( 4.02e+00 1.52e-02) 2.64e+00 ( 3.98e+00 4.54e-02) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.854 ( 4.92e+00 1.60e-01) 2.09e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 0.805 ( 2.18e+00 8.34e-02) 1.03e+00 ( 2.27e+00 2.81e-01) o 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.766 ( 9.75e+00 2.16e-01) 1.85e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.700 ( 4.21e+00 1.27e-02) 1.61e+00 ( 4.19e+00 3.15e-02) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.670 ( 1.15e+00 3.49e-02) 6.12e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.664 ( 1.27e+00 4.65e-02) 1.62e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 0.649 2bbl ............... -4.08e+00 5.43e-03 3.21e-01 idi ................ -2.89e+00 5.43e-03 9.77e-02 1bbl ............... -2.30e+00 5.43e-03 5.40e-02 spdi ............... -2.10e+00 5.43e-03 4.43e-02 4bbl ............... -1.03e+00 5.43e-03 1.52e-02 mpfi ............... 7.44e-01 9.62e-01 4.57e-01 mfi ................ -4.95e-03 5.43e-03 5.46e-03 spfi ............... -4.95e-03 5.43e-03 5.46e-03 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.631 ( 4.64e+00 3.21e-02) 1.59e+00 ( 4.59e+00 5.89e-02) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 0.503 ( 4.99e+00 1.51e-01) 1.28e+00 ( 4.80e+00 2.82e-01) 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.444 ( 2.97e+00 1.92e-01) 1.15e+00 ( 3.19e+00 2.56e-01) 21 05 D SM num-of-doors ....... 0.362 two ................ -2.01e+00 5.84e-02 4.34e-01 four ............... 5.13e-01 9.27e-01 5.55e-01 ? .................. 2.46e-01 1.45e-02 1.13e-02 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.347 ( 3.22e+00 1.92e-01) 9.40e-01 ( 3.40e+00 2.23e-01) 20 06 D SM body-style ......... 0.305 hatchback .......... -1.87e+00 5.26e-02 3.41e-01 hardtop ............ -1.52e+00 8.69e-03 3.98e-02 convertible ........ -1.24e+00 8.69e-03 3.01e-02 wagon .............. 6.14e-01 2.26e-01 1.22e-01 sedan .............. 4.10e-01 7.04e-01 4.67e-01 17 14 D SM engine-type ........ 0.226 ohcf ............... -2.47e+00 6.21e-03 7.35e-02 rotor .............. -1.18e+00 6.21e-03 2.01e-02 dohc ............... 1.12e+00 1.80e-01 5.89e-02 ohcv ............... 1.04e+00 1.80e-01 6.38e-02 ohc ................ -2.30e-01 5.71e-01 7.19e-01 l .................. -1.69e-01 4.98e-02 5.89e-02 dohcv .............. 1.13e-01 6.21e-03 5.55e-03 16 15 D SM num-of-cylinders ... 0.223 five ............... -2.16e+00 6.21e-03 5.41e-02 eight .............. -1.39e+00 6.21e-03 2.50e-02 two ................ -1.18e+00 6.21e-03 2.01e-02 six ................ 1.10e+00 3.54e-01 1.17e-01 four ............... -2.28e-01 6.15e-01 7.73e-01 three .............. 1.13e-01 6.21e-03 5.55e-03 twelve ............. 1.13e-01 6.21e-03 5.55e-03 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.215 ( 4.62e+00 1.96e-01) 7.31e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.61e-01 Prob-*k is known 8.00e-01 19 07 D SM drive-wheels ....... 0.182 4wd ................ -1.14e+00 1.45e-02 4.53e-02 fwd ................ -6.04e-01 3.19e-01 5.84e-01 rwd ................ 5.87e-01 6.66e-01 3.71e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.062 ( 8.57e+00 9.68e-02) 3.36e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 22 04 D SM aspiration ......... 0.060 turbo .............. 5.83e-01 3.26e-01 1.82e-01 std ................ -1.94e-01 6.74e-01 8.18e-01 23 03 D SM fuel-type .......... 0.048 diesel ............. -1.52e+00 2.17e-02 9.95e-02 gas ................ 8.28e-02 9.78e-01 9.00e-01 18 08 D SM engine-location .... 0.001 rear ............... 2.46e-01 2.17e-02 1.70e-02 front .............. -4.84e-03 9.78e-01 9.83e-01 CLASS 2 - weight 18 normalized weight 0.088 relative strength 1.29e-01 ******* class cross entropy w.r.t. global class 2.06e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 2.103 ( 2.22e+00 2.12e-02) 2.29e+00 ( 2.27e+00 2.81e-01) o 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.982 ( 4.24e+00 4.84e-02) 7.15e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 1.785 ( 4.16e+00 4.86e-03) 5.83e+00 ( 4.19e+00 3.15e-02) 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.746 ( 4.55e+00 7.93e-03) 5.05e+00 ( 4.59e+00 5.89e-02) 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.610 ( 7.62e+00 4.32e-02) 4.86e+00 ( 7.83e+00 1.97e-01) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.605 ( 4.57e+00 4.84e-02) 4.72e+00 ( 4.80e+00 2.82e-01) 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.558 ( 5.12e+00 1.09e-02) 3.85e+00 ( 5.16e+00 7.06e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 1.448 ( 1.15e+00 1.46e-02) 3.47e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.434 ( 8.92e+00 1.09e-01) 3.97e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 24 02 D SM make ............... 1.043 honda .............. -3.28e+00 2.39e-03 6.33e-02 mitsubishi ......... -3.28e+00 2.39e-03 6.33e-02 subaru ............. -3.20e+00 2.39e-03 5.85e-02 volkswagen ......... -3.20e+00 2.39e-03 5.85e-02 peugot ............. -3.11e+00 2.39e-03 5.36e-02 volvo .............. -3.11e+00 2.39e-03 5.36e-02 dodge .............. -2.91e+00 2.39e-03 4.39e-02 bmw ................ -2.79e+00 2.39e-03 3.91e-02 mercedes-benz ...... -2.79e+00 2.39e-03 3.91e-02 audi ............... -2.66e+00 2.39e-03 3.42e-02 plymouth ........... -2.66e+00 2.39e-03 3.42e-02 saab ............... -2.51e+00 2.39e-03 2.93e-02 porsche ............ -2.33e+00 2.39e-03 2.45e-02 isuzu .............. -2.10e+00 2.39e-03 1.96e-02 alfa-romero ........ -1.82e+00 2.39e-03 1.48e-02 chevrolet .......... -1.82e+00 2.39e-03 1.48e-02 jaguar ............. -1.82e+00 2.39e-03 1.48e-02 nissan ............. 1.69e+00 4.76e-01 8.76e-02 renault ............ -1.42e+00 2.39e-03 9.93e-03 toyota ............. 8.68e-01 3.71e-01 1.56e-01 mercury ............ -7.52e-01 2.39e-03 5.08e-03 mazda .............. 2.64e-01 1.08e-01 8.27e-02 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 0.964 mpfi ............... -4.24e+00 6.58e-03 4.57e-01 idi ................ -2.70e+00 6.58e-03 9.77e-02 1bbl ............... -2.10e+00 6.58e-03 5.40e-02 spdi ............... -1.91e+00 6.58e-03 4.43e-02 2bbl ............... 1.09e+00 9.54e-01 3.21e-01 4bbl ............... -8.35e-01 6.58e-03 1.52e-02 mfi ................ 1.87e-01 6.58e-03 5.46e-03 spfi ............... 1.87e-01 6.58e-03 5.46e-03 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.788 ( 3.98e+00 1.31e-02) 1.76e-01 ( 3.98e+00 4.54e-02) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.606 ( 1.15e+00 3.75e-02) 5.65e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.466 ( 3.42e+00 1.91e-01) 1.19e+00 ( 3.19e+00 2.56e-01) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.450 ( 3.61e+00 1.91e-01) 1.08e+00 ( 3.40e+00 2.23e-01) 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.268 ( 4.76e+00 2.02e-01) 2.13e-02 ( 4.76e+00 2.82e-01) Prob-jk is known 9.89e-01 Prob-*k is known 8.00e-01 17 14 D SM engine-type ........ 0.201 ohcf ............... -2.28e+00 7.52e-03 7.35e-02 ohcv ............... -2.14e+00 7.52e-03 6.38e-02 dohc ............... -2.06e+00 7.52e-03 5.89e-02 l .................. -2.06e+00 7.52e-03 5.89e-02 rotor .............. -9.83e-01 7.52e-03 2.01e-02 dohcv .............. 3.04e-01 7.52e-03 5.55e-03 ohc ................ 2.84e-01 9.55e-01 7.19e-01 19 07 D SM drive-wheels ....... 0.180 rwd ................ -1.10e+00 1.23e-01 3.71e-01 4wd ................ -9.48e-01 1.76e-02 4.53e-02 fwd ................ 3.86e-01 8.60e-01 5.84e-01 16 15 D SM num-of-cylinders ... 0.155 six ................ -2.75e+00 7.52e-03 1.17e-01 five ............... -1.97e+00 7.52e-03 5.41e-02 eight .............. -1.20e+00 7.52e-03 2.50e-02 two ................ -9.83e-01 7.52e-03 2.01e-02 three .............. 3.04e-01 7.52e-03 5.55e-03 twelve ............. 3.04e-01 7.52e-03 5.55e-03 four ............... 2.12e-01 9.55e-01 7.73e-01 22 04 D SM aspiration ......... 0.119 turbo .............. -1.93e+00 2.63e-02 1.82e-01 std ................ 1.74e-01 9.74e-01 8.18e-01 20 06 D SM body-style ......... 0.053 convertible ........ -1.05e+00 1.05e-02 3.01e-02 hardtop ............ 4.62e-01 6.32e-02 3.98e-02 hatchback .......... -4.34e-01 2.21e-01 3.41e-01 sedan .............. 2.33e-01 5.90e-01 4.67e-01 wagon .............. -5.46e-02 1.16e-01 1.22e-01 23 03 D SM fuel-type .......... 0.041 diesel ............. -1.33e+00 2.63e-02 9.95e-02 gas ................ 7.81e-02 9.74e-01 9.00e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.022 ( 8.52e+00 9.64e-02) 1.72e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 21 05 D SM num-of-doors ....... 0.006 ? .................. 4.38e-01 1.76e-02 1.13e-02 two ................ -1.16e-01 3.86e-01 4.34e-01 four ............... 7.17e-02 5.96e-01 5.55e-01 18 08 D SM engine-location .... 0.002 rear ............... 4.38e-01 2.63e-02 1.70e-02 front .............. -9.54e-03 9.74e-01 9.83e-01 CLASS 3 - weight 16 normalized weight 0.078 relative strength 1.00e+00 ******* class cross entropy w.r.t. global class 2.67e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 2.466 ( 2.25e+00 1.45e-02) 1.47e+00 ( 2.27e+00 2.81e-01) o 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 2.342 ( 4.54e+00 4.85e-03) 1.00e+01 ( 4.59e+00 5.89e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 2.091 ( 1.09e+00 1.45e-02) 7.29e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 2.019 ( 4.23e+00 4.83e-02) 7.40e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 2.005 ( 7.57e+00 3.70e-02) 6.80e+00 ( 7.83e+00 1.97e-01) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 1.868 ( 4.16e+00 4.85e-03) 6.40e+00 ( 4.19e+00 3.15e-02) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.812 ( 4.51e+00 4.83e-02) 6.03e+00 ( 4.80e+00 2.82e-01) 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.764 ( 8.76e+00 9.59e-02) 6.14e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 8.81e-01 Prob-*k is known 9.80e-01 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.623 ( 5.06e+00 2.06e-02) 4.46e+00 ( 5.16e+00 7.06e-02) 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 1.601 ( 3.93e+00 1.05e-02) 4.78e+00 ( 3.98e+00 4.54e-02) 24 02 D SM make ............... 1.567 toyota ............. -4.06e+00 2.67e-03 1.56e-01 nissan ............. -3.49e+00 2.67e-03 8.76e-02 mazda .............. -3.43e+00 2.67e-03 8.27e-02 honda .............. -3.16e+00 2.67e-03 6.33e-02 subaru ............. -3.08e+00 2.67e-03 5.85e-02 volkswagen ......... -3.08e+00 2.67e-03 5.85e-02 peugot ............. -3.00e+00 2.67e-03 5.36e-02 volvo .............. -3.00e+00 2.67e-03 5.36e-02 bmw ................ -2.68e+00 2.67e-03 3.91e-02 mercedes-benz ...... -2.68e+00 2.67e-03 3.91e-02 audi ............... -2.55e+00 2.67e-03 3.42e-02 saab ............... -2.40e+00 2.67e-03 2.93e-02 porsche ............ -2.21e+00 2.67e-03 2.45e-02 chevrolet .......... 2.10e+00 1.20e-01 1.48e-02 plymouth ........... 1.94e+00 2.38e-01 3.42e-02 dodge .............. 1.91e+00 2.97e-01 4.39e-02 isuzu .............. 1.81e+00 1.20e-01 1.96e-02 alfa-romero ........ -1.71e+00 2.67e-03 1.48e-02 jaguar ............. -1.71e+00 2.67e-03 1.48e-02 renault ............ -1.31e+00 2.67e-03 9.93e-03 mitsubishi ......... 1.04e+00 1.79e-01 6.33e-02 mercury ............ -6.41e-01 2.67e-03 5.08e-03 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.394 ( 1.16e+00 1.59e-02) 7.70e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 0.949 mpfi ............... -4.13e+00 7.35e-03 4.57e-01 idi ................ -2.59e+00 7.35e-03 9.77e-02 1bbl ............... -1.99e+00 7.35e-03 5.40e-02 spdi ............... -1.80e+00 7.35e-03 4.43e-02 2bbl ............... 1.08e+00 9.49e-01 3.21e-01 4bbl ............... -7.24e-01 7.35e-03 1.52e-02 mfi ................ 2.98e-01 7.35e-03 5.46e-03 spfi ............... 2.98e-01 7.35e-03 5.46e-03 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.863 ( 3.52e+00 1.90e-01) 1.69e+00 ( 3.19e+00 2.56e-01) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.811 ( 3.68e+00 1.90e-01) 1.47e+00 ( 3.40e+00 2.23e-01) 19 07 D SM drive-wheels ....... 0.404 rwd ................ -2.94e+00 1.96e-02 3.71e-01 4wd ................ -8.37e-01 1.96e-02 4.53e-02 fwd ................ 4.98e-01 9.61e-01 5.84e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.259 ( 8.61e+00 9.61e-02) 7.29e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.209 ( 4.90e+00 1.98e-01) 7.00e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.70e-01 Prob-*k is known 8.00e-01 17 14 D SM engine-type ........ 0.192 ohcf ............... -2.17e+00 8.41e-03 7.35e-02 ohcv ............... -2.03e+00 8.41e-03 6.38e-02 dohc ............... -1.95e+00 8.41e-03 5.89e-02 l .................. -1.95e+00 8.41e-03 5.89e-02 rotor .............. -8.72e-01 8.41e-03 2.01e-02 dohcv .............. 4.15e-01 8.41e-03 5.55e-03 ohc ................ 2.78e-01 9.50e-01 7.19e-01 20 06 D SM body-style ......... 0.156 wagon .............. -2.34e+00 1.18e-02 1.22e-01 hardtop ............ -1.22e+00 1.18e-02 3.98e-02 convertible ........ -9.39e-01 1.18e-02 3.01e-02 hatchback .......... 4.62e-01 5.41e-01 3.41e-01 sedan .............. -9.75e-02 4.24e-01 4.67e-01 16 15 D SM num-of-cylinders ... 0.149 six ................ -2.64e+00 8.41e-03 1.17e-01 five ............... -1.86e+00 8.41e-03 5.41e-02 eight .............. -1.09e+00 8.41e-03 2.50e-02 two ................ -8.72e-01 8.41e-03 2.01e-02 three .............. 4.15e-01 8.41e-03 5.55e-03 twelve ............. 4.15e-01 8.41e-03 5.55e-03 four ............... 2.06e-01 9.50e-01 7.73e-01 22 04 D SM aspiration ......... 0.112 turbo .............. -1.82e+00 2.94e-02 1.82e-01 std ................ 1.71e-01 9.71e-01 8.18e-01 23 03 D SM fuel-type .......... 0.037 diesel ............. -1.22e+00 2.94e-02 9.95e-02 gas ................ 7.50e-02 9.71e-01 9.00e-01 21 05 D SM num-of-doors ....... 0.010 ? .................. 5.49e-01 1.96e-02 1.13e-02 four ............... -1.24e-01 4.90e-01 5.55e-01 two ................ 1.22e-01 4.90e-01 4.34e-01 18 08 D SM engine-location .... 0.004 rear ............... 5.49e-01 2.94e-02 1.70e-02 front .............. -1.27e-02 9.71e-01 9.83e-01 CLASS 4 - weight 15 normalized weight 0.073 relative strength 6.14e-06 ******* class cross entropy w.r.t. global class 1.51e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.891 ( 2.20e+00 2.67e-02) 2.67e+00 ( 2.27e+00 2.81e-01) o 24 02 D SM make ............... 1.365 nissan ............. -3.43e+00 2.84e-03 8.76e-02 mazda .............. -3.37e+00 2.84e-03 8.27e-02 mitsubishi ......... -3.10e+00 2.84e-03 6.33e-02 honda .............. -3.10e+00 2.84e-03 6.33e-02 volkswagen ......... -3.02e+00 2.84e-03 5.85e-02 peugot ............. -2.94e+00 2.84e-03 5.36e-02 volvo .............. -2.94e+00 2.84e-03 5.36e-02 dodge .............. -2.74e+00 2.84e-03 4.39e-02 bmw ................ -2.62e+00 2.84e-03 3.91e-02 mercedes-benz ...... -2.62e+00 2.84e-03 3.91e-02 audi ............... -2.49e+00 2.84e-03 3.42e-02 plymouth ........... -2.49e+00 2.84e-03 3.42e-02 saab ............... -2.34e+00 2.84e-03 2.93e-02 porsche ............ -2.15e+00 2.84e-03 2.45e-02 subaru ............. 2.15e+00 5.03e-01 5.85e-02 alfa-romero ........ -1.65e+00 2.84e-03 1.48e-02 chevrolet .......... -1.65e+00 2.84e-03 1.48e-02 jaguar ............. -1.65e+00 2.84e-03 1.48e-02 renault ............ -1.25e+00 2.84e-03 9.93e-03 isuzu .............. 1.20e+00 6.53e-02 1.96e-02 toyota ............. 8.88e-01 3.78e-01 1.56e-01 mercury ............ -5.81e-01 2.84e-03 5.08e-03 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.214 ( 8.87e+00 1.51e-01) 3.17e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 1.174 ( 4.47e+00 9.39e-02) 3.15e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 9.25e-01 Prob-*k is known 8.00e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.161 ( 4.56e+00 1.31e-02) 2.32e+00 ( 4.59e+00 5.89e-02) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.060 ( 4.27e+00 1.19e-01) 2.69e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.059 ( 4.61e+00 7.67e-02) 2.44e+00 ( 4.80e+00 2.82e-01) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.026 ( 1.03e+00 8.34e-02) 1.71e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 15 17 D SM fuel-system ........ 0.940 mpfi ............... -4.03e+00 8.09e-03 4.57e-01 idi ................ -2.53e+00 7.81e-03 9.77e-02 1bbl ............... -1.93e+00 7.81e-03 5.40e-02 spdi ............... -1.74e+00 7.81e-03 4.43e-02 2bbl ............... 1.08e+00 9.45e-01 3.21e-01 4bbl ............... -6.64e-01 7.81e-03 1.52e-02 spfi ............... 3.58e-01 7.81e-03 5.46e-03 mfi ................ 3.58e-01 7.81e-03 5.46e-03 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 17 14 D SM engine-type ........ 0.718 ohcv ............... -1.97e+00 8.92e-03 6.38e-02 ohcf ............... 1.93e+00 5.09e-01 7.35e-02 dohc ............... -1.89e+00 8.92e-03 5.89e-02 l .................. -1.89e+00 8.92e-03 5.89e-02 rotor .............. -8.13e-01 8.92e-03 2.01e-02 ohc ................ -4.77e-01 4.46e-01 7.19e-01 dohcv .............. 4.75e-01 8.92e-03 5.55e-03 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.709 ( 4.16e+00 1.57e-02) 1.77e+00 ( 4.19e+00 3.15e-02) 19 07 D SM drive-wheels ....... 0.540 4wd ................ 2.00e+00 3.33e-01 4.53e-02 rwd ................ -1.49e+00 8.33e-02 3.71e-01 fwd ................ -9.84e-04 5.84e-01 5.84e-01 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.446 ( 5.11e+00 3.92e-02) 1.15e+00 ( 5.16e+00 7.06e-02) 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.441 ( 7.72e+00 1.03e-01) 1.06e+00 ( 7.83e+00 1.97e-01) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.310 ( 8.46e+00 9.59e-02) 8.01e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.221 ( 3.33e+00 1.90e-01) 7.33e-01 ( 3.19e+00 2.56e-01) 20 06 D SM body-style ......... 0.191 hardtop ............ -1.16e+00 1.25e-02 3.98e-02 wagon .............. 9.77e-01 3.25e-01 1.22e-01 convertible ........ -8.79e-01 1.25e-02 3.01e-02 sedan .............. -5.76e-01 2.63e-01 4.67e-01 hatchback .......... 1.28e-01 3.87e-01 3.41e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.153 ( 4.01e+00 3.85e-02) 5.98e-01 ( 3.98e+00 4.54e-02) 16 15 D SM num-of-cylinders ... 0.145 six ................ -2.58e+00 8.92e-03 1.17e-01 five ............... -1.80e+00 8.92e-03 5.41e-02 eight .............. -1.03e+00 8.92e-03 2.50e-02 two ................ -8.13e-01 8.92e-03 2.01e-02 three .............. 4.75e-01 8.92e-03 5.55e-03 twelve ............. 4.75e-01 8.92e-03 5.55e-03 four ............... 2.03e-01 9.46e-01 7.73e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.115 ( 3.50e+00 1.90e-01) 5.04e-01 ( 3.40e+00 2.23e-01) 22 04 D SM aspiration ......... 0.109 turbo .............. -1.76e+00 3.12e-02 1.82e-01 std ................ 1.69e-01 9.69e-01 8.18e-01 23 03 D SM fuel-type .......... 0.035 diesel ............. -1.16e+00 3.12e-02 9.95e-02 gas ................ 7.31e-02 9.69e-01 9.00e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.027 ( 1.21e+00 7.94e-02) 1.56e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 21 05 D SM num-of-doors ....... 0.023 ? .................. 6.09e-01 2.08e-02 1.13e-02 two ................ -2.64e-01 3.33e-01 4.34e-01 four ............... 1.52e-01 6.46e-01 5.55e-01 18 08 D SM engine-location .... 0.005 rear ............... 6.09e-01 3.12e-02 1.70e-02 front .............. -1.46e-02 9.69e-01 9.83e-01 CLASS 5 - weight 14 normalized weight 0.068 relative strength 3.00e-09 ******* class cross entropy w.r.t. global class 2.54e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 2.838 ( 1.04e+01 1.81e-01) 5.85e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 2.594 ( 8.21e+00 6.43e-02) 6.00e+00 ( 7.83e+00 1.97e-01) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 2.558 ( 4.25e+00 1.56e-02) 4.28e+00 ( 4.19e+00 3.15e-02) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 2.374 ( 5.41e+00 2.22e-01) 2.73e+00 ( 4.80e+00 2.82e-01) 24 02 D SM make ............... 1.992 toyota ............. -3.94e+00 3.03e-03 1.56e-01 nissan ............. -3.36e+00 3.03e-03 8.76e-02 mazda .............. -3.31e+00 3.03e-03 8.27e-02 honda .............. -3.04e+00 3.03e-03 6.33e-02 mitsubishi ......... -3.04e+00 3.03e-03 6.33e-02 subaru ............. -2.96e+00 3.03e-03 5.85e-02 volkswagen ......... -2.96e+00 3.03e-03 5.85e-02 peugot ............. -2.87e+00 3.03e-03 5.36e-02 dodge .............. -2.67e+00 3.03e-03 4.39e-02 jaguar ............. 2.62e+00 2.03e-01 1.48e-02 mercedes-benz ...... 2.62e+00 5.36e-01 3.91e-02 audi ............... -2.42e+00 3.03e-03 3.42e-02 plymouth ........... -2.42e+00 3.03e-03 3.42e-02 saab ............... -2.27e+00 3.03e-03 2.93e-02 porsche ............ -2.09e+00 3.03e-03 2.45e-02 isuzu .............. -1.87e+00 3.03e-03 1.96e-02 alfa-romero ........ -1.59e+00 3.03e-03 1.48e-02 chevrolet .......... -1.59e+00 3.03e-03 1.48e-02 bmw ................ 1.25e+00 1.37e-01 3.91e-02 renault ............ -1.19e+00 3.03e-03 9.93e-03 mercury ............ -5.16e-01 3.03e-03 5.08e-03 volvo .............. 2.62e-01 6.97e-02 5.36e-02 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.672 ( 5.27e+00 3.54e-02) 3.29e+00 ( 5.16e+00 7.06e-02) 16 15 D SM num-of-cylinders ... 1.638 four ............... -4.40e+00 9.52e-03 7.73e-01 twelve ............. 2.62e+00 7.61e-02 5.55e-03 eight .............. 2.40e+00 2.76e-01 2.50e-02 five ............... 1.63e+00 2.76e-01 5.41e-02 six ................ 1.07e+00 3.43e-01 1.17e-01 two ................ -7.48e-01 9.52e-03 2.01e-02 three .............. 5.40e-01 9.52e-03 5.55e-03 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.626 ( 4.70e+00 5.34e-02) 1.98e+00 ( 4.59e+00 5.89e-02) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 1.451 ( 3.02e+00 1.89e-01) 1.99e+00 ( 3.40e+00 2.23e-01) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.058 ( 5.05e+00 2.27e-01) 2.06e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.932 ( 2.85e+00 2.08e-01) 1.65e+00 ( 3.19e+00 2.56e-01) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 19 07 D SM drive-wheels ....... 0.817 fwd ................ -3.27e+00 2.22e-02 5.84e-01 rwd ................ 9.47e-01 9.56e-01 3.71e-01 4wd ................ -7.13e-01 2.22e-02 4.53e-02 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 0.547 ( 2.46e+00 4.49e-01) 4.33e-01 ( 2.27e+00 2.81e-01) o 15 17 D SM fuel-system ........ 0.544 2bbl ............... -3.65e+00 8.33e-03 3.21e-01 1bbl ............... -1.87e+00 8.33e-03 5.40e-02 spdi ............... -1.67e+00 8.33e-03 4.43e-02 idi ................ 1.25e+00 3.41e-01 9.77e-02 4bbl ............... -6.00e-01 8.33e-03 1.52e-02 mfi ................ 4.22e-01 8.33e-03 5.46e-03 spfi ............... 4.22e-01 8.33e-03 5.46e-03 mpfi ............... 2.87e-01 6.09e-01 4.57e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.536 ( 1.27e+00 5.15e-02) 1.35e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 17 14 D SM engine-type ........ 0.467 ohcf ............... -2.04e+00 9.52e-03 7.35e-02 l .................. -1.82e+00 9.52e-03 5.89e-02 ohcv ............... 1.68e+00 3.43e-01 6.38e-02 dohc ............... 8.85e-01 1.43e-01 5.89e-02 rotor .............. -7.48e-01 9.52e-03 2.01e-02 dohcv .............. 5.40e-01 9.52e-03 5.55e-03 ohc ................ -4.12e-01 4.76e-01 7.19e-01 20 06 D SM body-style ......... 0.448 hatchback .......... -3.24e+00 1.33e-02 3.41e-01 hardtop ............ 1.30e+00 1.47e-01 3.98e-02 convertible ........ 9.77e-01 8.00e-02 3.01e-02 wagon .............. -4.25e-01 8.00e-02 1.22e-01 sedan .............. 3.76e-01 6.80e-01 4.67e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.346 ( 8.46e+00 9.57e-02) 8.49e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.299 ( 4.66e+00 1.88e-01) 5.52e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 5.20e-01 Prob-*k is known 8.00e-01 23 03 D SM fuel-type .......... 0.255 diesel ............. 1.30e+00 3.66e-01 9.95e-02 gas ................ -3.52e-01 6.34e-01 9.00e-01 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.223 ( 1.24e+00 1.03e-01) 6.43e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 22 04 D SM aspiration ......... 0.095 turbo .............. 7.00e-01 3.66e-01 1.82e-01 std ................ -2.55e-01 6.34e-01 8.18e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.070 ( 4.00e+00 4.95e-02) 3.25e-01 ( 3.98e+00 4.54e-02) 21 05 D SM num-of-doors ....... 0.016 ? .................. 6.73e-01 2.22e-02 1.13e-02 two ................ -1.99e-01 3.55e-01 4.34e-01 four ............... 1.15e-01 6.22e-01 5.55e-01 18 08 D SM engine-location .... 0.006 rear ............... 6.73e-01 3.33e-02 1.70e-02 front .............. -1.67e-02 9.67e-01 9.83e-01 CLASS 6 - weight 14 normalized weight 0.068 relative strength 8.62e-07 ******* class cross entropy w.r.t. global class 1.42e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 24 02 D SM make ............... 1.616 toyota ............. -3.94e+00 3.03e-03 1.56e-01 nissan ............. -3.36e+00 3.03e-03 8.76e-02 mazda .............. -3.31e+00 3.03e-03 8.27e-02 honda .............. -3.04e+00 3.03e-03 6.33e-02 mitsubishi ......... -3.04e+00 3.03e-03 6.33e-02 subaru ............. -2.96e+00 3.03e-03 5.85e-02 volvo .............. -2.87e+00 3.03e-03 5.36e-02 dodge .............. -2.67e+00 3.03e-03 4.39e-02 bmw ................ -2.56e+00 3.03e-03 3.91e-02 mercedes-benz ...... -2.56e+00 3.03e-03 3.91e-02 plymouth ........... -2.42e+00 3.03e-03 3.42e-02 audi ............... 2.29e+00 3.36e-01 3.42e-02 porsche ............ -2.09e+00 3.03e-03 2.45e-02 renault ............ 1.95e+00 6.97e-02 9.93e-03 isuzu .............. -1.87e+00 3.03e-03 1.96e-02 peugot ............. 1.84e+00 3.36e-01 5.36e-02 alfa-romero ........ -1.59e+00 3.03e-03 1.48e-02 chevrolet .......... -1.59e+00 3.03e-03 1.48e-02 jaguar ............. -1.59e+00 3.03e-03 1.48e-02 saab ............... 1.53e+00 1.36e-01 2.93e-02 mercury ............ -5.16e-01 3.03e-03 5.08e-03 volkswagen ......... 1.90e-01 7.07e-02 5.85e-02 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.607 ( 2.14e+00 3.79e-02) 3.24e+00 ( 2.27e+00 2.81e-01) o 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 1.197 ( 5.06e+00 4.81e-02) 6.19e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.86e-01 Prob-*k is known 8.00e-01 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.150 ( 4.85e+00 5.59e-02) 8.25e-01 ( 4.80e+00 2.82e-01) 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.898 ( 5.23e+00 3.53e-02) 2.15e+00 ( 5.16e+00 7.06e-02) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.830 ( 4.67e+00 1.00e-01) 8.63e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.33e-01 Prob-*k is known 9.90e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.748 ( 4.02e+00 2.18e-02) 1.86e+00 ( 3.98e+00 4.54e-02) 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.740 ( 7.97e+00 7.97e-02) 1.76e+00 ( 7.83e+00 1.97e-01) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.713 ( 1.12e+00 1.84e-01) 3.24e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.637 ( 4.22e+00 2.60e-02) 1.33e+00 ( 4.19e+00 3.15e-02) 16 15 D SM num-of-cylinders ... 0.607 six ................ -2.51e+00 9.52e-03 1.17e-01 five ............... 2.02e+00 4.09e-01 5.41e-02 eight .............. -9.64e-01 9.52e-03 2.50e-02 two ................ -7.48e-01 9.52e-03 2.01e-02 three .............. 5.40e-01 9.52e-03 5.55e-03 twelve ............. 5.40e-01 9.52e-03 5.55e-03 four ............... -3.53e-01 5.43e-01 7.73e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 0.603 2bbl ............... -3.65e+00 8.33e-03 3.21e-01 idi ................ -2.46e+00 8.33e-03 9.77e-02 1bbl ............... -1.87e+00 8.33e-03 5.40e-02 spdi ............... -1.67e+00 8.33e-03 4.43e-02 mpfi ............... 7.23e-01 9.42e-01 4.57e-01 4bbl ............... -5.99e-01 8.33e-03 1.52e-02 mfi ................ 4.22e-01 8.33e-03 5.46e-03 spfi ............... 4.22e-01 8.33e-03 5.46e-03 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.570 ( 9.62e+00 2.21e-01) 1.21e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.488 ( 2.96e+00 1.89e-01) 1.23e+00 ( 3.19e+00 2.56e-01) 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.487 ( 4.65e+00 5.04e-02) 1.13e+00 ( 4.59e+00 5.89e-02) 17 14 D SM engine-type ........ 0.446 ohcf ............... -2.04e+00 9.52e-03 7.35e-02 ohcv ............... -1.90e+00 9.52e-03 6.38e-02 dohc ............... -1.82e+00 9.52e-03 5.89e-02 l .................. 1.76e+00 3.43e-01 5.89e-02 rotor .............. -7.48e-01 9.52e-03 2.01e-02 dohcv .............. 5.40e-01 9.52e-03 5.55e-03 ohc ................ -1.65e-01 6.10e-01 7.19e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.394 ( 3.21e+00 1.89e-01) 1.01e+00 ( 3.40e+00 2.23e-01) 20 06 D SM body-style ......... 0.170 hardtop ............ -1.09e+00 1.33e-02 3.98e-02 hatchback .......... -8.48e-01 1.46e-01 3.41e-01 wagon .............. 8.31e-01 2.81e-01 1.22e-01 convertible ........ -8.14e-01 1.33e-02 3.01e-02 sedan .............. 1.57e-01 5.46e-01 4.67e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.121 ( 8.57e+00 9.57e-02) 3.24e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.33e-01 Prob-*k is known 9.90e-01 21 05 D SM num-of-doors ....... 0.100 ? .................. 6.74e-01 2.22e-02 1.13e-02 two ................ -6.72e-01 2.22e-01 4.34e-01 four ............... 3.09e-01 7.56e-01 5.55e-01 23 03 D SM fuel-type .......... 0.032 diesel ............. -1.09e+00 3.33e-02 9.95e-02 gas ................ 7.09e-02 9.67e-01 9.00e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.032 ( 1.18e+00 7.92e-02) 1.87e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 22 04 D SM aspiration ......... 0.026 turbo .............. -5.99e-01 1.00e-01 1.82e-01 std ................ 9.56e-02 9.00e-01 8.18e-01 19 07 D SM drive-wheels ....... 0.017 4wd ................ 6.74e-01 8.89e-02 4.53e-02 fwd ................ -4.98e-02 5.56e-01 5.84e-01 rwd ................ -4.19e-02 3.55e-01 3.71e-01 18 08 D SM engine-location .... 0.006 rear ............... 6.74e-01 3.33e-02 1.70e-02 front .............. -1.68e-02 9.67e-01 9.83e-01 CLASS 7 - weight 13 normalized weight 0.063 relative strength 5.30e-04 ******* class cross entropy w.r.t. global class 1.66e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.590 ( 5.16e+00 8.84e-03) 7.75e-01 ( 5.16e+00 7.06e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 1.569 ( 1.28e+00 1.64e-02) 4.63e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 1.150 ( 4.19e+00 6.17e-03) 9.89e-02 ( 4.19e+00 3.15e-02) 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.134 ( 4.58e+00 1.22e-02) 1.27e+00 ( 4.59e+00 5.89e-02) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.105 ( 4.99e+00 7.26e-02) 2.55e+00 ( 4.80e+00 2.82e-01) 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 1.033 ( 3.94e+00 1.80e-02) 2.59e+00 ( 3.98e+00 4.54e-02) 15 17 D SM fuel-system ........ 0.971 2bbl ............... -3.58e+00 8.93e-03 3.21e-01 mfi ................ 2.69e+00 8.04e-02 5.46e-03 spfi ............... 2.69e+00 8.04e-02 5.46e-03 idi ................ -2.39e+00 8.93e-03 9.77e-02 spdi ............... 1.89e+00 2.95e-01 4.43e-02 1bbl ............... -1.80e+00 8.93e-03 5.40e-02 4bbl ............... -5.30e-01 8.93e-03 1.52e-02 mpfi ............... 1.08e-01 5.09e-01 4.57e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.942 ( 4.91e+00 4.79e-02) 3.03e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.57e-01 Prob-*k is known 8.00e-01 24 02 D SM make ............... 0.936 nissan ............. -3.30e+00 3.25e-03 8.76e-02 mazda .............. -3.24e+00 3.25e-03 8.27e-02 honda .............. -2.97e+00 3.25e-03 6.33e-02 subaru ............. -2.89e+00 3.25e-03 5.85e-02 volkswagen ......... -2.89e+00 3.25e-03 5.85e-02 peugot ............. -2.80e+00 3.25e-03 5.36e-02 volvo .............. -2.80e+00 3.25e-03 5.36e-02 bmw ................ -2.49e+00 3.25e-03 3.91e-02 mercedes-benz ...... -2.49e+00 3.25e-03 3.91e-02 audi ............... -2.35e+00 3.25e-03 3.42e-02 saab ............... -2.20e+00 3.25e-03 2.93e-02 porsche ............ -2.02e+00 3.25e-03 2.45e-02 renault ............ 2.02e+00 7.47e-02 9.93e-03 alfa-romero ........ -1.52e+00 3.25e-03 1.48e-02 chevrolet .......... -1.52e+00 3.25e-03 1.48e-02 jaguar ............. -1.52e+00 3.25e-03 1.48e-02 isuzu .............. 1.34e+00 7.47e-02 1.96e-02 mitsubishi ......... 1.23e+00 2.18e-01 6.33e-02 toyota ............. 1.02e+00 4.32e-01 1.56e-01 plymouth ........... 7.81e-01 7.47e-02 3.42e-02 dodge .............. 5.31e-01 7.47e-02 4.39e-02 mercury ............ -4.47e-01 3.25e-03 5.08e-03 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.907 ( 1.29e+00 5.78e-02) 2.04e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.864 ( 7.91e+00 5.76e-02) 1.46e+00 ( 7.83e+00 1.97e-01) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 20 06 D SM body-style ......... 0.840 sedan .............. -3.49e+00 1.43e-02 4.67e-01 wagon .............. -2.15e+00 1.43e-02 1.22e-01 hardtop ............ 1.75e+00 2.29e-01 3.98e-02 convertible ........ 1.05e+00 8.57e-02 3.01e-02 hatchback .......... 6.57e-01 6.57e-01 3.41e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.762 ( 4.82e+00 1.39e-01) 1.73e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.28e-01 Prob-*k is known 9.90e-01 21 05 D SM num-of-doors ....... 0.692 four ............... -3.15e+00 2.38e-02 5.55e-01 two ................ 7.87e-01 9.52e-01 4.34e-01 ? .................. 7.43e-01 2.38e-02 1.13e-02 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.562 ( 9.38e+00 1.88e-01) 1.62e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 0.526 ( 2.12e+00 1.30e-01) 1.17e+00 ( 2.27e+00 2.81e-01) o 17 14 D SM engine-type ........ 0.175 ohcf ............... -1.97e+00 1.02e-02 7.35e-02 ohcv ............... -1.83e+00 1.02e-02 6.38e-02 dohc ............... -1.75e+00 1.02e-02 5.89e-02 l .................. -1.75e+00 1.02e-02 5.89e-02 rotor .............. -6.78e-01 1.02e-02 2.01e-02 dohcv .............. 6.09e-01 1.02e-02 5.55e-03 ohc ................ 2.67e-01 9.39e-01 7.19e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.167 ( 3.09e+00 1.89e-01) 5.78e-01 ( 3.19e+00 2.56e-01) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.163 ( 8.50e+00 9.54e-02) 4.25e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.28e-01 Prob-*k is known 9.90e-01 16 15 D SM num-of-cylinders ... 0.137 six ................ -2.44e+00 1.02e-02 1.17e-01 five ............... -1.67e+00 1.02e-02 5.41e-02 eight .............. -8.95e-01 1.02e-02 2.50e-02 two ................ -6.78e-01 1.02e-02 2.01e-02 three .............. 6.09e-01 1.02e-02 5.55e-03 twelve ............. 6.09e-01 1.02e-02 5.55e-03 four ............... 1.95e-01 9.39e-01 7.73e-01 22 04 D SM aspiration ......... 0.121 turbo .............. 7.69e-01 3.93e-01 1.82e-01 std ................ -2.98e-01 6.07e-01 8.18e-01 19 07 D SM drive-wheels ....... 0.104 4wd ................ -6.43e-01 2.38e-02 4.53e-02 rwd ................ 4.74e-01 5.95e-01 3.71e-01 fwd ................ -4.27e-01 3.81e-01 5.84e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.098 ( 3.32e+00 1.89e-01) 4.51e-01 ( 3.40e+00 2.23e-01) 23 03 D SM fuel-type .......... 0.029 diesel ............. -1.02e+00 3.57e-02 9.95e-02 gas ................ 6.85e-02 9.64e-01 9.00e-01 18 08 D SM engine-location .... 0.008 rear ............... 7.43e-01 3.57e-02 1.70e-02 front .............. -1.92e-02 9.64e-01 9.83e-01 CLASS 8 - weight 11 normalized weight 0.054 relative strength 2.98e-07 ******* class cross entropy w.r.t. global class 1.06e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 24 02 D SM make ............... 1.197 nissan ............. -3.14e+00 3.79e-03 8.76e-02 mitsubishi ......... -2.82e+00 3.79e-03 6.33e-02 honda .............. -2.82e+00 3.79e-03 6.33e-02 volkswagen ......... -2.74e+00 3.79e-03 5.85e-02 peugot ............. -2.65e+00 3.79e-03 5.36e-02 volvo .............. -2.65e+00 3.79e-03 5.36e-02 dodge .............. -2.45e+00 3.79e-03 4.39e-02 mercedes-benz ...... -2.33e+00 3.79e-03 3.91e-02 plymouth ........... -2.20e+00 3.79e-03 3.42e-02 audi ............... -2.19e+00 3.82e-03 3.42e-02 bmw ................ 2.16e+00 3.37e-01 3.91e-02 saab ............... -2.05e+00 3.79e-03 2.93e-02 porsche ............ -1.87e+00 3.79e-03 2.45e-02 subaru ............. 1.75e+00 3.37e-01 5.85e-02 isuzu .............. -1.64e+00 3.79e-03 1.96e-02 alfa-romero ........ -1.36e+00 3.79e-03 1.48e-02 chevrolet .......... -1.36e+00 3.79e-03 1.48e-02 jaguar ............. -1.36e+00 3.79e-03 1.48e-02 renault ............ -9.63e-01 3.79e-03 9.93e-03 mercury ............ -2.92e-01 3.79e-03 5.08e-03 toyota ............. 9.17e-02 1.71e-01 1.56e-01 mazda .............. 5.19e-02 8.71e-02 8.27e-02 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.033 ( 2.16e+00 6.69e-02) 1.57e+00 ( 2.27e+00 2.81e-01) o 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.989 ( 5.16e+00 1.64e-02) 2.13e-02 ( 5.16e+00 7.06e-02) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.971 ( 4.69e+00 8.58e-02) 1.19e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.896 ( 4.17e+00 8.90e-03) 1.52e+00 ( 4.19e+00 3.15e-02) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.709 ( 1.06e+00 7.66e-02) 1.50e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.676 ( 7.82e+00 6.41e-02) 9.44e-02 ( 7.83e+00 1.97e-01) 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.648 ( 3.98e+00 1.53e-02) 1.18e-01 ( 3.98e+00 4.54e-02) 15 17 D SM fuel-system ........ 0.574 2bbl ............... -3.42e+00 1.05e-02 3.21e-01 idi ................ -2.24e+00 1.04e-02 9.77e-02 1bbl ............... -1.65e+00 1.04e-02 5.40e-02 spdi ............... -1.45e+00 1.04e-02 4.43e-02 mpfi ............... 7.07e-01 9.27e-01 4.57e-01 spfi ............... 6.47e-01 1.04e-02 5.46e-03 mfi ................ 6.46e-01 1.04e-02 5.46e-03 4bbl ............... -3.76e-01 1.04e-02 1.52e-02 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 17 14 D SM engine-type ........ 0.474 ohcv ............... -1.68e+00 1.19e-02 6.38e-02 l .................. -1.60e+00 1.19e-02 5.89e-02 ohcf ............... 1.55e+00 3.45e-01 7.35e-02 dohc ............... 1.11e+00 1.79e-01 5.89e-02 dohcv .............. 7.64e-01 1.19e-02 5.55e-03 rotor .............. -5.24e-01 1.19e-02 2.01e-02 ohc ................ -5.17e-01 4.29e-01 7.19e-01 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.391 ( 1.25e+00 4.79e-02) 9.94e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 19 07 D SM drive-wheels ....... 0.376 4wd ................ 1.46e+00 1.95e-01 4.53e-02 fwd ................ -1.10e+00 1.94e-01 5.84e-01 rwd ................ 5.01e-01 6.11e-01 3.71e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.325 ( 4.92e+00 3.06e-01) 5.23e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.83e-01 Prob-*k is known 8.00e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.293 ( 4.59e+00 3.05e-02) 5.61e-02 ( 4.59e+00 5.89e-02) 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.260 ( 9.51e+00 2.97e-01) 5.31e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.193 ( 8.56e+00 1.39e-01) 1.93e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 20 06 D SM body-style ......... 0.187 hatchback .......... -1.23e+00 1.00e-01 3.41e-01 hardtop ............ -8.70e-01 1.67e-02 3.98e-02 convertible ........ -5.91e-01 1.67e-02 3.01e-02 wagon .............. 4.03e-01 1.83e-01 1.22e-01 sedan .............. 3.81e-01 6.84e-01 4.67e-01 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 0.182 ( 4.76e+00 1.74e-01) 2.07e-01 ( 4.80e+00 2.82e-01) 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.096 ( 3.15e+00 1.88e-01) 2.56e-01 ( 3.19e+00 2.56e-01) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.054 ( 3.35e+00 1.88e-01) 2.79e-01 ( 3.40e+00 2.23e-01) 16 15 D SM num-of-cylinders ... 0.050 five ............... -1.51e+00 1.19e-02 5.41e-02 three .............. 7.64e-01 1.19e-02 5.55e-03 twelve ............. 7.64e-01 1.19e-02 5.55e-03 eight .............. -7.40e-01 1.19e-02 2.50e-02 two ................ -5.24e-01 1.19e-02 2.01e-02 six ................ 4.21e-01 1.79e-01 1.17e-01 four ............... -1.40e-02 7.62e-01 7.73e-01 23 03 D SM fuel-type .......... 0.023 diesel ............. -8.70e-01 4.17e-02 9.95e-02 gas ................ 6.22e-02 9.58e-01 9.00e-01 21 05 D SM num-of-doors ....... 0.018 ? .................. 8.97e-01 2.78e-02 1.13e-02 two ................ -1.83e-01 3.61e-01 4.34e-01 four ............... 9.61e-02 6.11e-01 5.55e-01 18 08 D SM engine-location .... 0.013 rear ............... 8.97e-01 4.17e-02 1.70e-02 front .............. -2.54e-02 9.58e-01 9.83e-01 22 04 D SM aspiration ......... 0.002 turbo .............. 1.35e-01 2.08e-01 1.82e-01 std ................ -3.28e-02 7.92e-01 8.18e-01 CLASS 9 - weight 10 normalized weight 0.049 relative strength 1.86e-06 ******* class cross entropy w.r.t. global class 1.76e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 1.791 ( 5.29e+00 6.55e-02) 8.11e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.18e-01 Prob-*k is known 8.00e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.630 ( 5.15e+00 1.81e-01) 3.13e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.502 ( 9.78e+00 9.88e-02) 4.40e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 8.16e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.344 ( 8.01e+00 5.07e-02) 3.70e+00 ( 7.83e+00 1.97e-01) 24 02 D SM make ............... 1.137 mazda .............. -3.00e+00 4.13e-03 8.27e-02 mercury ............ 2.93e+00 9.50e-02 5.08e-03 honda .............. -2.73e+00 4.13e-03 6.33e-02 mitsubishi ......... -2.73e+00 4.13e-03 6.33e-02 subaru ............. -2.65e+00 4.13e-03 5.85e-02 volkswagen ......... -2.65e+00 4.13e-03 5.85e-02 peugot ............. -2.56e+00 4.13e-03 5.36e-02 volvo .............. -2.56e+00 4.13e-03 5.36e-02 dodge .............. -2.36e+00 4.13e-03 4.39e-02 bmw ................ -2.25e+00 4.13e-03 3.91e-02 mercedes-benz ...... -2.25e+00 4.13e-03 3.91e-02 plymouth ........... -2.11e+00 4.13e-03 3.42e-02 porsche ............ 2.03e+00 1.86e-01 2.45e-02 saab ............... -1.96e+00 4.13e-03 2.93e-02 alfa-romero ........ 1.86e+00 9.50e-02 1.48e-02 isuzu .............. -1.56e+00 4.13e-03 1.96e-02 chevrolet .......... -1.27e+00 4.13e-03 1.48e-02 jaguar ............. -1.27e+00 4.13e-03 1.48e-02 nissan ............. 1.15e+00 2.77e-01 8.76e-02 audi ............... 1.02e+00 9.50e-02 3.42e-02 renault ............ -8.77e-01 4.13e-03 9.93e-03 toyota ............. 1.79e-01 1.86e-01 1.56e-01 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.008 ( 5.10e+00 1.23e-01) 2.48e+00 ( 4.80e+00 2.82e-01) 17 14 D SM engine-type ........ 0.894 dohcv .............. 2.93e+00 1.04e-01 5.55e-03 ohcv ............... 1.78e+00 3.77e-01 6.38e-02 ohcf ............... -1.73e+00 1.30e-02 7.35e-02 l .................. -1.51e+00 1.30e-02 5.89e-02 dohc ............... 1.20e+00 1.95e-01 5.89e-02 ohc ................ -9.23e-01 2.86e-01 7.19e-01 rotor .............. -4.37e-01 1.30e-02 2.01e-02 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 16 15 D SM num-of-cylinders ... 0.836 six ................ 1.56e+00 5.58e-01 1.17e-01 eight .............. 1.43e+00 1.04e-01 2.50e-02 four ............... -1.38e+00 1.95e-01 7.73e-01 three .............. 8.51e-01 1.30e-02 5.55e-03 twelve ............. 8.51e-01 1.30e-02 5.55e-03 five ............... 6.53e-01 1.04e-01 5.41e-02 two ................ -4.37e-01 1.30e-02 2.01e-02 20 06 D SM body-style ......... 0.811 sedan .............. -3.25e+00 1.82e-02 4.67e-01 wagon .............. -1.91e+00 1.82e-02 1.22e-01 hatchback .......... 1.00e+00 9.27e-01 3.41e-01 hardtop ............ -7.84e-01 1.82e-02 3.98e-02 convertible ........ -5.04e-01 1.82e-02 3.01e-02 19 07 D SM drive-wheels ....... 0.733 fwd ................ -2.96e+00 3.03e-02 5.84e-01 4wd ................ 9.84e-01 1.21e-01 4.53e-02 rwd ................ 8.28e-01 8.48e-01 3.71e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.691 ( 2.91e+00 1.87e-01) 1.51e+00 ( 3.19e+00 2.56e-01) 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 0.676 ( 2.17e+00 9.79e-02) 1.01e+00 ( 2.27e+00 2.81e-01) o 21 05 D SM num-of-doors ....... 0.668 four ............... -2.91e+00 3.03e-02 5.55e-01 ? .................. 9.84e-01 3.03e-02 1.13e-02 two ................ 7.73e-01 9.39e-01 4.34e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.655 ( 3.94e+00 2.87e-02) 1.58e+00 ( 3.98e+00 4.54e-02) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.654 ( 4.22e+00 2.18e-02) 1.51e+00 ( 4.19e+00 3.15e-02) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.648 ( 1.18e+00 3.50e-02) 2.70e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 15 17 D SM fuel-system ........ 0.562 2bbl ............... -3.34e+00 1.14e-02 3.21e-01 idi ................ -2.15e+00 1.14e-02 9.77e-02 1bbl ............... -1.56e+00 1.14e-02 5.40e-02 spdi ............... -1.36e+00 1.14e-02 4.43e-02 mfi ................ 7.33e-01 1.14e-02 5.46e-03 spfi ............... 7.33e-01 1.14e-02 5.46e-03 mpfi ............... 7.00e-01 9.20e-01 4.57e-01 4bbl ............... -2.89e-01 1.14e-02 1.52e-02 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.533 ( 5.17e+00 2.75e-02) 4.65e-01 ( 5.16e+00 7.06e-02) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.395 ( 3.21e+00 1.87e-01) 1.02e+00 ( 3.40e+00 2.23e-01) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.136 ( 1.23e+00 1.05e-01) 2.52e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.100 ( 4.58e+00 4.25e-02) 2.28e-01 ( 4.59e+00 5.89e-02) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.061 ( 8.57e+00 9.44e-02) 3.39e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 22 04 D SM aspiration ......... 0.054 turbo .............. 5.58e-01 3.18e-01 1.82e-01 std ................ -1.82e-01 6.82e-01 8.18e-01 23 03 D SM fuel-type .......... 0.020 diesel ............. -7.84e-01 4.55e-02 9.95e-02 gas ................ 5.83e-02 9.55e-01 9.00e-01 18 08 D SM engine-location .... 0.017 rear ............... 9.84e-01 4.55e-02 1.70e-02 front .............. -2.94e-02 9.55e-01 9.83e-01 CLASS 10 - weight 10 normalized weight 0.049 relative strength 5.15e-05 ******* class cross entropy w.r.t. global class 2.25e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 6.920 ( 3.12e+00 1.66e-02) 5.13e+01 ( 2.27e+00 2.81e-01) o 23 03 D SM fuel-type .......... 2.022 gas ................ -2.99e+00 4.55e-02 9.00e-01 diesel ............. 2.26e+00 9.55e-01 9.95e-02 15 17 D SM fuel-system ........ 1.965 mpfi ............... -3.69e+00 1.14e-02 4.57e-01 2bbl ............... -3.34e+00 1.14e-02 3.21e-01 idi ................ 2.24e+00 9.20e-01 9.77e-02 1bbl ............... -1.56e+00 1.14e-02 5.40e-02 spdi ............... -1.36e+00 1.14e-02 4.43e-02 mfi ................ 7.33e-01 1.14e-02 5.46e-03 spfi ............... 7.33e-01 1.14e-02 5.46e-03 4bbl ............... -2.89e-01 1.14e-02 1.52e-02 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.543 ( 4.11e+00 1.21e-01) 3.88e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.182 ( 1.23e+00 2.25e-02) 2.34e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 1.151 ( 3.57e+00 1.87e-01) 2.00e+00 ( 3.19e+00 2.56e-01) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 1.081 ( 3.73e+00 1.87e-01) 1.73e+00 ( 3.40e+00 2.23e-01) 24 02 D SM make ............... 0.852 honda .............. -2.73e+00 4.13e-03 6.33e-02 mitsubishi ......... -2.73e+00 4.13e-03 6.33e-02 subaru ............. -2.65e+00 4.13e-03 5.85e-02 peugot ............. -2.56e+00 4.13e-03 5.36e-02 volvo .............. -2.56e+00 4.13e-03 5.36e-02 dodge .............. -2.36e+00 4.13e-03 4.39e-02 bmw ................ -2.25e+00 4.13e-03 3.91e-02 mercedes-benz ...... -2.25e+00 4.13e-03 3.91e-02 audi ............... -2.11e+00 4.13e-03 3.42e-02 plymouth ........... -2.11e+00 4.13e-03 3.42e-02 saab ............... -1.96e+00 4.13e-03 2.93e-02 volkswagen ......... 1.84e+00 3.68e-01 5.85e-02 porsche ............ -1.78e+00 4.13e-03 2.45e-02 isuzu .............. -1.56e+00 4.13e-03 1.96e-02 alfa-romero ........ -1.27e+00 4.13e-03 1.48e-02 chevrolet .......... -1.27e+00 4.13e-03 1.48e-02 jaguar ............. -1.27e+00 4.13e-03 1.48e-02 renault ............ -8.77e-01 4.13e-03 9.93e-03 mazda .............. 8.10e-01 1.86e-01 8.27e-02 toyota ............. 5.76e-01 2.77e-01 1.56e-01 mercury ............ -2.06e-01 4.13e-03 5.08e-03 nissan ............. 8.15e-02 9.50e-02 8.76e-02 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 0.699 ( 4.67e+00 1.00e-01) 1.26e+00 ( 4.80e+00 2.82e-01) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.618 ( 8.43e+00 9.44e-02) 1.16e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.597 ( 4.00e+00 1.80e-02) 1.08e+00 ( 3.98e+00 4.54e-02) 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.596 ( 7.76e+00 7.43e-02) 8.36e-01 ( 7.83e+00 1.97e-01) 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 0.550 ( 5.15e+00 2.67e-02) 3.29e-01 ( 5.16e+00 7.06e-02) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.408 ( 4.18e+00 1.43e-02) 3.94e-01 ( 4.19e+00 3.15e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 0.364 ( 1.15e+00 5.02e-02) 9.60e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.319 ( 9.18e+00 2.74e-01) 6.08e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.314 ( 4.59e+00 2.96e-02) 6.79e-02 ( 4.59e+00 5.89e-02) 20 06 D SM body-style ......... 0.305 wagon .............. -1.91e+00 1.82e-02 1.22e-01 hatchback .......... -1.14e+00 1.09e-01 3.41e-01 hardtop ............ -7.84e-01 1.82e-02 3.98e-02 sedan .............. 5.83e-01 8.36e-01 4.67e-01 convertible ........ -5.04e-01 1.82e-02 3.01e-02 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.273 ( 4.56e+00 1.97e-01) 1.01e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 7.09e-01 Prob-*k is known 8.00e-01 21 05 D SM num-of-doors ....... 0.258 ? .................. 2.37e+00 1.21e-01 1.13e-02 two ................ -7.15e-01 2.12e-01 4.34e-01 four ............... 1.83e-01 6.67e-01 5.55e-01 19 07 D SM drive-wheels ....... 0.169 rwd ................ -1.12e+00 1.21e-01 3.71e-01 4wd ................ -4.02e-01 3.03e-02 4.53e-02 fwd ................ 3.73e-01 8.48e-01 5.84e-01 17 14 D SM engine-type ........ 0.152 ohcf ............... -1.73e+00 1.30e-02 7.35e-02 ohcv ............... -1.59e+00 1.30e-02 6.38e-02 dohc ............... -1.51e+00 1.30e-02 5.89e-02 l .................. -1.51e+00 1.30e-02 5.89e-02 dohcv .............. 8.51e-01 1.30e-02 5.55e-03 rotor .............. -4.37e-01 1.30e-02 2.01e-02 ohc ................ 2.49e-01 9.22e-01 7.19e-01 16 15 D SM num-of-cylinders ... 0.124 six ................ -2.20e+00 1.30e-02 1.17e-01 five ............... -1.43e+00 1.30e-02 5.41e-02 three .............. 8.51e-01 1.30e-02 5.55e-03 twelve ............. 8.51e-01 1.30e-02 5.55e-03 eight .............. -6.54e-01 1.30e-02 2.50e-02 two ................ -4.37e-01 1.30e-02 2.01e-02 four ............... 1.77e-01 9.22e-01 7.73e-01 22 04 D SM aspiration ......... 0.054 turbo .............. 5.58e-01 3.18e-01 1.82e-01 std ................ -1.82e-01 6.82e-01 8.18e-01 18 08 D SM engine-location .... 0.017 rear ............... 9.84e-01 4.55e-02 1.70e-02 front .............. -2.94e-02 9.55e-01 9.83e-01 CLASS 11 - weight 9 normalized weight 0.044 relative strength 3.58e-05 ******* class cross entropy w.r.t. global class 2.22e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 2.201 ( 5.02e+00 4.09e-02) 3.44e+00 ( 5.16e+00 7.06e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 2.074 ( 1.08e+00 1.81e-02) 6.52e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.817 ( 7.51e+00 7.68e-02) 4.14e+00 ( 7.83e+00 1.97e-01) 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.810 ( 8.72e+00 1.13e-01) 5.56e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.616 ( 2.23e+00 3.44e-02) 1.07e+00 ( 2.27e+00 2.81e-01) o 24 02 D SM make ............... 1.475 toyota ............. -3.45e+00 4.94e-03 1.56e-01 nissan ............. -2.96e+00 4.54e-03 8.76e-02 mitsubishi ......... -2.64e+00 4.54e-03 6.33e-02 subaru ............. -2.56e+00 4.54e-03 5.85e-02 volkswagen ......... -2.56e+00 4.54e-03 5.85e-02 peugot ............. -2.47e+00 4.54e-03 5.36e-02 volvo .............. -2.47e+00 4.54e-03 5.36e-02 dodge .............. -2.27e+00 4.54e-03 4.39e-02 bmw ................ -2.15e+00 4.54e-03 3.91e-02 mercedes-benz ...... -2.15e+00 4.54e-03 3.91e-02 honda .............. 2.08e+00 5.05e-01 6.33e-02 audi ............... -2.02e+00 4.54e-03 3.42e-02 plymouth ........... -2.02e+00 4.54e-03 3.42e-02 chevrolet .......... 1.96e+00 1.05e-01 1.48e-02 saab ............... -1.87e+00 4.54e-03 2.93e-02 porsche ............ -1.69e+00 4.54e-03 2.45e-02 isuzu .............. -1.47e+00 4.54e-03 1.96e-02 mazda .............. 1.30e+00 3.04e-01 8.27e-02 alfa-romero ........ -1.18e+00 4.54e-03 1.48e-02 jaguar ............. -1.18e+00 4.54e-03 1.48e-02 renault ............ -7.83e-01 4.54e-03 9.93e-03 mercury ............ -1.12e-01 4.54e-03 5.08e-03 15 17 D SM fuel-system ........ 1.189 mpfi ............... -3.60e+00 1.25e-02 4.57e-01 1bbl ............... 2.25e+00 5.13e-01 5.40e-02 idi ................ -2.06e+00 1.25e-02 9.77e-02 spdi ............... -1.27e+00 1.25e-02 4.43e-02 mfi ................ 8.26e-01 1.25e-02 5.46e-03 spfi ............... 8.26e-01 1.25e-02 5.46e-03 2bbl ............... 2.50e-01 4.12e-01 3.21e-01 4bbl ............... -1.96e-01 1.25e-02 1.52e-02 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.164 ( 4.46e+00 1.24e-01) 2.78e+00 ( 4.80e+00 2.82e-01) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.164 ( 4.19e+00 1.38e-01) 2.88e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.115 ( 4.51e+00 3.13e-02) 2.44e+00 ( 4.59e+00 5.89e-02) 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.992 ( 3.54e+00 1.86e-01) 1.85e+00 ( 3.19e+00 2.56e-01) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.825 ( 4.15e+00 1.81e-02) 1.92e+00 ( 4.19e+00 3.15e-02) 20 06 D SM body-style ......... 0.787 sedan .............. -3.09e+00 2.13e-02 4.67e-01 wagon .............. -1.81e+00 2.00e-02 1.22e-01 hatchback .......... 9.92e-01 9.19e-01 3.41e-01 hardtop ............ -6.90e-01 2.00e-02 3.98e-02 convertible ........ -4.11e-01 2.00e-02 3.01e-02 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.781 ( 3.67e+00 1.86e-01) 1.47e+00 ( 3.40e+00 2.23e-01) 21 05 D SM num-of-doors ....... 0.653 four ............... -2.77e+00 3.46e-02 5.55e-01 ? .................. 1.08e+00 3.33e-02 1.13e-02 two ................ 7.65e-01 9.32e-01 4.34e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.631 ( 4.71e+00 1.16e-01) 4.38e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.80e-01 Prob-*k is known 8.00e-01 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 0.429 ( 1.18e+00 4.50e-02) 5.25e-03 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.412 ( 3.97e+00 2.20e-02) 7.76e-01 ( 3.98e+00 4.54e-02) 19 07 D SM drive-wheels ....... 0.347 rwd ................ -2.41e+00 3.33e-02 3.71e-01 fwd ................ 4.69e-01 9.33e-01 5.84e-01 4wd ................ -3.09e-01 3.33e-02 4.53e-02 16 15 D SM num-of-cylinders ... 0.340 three .............. 3.02e+00 1.14e-01 5.55e-03 six ................ -2.11e+00 1.43e-02 1.17e-01 five ............... -1.33e+00 1.43e-02 5.41e-02 twelve ............. 9.44e-01 1.43e-02 5.55e-03 eight .............. -5.60e-01 1.43e-02 2.50e-02 two ................ -3.44e-01 1.43e-02 2.01e-02 four ............... 5.31e-02 8.15e-01 7.73e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.128 ( 8.59e+00 9.39e-02) 5.15e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 17 14 D SM engine-type ........ 0.120 ohcf ............... -1.64e+00 1.43e-02 7.35e-02 ohcv ............... -1.50e+00 1.43e-02 6.38e-02 dohc ............... -1.42e+00 1.43e-02 5.89e-02 dohcv .............. 9.44e-01 1.43e-02 5.55e-03 l .................. 6.60e-01 1.14e-01 5.89e-02 rotor .............. -3.44e-01 1.43e-02 2.01e-02 ohc ................ 1.25e-01 8.15e-01 7.19e-01 22 04 D SM aspiration ......... 0.078 turbo .............. -1.29e+00 4.99e-02 1.82e-01 std ................ 1.50e-01 9.50e-01 8.18e-01 18 08 D SM engine-location .... 0.021 rear ............... 1.08e+00 4.99e-02 1.70e-02 front .............. -3.40e-02 9.50e-01 9.83e-01 23 03 D SM fuel-type .......... 0.017 diesel ............. -6.90e-01 4.99e-02 9.95e-02 gas ................ 5.36e-02 9.50e-01 9.00e-01 CLASS 12 - weight 9 normalized weight 0.044 relative strength 9.06e-05 ******* class cross entropy w.r.t. global class 2.56e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 2.278 ( 5.13e+00 4.74e-03) 5.78e+00 ( 5.16e+00 7.06e-02) 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 2.180 ( 2.23e+00 1.94e-02) 1.69e+00 ( 2.27e+00 2.81e-01) o 17 14 D SM engine-type ........ 1.902 ohc ................ -3.92e+00 1.43e-02 7.19e-01 rotor .............. 3.03e+00 4.14e-01 2.01e-02 ohcv ............... -1.50e+00 1.43e-02 6.38e-02 ohcf ............... 1.45e+00 3.14e-01 7.35e-02 l .................. -1.42e+00 1.43e-02 5.89e-02 dohc ............... 1.29e+00 2.14e-01 5.89e-02 dohcv .............. 9.46e-01 1.43e-02 5.55e-03 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.850 ( 1.03e+00 3.67e-02) 3.87e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 5.98e-01 Prob-*k is known 9.80e-01 24 02 D SM make ............... 1.770 toyota ............. -3.53e+00 4.55e-03 1.56e-01 nissan ............. -2.96e+00 4.55e-03 8.76e-02 honda .............. -2.63e+00 4.55e-03 6.33e-02 mitsubishi ......... -2.63e+00 4.55e-03 6.33e-02 alfa-romero ........ 2.63e+00 2.05e-01 1.48e-02 subaru ............. -2.55e+00 4.55e-03 5.85e-02 volkswagen ......... -2.55e+00 4.55e-03 5.85e-02 porsche ............ 2.52e+00 3.05e-01 2.45e-02 peugot ............. -2.47e+00 4.55e-03 5.36e-02 volvo .............. -2.47e+00 4.55e-03 5.36e-02 dodge .............. -2.27e+00 4.55e-03 4.39e-02 bmw ................ -2.15e+00 4.55e-03 3.91e-02 mercedes-benz ...... -2.15e+00 4.55e-03 3.91e-02 audi ............... -2.02e+00 4.55e-03 3.42e-02 plymouth ........... -2.02e+00 4.55e-03 3.42e-02 saab ............... -1.87e+00 4.55e-03 2.93e-02 mazda .............. 1.59e+00 4.05e-01 8.27e-02 isuzu .............. -1.46e+00 4.55e-03 1.96e-02 chevrolet .......... -1.18e+00 4.55e-03 1.48e-02 jaguar ............. -1.18e+00 4.55e-03 1.48e-02 renault ............ -7.81e-01 4.55e-03 9.93e-03 mercury ............ -1.10e-01 4.55e-03 5.08e-03 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 1.542 ( 1.29e+00 3.48e-02) 2.57e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 5.98e-01 Prob-*k is known 9.80e-01 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 1.533 ( 3.91e+00 2.10e-02) 3.29e+00 ( 3.98e+00 4.54e-02) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 16 15 D SM num-of-cylinders ... 1.289 two ................ 3.03e+00 4.14e-01 2.01e-02 five ............... -1.33e+00 1.43e-02 5.41e-02 four ............... -1.28e+00 2.14e-01 7.73e-01 six ................ 9.86e-01 3.14e-01 1.17e-01 three .............. 9.46e-01 1.43e-02 5.55e-03 twelve ............. 9.46e-01 1.43e-02 5.55e-03 eight .............. -5.58e-01 1.43e-02 2.50e-02 20 06 D SM body-style ......... 1.121 sedan .............. -3.15e+00 2.00e-02 4.67e-01 convertible ........ 2.36e+00 3.20e-01 3.01e-02 wagon .............. -1.81e+00 2.00e-02 1.22e-01 hardtop ............ 1.71e+00 2.20e-01 3.98e-02 hatchback .......... 2.09e-01 4.20e-01 3.41e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 1.060 ( 5.01e+00 4.72e-02) 5.23e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 4.80e-01 Prob-*k is known 8.00e-01 15 17 D SM fuel-system ........ 1.045 2bbl ............... -3.25e+00 1.25e-02 3.21e-01 4bbl ............... 3.03e+00 3.12e-01 1.52e-02 idi ................ -2.06e+00 1.25e-02 9.77e-02 1bbl ............... -1.46e+00 1.25e-02 5.40e-02 spdi ............... -1.27e+00 1.25e-02 4.43e-02 mfi ................ 8.28e-01 1.25e-02 5.46e-03 spfi ............... 8.28e-01 1.25e-02 5.46e-03 mpfi ............... 2.93e-01 6.13e-01 4.57e-01 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 0.984 ( 4.52e+00 3.17e-02) 2.23e+00 ( 4.59e+00 5.89e-02) 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.859 ( 2.88e+00 1.86e-01) 1.71e+00 ( 3.19e+00 2.56e-01) 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 0.855 ( 4.18e+00 9.07e-03) 1.29e+00 ( 4.19e+00 3.15e-02) 18 08 D SM engine-location .... 0.790 rear ............... 3.03e+00 3.50e-01 1.70e-02 front .............. -4.14e-01 6.50e-01 9.83e-01 19 07 D SM drive-wheels ....... 0.757 fwd ................ -2.86e+00 3.33e-02 5.84e-01 rwd ................ 9.24e-01 9.33e-01 3.71e-01 4wd ................ -3.07e-01 3.33e-02 4.53e-02 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 0.751 ( 7.85e+00 5.93e-02) 3.39e-01 ( 7.83e+00 1.97e-01) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.684 ( 8.65e+00 9.39e-02) 1.22e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 21 05 D SM num-of-doors ....... 0.658 four ............... -2.81e+00 3.33e-02 5.55e-01 ? .................. 1.08e+00 3.33e-02 1.13e-02 two ................ 7.67e-01 9.33e-01 4.34e-01 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.466 ( 9.82e+00 4.33e-01) 1.08e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.456 ( 4.90e+00 2.96e-01) 1.08e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.433 ( 3.20e+00 1.86e-01) 1.08e+00 ( 3.40e+00 2.23e-01) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 0.219 ( 4.74e+00 4.15e-01) 1.40e-01 ( 4.80e+00 2.82e-01) 22 04 D SM aspiration ......... 0.078 turbo .............. -1.29e+00 5.00e-02 1.82e-01 std ................ 1.50e-01 9.50e-01 8.18e-01 23 03 D SM fuel-type .......... 0.016 diesel ............. -6.88e-01 5.00e-02 9.95e-02 gas ................ 5.35e-02 9.50e-01 9.00e-01 CLASS 13 - weight 8 normalized weight 0.039 relative strength 2.67e-05 ******* class cross entropy w.r.t. global class 1.72e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 1.871 ( 4.16e+00 4.71e-03) 6.41e+00 ( 4.19e+00 3.15e-02) 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.623 ( 5.07e+00 1.83e-02) 4.69e+00 ( 5.16e+00 7.06e-02) 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.607 ( 1.22e+00 1.41e-02) 3.38e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 1.543 ( 7.67e+00 3.60e-02) 4.42e+00 ( 7.83e+00 1.97e-01) 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.374 ( 4.60e+00 5.74e-02) 3.56e+00 ( 4.80e+00 2.82e-01) 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 1.351 ( 4.55e+00 1.22e-02) 3.45e+00 ( 4.59e+00 5.89e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 1.145 ( 1.11e+00 3.02e-02) 2.90e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 24 02 D SM make ............... 1.029 toyota ............. -3.43e+00 5.06e-03 1.56e-01 nissan ............. -2.85e+00 5.06e-03 8.76e-02 mazda .............. -2.79e+00 5.06e-03 8.27e-02 subaru ............. -2.45e+00 5.06e-03 5.85e-02 peugot ............. -2.36e+00 5.06e-03 5.36e-02 volvo .............. -2.36e+00 5.06e-03 5.36e-02 bmw ................ -2.04e+00 5.06e-03 3.91e-02 mercedes-benz ...... -2.04e+00 5.06e-03 3.91e-02 audi ............... -1.91e+00 5.06e-03 3.42e-02 saab ............... -1.76e+00 5.06e-03 2.93e-02 dodge .............. 1.65e+00 2.28e-01 4.39e-02 porsche ............ -1.58e+00 5.06e-03 2.45e-02 volkswagen ......... 1.36e+00 2.28e-01 5.85e-02 isuzu .............. -1.36e+00 5.06e-03 1.96e-02 honda .............. 1.27e+00 2.26e-01 6.33e-02 plymouth ........... 1.22e+00 1.16e-01 3.42e-02 alfa-romero ........ -1.07e+00 5.06e-03 1.48e-02 chevrolet .......... -1.07e+00 5.06e-03 1.48e-02 jaguar ............. -1.07e+00 5.06e-03 1.48e-02 renault ............ -6.74e-01 5.06e-03 9.93e-03 mitsubishi ......... 6.08e-01 1.16e-01 6.33e-02 mercury ............ -3.29e-03 5.06e-03 5.08e-03 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 1.012 ( 2.10e+00 7.61e-02) 2.15e+00 ( 2.27e+00 2.81e-01) o 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 0.985 ( 9.04e+00 1.44e-01) 2.14e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 0.698 2bbl ............... -3.14e+00 1.39e-02 3.21e-01 idi ................ -1.95e+00 1.39e-02 9.77e-02 spdi ............... 1.67e+00 2.36e-01 4.43e-02 1bbl ............... 1.47e+00 2.35e-01 5.40e-02 mfi ................ 9.35e-01 1.39e-02 5.46e-03 spfi ............... 9.35e-01 1.39e-02 5.46e-03 4bbl ............... -8.67e-02 1.39e-02 1.52e-02 mpfi ............... 4.63e-03 4.59e-01 4.57e-01 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 0.686 ( 4.52e+00 1.14e-01) 5.59e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 0.477 ( 8.63e+00 9.33e-02) 1.02e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 21 05 D SM num-of-doors ....... 0.370 ? .................. 2.57e+00 1.48e-01 1.13e-02 four ............... -7.66e-01 2.58e-01 5.55e-01 two ................ 3.14e-01 5.94e-01 4.34e-01 19 07 D SM drive-wheels ....... 0.334 rwd ................ -2.30e+00 3.71e-02 3.71e-01 fwd ................ 4.61e-01 9.26e-01 5.84e-01 4wd ................ -2.00e-01 3.71e-02 4.53e-02 22 04 D SM aspiration ......... 0.260 turbo .............. 1.01e+00 5.01e-01 1.82e-01 std ................ -4.94e-01 4.99e-01 8.18e-01 20 06 D SM body-style ......... 0.187 convertible ........ 1.49e+00 1.34e-01 3.01e-02 sedan .............. -6.52e-01 2.43e-01 4.67e-01 hardtop ............ -5.81e-01 2.23e-02 3.98e-02 hatchback .......... 3.16e-01 4.67e-01 3.41e-01 wagon .............. 8.76e-02 1.34e-01 1.22e-01 17 14 D SM engine-type ........ 0.132 ohcf ............... -1.53e+00 1.59e-02 7.35e-02 ohcv ............... -1.39e+00 1.59e-02 6.38e-02 dohc ............... -1.31e+00 1.59e-02 5.89e-02 l .................. -1.31e+00 1.59e-02 5.89e-02 dohcv .............. 1.05e+00 1.59e-02 5.55e-03 rotor .............. -2.35e-01 1.59e-02 2.01e-02 ohc ................ 2.29e-01 9.05e-01 7.19e-01 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 0.115 ( 4.89e+00 3.24e-01) 3.81e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.66e-01 Prob-*k is known 8.00e-01 16 15 D SM num-of-cylinders ... 0.114 six ................ -2.00e+00 1.59e-02 1.17e-01 five ............... -1.22e+00 1.59e-02 5.41e-02 three .............. 1.05e+00 1.59e-02 5.55e-03 twelve ............. 1.05e+00 1.59e-02 5.55e-03 eight .............. -4.51e-01 1.59e-02 2.50e-02 two ................ -2.35e-01 1.59e-02 2.01e-02 four ............... 1.58e-01 9.05e-01 7.73e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.097 ( 3.23e+00 1.85e-01) 2.08e-01 ( 3.19e+00 2.56e-01) 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 0.067 ( 3.97e+00 4.77e-02) 3.43e-01 ( 3.98e+00 4.54e-02) 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.037 ( 3.42e+00 1.85e-01) 1.22e-01 ( 3.40e+00 2.23e-01) 18 08 D SM engine-location .... 0.028 rear ............... 1.19e+00 5.56e-02 1.70e-02 front .............. -4.01e-02 9.44e-01 9.83e-01 23 03 D SM fuel-type .......... 0.013 diesel ............. -5.81e-01 5.56e-02 9.95e-02 gas ................ 4.76e-02 9.44e-01 9.00e-01 CLASS 14 - weight 5 normalized weight 0.024 relative strength 3.52e-01 ******* class cross entropy w.r.t. global class 3.92e+01 ******* Model file: /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): (single_multinomial SM) (single_normal_cm SNcm) (single_normal_cn SNcn) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 12 38 R SNcn Log compression-ratio12 38 R SNcn Log compression-rati 6.351 ( 3.04e+00 1.37e-02) 5.68e+01 ( 2.27e+00 2.81e-01) o 10 36 R SNcn Log curb-weight ....10 36 R SNcn Log curb-weight .... 2.390 ( 8.11e+00 3.08e-02) 9.14e+00 ( 7.83e+00 1.97e-01) 06 32 R SNcn Log wheel-base .....06 32 R SNcn Log wheel-base ..... 2.258 ( 4.70e+00 2.54e-02) 4.43e+00 ( 4.59e+00 5.89e-02) 01 27 R SNcm Log bore ...........01 27 R SNcm Log bore ........... 2.185 ( 1.31e+00 1.37e-02) 7.95e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 17 14 D SM engine-type ........ 2.180 ohc ................ -3.41e+00 2.38e-02 7.19e-01 l .................. 2.68e+00 8.57e-01 5.89e-02 dohcv .............. 1.46e+00 2.38e-02 5.55e-03 ohcf ............... -1.13e+00 2.38e-02 7.35e-02 ohcv ............... -9.86e-01 2.38e-02 6.38e-02 dohc ............... -9.07e-01 2.38e-02 5.89e-02 rotor .............. 1.69e-01 2.38e-02 2.01e-02 08 34 R SNcn Log width ..........08 34 R SNcn Log width .......... 2.155 ( 4.23e+00 4.56e-03) 8.24e+00 ( 4.19e+00 3.15e-02) 04 30 R SNcm Log peak-rpm .......04 30 R SNcm Log peak-rpm ....... 2.137 ( 8.33e+00 9.04e-02) 2.25e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.90e-01 24 02 D SM make ............... 2.076 toyota ............. -3.02e+00 7.58e-03 1.56e-01 peugot ............. 2.75e+00 8.41e-01 5.36e-02 nissan ............. -2.45e+00 7.58e-03 8.76e-02 mazda .............. -2.39e+00 7.58e-03 8.27e-02 honda .............. -2.12e+00 7.58e-03 6.33e-02 mitsubishi ......... -2.12e+00 7.58e-03 6.33e-02 subaru ............. -2.04e+00 7.58e-03 5.85e-02 volkswagen ......... -2.04e+00 7.58e-03 5.85e-02 volvo .............. -1.96e+00 7.58e-03 5.36e-02 dodge .............. -1.76e+00 7.58e-03 4.39e-02 bmw ................ -1.64e+00 7.58e-03 3.91e-02 mercedes-benz ...... -1.64e+00 7.58e-03 3.91e-02 audi ............... -1.51e+00 7.58e-03 3.42e-02 plymouth ........... -1.51e+00 7.58e-03 3.42e-02 saab ............... -1.35e+00 7.58e-03 2.93e-02 porsche ............ -1.17e+00 7.58e-03 2.45e-02 isuzu .............. -9.53e-01 7.58e-03 1.96e-02 alfa-romero ........ -6.69e-01 7.58e-03 1.48e-02 chevrolet .......... -6.69e-01 7.58e-03 1.48e-02 jaguar ............. -6.69e-01 7.58e-03 1.48e-02 mercury ............ 4.01e-01 7.58e-03 5.08e-03 renault ............ -2.71e-01 7.58e-03 9.93e-03 02 28 R SNcm Log stroke .........02 28 R SNcm Log stroke ......... 1.855 ( 1.26e+00 1.37e-02) 6.08e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 23 03 D SM fuel-type .......... 1.837 gas ................ -2.38e+00 8.33e-02 9.00e-01 diesel ............. 2.22e+00 9.17e-01 9.95e-02 09 35 R SNcn Log height .........09 35 R SNcn Log height ......... 1.771 ( 4.05e+00 1.55e-02) 4.42e+00 ( 3.98e+00 4.54e-02) DISCRETE ATTRIBUTE (t = D) log( numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob t a Prob-*kl) -jkl -*kl REAL ATTRIBUTE (t = R) |Mean-jk - numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev t a -jk -jk StDev-jk -*k -*k 15 17 D SM fuel-system ........ 1.758 mpfi ............... -3.09e+00 2.08e-02 4.57e-01 2bbl ............... -2.73e+00 2.08e-02 3.21e-01 idi ................ 2.17e+00 8.54e-01 9.77e-02 mfi ................ 1.34e+00 2.08e-02 5.46e-03 spfi ............... 1.34e+00 2.08e-02 5.46e-03 1bbl ............... -9.53e-01 2.08e-02 5.40e-02 spdi ............... -7.54e-01 2.08e-02 4.43e-02 4bbl ............... 3.17e-01 2.08e-02 1.52e-02 11 37 R SNcn Log engine-size ....11 37 R SNcn Log engine-size .... 1.653 ( 5.02e+00 4.54e-02) 4.92e+00 ( 4.80e+00 2.82e-01) 03 29 R SNcm Log horse-power ....03 29 R SNcm Log horse-power .... 1.541 ( 4.55e+00 4.54e-02) 6.65e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.90e-01 07 33 R SNcn Log length .........07 33 R SNcn Log length ......... 1.450 ( 5.25e+00 2.83e-02) 3.45e+00 ( 5.16e+00 7.06e-02) 00 26 R SNcm Log normalized-loses00 26 R SNcm Log normalized-loses 1.323 ( 5.08e+00 4.54e-02) 6.99e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.33e-01 Prob-*k is known 8.00e-01 22 04 D SM aspiration ......... 1.291 std ................ -2.28e+00 8.33e-02 8.18e-01 turbo .............. 1.62e+00 9.17e-01 1.82e-01 05 31 R SNcm Log price ..........05 31 R SNcm Log price .......... 1.213 ( 9.66e+00 1.13e-01) 2.75e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 19 07 D SM drive-wheels ....... 0.658 fwd ................ -2.35e+00 5.56e-02 5.84e-01 rwd ................ 8.75e-01 8.89e-01 3.71e-01 4wd ................ 2.04e-01 5.56e-02 4.53e-02 20 06 D SM body-style ......... 0.393 hatchback .......... -2.32e+00 3.33e-02 3.41e-01 wagon .............. 1.10e+00 3.67e-01 1.22e-01 hardtop ............ -1.77e-01 3.33e-02 3.98e-02 sedan .............. 1.33e-01 5.33e-01 4.67e-01 convertible ........ 1.02e-01 3.33e-02 3.01e-02 21 05 D SM num-of-doors ....... 0.393 two ................ -2.05e+00 5.56e-02 4.34e-01 ? .................. 1.59e+00 5.56e-02 1.13e-02 four ............... 4.71e-01 8.89e-01 5.55e-01 13 39 R SNcn Log city-mpg .......13 39 R SNcn Log city-mpg ....... 0.165 ( 3.29e+00 1.79e-01) 5.07e-01 ( 3.19e+00 2.56e-01) 16 15 D SM num-of-cylinders ... 0.104 six ................ -1.59e+00 2.38e-02 1.17e-01 three .............. 1.46e+00 2.38e-02 5.55e-03 twelve ............. 1.46e+00 2.38e-02 5.55e-03 five ............... -8.21e-01 2.38e-02 5.41e-02 two ................ 1.69e-01 2.38e-02 2.01e-02 four ............... 1.04e-01 8.57e-01 7.73e-01 eight .............. -4.74e-02 2.38e-02 2.50e-02 18 08 D SM engine-location .... 0.068 rear ............... 1.59e+00 8.33e-02 1.70e-02 front .............. -6.99e-02 9.17e-01 9.83e-01 14 40 R SNcn Log highway-mpg ....14 40 R SNcn Log highway-mpg .... 0.045 ( 3.39e+00 1.79e-01) 8.63e-02 ( 3.40e+00 2.23e-01) autoclass-3.3.6.dfsg.1/sample/semantic.cache0000644000175000017500000000051111247310756016756 0ustar areare;; Object sample/ ;; SEMANTICDB Tags save file (semanticdb-project-database-file "sample/" :tables (list (semanticdb-table "read.me.c" :major-mode 'c-mode :tags 'nil :file "read.me.c" :pointmax 1055 ) ) :file "semantic.cache" :semantic-tag-version "2.0beta3" :semanticdb-version "2.0beta3" ) autoclass-3.3.6.dfsg.1/sample/imports-85c.search0000644000175000017500000006715411247310756017467 0ustar arearesearch_DS n, time, n_dups, n_dup_tries 250 21 61 2247 last try reported 0 tries from best on down for n_tries 189 search_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 161 0 15 15 -1.62636315e+04 28 200 n_dups 6 search_try_DS 0 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 162 0 15 15 -1.62636315e+04 28 200 search_try_DS 0 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 163 0 15 15 -1.62636315e+04 28 200 search_try_DS 0 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 164 0 15 15 -1.62636315e+04 28 200 search_try_DS 0 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 165 0 15 15 -1.62636315e+04 28 200 search_try_DS 0 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 166 0 15 15 -1.62636315e+04 28 200 search_try_DS 0 dup_try_DS 5 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 167 1 15 15 -1.62636315e+04 28 200 search_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 82 0 12 11 -1.62792399e+04 23 200 n_dups 0 search_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 183 0 13 13 -1.62916655e+04 12 200 n_dups 0 search_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 83 0 13 12 -1.62958097e+04 25 200 n_dups 0 search_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 181 0 12 12 -1.62991049e+04 12 200 n_dups 5 search_try_DS 4 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 185 0 12 12 -1.62991049e+04 12 200 search_try_DS 4 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 187 0 12 12 -1.62991049e+04 12 200 search_try_DS 4 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 189 0 12 12 -1.62991049e+04 12 200 search_try_DS 4 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 191 0 12 12 -1.62991049e+04 12 200 search_try_DS 4 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 193 0 12 12 -1.62991049e+04 12 200 search_try_DS 5 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 5 1 10 10 -1.63266990e+04 20 200 n_dups 0 search_try_DS 6 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 210 0 12 12 -1.63280281e+04 23 200 n_dups 0 search_try_DS 7 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 65 0 10 10 -1.63284382e+04 24 200 n_dups 0 search_try_DS 8 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 8 0 9 9 -1.63286639e+04 10 200 n_dups 0 search_try_DS 9 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 150 0 9 9 -1.63372396e+04 15 200 n_dups 0 search_try_DS 10 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 118 0 13 13 -1.63414156e+04 36 200 n_dups 4 search_try_DS 10 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 120 0 13 13 -1.63414156e+04 36 200 search_try_DS 10 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 122 0 13 13 -1.63414156e+04 36 200 search_try_DS 10 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 124 0 13 13 -1.63414156e+04 36 200 search_try_DS 10 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 126 1 13 13 -1.63414156e+04 36 200 search_try_DS 11 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 130 0 18 18 -1.63501692e+04 39 200 n_dups 0 search_try_DS 12 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 54 0 16 16 -1.63515599e+04 35 200 n_dups 0 search_try_DS 13 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 222 0 10 10 -1.63529086e+04 15 200 n_dups 0 search_try_DS 14 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 73 0 12 12 -1.63553195e+04 35 200 n_dups 2 search_try_DS 14 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 76 0 12 12 -1.63553195e+04 35 200 search_try_DS 14 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 79 0 12 12 -1.63553195e+04 35 200 search_try_DS 15 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 128 0 11 11 -1.63605728e+04 18 200 n_dups 0 search_try_DS 16 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 127 0 14 14 -1.63700181e+04 21 200 n_dups 0 search_try_DS 17 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 57 0 12 12 -1.63720909e+04 57 200 n_dups 0 search_try_DS 18 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 119 0 14 14 -1.63806778e+04 17 200 n_dups 3 search_try_DS 18 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 121 0 14 14 -1.63806777e+04 17 200 search_try_DS 18 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 123 0 14 14 -1.63806777e+04 17 200 search_try_DS 18 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 125 0 14 14 -1.63806777e+04 17 200 search_try_DS 19 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 102 0 13 13 -1.63838871e+04 14 200 n_dups 15 search_try_DS 19 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 103 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 104 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 105 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 106 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 107 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 5 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 108 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 6 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 109 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 7 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 110 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 8 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 111 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 9 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 112 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 10 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 113 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 11 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 114 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 12 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 115 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 13 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 116 0 13 13 -1.63838870e+04 14 200 search_try_DS 19 dup_try_DS 14 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 117 1 13 13 -1.63838870e+04 14 200 search_try_DS 20 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 147 0 11 11 -1.63882675e+04 18 200 n_dups 0 search_try_DS 21 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 153 0 11 11 -1.63882675e+04 18 200 n_dups 0 search_try_DS 22 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 168 0 15 15 -1.63887901e+04 9 200 n_dups 0 search_try_DS 23 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 66 0 14 14 -1.63897605e+04 19 200 n_dups 0 search_try_DS 24 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 184 0 16 16 -1.63903518e+04 10 200 n_dups 0 search_try_DS 25 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 249 0 10 10 -1.63936260e+04 22 200 n_dups 0 search_try_DS 26 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 129 0 10 10 -1.63958699e+04 14 200 n_dups 0 search_try_DS 27 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 101 0 9 9 -1.63964468e+04 14 200 n_dups 0 search_try_DS 28 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 151 0 10 10 -1.63984698e+04 20 200 n_dups 0 search_try_DS 29 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 170 0 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 30 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 172 0 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 31 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 174 0 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 32 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 176 0 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 33 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 178 0 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 34 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 180 1 13 13 -1.63991086e+04 18 200 n_dups 0 search_try_DS 35 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 4 0 7 7 -1.64066292e+04 22 200 n_dups 0 search_try_DS 36 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 152 0 16 16 -1.64151364e+04 19 200 n_dups 0 search_try_DS 37 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 221 0 16 15 -1.64157494e+04 16 200 n_dups 0 search_try_DS 38 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 23 0 11 11 -1.64159914e+04 15 200 n_dups 8 search_try_DS 38 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 25 0 11 11 -1.64159914e+04 15 200 search_try_DS 38 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 27 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 29 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 31 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 33 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 5 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 35 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 6 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 37 0 11 11 -1.64159915e+04 15 200 search_try_DS 38 dup_try_DS 7 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 39 0 11 11 -1.64159915e+04 15 200 search_try_DS 39 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 55 0 15 15 -1.64205958e+04 36 200 n_dups 0 search_try_DS 40 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 10 0 17 17 -1.64212238e+04 18 200 n_dups 0 search_try_DS 41 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 74 0 9 9 -1.64230308e+04 45 200 n_dups 0 search_try_DS 42 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 77 0 9 9 -1.64230308e+04 45 200 n_dups 0 search_try_DS 43 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 80 0 9 9 -1.64230308e+04 45 200 n_dups 0 search_try_DS 44 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 63 0 11 11 -1.64263819e+04 13 200 n_dups 0 search_try_DS 45 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 169 0 12 12 -1.64282873e+04 18 200 n_dups 0 search_try_DS 46 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 171 0 12 12 -1.64282874e+04 18 200 n_dups 0 search_try_DS 47 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 173 0 12 12 -1.64282874e+04 18 200 n_dups 0 search_try_DS 48 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 175 0 12 12 -1.64282874e+04 18 200 n_dups 0 search_try_DS 49 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 177 0 12 12 -1.64282874e+04 18 200 n_dups 0 search_try_DS 50 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 179 0 12 12 -1.64282874e+04 18 200 n_dups 0 search_try_DS 51 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 156 0 8 8 -1.64294361e+04 14 200 n_dups 0 search_try_DS 52 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 6 0 15 15 -1.64413298e+04 18 200 n_dups 0 search_try_DS 53 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 139 0 16 15 -1.64414441e+04 26 200 n_dups 0 search_try_DS 54 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 142 0 16 15 -1.64414441e+04 26 200 n_dups 0 search_try_DS 55 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 145 0 16 15 -1.64414441e+04 26 200 n_dups 0 search_try_DS 56 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 149 0 12 12 -1.64428033e+04 36 200 n_dups 0 search_try_DS 57 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 155 0 12 12 -1.64428033e+04 36 200 n_dups 0 search_try_DS 58 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 158 1 12 12 -1.64428033e+04 36 200 n_dups 0 search_try_DS 59 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 67 0 12 12 -1.64438233e+04 24 200 n_dups 0 search_try_DS 60 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 69 0 12 12 -1.64438233e+04 24 200 n_dups 0 search_try_DS 61 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 71 0 12 12 -1.64438233e+04 24 200 n_dups 0 search_try_DS 62 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 131 0 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 63 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 132 0 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 64 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 133 0 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 65 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 134 0 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 66 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 135 0 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 67 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 136 1 15 15 -1.64490762e+04 22 200 n_dups 0 search_try_DS 68 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 85 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 69 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 86 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 70 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 87 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 71 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 88 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 72 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 89 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 73 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 90 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 74 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 91 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 75 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 92 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 76 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 93 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 77 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 94 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 78 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 95 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 79 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 96 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 80 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 97 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 81 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 98 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 82 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 99 0 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 83 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 100 1 9 9 -1.64497608e+04 15 200 n_dups 0 search_try_DS 84 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 84 0 9 9 -1.64497609e+04 15 200 n_dups 0 search_try_DS 85 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 160 0 11 11 -1.64517937e+04 19 200 n_dups 0 search_try_DS 86 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 137 0 15 15 -1.64520930e+04 21 200 n_dups 0 search_try_DS 87 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 140 0 15 15 -1.64520930e+04 21 200 n_dups 0 search_try_DS 88 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 143 0 15 15 -1.64520930e+04 21 200 n_dups 0 search_try_DS 89 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 146 1 15 15 -1.64520930e+04 21 200 n_dups 0 search_try_DS 90 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 212 0 13 13 -1.64535472e+04 13 200 n_dups 0 search_try_DS 91 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 211 0 14 14 -1.64535907e+04 14 200 n_dups 0 search_try_DS 92 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 240 0 10 10 -1.64623544e+04 20 200 n_dups 0 search_try_DS 93 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 243 0 10 10 -1.64623544e+04 20 200 n_dups 0 search_try_DS 94 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 246 0 10 10 -1.64623544e+04 20 200 n_dups 0 search_try_DS 95 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 223 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 96 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 224 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 97 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 225 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 98 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 226 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 99 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 227 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 100 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 228 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 101 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 229 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 102 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 230 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 103 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 231 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 104 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 232 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 105 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 233 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 106 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 234 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 107 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 235 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 108 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 236 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 109 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 237 0 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 110 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 238 1 12 12 -1.64643559e+04 12 200 n_dups 0 search_try_DS 111 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 41 0 11 11 -1.64671384e+04 12 200 n_dups 0 search_try_DS 112 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 213 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 113 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 214 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 114 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 215 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 115 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 216 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 116 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 217 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 117 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 218 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 118 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 219 0 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 119 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 220 1 16 16 -1.64721649e+04 21 200 n_dups 0 search_try_DS 120 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 64 0 13 13 -1.64756177e+04 19 200 n_dups 0 search_try_DS 121 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 250 0 13 13 -1.64827911e+04 23 200 n_dups 0 search_try_DS 122 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 44 0 13 13 -1.64891188e+04 17 200 n_dups 0 search_try_DS 123 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 159 0 8 8 -1.64897525e+04 16 200 n_dups 0 search_try_DS 124 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 182 0 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 125 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 186 0 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 126 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 188 0 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 127 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 190 0 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 128 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 192 0 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 129 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 194 1 9 9 -1.64923466e+04 29 200 n_dups 0 search_try_DS 130 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 46 0 14 13 -1.64953080e+04 18 200 n_dups 0 search_try_DS 131 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 42 0 8 8 -1.64992463e+04 21 200 n_dups 1 search_try_DS 131 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 47 0 8 8 -1.64992465e+04 21 200 search_try_DS 132 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 68 0 15 15 -1.65030171e+04 26 200 n_dups 0 search_try_DS 133 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 70 0 15 15 -1.65030171e+04 26 200 n_dups 0 search_try_DS 134 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 72 1 15 15 -1.65030171e+04 26 200 n_dups 0 search_try_DS 135 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 48 0 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 136 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 49 0 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 137 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 50 0 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 138 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 51 0 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 139 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 52 0 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 140 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 53 1 16 15 -1.65039219e+04 18 200 n_dups 0 search_try_DS 141 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 22 0 5 5 -1.65101133e+04 23 200 n_dups 0 search_try_DS 142 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 43 0 18 17 -1.65214998e+04 11 200 n_dups 0 search_try_DS 143 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 241 0 11 11 -1.65283240e+04 19 200 n_dups 0 search_try_DS 144 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 244 0 11 11 -1.65283240e+04 19 200 n_dups 0 search_try_DS 145 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 247 0 11 11 -1.65283240e+04 19 200 n_dups 0 search_try_DS 146 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 56 0 8 8 -1.65287219e+04 32 200 n_dups 0 search_try_DS 147 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 7 0 25 21 -1.65388893e+04 12 200 n_dups 0 search_try_DS 148 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 24 0 6 6 -1.65396514e+04 22 200 n_dups 8 search_try_DS 148 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 26 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 28 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 30 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 32 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 34 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 5 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 36 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 6 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 38 0 6 6 -1.65396514e+04 22 200 search_try_DS 148 dup_try_DS 7 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 40 1 6 6 -1.65396514e+04 22 200 search_try_DS 149 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 45 0 15 14 -1.65433581e+04 18 200 n_dups 0 search_try_DS 150 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 196 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 151 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 197 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 152 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 198 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 153 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 199 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 154 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 200 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 155 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 201 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 156 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 202 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 157 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 203 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 158 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 204 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 159 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 205 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 160 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 206 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 161 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 207 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 162 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 208 0 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 163 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 209 1 12 12 -1.65454065e+04 17 200 n_dups 0 search_try_DS 164 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 195 0 12 12 -1.65454066e+04 17 200 n_dups 0 search_try_DS 165 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 12 0 5 5 -1.65531956e+04 35 200 n_dups 4 search_try_DS 165 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 14 0 5 5 -1.65531956e+04 35 200 search_try_DS 165 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 16 0 5 5 -1.65531956e+04 35 200 search_try_DS 165 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 18 0 5 5 -1.65531956e+04 35 200 search_try_DS 165 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 20 0 5 5 -1.65531956e+04 35 200 search_try_DS 166 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 58 0 11 11 -1.65558662e+04 25 200 n_dups 0 search_try_DS 167 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 59 0 11 11 -1.65558662e+04 25 200 n_dups 0 search_try_DS 168 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 60 0 11 11 -1.65558662e+04 25 200 n_dups 0 search_try_DS 169 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 61 0 11 11 -1.65558662e+04 25 200 n_dups 0 search_try_DS 170 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 62 1 11 11 -1.65558662e+04 25 200 n_dups 0 search_try_DS 171 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 11 0 8 8 -1.65595451e+04 21 200 n_dups 5 search_try_DS 171 dup_try_DS 0 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 13 0 8 8 -1.65595451e+04 21 200 search_try_DS 171 dup_try_DS 1 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 15 0 8 8 -1.65595451e+04 21 200 search_try_DS 171 dup_try_DS 2 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 17 0 8 8 -1.65595451e+04 21 200 search_try_DS 171 dup_try_DS 3 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 19 0 8 8 -1.65595451e+04 21 200 search_try_DS 171 dup_try_DS 4 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 21 1 8 8 -1.65595451e+04 21 200 search_try_DS 172 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 148 0 14 14 -1.65683017e+04 21 200 n_dups 0 search_try_DS 173 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 154 0 14 14 -1.65683017e+04 21 200 n_dups 0 search_try_DS 174 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 157 0 14 14 -1.65683017e+04 21 200 n_dups 0 search_try_DS 175 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 138 0 11 11 -1.65863003e+04 23 200 n_dups 0 search_try_DS 176 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 141 0 11 11 -1.65863003e+04 23 200 n_dups 0 search_try_DS 177 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 144 0 11 11 -1.65863003e+04 23 200 n_dups 0 search_try_DS 178 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 75 0 16 16 -1.65914234e+04 16 200 n_dups 0 search_try_DS 179 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 78 0 16 16 -1.65914234e+04 16 200 n_dups 0 search_try_DS 180 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 81 1 16 16 -1.65914234e+04 16 200 n_dups 0 search_try_DS 181 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 239 0 12 11 -1.65917751e+04 40 200 n_dups 0 search_try_DS 182 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 242 0 12 11 -1.65917751e+04 40 200 n_dups 0 search_try_DS 183 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 245 0 12 11 -1.65917751e+04 40 200 n_dups 0 search_try_DS 184 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 248 1 12 11 -1.65917751e+04 40 200 n_dups 0 search_try_DS 185 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 3 0 5 5 -1.66555216e+04 9 200 n_dups 0 search_try_DS 186 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 2 0 3 3 -1.68407251e+04 24 200 n_dups 0 search_try_DS 187 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 9 0 3 3 -1.70093039e+04 9 200 n_dups 0 search_try_DS 188 n, time, j_in, j_out, ln_p, num_cycles, max_cycles 1 0 2 2 -1.72656173e+04 15 200 n_dups 0 start_j_list -999 n_final_summary, n_save 10 2 autoclass-3.3.6.dfsg.1/sample/imports-85c.hd20000644000175000017500000000432311247310756016664 0ustar areare!#; AutoClass C header file -- extension .hd2 !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 ;#! num_db2_format_defs num_db2_format_defs 2 ;; required number_of_attributes 26 ;; optional - default values are specified ;; separator_char ' ' ;; comment_char ';' ;; unknown_token '?' separator_char ',' ;; 0 discrete nominal "symboling" range 7 1 real scalar "normalized-loses" zero_point 0.0 rel_error 0.01 2 discrete nominal "make" range 22 3 discrete nominal "fuel-type" range 2 4 discrete nominal "aspiration" range 2 5 discrete nominal "num-of-doors" range 2 6 discrete nominal "body-style" range 5 7 discrete nominal "drive-wheels" range 3 8 discrete nominal "engine-location" range 2 9 real scalar "wheel-base" zero_point 0.0 rel_error 0.001 10 real scalar "length" zero_point 0.0 rel_error 0.001 11 real scalar "width" zero_point 0.0 rel_error 0.001 12 real scalar "height" zero_point 0.0 rel_error 0.001 13 real scalar "curb-weight" zero_point 0.0 rel_error 0.0002 14 discrete nominal "engine-type" range 7 15 discrete nominal "num-of-cylinders" range 7 16 real scalar "engine-size" zero_point 0.0 rel_error 0.01 17 discrete nominal "fuel-system" range 8 18 real scalar "bore" zero_point 0.0 rel_error 0.003 19 real scalar "stroke" zero_point 0.0 rel_error 0.003 20 real scalar "compression-ratio" zero_point 0.0 rel_error 0.003 21 real scalar "horse-power" zero_point 0.0 rel_error 0.01 22 real scalar "peak-rpm" zero_point 0.0 rel_error 0.02 23 real scalar "city-mpg" zero_point 0.0 rel_error 0.04 24 real scalar "highway-mpg" zero_point 0.0 rel_error 0.04 25 real scalar "price" zero_point 0.0 rel_error 0.001 autoclass-3.3.6.dfsg.1/sample/imports-85c.class-data-10000644000175000017500000003732311247310756020367 0ustar areare# CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP # # DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 # with log-A (approximate marginal likelihood) = -16230.401 # from classification results file # /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin # and using models # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 # # # DATA_CLASS 0 # CLASS = 0 # #Case # make num-of-doors body-style (Cls Prob) 004 audi four sedan 1.000 029 dodge four wagon 1.000 038 honda two hatchback 1.000 039 honda two hatchback 1.000 040 honda four sedan 1.000 041 honda four sedan 1.000 042 honda four sedan 1.000 043 honda two sedan 1.000 060 mazda two hatchback 1.000 061 mazda four sedan 1.000 062 mazda two hatchback 1.000 063 mazda four sedan 1.000 065 mazda four hatchback 1.000 081 mitsubishi two hatchback 1.000 082 mitsubishi two hatchback 1.000 086 mitsubishi four sedan 1.000 087 mitsubishi four sedan 1.000 088 mitsubishi four sedan 1.000 089 mitsubishi four sedan 1.000 100 nissan four hatchback 1.000 101 nissan four sedan 1.000 124 plymouth four wagon 1.000 174 toyota four sedan 1.000 176 toyota four hatchback 1.000 177 toyota four sedan 1.000 178 toyota four hatchback 1.000 184 volkswagen two sedan 1.000 186 volkswagen four sedan 1.000 187 volkswagen four sedan 1.000 189 volkswagen four sedan 1.000 194 volkswagen four wagon 0.985 6 0.015 # # # DATA_CLASS 1 # CLASS = 1 # #Case # make num-of-doors body-style (Cls Prob) 015 bmw four sedan 1.000 016 bmw four sedan 0.991 5 0.009 102 nissan four sedan 1.000 103 nissan four wagon 1.000 104 nissan four sedan 1.000 118 peugot four sedan 1.000 134 saab four sedan 1.000 136 saab four sedan 1.000 137 saab two hatchback 1.000 138 saab four sedan 1.000 181 toyota four sedan 1.000 182 toyota four wagon 1.000 195 volvo four sedan 1.000 196 volvo four wagon 1.000 197 volvo four sedan 1.000 198 volvo four wagon 1.000 199 volvo four sedan 1.000 200 volvo four wagon 1.000 201 volvo four sedan 1.000 202 volvo four sedan 1.000 203 volvo four sedan 1.000 205 volvo four sedan 1.000 # # # DATA_CLASS 2 # CLASS = 2 # #Case # make num-of-doors body-style (Cls Prob) 054 mazda four sedan 1.000 055 mazda four sedan 1.000 090 nissan two sedan 1.000 092 nissan two sedan 1.000 093 nissan four sedan 1.000 094 nissan four wagon 1.000 095 nissan two sedan 1.000 096 nissan two hatchback 1.000 097 nissan four sedan 1.000 098 nissan four wagon 1.000 099 nissan two hardtop 1.000 157 toyota four sedan 1.000 158 toyota four hatchback 0.998 4 0.002 161 toyota four sedan 1.000 162 toyota four hatchback 0.996 4 0.004 163 toyota four sedan 0.998 4 0.002 164 toyota two sedan 1.000 165 toyota two hatchback 1.000 # # # DATA_CLASS 3 # CLASS = 3 # #Case # make num-of-doors body-style (Cls Prob) 020 chevrolet two hatchback 0.997 11 0.003 021 chevrolet four sedan 1.000 022 dodge two hatchback 1.000 023 dodge two hatchback 1.000 025 dodge four hatchback 1.000 026 dodge four sedan 1.000 027 dodge four sedan 1.000 045 isuzu two sedan 1.000 046 isuzu four sedan 1.000 077 mitsubishi two hatchback 1.000 078 mitsubishi two hatchback 1.000 079 mitsubishi two hatchback 1.000 119 plymouth two hatchback 1.000 121 plymouth four hatchback 1.000 122 plymouth four sedan 1.000 123 plymouth four sedan 1.000 # # # DATA_CLASS 4 # CLASS = 4 # #Case # make num-of-doors body-style (Cls Prob) 044 isuzu four sedan 1.000 139 subaru two hatchback 1.000 140 subaru two hatchback 1.000 141 subaru two hatchback 1.000 142 subaru four sedan 1.000 143 subaru four sedan 1.000 145 subaru four sedan 1.000 147 subaru four wagon 1.000 149 subaru four wagon 1.000 151 toyota two hatchback 0.996 11 0.004 152 toyota two hatchback 0.999 153 toyota four hatchback 1.000 154 toyota four wagon 1.000 155 toyota four wagon 1.000 156 toyota four wagon 1.000 # # # DATA_CLASS 5 # CLASS = 5 # #Case # make num-of-doors body-style (Cls Prob) 017 bmw two sedan 1.000 018 bmw four sedan 1.000 048 jaguar four sedan 1.000 049 jaguar four sedan 1.000 050 jaguar two sedan 1.000 068 mercedes-benz four sedan 1.000 069 mercedes-benz four wagon 1.000 070 mercedes-benz two hardtop 1.000 071 mercedes-benz four sedan 1.000 072 mercedes-benz four sedan 1.000 073 mercedes-benz two convertible 1.000 074 mercedes-benz four sedan 1.000 075 mercedes-benz two hardtop 1.000 204 volvo four sedan 1.000 # # # DATA_CLASS 6 # CLASS = 6 # #Case # make num-of-doors body-style (Cls Prob) 005 audi four sedan 1.000 006 audi two sedan 1.000 007 audi four sedan 1.000 008 audi four wagon 1.000 009 audi four sedan 1.000 108 peugot four sedan 1.000 110 peugot four wagon 1.000 112 peugot four sedan 1.000 114 peugot four wagon 1.000 116 peugot four sedan 0.999 1 0.001 131 renault four wagon 1.000 133 saab two hatchback 0.990 1 0.010 135 saab two hatchback 1.000 192 volkswagen four sedan 1.000 # # # DATA_CLASS 7 # CLASS = 7 # #Case # make num-of-doors body-style (Cls Prob) 030 dodge two hatchback 1.000 047 isuzu two hatchback 1.000 083 mitsubishi two hatchback 1.000 084 mitsubishi two hatchback 1.000 085 mitsubishi two hatchback 1.000 125 plymouth two hatchback 1.000 132 renault two hatchback 1.000 168 toyota two hardtop 1.000 169 toyota two hardtop 1.000 170 toyota two hatchback 1.000 171 toyota two hardtop 1.000 172 toyota two hatchback 1.000 173 toyota two convertible 1.000 # # # DATA_CLASS 8 # CLASS = 8 # #Case # make num-of-doors body-style (Cls Prob) 011 bmw two sedan 1.000 012 bmw four sedan 1.000 013 bmw two sedan 1.000 014 bmw four sedan 1.000 066 mazda four sedan 1.000 144 subaru four sedan 0.999 4 0.001 146 subaru four sedan 1.000 148 subaru four wagon 0.996 4 0.004 150 subaru four wagon 1.000 166 toyota two sedan 1.000 167 toyota two hatchback 1.000 # # # DATA_CLASS 9 # CLASS = 9 # #Case # make num-of-doors body-style (Cls Prob) 003 alfa-romero two hatchback 1.000 010 audi two hatchback 1.000 076 mercury two hatchback 1.000 105 nissan two hatchback 1.000 106 nissan two hatchback 1.000 107 nissan two hatchback 1.000 126 porsche two hatchback 1.000 130 porsche two hatchback 1.000 179 toyota two hatchback 1.000 180 toyota two hatchback 1.000 # # # DATA_CLASS 10 # CLASS = 10 # #Case # make num-of-doors body-style (Cls Prob) 064 mazda ? sedan 1.000 067 mazda four sedan 1.000 091 nissan two sedan 1.000 159 toyota four sedan 1.000 160 toyota four hatchback 1.000 175 toyota four sedan 1.000 183 volkswagen two sedan 1.000 185 volkswagen four sedan 1.000 188 volkswagen four sedan 1.000 193 volkswagen four sedan 1.000 # # # DATA_CLASS 11 # CLASS = 11 # #Case # make num-of-doors body-style (Cls Prob) 019 chevrolet two hatchback 1.000 031 honda two hatchback 1.000 032 honda two hatchback 1.000 033 honda two hatchback 1.000 034 honda two hatchback 1.000 035 honda two hatchback 1.000 051 mazda two hatchback 1.000 052 mazda two hatchback 1.000 053 mazda two hatchback 1.000 # # # DATA_CLASS 12 # CLASS = 12 # #Case # make num-of-doors body-style (Cls Prob) 001 alfa-romero two convertible 1.000 002 alfa-romero two convertible 1.000 056 mazda two hatchback 1.000 057 mazda two hatchback 1.000 058 mazda two hatchback 1.000 059 mazda two hatchback 1.000 127 porsche two hardtop 1.000 128 porsche two hardtop 1.000 129 porsche two convertible 1.000 # # # DATA_CLASS 13 # CLASS = 13 # #Case # make num-of-doors body-style (Cls Prob) 024 dodge two hatchback 1.000 028 dodge ? sedan 1.000 036 honda four sedan 0.986 11 0.014 037 honda four wagon 1.000 080 mitsubishi two hatchback 1.000 120 plymouth two hatchback 1.000 190 volkswagen two convertible 1.000 191 volkswagen two hatchback 1.000 # # # DATA_CLASS 14 # CLASS = 14 # #Case # make num-of-doors body-style (Cls Prob) 109 peugot four sedan 1.000 111 peugot four wagon 1.000 113 peugot four sedan 1.000 115 peugot four wagon 1.000 117 peugot four sedan 1.000 autoclass-3.3.6.dfsg.1/sample/imports-85c.s-params0000644000175000017500000002170211247310756017732 0ustar areare# PARAMETERS TO AUTOCLASS-SEARCH -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # rel_error = 0.01 ! passed to clsf-DS-%= when deciding if a new clsf is duplicate of old # start_j_list = 2, 3, 5, 7, 10, 15, 25 ! initially try these numbers of classes, so not to narrow the search ! too quickly. the state of this list is saved in the <..>.search file ! and used on restarts, unless an override specification of start_j_list ! is made in this file for the restart run. ! start_j_list = -999 specifies an empty list (allowed only on restarts) # n_classes_fn_type = "random_ln_normal" ! will call this function to decide how many classes to start next try ! with, based on best clsfs found so far. ! only "random_ln_normal" so far # fixed_j = 0 ! if not 0, overrides start_j_list and n_classes_fn_type, and always uses ! this value as j-in # min_report_period = 30 ! wait at least this time (in seconds) since last report until ! reporting verbosely again # max_duration = 0 ! the search will end this time (in seconds) from start if it hasn't already # max_n_tries = 0 ! if > 0, search will end after this many clsf tries have been done # n_save = 2 ! save this many clsfs to disk in the .results[-bin] and .search files. ! if 0, don't save anything (no .search & .results[-bin] files) # log_file_p = true ! if false, do not write a log file # search_file_p = true ! if false, do not write a search file # results_file_p = true ! if false, do not write a results file # min_save_period = 1800 ! to protect against possible cpu crash, will save to disk this often ! (in seconds => 30 minutes) # max_n_store = 10 ! don't store any more than this many clsfs internally # n_final_summary = 10 ! print out descriptions of this many of the trials at the end of the search # start_fn_type = "random" ! clsf start function: "random" or "block" ! "block" is used for testing -- it produces repeatable searches. # try_fn_type = "converge_search_3" ! clsf try function: "converge_search_3", "converge_search_4" or "converge" ! "converge_search_3" uses an absolute stopping criterion for maximum ! class variation between successive convergence cycles. ! "converge_search_4" uses an absolute stopping criterion for the slope of ! class variation over sigma_beta_n_values cycles. ! "converge" uses a criterion which tests the variation all the classes ! aggregated together. # initial_cycles_p = true ! if true, perform base_cycle in initialize_parameters ! false is used only for testing # save_compact_p = true ! true saves classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false saves as ascii text (.results & .chkpt) # read_compact_p = true ! true reads classifications as machine dependent binary (.results-bin & ! .chkpt-bin); false reads as ascii text (.results & .chkpt) # randomize_random_p = true ! false uses 1 as the seed for rand, the pseudo-random number function (this ! facilitates producing repeatable test cases); true uses universal time ! clock as the seed # n_data = 0 ! if > 0, will only read this many datum from .db2, rather than the whole file # halt_range = 0.5 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # halt_factor = 0.0001 ! passed to try_fn_type "converge" ! one of two candidate tests for log_marginal (clsf->log_a_x_h) delta between ! successive convergence cycles. the largest of halt_range and (halt_factor * ! current_log_marginal) is used. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # rel_delta_range = 0.0025 ! passed to try function "converge_search_3" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) between successive convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # cs4_delta_range = 0.0025 ! passed to try function "converge_search_4" ! delta for each class of log aprox-marginal-likelihood of class statistics ! with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by the ! class weight (class->w_j) over sigma_beta_n_values convergence cycles. ! increasing this value loosens the convergence and reduces the number of ! cycles. decreasing this value tightens the convergence and increases the ! number of cycles # n_average = 3 ! passed to try functions "converge_search_3" and "converge" ! The number of cycles for which the convergence criterion must be satisfied ! for the trial to terminate. # sigma_beta_n_values = 6 ! passed to try_fn_type "converge_search_4" ! number of past values to use in computing sigma^2 (noise) and beta^2 ! (signal). # max_cycles = 200 ! passed to all try functions. They will end a trial if this many cycles ! have been done and the convergence criterion has not been satisfied. # converge_print_p = false ! if true, the selected try function will print to the screen values useful in ! specifying non-default values for halt_range, halt_factor, rel_delta_range, ! n_average, sigma_beta_n_values, and range_factor. # force_new_search_p = true ! If true, will ignore any previous search results, discarding the ! existing .search & .results[-bin] files after confirmation by the ! user; if false, will continue the search using the existing ! .search & .results[-bin] files. ! For repeatable results, also see min_report_period, start_fn_type ! and randomize_random_p. # checkpoint_p = false ! if true, checkpoints of the current classification will be output every ! min_checkpoint_period seconds. file extension is .chkpt[-bin] -- useful ! for very large classifications # min_checkpoint_period = 10800 ! if checkpoint_p = true, the checkpointed classification will be written ! this often - in seconds (= 3 hours) # reconverge_type = "" ! can be either "chkpt" or "results" ! if "chkpt", continue convergence of the classification contained in ! <...>.chkpt[-bin] -- checkpoint_p must be true. ! if "results", continue convergence of the best classification ! contained in <...>.results[-bin] -- checkpoint_p must be false. # screen_output_p = true ! if false, no output is directed to the screen. Assuming log_file_p = true, ! output will be directed to the log file only. # interactive_p = true ! if false, standard input is not queried each cycle for the character q. ! Thus either parameter max_n_tires or max_duration must be specified, or ! AutoClass will run forever. # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. 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W"> &!A!AL=X5)   ?@=?@}C>ӿ=SA =xL@7-?XZ>f> '!A!AL=hDO& 5)   P@=tP@}C>ӿ=SA =Y@7-?8Ed>f>(!A!AL=hDO& 5)   q@@>`?`% `% `% 5!A!AL=5)    !A }t?;;;;;;; 7lllllllۭ@@%I>m[?c% b% b% !A!AL=5)    A6@@@?:!>R,>[;[;[;[;-^?*&&&&ۭ@@%I>m[?b% a% a% !A!AL=5)   O@4);A=+;J[?[;[;[;[;|&&&&m???h% ph% `h% !A!AL=5)   !Ay?<Gϼ@ lh@>*?g% g% g% !A!AL=5)    /@-0A ? />?S}= O~MU- ?@L>L?e% e% e% !A!AL=5)   @@pA?Y#<#>B?Sv=Y#<abu`3abh@>*?f% f% f% !A!AL=5)    @60PA >~*?s< VCrϾ m???d% d% d% !A!AL=5)   0pAg@nF?RDf>=k}m???d% c% c% !A!AL=5)   !Ay?<Gϼ@ lv&BA.:=]t?Z% @q% q% !A!AL=5)   X@~ܟ@?K?4);@K;?\7X;1> >;;;;;U=;U=;\-;;;;;=>;)$;OV=;X7yn<7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (12). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 10:08:23 2001 [j_in=2] [cs-3: cycles 13] best2->2(1) [j_in=3] [cs-3: cycles 11] best3->3(2) [j_in=5] [cs-3: cycles 31] best5->5(3) [j_in=7] [cs-3: cycles 27] best7->7(4) [j_in=10] [cs-3: cycles 50] 10->10(5) [j_in=15] [cs-3: cycles 18] 15->14(6) [j_in=25] [cs-3: cycles 8] 25->21(7) [j_in=1] [cs-3: cycles 4] 1->1(8) [j_in=13] [cs-3: cycles 27] 13->13(9) [j_in=15] [cs-3: cycles 19] best15->15(10) [j_in=10] [cs-3: cycles 13] 10->10(11) [j_in=6] [cs-3: cycles 43] 6->6(12) ENDING SEARCH because max number of tries reached at Mon Jun 11 10:08:26 2001 after a total of 12 tries over 4 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16410.967) N_CLASSES 15 FOUND ON TRY 10 *SAVED* PROBABILITY exp(-16437.588) N_CLASSES 7 FOUND ON TRY 4 *SAVED* PROBABILITY exp(-16476.523) N_CLASSES 5 FOUND ON TRY 3 PROBABILITY exp(-16512.283) N_CLASSES 10 FOUND ON TRY 5 PROBABILITY exp(-16514.296) N_CLASSES 6 FOUND ON TRY 12 PROBABILITY exp(-16549.938) N_CLASSES 21 FOUND ON TRY 7 PROBABILITY exp(-16583.874) N_CLASSES 10 FOUND ON TRY 11 PROBABILITY exp(-16639.164) N_CLASSES 14 FOUND ON TRY 6 PROBABILITY exp(-16641.226) N_CLASSES 13 FOUND ON TRY 9 PROBABILITY exp(-16844.588) N_CLASSES 3 FOUND ON TRY 2 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 10 num_cycles 19 max_cycles 200 convergent try 4 num_cycles 27 max_cycles 200 convergent try 3 num_cycles 31 max_cycles 200 convergent try 5 num_cycles 50 max_cycles 200 convergent try 12 num_cycles 43 max_cycles 200 convergent try 7 num_cycles 8 max_cycles 200 convergent try 11 num_cycles 13 max_cycles 200 convergent try 6 num_cycles 18 max_cycles 200 convergent try 9 num_cycles 27 max_cycles 200 convergent try 2 num_cycles 11 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:08:26 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:08:49 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=10; force_new_search_p=false ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 12 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search ADVISORY: start_j_list=(2,3,5,7,10,15,25) has been overridden by () from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (22). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log RESTARTING SEARCH at Mon Jun 11 10:08:49 2001 [j_in=7] [cs-3: cycles 40] 7->7(13) [j_in=9] [cs-3: cycles 29] 9->9(14) [j_in=7] [cs-3: cycles 96] 7->7(15) [j_in=4] [cs-3: cycles 29] 4->4(16) [j_in=3] [cs-3: cycles 33] 3->3(17) [j_in=8] [cs-3: cycles 38] 8->8(18) [j_in=9] [cs-3: cycles 31] best9->9(19) [j_in=12] [cs-3: cycles 16] 12->11(20) [j_in=11] [cs-3: cycles 11] best11->10(21) [j_in=8] [cs-3: cycles 30] 8->8(22) ENDING SEARCH because max number of tries reached at Mon Jun 11 10:08:53 2001 after a total of 22 tries over 9 seconds This invocation of "autoclass -search" took 4 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16347.467) N_CLASSES 10 FOUND ON TRY 21 *SAVED* PROBABILITY exp(-16376.557) N_CLASSES 9 FOUND ON TRY 19 *SAVED* PROBABILITY exp(-16384.448) N_CLASSES 8 FOUND ON TRY 22 PROBABILITY exp(-16410.967) N_CLASSES 15 FOUND ON TRY 10 PROBABILITY exp(-16411.990) N_CLASSES 11 FOUND ON TRY 20 PROBABILITY exp(-16437.588) N_CLASSES 7 FOUND ON TRY 4 PROBABILITY exp(-16467.782) N_CLASSES 9 FOUND ON TRY 14 PROBABILITY exp(-16476.523) N_CLASSES 5 FOUND ON TRY 3 PROBABILITY exp(-16494.730) N_CLASSES 8 FOUND ON TRY 18 PROBABILITY exp(-16505.551) N_CLASSES 7 FOUND ON TRY 15 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 21 num_cycles 11 max_cycles 200 convergent try 19 num_cycles 31 max_cycles 200 convergent try 22 num_cycles 30 max_cycles 200 convergent try 10 num_cycles 19 max_cycles 200 convergent try 20 num_cycles 16 max_cycles 200 convergent try 4 num_cycles 27 max_cycles 200 convergent try 14 num_cycles 29 max_cycles 200 convergent try 3 num_cycles 31 max_cycles 200 convergent try 18 num_cycles 38 max_cycles 200 convergent try 15 num_cycles 96 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:08:53 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:13:51 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_duration=120; force_new_search_p=false ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 22 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search ADVISORY: start_j_list=(2,3,5,7,10,15,25) has been overridden by () from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until Mon Jun 11 10:15:51 2001. 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log RESTARTING SEARCH at Mon Jun 11 10:13:51 2001 [j_in=9] [cs-3: cycles 15] 9->9(23) [j_in=11] [cs-3: cycles 24] 11->10(24) [j_in=12] [cs-3: cycles 12] 12->12(25) [j_in=17] [cs-3: cycles 12] 17->16(26) [j_in=7] [cs-3: cycles 16] 7->7(27) [j_in=13] [cs-3: cycles 10] 13->13(28) [j_in=13] [cs-3: cycles 10] 13->13(29) [j_in=13] [cs-3: cycles 13] 13->13(30) [j_in=8] [cs-3: cycles 15] 8->8(31) [j_in=10] [cs-3: cycles 25] 10->10(32) [j_in=18] [cs-3: cycles 28] best18->17(33) [j_in=8] [cs-3: cycles 16] 8->8(34) [j_in=12] [cs-3: cycles 18] 12->12(35) [j_in=15] [cs-3: cycles 22] 15->15(36) [j_in=14] [cs-3: cycles 13] 14->13(37) [j_in=15] [cs-3: cycles 14] 15->14(38) [j_in=15] [cs-3: cycles 14] dup15->14(39) [j_in=15] [cs-3: cycles 14] dup15->14(40) [j_in=15] [cs-3: cycles 35] 15->14(41) [j_in=16] [cs-3: cycles 35] dup16->14(42) [j_in=11] [cs-3: cycles 38] 11->10(43) [j_in=8] [cs-3: cycles 16] 8->8(44) [j_in=15] [cs-3: cycles 14] 15->13(45) [j_in=10] [cs-3: cycles 30] 10->10(46) [j_in=17] [cs-3: cycles 11] 17->17(47) [j_in=13] [cs-3: cycles 23] 13->13(48) [j_in=14] [cs-3: cycles 23] 14->14(49) [j_in=12] [cs-3: cycles 31] 12->12(50) [j_in=11] [cs-3: cycles 24] 11->11(51) [j_in=19] [cs-3: cycles 26] 19->18(52) [j_in=18] [cs-3: cycles 14] 18->17(53) [j_in=15] [cs-3: cycles 31] 15->15(54) [j_in=20] [cs-3: cycles 16] 20->20(55) [j_in=15] [cs-3: cycles 18] 15->15(56) [j_in=15] [cs-3: cycles 18] dup15->15(57) [j_in=15] [cs-3: cycles 14] 15->14(58) [j_in=20] [cs-3: cycles 15] 20->18(59) [j_in=14] [cs-3: cycles 15] 14->13(60) [j_in=14] [cs-3: cycles 43] 14->14(61) [j_in=15] [cs-3: cycles 26] 15->14(62) [j_in=17] [cs-3: cycles 13] 17->16(63) [j_in=14] [cs-3: cycles 16] 14->14(64) [j_in=16] [cs-3: cycles 14] 16->16(65) [j_in=22] [cs-3: cycles 11] 22->20(66) [j_in=20] [cs-3: cycles 15] 20->19(67) [j_in=14] [cs-3: cycles 17] 14->14(68) [j_in=13] [cs-3: cycles 17] 13->13(69) [j_in=15] [cs-3: cycles 7] best15->15(70) [j_in=14] [cs-3: cycles 38] 14->14(71) [j_in=14] [cs-3: cycles 38] 14->14(72) [j_in=14] [cs-3: cycles 16] best14->13(73) [j_in=13] [cs-3: cycles 19] 13->12(74) [j_in=17] [cs-3: cycles 16] best17->15(75) [j_in=13] [cs-3: cycles 38] best13->12(76) [j_in=17] [cs-3: cycles 11] 17->16(77) [j_in=13] [cs-3: cycles 38] dup13->12(78) [j_in=17] [cs-3: cycles 13] 17->15(79) [j_in=15] [cs-3: cycles 13] 15->14(80) [j_in=19] [cs-3: cycles 13] 19->17(81) [j_in=17] [cs-3: cycles 9] 17->15(82) [j_in=15] [cs-3: cycles 16] 15->14(83) [j_in=14] [cs-3: cycles 11] 14->14(84) [j_in=17] [cs-3: cycles 9] dup17->15(85) [j_in=15] [cs-3: cycles 7] 15->15(86) [j_in=15] [cs-3: cycles 7] 15->15(87) [j_in=15] [cs-3: cycles 7] 15->15(88) [j_in=15] [cs-3: cycles 7] 15->15(89) [j_in=15] [cs-3: cycles 7] 15->15(90) [j_in=15] [cs-3: cycles 7] 15->15(91) [j_in=15] [cs-3: cycles 7] 15->15(92) [j_in=15] [cs-3: cycles 13] 15->15(93) [j_in=11] [cs-3: cycles 22] 11->11(94) [j_in=15] [cs-3: cycles 13] 15->15(95) [j_in=11] [cs-3: cycles 22] 11->11(96) [j_in=15] [cs-3: cycles 13] 15->15(97) [j_in=14] [cs-3: cycles 34] 14->14(98) [j_in=16] [cs-3: cycles 13] 16->16(99) [j_in=15] [cs-3: cycles 9] 15->15(100) [j_in=15] [cs-3: cycles 9] 15->15(101) [j_in=15] [cs-3: cycles 9] 15->15(102) [j_in=15] [cs-3: cycles 9] 15->15(103) [j_in=15] [cs-3: cycles 9] 15->15(104) [j_in=15] [cs-3: cycles 23] 15->15(105) [j_in=13] [cs-3: cycles 19] 13->13(106) [j_in=11] [cs-3: cycles 18] 11->11(107) [j_in=17] [cs-3: cycles 27] 17->17(108) [j_in=15] [cs-3: cycles 17] 15->15(109) [j_in=15] [cs-3: cycles 17] 15->15(110) [j_in=15] [cs-3: cycles 12] 15->15(111) [j_in=18] [cs-3: cycles 16] 18->18(112) [j_in=15] [cs-3: cycles 12] 15->15(113) [j_in=18] [cs-3: cycles 16] 18->18(114) [j_in=15] [cs-3: cycles 17] 15->14(115) [j_in=15] [cs-3: cycles 17] 15->14(116) [j_in=15] [cs-3: cycles 17] 15->14(117) [j_in=15] [cs-3: cycles 25] 15->15(118) [j_in=15] [cs-3: cycles 25] 15->15(119) [j_in=15] [cs-3: cycles 25] 15->15(120) [j_in=15] [cs-3: cycles 12] 15->15(121) [j_in=17] [cs-3: cycles 12] 17->16(122) [j_in=16] [cs-3: cycles 12] 16->16(123) [j_in=16] [cs-3: cycles 12] 16->16(124) [j_in=15] [cs-3: cycles 11] 15->15(125) [j_in=14] [cs-3: cycles 15] 14->14(126) [j_in=15] [cs-3: cycles 11] 15->15(127) [j_in=14] [cs-3: cycles 15] 14->14(128) ---------------- NEW BEST CLASSIFICATION FOUND on try 76 ------------- It has 12 CLASSES with WEIGHTS 30 24 24 22 21 20 17 13 12 9 7 6 PROBABILITY of both the data and the classification = exp(-16211.601) (Also found 75 other better than last report.) ----------- SEARCH STATUS as of Mon Jun 11 10:14:22 2001 ----------- It just took 31 seconds since beginning. Estimate < 40 seconds to find a classification exp(22.5) [= 5.9e+09] times more probable. Estimate >> 7 seconds to find the very best classification, which may be exp(0.0) to exp(3800.8) times more probable. Have seen 122 of the estimated > 144 possible classifications (based on 6 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16460.8, S(igma) 90.0 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [12.5, 14.6, 16.9] Overhead time is 7.3 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=16] [cs-3: cycles 29] 16->16(129) [j_in=17] [cs-3: cycles 31] 17->17(130) [j_in=14] [cs-3: cycles 29] 14->14(131) [j_in=21] [cs-3: cycles 14] 21->21(132) [j_in=17] [cs-3: cycles 13] 17->17(133) [j_in=15] [cs-3: cycles 19] 15->15(134) [j_in=16] [cs-3: cycles 20] 16->16(135) [j_in=14] [cs-3: cycles 19] 14->14(136) [j_in=16] [cs-3: cycles 22] 16->16(137) [j_in=19] [cs-3: cycles 17] 19->19(138) [j_in=14] [cs-3: cycles 11] 14->14(139) [j_in=15] [cs-3: cycles 12] 15->15(140) [j_in=14] [cs-3: cycles 11] dup14->14(141) [j_in=15] [cs-3: cycles 12] dup15->15(142) [j_in=14] [cs-3: cycles 11] dup14->14(143) [j_in=15] [cs-3: cycles 29] 15->15(144) [j_in=18] [cs-3: cycles 12] 18->18(145) [j_in=13] [cs-3: cycles 11] 13->13(146) [j_in=12] [cs-3: cycles 13] 12->11(147) [j_in=17] [cs-3: cycles 17] 17->16(148) [j_in=15] [cs-3: cycles 20] 15->14(149) [j_in=16] [cs-3: cycles 18] 16->15(150) [j_in=20] [cs-3: cycles 13] 20->17(151) [j_in=16] [cs-3: cycles 20] 16->15(152) [j_in=16] [cs-3: cycles 20] dup16->15(153) [j_in=16] [cs-3: cycles 14] 16->15(154) [j_in=17] [cs-3: cycles 14] 17->16(155) [j_in=15] [cs-3: cycles 14] 15->14(156) [j_in=15] [cs-3: cycles 14] 15->14(157) [j_in=15] [cs-3: cycles 9] 15->15(158) [j_in=14] [cs-3: cycles 20] 14->14(159) [j_in=16] [cs-3: cycles 9] 16->16(160) [j_in=15] [cs-3: cycles 9] 15->15(161) [j_in=14] [cs-3: cycles 26] 14->12(162) [j_in=15] [cs-3: cycles 22] 15->13(163) [j_in=12] [cs-3: cycles 19] 12->11(164) [j_in=14] [cs-3: cycles 9] 14->14(165) [j_in=16] [cs-3: cycles 15] 16->16(166) [j_in=15] [cs-3: cycles 14] 15->15(167) [j_in=18] [cs-3: cycles 14] 18->18(168) [j_in=13] [cs-3: cycles 14] 13->12(169) [j_in=13] [cs-3: cycles 14] 13->12(170) [j_in=13] [cs-3: cycles 14] 13->12(171) [j_in=13] [cs-3: cycles 14] 13->12(172) [j_in=13] [cs-3: cycles 14] 13->12(173) [j_in=13] [cs-3: cycles 23] 13->13(174) [j_in=13] [cs-3: cycles 23] 13->13(175) [j_in=13] [cs-3: cycles 23] 13->13(176) [j_in=13] [cs-3: cycles 47] 13->12(177) [j_in=15] [cs-3: cycles 19] 15->14(178) [j_in=17] [cs-3: cycles 14] 17->16(179) [j_in=16] [cs-3: cycles 17] 16->15(180) [j_in=14] [cs-3: cycles 22] 14->13(181) [j_in=16] [cs-3: cycles 12] 16->16(182) [j_in=13] [cs-3: cycles 41] 13->13(183) [j_in=15] [cs-3: cycles 12] 15->15(184) [j_in=14] [cs-3: cycles 12] 14->14(185) [j_in=15] [cs-3: cycles 20] 15->15(186) [j_in=16] [cs-3: cycles 21] 16->16(187) [j_in=16] [cs-3: cycles 13] 16->16(188) [j_in=15] [cs-3: cycles 13] 15->15(189) [j_in=13] [cs-3: cycles 24] 13->13(190) [j_in=15] [cs-3: cycles 9] 15->15(191) [j_in=17] [cs-3: cycles 9] 17->17(192) [j_in=16] [cs-3: cycles 9] 16->16(193) [j_in=15] [cs-3: cycles 9] 15->15(194) [j_in=17] [cs-3: cycles 9] 17->17(195) [j_in=16] [cs-3: cycles 9] dup16->16(196) [j_in=15] [cs-3: cycles 20] 15->15(197) [j_in=15] [cs-3: cycles 20] 15->15(198) [j_in=15] [cs-3: cycles 20] 15->15(199) [j_in=15] [cs-3: cycles 43] 15->15(200) [j_in=15] [cs-3: cycles 43] 15->15(201) [j_in=15] [cs-3: cycles 20] 15->15(202) [j_in=17] [cs-3: cycles 11] 17->16(203) [j_in=15] [cs-3: cycles 13] 15->15(204) [j_in=17] [cs-3: cycles 13] 17->17(205) [j_in=16] [cs-3: cycles 13] 16->16(206) [j_in=16] [cs-3: cycles 13] 16->16(207) [j_in=16] [cs-3: cycles 12] 16->16(208) [j_in=15] [cs-3: cycles 12] 15->15(209) [j_in=15] [cs-3: cycles 12] dup15->15(210) [j_in=15] [cs-3: cycles 12] dup15->15(211) [j_in=15] [cs-3: cycles 15] 15->13(212) [j_in=14] [cs-3: cycles 15] 14->14(213) [j_in=14] [cs-3: cycles 15] 14->14(214) [j_in=14] [cs-3: cycles 15] 14->14(215) [j_in=14] [cs-3: cycles 21] 14->13(216) [j_in=16] [cs-3: cycles 26] 16->15(217) [j_in=15] [cs-3: cycles 26] 15->14(218) [j_in=15] [cs-3: cycles 33] 15->15(219) [j_in=16] [cs-3: cycles 14] 16->16(220) [j_in=14] [cs-3: cycles 18] 14->14(221) [j_in=13] [cs-3: cycles 24] 13->13(222) [j_in=16] [cs-3: cycles 13] 16->16(223) [j_in=15] [cs-3: cycles 12] 15->15(224) [j_in=14] [cs-3: cycles 14] 14->14(225) [j_in=13] [cs-3: cycles 15] 13->13(226) [j_in=17] [cs-3: cycles 13] 17->16(227) [j_in=16] [cs-3: cycles 13] 16->16(228) [j_in=15] [cs-3: cycles 23] 15->15(229) [j_in=15] [cs-3: cycles 23] 15->15(230) [j_in=15] [cs-3: cycles 14] 15->14(231) [j_in=17] [cs-3: cycles 21] 17->16(232) [j_in=12] [cs-3: cycles 19] 12->12(233) [j_in=15] [cs-3: cycles 14] 15->14(234) [j_in=17] [cs-3: cycles 20] 17->16(235) [j_in=14] [cs-3: cycles 14] 14->14(236) [j_in=17] [cs-3: cycles 20] 17->16(237) [j_in=14] [cs-3: cycles 10] 14->14(238) [j_in=15] [cs-3: cycles 12] 15->15(239) [j_in=16] [cs-3: cycles 13] 16->16(240) [j_in=15] [cs-3: cycles 12] 15->15(241) [j_in=16] [cs-3: cycles 29] 16->16(242) [j_in=16] [cs-3: cycles 29] 16->16(243) [j_in=16] [cs-3: cycles 25] 16->16(244) [j_in=14] [cs-3: cycles 16] 14->14(245) [j_in=19] [cs-3: cycles 24] 19->19(246) [j_in=15] [cs-3: cycles 24] 15->14(247) [j_in=15] [cs-3: cycles 24] 15->14(248) [j_in=15] [cs-3: cycles 21] 15->14(249) [j_in=16] [cs-3: cycles 20] 16->15(250) [j_in=15] [cs-3: cycles 21] 15->14(251) [j_in=16] [cs-3: cycles 9] 16->16(252) [j_in=17] [cs-3: cycles 10] 17->17(253) [j_in=19] [cs-3: cycles 15] 19->19(254) [j_in=16] [cs-3: cycles 9] 16->16(255) [j_in=17] [cs-3: cycles 23] 17->16(256) [j_in=19] [cs-3: cycles 15] 19->18(257) [j_in=16] [cs-3: cycles 22] 16->15(258) [j_in=18] [cs-3: cycles 8] 18->16(259) [j_in=18] [cs-3: cycles 8] 18->16(260) [j_in=18] [cs-3: cycles 8] 18->16(261) [j_in=18] [cs-3: cycles 8] 18->16(262) [j_in=18] [cs-3: cycles 8] 18->16(263) [j_in=18] [cs-3: cycles 11] 18->18(264) [j_in=16] [cs-3: cycles 22] 16->16(265) [j_in=19] [cs-3: cycles 20] 19->19(266) [j_in=15] [cs-3: cycles 31] 15->15(267) [j_in=16] [cs-3: cycles 13] 16->16(268) [j_in=17] [cs-3: cycles 19] 17->17(269) [j_in=14] [cs-3: cycles 20] 14->14(270) [j_in=17] [cs-3: cycles 48] 17->17(271) [j_in=18] [cs-3: cycles 9] 18->18(272) [j_in=18] [cs-3: cycles 9] 18->18(273) [j_in=18] [cs-3: cycles 9] 18->18(274) [j_in=18] [cs-3: cycles 13] 18->17(275) [j_in=16] [cs-3: cycles 21] 16->16(276) [j_in=18] [cs-3: cycles 13] 18->17(277) [j_in=16] [cs-3: cycles 21] 16->16(278) [j_in=18] [cs-3: cycles 20] 18->18(279) [j_in=14] [cs-3: cycles 12] 14->14(280) [j_in=15] [cs-3: cycles 19] 15->15(281) [j_in=14] [cs-3: cycles 30] 14->14(282) [j_in=15] [cs-3: cycles 30] 15->15(283) [j_in=16] [cs-3: cycles 18] 16->16(284) [j_in=16] [cs-3: cycles 18] 16->16(285) [j_in=16] [cs-3: cycles 18] 16->16(286) [j_in=16] [cs-3: cycles 17] 16->16(287) [j_in=15] [cs-3: cycles 17] 15->15(288) [j_in=17] [cs-3: cycles 14] 17->17(289) [j_in=12] [cs-3: cycles 17] 12->11(290) [j_in=15] [cs-3: cycles 13] 15->14(291) [j_in=15] [cs-3: cycles 13] 15->14(292) [j_in=15] [cs-3: cycles 13] 15->14(293) [j_in=15] [cs-3: cycles 27] 15->15(294) [j_in=16] [cs-3: cycles 27] 16->15(295) [j_in=15] [cs-3: cycles 9] 15->15(296) [j_in=18] [cs-3: cycles 21] 18->18(297) [j_in=14] [cs-3: cycles 12] 14->14(298) [j_in=12] [cs-3: cycles 13] 12->12(299) [j_in=17] [cs-3: cycles 13] 17->17(300) [j_in=19] [cs-3: cycles 15] 19->19(301) [j_in=14] [cs-3: cycles 28] 14->14(302) [j_in=14] [cs-3: cycles 18] 14->14(303) [j_in=15] [cs-3: cycles 22] 15->15(304) [j_in=16] [cs-3: cycles 22] 16->16(305) [j_in=15] [cs-3: cycles 15] 15->15(306) [j_in=18] [cs-3: cycles 26] 18->17(307) [j_in=16] [cs-3: cycles 9] 16->16(308) [j_in=19] [cs-3: cycles 41] 19->18(309) [j_in=19] [cs-3: cycles 41] 19->18(310) [j_in=19] [cs-3: cycles 13] 19->18(311) [j_in=17] [cs-3: cycles 9] 17->17(312) [j_in=16] [cs-3: cycles 22] 16->16(313) [j_in=16] [cs-3: cycles 22] 16->16(314) [j_in=16] [cs-3: cycles 22] 16->16(315) [j_in=16] [cs-3: cycles 17] 16->15(316) [j_in=16] [cs-3: cycles 17] 16->15(317) [j_in=16] [cs-3: cycles 16] 16->16(318) [j_in=16] [cs-3: cycles 16] 16->16(319) [j_in=16] [cs-3: cycles 16] 16->16(320) [j_in=16] [cs-3: cycles 16] 16->16(321) [j_in=16] [cs-3: cycles 14] 16->15(322) [j_in=16] [cs-3: cycles 14] 16->15(323) [j_in=16] [cs-3: cycles 14] 16->15(324) [j_in=16] [cs-3: cycles 14] 16->15(325) [j_in=16] [cs-3: cycles 10] 16->15(326) [j_in=16] [cs-3: cycles 10] 16->15(327) [j_in=16] [cs-3: cycles 10] 16->15(328) [j_in=16] [cs-3: cycles 10] 16->15(329) [j_in=16] [cs-3: cycles 14] 16->16(330) [j_in=16] [cs-3: cycles 14] 16->16(331) [j_in=16] [cs-3: cycles 14] 16->16(332) [j_in=16] [cs-3: cycles 14] 16->16(333) [j_in=16] [cs-3: cycles 23] 16->14(334) [j_in=16] [cs-3: cycles 23] 16->14(335) [j_in=16] [cs-3: cycles 23] 16->14(336) [j_in=16] [cs-3: cycles 33] 16->14(337) [j_in=13] [cs-3: cycles 21] 13->12(338) [j_in=14] [cs-3: cycles 32] 14->14(339) [j_in=18] [cs-3: cycles 13] 18->18(340) [j_in=18] [cs-3: cycles 13] 18->18(341) [j_in=18] [cs-3: cycles 12] 18->18(342) [j_in=17] [cs-3: cycles 18] 17->17(343) [j_in=16] [cs-3: cycles 18] 16->16(344) [j_in=13] [cs-3: cycles 12] 13->13(345) [j_in=13] [cs-3: cycles 12] 13->13(346) [j_in=13] [cs-3: cycles 12] 13->13(347) [j_in=13] [cs-3: cycles 12] 13->13(348) [j_in=13] [cs-3: cycles 12] 13->13(349) [j_in=13] [cs-3: cycles 23] 13->13(350) [j_in=14] [cs-3: cycles 16] 14->13(351) [j_in=14] [cs-3: cycles 16] dup14->13(352) [j_in=14] [cs-3: cycles 16] dup14->13(353) [j_in=14] [cs-3: cycles 28] 14->14(354) [j_in=17] [cs-3: cycles 17] 17->17(355) [j_in=15] [cs-3: cycles 20] 15->15(356) [j_in=15] [cs-3: cycles 20] 15->15(357) [j_in=15] [cs-3: cycles 20] 15->15(358) [j_in=15] [cs-3: cycles 13] 15->15(359) [j_in=16] [cs-3: cycles 17] 16->16(360) [j_in=17] [cs-3: cycles 13] 17->17(361) [j_in=16] [cs-3: cycles 17] 16->16(362) [j_in=17] [cs-3: cycles 16] 17->17(363) [j_in=15] [cs-3: cycles 12] 15->15(364) [j_in=16] [cs-3: cycles 18] 16->16(365) [j_in=16] [cs-3: cycles 15] 16->15(366) [j_in=14] [cs-3: cycles 15] 14->13(367) [j_in=16] [cs-3: cycles 15] 16->15(368) [j_in=14] [cs-3: cycles 15] 14->13(369) [j_in=16] [cs-3: cycles 46] 16->15(370) [j_in=16] [cs-3: cycles 46] 16->15(371) [j_in=16] [cs-3: cycles 10] 16->15(372) [j_in=17] [cs-3: cycles 10] 17->16(373) [j_in=14] [cs-3: cycles 14] 14->13(374) [j_in=16] [cs-3: cycles 11] 16->16(375) [j_in=16] [cs-3: cycles 11] 16->16(376) [j_in=16] [cs-3: cycles 11] 16->16(377) [j_in=16] [cs-3: cycles 11] 16->16(378) [j_in=16] [cs-3: cycles 13] 16->15(379) [j_in=19] [cs-3: cycles 12] 19->18(380) [j_in=14] [cs-3: cycles 13] 14->13(381) [j_in=13] [cs-3: cycles 20] 13->12(382) [j_in=15] [cs-3: cycles 9] 15->15(383) [j_in=17] [cs-3: cycles 9] 17->17(384) [j_in=15] [cs-3: cycles 9] 15->15(385) [j_in=17] [cs-3: cycles 9] 17->17(386) [j_in=15] [cs-3: cycles 9] 15->15(387) [j_in=17] [cs-3: cycles 18] 17->16(388) [j_in=14] [cs-3: cycles 11] 14->14(389) [j_in=18] [cs-3: cycles 18] 18->17(390) [j_in=14] [cs-3: cycles 11] 14->14(391) [j_in=18] [cs-3: cycles 15] 18->16(392) [j_in=14] [cs-3: cycles 12] 14->14(393) [j_in=16] [cs-3: cycles 16] 16->15(394) [j_in=19] [cs-3: cycles 9] 19->17(395) [j_in=14] [cs-3: cycles 13] 14->14(396) [j_in=19] [cs-3: cycles 25] 19->19(397) [j_in=16] [cs-3: cycles 13] 16->16(398) [j_in=14] [cs-3: cycles 22] 14->14(399) [j_in=17] [cs-3: cycles 11] 17->17(400) [j_in=15] [cs-3: cycles 11] 15->15(401) [j_in=13] [cs-3: cycles 22] 13->13(402) [j_in=17] [cs-3: cycles 21] 17->17(403) [j_in=16] [cs-3: cycles 18] 16->16(404) [j_in=13] [cs-3: cycles 23] 13->12(405) [j_in=15] [cs-3: cycles 16] 15->14(406) [j_in=14] [cs-3: cycles 12] 14->13(407) [j_in=16] [cs-3: cycles 12] 16->15(408) [j_in=14] [cs-3: cycles 22] 14->14(409) [j_in=15] [cs-3: cycles 14] 15->15(410) [j_in=19] [cs-3: cycles 14] 19->19(411) [j_in=14] [cs-3: cycles 22] 14->14(412) [j_in=15] [cs-3: cycles 11] 15->15(413) [j_in=18] [cs-3: cycles 21] 18->18(414) [j_in=13] [cs-3: cycles 11] 13->13(415) [j_in=16] [cs-3: cycles 15] 16->15(416) [j_in=15] [cs-3: cycles 15] 15->15(417) [j_in=17] [cs-3: cycles 15] 17->16(418) [j_in=13] [cs-3: cycles 44] 13->13(419) [j_in=16] [cs-3: cycles 26] 16->16(420) ENDING SEARCH because max duration has expired at Mon Jun 11 10:15:52 2001 after a total of 420 tries over 2 minutes 11 seconds This invocation of "autoclass -search" took 2 minutes 1 second A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16211.601) N_CLASSES 12 FOUND ON TRY 76 DUPS 1 *SAVED* PROBABILITY exp(-16216.971) N_CLASSES 13 FOUND ON TRY 351 DUPS 2 *SAVED* PROBABILITY exp(-16241.062) N_CLASSES 14 FOUND ON TRY 245 PROBABILITY exp(-16245.009) N_CLASSES 16 FOUND ON TRY 135 PROBABILITY exp(-16247.711) N_CLASSES 16 FOUND ON TRY 193 DUPS 1 PROBABILITY exp(-16247.786) N_CLASSES 13 FOUND ON TRY 183 PROBABILITY exp(-16248.708) N_CLASSES 16 FOUND ON TRY 208 PROBABILITY exp(-16254.312) N_CLASSES 15 FOUND ON TRY 75 PROBABILITY exp(-16254.821) N_CLASSES 17 FOUND ON TRY 133 PROBABILITY exp(-16255.435) N_CLASSES 15 FOUND ON TRY 258 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 76 num_cycles 38 max_cycles 200 convergent try 351 num_cycles 16 max_cycles 200 convergent try 245 num_cycles 16 max_cycles 200 convergent try 135 num_cycles 20 max_cycles 200 convergent try 193 num_cycles 9 max_cycles 200 convergent try 183 num_cycles 41 max_cycles 200 convergent try 208 num_cycles 12 max_cycles 200 convergent try 75 num_cycles 16 max_cycles 200 convergent try 133 num_cycles 13 max_cycles 200 convergent try 258 num_cycles 22 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:15:52 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 11:54:25 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=12 ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (12). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 11:54:27 2001 [j_in=2] [cs-3: cycles 8] best2->2(1) [j_in=3] [cs-3: cycles 14] best3->3(2) [j_in=5] [cs-3: cycles 38] best5->5(3) [j_in=7] [cs-3: cycles 24] best7->7(4) [j_in=10] [cs-3: cycles 22] best10->10(5) [j_in=15] [cs-3: cycles 38] best15->15(6) [j_in=25] [cs-3: cycles 9] 25->23(7) [j_in=16] [cs-3: cycles 45] best16->15(8) [j_in=4] [cs-3: cycles 13] 4->4(9) [j_in=14] [cs-3: cycles 11] 14->13(10) [j_in=20] [cs-3: cycles 10] 20->19(11) [j_in=10] [cs-3: cycles 20] 10->10(12) ENDING SEARCH because max number of tries reached at Mon Jun 11 11:54:30 2001 after a total of 12 tries over 4 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16176.428) N_CLASSES 15 FOUND ON TRY 8 *SAVED* PROBABILITY exp(-16210.935) N_CLASSES 15 FOUND ON TRY 6 *SAVED* PROBABILITY exp(-16462.450) N_CLASSES 23 FOUND ON TRY 7 PROBABILITY exp(-16494.516) N_CLASSES 13 FOUND ON TRY 10 PROBABILITY exp(-16514.158) N_CLASSES 10 FOUND ON TRY 5 PROBABILITY exp(-16521.458) N_CLASSES 7 FOUND ON TRY 4 PROBABILITY exp(-16522.034) N_CLASSES 10 FOUND ON TRY 12 PROBABILITY exp(-16602.618) N_CLASSES 5 FOUND ON TRY 3 PROBABILITY exp(-16642.879) N_CLASSES 19 FOUND ON TRY 11 PROBABILITY exp(-16659.395) N_CLASSES 4 FOUND ON TRY 9 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 8 num_cycles 45 max_cycles 200 convergent try 6 num_cycles 38 max_cycles 200 convergent try 7 num_cycles 9 max_cycles 200 convergent try 10 num_cycles 11 max_cycles 200 convergent try 5 num_cycles 22 max_cycles 200 convergent try 4 num_cycles 24 max_cycles 200 convergent try 12 num_cycles 20 max_cycles 200 convergent try 3 num_cycles 38 max_cycles 200 convergent try 11 num_cycles 10 max_cycles 200 convergent try 9 num_cycles 13 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 11:54:30 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 11:55:57 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=350; start_fn_type="block"; randomize_random_p=false ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (350). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 11:55:58 2001 [j_in=2] [cs-3: cycles 22] best2->2(1) [j_in=3] [cs-3: cycles 16] best3->3(2) [j_in=5] [cs-3: cycles 37] best5->5(3) [j_in=7] [cs-3: cycles 32] best7->7(4) [j_in=10] [cs-3: cycles 23] best10->10(5) [j_in=15] [cs-3: cycles 25] 15->14(6) [j_in=25] [cs-3: cycles 12] 25->24(7) [j_in=2] [cs-3: cycles 22] dup2->2(8) [j_in=10] [cs-3: cycles 23] dup10->10(9) [j_in=30] [cs-3: cycles 28] best30->30(10) [j_in=8] [cs-3: cycles 37] 8->8(11) [j_in=13] [cs-3: cycles 13] 13->13(12) [j_in=8] [cs-3: cycles 37] dup8->8(13) [j_in=5] [cs-3: cycles 37] dup5->5(14) [j_in=6] [cs-3: cycles 38] 6->6(15) [j_in=9] [cs-3: cycles 16] 9->9(16) [j_in=12] [cs-3: cycles 15] 12->12(17) [j_in=11] [cs-3: cycles 17] 11->11(18) [j_in=6] [cs-3: cycles 38] 6->6(19) [j_in=13] [cs-3: cycles 13] dup13->13(20) [j_in=12] [cs-3: cycles 15] dup12->12(21) [j_in=13] [cs-3: cycles 13] dup13->13(22) [j_in=9] [cs-3: cycles 16] dup9->9(23) [j_in=13] [cs-3: cycles 13] dup13->13(24) [j_in=20] [cs-3: cycles 18] 20->20(25) [j_in=11] [cs-3: cycles 17] dup11->11(26) [j_in=20] [cs-3: cycles 18] dup20->20(27) [j_in=13] [cs-3: cycles 13] dup13->13(28) [j_in=12] [cs-3: cycles 15] dup12->12(29) [j_in=14] [cs-3: cycles 19] 14->13(30) [j_in=8] [cs-3: cycles 37] 8->8(31) [j_in=15] [cs-3: cycles 25] dup15->14(32) [j_in=6] [cs-3: cycles 38] 6->6(33) [j_in=14] [cs-3: cycles 19] dup14->13(34) [j_in=14] [cs-3: cycles 19] dup14->13(35) [j_in=10] [cs-3: cycles 23] dup10->10(36) [j_in=18] [cs-3: cycles 21] 18->17(37) [j_in=21] [cs-3: cycles 13] 21->21(38) [j_in=19] [cs-3: cycles 15] 19->19(39) [j_in=9] [cs-3: cycles 16] dup9->9(40) [j_in=13] [cs-3: cycles 13] dup13->13(41) [j_in=11] [cs-3: cycles 17] dup11->11(42) [j_in=21] [cs-3: cycles 13] 21->21(43) [j_in=5] [cs-3: cycles 37] 5->5(44) [j_in=38] [cs-3: cycles 10] 38->36(45) [j_in=10] [cs-3: cycles 23] dup10->10(46) [j_in=13] [cs-3: cycles 13] dup13->13(47) [j_in=17] [cs-3: cycles 24] 17->17(48) [j_in=13] [cs-3: cycles 13] dup13->13(49) [j_in=23] [cs-3: cycles 21] 23->23(50) [j_in=22] [cs-3: cycles 26] 22->22(51) [j_in=12] [cs-3: cycles 15] 12->12(52) [j_in=12] [cs-3: cycles 15] 12->12(53) [j_in=14] [cs-3: cycles 19] 14->13(54) [j_in=24] [cs-3: cycles 14] 24->23(55) [j_in=11] [cs-3: cycles 17] dup11->11(56) [j_in=20] [cs-3: cycles 18] dup20->20(57) [j_in=10] [cs-3: cycles 23] dup10->10(58) [j_in=32] [cs-3: cycles 28] best32->30(59) [j_in=8] [cs-3: cycles 37] 8->8(60) [j_in=10] [cs-3: cycles 23] dup10->10(61) [j_in=7] [cs-3: cycles 32] dup7->7(62) [j_in=15] [cs-3: cycles 25] 15->14(63) [j_in=11] [cs-3: cycles 17] dup11->11(64) [j_in=17] [cs-3: cycles 24] 17->17(65) [j_in=26] [cs-3: cycles 10] 26->24(66) [j_in=17] [cs-3: cycles 24] 17->17(67) [j_in=17] [cs-3: cycles 24] 17->17(68) [j_in=25] [cs-3: cycles 12] 25->24(69) [j_in=35] [cs-3: cycles 10] 35->34(70) [j_in=21] [cs-3: cycles 13] 21->21(71) [j_in=14] [cs-3: cycles 19] 14->13(72) [j_in=8] [cs-3: cycles 37] 8->8(73) [j_in=34] [cs-3: cycles 28] 34->32(74) [j_in=11] [cs-3: cycles 17] dup11->11(75) [j_in=24] [cs-3: cycles 14] 24->23(76) [j_in=18] [cs-3: cycles 21] dup18->17(77) [j_in=16] [cs-3: cycles 24] 16->16(78) [j_in=26] [cs-3: cycles 10] 26->24(79) [j_in=21] [cs-3: cycles 13] 21->21(80) [j_in=22] [cs-3: cycles 26] dup22->22(81) [j_in=15] [cs-3: cycles 25] 15->14(82) [j_in=23] [cs-3: cycles 21] 23->23(83) [j_in=28] [cs-3: cycles 13] 28->27(84) [j_in=21] [cs-3: cycles 13] 21->21(85) [j_in=25] [cs-3: cycles 12] 25->24(86) [j_in=13] [cs-3: cycles 13] 13->13(87) [j_in=13] [cs-3: cycles 13] 13->13(88) [j_in=17] [cs-3: cycles 24] 17->17(89) ---------------- NEW BEST CLASSIFICATION FOUND on try 59 ------------- It has 30 CLASSES with WEIGHTS 21 13 13 10 10 10 9 9 7 7 7 7 7 6 6 6 6 6 5 4 4 4 4 4 4 4 3 3 3 3 PROBABILITY of both the data and the classification = exp(-16266.782) (Also found 58 other better than last report.) ----------- SEARCH STATUS as of Mon Jun 11 11:56:29 2001 ----------- It just took 31 seconds since beginning. Estimate < 31 seconds to find a classification exp(32.3) [= 1.1e+14] times more probable. Estimate >> 0 seconds to find the very best classification, which may be exp(0.0) to exp(6901.4) times more probable. Have seen 57 of the estimated > 22 possible classifications (based on 32 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16615.1, S(igma) 120.4 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [11.9, 19.9, 33.3] Overhead time is 3.1 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=22] [cs-3: cycles 26] dup22->22(90) [j_in=6] [cs-3: cycles 38] 6->6(91) [j_in=16] [cs-3: cycles 24] 16->16(92) [j_in=22] [cs-3: cycles 26] dup22->22(93) [j_in=12] [cs-3: cycles 15] 12->12(94) [j_in=13] [cs-3: cycles 13] 13->13(95) [j_in=24] [cs-3: cycles 14] 24->23(96) [j_in=20] [cs-3: cycles 18] dup20->20(97) [j_in=13] [cs-3: cycles 13] 13->13(98) [j_in=29] [cs-3: cycles 11] 29->27(99) [j_in=20] [cs-3: cycles 18] dup20->20(100) [j_in=29] [cs-3: cycles 11] dup29->27(101) [j_in=47] [cs-3: cycles 13] 47->35(102) [j_in=49] [cs-3: cycles 17] 49->37(103) [j_in=23] [cs-3: cycles 21] 23->23(104) [j_in=10] [cs-3: cycles 23] dup10->10(105) [j_in=26] [cs-3: cycles 10] 26->24(106) [j_in=21] [cs-3: cycles 13] 21->21(107) [j_in=23] [cs-3: cycles 21] 23->23(108) [j_in=35] [cs-3: cycles 10] 35->34(109) [j_in=11] [cs-3: cycles 17] 11->11(110) [j_in=28] [cs-3: cycles 13] dup28->27(111) [j_in=37] [cs-3: cycles 10] 37->35(112) [j_in=16] [cs-3: cycles 24] 16->16(113) [j_in=18] [cs-3: cycles 21] dup18->17(114) [j_in=25] [cs-3: cycles 12] 25->24(115) [j_in=26] [cs-3: cycles 10] 26->24(116) [j_in=26] [cs-3: cycles 10] 26->24(117) [j_in=32] [cs-3: cycles 28] dup32->30(118) [j_in=48] [cs-3: cycles 17] 48->37(119) [j_in=52] [cs-3: cycles 16] 52->47(120) [j_in=69] [cs-3: cycles 15] 69->24(121) [j_in=15] [cs-3: cycles 25] 15->14(122) [j_in=25] [cs-3: cycles 12] 25->24(123) [j_in=18] [cs-3: cycles 21] dup18->17(124) [j_in=39] [cs-3: cycles 10] 39->36(125) [j_in=18] [cs-3: cycles 21] dup18->17(126) [j_in=16] [cs-3: cycles 24] 16->16(127) [j_in=5] [cs-3: cycles 37] 5->5(128) [j_in=16] [cs-3: cycles 24] 16->16(129) [j_in=28] [cs-3: cycles 13] dup28->27(130) [j_in=25] [cs-3: cycles 12] 25->24(131) [j_in=32] [cs-3: cycles 28] dup32->30(132) [j_in=7] [cs-3: cycles 32] dup7->7(133) [j_in=18] [cs-3: cycles 21] dup18->17(134) [j_in=8] [cs-3: cycles 37] 8->8(135) [j_in=10] [cs-3: cycles 23] dup10->10(136) [j_in=29] [cs-3: cycles 11] dup29->27(137) [j_in=23] [cs-3: cycles 21] 23->23(138) [j_in=22] [cs-3: cycles 26] 22->22(139) [j_in=34] [cs-3: cycles 28] dup34->32(140) [j_in=21] [cs-3: cycles 13] 21->21(141) [j_in=17] [cs-3: cycles 24] 17->17(142) [j_in=24] [cs-3: cycles 14] 24->23(143) [j_in=21] [cs-3: cycles 13] 21->21(144) [j_in=11] [cs-3: cycles 17] 11->11(145) [j_in=12] [cs-3: cycles 15] 12->12(146) [j_in=24] [cs-3: cycles 14] 24->23(147) [j_in=19] [cs-3: cycles 15] dup19->19(148) [j_in=35] [cs-3: cycles 10] 35->34(149) [j_in=19] [cs-3: cycles 15] dup19->19(150) [j_in=13] [cs-3: cycles 13] 13->13(151) [j_in=33] [cs-3: cycles 28] 33->32(152) [j_in=22] [cs-3: cycles 26] 22->22(153) [j_in=22] [cs-3: cycles 26] 22->22(154) [j_in=14] [cs-3: cycles 19] 14->13(155) [j_in=23] [cs-3: cycles 21] 23->23(156) [j_in=71] [cs-3: cycles 25] 71->25(157) [j_in=16] [cs-3: cycles 24] 16->16(158) [j_in=26] [cs-3: cycles 10] 26->24(159) [j_in=8] [cs-3: cycles 37] 8->8(160) [j_in=30] [cs-3: cycles 28] dup30->30(161) [j_in=25] [cs-3: cycles 12] 25->24(162) [j_in=22] [cs-3: cycles 26] 22->22(163) [j_in=75] [cs-3: cycles 13] 75->25(164) [j_in=14] [cs-3: cycles 19] 14->13(165) [j_in=12] [cs-3: cycles 15] 12->12(166) [j_in=59] [cs-3: cycles 19] 59->49(167) [j_in=10] [cs-3: cycles 23] dup10->10(168) [j_in=26] [cs-3: cycles 10] 26->24(169) [j_in=15] [cs-3: cycles 25] 15->14(170) [j_in=29] [cs-3: cycles 11] dup29->27(171) [j_in=62] [cs-3: cycles 15] 62->50(172) [j_in=15] [cs-3: cycles 25] 15->14(173) [j_in=18] [cs-3: cycles 21] 18->17(174) [j_in=10] [cs-3: cycles 23] dup10->10(175) [j_in=47] [cs-3: cycles 13] 47->35(176) [j_in=78] [cs-3: cycles 19] 78->25(177) [j_in=12] [cs-3: cycles 15] 12->12(178) [j_in=15] [cs-3: cycles 25] 15->14(179) [j_in=10] [cs-3: cycles 23] dup10->10(180) [j_in=19] [cs-3: cycles 15] dup19->19(181) [j_in=37] [cs-3: cycles 10] 37->35(182) [j_in=18] [cs-3: cycles 21] 18->17(183) [j_in=19] [cs-3: cycles 15] dup19->19(184) [j_in=23] [cs-3: cycles 21] 23->23(185) [j_in=12] [cs-3: cycles 15] 12->12(186) [j_in=19] [cs-3: cycles 15] dup19->19(187) [j_in=18] [cs-3: cycles 21] 18->17(188) [j_in=5] [cs-3: cycles 37] 5->5(189) [j_in=29] [cs-3: cycles 11] dup29->27(190) [j_in=23] [cs-3: cycles 21] 23->23(191) [j_in=11] [cs-3: cycles 17] 11->11(192) [j_in=22] [cs-3: cycles 26] 22->22(193) [j_in=28] [cs-3: cycles 13] dup28->27(194) [j_in=18] [cs-3: cycles 21] 18->17(195) [j_in=21] [cs-3: cycles 13] 21->21(196) [j_in=37] [cs-3: cycles 10] 37->35(197) [j_in=10] [cs-3: cycles 23] dup10->10(198) [j_in=27] [cs-3: cycles 12] 27->25(199) [j_in=27] [cs-3: cycles 12] 27->25(200) [j_in=26] [cs-3: cycles 10] 26->24(201) [j_in=15] [cs-3: cycles 25] 15->14(202) [j_in=32] [cs-3: cycles 28] dup32->30(203) [j_in=14] [cs-3: cycles 19] 14->13(204) [j_in=20] [cs-3: cycles 18] dup20->20(205) [j_in=61] [cs-3: cycles 14] 61->49(206) [j_in=12] [cs-3: cycles 15] 12->12(207) [j_in=27] [cs-3: cycles 12] 27->25(208) [j_in=12] [cs-3: cycles 15] 12->12(209) [j_in=15] [cs-3: cycles 25] 15->14(210) [j_in=55] [cs-3: cycles 18] 55->47(211) [j_in=22] [cs-3: cycles 26] 22->22(212) [j_in=28] [cs-3: cycles 13] dup28->27(213) [j_in=13] [cs-3: cycles 13] 13->13(214) [j_in=15] [cs-3: cycles 25] 15->14(215) [j_in=21] [cs-3: cycles 13] 21->21(216) [j_in=33] [cs-3: cycles 28] dup33->32(217) [j_in=24] [cs-3: cycles 14] 24->23(218) [j_in=18] [cs-3: cycles 21] 18->17(219) [j_in=15] [cs-3: cycles 25] 15->14(220) [j_in=25] [cs-3: cycles 12] 25->24(221) [j_in=24] [cs-3: cycles 14] 24->23(222) [j_in=31] [cs-3: cycles 28] 31->30(223) [j_in=20] [cs-3: cycles 18] dup20->20(224) [j_in=27] [cs-3: cycles 12] 27->25(225) [j_in=11] [cs-3: cycles 17] 11->11(226) [j_in=34] [cs-3: cycles 28] dup34->32(227) [j_in=11] [cs-3: cycles 17] 11->11(228) [j_in=42] [cs-3: cycles 23] 42->34(229) [j_in=23] [cs-3: cycles 21] 23->23(230) [j_in=26] [cs-3: cycles 10] 26->24(231) [j_in=19] [cs-3: cycles 15] dup19->19(232) [j_in=20] [cs-3: cycles 18] dup20->20(233) [j_in=25] [cs-3: cycles 12] 25->24(234) [j_in=45] [cs-3: cycles 13] 45->36(235) [j_in=19] [cs-3: cycles 15] dup19->19(236) [j_in=17] [cs-3: cycles 24] 17->17(237) [j_in=18] [cs-3: cycles 21] 18->17(238) [j_in=29] [cs-3: cycles 11] dup29->27(239) [j_in=12] [cs-3: cycles 15] 12->12(240) [j_in=34] [cs-3: cycles 28] dup34->32(241) [j_in=49] [cs-3: cycles 17] 49->37(242) [j_in=6] [cs-3: cycles 38] 6->6(243) [j_in=30] [cs-3: cycles 28] dup30->30(244) [j_in=21] [cs-3: cycles 13] 21->21(245) [j_in=41] [cs-3: cycles 10] 41->38(246) [j_in=32] [cs-3: cycles 28] dup32->30(247) [j_in=21] [cs-3: cycles 13] 21->21(248) [j_in=19] [cs-3: cycles 15] dup19->19(249) [j_in=13] [cs-3: cycles 13] 13->13(250) [j_in=39] [cs-3: cycles 10] 39->36(251) [j_in=15] [cs-3: cycles 25] 15->14(252) [j_in=32] [cs-3: cycles 28] dup32->30(253) [j_in=22] [cs-3: cycles 26] 22->22(254) [j_in=20] [cs-3: cycles 18] dup20->20(255) [j_in=12] [cs-3: cycles 15] 12->12(256) [j_in=5] [cs-3: cycles 37] 5->5(257) [j_in=47] [cs-3: cycles 13] 47->35(258) [j_in=29] [cs-3: cycles 11] dup29->27(259) [j_in=31] [cs-3: cycles 28] dup31->30(260) [j_in=39] [cs-3: cycles 10] 39->36(261) [j_in=14] [cs-3: cycles 19] 14->13(262) [j_in=29] [cs-3: cycles 11] dup29->27(263) [j_in=25] [cs-3: cycles 12] 25->24(264) [j_in=20] [cs-3: cycles 18] dup20->20(265) [j_in=20] [cs-3: cycles 18] dup20->20(266) [j_in=23] [cs-3: cycles 21] 23->23(267) [j_in=9] [cs-3: cycles 16] 9->9(268) [j_in=33] [cs-3: cycles 28] dup33->32(269) [j_in=26] [cs-3: cycles 10] 26->24(270) [j_in=19] [cs-3: cycles 15] dup19->19(271) [j_in=13] [cs-3: cycles 13] 13->13(272) [j_in=25] [cs-3: cycles 12] 25->24(273) [j_in=42] [cs-3: cycles 23] 42->34(274) [j_in=14] [cs-3: cycles 19] 14->13(275) [j_in=5] [cs-3: cycles 37] 5->5(276) [j_in=22] [cs-3: cycles 26] 22->22(277) [j_in=59] [cs-3: cycles 19] 59->49(278) [j_in=13] [cs-3: cycles 13] 13->13(279) [j_in=35] [cs-3: cycles 10] 35->34(280) [j_in=59] [cs-3: cycles 19] 59->49(281) [j_in=11] [cs-3: cycles 17] 11->11(282) [j_in=21] [cs-3: cycles 13] 21->21(283) [j_in=17] [cs-3: cycles 24] 17->17(284) [j_in=56] [cs-3: cycles 16] 56->48(285) [j_in=34] [cs-3: cycles 28] dup34->32(286) [j_in=53] [cs-3: cycles 16] 53->48(287) [j_in=47] [cs-3: cycles 13] 47->35(288) [j_in=9] [cs-3: cycles 16] 9->9(289) [j_in=13] [cs-3: cycles 13] 13->13(290) [j_in=36] [cs-3: cycles 10] 36->34(291) [j_in=33] [cs-3: cycles 28] dup33->32(292) [j_in=10] [cs-3: cycles 23] dup10->10(293) [j_in=11] [cs-3: cycles 17] 11->11(294) [j_in=14] [cs-3: cycles 19] 14->13(295) [j_in=19] [cs-3: cycles 15] dup19->19(296) [j_in=18] [cs-3: cycles 21] 18->17(297) [j_in=16] [cs-3: cycles 24] 16->16(298) [j_in=51] [cs-3: cycles 9] 51->38(299) [j_in=31] [cs-3: cycles 28] dup31->30(300) [j_in=10] [cs-3: cycles 23] dup10->10(301) [j_in=30] [cs-3: cycles 28] dup30->30(302) [j_in=20] [cs-3: cycles 18] dup20->20(303) [j_in=13] [cs-3: cycles 13] 13->13(304) [j_in=9] [cs-3: cycles 16] 9->9(305) [j_in=25] [cs-3: cycles 12] 25->24(306) [j_in=72] [cs-3: cycles 16] 72->25(307) [j_in=33] [cs-3: cycles 28] dup33->32(308) [j_in=15] [cs-3: cycles 25] 15->14(309) [j_in=12] [cs-3: cycles 15] 12->12(310) [j_in=32] [cs-3: cycles 28] dup32->30(311) [j_in=69] [cs-3: cycles 15] 69->24(312) [j_in=27] [cs-3: cycles 12] 27->25(313) [j_in=35] [cs-3: cycles 10] 35->34(314) [j_in=18] [cs-3: cycles 21] 18->17(315) [j_in=18] [cs-3: cycles 21] 18->17(316) [j_in=11] [cs-3: cycles 17] 11->11(317) [j_in=33] [cs-3: cycles 28] dup33->32(318) [j_in=21] [cs-3: cycles 13] 21->21(319) [j_in=8] [cs-3: cycles 37] 8->8(320) [j_in=24] [cs-3: cycles 14] 24->23(321) [j_in=26] [cs-3: cycles 10] 26->24(322) [j_in=29] [cs-3: cycles 11] dup29->27(323) [j_in=28] [cs-3: cycles 13] 28->27(324) [j_in=18] [cs-3: cycles 21] 18->17(325) [j_in=32] [cs-3: cycles 28] dup32->30(326) [j_in=18] [cs-3: cycles 21] 18->17(327) [j_in=12] [cs-3: cycles 15] 12->12(328) [j_in=29] [cs-3: cycles 11] dup29->27(329) [j_in=7] [cs-3: cycles 32] dup7->7(330) [j_in=40] [cs-3: cycles 10] 40->37(331) [j_in=22] [cs-3: cycles 26] 22->22(332) [j_in=33] [cs-3: cycles 28] dup33->32(333) [j_in=14] [cs-3: cycles 19] 14->13(334) [j_in=23] [cs-3: cycles 21] 23->23(335) [j_in=22] [cs-3: cycles 26] 22->22(336) [j_in=20] [cs-3: cycles 18] dup20->20(337) [j_in=40] [cs-3: cycles 10] 40->37(338) [j_in=9] [cs-3: cycles 16] 9->9(339) [j_in=22] [cs-3: cycles 26] 22->22(340) [j_in=24] [cs-3: cycles 14] 24->23(341) [j_in=85] [cs-3: cycles 21] 85->24(342) [j_in=55] [cs-3: cycles 18] 55->47(343) [j_in=26] [cs-3: cycles 10] 26->24(344) [j_in=13] [cs-3: cycles 13] 13->13(345) [j_in=17] [cs-3: cycles 24] 17->17(346) [j_in=25] [cs-3: cycles 12] 25->24(347) [j_in=50] [cs-3: cycles 10] 50->37(348) [j_in=13] [cs-3: cycles 13] 13->13(349) [j_in=13] [cs-3: cycles 13] 13->13(350) ENDING SEARCH because max number of tries reached at Mon Jun 11 11:58:22 2001 after a total of 350 tries over 2 minutes 25 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16266.782) N_CLASSES 30 FOUND ON TRY 59 DUPS 7 *SAVED* PROBABILITY exp(-16283.605) N_CLASSES 30 FOUND ON TRY 223 DUPS 2 *SAVED* PROBABILITY exp(-16305.340) N_CLASSES 32 FOUND ON TRY 74 DUPS 4 PROBABILITY exp(-16339.997) N_CLASSES 32 FOUND ON TRY 152 DUPS 6 PROBABILITY exp(-16362.340) N_CLASSES 27 FOUND ON TRY 99 DUPS 9 PROBABILITY exp(-16399.962) N_CLASSES 30 FOUND ON TRY 10 DUPS 3 PROBABILITY exp(-16427.763) N_CLASSES 10 FOUND ON TRY 5 DUPS 13 PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 DUPS 3 PROBABILITY exp(-16455.829) N_CLASSES 19 FOUND ON TRY 39 DUPS 10 PROBABILITY exp(-16477.439) N_CLASSES 20 FOUND ON TRY 25 DUPS 12 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 59 num_cycles 28 max_cycles 200 convergent try 223 num_cycles 28 max_cycles 200 convergent try 74 num_cycles 28 max_cycles 200 convergent try 152 num_cycles 28 max_cycles 200 convergent try 99 num_cycles 11 max_cycles 200 convergent try 10 num_cycles 28 max_cycles 200 convergent try 5 num_cycles 23 max_cycles 200 convergent try 4 num_cycles 32 max_cycles 200 convergent try 39 num_cycles 15 max_cycles 200 convergent try 25 num_cycles 18 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 11:58:22 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 12:07:03 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=350; start_fn_type="block"; randomize_random_p=false; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (350). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 12:07:05 2001 [j_in=2] [cs-3: cycles 22] best2->2(1) [j_in=3] [cs-3: cycles 16] best3->3(2) [j_in=5] [cs-3: cycles 37] best5->5(3) [j_in=7] [cs-3: cycles 32] best7->7(4) [j_in=10] [cs-3: cycles 23] best10->10(5) [j_in=15] [cs-3: cycles 25] 15->14(6) [j_in=25] [cs-3: cycles 12] 25->24(7) [j_in=2] [cs-3: cycles 22] dup2->2(8) [j_in=10] [cs-3: cycles 23] dup10->10(9) [j_in=30] [cs-3: cycles 28] best30->30(10) [j_in=8] [cs-3: cycles 37] 8->8(11) [j_in=13] [cs-3: cycles 13] 13->13(12) [j_in=8] [cs-3: cycles 37] dup8->8(13) [j_in=5] [cs-3: cycles 37] dup5->5(14) [j_in=6] [cs-3: cycles 38] 6->6(15) [j_in=9] [cs-3: cycles 16] 9->9(16) [j_in=12] [cs-3: cycles 15] 12->12(17) [j_in=11] [cs-3: cycles 17] 11->11(18) [j_in=6] [cs-3: cycles 38] 6->6(19) [j_in=13] [cs-3: cycles 13] dup13->13(20) [j_in=12] [cs-3: cycles 15] dup12->12(21) [j_in=13] [cs-3: cycles 13] dup13->13(22) [j_in=9] [cs-3: cycles 16] dup9->9(23) [j_in=13] [cs-3: cycles 13] dup13->13(24) [j_in=20] [cs-3: cycles 18] 20->20(25) [j_in=11] [cs-3: cycles 17] dup11->11(26) [j_in=20] [cs-3: cycles 18] dup20->20(27) [j_in=13] [cs-3: cycles 13] dup13->13(28) [j_in=12] [cs-3: cycles 15] dup12->12(29) [j_in=14] [cs-3: cycles 19] 14->13(30) [j_in=8] [cs-3: cycles 37] 8->8(31) [j_in=15] [cs-3: cycles 25] dup15->14(32) [j_in=6] [cs-3: cycles 38] 6->6(33) [j_in=14] [cs-3: cycles 19] dup14->13(34) [j_in=14] [cs-3: cycles 19] dup14->13(35) [j_in=10] [cs-3: cycles 23] dup10->10(36) [j_in=18] [cs-3: cycles 21] 18->17(37) [j_in=21] [cs-3: cycles 13] 21->21(38) [j_in=19] [cs-3: cycles 15] 19->19(39) [j_in=9] [cs-3: cycles 16] dup9->9(40) [j_in=13] [cs-3: cycles 13] dup13->13(41) [j_in=11] [cs-3: cycles 17] dup11->11(42) [j_in=21] [cs-3: cycles 13] 21->21(43) [j_in=5] [cs-3: cycles 37] 5->5(44) [j_in=38] [cs-3: cycles 10] 38->36(45) [j_in=10] [cs-3: cycles 23] dup10->10(46) [j_in=13] [cs-3: cycles 13] dup13->13(47) [j_in=17] [cs-3: cycles 24] 17->17(48) [j_in=13] [cs-3: cycles 13] dup13->13(49) [j_in=23] [cs-3: cycles 21] 23->23(50) [j_in=22] [cs-3: cycles 26] 22->22(51) [j_in=12] [cs-3: cycles 15] 12->12(52) [j_in=12] [cs-3: cycles 15] 12->12(53) [j_in=14] [cs-3: cycles 19] 14->13(54) [j_in=24] [cs-3: cycles 14] 24->23(55) [j_in=11] [cs-3: cycles 17] dup11->11(56) [j_in=20] [cs-3: cycles 18] dup20->20(57) [j_in=10] [cs-3: cycles 23] dup10->10(58) [j_in=32] [cs-3: cycles 28] best32->30(59) [j_in=8] [cs-3: cycles 37] 8->8(60) [j_in=10] [cs-3: cycles 23] dup10->10(61) [j_in=7] [cs-3: cycles 32] dup7->7(62) [j_in=15] [cs-3: cycles 25] 15->14(63) [j_in=11] [cs-3: cycles 17] dup11->11(64) [j_in=17] [cs-3: cycles 24] 17->17(65) [j_in=26] [cs-3: cycles 10] 26->24(66) [j_in=17] [cs-3: cycles 24] 17->17(67) [j_in=17] [cs-3: cycles 24] 17->17(68) [j_in=25] [cs-3: cycles 12] 25->24(69) [j_in=35] [cs-3: cycles 10] 35->34(70) [j_in=21] [cs-3: cycles 13] 21->21(71) [j_in=14] [cs-3: cycles 19] 14->13(72) [j_in=8] [cs-3: cycles 37] 8->8(73) [j_in=34] [cs-3: cycles 28] 34->32(74) [j_in=11] [cs-3: cycles 17] dup11->11(75) [j_in=24] [cs-3: cycles 14] 24->23(76) [j_in=18] [cs-3: cycles 21] dup18->17(77) [j_in=16] [cs-3: cycles 24] 16->16(78) [j_in=26] [cs-3: cycles 10] 26->24(79) [j_in=21] [cs-3: cycles 13] 21->21(80) [j_in=22] [cs-3: cycles 26] dup22->22(81) [j_in=15] [cs-3: cycles 25] 15->14(82) [j_in=23] [cs-3: cycles 21] 23->23(83) [j_in=28] [cs-3: cycles 13] 28->27(84) [j_in=21] [cs-3: cycles 13] 21->21(85) [j_in=25] [cs-3: cycles 12] 25->24(86) [j_in=13] [cs-3: cycles 13] 13->13(87) [j_in=13] [cs-3: cycles 13] 13->13(88) [j_in=17] [cs-3: cycles 24] 17->17(89) ---------------- NEW BEST CLASSIFICATION FOUND on try 59 ------------- It has 30 CLASSES with WEIGHTS 21 13 13 10 10 10 9 9 7 7 7 7 7 6 6 6 6 6 5 4 4 4 4 4 4 4 3 3 3 3 PROBABILITY of both the data and the classification = exp(-16266.782) (Also found 58 other better than last report.) ----------- SEARCH STATUS as of Mon Jun 11 12:07:36 2001 ----------- It just took 31 seconds since beginning. Estimate < 31 seconds to find a classification exp(32.3) [= 1.1e+14] times more probable. Estimate >> 0 seconds to find the very best classification, which may be exp(0.0) to exp(6901.4) times more probable. Have seen 57 of the estimated > 22 possible classifications (based on 32 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16615.1, S(igma) 120.4 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [11.9, 19.9, 33.3] Overhead time is 3.1 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=22] [cs-3: cycles 26] dup22->22(90) [j_in=6] [cs-3: cycles 38] 6->6(91) [j_in=16] [cs-3: cycles 24] 16->16(92) [j_in=22] [cs-3: cycles 26] dup22->22(93) [j_in=12] [cs-3: cycles 15] 12->12(94) [j_in=13] [cs-3: cycles 13] 13->13(95) [j_in=24] [cs-3: cycles 14] 24->23(96) [j_in=20] [cs-3: cycles 18] dup20->20(97) [j_in=13] [cs-3: cycles 13] 13->13(98) [j_in=29] [cs-3: cycles 11] 29->27(99) [j_in=20] [cs-3: cycles 18] dup20->20(100) [j_in=29] [cs-3: cycles 11] dup29->27(101) [j_in=47] [cs-3: cycles 13] 47->35(102) [j_in=49] [cs-3: cycles 17] 49->37(103) [j_in=23] [cs-3: cycles 21] 23->23(104) [j_in=10] [cs-3: cycles 23] dup10->10(105) [j_in=26] [cs-3: cycles 10] 26->24(106) [j_in=21] [cs-3: cycles 13] 21->21(107) [j_in=23] [cs-3: cycles 21] 23->23(108) [j_in=35] [cs-3: cycles 10] 35->34(109) [j_in=11] [cs-3: cycles 17] 11->11(110) [j_in=28] [cs-3: cycles 13] dup28->27(111) [j_in=37] [cs-3: cycles 10] 37->35(112) [j_in=16] [cs-3: cycles 24] 16->16(113) [j_in=18] [cs-3: cycles 21] dup18->17(114) [j_in=25] [cs-3: cycles 12] 25->24(115) [j_in=26] [cs-3: cycles 10] 26->24(116) [j_in=26] [cs-3: cycles 10] 26->24(117) [j_in=32] [cs-3: cycles 28] dup32->30(118) [j_in=48] [cs-3: cycles 17] 48->37(119) [j_in=52] [cs-3: cycles 16] 52->47(120) [j_in=69] [cs-3: cycles 15] 69->24(121) [j_in=15] [cs-3: cycles 25] 15->14(122) [j_in=25] [cs-3: cycles 12] 25->24(123) [j_in=18] [cs-3: cycles 21] dup18->17(124) [j_in=39] [cs-3: cycles 10] 39->36(125) [j_in=18] [cs-3: cycles 21] dup18->17(126) [j_in=16] [cs-3: cycles 24] 16->16(127) [j_in=5] [cs-3: cycles 37] 5->5(128) [j_in=16] [cs-3: cycles 24] 16->16(129) [j_in=28] [cs-3: cycles 13] dup28->27(130) [j_in=25] [cs-3: cycles 12] 25->24(131) [j_in=32] [cs-3: cycles 28] dup32->30(132) [j_in=7] [cs-3: cycles 32] dup7->7(133) [j_in=18] [cs-3: cycles 21] dup18->17(134) [j_in=8] [cs-3: cycles 37] 8->8(135) [j_in=10] [cs-3: cycles 23] dup10->10(136) [j_in=29] [cs-3: cycles 11] dup29->27(137) [j_in=23] [cs-3: cycles 21] 23->23(138) [j_in=22] [cs-3: cycles 26] 22->22(139) [j_in=34] [cs-3: cycles 28] dup34->32(140) [j_in=21] [cs-3: cycles 13] 21->21(141) [j_in=17] [cs-3: cycles 24] 17->17(142) [j_in=24] [cs-3: cycles 14] 24->23(143) [j_in=21] [cs-3: cycles 13] 21->21(144) [j_in=11] [cs-3: cycles 17] 11->11(145) [j_in=12] [cs-3: cycles 15] 12->12(146) [j_in=24] [cs-3: cycles 14] 24->23(147) [j_in=19] [cs-3: cycles 15] dup19->19(148) [j_in=35] [cs-3: cycles 10] 35->34(149) [j_in=19] [cs-3: cycles 15] dup19->19(150) [j_in=13] [cs-3: cycles 13] 13->13(151) [j_in=33] [cs-3: cycles 28] 33->32(152) [j_in=22] [cs-3: cycles 26] 22->22(153) [j_in=22] [cs-3: cycles 26] 22->22(154) [j_in=14] [cs-3: cycles 19] 14->13(155) [j_in=23] [cs-3: cycles 21] 23->23(156) [j_in=71] [cs-3: cycles 25] 71->25(157) [j_in=16] [cs-3: cycles 24] 16->16(158) [j_in=26] [cs-3: cycles 10] 26->24(159) [j_in=8] [cs-3: cycles 37] 8->8(160) [j_in=30] [cs-3: cycles 28] dup30->30(161) [j_in=25] [cs-3: cycles 12] 25->24(162) [j_in=22] [cs-3: cycles 26] 22->22(163) [j_in=75] [cs-3: cycles 13] 75->25(164) [j_in=14] [cs-3: cycles 19] 14->13(165) [j_in=12] [cs-3: cycles 15] 12->12(166) [j_in=59] [cs-3: cycles 19] 59->49(167) [j_in=10] [cs-3: cycles 23] dup10->10(168) [j_in=26] [cs-3: cycles 10] 26->24(169) [j_in=15] [cs-3: cycles 25] 15->14(170) [j_in=29] [cs-3: cycles 11] dup29->27(171) [j_in=62] [cs-3: cycles 15] 62->50(172) [j_in=15] [cs-3: cycles 25] 15->14(173) [j_in=18] [cs-3: cycles 21] 18->17(174) [j_in=10] [cs-3: cycles 23] dup10->10(175) [j_in=47] [cs-3: cycles 13] 47->35(176) [j_in=78] [cs-3: cycles 19] 78->25(177) [j_in=12] [cs-3: cycles 15] 12->12(178) [j_in=15] [cs-3: cycles 25] 15->14(179) [j_in=10] [cs-3: cycles 23] dup10->10(180) [j_in=19] [cs-3: cycles 15] dup19->19(181) [j_in=37] [cs-3: cycles 10] 37->35(182) [j_in=18] [cs-3: cycles 21] 18->17(183) [j_in=19] [cs-3: cycles 15] dup19->19(184) [j_in=23] [cs-3: cycles 21] 23->23(185) [j_in=12] [cs-3: cycles 15] 12->12(186) [j_in=19] [cs-3: cycles 15] dup19->19(187) [j_in=18] [cs-3: cycles 21] 18->17(188) [j_in=5] [cs-3: cycles 37] 5->5(189) [j_in=29] [cs-3: cycles 11] dup29->27(190) [j_in=23] [cs-3: cycles 21] 23->23(191) [j_in=11] [cs-3: cycles 17] 11->11(192) [j_in=22] [cs-3: cycles 26] 22->22(193) [j_in=28] [cs-3: cycles 13] dup28->27(194) [j_in=18] [cs-3: cycles 21] 18->17(195) [j_in=21] [cs-3: cycles 13] 21->21(196) [j_in=37] [cs-3: cycles 10] 37->35(197) [j_in=10] [cs-3: cycles 23] dup10->10(198) [j_in=27] [cs-3: cycles 12] 27->25(199) [j_in=27] [cs-3: cycles 12] 27->25(200) [j_in=26] [cs-3: cycles 10] 26->24(201) [j_in=15] [cs-3: cycles 25] 15->14(202) [j_in=32] [cs-3: cycles 28] dup32->30(203) [j_in=14] [cs-3: cycles 19] 14->13(204) [j_in=20] [cs-3: cycles 18] dup20->20(205) [j_in=61] [cs-3: cycles 14] 61->49(206) [j_in=12] [cs-3: cycles 15] 12->12(207) [j_in=27] [cs-3: cycles 12] 27->25(208) [j_in=12] [cs-3: cycles 15] 12->12(209) [j_in=15] [cs-3: cycles 25] 15->14(210) [j_in=55] [cs-3: cycles 18] 55->47(211) [j_in=22] [cs-3: cycles 26] 22->22(212) [j_in=28] [cs-3: cycles 13] dup28->27(213) [j_in=13] [cs-3: cycles 13] 13->13(214) [j_in=15] [cs-3: cycles 25] 15->14(215) [j_in=21] [cs-3: cycles 13] 21->21(216) [j_in=33] [cs-3: cycles 28] dup33->32(217) [j_in=24] [cs-3: cycles 14] 24->23(218) [j_in=18] [cs-3: cycles 21] 18->17(219) [j_in=15] [cs-3: cycles 25] 15->14(220) [j_in=25] [cs-3: cycles 12] 25->24(221) [j_in=24] [cs-3: cycles 14] 24->23(222) [j_in=31] [cs-3: cycles 28] 31->30(223) [j_in=20] [cs-3: cycles 18] dup20->20(224) [j_in=27] [cs-3: cycles 12] 27->25(225) [j_in=11] [cs-3: cycles 17] 11->11(226) [j_in=34] [cs-3: cycles 28] dup34->32(227) [j_in=11] [cs-3: cycles 17] 11->11(228) [j_in=42] [cs-3: cycles 23] 42->34(229) [j_in=23] [cs-3: cycles 21] 23->23(230) [j_in=26] [cs-3: cycles 10] 26->24(231) [j_in=19] [cs-3: cycles 15] dup19->19(232) [j_in=20] [cs-3: cycles 18] dup20->20(233) [j_in=25] [cs-3: cycles 12] 25->24(234) [j_in=45] [cs-3: cycles 13] 45->36(235) [j_in=19] [cs-3: cycles 15] dup19->19(236) [j_in=17] [cs-3: cycles 24] 17->17(237) [j_in=18] [cs-3: cycles 21] 18->17(238) [j_in=29] [cs-3: cycles 11] dup29->27(239) [j_in=12] [cs-3: cycles 15] 12->12(240) [j_in=34] [cs-3: cycles 28] dup34->32(241) [j_in=49] [cs-3: cycles 17] 49->37(242) [j_in=6] [cs-3: cycles 38] 6->6(243) [j_in=30] [cs-3: cycles 28] dup30->30(244) [j_in=21] [cs-3: cycles 13] 21->21(245) [j_in=41] [cs-3: cycles 10] 41->38(246) [j_in=32] [cs-3: cycles 28] dup32->30(247) [j_in=21] [cs-3: cycles 13] 21->21(248) [j_in=19] [cs-3: cycles 15] dup19->19(249) [j_in=13] [cs-3: cycles 13] 13->13(250) [j_in=39] [cs-3: cycles 10] 39->36(251) [j_in=15] [cs-3: cycles 25] 15->14(252) [j_in=32] [cs-3: cycles 28] dup32->30(253) [j_in=22] [cs-3: cycles 26] 22->22(254) [j_in=20] [cs-3: cycles 18] dup20->20(255) [j_in=12] [cs-3: cycles 15] 12->12(256) [j_in=5] [cs-3: cycles 37] 5->5(257) [j_in=47] [cs-3: cycles 13] 47->35(258) [j_in=29] [cs-3: cycles 11] dup29->27(259) [j_in=31] [cs-3: cycles 28] dup31->30(260) [j_in=39] [cs-3: cycles 10] 39->36(261) [j_in=14] [cs-3: cycles 19] 14->13(262) [j_in=29] [cs-3: cycles 11] dup29->27(263) [j_in=25] [cs-3: cycles 12] 25->24(264) [j_in=20] [cs-3: cycles 18] dup20->20(265) [j_in=20] [cs-3: cycles 18] dup20->20(266) [j_in=23] [cs-3: cycles 21] 23->23(267) [j_in=9] [cs-3: cycles 16] 9->9(268) [j_in=33] [cs-3: cycles 28] dup33->32(269) [j_in=26] [cs-3: cycles 10] 26->24(270) [j_in=19] [cs-3: cycles 15] dup19->19(271) [j_in=13] [cs-3: cycles 13] 13->13(272) [j_in=25] [cs-3: cycles 12] 25->24(273) [j_in=42] [cs-3: cycles 23] 42->34(274) [j_in=14] [cs-3: cycles 19] 14->13(275) [j_in=5] [cs-3: cycles 37] 5->5(276) [j_in=22] [cs-3: cycles 26] 22->22(277) [j_in=59] [cs-3: cycles 19] 59->49(278) [j_in=13] [cs-3: cycles 13] 13->13(279) [j_in=35] [cs-3: cycles 10] 35->34(280) [j_in=59] [cs-3: cycles 19] 59->49(281) [j_in=11] [cs-3: cycles 17] 11->11(282) [j_in=21] [cs-3: cycles 13] 21->21(283) [j_in=17] [cs-3: cycles 24] 17->17(284) [j_in=56] [cs-3: cycles 16] 56->48(285) [j_in=34] [cs-3: cycles 28] dup34->32(286) [j_in=53] [cs-3: cycles 16] 53->48(287) [j_in=47] [cs-3: cycles 13] 47->35(288) [j_in=9] [cs-3: cycles 16] 9->9(289) [j_in=13] [cs-3: cycles 13] 13->13(290) [j_in=36] [cs-3: cycles 10] 36->34(291) [j_in=33] [cs-3: cycles 28] dup33->32(292) [j_in=10] [cs-3: cycles 23] dup10->10(293) [j_in=11] [cs-3: cycles 17] 11->11(294) [j_in=14] [cs-3: cycles 19] 14->13(295) [j_in=19] [cs-3: cycles 15] dup19->19(296) [j_in=18] [cs-3: cycles 21] 18->17(297) [j_in=16] [cs-3: cycles 24] 16->16(298) [j_in=51] [cs-3: cycles 9] 51->38(299) [j_in=31] [cs-3: cycles 28] dup31->30(300) [j_in=10] [cs-3: cycles 23] dup10->10(301) [j_in=30] [cs-3: cycles 28] dup30->30(302) [j_in=20] [cs-3: cycles 18] dup20->20(303) [j_in=13] [cs-3: cycles 13] 13->13(304) [j_in=9] [cs-3: cycles 16] 9->9(305) [j_in=25] [cs-3: cycles 12] 25->24(306) [j_in=72] [cs-3: cycles 16] 72->25(307) [j_in=33] [cs-3: cycles 28] dup33->32(308) [j_in=15] [cs-3: cycles 25] 15->14(309) [j_in=12] [cs-3: cycles 15] 12->12(310) [j_in=32] [cs-3: cycles 28] dup32->30(311) [j_in=69] [cs-3: cycles 15] 69->24(312) [j_in=27] [cs-3: cycles 12] 27->25(313) [j_in=35] [cs-3: cycles 10] 35->34(314) [j_in=18] [cs-3: cycles 21] 18->17(315) [j_in=18] [cs-3: cycles 21] 18->17(316) [j_in=11] [cs-3: cycles 17] 11->11(317) [j_in=33] [cs-3: cycles 28] dup33->32(318) [j_in=21] [cs-3: cycles 13] 21->21(319) [j_in=8] [cs-3: cycles 37] 8->8(320) [j_in=24] [cs-3: cycles 14] 24->23(321) [j_in=26] [cs-3: cycles 10] 26->24(322) [j_in=29] [cs-3: cycles 11] dup29->27(323) [j_in=28] [cs-3: cycles 13] 28->27(324) [j_in=18] [cs-3: cycles 21] 18->17(325) [j_in=32] [cs-3: cycles 28] dup32->30(326) [j_in=18] [cs-3: cycles 21] 18->17(327) [j_in=12] [cs-3: cycles 15] 12->12(328) [j_in=29] [cs-3: cycles 11] dup29->27(329) [j_in=7] [cs-3: cycles 32] dup7->7(330) [j_in=40] [cs-3: cycles 10] 40->37(331) [j_in=22] [cs-3: cycles 26] 22->22(332) [j_in=33] [cs-3: cycles 28] dup33->32(333) [j_in=14] [cs-3: cycles 19] 14->13(334) [j_in=23] [cs-3: cycles 21] 23->23(335) [j_in=22] [cs-3: cycles 26] 22->22(336) [j_in=20] [cs-3: cycles 18] dup20->20(337) [j_in=40] [cs-3: cycles 10] 40->37(338) [j_in=9] [cs-3: cycles 16] 9->9(339) [j_in=22] [cs-3: cycles 26] 22->22(340) [j_in=24] [cs-3: cycles 14] 24->23(341) [j_in=85] [cs-3: cycles 21] 85->24(342) [j_in=55] [cs-3: cycles 18] 55->47(343) [j_in=26] [cs-3: cycles 10] 26->24(344) [j_in=13] [cs-3: cycles 13] 13->13(345) [j_in=17] [cs-3: cycles 24] 17->17(346) [j_in=25] [cs-3: cycles 12] 25->24(347) [j_in=50] [cs-3: cycles 10] 50->37(348) [j_in=13] [cs-3: cycles 13] 13->13(349) [j_in=13] [cs-3: cycles 13] 13->13(350) ENDING SEARCH because max number of tries reached at Mon Jun 11 12:09:30 2001 after a total of 350 tries over 2 minutes 26 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16266.782) N_CLASSES 30 FOUND ON TRY 59 DUPS 7 *SAVED* PROBABILITY exp(-16283.605) N_CLASSES 30 FOUND ON TRY 223 DUPS 2 *SAVED* PROBABILITY exp(-16305.340) N_CLASSES 32 FOUND ON TRY 74 DUPS 4 PROBABILITY exp(-16339.997) N_CLASSES 32 FOUND ON TRY 152 DUPS 6 PROBABILITY exp(-16362.340) N_CLASSES 27 FOUND ON TRY 99 DUPS 9 PROBABILITY exp(-16399.962) N_CLASSES 30 FOUND ON TRY 10 DUPS 3 PROBABILITY exp(-16427.763) N_CLASSES 10 FOUND ON TRY 5 DUPS 13 PROBABILITY exp(-16453.536) N_CLASSES 7 FOUND ON TRY 4 DUPS 3 PROBABILITY exp(-16455.829) N_CLASSES 19 FOUND ON TRY 39 DUPS 10 PROBABILITY exp(-16477.439) N_CLASSES 20 FOUND ON TRY 25 DUPS 12 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 59 num_cycles 28 max_cycles 200 convergent try 223 num_cycles 28 max_cycles 200 convergent try 74 num_cycles 28 max_cycles 200 convergent try 152 num_cycles 28 max_cycles 200 convergent try 99 num_cycles 11 max_cycles 200 convergent try 10 num_cycles 28 max_cycles 200 convergent try 5 num_cycles 23 max_cycles 200 convergent try 4 num_cycles 32 max_cycles 200 convergent try 39 num_cycles 15 max_cycles 200 convergent try 25 num_cycles 18 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 12:09:30 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 14:47:36 2001 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=250; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log During loading of: [1] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2, [2] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2, [3] /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 1.2485e+03) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 3.6085e+01) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.5147e+02) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 4.5795e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 5.9417e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 2.6979e+05) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 1.7257e+03) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 7.4451e-02) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 9.9811e-02) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 1.5700e+01) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 1.5695e+03) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 2.2863e+05) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 4.2591e+01) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 4.7192e+01) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 6.2842e+07) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 8.0123e-02) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 6.7855e-03) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0509e-02) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 1.1947e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 8.8318e-03) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 2.5100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 3.5069e-03) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 5.0053e-03) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 1.0268e-03) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 2.0614e-03) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 3.8996e-02) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 7.9679e-02) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 7.9267e-02) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 6.5718e-02) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 5.0072e-02) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (250). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin and a description of the search to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search 9) A record of this search will be printed to file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log BEGINNING SEARCH at Mon Jun 11 14:47:38 2001 [j_in=2] [cs-3: cycles 18] best2->2(1) [j_in=3] [cs-3: cycles 10] best3->3(2) [j_in=5] [cs-3: cycles 35] best5->5(3) [j_in=7] [cs-3: cycles 38] best7->7(4) [j_in=10] [cs-3: cycles 27] 10->10(5) [j_in=15] [cs-3: cycles 13] 15->15(6) [j_in=25] [cs-3: cycles 19] 25->24(7) [j_in=37] [cs-3: cycles 14] 37->29(8) [j_in=4] [cs-3: cycles 31] 4->4(9) [j_in=12] [cs-3: cycles 20] 12->12(10) [j_in=14] [cs-3: cycles 14] best14->14(11) [j_in=5] [cs-3: cycles 14] 5->5(12) [j_in=10] [cs-3: cycles 19] 10->10(13) [j_in=14] [cs-3: cycles 14] dup14->14(14) [j_in=7] [cs-3: cycles 13] 7->7(15) [j_in=39] [cs-3: cycles 13] 39->32(16) [j_in=8] [cs-3: cycles 18] 8->8(17) [j_in=8] [cs-3: cycles 18] dup8->8(18) [j_in=8] [cs-3: cycles 22] 8->8(19) [j_in=7] [cs-3: cycles 22] 7->7(20) [j_in=28] [cs-3: cycles 18] 28->26(21) [j_in=16] [cs-3: cycles 15] 16->16(22) [j_in=19] [cs-3: cycles 19] 19->17(23) [j_in=22] [cs-3: cycles 12] 22->20(24) [j_in=35] [cs-3: cycles 9] 35->30(25) [j_in=14] [cs-3: cycles 17] 14->14(26) [j_in=10] [cs-3: cycles 13] 10->10(27) [j_in=6] [cs-3: cycles 34] 6->6(28) [j_in=22] [cs-3: cycles 21] 22->19(29) [j_in=7] [cs-3: cycles 22] 7->7(30) [j_in=6] [cs-3: cycles 47] 6->6(31) [j_in=27] [cs-3: cycles 17] 27->27(32) [j_in=9] [cs-3: cycles 22] 9->9(33) [j_in=16] [cs-3: cycles 48] 16->16(34) [j_in=8] [cs-3: cycles 25] 8->8(35) [j_in=11] [cs-3: cycles 22] 11->11(36) [j_in=8] [cs-3: cycles 21] 8->8(37) [j_in=9] [cs-3: cycles 23] 9->9(38) [j_in=32] [cs-3: cycles 16] 32->26(39) [j_in=6] [cs-3: cycles 12] 6->6(40) [j_in=10] [cs-3: cycles 31] 10->10(41) [j_in=16] [cs-3: cycles 21] best16->16(42) [j_in=9] [cs-3: cycles 22] 9->9(43) [j_in=12] [cs-3: cycles 17] 12->12(44) [j_in=24] [cs-3: cycles 12] 24->21(45) [j_in=7] [cs-3: cycles 21] 7->7(46) [j_in=23] [cs-3: cycles 9] 23->22(47) [j_in=14] [cs-3: cycles 22] 14->14(48) [j_in=13] [cs-3: cycles 22] 13->13(49) [j_in=22] [cs-3: cycles 9] 22->22(50) [j_in=12] [cs-3: cycles 29] 12->12(51) [j_in=20] [cs-3: cycles 16] 20->19(52) [j_in=18] [cs-3: cycles 15] 18->17(53) [j_in=17] [cs-3: cycles 21] 17->17(54) [j_in=24] [cs-3: cycles 19] 24->22(55) [j_in=11] [cs-3: cycles 27] 11->11(56) [j_in=10] [cs-3: cycles 10] 10->10(57) [j_in=11] [cs-3: cycles 27] dup11->11(58) [j_in=10] [cs-3: cycles 11] 10->10(59) [j_in=15] [cs-3: cycles 17] 15->15(60) [j_in=18] [cs-3: cycles 15] 18->18(61) [j_in=13] [cs-3: cycles 13] 13->13(62) [j_in=17] [cs-3: cycles 14] 17->17(63) [j_in=13] [cs-3: cycles 9] 13->13(64) [j_in=11] [cs-3: cycles 13] 11->11(65) [j_in=10] [cs-3: cycles 11] 10->10(66) [j_in=17] [cs-3: cycles 14] 17->17(67) [j_in=13] [cs-3: cycles 15] 13->13(68) [j_in=15] [cs-3: cycles 14] 15->14(69) [j_in=15] [cs-3: cycles 14] 15->14(70) [j_in=15] [cs-3: cycles 14] 15->14(71) [j_in=15] [cs-3: cycles 12] 15->15(72) [j_in=15] [cs-3: cycles 12] 15->15(73) [j_in=15] [cs-3: cycles 12] 15->15(74) [j_in=15] [cs-3: cycles 12] 15->15(75) [j_in=15] [cs-3: cycles 14] 15->15(76) [j_in=17] [cs-3: cycles 9] 17->17(77) [j_in=14] [cs-3: cycles 11] 14->14(78) [j_in=13] [cs-3: cycles 19] 13->13(79) [j_in=17] [cs-3: cycles 67] 17->17(80) [j_in=16] [cs-3: cycles 26] 16->16(81) [j_in=16] [cs-3: cycles 26] dup16->16(82) [j_in=16] [cs-3: cycles 13] 16->16(83) [j_in=16] [cs-3: cycles 13] dup16->16(84) [j_in=16] [cs-3: cycles 13] dup16->16(85) [j_in=16] [cs-3: cycles 13] dup16->16(86) [j_in=16] [cs-3: cycles 9] 16->16(87) [j_in=18] [cs-3: cycles 16] 18->18(88) [j_in=17] [cs-3: cycles 10] 17->17(89) [j_in=16] [cs-3: cycles 9] 16->16(90) [j_in=19] [cs-3: cycles 10] 19->19(91) [j_in=12] [cs-3: cycles 16] 12->11(92) [j_in=12] [cs-3: cycles 16] 12->11(93) [j_in=12] [cs-3: cycles 16] 12->11(94) [j_in=12] [cs-3: cycles 16] 12->11(95) [j_in=12] [cs-3: cycles 20] 12->12(96) [j_in=22] [cs-3: cycles 27] 22->21(97) [j_in=16] [cs-3: cycles 13] 16->16(98) [j_in=13] [cs-3: cycles 9] 13->13(99) [j_in=12] [cs-3: cycles 16] 12->12(100) [j_in=19] [cs-3: cycles 14] 19->18(101) [j_in=14] [cs-3: cycles 36] 14->14(102) [j_in=18] [cs-3: cycles 37] 18->18(103) ---------------- NEW BEST CLASSIFICATION FOUND on try 42 ------------- It has 16 CLASSES with WEIGHTS 34 22 16 14 14 13 12 11 11 11 10 9 9 9 5 5 PROBABILITY of both the data and the classification = exp(-16282.909) (Also found 41 other better than last report.) ----------- SEARCH STATUS as of Mon Jun 11 14:48:09 2001 ----------- It just took 31 seconds since beginning. Estimate < 31 seconds to find a classification exp(21.2) [= 1.5e+09] times more probable. Estimate >> 7 seconds to find the very best classification, which may be exp(45.1) to exp(3293.8) times more probable. Have seen 96 of the estimated > 117 possible classifications (based on 7 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16435.9, S(igma) 83.0 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [11.9, 14.2, 16.9] Overhead time is 3.1 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=11] [cs-3: cycles 21] 11->11(104) [j_in=15] [cs-3: cycles 26] 15->15(105) [j_in=19] [cs-3: cycles 9] 19->17(106) [j_in=17] [cs-3: cycles 21] 17->15(107) [j_in=11] [cs-3: cycles 23] 11->11(108) [j_in=12] [cs-3: cycles 24] 12->12(109) [j_in=14] [cs-3: cycles 14] 14->14(110) [j_in=17] [cs-3: cycles 9] 17->16(111) [j_in=19] [cs-3: cycles 9] 19->18(112) [j_in=20] [cs-3: cycles 9] 20->19(113) [j_in=21] [cs-3: cycles 15] 21->20(114) [j_in=21] [cs-3: cycles 15] 21->20(115) [j_in=21] [cs-3: cycles 15] 21->20(116) [j_in=21] [cs-3: cycles 17] 21->20(117) [j_in=19] [cs-3: cycles 16] 19->18(118) [j_in=15] [cs-3: cycles 25] 15->15(119) [j_in=16] [cs-3: cycles 15] 16->16(120) [j_in=14] [cs-3: cycles 22] 14->14(121) [j_in=19] [cs-3: cycles 19] 19->19(122) [j_in=15] [cs-3: cycles 17] 15->15(123) [j_in=17] [cs-3: cycles 9] 17->17(124) [j_in=14] [cs-3: cycles 17] 14->14(125) [j_in=16] [cs-3: cycles 17] 16->16(126) [j_in=21] [cs-3: cycles 10] 21->21(127) [j_in=17] [cs-3: cycles 16] 17->17(128) [j_in=19] [cs-3: cycles 55] 19->17(129) [j_in=15] [cs-3: cycles 19] 15->15(130) [j_in=13] [cs-3: cycles 20] 13->13(131) [j_in=13] [cs-3: cycles 20] 13->13(132) [j_in=13] [cs-3: cycles 34] 13->13(133) [j_in=13] [cs-3: cycles 34] 13->13(134) [j_in=13] [cs-3: cycles 17] 13->13(135) [j_in=17] [cs-3: cycles 21] 17->17(136) [j_in=16] [cs-3: cycles 16] 16->16(137) [j_in=18] [cs-3: cycles 44] 18->17(138) [j_in=15] [cs-3: cycles 14] 15->14(139) [j_in=18] [cs-3: cycles 25] 18->17(140) [j_in=17] [cs-3: cycles 26] 17->16(141) [j_in=12] [cs-3: cycles 36] 12->12(142) [j_in=14] [cs-3: cycles 28] 14->14(143) [j_in=13] [cs-3: cycles 16] 13->13(144) [j_in=12] [cs-3: cycles 19] 12->12(145) [j_in=13] [cs-3: cycles 16] 13->13(146) [j_in=12] [cs-3: cycles 19] 12->12(147) [j_in=13] [cs-3: cycles 24] 13->12(148) [j_in=14] [cs-3: cycles 15] 14->13(149) [j_in=16] [cs-3: cycles 14] 16->15(150) [j_in=16] [cs-3: cycles 19] 16->16(151) [j_in=20] [cs-3: cycles 15] 20->20(152) [j_in=18] [cs-3: cycles 17] 18->17(153) [j_in=20] [cs-3: cycles 17] 20->19(154) [j_in=12] [cs-3: cycles 52] 12->11(155) [j_in=20] [cs-3: cycles 16] 20->20(156) [j_in=10] [cs-3: cycles 11] 10->10(157) [j_in=16] [cs-3: cycles 25] 16->15(158) [j_in=16] [cs-3: cycles 25] 16->15(159) [j_in=16] [cs-3: cycles 25] 16->15(160) [j_in=16] [cs-3: cycles 16] 16->16(161) [j_in=15] [cs-3: cycles 13] 15->15(162) [j_in=18] [cs-3: cycles 16] 18->17(163) [j_in=13] [cs-3: cycles 18] 13->13(164) [j_in=16] [cs-3: cycles 30] 16->16(165) [j_in=15] [cs-3: cycles 15] 15->15(166) [j_in=19] [cs-3: cycles 19] 19->19(167) [j_in=17] [cs-3: cycles 18] 17->17(168) [j_in=13] [cs-3: cycles 21] 13->12(169) [j_in=15] [cs-3: cycles 20] 15->14(170) [j_in=13] [cs-3: cycles 21] 13->12(171) [j_in=15] [cs-3: cycles 13] 15->14(172) [j_in=20] [cs-3: cycles 10] 20->18(173) [j_in=13] [cs-3: cycles 15] 13->13(174) [j_in=15] [cs-3: cycles 13] 15->14(175) [j_in=20] [cs-3: cycles 16] 20->19(176) [j_in=20] [cs-3: cycles 16] 20->19(177) [j_in=20] [cs-3: cycles 16] 20->19(178) [j_in=20] [cs-3: cycles 9] 20->20(179) [j_in=11] [cs-3: cycles 29] 11->11(180) [j_in=14] [cs-3: cycles 27] 14->14(181) [j_in=17] [cs-3: cycles 28] 17->16(182) [j_in=18] [cs-3: cycles 27] 18->17(183) [j_in=16] [cs-3: cycles 20] 16->15(184) [j_in=13] [cs-3: cycles 17] 13->12(185) [j_in=13] [cs-3: cycles 17] 13->12(186) [j_in=13] [cs-3: cycles 16] 13->13(187) [j_in=17] [cs-3: cycles 13] 17->17(188) [j_in=13] [cs-3: cycles 16] 13->13(189) [j_in=17] [cs-3: cycles 13] 17->17(190) [j_in=13] [cs-3: cycles 9] 13->13(191) [j_in=14] [cs-3: cycles 13] 14->14(192) [j_in=12] [cs-3: cycles 13] 12->12(193) [j_in=13] [cs-3: cycles 9] 13->13(194) [j_in=14] [cs-3: cycles 13] 14->14(195) [j_in=12] [cs-3: cycles 54] 12->11(196) [j_in=18] [cs-3: cycles 16] 18->17(197) [j_in=15] [cs-3: cycles 15] 15->15(198) [j_in=17] [cs-3: cycles 31] 17->16(199) [j_in=15] [cs-3: cycles 11] 15->15(200) [j_in=17] [cs-3: cycles 9] 17->17(201) [j_in=18] [cs-3: cycles 12] 18->18(202) [j_in=15] [cs-3: cycles 11] 15->15(203) [j_in=18] [cs-3: cycles 12] dup18->18(204) [j_in=15] [cs-3: cycles 17] 15->15(205) [j_in=15] [cs-3: cycles 17] 15->15(206) [j_in=15] [cs-3: cycles 17] 15->15(207) [j_in=15] [cs-3: cycles 22] 15->15(208) [j_in=17] [cs-3: cycles 21] 17->17(209) [j_in=21] [cs-3: cycles 11] 21->21(210) [j_in=19] [cs-3: cycles 8] 19->19(211) [j_in=9] [cs-3: cycles 11] best9->9(212) ---------------- NEW BEST CLASSIFICATION FOUND on try 212 ------------- It has 9 CLASSES with WEIGHTS 52 35 33 21 16 14 14 10 10 PROBABILITY of both the data and the classification = exp(-16272.233) ----------- SEARCH STATUS as of Mon Jun 11 14:48:46 2001 ----------- It just took 37 seconds to find a classification exp(10.7) [= 4.3e+04] times more probable. Estimate < 1 minute 8 seconds to find a classification exp(20.2) [= 5.8e+08] times more probable. Estimate >> 7 seconds to find the very best classification, which may be exp(51.8) to exp(3520.2) times more probable. Have seen 204 of the estimated > 225 possible classifications (based on 8 duplicates do far). Log-Normal fit to classifications probabilities has M(ean) -16445.8, S(igma) 85.9 Choosing initial n-classes randomly from a log_normal [M-S, M, M+S] = [11.4, 14.5, 18.5] Overhead time is 1.4 % of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic [j_in=15] [cs-3: cycles 29] best15->15(213) [j_in=16] [cs-3: cycles 14] 16->16(214) [j_in=21] [cs-3: cycles 14] 21->20(215) [j_in=14] [cs-3: cycles 9] 14->14(216) [j_in=10] [cs-3: cycles 22] 10->10(217) [j_in=17] [cs-3: cycles 12] 17->17(218) [j_in=12] [cs-3: cycles 17] 12->12(219) [j_in=21] [cs-3: cycles 22] 21->21(220) [j_in=19] [cs-3: cycles 17] 19->18(221) [j_in=12] [cs-3: cycles 17] 12->12(222) [j_in=18] [cs-3: cycles 14] 18->17(223) [j_in=19] [cs-3: cycles 25] 19->18(224) [j_in=15] [cs-3: cycles 19] 15->15(225) [j_in=12] [cs-3: cycles 12] 12->12(226) [j_in=14] [cs-3: cycles 20] 14->14(227) [j_in=21] [cs-3: cycles 25] 21->20(228) [j_in=18] [cs-3: cycles 10] 18->18(229) [j_in=13] [cs-3: cycles 15] 13->13(230) [j_in=19] [cs-3: cycles 14] 19->19(231) [j_in=15] [cs-3: cycles 28] 15->15(232) [j_in=12] [cs-3: cycles 23] 12->12(233) [j_in=14] [cs-3: cycles 34] 14->14(234) [j_in=11] [cs-3: cycles 32] 11->10(235) [j_in=18] [cs-3: cycles 39] 18->16(236) [j_in=13] [cs-3: cycles 10] 13->13(237) [j_in=16] [cs-3: cycles 20] 16->16(238) [j_in=16] [cs-3: cycles 20] 16->16(239) [j_in=16] [cs-3: cycles 28] 16->16(240) [j_in=19] [cs-3: cycles 18] 19->19(241) [j_in=14] [cs-3: cycles 16] 14->13(242) [j_in=12] [cs-3: cycles 14] 12->12(243) [j_in=13] [cs-3: cycles 16] 13->12(244) [j_in=16] [cs-3: cycles 9] 16->15(245) [j_in=14] [cs-3: cycles 16] 14->13(246) [j_in=12] [cs-3: cycles 14] 12->12(247) [j_in=14] [cs-3: cycles 14] 14->14(248) [j_in=19] [cs-3: cycles 26] 19->19(249) [j_in=19] [cs-3: cycles 18] 19->19(250) ENDING SEARCH because max number of tries reached at Mon Jun 11 14:48:59 2001 after a total of 250 tries over 1 minute 22 seconds A log of this search is in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.log The search results are stored in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16230.401) N_CLASSES 15 FOUND ON TRY 213 *SAVED* PROBABILITY exp(-16245.405) N_CLASSES 16 FOUND ON TRY 240 *SAVED* PROBABILITY exp(-16258.217) N_CLASSES 14 FOUND ON TRY 248 PROBABILITY exp(-16272.233) N_CLASSES 9 FOUND ON TRY 212 PROBABILITY exp(-16282.909) N_CLASSES 16 FOUND ON TRY 42 PROBABILITY exp(-16285.766) N_CLASSES 16 FOUND ON TRY 83 DUPS 3 PROBABILITY exp(-16289.486) N_CLASSES 13 FOUND ON TRY 99 PROBABILITY exp(-16292.727) N_CLASSES 19 FOUND ON TRY 113 PROBABILITY exp(-16295.621) N_CLASSES 12 FOUND ON TRY 51 PROBABILITY exp(-16297.324) N_CLASSES 15 FOUND ON TRY 184 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 213 num_cycles 29 max_cycles 200 convergent try 240 num_cycles 28 max_cycles 200 convergent try 248 num_cycles 14 max_cycles 200 convergent try 212 num_cycles 11 max_cycles 200 convergent try 42 num_cycles 21 max_cycles 200 convergent try 83 num_cycles 13 max_cycles 200 convergent try 99 num_cycles 9 max_cycles 200 convergent try 113 num_cycles 9 max_cycles 200 convergent try 51 num_cycles 29 max_cycles 200 convergent try 184 num_cycles 20 max_cycles 200 convergent AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 14:48:59 2001 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 25 13:36:27 2009 AUTOCLASS -SEARCH default parameters: rel_error=1.000000e-02; start_j_list=(2,3,5,7,10,15,25); n_classes_fn_type="random_ln_normal"; fixed_j=0; min_report_period=30; max_duration=0; max_n_tries=0; n_save=2; log_file_p=true; search_file_p=true; results_file_p=true; min_save_period=1800; max_n_store=10; n_final_summary=10; start_fn_type="random"; try_fn_type="converge_search_3"; initial_cycles_p=true; save_compact_p=true; read_compact_p=true; randomize_random_p=true; n_data=0; halt_range=5.000000e-01; halt_factor=1.000000e-04; rel_delta_range=2.500000e-03; n_average=3; cs4_delta_range=2.500000e-03; sigma_beta_n_values=6; max_cycles=200; converge_print_p=false; force_new_search_p=true; checkpoint_p=false; min_checkpoint_period=10800; reconverge_type=""; screen_output_p=true; interactive_p=true; break_on_warnings_p=true; free_storage_p=true USER supplied parameters which override the defaults: max_n_tries=250; force_new_search_p=true ############## Starting Input Check ############### To log file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.log During loading of: [1] /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.db2, [2] /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.hd2, [3] /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.model. [Attribute #, value #, and datum # are zero based.] ADVISORY[2]: data_file_format settings: separator_char = ',', comment_char = ';', unknown_token = '?' ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.hd2 ADVISORY[2]: for attribute #5: "num-of-doors" range increased to 3, for value 2 -- translator (2 ?). ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.db2 ADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- Attribute #0, "symboling": [ (0 3):27 (1 1):54 (2 2):32 (3 0):67 (4 -1):22 (5 -2):3 ] Attribute #2, "make": [ (0 alfa-romero):3 (1 audi):7 (2 bmw):8 (3 chevrolet):3 (4 dodge):9 (5 honda):13 (6 isuzu):4 (7 jaguar):3 (8 mazda):17 (9 mercedes-benz):8 (10 mercury):1 (11 mitsubishi):13 (12 nissan):18 (13 peugot):11 (14 plymouth):7 (15 porsche):5 (16 renault):2 (17 saab):6 (18 subaru):12 (19 toyota):32 (20 volkswagen):12 (21 volvo):11 ] Attribute #3, "fuel-type": [ (0 gas):185 (1 diesel):20 ] Attribute #4, "aspiration": [ (0 std):168 (1 turbo):37 ] Attribute #5, "num-of-doors": [ (0 two):89 (1 four):114 (2 ?):2 ] Attribute #6, "body-style": [ (0 convertible):6 (1 hatchback):70 (2 sedan):96 (3 wagon):25 (4 hardtop):8 ] Attribute #7, "drive-wheels": [ (0 rwd):76 (1 fwd):120 (2 4wd):9 ] Attribute #8, "engine-location": [ (0 front):202 (1 rear):3 ] Attribute #14, "engine-type": [ (0 dohc):12 (1 ohcv):13 (2 ohc):148 (3 l):12 (4 rotor):4 (5 ohcf):15 (6 dohcv):1 ] Attribute #15, "num-of-cylinders": [ (0 four):159 (1 six):24 (2 five):11 (3 three):1 (4 twelve):1 (5 two):4 (6 eight):5 ] Attribute #17, "fuel-system": [ (0 mpfi):94 (1 2bbl):66 (2 mfi):1 (3 1bbl):11 (4 spfi):1 (5 4bbl):3 (6 idi):20 (7 spdi):9 ] ADVISORY[1]: real statistics [ min < (mean : std dev) < max ] built from input data -- Attribute #1, "normalized-loses": [ 6.5000e+01 < ( 1.2200e+02 : 3.5334e+01) < 2.5600e+02 ] Attribute #9, "wheel-base": [ 8.6600e+01 < ( 9.8757e+01 : 6.0071e+00) < 1.2090e+02 ] Attribute #10, "length": [ 1.4110e+02 < ( 1.7405e+02 : 1.2307e+01) < 2.0810e+02 ] Attribute #11, "width": [ 6.0300e+01 < ( 6.5908e+01 : 2.1400e+00) < 7.2300e+01 ] Attribute #12, "height": [ 4.7800e+01 < ( 5.3725e+01 : 2.4376e+00) < 5.9800e+01 ] Attribute #13, "curb-weight": [ 1.4880e+03 < ( 2.5556e+03 : 5.1941e+02) < 4.0660e+03 ] Attribute #16, "engine-size": [ 6.1000e+01 < ( 1.2691e+02 : 4.1541e+01) < 3.2600e+02 ] Attribute #18, "bore": [ 2.5400e+00 < ( 3.3298e+00 : 2.7286e-01) < 3.9400e+00 ] Attribute #19, "stroke": [ 2.0700e+00 < ( 3.2554e+00 : 3.1593e-01) < 4.1700e+00 ] Attribute #20, "compression-ratio": [ 7.0000e+00 < ( 1.0143e+01 : 3.9623e+00) < 2.3000e+01 ] Attribute #21, "horse-power": [ 4.8000e+01 < ( 1.0426e+02 : 3.9616e+01) < 2.8800e+02 ] Attribute #22, "peak-rpm": [ 4.1500e+03 < ( 5.1254e+03 : 4.7815e+02) < 6.6000e+03 ] Attribute #23, "city-mpg": [ 1.3000e+01 < ( 2.5220e+01 : 6.5262e+00) < 4.9000e+01 ] Attribute #24, "highway-mpg": [ 1.6000e+01 < ( 3.0751e+01 : 6.8696e+00) < 5.4000e+01 ] Attribute #25, "price": [ 5.1180e+03 < ( 1.3207e+04 : 7.9273e+03) < 4.5400e+04 ] ADVISORY[3]: the default model term type, single_multinomial, will be used for these attributes: #2: "make" #3: "fuel-type" #4: "aspiration" #5: "num-of-doors" #6: "body-style" #7: "drive-wheels" #8: "engine-location" #14: "engine-type" #15: "num-of-cylinders" #17: "fuel-system" ADVISORY[3]: read 1 model def from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.model ADVISORY[2]: log_transform is being applied to attribute #1: "normalized-loses" and will be stored as attribute #26. Attribute #26, "Log normalized-loses": [ 4.1744e+00 < ( 4.7637e+00 : 2.8306e-01) < 5.5452e+00 ] ADVISORY[2]: log_transform is being applied to attribute #18: "bore" and will be stored as attribute #27. Attribute #27, "Log bore": [ 9.3216e-01 < ( 1.1995e+00 : 8.2374e-02) < 1.3712e+00 ] ADVISORY[2]: log_transform is being applied to attribute #19: "stroke" and will be stored as attribute #28. Attribute #28, "Log stroke": [ 7.2755e-01 < ( 1.1753e+00 : 1.0251e-01) < 1.4279e+00 ] ADVISORY[2]: log_transform is being applied to attribute #21: "horse-power" and will be stored as attribute #29. Attribute #29, "Log horse-power": [ 3.8712e+00 < ( 4.5841e+00 : 3.4564e-01) < 5.6630e+00 ] ADVISORY[2]: log_transform is being applied to attribute #22: "peak-rpm" and will be stored as attribute #30. Attribute #30, "Log peak-rpm": [ 8.3309e+00 < ( 8.5376e+00 : 9.3977e-02) < 8.7948e+00 ] ADVISORY[2]: log_transform is being applied to attribute #25: "price" and will be stored as attribute #31. Attribute #31, "Log price": [ 8.5405e+00 < ( 9.3501e+00 : 5.0100e-01) < 1.0723e+01 ] ADVISORY[2]: log_transform is being applied to attribute #9: "wheel-base" and will be stored as attribute #32. Attribute #32, "Log wheel-base": [ 4.4613e+00 < ( 4.5909e+00 : 5.9219e-02) < 4.7950e+00 ] ADVISORY[2]: log_transform is being applied to attribute #10: "length" and will be stored as attribute #33. Attribute #33, "Log length": [ 4.9495e+00 < ( 5.1568e+00 : 7.0748e-02) < 5.3380e+00 ] ADVISORY[2]: log_transform is being applied to attribute #11: "width" and will be stored as attribute #34. Attribute #34, "Log width": [ 4.0993e+00 < ( 4.1877e+00 : 3.2044e-02) < 4.2808e+00 ] ADVISORY[2]: log_transform is being applied to attribute #12: "height" and will be stored as attribute #35. Attribute #35, "Log height": [ 3.8670e+00 < ( 3.9828e+00 : 4.5402e-02) < 4.0910e+00 ] ADVISORY[2]: log_transform is being applied to attribute #13: "curb-weight" and will be stored as attribute #36. Attribute #36, "Log curb-weight": [ 7.3052e+00 < ( 7.8262e+00 : 1.9747e-01) < 8.3104e+00 ] ADVISORY[2]: log_transform is being applied to attribute #16: "engine-size" and will be stored as attribute #37. Attribute #37, "Log engine-size": [ 4.1109e+00 < ( 4.8002e+00 : 2.8227e-01) < 5.7869e+00 ] ADVISORY[2]: log_transform is being applied to attribute #20: "compression-ratio" and will be stored as attribute #38. Attribute #38, "Log compression-ratio": [ 1.9459e+00 < ( 2.2674e+00 : 2.8154e-01) < 3.1355e+00 ] ADVISORY[2]: log_transform is being applied to attribute #23: "city-mpg" and will be stored as attribute #39. Attribute #39, "Log city-mpg": [ 2.5649e+00 < ( 3.1948e+00 : 2.5635e-01) < 3.8918e+00 ] ADVISORY[2]: log_transform is being applied to attribute #24: "highway-mpg" and will be stored as attribute #40. Attribute #40, "Log highway-mpg": [ 2.7726e+00 < ( 3.4011e+00 : 2.2377e-01) < 3.9890e+00 ] ############ Input Check Concluded ############## WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than 30 seconds have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until I complete trial number (250). 8) If needed, every 30 minutes I will save the best 2 classifications so far to file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin and a description of the search to file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search 9) A record of this search will be printed to file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.log BEGINNING SEARCH at Tue Aug 25 13:36:43 2009 [j_in=2] [cs-3: cycles 15] best2->2(1) [j_in=3] [cs-3: cycles 24] best3->3(2) [j_in=5] [cs-3: cycles 9] best5->5(3) [j_in=7] [cs-3: cycles 22] best7->7(4) [j_in=10] [cs-3: cycles 20] best10->10(5) [j_in=15] [cs-3: cycles 18] 15->15(6) [j_in=25] [cs-3: cycles 12] 25->21(7) [j_in=9] [cs-3: cycles 10] 9->9(8) [j_in=3] [cs-3: cycles 9] 3->3(9) [j_in=17] [cs-3: cycles 18] 17->17(10) [j_in=8] [cs-3: cycles 21] 8->8(11) [j_in=5] [cs-3: cycles 35] 5->5(12) [j_in=8] [cs-3: cycles 21] dup8->8(13) [j_in=5] [cs-3: cycles 35] dup5->5(14) [j_in=8] [cs-3: cycles 21] dup8->8(15) [j_in=5] [cs-3: cycles 35] dup5->5(16) [j_in=8] [cs-3: cycles 21] dup8->8(17) [j_in=5] [cs-3: cycles 35] dup5->5(18) [j_in=8] [cs-3: cycles 21] dup8->8(19) [j_in=5] [cs-3: cycles 35] dup5->5(20) [j_in=8] [cs-3: cycles 21] dup8->8(21) [j_in=5] [cs-3: cycles 23] 5->5(22) [j_in=11] [cs-3: cycles 15] 11->11(23) [j_in=6] [cs-3: cycles 22] 6->6(24) [j_in=11] [cs-3: cycles 15] dup11->11(25) [j_in=6] [cs-3: cycles 22] dup6->6(26) [j_in=11] [cs-3: cycles 15] dup11->11(27) [j_in=6] [cs-3: cycles 22] dup6->6(28) [j_in=11] [cs-3: cycles 15] dup11->11(29) [j_in=6] [cs-3: cycles 22] dup6->6(30) [j_in=11] [cs-3: cycles 15] dup11->11(31) [j_in=6] [cs-3: cycles 22] dup6->6(32) [j_in=11] [cs-3: cycles 15] dup11->11(33) [j_in=6] [cs-3: cycles 22] dup6->6(34) [j_in=11] [cs-3: cycles 15] dup11->11(35) [j_in=6] [cs-3: cycles 22] dup6->6(36) [j_in=11] [cs-3: cycles 15] dup11->11(37) [j_in=6] [cs-3: cycles 22] dup6->6(38) [j_in=11] [cs-3: cycles 15] dup11->11(39) [j_in=6] [cs-3: cycles 22] dup6->6(40) [j_in=11] [cs-3: cycles 12] 11->11(41) [j_in=8] [cs-3: cycles 21] 8->8(42) [j_in=18] [cs-3: cycles 11] 18->17(43) [j_in=13] [cs-3: cycles 17] 13->13(44) [j_in=15] [cs-3: cycles 18] 15->14(45) [j_in=14] [cs-3: cycles 18] 14->13(46) [j_in=8] [cs-3: cycles 21] dup8->8(47) [j_in=16] [cs-3: cycles 18] 16->15(48) [j_in=16] [cs-3: cycles 18] 16->15(49) [j_in=16] [cs-3: cycles 18] 16->15(50) [j_in=16] [cs-3: cycles 18] 16->15(51) [j_in=16] [cs-3: cycles 18] 16->15(52) [j_in=16] [cs-3: cycles 18] 16->15(53) [j_in=16] [cs-3: cycles 35] 16->16(54) [j_in=15] [cs-3: cycles 36] 15->15(55) [j_in=8] [cs-3: cycles 32] 8->8(56) [j_in=12] [cs-3: cycles 57] 12->12(57) [j_in=11] [cs-3: cycles 25] 11->11(58) [j_in=11] [cs-3: cycles 25] 11->11(59) [j_in=11] [cs-3: cycles 25] 11->11(60) [j_in=11] [cs-3: cycles 25] 11->11(61) [j_in=11] [cs-3: cycles 25] 11->11(62) [j_in=11] [cs-3: cycles 13] 11->11(63) [j_in=13] [cs-3: cycles 19] 13->13(64) [j_in=10] [cs-3: cycles 24] 10->10(65) [j_in=14] [cs-3: cycles 19] 14->14(66) [j_in=12] [cs-3: cycles 24] 12->12(67) [j_in=15] [cs-3: cycles 26] 15->15(68) [j_in=12] [cs-3: cycles 24] 12->12(69) [j_in=15] [cs-3: cycles 26] 15->15(70) [j_in=12] [cs-3: cycles 24] 12->12(71) [j_in=15] [cs-3: cycles 26] 15->15(72) [j_in=12] [cs-3: cycles 35] 12->12(73) [j_in=9] [cs-3: cycles 45] 9->9(74) [j_in=16] [cs-3: cycles 16] 16->16(75) [j_in=12] [cs-3: cycles 35] dup12->12(76) [j_in=9] [cs-3: cycles 45] 9->9(77) [j_in=16] [cs-3: cycles 16] 16->16(78) [j_in=12] [cs-3: cycles 35] dup12->12(79) [j_in=9] [cs-3: cycles 45] 9->9(80) [j_in=16] [cs-3: cycles 16] 16->16(81) [j_in=12] [cs-3: cycles 23] best12->11(82) [j_in=13] [cs-3: cycles 25] 13->12(83) [j_in=9] [cs-3: cycles 15] 9->9(84) [j_in=9] [cs-3: cycles 15] 9->9(85) [j_in=9] [cs-3: cycles 15] 9->9(86) [j_in=9] [cs-3: cycles 15] 9->9(87) [j_in=9] [cs-3: cycles 15] 9->9(88) [j_in=9] [cs-3: cycles 15] 9->9(89) [j_in=9] [cs-3: cycles 15] 9->9(90) [j_in=9] [cs-3: cycles 15] 9->9(91) [j_in=9] [cs-3: cycles 15] 9->9(92) [j_in=9] [cs-3: cycles 15] 9->9(93) [j_in=9] [cs-3: cycles 15] 9->9(94) [j_in=9] [cs-3: cycles 15] 9->9(95) [j_in=9] [cs-3: cycles 15] 9->9(96) [j_in=9] [cs-3: cycles 15] 9->9(97) [j_in=9] [cs-3: cycles 15] 9->9(98) [j_in=9] [cs-3: cycles 15] 9->9(99) [j_in=9] [cs-3: cycles 15] 9->9(100) [j_in=9] [cs-3: cycles 14] 9->9(101) [j_in=13] [cs-3: cycles 14] 13->13(102) [j_in=13] [cs-3: cycles 14] dup13->13(103) [j_in=13] [cs-3: cycles 14] dup13->13(104) [j_in=13] [cs-3: cycles 14] dup13->13(105) [j_in=13] [cs-3: cycles 14] dup13->13(106) [j_in=13] [cs-3: cycles 14] dup13->13(107) [j_in=13] [cs-3: cycles 14] dup13->13(108) [j_in=13] [cs-3: cycles 14] dup13->13(109) [j_in=13] [cs-3: cycles 14] dup13->13(110) [j_in=13] [cs-3: cycles 14] dup13->13(111) [j_in=13] [cs-3: cycles 14] dup13->13(112) [j_in=13] [cs-3: cycles 14] dup13->13(113) [j_in=13] [cs-3: cycles 14] dup13->13(114) [j_in=13] [cs-3: cycles 14] dup13->13(115) [j_in=13] [cs-3: cycles 14] dup13->13(116) [j_in=13] [cs-3: cycles 14] dup13->13(117) [j_in=13] [cs-3: cycles 36] 13->13(118) [j_in=14] [cs-3: cycles 17] 14->14(119) [j_in=13] [cs-3: cycles 36] dup13->13(120) [j_in=14] [cs-3: cycles 17] dup14->14(121) [j_in=13] [cs-3: cycles 36] dup13->13(122) [j_in=14] [cs-3: cycles 17] dup14->14(123) [j_in=13] [cs-3: cycles 36] dup13->13(124) [j_in=14] [cs-3: cycles 17] dup14->14(125) [j_in=13] [cs-3: cycles 36] dup13->13(126) [j_in=14] [cs-3: cycles 21] 14->14(127) [j_in=11] [cs-3: cycles 18] 11->11(128) [j_in=10] [cs-3: cycles 14] 10->10(129) [j_in=18] [cs-3: cycles 39] 18->18(130) [j_in=15] [cs-3: cycles 22] 15->15(131) [j_in=15] [cs-3: cycles 22] 15->15(132) [j_in=15] [cs-3: cycles 22] 15->15(133) [j_in=15] [cs-3: cycles 22] 15->15(134) [j_in=15] [cs-3: cycles 22] 15->15(135) [j_in=15] [cs-3: cycles 22] 15->15(136) [j_in=15] [cs-3: cycles 21] 15->15(137) [j_in=11] [cs-3: cycles 23] 11->11(138) [j_in=16] [cs-3: cycles 26] 16->15(139) [j_in=15] [cs-3: cycles 21] 15->15(140) [j_in=11] [cs-3: cycles 23] 11->11(141) [j_in=16] [cs-3: cycles 26] 16->15(142) [j_in=15] [cs-3: cycles 21] 15->15(143) [j_in=11] [cs-3: cycles 23] 11->11(144) [j_in=16] [cs-3: cycles 26] 16->15(145) [j_in=15] [cs-3: cycles 21] 15->15(146) [j_in=11] [cs-3: cycles 18] 11->11(147) [j_in=14] [cs-3: cycles 21] 14->14(148) [j_in=12] [cs-3: cycles 36] 12->12(149) [j_in=9] [cs-3: cycles 15] 9->9(150) [j_in=10] [cs-3: cycles 20] 10->10(151) [j_in=16] [cs-3: cycles 19] 16->16(152) [j_in=11] [cs-3: cycles 18] 11->11(153) [j_in=14] [cs-3: cycles 21] 14->14(154) [j_in=12] [cs-3: cycles 36] 12->12(155) [j_in=8] [cs-3: cycles 14] 8->8(156) [j_in=14] [cs-3: cycles 21] 14->14(157) [j_in=12] [cs-3: cycles 36] 12->12(158) [j_in=8] [cs-3: cycles 16] 8->8(159) [j_in=11] [cs-3: cycles 19] 11->11(160) [j_in=15] [cs-3: cycles 28] best15->15(161) [j_in=15] [cs-3: cycles 28] dup15->15(162) [j_in=15] [cs-3: cycles 28] dup15->15(163) [j_in=15] [cs-3: cycles 28] dup15->15(164) [j_in=15] [cs-3: cycles 28] dup15->15(165) [j_in=15] [cs-3: cycles 28] dup15->15(166) [j_in=15] [cs-3: cycles 28] dup15->15(167) [j_in=15] [cs-3: cycles 9] 15->15(168) [j_in=12] [cs-3: cycles 18] 12->12(169) [j_in=13] [cs-3: cycles 18] 13->13(170) [j_in=12] [cs-3: cycles 18] 12->12(171) [j_in=13] [cs-3: cycles 18] 13->13(172) [j_in=12] [cs-3: cycles 18] 12->12(173) [j_in=13] [cs-3: cycles 18] 13->13(174) [j_in=12] [cs-3: cycles 18] 12->12(175) [j_in=13] [cs-3: cycles 18] 13->13(176) [j_in=12] [cs-3: cycles 18] 12->12(177) [j_in=13] [cs-3: cycles 18] 13->13(178) [j_in=12] [cs-3: cycles 18] 12->12(179) [j_in=13] [cs-3: cycles 18] 13->13(180) [j_in=12] [cs-3: cycles 12] 12->12(181) [j_in=9] [cs-3: cycles 29] 9->9(182) [j_in=13] [cs-3: cycles 12] 13->13(183) [j_in=16] [cs-3: cycles 10] 16->16(184) [j_in=12] [cs-3: cycles 12] dup12->12(185) [j_in=9] [cs-3: cycles 29] 9->9(186) [j_in=12] [cs-3: cycles 12] dup12->12(187) [j_in=9] [cs-3: cycles 29] 9->9(188) [j_in=12] [cs-3: cycles 12] dup12->12(189) [j_in=9] [cs-3: cycles 29] 9->9(190) [j_in=12] [cs-3: cycles 12] dup12->12(191) [j_in=9] [cs-3: cycles 29] 9->9(192) [j_in=12] [cs-3: cycles 12] dup12->12(193) [j_in=9] [cs-3: cycles 29] 9->9(194) [j_in=12] [cs-3: cycles 17] 12->12(195) [j_in=12] [cs-3: cycles 17] 12->12(196) [j_in=12] [cs-3: cycles 17] 12->12(197) [j_in=12] [cs-3: cycles 17] 12->12(198) [j_in=12] [cs-3: cycles 17] 12->12(199) [j_in=12] [cs-3: cycles 17] 12->12(200) [j_in=12] [cs-3: cycles 17] 12->12(201) [j_in=12] [cs-3: cycles 17] 12->12(202) [j_in=12] [cs-3: cycles 17] 12->12(203) [j_in=12] [cs-3: cycles 17] 12->12(204) [j_in=12] [cs-3: cycles 17] 12->12(205) [j_in=12] [cs-3: cycles 17] 12->12(206) [j_in=12] [cs-3: cycles 17] 12->12(207) [j_in=12] [cs-3: cycles 17] 12->12(208) [j_in=12] [cs-3: cycles 17] 12->12(209) [j_in=12] [cs-3: cycles 23] 12->12(210) [j_in=14] [cs-3: cycles 14] 14->14(211) [j_in=13] [cs-3: cycles 13] 13->13(212) [j_in=16] [cs-3: cycles 21] 16->16(213) [j_in=16] [cs-3: cycles 21] 16->16(214) [j_in=16] [cs-3: cycles 21] 16->16(215) [j_in=16] [cs-3: cycles 21] 16->16(216) [j_in=16] [cs-3: cycles 21] 16->16(217) [j_in=16] [cs-3: cycles 21] 16->16(218) [j_in=16] [cs-3: cycles 21] 16->16(219) [j_in=16] [cs-3: cycles 21] 16->16(220) [j_in=16] [cs-3: cycles 16] 16->15(221) [j_in=10] [cs-3: cycles 15] 10->10(222) [j_in=12] [cs-3: cycles 12] 12->12(223) [j_in=12] [cs-3: cycles 12] 12->12(224) [j_in=12] [cs-3: cycles 12] 12->12(225) [j_in=12] [cs-3: cycles 12] 12->12(226) [j_in=12] [cs-3: cycles 12] 12->12(227) [j_in=12] [cs-3: cycles 12] 12->12(228) [j_in=12] [cs-3: cycles 12] 12->12(229) [j_in=12] [cs-3: cycles 12] 12->12(230) [j_in=12] [cs-3: cycles 12] 12->12(231) [j_in=12] [cs-3: cycles 12] 12->12(232) [j_in=12] [cs-3: cycles 12] 12->12(233) [j_in=12] [cs-3: cycles 12] 12->12(234) [j_in=12] [cs-3: cycles 12] 12->12(235) [j_in=12] [cs-3: cycles 12] 12->12(236) [j_in=12] [cs-3: cycles 12] 12->12(237) [j_in=12] [cs-3: cycles 12] 12->12(238) [j_in=12] [cs-3: cycles 40] 12->11(239) [j_in=10] [cs-3: cycles 20] 10->10(240) [j_in=11] [cs-3: cycles 19] 11->11(241) [j_in=12] [cs-3: cycles 40] 12->11(242) [j_in=10] [cs-3: cycles 20] 10->10(243) [j_in=11] [cs-3: cycles 19] 11->11(244) [j_in=12] [cs-3: cycles 40] 12->11(245) [j_in=10] [cs-3: cycles 20] 10->10(246) [j_in=11] [cs-3: cycles 19] 11->11(247) [j_in=12] [cs-3: cycles 40] 12->11(248) [j_in=10] [cs-3: cycles 22] 10->10(249) [j_in=13] [cs-3: cycles 23] 13->13(250) ENDING SEARCH because max number of tries reached at Tue Aug 25 13:37:03 2009 after a total of 250 tries over 21 seconds A log of this search is in file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.log The search results are stored in file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin This search can be restarted by having "force_new_search_p = false" in file: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.s-params and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF 10 BEST RESULTS ------------------ PROBABILITY exp(-16263.632) N_CLASSES 15 FOUND ON TRY 161 DUPS 6 *SAVED* PROBABILITY exp(-16279.240) N_CLASSES 11 FOUND ON TRY 82 *SAVED* PROBABILITY exp(-16291.665) N_CLASSES 13 FOUND ON TRY 183 PROBABILITY exp(-16295.810) N_CLASSES 12 FOUND ON TRY 83 PROBABILITY exp(-16299.105) N_CLASSES 12 FOUND ON TRY 181 DUPS 5 PROBABILITY exp(-16326.699) N_CLASSES 10 FOUND ON TRY 5 PROBABILITY exp(-16328.028) N_CLASSES 12 FOUND ON TRY 210 PROBABILITY exp(-16328.438) N_CLASSES 10 FOUND ON TRY 65 PROBABILITY exp(-16328.664) N_CLASSES 9 FOUND ON TRY 8 PROBABILITY exp(-16337.240) N_CLASSES 9 FOUND ON TRY 150 ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try 161 num_cycles 28 max_cycles 200 convergent try 82 num_cycles 23 max_cycles 200 convergent try 183 num_cycles 12 max_cycles 200 convergent try 83 num_cycles 25 max_cycles 200 convergent try 181 num_cycles 12 max_cycles 200 convergent try 5 num_cycles 20 max_cycles 200 convergent try 210 num_cycles 23 max_cycles 200 convergent try 65 num_cycles 24 max_cycles 200 convergent try 8 num_cycles 10 max_cycles 200 convergent try 150 num_cycles 15 max_cycles 200 convergent AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 25 13:37:03 2009 autoclass-3.3.6.dfsg.1/sample/imports-85c.rlog0000644000175000017500000023127511247310756017162 0ustar areare AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:29:32 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 405 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.influ-o-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.case-text-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.class-text-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:29:32 2001 AUTOCLASS C (version 3.3.4unx) STARTING at Mon Jun 11 10:30:46 2001 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; sigma_contours_att_list=(18,19,20,21,22,23) ADVISORY[2]: read 26 attribute defs from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.results-bin ADVISORY: read 405 search trials from /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.search File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.influ-o-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.case-data-1 File written: /home/wtaylor/AC/3.3.4/autoclass-c/sample/imports-85c.class-data-1 AUTOCLASS C (version 3.3.4unx) STOPPING at Mon Jun 11 10:30:46 2001 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 14:55:27 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-text-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-text-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-text-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 14:55:27 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 14:56:39 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 14:56:39 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:02:48 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:02:48 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:05:12 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:05:12 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:15:02 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:15:02 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:20:48 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:20:48 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:23:26 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:23:26 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:25:56 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:25:56 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:29:15 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:29:15 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:34:54 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:34:54 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:36:29 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:36:30 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 15:39:05 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 15:39:05 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 17:51:58 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 17:51:58 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 18:19:22 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 18:19:22 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Mon Aug 17 18:36:09 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Mon Aug 17 18:36:09 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:32:41 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:32:41 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:40:33 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:40:33 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:43:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:43:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:45:32 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:45:33 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:54:30 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:54:30 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 10:58:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 10:58:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 18 11:00:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 18 11:00:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 11:47:00 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 11:47:00 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 11:47:53 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 11:47:53 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 11:54:14 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 11:54:14 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 11:56:41 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 11:56:41 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 12:04:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 12:04:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 12:05:37 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 12:05:37 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Wed Aug 19 12:13:16 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Wed Aug 19 12:13:16 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 10:51:18 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 10:51:18 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 10:55:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 10:55:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:14:20 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:14:20 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:16:14 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:16:14 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:26:09 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:26:09 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:28:32 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:28:32 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:29:57 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:29:57 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Thu Aug 20 11:38:37 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Thu Aug 20 11:38:37 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:23:02 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:23:02 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:27:41 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:27:41 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:33:47 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:33:47 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:35:25 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:35:25 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:39:11 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:39:11 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:40:54 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:40:54 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:42:36 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:42:36 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:45:35 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:45:35 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:52:23 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:52:23 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 10:54:10 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 10:54:11 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:00:17 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:00:17 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:00:25 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:00:25 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:01:57 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:01:57 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:11:16 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:11:16 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:21:03 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:21:03 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:25:33 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:25:33 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Fri Aug 21 11:27:35 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Fri Aug 21 11:27:35 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 25 10:38:03 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 25 10:38:03 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 25 10:45:59 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 25 10:45:59 2009 AUTOCLASS C (version 3.3.6unx) STARTING at Tue Aug 25 10:46:42 2009 AUTOCLASS -REPORTS default parameters: n_clsfs=1; clsf_n_list=(); report_type="all"; report_mode="text"; comment_data_headers_p=false; num_atts_to_list=999; xref_class_report_att_list=(); order_attributes_by_influence_p=true; break_on_warnings_p=true; free_storage_p=true; max_num_xref_class_probs=5; sigma_contours_att_list=() USER supplied parameters which override the defaults: report_mode="data"; comment_data_headers_p=true; xref_class_report_att_list=(2,5,6) ADVISORY[2]: read 26 attribute defs from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 ADVISORY[1]: read 205 datum from /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 ADVISORY: loaded 2 classifications from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin ADVISORY: read 242 search trials from /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.search File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.influ-o-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.case-data-1 File written: /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.class-data-1 AUTOCLASS C (version 3.3.6unx) STOPPING at Tue Aug 25 10:46:42 2009 autoclass-3.3.6.dfsg.1/sample/imports-85.names0000644000175000017500000001105011247310756017142 0ustar areare1. Title: 1985 Auto Imports Database 2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037 3. Past Usage: -- None published (none known) -- Unpublished: -- David W. Aha (June, 1988) -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation 4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year. -- Note: Several of the attributes in the database could be used as a "class" attribute. 5. Number of Instances: 205 6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal 7. Attribute Information: Attribute: Attribute Range: ------------------ ----------------------------------------------- 1. symboling: -3, -2, -1, 0, 1, 2, 3. 2. normalized-losses: continuous from 65 to 256. 3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo 4. fuel-type: diesel, gas. 5. aspiration: std, turbo. 6. num-of-doors: four, two. 7. body-style: hardtop, wagon, sedan, hatchback, convertible. 8. drive-wheels: 4wd, fwd, rwd. 9. engine-location: front, rear. 10. wheel-base: continuous from 86.6 120.9. 11. length: continuous from 141.1 to 208.1. 12. width: continuous from 60.3 to 72.3. 13. height: continuous from 47.8 to 59.8. 14. curb-weight: continuous from 1488 to 4066. 15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor. 16. num-of-cylinders: eight, five, four, six, three, twelve, two. 17. engine-size: continuous from 61 to 326. 18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi. 19. bore: continuous from 2.54 to 3.94. 20. stroke: continuous from 2.07 to 4.17. 21. compression-ratio: continuous from 7 to 23. 22. horsepower: continuous from 48 to 288. 23. peak-rpm: continuous from 4150 to 6600. 24. city-mpg: continuous from 13 to 49. 25. highway-mpg: continuous from 16 to 54. 26. price: continuous from 5118 to 45400. 8. Missing Attribute Values: Attribute #: Number of instances missing a value: 2. 41 6. 2 19. 4 20. 4 22. 2 23. 2 26. 4 autoclass-3.3.6.dfsg.1/sample/read.me.c0000644000175000017500000000203611247310756015651 0ustar areare Files in this directory describe a sample run of Autoclass, applied to a database of Imported Car Models sold in 1985. Although the results from this run aren't particularly spectacular, it has been chosen because most readers will have some intuition for the subject, and know what most of the attributes used mean. The procedure by which these files were created is described in: scriptc.text A direct dump of the screen from the run is in: screenc.text Raw Input: imports-85.names imports-85c.db2 (renamed from imports-85.data) Input descriptions processed for Autoclass: imports-85c.hd2 imports-85c.model imports-85c.s-params Outputs from the direct run: imports-85c.log imports-85c.search ;; imports-85c.results-bin not included to save space Outputs from post processing of the results file: imports-85c.rlog imports-85c.influ-o-text-1 imports-85c.case-text-1 imports-85c.class-text-1 imports-85c.influ-o-data-1 imports-85c.case-data-1 imports-85c.class-data-1 autoclass-3.3.6.dfsg.1/sample/imports-85c.case-data-10000644000175000017500000001235311247310756020171 0ustar areare# CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS # # DATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 # with log-A (approximate marginal likelihood) = -16230.401 # from classification results file # /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin # and using models # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 # # DATA_CASE_TO_CLASS #Case# Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) 001 12 1.000 002 12 1.000 003 9 1.000 004 0 0.999 005 6 1.000 006 6 1.000 007 6 1.000 008 6 1.000 009 6 1.000 010 9 1.000 011 8 1.000 012 8 1.000 013 8 1.000 014 8 1.000 015 1 1.000 016 1 0.991 5 0.009 017 5 1.000 018 5 1.000 019 11 1.000 020 3 0.997 11 0.003 021 3 1.000 022 3 1.000 023 3 1.000 024 13 1.000 025 3 1.000 026 3 1.000 027 3 1.000 028 13 1.000 029 0 1.000 030 7 1.000 031 11 1.000 032 11 1.000 033 11 1.000 034 11 1.000 035 11 0.999 036 13 0.986 11 0.014 037 13 0.999 038 0 1.000 039 0 1.000 040 0 1.000 041 0 1.000 042 0 0.999 043 0 1.000 044 4 1.000 045 3 1.000 046 3 1.000 047 7 0.999 048 5 1.000 049 5 1.000 050 5 1.000 051 11 1.000 052 11 0.999 053 11 0.999 054 2 1.000 055 2 1.000 056 12 1.000 057 12 1.000 058 12 1.000 059 12 1.000 060 0 1.000 061 0 1.000 062 0 1.000 063 0 1.000 064 10 1.000 065 0 1.000 066 8 1.000 067 10 1.000 068 5 1.000 069 5 1.000 070 5 1.000 071 5 1.000 072 5 1.000 073 5 1.000 074 5 1.000 075 5 1.000 076 9 1.000 077 3 1.000 078 3 1.000 079 3 1.000 080 13 1.000 081 0 1.000 082 0 1.000 083 7 1.000 084 7 1.000 085 7 1.000 086 0 1.000 087 0 1.000 088 0 1.000 089 0 1.000 090 2 1.000 091 10 1.000 092 2 1.000 093 2 1.000 094 2 1.000 095 2 1.000 096 2 1.000 097 2 1.000 098 2 1.000 099 2 1.000 100 0 1.000 101 0 1.000 102 1 1.000 103 1 1.000 104 1 1.000 105 9 1.000 106 9 1.000 107 9 1.000 108 6 0.999 109 14 1.000 110 6 1.000 111 14 1.000 112 6 1.000 113 14 1.000 114 6 1.000 115 14 1.000 116 6 0.999 1 0.001 117 14 1.000 118 1 1.000 119 3 1.000 120 13 1.000 121 3 1.000 122 3 1.000 123 3 1.000 124 0 1.000 125 7 1.000 126 9 1.000 127 12 1.000 128 12 1.000 129 12 1.000 130 9 1.000 131 6 1.000 132 7 1.000 133 6 0.990 1 0.010 134 1 1.000 135 6 1.000 136 1 1.000 137 1 0.999 138 1 1.000 139 4 1.000 140 4 1.000 141 4 1.000 142 4 1.000 143 4 1.000 144 8 0.999 4 0.001 145 4 0.999 146 8 1.000 147 4 1.000 148 8 0.996 4 0.004 149 4 1.000 150 8 1.000 151 4 0.996 11 0.004 152 4 0.999 153 4 0.999 154 4 1.000 155 4 1.000 156 4 1.000 157 2 0.999 158 2 0.998 4 0.002 159 10 1.000 160 10 1.000 161 2 0.999 162 2 0.996 4 0.004 163 2 0.998 4 0.002 164 2 1.000 165 2 1.000 166 8 1.000 167 8 1.000 168 7 1.000 169 7 1.000 170 7 1.000 171 7 1.000 172 7 1.000 173 7 1.000 174 0 1.000 175 10 1.000 176 0 1.000 177 0 1.000 178 0 1.000 179 9 1.000 180 9 1.000 181 1 1.000 182 1 0.999 183 10 1.000 184 0 1.000 185 10 1.000 186 0 1.000 187 0 1.000 188 10 1.000 189 0 1.000 190 13 1.000 191 13 0.999 192 6 1.000 193 10 1.000 194 0 0.985 6 0.015 195 1 1.000 196 1 1.000 197 1 1.000 198 1 1.000 199 1 1.000 200 1 1.000 201 1 1.000 202 1 1.000 203 1 1.000 204 5 1.000 205 1 1.000 autoclass-3.3.6.dfsg.1/sample/imports-85c.case-text-10000644000175000017500000001445311247310756020247 0ustar areare CROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS AutoClass CLASSIFICATION for the 205 cases in /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 with log-A (approximate marginal likelihood) = -16230.401 from classification results file /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin and using models /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 1 12 1.000 47 7 0.999 93 2 1.000 2 12 1.000 48 5 1.000 94 2 1.000 3 9 1.000 49 5 1.000 95 2 1.000 4 0 0.999 50 5 1.000 96 2 1.000 5 6 1.000 51 11 1.000 97 2 1.000 6 6 1.000 52 11 0.999 98 2 1.000 7 6 1.000 53 11 0.999 99 2 1.000 8 6 1.000 54 2 1.000 100 0 1.000 9 6 1.000 55 2 1.000 101 0 1.000 10 9 1.000 56 12 1.000 102 1 1.000 11 8 1.000 57 12 1.000 103 1 1.000 12 8 1.000 58 12 1.000 104 1 1.000 13 8 1.000 59 12 1.000 105 9 1.000 14 8 1.000 60 0 1.000 106 9 1.000 15 1 1.000 61 0 1.000 107 9 1.000 16 1 0.991 62 0 1.000 108 6 0.999 17 5 1.000 63 0 1.000 109 14 1.000 18 5 1.000 64 10 1.000 110 6 1.000 19 11 1.000 65 0 1.000 111 14 1.000 20 3 0.997 66 8 1.000 112 6 1.000 21 3 1.000 67 10 1.000 113 14 1.000 22 3 1.000 68 5 1.000 114 6 1.000 23 3 1.000 69 5 1.000 115 14 1.000 24 13 1.000 70 5 1.000 116 6 0.999 25 3 1.000 71 5 1.000 117 14 1.000 26 3 1.000 72 5 1.000 118 1 1.000 27 3 1.000 73 5 1.000 119 3 1.000 28 13 1.000 74 5 1.000 120 13 1.000 29 0 1.000 75 5 1.000 121 3 1.000 30 7 1.000 76 9 1.000 122 3 1.000 31 11 1.000 77 3 1.000 123 3 1.000 32 11 1.000 78 3 1.000 124 0 1.000 33 11 1.000 79 3 1.000 125 7 1.000 34 11 1.000 80 13 1.000 126 9 1.000 35 11 0.999 81 0 1.000 127 12 1.000 36 13 0.986 82 0 1.000 128 12 1.000 37 13 0.999 83 7 1.000 129 12 1.000 38 0 1.000 84 7 1.000 130 9 1.000 39 0 1.000 85 7 1.000 131 6 1.000 40 0 1.000 86 0 1.000 132 7 1.000 41 0 1.000 87 0 1.000 133 6 0.990 42 0 0.999 88 0 1.000 134 1 1.000 43 0 1.000 89 0 1.000 135 6 1.000 44 4 1.000 90 2 1.000 136 1 1.000 45 3 1.000 91 10 1.000 137 1 0.999 46 3 1.000 92 2 1.000 138 1 1.000 Case # Class Prob Case # Class Prob Case # Class Prob ------------------------------------------------------------------------------------------ 139 4 1.000 162 2 0.996 185 10 1.000 140 4 1.000 163 2 0.998 186 0 1.000 141 4 1.000 164 2 1.000 187 0 1.000 142 4 1.000 165 2 1.000 188 10 1.000 143 4 1.000 166 8 1.000 189 0 1.000 144 8 0.999 167 8 1.000 190 13 1.000 145 4 0.999 168 7 1.000 191 13 0.999 146 8 1.000 169 7 1.000 192 6 1.000 147 4 1.000 170 7 1.000 193 10 1.000 148 8 0.996 171 7 1.000 194 0 0.985 149 4 1.000 172 7 1.000 195 1 1.000 150 8 1.000 173 7 1.000 196 1 1.000 151 4 0.996 174 0 1.000 197 1 1.000 152 4 0.999 175 10 1.000 198 1 1.000 153 4 0.999 176 0 1.000 199 1 1.000 154 4 1.000 177 0 1.000 200 1 1.000 155 4 1.000 178 0 1.000 201 1 1.000 156 4 1.000 179 9 1.000 202 1 1.000 157 2 0.999 180 9 1.000 203 1 1.000 158 2 0.998 181 1 1.000 204 5 1.000 159 10 1.000 182 1 0.999 205 1 1.000 160 10 1.000 183 10 1.000 161 2 0.999 184 0 1.000 autoclass-3.3.6.dfsg.1/sample/imports-85c.db20000644000175000017500000006420411247310756016662 0ustar areare!#; AutoClass C data file -- extension .db2 !#; prior to the first non-comment line being read !#; the following chars in column 1 make the line a comment: !#; '!', '#', ';', ' ', and '\n' (empty line) !#; after the first non-comment line is read, the only column 1 comment characters are !#; ' ', '\n' (empty line), and comment_char (data file format def in .hd2 file) ; -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) ; -- Date: 19 May 1987 ; -- Sources: ; 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's ; Automotive Yearbook. ; 2) Personal Auto Manuals, Insurance Services Office, 160 Water ; Street, New York, NY 10038 ; 3) Insurance Collision Report, Insurance Institute for Highway ; Safety, Watergate 600, Washington, DC 20037 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,13495 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.60,168.80,64.10,48.80,2548,dohc,four,130,mpfi,3.47,2.68,9.00,111,5000,21,27,16500 1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.50,171.20,65.50,52.40,2823,ohcv,six,152,mpfi,2.68,3.47,9.00,154,5000,19,26,16500 2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950 2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450 2,?,audi,gas,std,two,sedan,fwd,front,99.80,177.30,66.30,53.10,2507,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,15250 1,158,audi,gas,std,four,sedan,fwd,front,105.80,192.70,71.40,55.70,2844,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,17710 1,?,audi,gas,std,four,wagon,fwd,front,105.80,192.70,71.40,55.70,2954,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,18920 1,158,audi,gas,turbo,four,sedan,fwd,front,105.80,192.70,71.40,55.90,3086,ohc,five,131,mpfi,3.13,3.40,8.30,140,5500,17,20,23875 0,?,audi,gas,turbo,two,hatchback,4wd,front,99.50,178.20,67.90,52.00,3053,ohc,five,131,mpfi,3.13,3.40,7.00,160,5500,16,22,? 2,192,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16430 0,192,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16925 0,188,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2710,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,20970 0,188,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2765,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,21105 1,?,bmw,gas,std,four,sedan,rwd,front,103.50,189.00,66.90,55.70,3055,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,20,25,24565 0,?,bmw,gas,std,four,sedan,rwd,front,103.50,189.00,66.90,55.70,3230,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,16,22,30760 0,?,bmw,gas,std,two,sedan,rwd,front,103.50,193.80,67.90,53.70,3380,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,16,22,41315 0,?,bmw,gas,std,four,sedan,rwd,front,110.00,197.00,70.90,56.30,3505,ohc,six,209,mpfi,3.62,3.39,8.00,182,5400,15,20,36880 2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.40,141.10,60.30,53.20,1488,l,three,61,2bbl,2.91,3.03,9.50,48,5100,47,53,5151 1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.50,155.90,63.60,52.00,1874,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6295 0,81,chevrolet,gas,std,four,sedan,fwd,front,94.50,158.80,63.60,52.00,1909,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6575 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6377 1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,mpfi,3.03,3.39,7.60,102,5500,24,30,7957 1,148,dodge,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 1,148,dodge,gas,turbo,?,sedan,fwd,front,93.70,157.30,63.80,50.60,2191,ohc,four,98,mpfi,3.03,3.39,7.60,102,5500,24,30,8558 -1,110,dodge,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.34,3.46,8.50,88,5000,24,30,8921 3,145,dodge,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2811,ohc,four,156,mfi,3.60,3.90,7.00,145,5000,19,24,12964 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1713,ohc,four,92,1bbl,2.91,3.41,9.60,58,4800,49,54,6479 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1819,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,31,38,6855 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1837,ohc,four,79,1bbl,2.91,3.07,10.10,60,5500,38,42,5399 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1940,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,6529 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1956,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7129 0,110,honda,gas,std,four,sedan,fwd,front,96.50,163.40,64.00,54.50,2010,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7295 0,78,honda,gas,std,four,wagon,fwd,front,96.50,157.10,63.90,58.30,2024,ohc,four,92,1bbl,2.92,3.41,9.20,76,6000,30,34,7295 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2236,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,7895 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2289,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,9095 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2304,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,8845 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,62.50,54.10,2372,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,10295 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2465,ohc,four,110,mpfi,3.15,3.58,9.00,101,5800,24,28,12945 1,107,honda,gas,std,two,sedan,fwd,front,96.50,169.10,66.00,51.00,2293,ohc,four,110,2bbl,3.15,3.58,9.10,100,5500,25,31,10345 0,?,isuzu,gas,std,four,sedan,rwd,front,94.30,170.70,61.80,53.50,2337,ohc,four,111,2bbl,3.31,3.23,8.50,78,4800,24,29,6785 1,?,isuzu,gas,std,two,sedan,fwd,front,94.50,155.90,63.60,52.00,1874,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,? 0,?,isuzu,gas,std,four,sedan,fwd,front,94.50,155.90,63.60,52.00,1909,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,? 2,?,isuzu,gas,std,two,hatchback,rwd,front,96.00,172.60,65.20,51.40,2734,ohc,four,119,spfi,3.43,3.23,9.20,90,5000,24,29,11048 0,145,jaguar,gas,std,four,sedan,rwd,front,113.00,199.60,69.60,52.80,4066,dohc,six,258,mpfi,3.63,4.17,8.10,176,4750,15,19,32250 0,?,jaguar,gas,std,four,sedan,rwd,front,113.00,199.60,69.60,52.80,4066,dohc,six,258,mpfi,3.63,4.17,8.10,176,4750,15,19,35550 0,?,jaguar,gas,std,two,sedan,rwd,front,102.00,191.70,70.60,47.80,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.50,262,5000,13,17,36000 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1890,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,30,31,5195 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1900,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6095 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1905,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6795 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1945,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6695 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1950,ohc,four,91,2bbl,3.08,3.15,9.00,68,5000,31,38,7395 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2380,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,10945 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2380,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,11845 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2385,rotor,two,70,4bbl,?,?,9.40,101,6000,17,23,13645 3,150,mazda,gas,std,two,hatchback,rwd,front,95.30,169.00,65.70,49.60,2500,rotor,two,80,mpfi,?,?,9.40,135,6000,16,23,15645 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8845 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8495 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10595 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10245 0,?,mazda,diesel,std,?,sedan,fwd,front,98.80,177.80,66.50,55.50,2443,ohc,four,122,idi,3.39,3.39,22.70,64,4650,36,42,10795 0,115,mazda,gas,std,four,hatchback,fwd,front,98.80,177.80,66.50,55.50,2425,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,11245 0,118,mazda,gas,std,four,sedan,rwd,front,104.90,175.00,66.10,54.40,2670,ohc,four,140,mpfi,3.76,3.16,8.00,120,5000,19,27,18280 0,?,mazda,diesel,std,four,sedan,rwd,front,104.90,175.00,66.10,54.40,2700,ohc,four,134,idi,3.43,3.64,22.00,72,4200,31,39,18344 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,110.00,190.90,70.30,56.50,3515,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,25552 -1,93,mercedes-benz,diesel,turbo,four,wagon,rwd,front,110.00,190.90,70.30,58.70,3750,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28248 0,93,mercedes-benz,diesel,turbo,two,hardtop,rwd,front,106.70,187.50,70.30,54.90,3495,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28176 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,115.60,202.60,71.70,56.30,3770,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,31600 -1,?,mercedes-benz,gas,std,four,sedan,rwd,front,115.60,202.60,71.70,56.50,3740,ohcv,eight,234,mpfi,3.46,3.10,8.30,155,4750,16,18,34184 3,142,mercedes-benz,gas,std,two,convertible,rwd,front,96.60,180.30,70.50,50.80,3685,ohcv,eight,234,mpfi,3.46,3.10,8.30,155,4750,16,18,35056 0,?,mercedes-benz,gas,std,four,sedan,rwd,front,120.90,208.10,71.70,56.70,3900,ohcv,eight,308,mpfi,3.80,3.35,8.00,184,4500,14,16,40960 1,?,mercedes-benz,gas,std,two,hardtop,rwd,front,112.00,199.20,72.00,55.40,3715,ohcv,eight,304,mpfi,3.80,3.35,8.00,184,4500,14,16,45400 1,?,mercury,gas,turbo,two,hatchback,rwd,front,102.70,178.40,68.00,54.80,2910,ohc,four,140,mpfi,3.78,3.12,8.00,175,5000,19,24,16503 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1918,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,37,41,5389 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1944,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6189 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,2004,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6669 1,161,mitsubishi,gas,turbo,two,hatchback,fwd,front,93,157.30,63.80,50.80,2145,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7689 3,153,mitsubishi,gas,turbo,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2370,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9959 3,153,mitsubishi,gas,std,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2328,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8499 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2833,ohc,four,156,spdi,3.58,3.86,7.00,145,5000,19,24,12629 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2921,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,14869 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2926,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,14489 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2365,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,6989 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2405,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8189 1,125,mitsubishi,gas,turbo,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 -1,137,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1889,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,5499 1,128,nissan,diesel,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,2017,ohc,four,103,idi,2.99,3.47,21.90,55,4800,45,50,7099 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1918,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6649 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1938,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6849 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2024,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7349 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1951,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7299 1,128,nissan,gas,std,two,hatchback,fwd,front,94.50,165.60,63.80,53.30,2028,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7799 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1971,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7499 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2037,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7999 2,168,nissan,gas,std,two,hardtop,fwd,front,95.10,162.40,63.80,53.30,2008,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,8249 0,106,nissan,gas,std,four,hatchback,fwd,front,97.20,173.40,65.20,54.70,2324,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,8949 0,106,nissan,gas,std,four,sedan,fwd,front,97.20,173.40,65.20,54.70,2302,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,9549 0,128,nissan,gas,std,four,sedan,fwd,front,100.40,181.70,66.50,55.10,3095,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,13499 0,108,nissan,gas,std,four,wagon,fwd,front,100.40,184.60,66.50,56.10,3296,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,14399 0,108,nissan,gas,std,four,sedan,fwd,front,100.40,184.60,66.50,55.10,3060,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,19,25,13499 3,194,nissan,gas,std,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3071,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,17199 3,194,nissan,gas,turbo,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,7.80,200,5200,17,23,19699 1,231,nissan,gas,std,two,hatchback,rwd,front,99.20,178.50,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,18399 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3020,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,11900 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3197,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,13200 0,?,peugot,gas,std,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3230,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,12440 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3430,l,four,152,idi,3.70,3.52,21.00,95,4150,25,25,13860 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,2.19,8.40,95,5000,19,24,15580 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,16900 0,?,peugot,gas,std,four,wagon,rwd,front,114.20,198.90,68.40,56.70,3285,l,four,120,mpfi,3.46,2.19,8.40,95,5000,19,24,16695 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.20,198.90,68.40,58.70,3485,l,four,152,idi,3.70,3.52,21.00,95,4150,25,25,17075 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,16630 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,17950 0,161,peugot,gas,turbo,four,sedan,rwd,front,108.00,186.70,68.30,56.00,3130,l,four,134,mpfi,3.61,3.21,7.00,142,5600,18,24,18150 1,119,plymouth,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1918,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,37,41,5572 1,119,plymouth,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7957 1,154,plymouth,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,2191,ohc,four,98,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 -1,74,plymouth,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,24,30,8921 3,?,plymouth,gas,turbo,two,hatchback,rwd,front,95.90,173.20,66.30,50.20,2818,ohc,four,156,spdi,3.59,3.86,7.00,145,5000,19,24,12764 3,186,porsche,gas,std,two,hatchback,rwd,front,94.50,168.90,68.30,50.20,2778,ohc,four,151,mpfi,3.94,3.11,9.50,143,5500,19,27,22018 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.50,168.90,65.00,51.60,2756,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,32528 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.50,168.90,65.00,51.60,2756,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,34028 3,?,porsche,gas,std,two,convertible,rwd,rear,89.50,168.90,65.00,51.60,2800,ohcf,six,194,mpfi,3.74,2.90,9.50,207,5900,17,25,37028 1,?,porsche,gas,std,two,hatchback,rwd,front,98.40,175.70,72.30,50.50,3366,dohcv,eight,203,mpfi,3.94,3.11,10.00,288,5750,17,28,? 0,?,renault,gas,std,four,wagon,fwd,front,96.10,181.50,66.50,55.20,2579,ohc,four,132,mpfi,3.46,3.90,8.70,?,?,23,31,9295 2,?,renault,gas,std,two,hatchback,fwd,front,96.10,176.80,66.60,50.50,2460,ohc,four,132,mpfi,3.46,3.90,8.70,?,?,23,31,9895 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2695,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,12170 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2707,ohc,four,121,mpfi,2.54,2.07,9.30,110,5250,21,28,15040 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2758,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,15510 3,150,saab,gas,turbo,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2808,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18150 2,104,saab,gas,turbo,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2847,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18620 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,156.90,63.40,53.70,2050,ohcf,four,97,2bbl,3.62,2.36,9.00,69,4900,31,36,5118 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,157.90,63.60,53.70,2120,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7053 2,83,subaru,gas,std,two,hatchback,4wd,front,93.30,157.30,63.80,55.70,2240,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7603 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2145,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4800,32,37,7126 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2190,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4400,28,33,7775 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2340,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,26,32,9960 0,102,subaru,gas,std,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2385,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,24,25,9233 0,102,subaru,gas,turbo,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2510,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,24,29,11259 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2290,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,28,32,7463 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2455,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,25,31,10198 0,85,subaru,gas,std,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2420,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,23,29,8013 0,85,subaru,gas,turbo,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2650,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,23,23,11694 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,1985,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,35,39,5348 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,2040,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6338 1,74,toyota,gas,std,four,hatchback,fwd,front,95.70,158.70,63.60,54.50,2015,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6488 0,77,toyota,gas,std,four,wagon,fwd,front,95.70,169.70,63.60,59.10,2280,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,37,6918 0,81,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,2290,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,7898 0,91,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,3110,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,8778 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2081,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,6938 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2109,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,7198 0,91,toyota,diesel,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,34,36,7898 0,91,toyota,diesel,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,38,47,7788 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2094,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,38,47,7738 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2122,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,8358 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,52.80,2140,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,9258 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2169,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8058 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2204,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8238 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2265,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9298 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2300,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9538 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2540,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,8449 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2536,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9639 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2551,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9989 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2679,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11199 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2714,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11549 2,134,toyota,gas,std,two,convertible,rwd,front,98.40,176.20,65.60,53.00,2975,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,17669 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2326,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,29,34,8948 -1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2480,ohc,four,110,idi,3.27,3.35,22.50,73,4500,30,33,10698 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,9988 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,10898 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2458,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,11248 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,2976,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,20,24,16558 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,3016,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,19,24,15998 -1,90,toyota,gas,std,four,sedan,rwd,front,104.50,187.80,66.50,54.10,3131,dohc,six,171,mpfi,3.27,3.35,9.20,156,5200,20,24,15690 -1,?,toyota,gas,std,four,wagon,rwd,front,104.50,187.80,66.50,54.10,3151,dohc,six,161,mpfi,3.27,3.35,9.20,156,5200,19,24,15750 2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2261,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7775 2,122,volkswagen,gas,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2209,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,7975 2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2264,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7995 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2212,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8195 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2275,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8495 2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2319,ohc,four,97,idi,3.01,3.40,23.00,68,4500,37,42,9495 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2300,ohc,four,109,mpfi,3.19,3.40,10.00,100,5500,26,32,9995 3,?,volkswagen,gas,std,two,convertible,fwd,front,94.50,159.30,64.20,55.60,2254,ohc,four,109,mpfi,3.19,3.40,8.50,90,5500,24,29,11595 3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.50,165.70,64.00,51.40,2221,ohc,four,109,mpfi,3.19,3.40,8.50,90,5500,24,29,9980 0,?,volkswagen,gas,std,four,sedan,fwd,front,100.40,180.20,66.90,55.10,2661,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,24,13295 0,?,volkswagen,diesel,turbo,four,sedan,fwd,front,100.40,180.20,66.90,55.10,2579,ohc,four,97,idi,3.01,3.40,23.00,68,4500,33,38,13845 0,?,volkswagen,gas,std,four,wagon,fwd,front,100.40,183.10,66.90,55.10,2563,ohc,four,109,mpfi,3.19,3.40,9.00,88,5500,25,31,12290 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2912,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,12940 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3034,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,13415 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2935,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,15985 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3042,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,16515 -2,103,volvo,gas,turbo,four,sedan,rwd,front,104.30,188.80,67.20,56.20,3045,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18420 -1,74,volvo,gas,turbo,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3157,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18950 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,2952,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,16845 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.80,55.50,3049,ohc,four,141,mpfi,3.78,3.15,8.70,160,5300,19,25,19045 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3012,ohcv,six,173,mpfi,3.58,2.87,8.80,134,5500,18,23,21485 -1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3217,ohc,six,145,idi,3.01,3.40,23.00,106,4800,26,27,22470 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3062,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,19,25,22625 autoclass-3.3.6.dfsg.1/sample/imports-85c.r-params0000644000175000017500000001111311247310756017724 0ustar areare# PARAMETERS TO AUTOCLASS-REPORTS -- AutoClass C # --------------------------------------------------------------- # as the first character makes the line a comment, or ! as the first character makes the line a comment, or ; as the first character makes the line a comment, or ;;; '\n' as the first character (empty line) makes the line a comment. # to override the following default parameters, # enter below the line => #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # = , or # , or # separator is a space # \tab. # note: blanks/spaces are ignored if '=', or '\tab' are separators; # note: no trailing ';'s. # --------------------------------------------------------------- # DEFAULT PARAMETERS # --------------------------------------------------------------- # n_clsfs = 1 ! number of clsfs in the .results file for which to generate reports, ! starting with the first or "best". # clsf_n_list = ! if specified, this is a one-based index list of clsfs in the clsf ! sequence read from the .results file. It overrides "n_clsfs". ! For example: clsf_n_list = 1, 2 ! will produce the same output as ! n_clsfs = 2 ! but ! clsf_n_list = 2 ! will only output the "second best" classification report. # report_type = "all" ! type of reports to generate: "all", "influence_values", "xref_case", or ! "xref_class". # report_mode = "text" ! mode of reports to generate. "text" is formatted text layout. "data" ! is numerical -- suitable for further processing. # comment_data_headers_p = false ! the default value does not insert # in column 1 of most ! report_mode = "data" header lines. If specified as true, the comment ! character will be inserted in most header lines. # num_atts_to_list = ! if specified, the number of attributes to list in influence values report. ! if not specified, *all* attributes will be listed. ! (e.g. num_atts_to_list = 5) # xref_class_report_att_list = ! if specified, a list of attribute numbers (zero-based), whose values will ! be output in the "xref_class" report along with the case probabilities. ! if not specified, no attributes values will be output. ! (e.g. xref_class_report_att_list = 1, 2, 3) # order_attributes_by_influence_p = true ! The default value lists each class's attributes in descending order of ! attribute influence value, and uses ".influ-o-text-n" as the ! influence values report file type. If specified as false, then each ! class's attributes will be listed in ascending order by attribute number. ! The extension of the file generated will be "influ-no-text-n". # break_on_warnings_p = true ! The default value asks the user whether to coninue or not when data ! definition warnings are found. If specified as false, then AutoClass ! will continue, despite warnings -- the warning will continue to be ! output to the terminal. # free_storage_p = true ! The default value tells AutoClass to free the majority of its allocated ! storage. This is not required, and in the case of DEC Alpha's causes ! core dump. If specified as false, AutoClass will not attempt to free ! storage. # max_num_xref_class_probs = 5 ! Determines how many lessor class probabilities will be printed for the ! case and class cross-reference reports. The default is to print the ! most probable class probability value and up to 4 lessor class prob- ! ibilities. Note this is true for both the "text" and "data" class ! cross-reference reports, but only true for the "data" case cross- ! reference report. The "text" case cross-reference report only has the ! most probable class probability. # sigma_contours_att_list = ! If specified, a list of real valued attribute indices (from .hd2 file) ! will be to compute sigma class contour values, when generating ! influence values report with the data option (report_mode = "data"). ! If not specified, there will be no sigma class contour output. ! (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15) #!#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; # OVERRIDE PARAMETERS #!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!;#!; xref_class_report_att_list = 2, 5, 6 report_mode = "data" comment_data_headers_p = true ;; sigma_contours_att_list = 18, 19, 20, 21, 22, 23,autoclass-3.3.6.dfsg.1/sample/imports-85c.class-text-10000644000175000017500000004312211247310756020434 0ustar areare CROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP AutoClass CLASSIFICATION for the 205 cases in /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 with log-A (approximate marginal likelihood) = -16230.401 from classification results file /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin and using models /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 CLASS = 0 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 4 audi four sedan 1.000 29 dodge four wagon 1.000 38 honda two hatchback 1.000 39 honda two hatchback 1.000 40 honda four sedan 1.000 41 honda four sedan 1.000 42 honda four sedan 1.000 43 honda two sedan 1.000 60 mazda two hatchback 1.000 61 mazda four sedan 1.000 62 mazda two hatchback 1.000 63 mazda four sedan 1.000 65 mazda four hatchback 1.000 81 mitsubishi two hatchback 1.000 82 mitsubishi two hatchback 1.000 86 mitsubishi four sedan 1.000 87 mitsubishi four sedan 1.000 88 mitsubishi four sedan 1.000 89 mitsubishi four sedan 1.000 100 nissan four hatchback 1.000 101 nissan four sedan 1.000 124 plymouth four wagon 1.000 174 toyota four sedan 1.000 176 toyota four hatchback 1.000 177 toyota four sedan 1.000 178 toyota four hatchback 1.000 184 volkswagen two sedan 1.000 186 volkswagen four sedan 1.000 187 volkswagen four sedan 1.000 189 volkswagen four sedan 1.000 194 volkswagen four wagon 0.985 6 0.015 CLASS = 1 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 15 bmw four sedan 1.000 16 bmw four sedan 0.991 5 0.009 102 nissan four sedan 1.000 103 nissan four wagon 1.000 104 nissan four sedan 1.000 118 peugot four sedan 1.000 134 saab four sedan 1.000 136 saab four sedan 1.000 137 saab two hatchback 1.000 138 saab four sedan 1.000 181 toyota four sedan 1.000 182 toyota four wagon 1.000 195 volvo four sedan 1.000 196 volvo four wagon 1.000 197 volvo four sedan 1.000 198 volvo four wagon 1.000 199 volvo four sedan 1.000 200 volvo four wagon 1.000 201 volvo four sedan 1.000 202 volvo four sedan 1.000 203 volvo four sedan 1.000 205 volvo four sedan 1.000 CLASS = 2 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 54 mazda four sedan 1.000 55 mazda four sedan 1.000 90 nissan two sedan 1.000 92 nissan two sedan 1.000 93 nissan four sedan 1.000 94 nissan four wagon 1.000 95 nissan two sedan 1.000 96 nissan two hatchback 1.000 97 nissan four sedan 1.000 98 nissan four wagon 1.000 99 nissan two hardtop 1.000 157 toyota four sedan 1.000 158 toyota four hatchback 0.998 4 0.002 161 toyota four sedan 1.000 162 toyota four hatchback 0.996 4 0.004 163 toyota four sedan 0.998 4 0.002 164 toyota two sedan 1.000 165 toyota two hatchback 1.000 CLASS = 3 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 20 chevrolet two hatchback 0.997 11 0.003 21 chevrolet four sedan 1.000 22 dodge two hatchback 1.000 23 dodge two hatchback 1.000 25 dodge four hatchback 1.000 26 dodge four sedan 1.000 27 dodge four sedan 1.000 45 isuzu two sedan 1.000 46 isuzu four sedan 1.000 77 mitsubishi two hatchback 1.000 78 mitsubishi two hatchback 1.000 79 mitsubishi two hatchback 1.000 119 plymouth two hatchback 1.000 121 plymouth four hatchback 1.000 122 plymouth four sedan 1.000 123 plymouth four sedan 1.000 CLASS = 4 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 44 isuzu four sedan 1.000 139 subaru two hatchback 1.000 140 subaru two hatchback 1.000 141 subaru two hatchback 1.000 142 subaru four sedan 1.000 143 subaru four sedan 1.000 145 subaru four sedan 1.000 147 subaru four wagon 1.000 149 subaru four wagon 1.000 151 toyota two hatchback 0.996 11 0.004 152 toyota two hatchback 0.999 153 toyota four hatchback 1.000 154 toyota four wagon 1.000 155 toyota four wagon 1.000 156 toyota four wagon 1.000 CLASS = 5 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 17 bmw two sedan 1.000 18 bmw four sedan 1.000 48 jaguar four sedan 1.000 49 jaguar four sedan 1.000 50 jaguar two sedan 1.000 68 mercedes-benz four sedan 1.000 69 mercedes-benz four wagon 1.000 70 mercedes-benz two hardtop 1.000 CLASS = 5 (continued) Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 71 mercedes-benz four sedan 1.000 72 mercedes-benz four sedan 1.000 73 mercedes-benz two convertible 1.000 74 mercedes-benz four sedan 1.000 75 mercedes-benz two hardtop 1.000 204 volvo four sedan 1.000 CLASS = 6 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 5 audi four sedan 1.000 6 audi two sedan 1.000 7 audi four sedan 1.000 8 audi four wagon 1.000 9 audi four sedan 1.000 108 peugot four sedan 1.000 110 peugot four wagon 1.000 112 peugot four sedan 1.000 114 peugot four wagon 1.000 116 peugot four sedan 0.999 1 0.001 131 renault four wagon 1.000 133 saab two hatchback 0.990 1 0.010 135 saab two hatchback 1.000 192 volkswagen four sedan 1.000 CLASS = 7 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 30 dodge two hatchback 1.000 47 isuzu two hatchback 1.000 83 mitsubishi two hatchback 1.000 84 mitsubishi two hatchback 1.000 85 mitsubishi two hatchback 1.000 125 plymouth two hatchback 1.000 132 renault two hatchback 1.000 168 toyota two hardtop 1.000 169 toyota two hardtop 1.000 170 toyota two hatchback 1.000 171 toyota two hardtop 1.000 172 toyota two hatchback 1.000 173 toyota two convertible 1.000 CLASS = 8 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 11 bmw two sedan 1.000 12 bmw four sedan 1.000 13 bmw two sedan 1.000 14 bmw four sedan 1.000 66 mazda four sedan 1.000 144 subaru four sedan 0.999 4 0.001 146 subaru four sedan 1.000 148 subaru four wagon 0.996 4 0.004 150 subaru four wagon 1.000 166 toyota two sedan 1.000 167 toyota two hatchback 1.000 CLASS = 9 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 3 alfa-romero two hatchback 1.000 10 audi two hatchback 1.000 76 mercury two hatchback 1.000 105 nissan two hatchback 1.000 106 nissan two hatchback 1.000 107 nissan two hatchback 1.000 126 porsche two hatchback 1.000 130 porsche two hatchback 1.000 179 toyota two hatchback 1.000 180 toyota two hatchback 1.000 CLASS = 10 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 64 mazda ? sedan 1.000 67 mazda four sedan 1.000 91 nissan two sedan 1.000 159 toyota four sedan 1.000 160 toyota four hatchback 1.000 175 toyota four sedan 1.000 183 volkswagen two sedan 1.000 185 volkswagen four sedan 1.000 188 volkswagen four sedan 1.000 193 volkswagen four sedan 1.000 CLASS = 11 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 19 chevrolet two hatchback 1.000 31 honda two hatchback 1.000 32 honda two hatchback 1.000 33 honda two hatchback 1.000 34 honda two hatchback 1.000 35 honda two hatchback 1.000 51 mazda two hatchback 1.000 52 mazda two hatchback 1.000 53 mazda two hatchback 1.000 CLASS = 12 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 1 alfa-romero two convertible 1.000 2 alfa-romero two convertible 1.000 56 mazda two hatchback 1.000 57 mazda two hatchback 1.000 58 mazda two hatchback 1.000 59 mazda two hatchback 1.000 127 porsche two hardtop 1.000 128 porsche two hardtop 1.000 129 porsche two convertible 1.000 CLASS = 13 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 24 dodge two hatchback 1.000 28 dodge ? sedan 1.000 36 honda four sedan 0.986 11 0.014 37 honda four wagon 1.000 80 mitsubishi two hatchback 1.000 120 plymouth two hatchback 1.000 190 volkswagen two convertible 1.000 191 volkswagen two hatchback 1.000 CLASS = 14 Case # make num-of-doors body-style (Cls Prob) ------------------------------------------------------------------------------------------ 109 peugot four sedan 1.000 111 peugot four wagon 1.000 113 peugot four sedan 1.000 115 peugot four wagon 1.000 117 peugot four sedan 1.000autoclass-3.3.6.dfsg.1/sample/scriptc.text0000644000175000017500000000663011247310756016553 0ustar areare;;; NOTE: This script is for the UN*X environment. ;;; The files in this directory were created by the following process: ;;; To facilitate comparisons with runs that you make, copy ;;; sample/imports-85c.s-params to sample/.s-params, and ;;; use this file for the search arguments => all output files you ;;; create will have in their names. ;;; the file screenc.text was created by 'cat' ing files created by % autoclass -search sample/imports-85c.db2 sample/imports-85c.hd2 \ sample/imports-85c.model sample/imports-85c.s-params \ > & sample/screencN.text ..... => sample/screenc.text ;;; (1) GET SOME DATA ;we got many data sets from the machine learning repository at UCI http://www.ics.uci.edu/AI/ML/MLDBRepository.html ; resulting data in files: imports-85.data and imports-85.names ;;; (2) CONVERT TO AUTOCLASS FORMAT Rename File imports-85.data to imports-85c.db2 ; using imports-85.names as a source, create files: ; imports-85c.hd2 and imports-85c.model ; based on autoclass-c/doc/preparation.text documentation file ;;; (3) SEARCH FOR GOOD CLASSIFICATIONS % ./autoclass -search sample/imports-85c.db2 \ sample/imports-85c.hd2 sample/imports-85c.model \ sample/imports-85c.s-params sample/imports-85c.s-params contains these overrides ==================================================== ## force_new_search_p = true is the default max_n_tries = 12 ;these files will be saved to disk: ; ~/autoclass-c/sample/imports-85c.log ; ~/autoclass-c/sample/imports-85c.search ; ~/autoclass-c/sample/imports-85c.results-bin ;will stop after 12 trials ;;; (4) RESTART SEARCH FOR 10 TRIALS ;continue the previous search % ./autoclass -search sample/imports-85c.db2 \ sample/imports-85c.hd2 sample/imports-85c.model \ sample/imports-85c.s-params sample/imports-85c.s-params contains these overrides ================================================== force_new_search_p = false max_n_tries = 10 ;will stop after it completes 10 more trials ;;; (5) RESTART SEARCH FOR 2 MINUTES % ./autoclass -search sample/imports-85c.db2 \ sample/imports-85c.hd2 sample/imports-85c.model \ sample/imports-85c.s-params sample/imports-85c.s-params contains these overrides ==================================================== force_new_search_p = false max_duration = 120 ;it stops itself this time after 2 minutes ;;; (6) TEXT REPORTS ON WHAT HAS BEEN FOUND % ./autoclass -reports sample/imports-85c.results-bin \ sample/imports-85c.search sample/imports-85c.r-params sample/imports-85c.r-params contains this override ==================================================== xref_class_report_att_list = 2, 5, 6x ;these files will show up on disk: ; ~/autoclass-c/sample/imports-85c.rlog ; ~/autoclass-c/sample/imports-85c.influ-o-text-1 ; ~/autoclass-c/sample/imports-85c.case-text-1 ; ~/autoclass-c/sample/imports-85c.class-text-1 ;;; (7) DATA REPORTS ON WHAT HAS BEEN FOUND % ./autoclass -reports sample/imports-85c.results-bin \ sample/imports-85c.search sample/imports-85c.r-params sample/imports-85c.r-params contains these overrides ==================================================== xref_class_report_att_list = 2, 5, 6 report_mode = "data" comment_data_headers_p = true sigma_contours_att_list = 18, 19, 20, 21, 22, 23, 24, 25 ;these reports show up: ; ~/autoclass-c/sample/imports-85c.influ-o-data-1 ; ~/autoclass-c/sample/imports-85c.case-data-1 ; ~/autoclass-c/sample/imports-85c.class-data-1 autoclass-3.3.6.dfsg.1/sample/imports-85c.influ-o-data-10000644000175000017500000045260511247310756020637 0ustar areareDATA_CLSF_HEADER # AutoClass CLASSIFICATION for the 205 cases in # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.db2 # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.hd2 # with log-A (approximate marginal likelihood) = -16230.401 # from classification results file # /home/taylor/taylor.old/AC/autoclass-c/sampleX/imports-85c.results-bin # and using models # /home/taylor/taylor.old/AC/autoclass-c/sample/imports-85c.model - index = 0 # # DATA_SEARCH_SUMMARY #SEARCH SUMMARY 250 tries over 1 minute 22 seconds #SUMMARY OF 10 BEST RESULTS PROBABILITY exp(-16230.401) N_CLASSES 15 FOUND ON TRY 213 *SAVED* -1 PROBABILITY exp(-16245.405) N_CLASSES 16 FOUND ON TRY 240 *SAVED* -2 PROBABILITY exp(-16258.217) N_CLASSES 14 FOUND ON TRY 248 PROBABILITY exp(-16272.233) N_CLASSES 9 FOUND ON TRY 212 PROBABILITY exp(-16282.909) N_CLASSES 16 FOUND ON TRY 42 PROBABILITY exp(-16285.766) N_CLASSES 16 FOUND ON TRY 83 DUPS 3 PROBABILITY exp(-16289.486) N_CLASSES 13 FOUND ON TRY 99 PROBABILITY exp(-16292.727) N_CLASSES 19 FOUND ON TRY 113 PROBABILITY exp(-16295.621) N_CLASSES 12 FOUND ON TRY 51 PROBABILITY exp(-16297.324) N_CLASSES 15 FOUND ON TRY 184 # # # DATA_POP_CLASSES CLASSIFICATION HAS 15 POPULATED CLASSES (max global influence value = 6.920) # # Class Log of class Relative Class Normalized # num strength class strength weight class weight # 00 -7.01e+01 1.20e-04 31 0.151 01 -7.29e+01 7.55e-06 22 0.107 02 -6.32e+01 1.29e-01 18 0.088 03 -6.11e+01 1.00e+00 16 0.078 04 -7.31e+01 6.14e-06 15 0.073 05 -8.07e+01 3.00e-09 14 0.068 06 -7.51e+01 8.62e-07 14 0.068 07 -6.86e+01 5.30e-04 13 0.063 08 -7.61e+01 2.98e-07 11 0.054 09 -7.43e+01 1.86e-06 10 0.049 10 -7.10e+01 5.15e-05 10 0.049 11 -7.13e+01 3.58e-05 9 0.044 12 -7.04e+01 9.06e-05 9 0.044 13 -7.16e+01 2.67e-05 8 0.039 14 -6.21e+01 3.52e-01 5 0.024# # # DATA_CLASS_DIVS #CLASS DIVERGENCES # # Class class cross entropy Class Normalized # num w.r.t. global class weight class weight # 00 1.15e+01 31 0.151 01 1.44e+01 22 0.107 02 2.06e+01 18 0.088 03 2.67e+01 16 0.078 04 1.51e+01 15 0.073 05 2.54e+01 14 0.068 06 1.42e+01 14 0.068 07 1.66e+01 13 0.063 08 1.06e+01 11 0.054 09 1.76e+01 10 0.049 10 2.25e+01 10 0.049 11 2.22e+01 9 0.044 12 2.56e+01 9 0.044 13 1.72e+01 8 0.039 14 3.92e+01 5 0.024 # # DATA_NORM_INF_VALS #ORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES # # num description I-*k # 038 Log compression-ratio 1.000 033 Log length 0.661 036 Log curb-weight 0.649 002 make 0.643 034 Log width 0.569 029 Log horse-power 0.564 037 Log engine-size 0.555 031 Log price 0.537 032 Log wheel-base 0.520 028 Log stroke 0.493 027 Log bore 0.482 017 fuel-system 0.440 035 Log height 0.361 026 Log normalized-loses 0.325 014 engine-type 0.276 039 Log city-mpg 0.250 040 Log highway-mpg 0.212 015 num-of-cylinders 0.198 007 drive-wheels 0.197 006 body-style 0.195 030 Log peak-rpm 0.181 003 fuel-type 0.145 005 num-of-doors 0.139 004 aspiration 0.081 008 engine-location 0.032 000 symboling ----- 001 normalized-loses ----- 009 wheel-base ----- 010 length ----- 011 width ----- 012 height ----- 013 curb-weight ----- 016 engine-size ----- 018 bore ----- 019 stroke ----- 020 compression-ratio ----- 021 horse-power ----- 022 peak-rpm ----- 023 city-mpg ----- 024 highway-mpg ----- 025 price ----- # # DATA_CLASS 0 #CLASS 0 - weight 31 normalized weight 0.151 relative strength 1.20e-04 ******* # class cross entropy w.r.t. global class 1.15e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 002 028 R SNcm Log stroke 1.398 ( 1.24e+00 1.99e-02) 3.46e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 1.260 ( 7.77e+00 3.59e-02) 1.57e+00 ( 7.83e+00 1.97e-01) 011 037 R SNcn Log engine-size 1.223 ( 4.75e+00 5.19e-02) 9.45e-01 ( 4.80e+00 2.82e-01) 012 038 R SNcn Log compression-ratio 1.110 ( 2.16e+00 6.17e-02) 1.71e+00 ( 2.27e+00 2.81e-01) 007 033 R SNcn Log length 0.894 ( 5.16e+00 1.81e-02) 1.71e-01 ( 5.16e+00 7.06e-02) 005 031 R SNcm Log price 0.853 ( 9.16e+00 1.47e-01) 1.27e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 003 029 R SNcm Log horse-power 0.850 ( 4.52e+00 9.52e-02) 6.76e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 1.00e+00 Prob-*k is known 9.90e-01 001 027 R SNcm Log bore 0.670 ( 1.18e+00 2.81e-02) 6.19e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 024 002 D SM make 0.560 subaru -3.72e+00 1.42e-03 5.85e-02 peugot -3.63e+00 1.42e-03 5.36e-02 volvo -3.63e+00 1.42e-03 5.36e-02 bmw -3.31e+00 1.42e-03 3.91e-02 mercedes-benz -3.31e+00 1.42e-03 3.91e-02 saab -3.03e+00 1.42e-03 2.93e-02 porsche -2.85e+00 1.42e-03 2.45e-02 isuzu -2.63e+00 1.42e-03 1.96e-02 alfa-romero -2.34e+00 1.42e-03 1.48e-02 chevrolet -2.34e+00 1.42e-03 1.48e-02 jaguar -2.34e+00 1.42e-03 1.48e-02 renault -1.94e+00 1.42e-03 9.93e-03 mercury -1.27e+00 1.42e-03 5.08e-03 mitsubishi 1.09e+00 1.89e-01 6.33e-02 honda 1.09e+00 1.89e-01 6.33e-02 volkswagen 9.89e-01 1.57e-01 5.85e-02 mazda 6.45e-01 1.58e-01 8.27e-02 nissan -3.15e-01 6.40e-02 8.76e-02 dodge -2.95e-01 3.27e-02 4.39e-02 toyota -2.07e-01 1.26e-01 1.56e-01 audi -4.56e-02 3.27e-02 3.42e-02 plymouth -4.53e-02 3.27e-02 3.42e-02 006 032 R SNcn Log wheel-base 0.500 ( 4.59e+00 2.35e-02) 8.52e-02 ( 4.59e+00 5.89e-02) 008 034 R SNcn Log width 0.488 ( 4.18e+00 1.28e-02) 2.82e-01 ( 4.19e+00 3.15e-02) 019 007 D SM drive-wheels 0.453 rwd -3.57e+00 1.04e-02 3.71e-01 4wd -1.47e+00 1.04e-02 4.53e-02 fwd 5.17e-01 9.79e-01 5.84e-01 017 014 D SM engine-type 0.239 ohcf -2.80e+00 4.47e-03 7.35e-02 ohcv -2.66e+00 4.47e-03 6.38e-02 dohc -2.58e+00 4.47e-03 5.89e-02 l -2.58e+00 4.47e-03 5.89e-02 rotor -1.50e+00 4.47e-03 2.01e-02 ohc 3.03e-01 9.73e-01 7.19e-01 dohcv -2.17e-01 4.47e-03 5.55e-03 000 026 R SNcm Log normalized-loses 0.213 ( 4.64e+00 2.53e-01) 4.71e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.63e-01 Prob-*k is known 8.00e-01 016 015 D SM num-of-cylinders 0.183 six -3.27e+00 4.47e-03 1.17e-01 five -2.49e+00 4.47e-03 5.41e-02 eight -1.72e+00 4.47e-03 2.50e-02 two -1.50e+00 4.47e-03 2.01e-02 four 2.31e-01 9.73e-01 7.73e-01 three -2.17e-01 4.47e-03 5.55e-03 twelve -2.17e-01 4.47e-03 5.55e-03 015 017 D SM fuel-system 0.175 idi -3.22e+00 3.91e-03 9.77e-02 4bbl -1.36e+00 3.91e-03 1.52e-02 1bbl 8.70e-01 1.29e-01 5.40e-02 spdi 7.91e-01 9.77e-02 4.43e-02 mfi -3.35e-01 3.91e-03 5.46e-03 spfi -3.35e-01 3.91e-03 5.46e-03 mpfi -2.74e-01 3.47e-01 4.57e-01 2bbl 2.46e-01 4.10e-01 3.21e-01 013 039 R SNcn Log city-mpg 0.087 ( 3.25e+00 1.93e-01) 2.73e-01 ( 3.19e+00 2.56e-01) 021 005 D SM num-of-doors 0.065 two -5.09e-01 2.61e-01 4.34e-01 four 2.73e-01 7.29e-01 5.55e-01 ? -8.33e-02 1.04e-02 1.13e-02 023 003 D SM fuel-type 0.059 diesel -1.85e+00 1.56e-02 9.95e-02 gas 8.91e-02 9.84e-01 9.00e-01 014 040 R SNcn Log highway-mpg 0.057 ( 3.46e+00 1.93e-01) 3.18e-01 ( 3.40e+00 2.23e-01) 020 006 D SM body-style 0.049 hardtop -1.85e+00 6.25e-03 3.98e-02 convertible -1.57e+00 6.25e-03 3.01e-02 wagon -2.06e-01 9.96e-02 1.22e-01 sedan 1.98e-01 5.69e-01 4.67e-01 hatchback -6.63e-02 3.19e-01 3.41e-01 022 004 D SM aspiration 0.044 turbo -8.45e-01 7.82e-02 1.82e-01 std 1.20e-01 9.22e-01 8.18e-01 009 035 R SNcn Log height 0.020 ( 3.99e+00 4.18e-02) 1.78e-01 ( 3.98e+00 4.54e-02) 004 030 R SNcm Log peak-rpm 0.009 ( 8.54e+00 9.75e-02) 3.30e-02 ( 8.54e+00 9.88e-02) Prob-jk is known 1.00e+00 Prob-*k is known 9.90e-01 018 008 D SM engine-location 0.000 rear -8.33e-02 1.56e-02 1.70e-02 front 1.38e-03 9.84e-01 9.83e-01 # # DATA_CLASS 1 #CLASS 1 - weight 22 normalized weight 0.107 relative strength 7.55e-06 ******* # class cross entropy w.r.t. global class 1.44e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 007 033 R SNcn Log length 2.083 ( 5.23e+00 9.76e-03) 7.89e+00 ( 5.16e+00 7.06e-02) 010 036 R SNcn Log curb-weight 1.390 ( 8.01e+00 4.78e-02) 3.88e+00 ( 7.83e+00 1.97e-01) 024 002 D SM make 1.218 mazda -3.73e+00 1.98e-03 8.27e-02 honda -3.47e+00 1.98e-03 6.33e-02 mitsubishi -3.47e+00 1.98e-03 6.33e-02 subaru -3.39e+00 1.98e-03 5.85e-02 volkswagen -3.39e+00 1.98e-03 5.85e-02 dodge -3.10e+00 1.98e-03 4.39e-02 mercedes-benz -2.98e+00 1.98e-03 3.91e-02 audi -2.85e+00 1.98e-03 3.42e-02 plymouth -2.85e+00 1.98e-03 3.42e-02 porsche -2.52e+00 1.98e-03 2.45e-02 isuzu -2.30e+00 1.98e-03 1.96e-02 volvo 2.10e+00 4.37e-01 5.36e-02 alfa-romero -2.01e+00 1.98e-03 1.48e-02 chevrolet -2.01e+00 1.98e-03 1.48e-02 jaguar -2.01e+00 1.98e-03 1.48e-02 saab 1.79e+00 1.76e-01 2.93e-02 renault -1.61e+00 1.98e-03 9.93e-03 mercury -9.43e-01 1.98e-03 5.08e-03 bmw 8.18e-01 8.85e-02 3.91e-02 toyota -5.59e-01 8.89e-02 1.56e-01 nissan 4.13e-01 1.32e-01 8.76e-02 peugot -1.64e-01 4.55e-02 5.36e-02 009 035 R SNcn Log height 1.042 ( 4.02e+00 1.52e-02) 2.64e+00 ( 3.98e+00 4.54e-02) 003 029 R SNcm Log horse-power 0.854 ( 4.92e+00 1.60e-01) 2.09e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 012 038 R SNcn Log compression-ratio 0.805 ( 2.18e+00 8.34e-02) 1.03e+00 ( 2.27e+00 2.81e-01) 005 031 R SNcm Log price 0.766 ( 9.75e+00 2.16e-01) 1.85e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 008 034 R SNcn Log width 0.700 ( 4.21e+00 1.27e-02) 1.61e+00 ( 4.19e+00 3.15e-02) 002 028 R SNcm Log stroke 0.670 ( 1.15e+00 3.49e-02) 6.12e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 001 027 R SNcm Log bore 0.664 ( 1.27e+00 4.65e-02) 1.62e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 015 017 D SM fuel-system 0.649 2bbl -4.08e+00 5.43e-03 3.21e-01 idi -2.89e+00 5.43e-03 9.77e-02 1bbl -2.30e+00 5.43e-03 5.40e-02 spdi -2.10e+00 5.43e-03 4.43e-02 4bbl -1.03e+00 5.43e-03 1.52e-02 mpfi 7.44e-01 9.62e-01 4.57e-01 mfi -4.95e-03 5.43e-03 5.46e-03 spfi -4.95e-03 5.43e-03 5.46e-03 006 032 R SNcn Log wheel-base 0.631 ( 4.64e+00 3.21e-02) 1.59e+00 ( 4.59e+00 5.89e-02) 011 037 R SNcn Log engine-size 0.503 ( 4.99e+00 1.51e-01) 1.28e+00 ( 4.80e+00 2.82e-01) 013 039 R SNcn Log city-mpg 0.444 ( 2.97e+00 1.92e-01) 1.15e+00 ( 3.19e+00 2.56e-01) 021 005 D SM num-of-doors 0.362 two -2.01e+00 5.84e-02 4.34e-01 four 5.13e-01 9.27e-01 5.55e-01 ? 2.46e-01 1.45e-02 1.13e-02 014 040 R SNcn Log highway-mpg 0.347 ( 3.22e+00 1.92e-01) 9.40e-01 ( 3.40e+00 2.23e-01) 020 006 D SM body-style 0.305 hatchback -1.87e+00 5.26e-02 3.41e-01 hardtop -1.52e+00 8.69e-03 3.98e-02 convertible -1.24e+00 8.69e-03 3.01e-02 wagon 6.14e-01 2.26e-01 1.22e-01 sedan 4.10e-01 7.04e-01 4.67e-01 017 014 D SM engine-type 0.226 ohcf -2.47e+00 6.21e-03 7.35e-02 rotor -1.18e+00 6.21e-03 2.01e-02 dohc 1.12e+00 1.80e-01 5.89e-02 ohcv 1.04e+00 1.80e-01 6.38e-02 ohc -2.30e-01 5.71e-01 7.19e-01 l -1.69e-01 4.98e-02 5.89e-02 dohcv 1.13e-01 6.21e-03 5.55e-03 016 015 D SM num-of-cylinders 0.223 five -2.16e+00 6.21e-03 5.41e-02 eight -1.39e+00 6.21e-03 2.50e-02 two -1.18e+00 6.21e-03 2.01e-02 six 1.10e+00 3.54e-01 1.17e-01 four -2.28e-01 6.15e-01 7.73e-01 three 1.13e-01 6.21e-03 5.55e-03 twelve 1.13e-01 6.21e-03 5.55e-03 000 026 R SNcm Log normalized-loses 0.215 ( 4.62e+00 1.96e-01) 7.31e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.61e-01 Prob-*k is known 8.00e-01 019 007 D SM drive-wheels 0.182 4wd -1.14e+00 1.45e-02 4.53e-02 fwd -6.04e-01 3.19e-01 5.84e-01 rwd 5.87e-01 6.66e-01 3.71e-01 004 030 R SNcm Log peak-rpm 0.062 ( 8.57e+00 9.68e-02) 3.36e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 022 004 D SM aspiration 0.060 turbo 5.83e-01 3.26e-01 1.82e-01 std -1.94e-01 6.74e-01 8.18e-01 023 003 D SM fuel-type 0.048 diesel -1.52e+00 2.17e-02 9.95e-02 gas 8.28e-02 9.78e-01 9.00e-01 018 008 D SM engine-location 0.001 rear 2.46e-01 2.17e-02 1.70e-02 front -4.84e-03 9.78e-01 9.83e-01 # # DATA_CLASS 2 #CLASS 2 - weight 18 normalized weight 0.088 relative strength 1.29e-01 ******* # class cross entropy w.r.t. global class 2.06e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 012 038 R SNcn Log compression-ratio 2.103 ( 2.22e+00 2.12e-02) 2.29e+00 ( 2.27e+00 2.81e-01) 003 029 R SNcm Log horse-power 1.982 ( 4.24e+00 4.84e-02) 7.15e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 008 034 R SNcn Log width 1.785 ( 4.16e+00 4.86e-03) 5.83e+00 ( 4.19e+00 3.15e-02) 006 032 R SNcn Log wheel-base 1.746 ( 4.55e+00 7.93e-03) 5.05e+00 ( 4.59e+00 5.89e-02) 010 036 R SNcn Log curb-weight 1.610 ( 7.62e+00 4.32e-02) 4.86e+00 ( 7.83e+00 1.97e-01) 011 037 R SNcn Log engine-size 1.605 ( 4.57e+00 4.84e-02) 4.72e+00 ( 4.80e+00 2.82e-01) 007 033 R SNcn Log length 1.558 ( 5.12e+00 1.09e-02) 3.85e+00 ( 5.16e+00 7.06e-02) 001 027 R SNcm Log bore 1.448 ( 1.15e+00 1.46e-02) 3.47e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 005 031 R SNcm Log price 1.434 ( 8.92e+00 1.09e-01) 3.97e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 024 002 D SM make 1.043 honda -3.28e+00 2.39e-03 6.33e-02 mitsubishi -3.28e+00 2.39e-03 6.33e-02 subaru -3.20e+00 2.39e-03 5.85e-02 volkswagen -3.20e+00 2.39e-03 5.85e-02 peugot -3.11e+00 2.39e-03 5.36e-02 volvo -3.11e+00 2.39e-03 5.36e-02 dodge -2.91e+00 2.39e-03 4.39e-02 bmw -2.79e+00 2.39e-03 3.91e-02 mercedes-benz -2.79e+00 2.39e-03 3.91e-02 audi -2.66e+00 2.39e-03 3.42e-02 plymouth -2.66e+00 2.39e-03 3.42e-02 saab -2.51e+00 2.39e-03 2.93e-02 porsche -2.33e+00 2.39e-03 2.45e-02 isuzu -2.10e+00 2.39e-03 1.96e-02 alfa-romero -1.82e+00 2.39e-03 1.48e-02 chevrolet -1.82e+00 2.39e-03 1.48e-02 jaguar -1.82e+00 2.39e-03 1.48e-02 nissan 1.69e+00 4.76e-01 8.76e-02 renault -1.42e+00 2.39e-03 9.93e-03 toyota 8.68e-01 3.71e-01 1.56e-01 mercury -7.52e-01 2.39e-03 5.08e-03 mazda 2.64e-01 1.08e-01 8.27e-02 015 017 D SM fuel-system 0.964 mpfi -4.24e+00 6.58e-03 4.57e-01 idi -2.70e+00 6.58e-03 9.77e-02 1bbl -2.10e+00 6.58e-03 5.40e-02 spdi -1.91e+00 6.58e-03 4.43e-02 2bbl 1.09e+00 9.54e-01 3.21e-01 4bbl -8.35e-01 6.58e-03 1.52e-02 mfi 1.87e-01 6.58e-03 5.46e-03 spfi 1.87e-01 6.58e-03 5.46e-03 009 035 R SNcn Log height 0.788 ( 3.98e+00 1.31e-02) 1.76e-01 ( 3.98e+00 4.54e-02) 002 028 R SNcm Log stroke 0.606 ( 1.15e+00 3.75e-02) 5.65e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 013 039 R SNcn Log city-mpg 0.466 ( 3.42e+00 1.91e-01) 1.19e+00 ( 3.19e+00 2.56e-01) 014 040 R SNcn Log highway-mpg 0.450 ( 3.61e+00 1.91e-01) 1.08e+00 ( 3.40e+00 2.23e-01) 000 026 R SNcm Log normalized-loses 0.268 ( 4.76e+00 2.02e-01) 2.13e-02 ( 4.76e+00 2.82e-01) Prob-jk is known 9.89e-01 Prob-*k is known 8.00e-01 017 014 D SM engine-type 0.201 ohcf -2.28e+00 7.52e-03 7.35e-02 ohcv -2.14e+00 7.52e-03 6.38e-02 dohc -2.06e+00 7.52e-03 5.89e-02 l -2.06e+00 7.52e-03 5.89e-02 rotor -9.83e-01 7.52e-03 2.01e-02 dohcv 3.04e-01 7.52e-03 5.55e-03 ohc 2.84e-01 9.55e-01 7.19e-01 019 007 D SM drive-wheels 0.180 rwd -1.10e+00 1.23e-01 3.71e-01 4wd -9.48e-01 1.76e-02 4.53e-02 fwd 3.86e-01 8.60e-01 5.84e-01 016 015 D SM num-of-cylinders 0.155 six -2.75e+00 7.52e-03 1.17e-01 five -1.97e+00 7.52e-03 5.41e-02 eight -1.20e+00 7.52e-03 2.50e-02 two -9.83e-01 7.52e-03 2.01e-02 three 3.04e-01 7.52e-03 5.55e-03 twelve 3.04e-01 7.52e-03 5.55e-03 four 2.12e-01 9.55e-01 7.73e-01 022 004 D SM aspiration 0.119 turbo -1.93e+00 2.63e-02 1.82e-01 std 1.74e-01 9.74e-01 8.18e-01 020 006 D SM body-style 0.053 convertible -1.05e+00 1.05e-02 3.01e-02 hardtop 4.62e-01 6.32e-02 3.98e-02 hatchback -4.34e-01 2.21e-01 3.41e-01 sedan 2.33e-01 5.90e-01 4.67e-01 wagon -5.46e-02 1.16e-01 1.22e-01 023 003 D SM fuel-type 0.041 diesel -1.33e+00 2.63e-02 9.95e-02 gas 7.81e-02 9.74e-01 9.00e-01 004 030 R SNcm Log peak-rpm 0.022 ( 8.52e+00 9.64e-02) 1.72e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 021 005 D SM num-of-doors 0.006 ? 4.38e-01 1.76e-02 1.13e-02 two -1.16e-01 3.86e-01 4.34e-01 four 7.17e-02 5.96e-01 5.55e-01 018 008 D SM engine-location 0.002 rear 4.38e-01 2.63e-02 1.70e-02 front -9.54e-03 9.74e-01 9.83e-01 # # DATA_CLASS 3 #CLASS 3 - weight 16 normalized weight 0.078 relative strength 1.00e+00 ******* # class cross entropy w.r.t. global class 2.67e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 012 038 R SNcn Log compression-ratio 2.466 ( 2.25e+00 1.45e-02) 1.47e+00 ( 2.27e+00 2.81e-01) 006 032 R SNcn Log wheel-base 2.342 ( 4.54e+00 4.85e-03) 1.00e+01 ( 4.59e+00 5.89e-02) 001 027 R SNcm Log bore 2.091 ( 1.09e+00 1.45e-02) 7.29e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 003 029 R SNcm Log horse-power 2.019 ( 4.23e+00 4.83e-02) 7.40e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 010 036 R SNcn Log curb-weight 2.005 ( 7.57e+00 3.70e-02) 6.80e+00 ( 7.83e+00 1.97e-01) 008 034 R SNcn Log width 1.868 ( 4.16e+00 4.85e-03) 6.40e+00 ( 4.19e+00 3.15e-02) 011 037 R SNcn Log engine-size 1.812 ( 4.51e+00 4.83e-02) 6.03e+00 ( 4.80e+00 2.82e-01) 005 031 R SNcm Log price 1.764 ( 8.76e+00 9.59e-02) 6.14e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 8.81e-01 Prob-*k is known 9.80e-01 007 033 R SNcn Log length 1.623 ( 5.06e+00 2.06e-02) 4.46e+00 ( 5.16e+00 7.06e-02) 009 035 R SNcn Log height 1.601 ( 3.93e+00 1.05e-02) 4.78e+00 ( 3.98e+00 4.54e-02) 024 002 D SM make 1.567 toyota -4.06e+00 2.67e-03 1.56e-01 nissan -3.49e+00 2.67e-03 8.76e-02 mazda -3.43e+00 2.67e-03 8.27e-02 honda -3.16e+00 2.67e-03 6.33e-02 subaru -3.08e+00 2.67e-03 5.85e-02 volkswagen -3.08e+00 2.67e-03 5.85e-02 peugot -3.00e+00 2.67e-03 5.36e-02 volvo -3.00e+00 2.67e-03 5.36e-02 bmw -2.68e+00 2.67e-03 3.91e-02 mercedes-benz -2.68e+00 2.67e-03 3.91e-02 audi -2.55e+00 2.67e-03 3.42e-02 saab -2.40e+00 2.67e-03 2.93e-02 porsche -2.21e+00 2.67e-03 2.45e-02 chevrolet 2.10e+00 1.20e-01 1.48e-02 plymouth 1.94e+00 2.38e-01 3.42e-02 dodge 1.91e+00 2.97e-01 4.39e-02 isuzu 1.81e+00 1.20e-01 1.96e-02 alfa-romero -1.71e+00 2.67e-03 1.48e-02 jaguar -1.71e+00 2.67e-03 1.48e-02 renault -1.31e+00 2.67e-03 9.93e-03 mitsubishi 1.04e+00 1.79e-01 6.33e-02 mercury -6.41e-01 2.67e-03 5.08e-03 002 028 R SNcm Log stroke 1.394 ( 1.16e+00 1.59e-02) 7.70e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 015 017 D SM fuel-system 0.949 mpfi -4.13e+00 7.35e-03 4.57e-01 idi -2.59e+00 7.35e-03 9.77e-02 1bbl -1.99e+00 7.35e-03 5.40e-02 spdi -1.80e+00 7.35e-03 4.43e-02 2bbl 1.08e+00 9.49e-01 3.21e-01 4bbl -7.24e-01 7.35e-03 1.52e-02 mfi 2.98e-01 7.35e-03 5.46e-03 spfi 2.98e-01 7.35e-03 5.46e-03 013 039 R SNcn Log city-mpg 0.863 ( 3.52e+00 1.90e-01) 1.69e+00 ( 3.19e+00 2.56e-01) 014 040 R SNcn Log highway-mpg 0.811 ( 3.68e+00 1.90e-01) 1.47e+00 ( 3.40e+00 2.23e-01) 019 007 D SM drive-wheels 0.404 rwd -2.94e+00 1.96e-02 3.71e-01 4wd -8.37e-01 1.96e-02 4.53e-02 fwd 4.98e-01 9.61e-01 5.84e-01 004 030 R SNcm Log peak-rpm 0.259 ( 8.61e+00 9.61e-02) 7.29e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 000 026 R SNcm Log normalized-loses 0.209 ( 4.90e+00 1.98e-01) 7.00e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.70e-01 Prob-*k is known 8.00e-01 017 014 D SM engine-type 0.192 ohcf -2.17e+00 8.41e-03 7.35e-02 ohcv -2.03e+00 8.41e-03 6.38e-02 dohc -1.95e+00 8.41e-03 5.89e-02 l -1.95e+00 8.41e-03 5.89e-02 rotor -8.72e-01 8.41e-03 2.01e-02 dohcv 4.15e-01 8.41e-03 5.55e-03 ohc 2.78e-01 9.50e-01 7.19e-01 020 006 D SM body-style 0.156 wagon -2.34e+00 1.18e-02 1.22e-01 hardtop -1.22e+00 1.18e-02 3.98e-02 convertible -9.39e-01 1.18e-02 3.01e-02 hatchback 4.62e-01 5.41e-01 3.41e-01 sedan -9.75e-02 4.24e-01 4.67e-01 016 015 D SM num-of-cylinders 0.149 six -2.64e+00 8.41e-03 1.17e-01 five -1.86e+00 8.41e-03 5.41e-02 eight -1.09e+00 8.41e-03 2.50e-02 two -8.72e-01 8.41e-03 2.01e-02 three 4.15e-01 8.41e-03 5.55e-03 twelve 4.15e-01 8.41e-03 5.55e-03 four 2.06e-01 9.50e-01 7.73e-01 022 004 D SM aspiration 0.112 turbo -1.82e+00 2.94e-02 1.82e-01 std 1.71e-01 9.71e-01 8.18e-01 023 003 D SM fuel-type 0.037 diesel -1.22e+00 2.94e-02 9.95e-02 gas 7.50e-02 9.71e-01 9.00e-01 021 005 D SM num-of-doors 0.010 ? 5.49e-01 1.96e-02 1.13e-02 four -1.24e-01 4.90e-01 5.55e-01 two 1.22e-01 4.90e-01 4.34e-01 018 008 D SM engine-location 0.004 rear 5.49e-01 2.94e-02 1.70e-02 front -1.27e-02 9.71e-01 9.83e-01 # # DATA_CLASS 4 #CLASS 4 - weight 15 normalized weight 0.073 relative strength 6.14e-06 ******* # class cross entropy w.r.t. global class 1.51e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 012 038 R SNcn Log compression-ratio 1.891 ( 2.20e+00 2.67e-02) 2.67e+00 ( 2.27e+00 2.81e-01) 024 002 D SM make 1.365 nissan -3.43e+00 2.84e-03 8.76e-02 mazda -3.37e+00 2.84e-03 8.27e-02 mitsubishi -3.10e+00 2.84e-03 6.33e-02 honda -3.10e+00 2.84e-03 6.33e-02 volkswagen -3.02e+00 2.84e-03 5.85e-02 peugot -2.94e+00 2.84e-03 5.36e-02 volvo -2.94e+00 2.84e-03 5.36e-02 dodge -2.74e+00 2.84e-03 4.39e-02 bmw -2.62e+00 2.84e-03 3.91e-02 mercedes-benz -2.62e+00 2.84e-03 3.91e-02 audi -2.49e+00 2.84e-03 3.42e-02 plymouth -2.49e+00 2.84e-03 3.42e-02 saab -2.34e+00 2.84e-03 2.93e-02 porsche -2.15e+00 2.84e-03 2.45e-02 subaru 2.15e+00 5.03e-01 5.85e-02 alfa-romero -1.65e+00 2.84e-03 1.48e-02 chevrolet -1.65e+00 2.84e-03 1.48e-02 jaguar -1.65e+00 2.84e-03 1.48e-02 renault -1.25e+00 2.84e-03 9.93e-03 isuzu 1.20e+00 6.53e-02 1.96e-02 toyota 8.88e-01 3.78e-01 1.56e-01 mercury -5.81e-01 2.84e-03 5.08e-03 005 031 R SNcm Log price 1.214 ( 8.87e+00 1.51e-01) 3.17e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 000 026 R SNcm Log normalized-loses 1.174 ( 4.47e+00 9.39e-02) 3.15e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 9.25e-01 Prob-*k is known 8.00e-01 006 032 R SNcn Log wheel-base 1.161 ( 4.56e+00 1.31e-02) 2.32e+00 ( 4.59e+00 5.89e-02) 003 029 R SNcm Log horse-power 1.060 ( 4.27e+00 1.19e-01) 2.69e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 011 037 R SNcn Log engine-size 1.059 ( 4.61e+00 7.67e-02) 2.44e+00 ( 4.80e+00 2.82e-01) 002 028 R SNcm Log stroke 1.026 ( 1.03e+00 8.34e-02) 1.71e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 015 017 D SM fuel-system 0.940 mpfi -4.03e+00 8.09e-03 4.57e-01 idi -2.53e+00 7.81e-03 9.77e-02 1bbl -1.93e+00 7.81e-03 5.40e-02 spdi -1.74e+00 7.81e-03 4.43e-02 2bbl 1.08e+00 9.45e-01 3.21e-01 4bbl -6.64e-01 7.81e-03 1.52e-02 spfi 3.58e-01 7.81e-03 5.46e-03 mfi 3.58e-01 7.81e-03 5.46e-03 017 014 D SM engine-type 0.718 ohcv -1.97e+00 8.92e-03 6.38e-02 ohcf 1.93e+00 5.09e-01 7.35e-02 dohc -1.89e+00 8.92e-03 5.89e-02 l -1.89e+00 8.92e-03 5.89e-02 rotor -8.13e-01 8.92e-03 2.01e-02 ohc -4.77e-01 4.46e-01 7.19e-01 dohcv 4.75e-01 8.92e-03 5.55e-03 008 034 R SNcn Log width 0.709 ( 4.16e+00 1.57e-02) 1.77e+00 ( 4.19e+00 3.15e-02) 019 007 D SM drive-wheels 0.540 4wd 2.00e+00 3.33e-01 4.53e-02 rwd -1.49e+00 8.33e-02 3.71e-01 fwd -9.84e-04 5.84e-01 5.84e-01 007 033 R SNcn Log length 0.446 ( 5.11e+00 3.92e-02) 1.15e+00 ( 5.16e+00 7.06e-02) 010 036 R SNcn Log curb-weight 0.441 ( 7.72e+00 1.03e-01) 1.06e+00 ( 7.83e+00 1.97e-01) 004 030 R SNcm Log peak-rpm 0.310 ( 8.46e+00 9.59e-02) 8.01e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 013 039 R SNcn Log city-mpg 0.221 ( 3.33e+00 1.90e-01) 7.33e-01 ( 3.19e+00 2.56e-01) 020 006 D SM body-style 0.191 hardtop -1.16e+00 1.25e-02 3.98e-02 wagon 9.77e-01 3.25e-01 1.22e-01 convertible -8.79e-01 1.25e-02 3.01e-02 sedan -5.76e-01 2.63e-01 4.67e-01 hatchback 1.28e-01 3.87e-01 3.41e-01 009 035 R SNcn Log height 0.153 ( 4.01e+00 3.85e-02) 5.98e-01 ( 3.98e+00 4.54e-02) 016 015 D SM num-of-cylinders 0.145 six -2.58e+00 8.92e-03 1.17e-01 five -1.80e+00 8.92e-03 5.41e-02 eight -1.03e+00 8.92e-03 2.50e-02 two -8.13e-01 8.92e-03 2.01e-02 three 4.75e-01 8.92e-03 5.55e-03 twelve 4.75e-01 8.92e-03 5.55e-03 four 2.03e-01 9.46e-01 7.73e-01 014 040 R SNcn Log highway-mpg 0.115 ( 3.50e+00 1.90e-01) 5.04e-01 ( 3.40e+00 2.23e-01) 022 004 D SM aspiration 0.109 turbo -1.76e+00 3.12e-02 1.82e-01 std 1.69e-01 9.69e-01 8.18e-01 023 003 D SM fuel-type 0.035 diesel -1.16e+00 3.12e-02 9.95e-02 gas 7.31e-02 9.69e-01 9.00e-01 001 027 R SNcm Log bore 0.027 ( 1.21e+00 7.94e-02) 1.56e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 021 005 D SM num-of-doors 0.023 ? 6.09e-01 2.08e-02 1.13e-02 two -2.64e-01 3.33e-01 4.34e-01 four 1.52e-01 6.46e-01 5.55e-01 018 008 D SM engine-location 0.005 rear 6.09e-01 3.12e-02 1.70e-02 front -1.46e-02 9.69e-01 9.83e-01 # # DATA_CLASS 5 #CLASS 5 - weight 14 normalized weight 0.068 relative strength 3.00e-09 ******* # class cross entropy w.r.t. global class 2.54e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 005 031 R SNcm Log price 2.838 ( 1.04e+01 1.81e-01) 5.85e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 2.594 ( 8.21e+00 6.43e-02) 6.00e+00 ( 7.83e+00 1.97e-01) 008 034 R SNcn Log width 2.558 ( 4.25e+00 1.56e-02) 4.28e+00 ( 4.19e+00 3.15e-02) 011 037 R SNcn Log engine-size 2.374 ( 5.41e+00 2.22e-01) 2.73e+00 ( 4.80e+00 2.82e-01) 024 002 D SM make 1.992 toyota -3.94e+00 3.03e-03 1.56e-01 nissan -3.36e+00 3.03e-03 8.76e-02 mazda -3.31e+00 3.03e-03 8.27e-02 honda -3.04e+00 3.03e-03 6.33e-02 mitsubishi -3.04e+00 3.03e-03 6.33e-02 subaru -2.96e+00 3.03e-03 5.85e-02 volkswagen -2.96e+00 3.03e-03 5.85e-02 peugot -2.87e+00 3.03e-03 5.36e-02 dodge -2.67e+00 3.03e-03 4.39e-02 jaguar 2.62e+00 2.03e-01 1.48e-02 mercedes-benz 2.62e+00 5.36e-01 3.91e-02 audi -2.42e+00 3.03e-03 3.42e-02 plymouth -2.42e+00 3.03e-03 3.42e-02 saab -2.27e+00 3.03e-03 2.93e-02 porsche -2.09e+00 3.03e-03 2.45e-02 isuzu -1.87e+00 3.03e-03 1.96e-02 alfa-romero -1.59e+00 3.03e-03 1.48e-02 chevrolet -1.59e+00 3.03e-03 1.48e-02 bmw 1.25e+00 1.37e-01 3.91e-02 renault -1.19e+00 3.03e-03 9.93e-03 mercury -5.16e-01 3.03e-03 5.08e-03 volvo 2.62e-01 6.97e-02 5.36e-02 007 033 R SNcn Log length 1.672 ( 5.27e+00 3.54e-02) 3.29e+00 ( 5.16e+00 7.06e-02) 016 015 D SM num-of-cylinders 1.638 four -4.40e+00 9.52e-03 7.73e-01 twelve 2.62e+00 7.61e-02 5.55e-03 eight 2.40e+00 2.76e-01 2.50e-02 five 1.63e+00 2.76e-01 5.41e-02 six 1.07e+00 3.43e-01 1.17e-01 two -7.48e-01 9.52e-03 2.01e-02 three 5.40e-01 9.52e-03 5.55e-03 006 032 R SNcn Log wheel-base 1.626 ( 4.70e+00 5.34e-02) 1.98e+00 ( 4.59e+00 5.89e-02) 014 040 R SNcn Log highway-mpg 1.451 ( 3.02e+00 1.89e-01) 1.99e+00 ( 3.40e+00 2.23e-01) 003 029 R SNcm Log horse-power 1.058 ( 5.05e+00 2.27e-01) 2.06e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 013 039 R SNcn Log city-mpg 0.932 ( 2.85e+00 2.08e-01) 1.65e+00 ( 3.19e+00 2.56e-01) 019 007 D SM drive-wheels 0.817 fwd -3.27e+00 2.22e-02 5.84e-01 rwd 9.47e-01 9.56e-01 3.71e-01 4wd -7.13e-01 2.22e-02 4.53e-02 012 038 R SNcn Log compression-ratio 0.547 ( 2.46e+00 4.49e-01) 4.33e-01 ( 2.27e+00 2.81e-01) 015 017 D SM fuel-system 0.544 2bbl -3.65e+00 8.33e-03 3.21e-01 1bbl -1.87e+00 8.33e-03 5.40e-02 spdi -1.67e+00 8.33e-03 4.43e-02 idi 1.25e+00 3.41e-01 9.77e-02 4bbl -6.00e-01 8.33e-03 1.52e-02 mfi 4.22e-01 8.33e-03 5.46e-03 spfi 4.22e-01 8.33e-03 5.46e-03 mpfi 2.87e-01 6.09e-01 4.57e-01 001 027 R SNcm Log bore 0.536 ( 1.27e+00 5.15e-02) 1.35e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 017 014 D SM engine-type 0.467 ohcf -2.04e+00 9.52e-03 7.35e-02 l -1.82e+00 9.52e-03 5.89e-02 ohcv 1.68e+00 3.43e-01 6.38e-02 dohc 8.85e-01 1.43e-01 5.89e-02 rotor -7.48e-01 9.52e-03 2.01e-02 dohcv 5.40e-01 9.52e-03 5.55e-03 ohc -4.12e-01 4.76e-01 7.19e-01 020 006 D SM body-style 0.448 hatchback -3.24e+00 1.33e-02 3.41e-01 hardtop 1.30e+00 1.47e-01 3.98e-02 convertible 9.77e-01 8.00e-02 3.01e-02 wagon -4.25e-01 8.00e-02 1.22e-01 sedan 3.76e-01 6.80e-01 4.67e-01 004 030 R SNcm Log peak-rpm 0.346 ( 8.46e+00 9.57e-02) 8.49e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 000 026 R SNcm Log normalized-loses 0.299 ( 4.66e+00 1.88e-01) 5.52e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 5.20e-01 Prob-*k is known 8.00e-01 023 003 D SM fuel-type 0.255 diesel 1.30e+00 3.66e-01 9.95e-02 gas -3.52e-01 6.34e-01 9.00e-01 002 028 R SNcm Log stroke 0.223 ( 1.24e+00 1.03e-01) 6.43e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 022 004 D SM aspiration 0.095 turbo 7.00e-01 3.66e-01 1.82e-01 std -2.55e-01 6.34e-01 8.18e-01 009 035 R SNcn Log height 0.070 ( 4.00e+00 4.95e-02) 3.25e-01 ( 3.98e+00 4.54e-02) 021 005 D SM num-of-doors 0.016 ? 6.73e-01 2.22e-02 1.13e-02 two -1.99e-01 3.55e-01 4.34e-01 four 1.15e-01 6.22e-01 5.55e-01 018 008 D SM engine-location 0.006 rear 6.73e-01 3.33e-02 1.70e-02 front -1.67e-02 9.67e-01 9.83e-01 # # DATA_CLASS 6 #CLASS 6 - weight 14 normalized weight 0.068 relative strength 8.62e-07 ******* # class cross entropy w.r.t. global class 1.42e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 024 002 D SM make 1.616 toyota -3.94e+00 3.03e-03 1.56e-01 nissan -3.36e+00 3.03e-03 8.76e-02 mazda -3.31e+00 3.03e-03 8.27e-02 honda -3.04e+00 3.03e-03 6.33e-02 mitsubishi -3.04e+00 3.03e-03 6.33e-02 subaru -2.96e+00 3.03e-03 5.85e-02 volvo -2.87e+00 3.03e-03 5.36e-02 dodge -2.67e+00 3.03e-03 4.39e-02 bmw -2.56e+00 3.03e-03 3.91e-02 mercedes-benz -2.56e+00 3.03e-03 3.91e-02 plymouth -2.42e+00 3.03e-03 3.42e-02 audi 2.29e+00 3.36e-01 3.42e-02 porsche -2.09e+00 3.03e-03 2.45e-02 renault 1.95e+00 6.97e-02 9.93e-03 isuzu -1.87e+00 3.03e-03 1.96e-02 peugot 1.84e+00 3.36e-01 5.36e-02 alfa-romero -1.59e+00 3.03e-03 1.48e-02 chevrolet -1.59e+00 3.03e-03 1.48e-02 jaguar -1.59e+00 3.03e-03 1.48e-02 saab 1.53e+00 1.36e-01 2.93e-02 mercury -5.16e-01 3.03e-03 5.08e-03 volkswagen 1.90e-01 7.07e-02 5.85e-02 012 038 R SNcn Log compression-ratio 1.607 ( 2.14e+00 3.79e-02) 3.24e+00 ( 2.27e+00 2.81e-01) 000 026 R SNcm Log normalized-loses 1.197 ( 5.06e+00 4.81e-02) 6.19e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.86e-01 Prob-*k is known 8.00e-01 011 037 R SNcn Log engine-size 1.150 ( 4.85e+00 5.59e-02) 8.25e-01 ( 4.80e+00 2.82e-01) 007 033 R SNcn Log length 0.898 ( 5.23e+00 3.53e-02) 2.15e+00 ( 5.16e+00 7.06e-02) 003 029 R SNcm Log horse-power 0.830 ( 4.67e+00 1.00e-01) 8.63e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.33e-01 Prob-*k is known 9.90e-01 009 035 R SNcn Log height 0.748 ( 4.02e+00 2.18e-02) 1.86e+00 ( 3.98e+00 4.54e-02) 010 036 R SNcn Log curb-weight 0.740 ( 7.97e+00 7.97e-02) 1.76e+00 ( 7.83e+00 1.97e-01) 002 028 R SNcm Log stroke 0.713 ( 1.12e+00 1.84e-01) 3.24e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 008 034 R SNcn Log width 0.637 ( 4.22e+00 2.60e-02) 1.33e+00 ( 4.19e+00 3.15e-02) 016 015 D SM num-of-cylinders 0.607 six -2.51e+00 9.52e-03 1.17e-01 five 2.02e+00 4.09e-01 5.41e-02 eight -9.64e-01 9.52e-03 2.50e-02 two -7.48e-01 9.52e-03 2.01e-02 three 5.40e-01 9.52e-03 5.55e-03 twelve 5.40e-01 9.52e-03 5.55e-03 four -3.53e-01 5.43e-01 7.73e-01 015 017 D SM fuel-system 0.603 2bbl -3.65e+00 8.33e-03 3.21e-01 idi -2.46e+00 8.33e-03 9.77e-02 1bbl -1.87e+00 8.33e-03 5.40e-02 spdi -1.67e+00 8.33e-03 4.43e-02 mpfi 7.23e-01 9.42e-01 4.57e-01 4bbl -5.99e-01 8.33e-03 1.52e-02 mfi 4.22e-01 8.33e-03 5.46e-03 spfi 4.22e-01 8.33e-03 5.46e-03 005 031 R SNcm Log price 0.570 ( 9.62e+00 2.21e-01) 1.21e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 013 039 R SNcn Log city-mpg 0.488 ( 2.96e+00 1.89e-01) 1.23e+00 ( 3.19e+00 2.56e-01) 006 032 R SNcn Log wheel-base 0.487 ( 4.65e+00 5.04e-02) 1.13e+00 ( 4.59e+00 5.89e-02) 017 014 D SM engine-type 0.446 ohcf -2.04e+00 9.52e-03 7.35e-02 ohcv -1.90e+00 9.52e-03 6.38e-02 dohc -1.82e+00 9.52e-03 5.89e-02 l 1.76e+00 3.43e-01 5.89e-02 rotor -7.48e-01 9.52e-03 2.01e-02 dohcv 5.40e-01 9.52e-03 5.55e-03 ohc -1.65e-01 6.10e-01 7.19e-01 014 040 R SNcn Log highway-mpg 0.394 ( 3.21e+00 1.89e-01) 1.01e+00 ( 3.40e+00 2.23e-01) 020 006 D SM body-style 0.170 hardtop -1.09e+00 1.33e-02 3.98e-02 hatchback -8.48e-01 1.46e-01 3.41e-01 wagon 8.31e-01 2.81e-01 1.22e-01 convertible -8.14e-01 1.33e-02 3.01e-02 sedan 1.57e-01 5.46e-01 4.67e-01 004 030 R SNcm Log peak-rpm 0.121 ( 8.57e+00 9.57e-02) 3.24e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.33e-01 Prob-*k is known 9.90e-01 021 005 D SM num-of-doors 0.100 ? 6.74e-01 2.22e-02 1.13e-02 two -6.72e-01 2.22e-01 4.34e-01 four 3.09e-01 7.56e-01 5.55e-01 023 003 D SM fuel-type 0.032 diesel -1.09e+00 3.33e-02 9.95e-02 gas 7.09e-02 9.67e-01 9.00e-01 001 027 R SNcm Log bore 0.032 ( 1.18e+00 7.92e-02) 1.87e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.80e-01 022 004 D SM aspiration 0.026 turbo -5.99e-01 1.00e-01 1.82e-01 std 9.56e-02 9.00e-01 8.18e-01 019 007 D SM drive-wheels 0.017 4wd 6.74e-01 8.89e-02 4.53e-02 fwd -4.98e-02 5.56e-01 5.84e-01 rwd -4.19e-02 3.55e-01 3.71e-01 018 008 D SM engine-location 0.006 rear 6.74e-01 3.33e-02 1.70e-02 front -1.68e-02 9.67e-01 9.83e-01 # # DATA_CLASS 7 #CLASS 7 - weight 13 normalized weight 0.063 relative strength 5.30e-04 ******* # class cross entropy w.r.t. global class 1.66e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 007 033 R SNcn Log length 1.590 ( 5.16e+00 8.84e-03) 7.75e-01 ( 5.16e+00 7.06e-02) 001 027 R SNcm Log bore 1.569 ( 1.28e+00 1.64e-02) 4.63e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 008 034 R SNcn Log width 1.150 ( 4.19e+00 6.17e-03) 9.89e-02 ( 4.19e+00 3.15e-02) 006 032 R SNcn Log wheel-base 1.134 ( 4.58e+00 1.22e-02) 1.27e+00 ( 4.59e+00 5.89e-02) 011 037 R SNcn Log engine-size 1.105 ( 4.99e+00 7.26e-02) 2.55e+00 ( 4.80e+00 2.82e-01) 009 035 R SNcn Log height 1.033 ( 3.94e+00 1.80e-02) 2.59e+00 ( 3.98e+00 4.54e-02) 015 017 D SM fuel-system 0.971 2bbl -3.58e+00 8.93e-03 3.21e-01 mfi 2.69e+00 8.04e-02 5.46e-03 spfi 2.69e+00 8.04e-02 5.46e-03 idi -2.39e+00 8.93e-03 9.77e-02 spdi 1.89e+00 2.95e-01 4.43e-02 1bbl -1.80e+00 8.93e-03 5.40e-02 4bbl -5.30e-01 8.93e-03 1.52e-02 mpfi 1.08e-01 5.09e-01 4.57e-01 000 026 R SNcm Log normalized-loses 0.942 ( 4.91e+00 4.79e-02) 3.03e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 5.57e-01 Prob-*k is known 8.00e-01 024 002 D SM make 0.936 nissan -3.30e+00 3.25e-03 8.76e-02 mazda -3.24e+00 3.25e-03 8.27e-02 honda -2.97e+00 3.25e-03 6.33e-02 subaru -2.89e+00 3.25e-03 5.85e-02 volkswagen -2.89e+00 3.25e-03 5.85e-02 peugot -2.80e+00 3.25e-03 5.36e-02 volvo -2.80e+00 3.25e-03 5.36e-02 bmw -2.49e+00 3.25e-03 3.91e-02 mercedes-benz -2.49e+00 3.25e-03 3.91e-02 audi -2.35e+00 3.25e-03 3.42e-02 saab -2.20e+00 3.25e-03 2.93e-02 porsche -2.02e+00 3.25e-03 2.45e-02 renault 2.02e+00 7.47e-02 9.93e-03 alfa-romero -1.52e+00 3.25e-03 1.48e-02 chevrolet -1.52e+00 3.25e-03 1.48e-02 jaguar -1.52e+00 3.25e-03 1.48e-02 isuzu 1.34e+00 7.47e-02 1.96e-02 mitsubishi 1.23e+00 2.18e-01 6.33e-02 toyota 1.02e+00 4.32e-01 1.56e-01 plymouth 7.81e-01 7.47e-02 3.42e-02 dodge 5.31e-01 7.47e-02 4.39e-02 mercury -4.47e-01 3.25e-03 5.08e-03 002 028 R SNcm Log stroke 0.907 ( 1.29e+00 5.78e-02) 2.04e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 0.864 ( 7.91e+00 5.76e-02) 1.46e+00 ( 7.83e+00 1.97e-01) 020 006 D SM body-style 0.840 sedan -3.49e+00 1.43e-02 4.67e-01 wagon -2.15e+00 1.43e-02 1.22e-01 hardtop 1.75e+00 2.29e-01 3.98e-02 convertible 1.05e+00 8.57e-02 3.01e-02 hatchback 6.57e-01 6.57e-01 3.41e-01 003 029 R SNcm Log horse-power 0.762 ( 4.82e+00 1.39e-01) 1.73e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.28e-01 Prob-*k is known 9.90e-01 021 005 D SM num-of-doors 0.692 four -3.15e+00 2.38e-02 5.55e-01 two 7.87e-01 9.52e-01 4.34e-01 ? 7.43e-01 2.38e-02 1.13e-02 005 031 R SNcm Log price 0.562 ( 9.38e+00 1.88e-01) 1.62e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 012 038 R SNcn Log compression-ratio 0.526 ( 2.12e+00 1.30e-01) 1.17e+00 ( 2.27e+00 2.81e-01) 017 014 D SM engine-type 0.175 ohcf -1.97e+00 1.02e-02 7.35e-02 ohcv -1.83e+00 1.02e-02 6.38e-02 dohc -1.75e+00 1.02e-02 5.89e-02 l -1.75e+00 1.02e-02 5.89e-02 rotor -6.78e-01 1.02e-02 2.01e-02 dohcv 6.09e-01 1.02e-02 5.55e-03 ohc 2.67e-01 9.39e-01 7.19e-01 013 039 R SNcn Log city-mpg 0.167 ( 3.09e+00 1.89e-01) 5.78e-01 ( 3.19e+00 2.56e-01) 004 030 R SNcm Log peak-rpm 0.163 ( 8.50e+00 9.54e-02) 4.25e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.28e-01 Prob-*k is known 9.90e-01 016 015 D SM num-of-cylinders 0.137 six -2.44e+00 1.02e-02 1.17e-01 five -1.67e+00 1.02e-02 5.41e-02 eight -8.95e-01 1.02e-02 2.50e-02 two -6.78e-01 1.02e-02 2.01e-02 three 6.09e-01 1.02e-02 5.55e-03 twelve 6.09e-01 1.02e-02 5.55e-03 four 1.95e-01 9.39e-01 7.73e-01 022 004 D SM aspiration 0.121 turbo 7.69e-01 3.93e-01 1.82e-01 std -2.98e-01 6.07e-01 8.18e-01 019 007 D SM drive-wheels 0.104 4wd -6.43e-01 2.38e-02 4.53e-02 rwd 4.74e-01 5.95e-01 3.71e-01 fwd -4.27e-01 3.81e-01 5.84e-01 014 040 R SNcn Log highway-mpg 0.098 ( 3.32e+00 1.89e-01) 4.51e-01 ( 3.40e+00 2.23e-01) 023 003 D SM fuel-type 0.029 diesel -1.02e+00 3.57e-02 9.95e-02 gas 6.85e-02 9.64e-01 9.00e-01 018 008 D SM engine-location 0.008 rear 7.43e-01 3.57e-02 1.70e-02 front -1.92e-02 9.64e-01 9.83e-01 # # DATA_CLASS 8 #CLASS 8 - weight 11 normalized weight 0.054 relative strength 2.98e-07 ******* # class cross entropy w.r.t. global class 1.06e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 024 002 D SM make 1.197 nissan -3.14e+00 3.79e-03 8.76e-02 mitsubishi -2.82e+00 3.79e-03 6.33e-02 honda -2.82e+00 3.79e-03 6.33e-02 volkswagen -2.74e+00 3.79e-03 5.85e-02 peugot -2.65e+00 3.79e-03 5.36e-02 volvo -2.65e+00 3.79e-03 5.36e-02 dodge -2.45e+00 3.79e-03 4.39e-02 mercedes-benz -2.33e+00 3.79e-03 3.91e-02 plymouth -2.20e+00 3.79e-03 3.42e-02 audi -2.19e+00 3.82e-03 3.42e-02 bmw 2.16e+00 3.37e-01 3.91e-02 saab -2.05e+00 3.79e-03 2.93e-02 porsche -1.87e+00 3.79e-03 2.45e-02 subaru 1.75e+00 3.37e-01 5.85e-02 isuzu -1.64e+00 3.79e-03 1.96e-02 alfa-romero -1.36e+00 3.79e-03 1.48e-02 chevrolet -1.36e+00 3.79e-03 1.48e-02 jaguar -1.36e+00 3.79e-03 1.48e-02 renault -9.63e-01 3.79e-03 9.93e-03 mercury -2.92e-01 3.79e-03 5.08e-03 toyota 9.17e-02 1.71e-01 1.56e-01 mazda 5.19e-02 8.71e-02 8.27e-02 012 038 R SNcn Log compression-ratio 1.033 ( 2.16e+00 6.69e-02) 1.57e+00 ( 2.27e+00 2.81e-01) 007 033 R SNcn Log length 0.989 ( 5.16e+00 1.64e-02) 2.13e-02 ( 5.16e+00 7.06e-02) 003 029 R SNcm Log horse-power 0.971 ( 4.69e+00 8.58e-02) 1.19e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 008 034 R SNcn Log width 0.896 ( 4.17e+00 8.90e-03) 1.52e+00 ( 4.19e+00 3.15e-02) 002 028 R SNcm Log stroke 0.709 ( 1.06e+00 7.66e-02) 1.50e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 0.676 ( 7.82e+00 6.41e-02) 9.44e-02 ( 7.83e+00 1.97e-01) 009 035 R SNcn Log height 0.648 ( 3.98e+00 1.53e-02) 1.18e-01 ( 3.98e+00 4.54e-02) 015 017 D SM fuel-system 0.574 2bbl -3.42e+00 1.05e-02 3.21e-01 idi -2.24e+00 1.04e-02 9.77e-02 1bbl -1.65e+00 1.04e-02 5.40e-02 spdi -1.45e+00 1.04e-02 4.43e-02 mpfi 7.07e-01 9.27e-01 4.57e-01 spfi 6.47e-01 1.04e-02 5.46e-03 mfi 6.46e-01 1.04e-02 5.46e-03 4bbl -3.76e-01 1.04e-02 1.52e-02 017 014 D SM engine-type 0.474 ohcv -1.68e+00 1.19e-02 6.38e-02 l -1.60e+00 1.19e-02 5.89e-02 ohcf 1.55e+00 3.45e-01 7.35e-02 dohc 1.11e+00 1.79e-01 5.89e-02 dohcv 7.64e-01 1.19e-02 5.55e-03 rotor -5.24e-01 1.19e-02 2.01e-02 ohc -5.17e-01 4.29e-01 7.19e-01 001 027 R SNcm Log bore 0.391 ( 1.25e+00 4.79e-02) 9.94e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 019 007 D SM drive-wheels 0.376 4wd 1.46e+00 1.95e-01 4.53e-02 fwd -1.10e+00 1.94e-01 5.84e-01 rwd 5.01e-01 6.11e-01 3.71e-01 000 026 R SNcm Log normalized-loses 0.325 ( 4.92e+00 3.06e-01) 5.23e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.83e-01 Prob-*k is known 8.00e-01 006 032 R SNcn Log wheel-base 0.293 ( 4.59e+00 3.05e-02) 5.61e-02 ( 4.59e+00 5.89e-02) 005 031 R SNcm Log price 0.260 ( 9.51e+00 2.97e-01) 5.31e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 004 030 R SNcm Log peak-rpm 0.193 ( 8.56e+00 1.39e-01) 1.93e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 020 006 D SM body-style 0.187 hatchback -1.23e+00 1.00e-01 3.41e-01 hardtop -8.70e-01 1.67e-02 3.98e-02 convertible -5.91e-01 1.67e-02 3.01e-02 wagon 4.03e-01 1.83e-01 1.22e-01 sedan 3.81e-01 6.84e-01 4.67e-01 011 037 R SNcn Log engine-size 0.182 ( 4.76e+00 1.74e-01) 2.07e-01 ( 4.80e+00 2.82e-01) 013 039 R SNcn Log city-mpg 0.096 ( 3.15e+00 1.88e-01) 2.56e-01 ( 3.19e+00 2.56e-01) 014 040 R SNcn Log highway-mpg 0.054 ( 3.35e+00 1.88e-01) 2.79e-01 ( 3.40e+00 2.23e-01) 016 015 D SM num-of-cylinders 0.050 five -1.51e+00 1.19e-02 5.41e-02 three 7.64e-01 1.19e-02 5.55e-03 twelve 7.64e-01 1.19e-02 5.55e-03 eight -7.40e-01 1.19e-02 2.50e-02 two -5.24e-01 1.19e-02 2.01e-02 six 4.21e-01 1.79e-01 1.17e-01 four -1.40e-02 7.62e-01 7.73e-01 023 003 D SM fuel-type 0.023 diesel -8.70e-01 4.17e-02 9.95e-02 gas 6.22e-02 9.58e-01 9.00e-01 021 005 D SM num-of-doors 0.018 ? 8.97e-01 2.78e-02 1.13e-02 two -1.83e-01 3.61e-01 4.34e-01 four 9.61e-02 6.11e-01 5.55e-01 018 008 D SM engine-location 0.013 rear 8.97e-01 4.17e-02 1.70e-02 front -2.54e-02 9.58e-01 9.83e-01 022 004 D SM aspiration 0.002 turbo 1.35e-01 2.08e-01 1.82e-01 std -3.28e-02 7.92e-01 8.18e-01 # # DATA_CLASS 9 #CLASS 9 - weight 10 normalized weight 0.049 relative strength 1.86e-06 ******* # class cross entropy w.r.t. global class 1.76e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 000 026 R SNcm Log normalized-loses 1.791 ( 5.29e+00 6.55e-02) 8.11e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.18e-01 Prob-*k is known 8.00e-01 003 029 R SNcm Log horse-power 1.630 ( 5.15e+00 1.81e-01) 3.13e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 005 031 R SNcm Log price 1.502 ( 9.78e+00 9.88e-02) 4.40e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 8.16e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 1.344 ( 8.01e+00 5.07e-02) 3.70e+00 ( 7.83e+00 1.97e-01) 024 002 D SM make 1.137 mazda -3.00e+00 4.13e-03 8.27e-02 mercury 2.93e+00 9.50e-02 5.08e-03 honda -2.73e+00 4.13e-03 6.33e-02 mitsubishi -2.73e+00 4.13e-03 6.33e-02 subaru -2.65e+00 4.13e-03 5.85e-02 volkswagen -2.65e+00 4.13e-03 5.85e-02 peugot -2.56e+00 4.13e-03 5.36e-02 volvo -2.56e+00 4.13e-03 5.36e-02 dodge -2.36e+00 4.13e-03 4.39e-02 bmw -2.25e+00 4.13e-03 3.91e-02 mercedes-benz -2.25e+00 4.13e-03 3.91e-02 plymouth -2.11e+00 4.13e-03 3.42e-02 porsche 2.03e+00 1.86e-01 2.45e-02 saab -1.96e+00 4.13e-03 2.93e-02 alfa-romero 1.86e+00 9.50e-02 1.48e-02 isuzu -1.56e+00 4.13e-03 1.96e-02 chevrolet -1.27e+00 4.13e-03 1.48e-02 jaguar -1.27e+00 4.13e-03 1.48e-02 nissan 1.15e+00 2.77e-01 8.76e-02 audi 1.02e+00 9.50e-02 3.42e-02 renault -8.77e-01 4.13e-03 9.93e-03 toyota 1.79e-01 1.86e-01 1.56e-01 011 037 R SNcn Log engine-size 1.008 ( 5.10e+00 1.23e-01) 2.48e+00 ( 4.80e+00 2.82e-01) 017 014 D SM engine-type 0.894 dohcv 2.93e+00 1.04e-01 5.55e-03 ohcv 1.78e+00 3.77e-01 6.38e-02 ohcf -1.73e+00 1.30e-02 7.35e-02 l -1.51e+00 1.30e-02 5.89e-02 dohc 1.20e+00 1.95e-01 5.89e-02 ohc -9.23e-01 2.86e-01 7.19e-01 rotor -4.37e-01 1.30e-02 2.01e-02 016 015 D SM num-of-cylinders 0.836 six 1.56e+00 5.58e-01 1.17e-01 eight 1.43e+00 1.04e-01 2.50e-02 four -1.38e+00 1.95e-01 7.73e-01 three 8.51e-01 1.30e-02 5.55e-03 twelve 8.51e-01 1.30e-02 5.55e-03 five 6.53e-01 1.04e-01 5.41e-02 two -4.37e-01 1.30e-02 2.01e-02 020 006 D SM body-style 0.811 sedan -3.25e+00 1.82e-02 4.67e-01 wagon -1.91e+00 1.82e-02 1.22e-01 hatchback 1.00e+00 9.27e-01 3.41e-01 hardtop -7.84e-01 1.82e-02 3.98e-02 convertible -5.04e-01 1.82e-02 3.01e-02 019 007 D SM drive-wheels 0.733 fwd -2.96e+00 3.03e-02 5.84e-01 4wd 9.84e-01 1.21e-01 4.53e-02 rwd 8.28e-01 8.48e-01 3.71e-01 013 039 R SNcn Log city-mpg 0.691 ( 2.91e+00 1.87e-01) 1.51e+00 ( 3.19e+00 2.56e-01) 012 038 R SNcn Log compression-ratio 0.676 ( 2.17e+00 9.79e-02) 1.01e+00 ( 2.27e+00 2.81e-01) 021 005 D SM num-of-doors 0.668 four -2.91e+00 3.03e-02 5.55e-01 ? 9.84e-01 3.03e-02 1.13e-02 two 7.73e-01 9.39e-01 4.34e-01 009 035 R SNcn Log height 0.655 ( 3.94e+00 2.87e-02) 1.58e+00 ( 3.98e+00 4.54e-02) 008 034 R SNcn Log width 0.654 ( 4.22e+00 2.18e-02) 1.51e+00 ( 4.19e+00 3.15e-02) 002 028 R SNcm Log stroke 0.648 ( 1.18e+00 3.50e-02) 2.70e-01 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 015 017 D SM fuel-system 0.562 2bbl -3.34e+00 1.14e-02 3.21e-01 idi -2.15e+00 1.14e-02 9.77e-02 1bbl -1.56e+00 1.14e-02 5.40e-02 spdi -1.36e+00 1.14e-02 4.43e-02 mfi 7.33e-01 1.14e-02 5.46e-03 spfi 7.33e-01 1.14e-02 5.46e-03 mpfi 7.00e-01 9.20e-01 4.57e-01 4bbl -2.89e-01 1.14e-02 1.52e-02 007 033 R SNcn Log length 0.533 ( 5.17e+00 2.75e-02) 4.65e-01 ( 5.16e+00 7.06e-02) 014 040 R SNcn Log highway-mpg 0.395 ( 3.21e+00 1.87e-01) 1.02e+00 ( 3.40e+00 2.23e-01) 001 027 R SNcm Log bore 0.136 ( 1.23e+00 1.05e-01) 2.52e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 006 032 R SNcn Log wheel-base 0.100 ( 4.58e+00 4.25e-02) 2.28e-01 ( 4.59e+00 5.89e-02) 004 030 R SNcm Log peak-rpm 0.061 ( 8.57e+00 9.44e-02) 3.39e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 022 004 D SM aspiration 0.054 turbo 5.58e-01 3.18e-01 1.82e-01 std -1.82e-01 6.82e-01 8.18e-01 023 003 D SM fuel-type 0.020 diesel -7.84e-01 4.55e-02 9.95e-02 gas 5.83e-02 9.55e-01 9.00e-01 018 008 D SM engine-location 0.017 rear 9.84e-01 4.55e-02 1.70e-02 front -2.94e-02 9.55e-01 9.83e-01 # # DATA_CLASS 10 #CLASS 10 - weight 10 normalized weight 0.049 relative strength 5.15e-05 ******* # class cross entropy w.r.t. global class 2.25e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 012 038 R SNcn Log compression-ratio 6.920 ( 3.12e+00 1.66e-02) 5.13e+01 ( 2.27e+00 2.81e-01) 023 003 D SM fuel-type 2.022 gas -2.99e+00 4.55e-02 9.00e-01 diesel 2.26e+00 9.55e-01 9.95e-02 015 017 D SM fuel-system 1.965 mpfi -3.69e+00 1.14e-02 4.57e-01 2bbl -3.34e+00 1.14e-02 3.21e-01 idi 2.24e+00 9.20e-01 9.77e-02 1bbl -1.56e+00 1.14e-02 5.40e-02 spdi -1.36e+00 1.14e-02 4.43e-02 mfi 7.33e-01 1.14e-02 5.46e-03 spfi 7.33e-01 1.14e-02 5.46e-03 4bbl -2.89e-01 1.14e-02 1.52e-02 003 029 R SNcm Log horse-power 1.543 ( 4.11e+00 1.21e-01) 3.88e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 002 028 R SNcm Log stroke 1.182 ( 1.23e+00 2.25e-02) 2.34e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 013 039 R SNcn Log city-mpg 1.151 ( 3.57e+00 1.87e-01) 2.00e+00 ( 3.19e+00 2.56e-01) 014 040 R SNcn Log highway-mpg 1.081 ( 3.73e+00 1.87e-01) 1.73e+00 ( 3.40e+00 2.23e-01) 024 002 D SM make 0.852 honda -2.73e+00 4.13e-03 6.33e-02 mitsubishi -2.73e+00 4.13e-03 6.33e-02 subaru -2.65e+00 4.13e-03 5.85e-02 peugot -2.56e+00 4.13e-03 5.36e-02 volvo -2.56e+00 4.13e-03 5.36e-02 dodge -2.36e+00 4.13e-03 4.39e-02 bmw -2.25e+00 4.13e-03 3.91e-02 mercedes-benz -2.25e+00 4.13e-03 3.91e-02 audi -2.11e+00 4.13e-03 3.42e-02 plymouth -2.11e+00 4.13e-03 3.42e-02 saab -1.96e+00 4.13e-03 2.93e-02 volkswagen 1.84e+00 3.68e-01 5.85e-02 porsche -1.78e+00 4.13e-03 2.45e-02 isuzu -1.56e+00 4.13e-03 1.96e-02 alfa-romero -1.27e+00 4.13e-03 1.48e-02 chevrolet -1.27e+00 4.13e-03 1.48e-02 jaguar -1.27e+00 4.13e-03 1.48e-02 renault -8.77e-01 4.13e-03 9.93e-03 mazda 8.10e-01 1.86e-01 8.27e-02 toyota 5.76e-01 2.77e-01 1.56e-01 mercury -2.06e-01 4.13e-03 5.08e-03 nissan 8.15e-02 9.50e-02 8.76e-02 011 037 R SNcn Log engine-size 0.699 ( 4.67e+00 1.00e-01) 1.26e+00 ( 4.80e+00 2.82e-01) 004 030 R SNcm Log peak-rpm 0.618 ( 8.43e+00 9.44e-02) 1.16e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 009 035 R SNcn Log height 0.597 ( 4.00e+00 1.80e-02) 1.08e+00 ( 3.98e+00 4.54e-02) 010 036 R SNcn Log curb-weight 0.596 ( 7.76e+00 7.43e-02) 8.36e-01 ( 7.83e+00 1.97e-01) 007 033 R SNcn Log length 0.550 ( 5.15e+00 2.67e-02) 3.29e-01 ( 5.16e+00 7.06e-02) 008 034 R SNcn Log width 0.408 ( 4.18e+00 1.43e-02) 3.94e-01 ( 4.19e+00 3.15e-02) 001 027 R SNcm Log bore 0.364 ( 1.15e+00 5.02e-02) 9.60e-01 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 005 031 R SNcm Log price 0.319 ( 9.18e+00 2.74e-01) 6.08e-01 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 006 032 R SNcn Log wheel-base 0.314 ( 4.59e+00 2.96e-02) 6.79e-02 ( 4.59e+00 5.89e-02) 020 006 D SM body-style 0.305 wagon -1.91e+00 1.82e-02 1.22e-01 hatchback -1.14e+00 1.09e-01 3.41e-01 hardtop -7.84e-01 1.82e-02 3.98e-02 sedan 5.83e-01 8.36e-01 4.67e-01 convertible -5.04e-01 1.82e-02 3.01e-02 000 026 R SNcm Log normalized-loses 0.273 ( 4.56e+00 1.97e-01) 1.01e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 7.09e-01 Prob-*k is known 8.00e-01 021 005 D SM num-of-doors 0.258 ? 2.37e+00 1.21e-01 1.13e-02 two -7.15e-01 2.12e-01 4.34e-01 four 1.83e-01 6.67e-01 5.55e-01 019 007 D SM drive-wheels 0.169 rwd -1.12e+00 1.21e-01 3.71e-01 4wd -4.02e-01 3.03e-02 4.53e-02 fwd 3.73e-01 8.48e-01 5.84e-01 017 014 D SM engine-type 0.152 ohcf -1.73e+00 1.30e-02 7.35e-02 ohcv -1.59e+00 1.30e-02 6.38e-02 dohc -1.51e+00 1.30e-02 5.89e-02 l -1.51e+00 1.30e-02 5.89e-02 dohcv 8.51e-01 1.30e-02 5.55e-03 rotor -4.37e-01 1.30e-02 2.01e-02 ohc 2.49e-01 9.22e-01 7.19e-01 016 015 D SM num-of-cylinders 0.124 six -2.20e+00 1.30e-02 1.17e-01 five -1.43e+00 1.30e-02 5.41e-02 three 8.51e-01 1.30e-02 5.55e-03 twelve 8.51e-01 1.30e-02 5.55e-03 eight -6.54e-01 1.30e-02 2.50e-02 two -4.37e-01 1.30e-02 2.01e-02 four 1.77e-01 9.22e-01 7.73e-01 022 004 D SM aspiration 0.054 turbo 5.58e-01 3.18e-01 1.82e-01 std -1.82e-01 6.82e-01 8.18e-01 018 008 D SM engine-location 0.017 rear 9.84e-01 4.55e-02 1.70e-02 front -2.94e-02 9.55e-01 9.83e-01 # # DATA_CLASS 11 #CLASS 11 - weight 9 normalized weight 0.044 relative strength 3.58e-05 ******* # class cross entropy w.r.t. global class 2.22e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 007 033 R SNcn Log length 2.201 ( 5.02e+00 4.09e-02) 3.44e+00 ( 5.16e+00 7.06e-02) 001 027 R SNcm Log bore 2.074 ( 1.08e+00 1.81e-02) 6.52e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 1.817 ( 7.51e+00 7.68e-02) 4.14e+00 ( 7.83e+00 1.97e-01) 005 031 R SNcm Log price 1.810 ( 8.72e+00 1.13e-01) 5.56e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 012 038 R SNcn Log compression-ratio 1.616 ( 2.23e+00 3.44e-02) 1.07e+00 ( 2.27e+00 2.81e-01) 024 002 D SM make 1.475 toyota -3.45e+00 4.94e-03 1.56e-01 nissan -2.96e+00 4.54e-03 8.76e-02 mitsubishi -2.64e+00 4.54e-03 6.33e-02 subaru -2.56e+00 4.54e-03 5.85e-02 volkswagen -2.56e+00 4.54e-03 5.85e-02 peugot -2.47e+00 4.54e-03 5.36e-02 volvo -2.47e+00 4.54e-03 5.36e-02 dodge -2.27e+00 4.54e-03 4.39e-02 bmw -2.15e+00 4.54e-03 3.91e-02 mercedes-benz -2.15e+00 4.54e-03 3.91e-02 honda 2.08e+00 5.05e-01 6.33e-02 audi -2.02e+00 4.54e-03 3.42e-02 plymouth -2.02e+00 4.54e-03 3.42e-02 chevrolet 1.96e+00 1.05e-01 1.48e-02 saab -1.87e+00 4.54e-03 2.93e-02 porsche -1.69e+00 4.54e-03 2.45e-02 isuzu -1.47e+00 4.54e-03 1.96e-02 mazda 1.30e+00 3.04e-01 8.27e-02 alfa-romero -1.18e+00 4.54e-03 1.48e-02 jaguar -1.18e+00 4.54e-03 1.48e-02 renault -7.83e-01 4.54e-03 9.93e-03 mercury -1.12e-01 4.54e-03 5.08e-03 015 017 D SM fuel-system 1.189 mpfi -3.60e+00 1.25e-02 4.57e-01 1bbl 2.25e+00 5.13e-01 5.40e-02 idi -2.06e+00 1.25e-02 9.77e-02 spdi -1.27e+00 1.25e-02 4.43e-02 mfi 8.26e-01 1.25e-02 5.46e-03 spfi 8.26e-01 1.25e-02 5.46e-03 2bbl 2.50e-01 4.12e-01 3.21e-01 4bbl -1.96e-01 1.25e-02 1.52e-02 011 037 R SNcn Log engine-size 1.164 ( 4.46e+00 1.24e-01) 2.78e+00 ( 4.80e+00 2.82e-01) 003 029 R SNcm Log horse-power 1.164 ( 4.19e+00 1.38e-01) 2.88e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 006 032 R SNcn Log wheel-base 1.115 ( 4.51e+00 3.13e-02) 2.44e+00 ( 4.59e+00 5.89e-02) 013 039 R SNcn Log city-mpg 0.992 ( 3.54e+00 1.86e-01) 1.85e+00 ( 3.19e+00 2.56e-01) 008 034 R SNcn Log width 0.825 ( 4.15e+00 1.81e-02) 1.92e+00 ( 4.19e+00 3.15e-02) 020 006 D SM body-style 0.787 sedan -3.09e+00 2.13e-02 4.67e-01 wagon -1.81e+00 2.00e-02 1.22e-01 hatchback 9.92e-01 9.19e-01 3.41e-01 hardtop -6.90e-01 2.00e-02 3.98e-02 convertible -4.11e-01 2.00e-02 3.01e-02 014 040 R SNcn Log highway-mpg 0.781 ( 3.67e+00 1.86e-01) 1.47e+00 ( 3.40e+00 2.23e-01) 021 005 D SM num-of-doors 0.653 four -2.77e+00 3.46e-02 5.55e-01 ? 1.08e+00 3.33e-02 1.13e-02 two 7.65e-01 9.32e-01 4.34e-01 000 026 R SNcm Log normalized-loses 0.631 ( 4.71e+00 1.16e-01) 4.38e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 9.80e-01 Prob-*k is known 8.00e-01 002 028 R SNcm Log stroke 0.429 ( 1.18e+00 4.50e-02) 5.25e-03 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 009 035 R SNcn Log height 0.412 ( 3.97e+00 2.20e-02) 7.76e-01 ( 3.98e+00 4.54e-02) 019 007 D SM drive-wheels 0.347 rwd -2.41e+00 3.33e-02 3.71e-01 fwd 4.69e-01 9.33e-01 5.84e-01 4wd -3.09e-01 3.33e-02 4.53e-02 016 015 D SM num-of-cylinders 0.340 three 3.02e+00 1.14e-01 5.55e-03 six -2.11e+00 1.43e-02 1.17e-01 five -1.33e+00 1.43e-02 5.41e-02 twelve 9.44e-01 1.43e-02 5.55e-03 eight -5.60e-01 1.43e-02 2.50e-02 two -3.44e-01 1.43e-02 2.01e-02 four 5.31e-02 8.15e-01 7.73e-01 004 030 R SNcm Log peak-rpm 0.128 ( 8.59e+00 9.39e-02) 5.15e-01 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 017 014 D SM engine-type 0.120 ohcf -1.64e+00 1.43e-02 7.35e-02 ohcv -1.50e+00 1.43e-02 6.38e-02 dohc -1.42e+00 1.43e-02 5.89e-02 dohcv 9.44e-01 1.43e-02 5.55e-03 l 6.60e-01 1.14e-01 5.89e-02 rotor -3.44e-01 1.43e-02 2.01e-02 ohc 1.25e-01 8.15e-01 7.19e-01 022 004 D SM aspiration 0.078 turbo -1.29e+00 4.99e-02 1.82e-01 std 1.50e-01 9.50e-01 8.18e-01 018 008 D SM engine-location 0.021 rear 1.08e+00 4.99e-02 1.70e-02 front -3.40e-02 9.50e-01 9.83e-01 023 003 D SM fuel-type 0.017 diesel -6.90e-01 4.99e-02 9.95e-02 gas 5.36e-02 9.50e-01 9.00e-01 # # DATA_CLASS 12 #CLASS 12 - weight 9 normalized weight 0.044 relative strength 9.06e-05 ******* # class cross entropy w.r.t. global class 2.56e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 007 033 R SNcn Log length 2.278 ( 5.13e+00 4.74e-03) 5.78e+00 ( 5.16e+00 7.06e-02) 012 038 R SNcn Log compression-ratio 2.180 ( 2.23e+00 1.94e-02) 1.69e+00 ( 2.27e+00 2.81e-01) 017 014 D SM engine-type 1.902 ohc -3.92e+00 1.43e-02 7.19e-01 rotor 3.03e+00 4.14e-01 2.01e-02 ohcv -1.50e+00 1.43e-02 6.38e-02 ohcf 1.45e+00 3.14e-01 7.35e-02 l -1.42e+00 1.43e-02 5.89e-02 dohc 1.29e+00 2.14e-01 5.89e-02 dohcv 9.46e-01 1.43e-02 5.55e-03 002 028 R SNcm Log stroke 1.850 ( 1.03e+00 3.67e-02) 3.87e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 5.98e-01 Prob-*k is known 9.80e-01 024 002 D SM make 1.770 toyota -3.53e+00 4.55e-03 1.56e-01 nissan -2.96e+00 4.55e-03 8.76e-02 honda -2.63e+00 4.55e-03 6.33e-02 mitsubishi -2.63e+00 4.55e-03 6.33e-02 alfa-romero 2.63e+00 2.05e-01 1.48e-02 subaru -2.55e+00 4.55e-03 5.85e-02 volkswagen -2.55e+00 4.55e-03 5.85e-02 porsche 2.52e+00 3.05e-01 2.45e-02 peugot -2.47e+00 4.55e-03 5.36e-02 volvo -2.47e+00 4.55e-03 5.36e-02 dodge -2.27e+00 4.55e-03 4.39e-02 bmw -2.15e+00 4.55e-03 3.91e-02 mercedes-benz -2.15e+00 4.55e-03 3.91e-02 audi -2.02e+00 4.55e-03 3.42e-02 plymouth -2.02e+00 4.55e-03 3.42e-02 saab -1.87e+00 4.55e-03 2.93e-02 mazda 1.59e+00 4.05e-01 8.27e-02 isuzu -1.46e+00 4.55e-03 1.96e-02 chevrolet -1.18e+00 4.55e-03 1.48e-02 jaguar -1.18e+00 4.55e-03 1.48e-02 renault -7.81e-01 4.55e-03 9.93e-03 mercury -1.10e-01 4.55e-03 5.08e-03 001 027 R SNcm Log bore 1.542 ( 1.29e+00 3.48e-02) 2.57e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 5.98e-01 Prob-*k is known 9.80e-01 009 035 R SNcn Log height 1.533 ( 3.91e+00 2.10e-02) 3.29e+00 ( 3.98e+00 4.54e-02) 016 015 D SM num-of-cylinders 1.289 two 3.03e+00 4.14e-01 2.01e-02 five -1.33e+00 1.43e-02 5.41e-02 four -1.28e+00 2.14e-01 7.73e-01 six 9.86e-01 3.14e-01 1.17e-01 three 9.46e-01 1.43e-02 5.55e-03 twelve 9.46e-01 1.43e-02 5.55e-03 eight -5.58e-01 1.43e-02 2.50e-02 020 006 D SM body-style 1.121 sedan -3.15e+00 2.00e-02 4.67e-01 convertible 2.36e+00 3.20e-01 3.01e-02 wagon -1.81e+00 2.00e-02 1.22e-01 hardtop 1.71e+00 2.20e-01 3.98e-02 hatchback 2.09e-01 4.20e-01 3.41e-01 000 026 R SNcm Log normalized-loses 1.060 ( 5.01e+00 4.72e-02) 5.23e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 4.80e-01 Prob-*k is known 8.00e-01 015 017 D SM fuel-system 1.045 2bbl -3.25e+00 1.25e-02 3.21e-01 4bbl 3.03e+00 3.12e-01 1.52e-02 idi -2.06e+00 1.25e-02 9.77e-02 1bbl -1.46e+00 1.25e-02 5.40e-02 spdi -1.27e+00 1.25e-02 4.43e-02 mfi 8.28e-01 1.25e-02 5.46e-03 spfi 8.28e-01 1.25e-02 5.46e-03 mpfi 2.93e-01 6.13e-01 4.57e-01 006 032 R SNcn Log wheel-base 0.984 ( 4.52e+00 3.17e-02) 2.23e+00 ( 4.59e+00 5.89e-02) 013 039 R SNcn Log city-mpg 0.859 ( 2.88e+00 1.86e-01) 1.71e+00 ( 3.19e+00 2.56e-01) 008 034 R SNcn Log width 0.855 ( 4.18e+00 9.07e-03) 1.29e+00 ( 4.19e+00 3.15e-02) 018 008 D SM engine-location 0.790 rear 3.03e+00 3.50e-01 1.70e-02 front -4.14e-01 6.50e-01 9.83e-01 019 007 D SM drive-wheels 0.757 fwd -2.86e+00 3.33e-02 5.84e-01 rwd 9.24e-01 9.33e-01 3.71e-01 4wd -3.07e-01 3.33e-02 4.53e-02 010 036 R SNcn Log curb-weight 0.751 ( 7.85e+00 5.93e-02) 3.39e-01 ( 7.83e+00 1.97e-01) 004 030 R SNcm Log peak-rpm 0.684 ( 8.65e+00 9.39e-02) 1.22e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 021 005 D SM num-of-doors 0.658 four -2.81e+00 3.33e-02 5.55e-01 ? 1.08e+00 3.33e-02 1.13e-02 two 7.67e-01 9.33e-01 4.34e-01 005 031 R SNcm Log price 0.466 ( 9.82e+00 4.33e-01) 1.08e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 003 029 R SNcm Log horse-power 0.456 ( 4.90e+00 2.96e-01) 1.08e+00 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 014 040 R SNcn Log highway-mpg 0.433 ( 3.20e+00 1.86e-01) 1.08e+00 ( 3.40e+00 2.23e-01) 011 037 R SNcn Log engine-size 0.219 ( 4.74e+00 4.15e-01) 1.40e-01 ( 4.80e+00 2.82e-01) 022 004 D SM aspiration 0.078 turbo -1.29e+00 5.00e-02 1.82e-01 std 1.50e-01 9.50e-01 8.18e-01 023 003 D SM fuel-type 0.016 diesel -6.88e-01 5.00e-02 9.95e-02 gas 5.35e-02 9.50e-01 9.00e-01 # # DATA_CLASS 13 #CLASS 13 - weight 8 normalized weight 0.039 relative strength 2.67e-05 ******* # class cross entropy w.r.t. global class 1.72e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 008 034 R SNcn Log width 1.871 ( 4.16e+00 4.71e-03) 6.41e+00 ( 4.19e+00 3.15e-02) 007 033 R SNcn Log length 1.623 ( 5.07e+00 1.83e-02) 4.69e+00 ( 5.16e+00 7.06e-02) 002 028 R SNcm Log stroke 1.607 ( 1.22e+00 1.41e-02) 3.38e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 010 036 R SNcn Log curb-weight 1.543 ( 7.67e+00 3.60e-02) 4.42e+00 ( 7.83e+00 1.97e-01) 011 037 R SNcn Log engine-size 1.374 ( 4.60e+00 5.74e-02) 3.56e+00 ( 4.80e+00 2.82e-01) 006 032 R SNcn Log wheel-base 1.351 ( 4.55e+00 1.22e-02) 3.45e+00 ( 4.59e+00 5.89e-02) 001 027 R SNcm Log bore 1.145 ( 1.11e+00 3.02e-02) 2.90e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 024 002 D SM make 1.029 toyota -3.43e+00 5.06e-03 1.56e-01 nissan -2.85e+00 5.06e-03 8.76e-02 mazda -2.79e+00 5.06e-03 8.27e-02 subaru -2.45e+00 5.06e-03 5.85e-02 peugot -2.36e+00 5.06e-03 5.36e-02 volvo -2.36e+00 5.06e-03 5.36e-02 bmw -2.04e+00 5.06e-03 3.91e-02 mercedes-benz -2.04e+00 5.06e-03 3.91e-02 audi -1.91e+00 5.06e-03 3.42e-02 saab -1.76e+00 5.06e-03 2.93e-02 dodge 1.65e+00 2.28e-01 4.39e-02 porsche -1.58e+00 5.06e-03 2.45e-02 volkswagen 1.36e+00 2.28e-01 5.85e-02 isuzu -1.36e+00 5.06e-03 1.96e-02 honda 1.27e+00 2.26e-01 6.33e-02 plymouth 1.22e+00 1.16e-01 3.42e-02 alfa-romero -1.07e+00 5.06e-03 1.48e-02 chevrolet -1.07e+00 5.06e-03 1.48e-02 jaguar -1.07e+00 5.06e-03 1.48e-02 renault -6.74e-01 5.06e-03 9.93e-03 mitsubishi 6.08e-01 1.16e-01 6.33e-02 mercury -3.29e-03 5.06e-03 5.08e-03 012 038 R SNcn Log compression-ratio 1.012 ( 2.10e+00 7.61e-02) 2.15e+00 ( 2.27e+00 2.81e-01) 005 031 R SNcm Log price 0.985 ( 9.04e+00 1.44e-01) 2.14e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.80e-01 015 017 D SM fuel-system 0.698 2bbl -3.14e+00 1.39e-02 3.21e-01 idi -1.95e+00 1.39e-02 9.77e-02 spdi 1.67e+00 2.36e-01 4.43e-02 1bbl 1.47e+00 2.35e-01 5.40e-02 mfi 9.35e-01 1.39e-02 5.46e-03 spfi 9.35e-01 1.39e-02 5.46e-03 4bbl -8.67e-02 1.39e-02 1.52e-02 mpfi 4.63e-03 4.59e-01 4.57e-01 003 029 R SNcm Log horse-power 0.686 ( 4.52e+00 1.14e-01) 5.59e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 004 030 R SNcm Log peak-rpm 0.477 ( 8.63e+00 9.33e-02) 1.02e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.99e-01 Prob-*k is known 9.90e-01 021 005 D SM num-of-doors 0.370 ? 2.57e+00 1.48e-01 1.13e-02 four -7.66e-01 2.58e-01 5.55e-01 two 3.14e-01 5.94e-01 4.34e-01 019 007 D SM drive-wheels 0.334 rwd -2.30e+00 3.71e-02 3.71e-01 fwd 4.61e-01 9.26e-01 5.84e-01 4wd -2.00e-01 3.71e-02 4.53e-02 022 004 D SM aspiration 0.260 turbo 1.01e+00 5.01e-01 1.82e-01 std -4.94e-01 4.99e-01 8.18e-01 020 006 D SM body-style 0.187 convertible 1.49e+00 1.34e-01 3.01e-02 sedan -6.52e-01 2.43e-01 4.67e-01 hardtop -5.81e-01 2.23e-02 3.98e-02 hatchback 3.16e-01 4.67e-01 3.41e-01 wagon 8.76e-02 1.34e-01 1.22e-01 017 014 D SM engine-type 0.132 ohcf -1.53e+00 1.59e-02 7.35e-02 ohcv -1.39e+00 1.59e-02 6.38e-02 dohc -1.31e+00 1.59e-02 5.89e-02 l -1.31e+00 1.59e-02 5.89e-02 dohcv 1.05e+00 1.59e-02 5.55e-03 rotor -2.35e-01 1.59e-02 2.01e-02 ohc 2.29e-01 9.05e-01 7.19e-01 000 026 R SNcm Log normalized-loses 0.115 ( 4.89e+00 3.24e-01) 3.81e-01 ( 4.76e+00 2.82e-01) Prob-jk is known 8.66e-01 Prob-*k is known 8.00e-01 016 015 D SM num-of-cylinders 0.114 six -2.00e+00 1.59e-02 1.17e-01 five -1.22e+00 1.59e-02 5.41e-02 three 1.05e+00 1.59e-02 5.55e-03 twelve 1.05e+00 1.59e-02 5.55e-03 eight -4.51e-01 1.59e-02 2.50e-02 two -2.35e-01 1.59e-02 2.01e-02 four 1.58e-01 9.05e-01 7.73e-01 013 039 R SNcn Log city-mpg 0.097 ( 3.23e+00 1.85e-01) 2.08e-01 ( 3.19e+00 2.56e-01) 009 035 R SNcn Log height 0.067 ( 3.97e+00 4.77e-02) 3.43e-01 ( 3.98e+00 4.54e-02) 014 040 R SNcn Log highway-mpg 0.037 ( 3.42e+00 1.85e-01) 1.22e-01 ( 3.40e+00 2.23e-01) 018 008 D SM engine-location 0.028 rear 1.19e+00 5.56e-02 1.70e-02 front -4.01e-02 9.44e-01 9.83e-01 023 003 D SM fuel-type 0.013 diesel -5.81e-01 5.56e-02 9.95e-02 gas 4.76e-02 9.44e-01 9.00e-01 # # DATA_CLASS 14 #CLASS 14 - weight 5 normalized weight 0.024 relative strength 3.52e-01 ******* # class cross entropy w.r.t. global class 3.92e+01 ******* # # #DISCRETE ATTRIBUTE (t = D) log( # numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob # t a Prob-*kl) -jkl -*kl # #REAL ATTRIBUTE (t = R) |Mean-jk - # numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev # t a -jk -jk StDev-jk -*k -*k # 012 038 R SNcn Log compression-ratio 6.351 ( 3.04e+00 1.37e-02) 5.68e+01 ( 2.27e+00 2.81e-01) 010 036 R SNcn Log curb-weight 2.390 ( 8.11e+00 3.08e-02) 9.14e+00 ( 7.83e+00 1.97e-01) 006 032 R SNcn Log wheel-base 2.258 ( 4.70e+00 2.54e-02) 4.43e+00 ( 4.59e+00 5.89e-02) 001 027 R SNcm Log bore 2.185 ( 1.31e+00 1.37e-02) 7.95e+00 ( 1.20e+00 8.22e-02) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 017 014 D SM engine-type 2.180 ohc -3.41e+00 2.38e-02 7.19e-01 l 2.68e+00 8.57e-01 5.89e-02 dohcv 1.46e+00 2.38e-02 5.55e-03 ohcf -1.13e+00 2.38e-02 7.35e-02 ohcv -9.86e-01 2.38e-02 6.38e-02 dohc -9.07e-01 2.38e-02 5.89e-02 rotor 1.69e-01 2.38e-02 2.01e-02 008 034 R SNcn Log width 2.155 ( 4.23e+00 4.56e-03) 8.24e+00 ( 4.19e+00 3.15e-02) 004 030 R SNcm Log peak-rpm 2.137 ( 8.33e+00 9.04e-02) 2.25e+00 ( 8.54e+00 9.88e-02) Prob-jk is known 9.98e-01 Prob-*k is known 9.90e-01 024 002 D SM make 2.076 toyota -3.02e+00 7.58e-03 1.56e-01 peugot 2.75e+00 8.41e-01 5.36e-02 nissan -2.45e+00 7.58e-03 8.76e-02 mazda -2.39e+00 7.58e-03 8.27e-02 honda -2.12e+00 7.58e-03 6.33e-02 mitsubishi -2.12e+00 7.58e-03 6.33e-02 subaru -2.04e+00 7.58e-03 5.85e-02 volkswagen -2.04e+00 7.58e-03 5.85e-02 volvo -1.96e+00 7.58e-03 5.36e-02 dodge -1.76e+00 7.58e-03 4.39e-02 bmw -1.64e+00 7.58e-03 3.91e-02 mercedes-benz -1.64e+00 7.58e-03 3.91e-02 audi -1.51e+00 7.58e-03 3.42e-02 plymouth -1.51e+00 7.58e-03 3.42e-02 saab -1.35e+00 7.58e-03 2.93e-02 porsche -1.17e+00 7.58e-03 2.45e-02 isuzu -9.53e-01 7.58e-03 1.96e-02 alfa-romero -6.69e-01 7.58e-03 1.48e-02 chevrolet -6.69e-01 7.58e-03 1.48e-02 jaguar -6.69e-01 7.58e-03 1.48e-02 mercury 4.01e-01 7.58e-03 5.08e-03 renault -2.71e-01 7.58e-03 9.93e-03 002 028 R SNcm Log stroke 1.855 ( 1.26e+00 1.37e-02) 6.08e+00 ( 1.18e+00 1.02e-01) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 023 003 D SM fuel-type 1.837 gas -2.38e+00 8.33e-02 9.00e-01 diesel 2.22e+00 9.17e-01 9.95e-02 009 035 R SNcn Log height 1.771 ( 4.05e+00 1.55e-02) 4.42e+00 ( 3.98e+00 4.54e-02) 015 017 D SM fuel-system 1.758 mpfi -3.09e+00 2.08e-02 4.57e-01 2bbl -2.73e+00 2.08e-02 3.21e-01 idi 2.17e+00 8.54e-01 9.77e-02 mfi 1.34e+00 2.08e-02 5.46e-03 spfi 1.34e+00 2.08e-02 5.46e-03 1bbl -9.53e-01 2.08e-02 5.40e-02 spdi -7.54e-01 2.08e-02 4.43e-02 4bbl 3.17e-01 2.08e-02 1.52e-02 011 037 R SNcn Log engine-size 1.653 ( 5.02e+00 4.54e-02) 4.92e+00 ( 4.80e+00 2.82e-01) 003 029 R SNcm Log horse-power 1.541 ( 4.55e+00 4.54e-02) 6.65e-01 ( 4.58e+00 3.45e-01) Prob-jk is known 9.98e-01 Prob-*k is known 9.90e-01 007 033 R SNcn Log length 1.450 ( 5.25e+00 2.83e-02) 3.45e+00 ( 5.16e+00 7.06e-02) 000 026 R SNcm Log normalized-loses 1.323 ( 5.08e+00 4.54e-02) 6.99e+00 ( 4.76e+00 2.82e-01) Prob-jk is known 6.33e-01 Prob-*k is known 8.00e-01 022 004 D SM aspiration 1.291 std -2.28e+00 8.33e-02 8.18e-01 turbo 1.62e+00 9.17e-01 1.82e-01 005 031 R SNcm Log price 1.213 ( 9.66e+00 1.13e-01) 2.75e+00 ( 9.35e+00 5.00e-01) Prob-jk is known 9.96e-01 Prob-*k is known 9.80e-01 019 007 D SM drive-wheels 0.658 fwd -2.35e+00 5.56e-02 5.84e-01 rwd 8.75e-01 8.89e-01 3.71e-01 4wd 2.04e-01 5.56e-02 4.53e-02 020 006 D SM body-style 0.393 hatchback -2.32e+00 3.33e-02 3.41e-01 wagon 1.10e+00 3.67e-01 1.22e-01 hardtop -1.77e-01 3.33e-02 3.98e-02 sedan 1.33e-01 5.33e-01 4.67e-01 convertible 1.02e-01 3.33e-02 3.01e-02 021 005 D SM num-of-doors 0.393 two -2.05e+00 5.56e-02 4.34e-01 ? 1.59e+00 5.56e-02 1.13e-02 four 4.71e-01 8.89e-01 5.55e-01 013 039 R SNcn Log city-mpg 0.165 ( 3.29e+00 1.79e-01) 5.07e-01 ( 3.19e+00 2.56e-01) 016 015 D SM num-of-cylinders 0.104 six -1.59e+00 2.38e-02 1.17e-01 three 1.46e+00 2.38e-02 5.55e-03 twelve 1.46e+00 2.38e-02 5.55e-03 five -8.21e-01 2.38e-02 5.41e-02 two 1.69e-01 2.38e-02 2.01e-02 four 1.04e-01 8.57e-01 7.73e-01 eight -4.74e-02 2.38e-02 2.50e-02 018 008 D SM engine-location 0.068 rear 1.59e+00 8.33e-02 1.70e-02 front -6.99e-02 9.17e-01 9.83e-01 014 040 R SNcn Log highway-mpg 0.045 ( 3.39e+00 1.79e-01) 8.63e-02 ( 3.40e+00 2.23e-01) autoclass-3.3.6.dfsg.1/autoclass.dmalloc0000755000175000017500000232757711247310756016275 0ustar areareELF4M 4 (444  ' '.00,0, /lib/ld-linux.so.2GNU%?:&0) <68-7%>="9+;13   $ #*'.!,4(2/5Ћ`9#z ~!<  -*0V0,@~"P;K`.xp"4=D7.Y",,ЌRAA> (_01@P1.`+,p83=qqЍS/ .,P+#U0@hq,P`6p.w 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r/usr/ucb/hostname?@@multiplemodulusmulti_multinomial_dmultinomialtypeerrorallowedmissingMM_Dprint_stringmm_d_params_DSparamsnominalorderedcircularn_discreteatt_trans_datan_att_trans_datasingle_multinomialsingle_equivalentmulti_multinomial_smulti_multinomial_choosemultiple_equivalentn_multiple_equivalentMM_Smulti_normal_cnnormalconstantnoMNcnmn_cn_params_DSlocationscalartransformn_realn_scalarsingle_normal_cnsingleSMsm_params_DSSNcnsn_cn_params_DSsingle_normal_cmyesSNcmsn_cm_params_DSn_argstypesn_types ERRORs have occurred! There is NO continuation possible WARNINGs have occurred! Do you want to EXIT - {y/n}? EXIT due to warning messages at user's request Run continues, even though warnings were found define_data_file_format%s %d %snumber_of_attributes%d separator_charcomment_charunknown_tokenERROR[2]: invalid data file format definition name: %s ADVISORY[2]: data_file_format settings: separator_char = '%c', comment_char = '%c', unknown_token = '%c' ERROR[2]: the number of attributes %d should be a positive integer. :read:expand ############ Input Check Concluded ############## log_header ############## Starting Input Check ############### To log file: %s%s During loading of: [1] %s%s, [2] %s%s, [3] %s%s. [Attribute #, value #, and datum # are zero based.] Skipping a reread of the database binaryrbasciirERROR: G_data_file_format "%s" not handled output_warning_msgsWARNING[2]: attribute #%d definition has not been specified -- type set to dummy WARNING[2]: attribute #%d: "%s" to improve sensitivity of classification, reduce range to %d. model_term_not_specifiedWARNING[3]: attribute #%d: "%s" model term type has not been specified and is set to ignore has only one unique value. Change model term type to ignore. ERROR: %s produced %d chars (max number is %d) output_error_msgsERROR[2]: attribute #%d: "%s" output_message_summary ******* SUMMARY OF ALL ERROR AND WARNING MESSAGES ******* %d WARNING message(s) occured: %d due to unspecified attribute type set to dummy %d due to excess type = discrete range(s) %d due to unspecified model term type set to ignore %d due to single valued attribute(s) %d due to model term type expansion %d ERROR message(s) occured: %d due to incomplete datum %d due to invalid type = real attribute value(s) output_messagesignore_model:read ****** Error & Warning Messages from READING Model Index = %d ****** ** Error & Warning Messages from READING & EXPANDING Model Index = %d ** output_db_error_messages ********** Error Messages from Data Base *********** ERROR[1]: in datum #%d, type = real attribute #%d: "%s" has non-number value, %s ERROR[1]: datum #%d is incomplete: it has %d attributes, instead of %d. read_data.db2-binERROR: %s%s, either "%s" is not the correct header string(".db2-bin") or %d is not the correct case length (%d) ERROR: read_data(1): out of memory, malloc returned NULL! ERROR: read_data(2): out of memory, realloc returned NULL! ERROR: read_data(3): out of memory, malloc returned NULL! ERROR: read_data(4): out of memory, realloc returned NULL! WARNING[1]: read_data found *ONLY* %d datum in "%s%s" ERROR[1]: no data read by read_data from "%s%s" readloadedADVISORY[1]: %s %d datum from %s%s process_attribute_definitionsERROR[2]: expecting integer attribute index %d, read %s ADVISORY[2]: read %d attribute defs from %s%s unspecified_attributenildummytrueprocess_attribute_defERROR[2]: expected at least %d items: read %d: %s, %s, %s, %s discretenominalERROR[2]: expected at least %d items: range read %d: %s, %s, %s, %s, %s, %s rangeERROR[2]: expected parameter range, got %s for attribute #%d: "%s" ERROR[2]: value of parameter range read, %s, was not an integer for attribute #%d: "%s" realscalarERROR[2]: expected at least %d items: zero_point rel_error read %d: %s, %s, %s, %s, %s, %s, %s, %s zero_pointERROR[2]: expected parameter zero_point, got %s and %s for attribute #%d: "%s" rel_errorERROR[2]: expected parameter rel_error, got %s and %s for attribute #%d: "%s" ERROR[2]: value of parameter zero_point read, %s, was not a float for attribute #%d: "%s" ERROR[2]: value of parameter rel_error read, %s, was not a float for attribute #%d: "%s" locationERROR[2]: expected at least %d items: error errorERROR[2]: expected parameter error, got %s for attribute #%d: "%s" ERROR[2]: value of parameter error read, %s, was not a float for attribute #%d: "%s" noneERROR[2]: expected sub_type nil or none, got %s for attribute #%d: "%s" ERROR[2]: unknown type/sub_type = %s/%s for attribute #%d: "%s" create_att_DSERROR[2]: length of attribute #%d description "%s" is longer than %d characters ADVISORY: the default translation will be usedADVISORY: no translations were provided for these type = discrete attributes #%d "%s"ERROR: process_translation called with commented code in io-read-data.c ERROR[2]: unknown attribute type: %s Mtranslate_discreteADVISORY[2]: for attribute #%d: "%s" range increased to %d, for value %d -- translator (%d %s). eofcommentERROR[1]: data is of type :vector or :list, but only :line is handled ERROR: read_from_string read a token longer than %d characters ERROR: read_line read a line longer than %d characters ERROR: (find_att_statistics) unknown attribute type: %s find_real_statsADVISORY[2]: attribute #%d: "%s", the error %f is %d%% of the range %f. M{Gz??Y@ADVISORY[1]: real statistics [ min < (mean : variance) < max ] built from input data -- output_real_att_statistics Attribute #%d, "%s": [ %11.4e < (%11.4e : %10.4e) < %11.4e ] ERROR: (output_real_att_statistics) attribute #%d: "%s", the variance exceeds %e MGoutput_created_translationsADVISORY[1]: discrete translations [ (internal external):count ... ] built from input data -- (%d %s):%d ] read_model_filesADVISORY[3]: read %d model def%s from %s%s ERROR[3]: No models read from "%s%s" read_model_doit<...>ERROR[3]: expected 3 items: model_index , read %d: %s %s %s %s model_indexERROR[3]: expected model_index, read %s ERROR[3]: model index read, %s, was not an integer ERROR[3]: expected model index %d, read %d ERROR[3]: num model definition lines read, %s, was not an integer ERROR[3]: expected %d model definition lines, only read %d %s()define_modelsMODEL-%dERROR[3]: No models read from model source: %s generate_attribute_infodefaultERROR[3]: for model index = %d, model term type = %s, is not handled ignoreERROR[3]: for model index = %d, model term type = ignore, attribute number read, %s, was not an integer ERROR[3]: for model index = %d, %d is an invalid model term type = ignore attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = ignore, attempt to re-use attribute %d ignore_modelsinglemultiERROR: No method for generating attribute sets for set_type %s ERROR[3]: for model index = %d, default model term type, %s, specified twice. extend_terms_singleERROR[3]: for model index = %d, model term type = %s, attribute number read, %s, was not an integer ERROR[3]: for model index = %d, model term type = %s, %d is an invalid attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = %s, attempt to re-use attribute %d dummy%dextend_terms_multiextend_default_termsERROR[3]: for model index = %d, ignore is not a valid default model term type ERROR[3]: for model index = %d, model term type = %s, %d is an invalid attribute number, must be less than %d ERROR[3]: for model index = %d, model term type = %s, attempt to re-use attribute %d ADVISORY[3]: the default model term type, %s, will be used for these attributes: #%d: "%s" transformed-attribute-ignoredatt_type_not_specifiedatt_type_is_dummymodel_term_not_specified## all code commented in get_sources_list sourcen_sourceERROR: get_source_list found circular reference ERROR: get_source_list found circularity of attribute source references %d=%d:%s ERROR: expand_clsf_wts(1): out of memory, malloc returned NULL! ERROR: expand_clsf_wts(2): out of memory, malloc returned NULL! save_clsf_seqresultsresults_binresults_tmp_binresults_tmpchkptcheckpoint_bincheckpoint_tmp_bincheckpointcheckpoint_tmpERROR: save file extension type %s not handled wbwrrm %smv %s %swrite_clsf_seq# ordered sequence of clsf_DS's: 0 -> %d # clsf_DS %d: log_a_x_h = %.7e ac_version %s write_clsf_DSclsf_DS %d log_p_x_h_pi_theta, log_a_x_h %.7e %.7e database_DS_ptr num_models %d model_DS_ptr %d n_classes min_class_wt %.7f chkpt_DS accumulated_try_time, current_try_j_in, current_cycle %d %d %d write_database_DSdatabase_DS data_file, header_file %s %s n_data, n_atts, input_n_atts write_att_DSatt_DS %d type, subtype, dscrp %s %s "%s" realreal_stats_DS count, max, min, mean, var %d %.7e %.7e %.7e %.7e discretediscrete_stats_DS range, n_observed %d %d dummydummy_stats_DS ERROR: att_info->type %s not handled n_props, range, zero_point, n_trans %d %d %f %d translations_DS %d %s props_DS int%s %s %d flt%s %s %f str%s %s %s ERROR: property list type %s, not handled! warn_err_DS unspecified_dummy_warning, single_valued_warning, num_expander_warnings, num_expander_errors NULL%s %s %d %d %s rel_error, error, missing %.7e %.7e %d write_model_DSmodel_DS %d id, file_index %s %d model_file %s data_file, header_file, n_data write_term_DSterm_DS %d n_atts, type write_tparm_DStparm_DS %d n_atts, tppt(type) %d %d write_tparms_DS: unknown type of enum MODEL_TYPES=%d n_term, n_att, n_att_indices, n_datum, n_data %d %d %d %d %d w_j, ranges, class_wt, disc_scale %.7e %.7e %.7e %.7e log_pi, log_att_delta, log_delta, wt_m, log_marginal %.7e %.7e %.7e %.7e %.7e  @write_mm_d_paramsmm_d_params row %d, size %d wts probs log_probs wts_vec probs_vec log_probs_vec write_mm_s_paramsmm_s_params count, wt, prob, log_prob %d %.7e %.7e %.7e write_mn_cn_paramsmn_cn_params ln_root log_ranges emp_means emp_covar means covariance factor min_sigma_2s write_sm_paramssm_params gamma_term, range, range_m1, inv_range, range_factor %.7e %d %.7e %.7e %.7e val_wts val_probs val_log_probs write_sn_cm_paramssn_cm_params known_wt, known_prob, known_log_prob, unknown_log_prob weighted_mean, weighted_var, mean %.7e %.7e %.7e sigma, log_sigma, variance, log_variance, inv_variance ll_min_diff, skewness, kurtosis prior_sigma_min_2, prior_mean_mean, prior_mean_sigma %.7e %.7e %.7e prior_sigmas_term, prior_sigma_max_2, prior_mean_var, prior_known_prior write_sn_cn_paramssn_cn_params write_priors_DSpriors_DS %d known_prior, sigma_min, sigma_max mean_mean, mean_sigma, mean_var minus_log_log_sigmas_ratio, minus_log_mean_sigma NULL write_class_DS_sclass_DS %d w_j, pi_j log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j known_parms_p, num_tparms num_wts %d data.db2header.hd2model.modelsearch params.s-paramsreports params.r-paramssearch.searchsearch_tmp.search-tmp.results.results-tmp.results-bin.results-tmp-binlog.logrlog.rlog.chkpt.chkpt-tmp.chkpt-bin.chkpt-tmp-bininflu_vals.influ-text-xref_class.class-text-xref_case.case-text-predict.predictERROR: file type: %s not handled! ERROR: pathname %s is greater than %d chars -- see autoclass.h ERROR: pathname %s does not contain '.' character ERROR: %s file: %s%s not found! ERROR: results file pathname %s does not contain '.' character asciibinaryERROR: type %s not handled by validate_results_pathname rbERROR: neither %s%s, nor %s%s were found! ERROR: %s%s, was not found! ERROR: data file pathname %s is greater than %d chars -- see autoclass.h ERROR: data file pathname %s does not contain '.' character .db2-binERROR: file_type %s not handled by get_clsf_seq read_clsf_seq%s %s#unxwinac_versionERROR: expecting "ac_version n.n", found "%s" ADVISORY: read %d classifications from %s%s clsf_DS%le %le %sdatabase_DS_ptrERROR: expecting "database_DS_ptr", found "%s" num_modelsERROR: expecting "num_models", found "%s" %dmodel_DS_ptrERROR: expecting "model_DS_ptr", found "%s" n_classesERROR: expecting "n_classes", found "%s" %fchkpt_DSERROR: expecting "chkpt_DS", found "%s" ERROR: expecting clsf_DS index %d, found "%s" database_DSERROR: expecting "database_DS", found "%s" %d %d %d%s %dmodel_DSERROR: expecting "model_DS" and model_index = %d, found "%s" %s %s %dclass_DSERROR: expecting "class_DS" and n_class = %d, found "%s" %e %e%e %le %le%d %d read_class_DS_s: %p, num_wts %d, wts:%p, wts-len:%d ERROR: expecting "model_DS_ptr" and file_index, found "%s" att_DSERROR: expecting "att_DS" and n_att = %d, found "%s" %d %e %e %e %eERROR: expecting observed[%d], found "%s" %d %s%s %s %sproperty list type %s, not handled! %s %s %d %d%e %e %dtparm_DSERROR: expecting "tparm_DS" and n_parm = %d, found "%s" read_tparms_DS: unknown type of ENUM MODEL_TYPES =%d %d %d %d %d %d%e %e %e %e%e %e %e %e %eVVVEV`VvVread_mm_d_params not converted from write_mm_d_params read_mm_s_params not converted from write_mm_s_params emp_meansread_mn_cn_params expected "emp_means", read "%s" emp_covarread_mn_cn_params expected "emp_covar", read "%s" meansread_mn_cn_params expected "means", read "%s" covarianceread_mn_cn_params expected "covariance", read "%s" factorread_mn_cn_params expected "factor", read "%s" min_sigma_2sread_mn_cn_params expected "min_sigma_2s", read "%s" %e %d %e %e %eval_wtsread_sm_params expected "val_wts", read "%s" val_probsread_sm_params expected "val_probs", read "%s" val_log_probsread_sm_params expected "val_log_probs", read "%s" %e %e %e%e %e %e %e %e %eERROR: fwrite failed -- called by %s ERROR: write failed -- called by %s ERROR: in %s, expecting data type %d, found %d dump_clsf_seq# ordered sequence of clsf_DS's: 0 -> %d# clsf_DS %d: log_a_x_h = %.7eac_version %sdump_clsf_DSdatabase_DS_ptrmodel_DS_ptr%s %ddump_database_DSdump_att_DSrealdiscretedummyERROR: att_info->type %s not handled int%s %s %dflt%s %s %fstr%s %s %sproperty list type %s, not handled! NULLdump_model_DSdump_term_DSdump_tparm_DS dump_tparms_DS: unknown type of ENUM MODEL_TYPES =%d nnnnnndump_mn_cn_paramsdump_sm_paramsdump_class_DS_sload_clsf_seq%s %sunxwinac_versionERROR: expecting "ac_version n.n", found "%s %s" sADVISORY: loaded %d classification%s from %s%s load_clsf%sERROR: expecting "database_DS_ptr", found "%s" ERROR: expecting "model_DS_ptr", found "%s" load_database_DSload_att_DS ERROR: att_info->type %s not handled load_model_DSload_class_DS_sERROR: load_class_DS_s: out of memory, malloc returned NULL! load_class_DS: %p, num_wts %d, wts:%p, wts-len:%d load_tparm_DS load_tparms_DS: unknown type of ENUM MODEL_TYPES =%d Є66load_mm_d_params not converted from dump_mm_d_params load_mm_s_params not converted from dump_mm_s_params load_mn_cn_paramsload_sm_paramsmulti_multinomial_dmulti_multinomial_smulti_normal_cnsingle_multinomialsingle_normal_cmsingle_normal_cnignoreERROR: unkown type in expand_model_terms: %s aqcheck_termatt_trans_datan_att_trans_datan_%sERROR[3]: model term type %s cannot handle type = %s, attribute #%d: "%s" Multiple sources for attribute transformERROR[3]: %s model terms cannot handle subtype %s of type %s attributes TRANSFORMED->%d%dERROR: unknown type in update_params;i,t=%d %d`rERROR: unknown type in log_likelihood; parm=%d, type= %d~~8FTbERROR: unknown type in update_l_approx; parm=%d, type=%dFF 0update_m_approx-fn called with w_j = %f, log_a_w_s_h_j not updated. ERROR: unknown type in update_m_approx; parm=%d, type=%dFF!0ERROR: unequal type in class_equiv;i,s=%d %d != %dERROR: unknown type in class_equivalence;i,s=%d %dhh 2DVERROR: unequal type in class_merge;i,s=%d %d != %dERROR: unknown type in class_merged_marginal;i,s=%d %d  ՚{Gz?M: 8MG? single_normal_cm is faulty due to large error-to-range ratio on sigma priors. @?@(?: 8att_trans_datasingle_normal_cmn_att_trans_dataAttribute %d: "%s" not one of those allowed for single_normal_cm terms. using single_normal_cm model on att which has NO missing values Attribute %d: "%s": attempting to use single_normal_cm model in a non-singleton attribute set Attribute %d: "%s", attempting to use single_normal_cm model with non-positive error value %f. %!g-DT! @@{Gz?M࿫: 8433333??: 8?-DT! @: 8{Gz?Mǫ: 8?Set known_wt to %f ? single_normal_cn is faulty due to large error-to-range ratio on sigma priors. @?@(?: 8att_trans_datasingle_normal_cnn_att_trans_dataAttribute %d not one allowed for single_normal_cn terms using single_normal_cn model on attribute with missing values Attribute %d using single_normal_cn model in non-singleton set Attribute %d using single_normal_cn model with non-positive error %!g-DT! @@࿫: 8࿫: 8?-DT! @: 8{Gz?M@: 8???multi_normal_cn: attempt to apply to non-multiple set %!g: 8h㈵>??@࿽HP?@࿫: 8h㈵>find_transformERROR[2]: Attempt to find unknown transform %s on attributes: %d n_argsERROR: Currently unable to deal with multiple argument transforms: %s generate_singleton_transformADVISORY[2]: %s is being applied to attribute #%d: "%s" and will be stored as attribute #%d. ERROR: (generate_singleton_transform) Undefined transform; %s intsourcestrsource_sub_typerealdiscrete?log_transformERROR: Attempt to apply log_transform to non-numerical attribute %d of type %s log transform of attribute# %d using mn %f rather than %f for zero_point. Suggest decreasing attribute's rel_error. zero_pointERROR: Attribute %d has no error property Log %s{Gz?Mlog_odds_transform_cminmaxlog %s{Gz?MMbP?@ff@@ff@ERROR: update_weights called without any classes ERROR: update_ln_p_x_pi_theta called without any classes .results%s file WARNING: "autoclass -search" running in checkpointing mode ADVISORY: start_j_list=() has been overridden by () from %s%s ADVISORY: start_j_list=() from %s%s ERROR: Haven't been given enough info to find a classification ADVISORY: start_j_list has been modified to: () ERROR: A new search must have at least one item in start_j_list RESTARTING SEARCH at %s BEGINNING SEARCH at %s best %s%d->%d(%d) dup %d->%d(%d) WARNING: trial %d terminated prior to convergence since number of cycles reached "max_cycles" (%d). as fixed at %doff of list: ( %d ).s-params.results-bin.results [saved %s/%s at %s] [saved .search at %s] you asked me tomax duration has expiredmax number of tries reached AUTOCLASS C (version %s) STOPPING at %s #<?8 #;print_initial_report WELCOME TO AUTOCLASS. 1) Each time I have finished a new 'trial', or attempt to find a good classification, I will print the number of classes that trial started and ended with, such as 9->7. 2) If that trial results in a duplicate of a previous run, I will print 'dup' first. 3) If that trial results in a classification better than any previous, I will print 'best' first. 4) If more than%s have passed since the last report, and a new classification has been found which is better than any previous ones, I will report on that classification and on the status of the search so far. 5) This report will include an estimate of the time it will take to find another even better classification, and how much better that will be. In addition, I will estimate a lower bound on how long it might take to find the very best classification, and how much better that might be. 6) If you are warned about too much time in overhead, you may want to change the parameters n_save, min_save_period, min_report_period, or min_checkpoint_period. 7) Since interactive_p = false, I will continue searching 7) To quit searching, type a 'q', hit , and wait. Otherwise I'll go on until %s. until I complete trial number (%d). forever. 8) If needed, every%s I will save the best %d classifications so far to file: %s%s and a description of the search to file: %s%s 9) A record of this search will be printed to file: %s%s print_report ---------------- NEW BEST CLASSIFICATION FOUND on try %d ------------- (Also found %d other better than last report.) ----------- SEARCH STATUS as of %s ----------- It just took%s since beginning. It just took%s to find a classification times more probable. Estimate <%s to find a classification Estimate >>%s to find the very best classification, which may betoHave seen %d of the estimated > %d possible classifications (based on %d duplicates do far). Log-Normal fit to classifications probabilities has M(ean) %.1f, S(igma) %.1f Choosing initial n-classes %s WARNING: %.1f %% of time so far spend doing non-try overhead tasks - should you save and/or report less? Overhead time is %.1f %% of total search time WARNING: You may be running out of peaks to find. Estimates are too optimistic ?Y@?print_final_report ENDING SEARCH because %s at %s after a total of %d tries over%s This invocation of "autoclass -search" took%s A log of this search is in file: %s%s The search results are stored in file: %s%s This search can be restarted by having "force_new_search_p = false" in file: %s%s and reinvoking the "autoclass -search ..." form ------------------ SUMMARY OF %d BEST RESULTS ------------------ ------------------ SUMMARY OF TRY CONVERGENCE ------------------ try %4d num_cycles %4d max_cycles %4d ... **** NON-CONVERGENT ***** convergent print_search_try#%s%sPROBABILITY exp(%.3f) N_CLASSES %2d FOUND ON TRY %3d DUPS %d *SAVED*%s%schkpt[reconverge "chkpt" j_in=%d] results[reconverge "results" j_in=%d] [j_in=%d] converge cnt %d n_cls %d no_chng %d ln_p %.3f h_rng %.3f, h_fact*(-ln_p) %.3f [c: cycles %d]converge_search_3converge_search_3 called with rel_delta_range < 0.0 cnt %d, n_class %d, range %.4f, diff %.4f, ln_p %.4f, n_no_chng %d [cs-3: cycles %d]converge_search_3aconverge_search_3a called with rel_delta_range < 0.0 cnt %d, n_cls %d, range %.4f[%.4f], diff %.4f, ln_p %.4f, n_no_chg %d, h %d [cs-3a: cycles %d]converge_search_4 n_cls %d, s^2/b^2 %.4f, beta %.4f, range %.4f, in_n %d cnt %d, num_cls %d, ln_p %.4f, n_in_noise %d, range %.4f, s_b_n_vals %d, h %d [cs-4: cycles %d]]tE??random_j_from_ln_normalrandomly from a log_normal [M-S, M, M+S] = [%.1f, %.1f, %.1f]?$@?333333?@@.@-DT! @@M6dM?ffffff?ERROR: average called for a zero length list! save_searchsearch_tmpwrrm %smv %s %swrite_search_DSsearch_DS n, time, n_dups, n_dup_tries %d %d %d %d last try reported %d tries from best on down for n_tries %d search_try_DS %dstart_j_list n_final_summary, n_save %d %d write_search_try_DS%s n, time, j_in, j_out, ln_p, num_cycles, max_cycles %d %d %d %d %.8e %d %d n_dups %d search_try_DS %d dup_try_DS %dERROR: number of search trials (%d) is less than the number of saved clsfs (%d) ERROR: .results[-bin] file and .search file not from the same run get_search_from_file%ssearch_DS ERROR: "%s" is not a valid search file %d %d %d %d%dstart_j_list ERROR: in "%s", expected "start_j_list", found "%s" %d %dADVISORY: read %d search trials from %s%s %s %dsearch_try_DS ERROR: in "%s", search_try index = %d not found %s %d %s %ddup_try_DS ERROR: in "%s", dup_try_index %d not found for search_try index = %d num_cycles%d %d %d %d %le%d %d %d %d %le %d %ddescribe_clsfIt has %d CLASSES with WEIGHTS %d PROBABILITY of both the data and the classification = exp(%.3f) exp(%.1f) [= %.1e] randomblock ERROR: start function type "%s" not handled! ERROR: try function type "%s" not handled! random_ln_normal ERROR: number of classes function type "%s" not handled! ERROR: start function type "%s" not handled! allowable types are "random" & "block" ERROR: try function type "%s" not handled! allowable types are "converge_search_3", "converge_search_4", and "converge", ERROR: number of classes function type "%s" not handled! allowable types are "random_ln_normal" only yesnotry %.2d: n %.2d j_in %.2d j_out %.2d clsf_ln_p %.3f clsf %s %d null classes stored from base-cycle. . %.4d clsf store_class_DS(%.2d [max=%d]): %p ERROR: get_class called with NULL model pop_class_DS(%.2d): %p build_class_DS: %p, num_wts %d, wts:%p, wts-len:%d ERROR: from_class->i_values is non NULL ERROR: copy class wt allocation error 1ERROR: copy class wt allocation error 2ERROR: unknown enum MODEL_TYPES in copy_tparm=%dfree_class(%s): %p discreteintegeratt_type integer not supported realADVISORY: unknown enum MODEL_TYPES in free_tparm_DS = %d G_model_list is NULL model-%d class_store %d: %p push_clsf(%.2d): %p pop_clsf(%.2d): %p displaying weights for %d classes %f create_clsf_DS: %p ? NULL clsf passed to list_clsf_storage clsf: %p model global clsf: %p search_try_clsf %d: %p G_clsf_store %d: %p free_clsf: %p clsf n_freed_classes = %.2d, n_create_classes_after_free = %.2d modelfree search_try_dup: %d of %d free search_try: %d free search {Gz?MblockrERROR: training classification & test data have different models and/or different attributes ERROR: -predict assumes only one model discreterealheaderrERROR: expand_database has db and comp_db from differing sources check: data_file_path, header_file_path, or n_data ERROR: expand_database found unmatched common attributes defs in <.results[-bin] file> and %s ERROR: extend_database has db and comp_db from differing sources ERROR: extend_database found unmatched common attributes in db and comp_dbsourceERROR: extend_database failed to produce corresponding attributesdummyERROR: att_type %s not handled {Gz?modelrfree_model(%d): %p to_screen_and_log_file%ss %d day%s %d hour%s %d minute%s %d second%s 0 seconds? [checkpt clsf (j=%d, cycle=%d) at %s] ERROR: checkpoint_clsf called with G_checkpoint_file = "" does nothing chkpt all code commented in delete_duplicates WARNING: new_random: unable to find an unused number Type "y" for yes or "n" for no. {Gz?M?write_vector_float %.7e UUUUUU%@write_matrix_floatrow %d UUUUUU%@write_matrix_integer%d UUUUUU%@%e UUUUUU%@UUUUUU%@UUUUUU%@ERROR: for %s, expected to read first ' from 'c', read %c instead! ,%dERROR: integer list of type "int_list" is full ERROR: fprintf returned %d -- called by %s ERROR: vsprintf produced %d chars (max number is %d) -- called by %s Attempted to take log_gamma %20.15f @?dg?UUUUUU?llf?@Hg?팛&5?@@h㈵>MGMG@h㈵>: 8alltextautoclass_reportsn_clsfsclsf_n_listreport_typereport_modecomment_data_headers_pnum_atts_to_listxref_class_report_att_listorder_attributes_by_influence_pbreak_on_warnings_pfree_storage_pmax_num_xref_class_probssigma_contours_att_list ### Starting Check of %s%s rdataERROR: report_mode must be either "text", or "data". allxref_casexref_classERROR: report_type must be either "all", "xref_case", or "xref_class". influence_valuesERROR: report_type must be either "all", "influence_values", "xref_case", or "xref_class". ERROR: report_mode must be "data" if comment_data_headers_p is true. ERROR: max_num_xref_class_probs must be greater than 0. ### Ending Check of %s%s a AUTOCLASS C (version %s) STARTING at %s -PREDICT-REPORTSAUTOCLASS %s default parameters: USER supplied parameters which override the defaults: ERROR: .results[-bin] file and .search file not from the same run ERROR: sigma_contours_att_list index %d cannot exceed %d -- use indices from .hd2 file. ERROR: sigma_contours_att_list length must be >= 2. caseclass AUTOCLASS C (version %s) STOPPING at %s p>influence_values_report_streamso-no-text-data-%dw File written: %s%s case_report_streamsclass_report_streamsERROR: .r-params file: xref_class_report_att_list index %d not in range: 0<->%d ERROR: xref_get_data(1): out of memory, malloc returned NULL! ERROR: xref_get_data(1): out of memory, realloc returned NULL! ERROR: xref_get_data(2): out of memory, malloc returned NULL! ERROR: xref_get_data(3): out of memory, malloc returned NULL! ERROR: xref_get_data(4): out of memory, malloc returned NULL! xref_get_data: case_num %d => class 9999 8*CbP?ERROR: autoclass_xref_by_case_report: out of memory, malloc returned NULL! # %s%6cCROSS REFERENCE CASE NUMBER => MOST PROBABLE CLASS %sDATA_CLSF_HEADER %s%6cAutoClass PREDICTION for the %d "TEST" cases in %s%8c%s%s %s%6cbased on the "TRAINING" classification of %d cases in %s%6cAutoClass CLASSIFICATION for the %d cases in %s%8c%s%s %s%6cwith log-A (approximate marginal likelihood) = %.3f %s%6cfrom classification results file %s%6cand using models %sDATA_CASE_TO_CLASS%03d %11d %4d %5.3f %c%4d %5.3f %11d %4d %5.3f %11d %4d %5.3f @+?------------------------------------------------------------------------------------------ %s Case # Class Prob Case # Class Prob Case # Class Prob %s Case # Class Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) %s Case # Prob (Class Prob) (Class Prob) (Class Prob) (Class Prob) %sCase # %s%%%dc (Cls Prob) %s%6cCROSS REFERENCE CLASS => CASE NUMBER MEMBERSHIP ERROR: xref_class_report_attributes(1): out of memory, malloc returned NULL! ERROR: xref_class_report_attributes(2): out of memory, malloc returned NULL! %%-%ds %%-%dgERROR: xref_class_report_attributes(3): out of memory, malloc returned NULL! ERROR: xref_class_report_attributes(3): out of memory, realloc returned NULL! DATA_CLASS %d (continued)%s%32c CLASS = %d %s ? %%%dc%2d %5.3f %03d %6d %11d %s %5.3f%s{Gz?Mclsftruefalse %6cI N F L U E N C E V A L U E S R E P O R T %6c order attributes by influence values = %s %6c============================================= %s num description I-*k DATA_POP_CLASSES %sCLASSIFICATION HAS %d POPULATED CLASSES (max global influence value = %5.3f) DATA_CLASS_DIVS %sCLASS DIVERGENCES DATA_NORM_INF_VALS %sORDERED LIST OF NORMALIZED ATTRIBUTE INFLUENCE VALUES SUMMED OVER ALL CLASSES This gives a rough heuristic measure of relative influence of each attribute in differentiating the classes from the overall data set. Note that "influence values" are only computable with respect to the model terms. When multiple attributes are modeled by a single dependent term (e.g. multi_normal_cn), the term influence value is distributed equally over the modeled attributes. %03d %-55s -----%6.3f CLASS LISTINGS: These listings are ordered by class weight -- * j is the zero-based class index, * k is the zero-based attribute index, and * l is the zero-based discrete attribute instance index. Within each class, the covariant and independent model terms are ordered by their term influence value I-jk. Covariant attributes and discrete attribute instance values are both ordered by their significance value. Significance values are computed with respect to a single class classification, using the divergence from it, abs( log( Prob-jkl / Prob-*kl)), for discrete attributes and the relative separation from it, abs( Mean-jk - Mean-*k) / StDev-jk, for numerical valued attributes. For the SNcm model, the value line is followed by the probabilities that the value is known, for that class and for the single class classification. Entries are attribute type dependent, and the corresponding headers are reproduced with each class. In these -- * num/t denotes model term number, * num/a denotes attribute number, * t denotes attribute type, * mtt denotes model term type, and * I-jk denotes the term influence value for attribute k in class j. This is the cross entropy or Kullback-Leibler distance between the class and full database probability distributions (see interpretation-c.text). * Mean StDev -jk -jk The estimated mean and standard deviation for attribute k in class j. * |Mean-jk - The absolute difference between the Mean-*k|/ two means, scaled w.r.t. the class StDev-jk standard deviation, to get a measure of the distance between the attribute means in the class and full data. * Mean StDev The estimated mean and standard -*k -*k deviation for attribute k when the model is applied to the data set as a whole. * Prob-jk is known 1.00e+00 Prob-*k is known 9.98e-01 The SNcm model allows for the possibility that data values are unknown, and models this with a discrete known/unknown probability. The gaussian normal for known values is then conditional on the known probability. In this instance, we have a class where all values are known, as opposed to a database where only 99.8%% of values are known. autoclass_class_influence_values_report DATA_CLASS %d %sCLASS %2d - weight %3d normalized weight %5.3f relative strength %9.2e ******* %s class cross entropy w.r.t. global class %9.2e ******* Model file: %s Numbers: numb/t = model term number; numb/a = attribute number Model term types (mtt): ignoreERROR: autoclass_class_influence_values_report: out of memory, malloc returned NULL! ERROR: autoclass_class_influence_values_report: out of memory, realloc returned NULL! discreterealprint_string %s(%s %s) reportMbP?ERROR: ordered_normalized_influence_values : out of memory, malloc returned NULL! ERROR: ordered_normalized_influence_values : out of memory, realloc returned NULL! ORDER OF PRESENTATION: * Summary of the generating search. * Weight ordered list of the classes found & class strength heuristic. * List of class cross entropies with respect to the global class. * Ordered list of attribute influence values summed over all classes. * Class listings, ordered by class weight. _____________________________________________________________________________ %sDATA_SEARCH_SUMMARY %sSEARCH SUMMARY %d tries over %s _______________ %sSUMMARY OF %d BEST RESULTS _________________________ ## -%d ## - report filenames suffix %s Class Log of class Relative Class Normalized %s num strength class strength weight class weight We give below a heuristic measure of class strength: the approximate geometric mean probability for instances belonging to each class, computed from the class parameters and statistics. This approximates the contribution made, by any one instance "belonging" to the class, to the log probability of the data set w.r.t. the classification. It thus provides a heuristic measure of how strongly each class predicts "its" instances. %02d %9.2e %9.2e %6d %6.3f %s Class class cross entropy Class Normalized %s num w.r.t. global class weight class weight The class divergence, or cross entropy w.r.t. the single class classification, is a measure of how strongly the class probability distribution function differs from that of the database as a whole. It is zero for identical distributions, going infinite when two discrete distributions place probability 1 on differing values of the same attribute. %9.2e %6d %6.3fERROR: pre_format_attributes: out of memory, malloc returned NULL! ERROR: pre_format_attributes: out of memory, realloc returned NULL! integerERROR: attribute type %s not supported MNcn %sDISCRETE ATTRIBUTE (t = D) log( %s numb t mtt description I-jk Value name/Index Prob-jkl/ Prob Prob %s t a Prob-*kl) -jkl -*kl %sREAL ATTRIBUTE (t = R) |Mean-jk - %s numb t mtt description I-jk Mean StDev Mean-*k|/ Mean StDev %s t a -jk -jk StDev-jk -*k -*k format_attributeDIRERROR: type %s not handled __ %02d %02d %s %-4s %20s%34s %6.3f %-20s %9.2e %9.2e %9.2e %-20s %9.2e %9.2e %9.2e ERROR: format_discrete_attribute: out of memory, malloc returned NULL! ERROR: format_discrete_attribute: out of memory, realloc returned NULL! %s%13c %-20s %6c %43cNot supported yet %33s %6.3f (%9.2e %9.2e) %9.2e (%9.2e %9.2e) Prob-jk is known %9.2e Prob-*k is known %9.2e SNcm%13c %-20s %s%36c DATA_CORR_MATRIX %s Correlation matrix (row & column indices are attribute numbers) %7d%02d %2d %6.3fERROR: sort_mncn_attributes: out of memory, malloc returned NULL! results WARNING: requested clsf number %d not found -- max number is %d ignore#%s%8c%s%s - index = %d get_class_model_source_info%s %s%s - index = %ddiscreteintegerrealignoreERROR: attribute type %s not handled ERROR: unknown type of ENUM MODEL_TYPES in influence_value: %d influence_value called with unknown attribute_type: %s @signore%06d %05d %13e %13e %13e %13e %13e # SIGMA CONTOURS %satt_x att_y mean_x sigma_x mean_y sigma_y rotation-rad realADVISORY: compute sigma contour => att_n %d ("%s") is not a type real attribute. ADVISORY: compute sigma contour => att_n %d ("%s") has been declared ignore in the .model file. normalADVISORY: compute sigma contour => term_type %s is not a `normal' term for att_n %d ("%s") %s?@?p>I?%d pointer passed is null for %s vector %s, n=%d float: %f, double %12.10f ****limiting n=%d to %d %g pointer passed is null for %s matrix %s, m,n=%d %d . . . skipping to %d row %d matrix %s, m,n=%d %d %d row %d,size=%d wtsprobslog_probswts_vecprobs_veclog_probs_vec mm_s_params count,wt,prob %d %g %g %g ln_root %g log_ranges %g emp meansemp_covarmeanscovariancefactorvaluestemp_vtemp_m sm_param gamma_term,range,range_m1,inv_range,range_factor %g %d %g %g %g val_wtsval_probsval_log_probs known_wt, known_prob, known_log_prob, unknown_log_prob %g %g %g %g weighted_mean, weighted_var, mean %g %g %g sigma, log_sigma, variance, log_variance, inv_variance %g %g %g %g %g ll_min_diff, skewness, kurtosis %g %g %g prior_sigma_min_2, prior_mean_mean, prior_mean_sigma %g %g %g prior_sigmas_term, prior_sigma_max_2, prior_mean_var prior_known_prior %g weighted_mean, weighted_var, mean sigma, log_sigma, variance, log_variance, inv_variance tparmDS %s;n_atts=%d type=%d in print_tparms_DS UNKNOWN TYPE=%d collect %d n_term, n_att, n_att_indices, n_datum, n_data %d %d %d %d %d w_j, ranges= %g %g class_wt,disc_scale %g %g log_pi, log_att_delta, log_delta wt_m, log_marginal %g %g datumatt_indices data Cf priors %s known_prior, sigma_min, sigma_max %g %g %g mean_mean, mean_sigma, mean_var %g %g %g minus_log_log_sigmas_ratio, minus_log_mean_sigma %g %g class %s w_j, pi_j %g %g log_pi_j, log_a_w_s_h_pi_theta, log_a_w_s_h_j %g %g %g known_parms_p, num_tparms %d %d from class void **i_values; N-attributes vector of influence value structures. i_sum,max_i_value %g %g wt vectorskipping call to print_model that is in print_class NOT next pointer is%sNULL term %s, n_atts,type=%d %s att_list from termfrom term real stats from %s count,max,min,mean,var %d %5g %g %g %g discrete stats from %s range,n_observed %d %d %d %d att %s, type,subtype,descrp=%s %s "%s" real rstats from att dstats from att n_props,range,zero_point,n_trans %d %d %f %d NOT translations triple pointer is%sNULL props triple pointer is%sNULL not printing warings and errors rel_error, error, missing %g %g %d not prinitng file pointers for data and header in db %s n_data,n_atts,input_n_atts,compressed_p %d %d %d %d %d th info from database separator_char,comment_char,unknown_token %c %c %c NOT num_tsp %d, translations_supplied_p is %sNULL num_invalid_value_errors %d, invalid_value_errors is %sNULL num_incomplete_datum %d, incomplete_datum is %sNULL model %s; id =%s, expanded_terms univ time=%d model file pointer not printed; file index =%d database in model this model contains %d terms ith term in model n_att_locs=%d %d %s n_att_ignore_ids=%d num_priors=%d priors num_class_store=%d; class_store is%sNULL not printing global clsf from model clsf %s log_p_x_h_pi_theta, log_a_x_h %g %g database pointer is%sNULL num_models=%d skipping 1 call for each to print_model in clsf n_classes=%d class pointer is%sNULL ith clsf classmin_class_wt %g clsf reports pointer is%sNULL clsf_store next pointer is%sNULL search try %s n,time,j_in,j_out,ln_p, %d %d %d %d %g number of duplicates=%d duplicate triesclsf from try search struct %s n, time, n_dups, n_dup_tries %d %d %d %d last try reported tries from best on down for n_tries =%d best to worst try start_j_list: %d, n_final_summary, n_save %d %d %s=; %s=truefalse%s"%s"%d%15.12e%e() of unknown paramtype=%dNone. ((((((= ERROR: undefined parameter: %s ERROR: no value given for: %s = ERROR: for parameter %s, neither true or false was read. ERROR: for parameter %s, first character of value is not a '"' ERROR: for parameter %s, more than %d characters. were input %cERROR: for parameter %s, number read, %s, was not an integer ERROR: for parameter %s, number read, %s, was not a float ERROR: for parameter %s, number read, %s, was not a double , ERROR: more than %d values input for %s=: ERROR: bad paramtype= %d for %s; parameter not set P+* ,,-- too many params; max = %d param name too long. limit is %d AUTOCLASS C (version %s) -search-reports-predictERROR: the second argument must be "-search", "-reports", or "-predict" ERROR: invalid number of arguments for "autoclass" ERROR: invalid number of arguments for "autoclass -search" headermodelsearch paramslogsearchresults ERROR: invalid number of arguments for "autoclass -reports" reports paramsinflu_valsxref_classxref_caserlog ERROR: invalid number of arguments for "autoclass -predict" > autoclass AutoClass Search: %s -search <.db2[-bin] file path> <.hd2 file path> <.model file path> <.s-params file path> AutoClass Reports: %s -reports <.results[-bin] file path> <.search file path> <.r-params file path> AutoClass Prediction: %s -predict L+'73.3.4unxA/ ?> #=e>L>>>io>>>?c>?w>333?PX>L?<>fff?A">? >̌?=??=ff?xz=33?pΈ=?u`=?}6=? =ff?C<33?Ǻ<@ ? @ A B C& D? EV Fo G H I N Q R/ SG T` Vy Y o r u {+ |H e  . 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DDLDaDs@$*$+7`DDDDDDoDDDDDD,DT$q+$)+7LDDDD;DKD$4+$K+) DDDD!D(D:DXDsD{DDDDDDDDDDD&D)D:DGDkDDDDDD+DFD^DyD U+@)@$_+$ dw+ )  D D  DDD$D2DYDD+@ )@ $+$+) DD!D#D$ D&,D0/D&1D(<D*ZD-D0D1D0D1D0D4D6D8D;D> D@.+@)@ a@ .$7+$DL+C)C DDDE DGDHDL$DM4DOIDP]+@C)@C$d+$T+S)S DTDU DV ,@S)@S$V,$[ 4,Z)Z D[D] D[D`D]D^D`2Db_?,@Z)@Z a@\_$_J,$eh _,d)d DeDfDhDi Dk(Dn?DoWDqjDsDvDsDvDxD{D}D2j,@d)@d$;u,$!,) DDDD!D,D/D1D2D7DADjD|DDDDDDDDD>Df,@)@ a@f$p,$#,) DDDD!D,D/D1D>DRDiD}DDDDDDDDDDDDDDDDDDDD'D*D1D2D7DBDLDNDXD\DnDoDtDDD,@)@ a@$,$$,) DDDD!D,D/D4D?D]D{DDDDDDDDDDDDD D>,@)@ a@>$H,$&-) DDDD D%D(D*D5DZDnDtDDDDD %-@)@ a@$0-$ &H- )  D DDD D(D+D-D8DTDhDyDDDDDDDDD DD DD!D"S-@ )@  a@$ d'#d'^-d',<;j-y;`z6`nd|/R A/,B/ Z"x""/""""mO<$/#$#A#]#{#P(K/b#1S+^j-$'|-- -DDD!D"'D$AD%GD&YD'`D)bD+wD,D.D0D2D3D4D5D6D7D8D9D:D;D=D>D?D@%DA'DB6DC9DE<DF?DGIDHUDIfDK~-@-@---@~$~.$\`).[.[ D\D]D_DdDf$DjODkcDjfDmhDkjDmpDoxDnDqDrDtDvDwDxDyDzD{D| D"x*DDD D/D8D8D8D8^-*D8D<D@DPDeDDDDDDDDDDDDD DDDD(D6D;DUD[D^DaDjDkDqDuDDDDDDDDDDDDDDDDD DDD!D.D3D6D<DNDQDXDdD}DDDDDDDDDDDDDDDDDDD"D'DBDDDGDJDMDUDYD\D_DgDmDpDvDDD"-DDDDDDDDD^-.DD".DSDUD`DgDgDgDgDgDgDgDg^-.DgDoDDDDDDDDDDDDDDD(D.D1D>D ^D q-.]M.@]X.^\b._Xce_Tr.@_pk_dl``.@`.@a.bl.@8888.@.@.@gg.@gggg.@gggggq$q/$/./ !/./;/F/DDDD&D,DxD"`0DDD^-`0D D!D'D!D"D#D$D%D'D(X/@g/@v/@/@/@/@$d0#d0/d0,<;j-y;`z6`nd|/R A/,B/ Z"x""/""""mO<$/#$#A#]#{#P(K/'d'.Gq/b#1S+^/$*0/)/) D*D2D5D62D7GD8\D:jD<oD?D@DADBDGDFDGDJDLDQ DS3DT8DXBDY\DZaD\tD]yD^D`D_D`DaDbDcDeDfDgDhDiDjDlDmDn"Do'Dr1DsCDtUDvjDxDyDzD|D}DDDDDD DD0D7DQDXD^DcDpDDDDDDDDD DDDD#D&D=DDDcDjDDDDDDDDDDD*D6/@)/@)/@+0+.0+H0@,k0@,0@,0@-0-0(. <0(.<0(.`=1(.>-1(/>(/@?A1(/?R1(/@n1(0 A1(0A1(0`B1(1C126$61$5DDDDDDD&D7DKDi1i$idQ6init.c/usr/src/RPM/BUILD/glibc-2.1.3/csu/gcc2_compiled.int:t(0,1)=r(0,1);0020000000000;0017777777777;char:t(0,2)=r(0,2);0;127;long int:t(0,3)=r(0,1);0020000000000;0017777777777;unsigned int:t(0,4)=r(0,1);0000000000000;0037777777777;long unsigned int:t(0,5)=r(0,1);0000000000000;0037777777777;long long int:t(0,6)=r(0,1);01000000000000000000000;0777777777777777777777;long long unsigned int:t(0,7)=r(0,1);0000000000000;01777777777777777777777;short int:t(0,8)=r(0,8);-32768;32767;short unsigned int:t(0,9)=r(0,9);0;65535;signed char:t(0,10)=r(0,10);-128;127;unsigned char:t(0,11)=r(0,11);0;255;float:t(0,12)=r(0,1);4;0;double:t(0,13)=r(0,1);8;0;long double:t(0,14)=r(0,1);12;0;complex int:t(0,15)=s8real:(0,1),0,32;imag:(0,1),32,32;;complex float:t(0,16)=r(0,16);4;0;complex double:t(0,17)=r(0,17);8;0;complex long double:t(0,18)=r(0,18);12;0;void:t(0,19)=(0,19)../include/libc-symbols.h/usr/src/RPM/BUILD/glibc-2.1.3/build-i386-linux/config.h../include/libintl.h../intl/libintl.h../include/features.h../include/sys/cdefs.h../misc/sys/cdefs.h/usr/lib/gcc-lib/i586-mandrake-linux/2.95.3/include/stddef.h../include/locale.h../locale/locale.hlconv:T(10,1)=s48decimal_point:(10,2)=*(0,2),0,32;thousands_sep:(10,2),32,32;grouping:(10,2),64,32;int_curr_symbol:(10,2),96,32;currency_symbol:(10,2),128,32;mon_decimal_point:(10,2),160,32;mon_thousands_sep:(10,2),192,32;mon_grouping:(10,2),224,32;positive_sign:(10,2),256,32;negative_sign:(10,2),288,32;int_frac_digits:(0,2),320,8;frac_digits:(0,2),328,8;p_cs_precedes:(0,2),336,8;p_sep_by_space:(0,2),344,8;n_cs_precedes:(0,2),352,8;n_sep_by_space:(0,2),360,8;p_sign_posn:(0,2),368,8;n_sign_posn:(0,2),376,8;;../include/xlocale.h../locale/xlocale.h__locale_struct:T(13,1)=s36__locales:(13,2)=ar(0,1);0;5;(13,3)=*(13,4)=xslocale_data:,0,192;__ctype_b:(13,5)=*(0,9),192,32;__ctype_tolower:(13,6)=*(0,1),224,32;__ctype_toupper:(13,6),256,32;;__locale_t:t(13,7)=(13,8)=*(13,1)../sysdeps/unix/sysv/linux/_G_config.h../sysdeps/unix/sysv/linux/bits/types.hsize_t:t(16,1)=(0,4)__u_char:t(15,1)=(0,11)__u_short:t(15,2)=(0,9)__u_int:t(15,3)=(0,4)__u_long:t(15,4)=(0,5)__u_quad_t:t(15,5)=(0,7)__quad_t:t(15,6)=(0,6)__int8_t:t(15,7)=(0,10)__uint8_t:t(15,8)=(0,11)__int16_t:t(15,9)=(0,8)__uint16_t:t(15,10)=(0,9)__int32_t:t(15,11)=(0,1)__uint32_t:t(15,12)=(0,4)__int64_t:t(15,13)=(0,6)__uint64_t:t(15,14)=(0,7)__qaddr_t:t(15,15)=(15,16)=*(15,6)__dev_t:t(15,17)=(15,5)__uid_t:t(15,18)=(15,3)__gid_t:t(15,19)=(15,3)__ino_t:t(15,20)=(15,4)__mode_t:t(15,21)=(15,3)__nlink_t:t(15,22)=(15,3)__off_t:t(15,23)=(0,3)__loff_t:t(15,24)=(15,6)__pid_t:t(15,25)=(0,1)__ssize_t:t(15,26)=(0,1)__rlim_t:t(15,27)=(0,3)__rlim64_t:t(15,28)=(15,6)__id_t:t(15,29)=(15,3)__fsid_t:t(15,30)=(15,31)=s8__val:(15,32)=ar(0,1);0;1;(0,1),0,64;;__daddr_t:t(15,33)=(0,1)__caddr_t:t(15,34)=(10,2)__time_t:t(15,35)=(0,3)__swblk_t:t(15,36)=(0,3)__clock_t:t(15,37)=(0,3)__fd_mask:t(15,38)=(0,5)__fd_set:t(15,39)=(15,40)=s128fds_bits:(15,41)=ar(0,1);0;31;(15,38),0,1024;;__key_t:t(15,42)=(0,1)__ipc_pid_t:t(15,43)=(0,9)__blkcnt_t:t(15,44)=(0,3)__blkcnt64_t:t(15,45)=(15,6)__fsblkcnt_t:t(15,46)=(15,4)__fsblkcnt64_t:t(15,47)=(15,5)__fsfilcnt_t:t(15,48)=(15,4)__fsfilcnt64_t:t(15,49)=(15,5)__ino64_t:t(15,50)=(15,4)__off64_t:t(15,51)=(15,24)__t_scalar_t:t(15,52)=(0,3)__t_uscalar_t:t(15,53)=(0,5)__intptr_t:t(15,54)=(0,1)../linuxthreads/sysdeps/pthread/bits/pthreadtypes.h../sysdeps/unix/sysv/linux/bits/sched.h__sched_param:T(18,1)=s4sched_priority:(0,1),0,32;;_pthread_fastlock:T(17,1)=s8__status:(0,3),0,32;__spinlock:(0,1),32,32;;_pthread_descr:t(17,2)=(17,3)=*(17,4)=xs_pthread_descr_struct:pthread_attr_t:t(17,5)=(17,6)=s36__detachstate:(0,1),0,32;__schedpolicy:(0,1),32,32;__schedparam:(18,1),64,32;__inheritsched:(0,1),96,32;__scope:(0,1),128,32;__guardsize:(16,1),160,32;__stackaddr_set:(0,1),192,32;__stackaddr:(17,7)=*(0,19),224,32;__stacksize:(16,1),256,32;;pthread_cond_t:t(17,8)=(17,9)=s12__c_lock:(17,1),0,64;__c_waiting:(17,2),64,32;;pthread_condattr_t:t(17,10)=(17,11)=s4__dummy:(0,1),0,32;;pthread_key_t:t(17,12)=(0,4)pthread_mutex_t:t(17,13)=(17,14)=s24__m_reserved:(0,1),0,32;__m_count:(0,1),32,32;__m_owner:(17,2),64,32;__m_kind:(0,1),96,32;__m_lock:(17,1),128,64;;pthread_mutexattr_t:t(17,15)=(17,16)=s4__mutexkind:(0,1),0,32;;pthread_once_t:t(17,17)=(0,1)_pthread_rwlock_t:T(17,18)=s32__rw_lock:(17,1),0,64;__rw_readers:(0,1),64,32;__rw_writer:(17,2),96,32;__rw_read_waiting:(17,2),128,32;__rw_write_waiting:(17,2),160,32;__rw_kind:(0,1),192,32;__rw_pshared:(0,1),224,32;;pthread_rwlock_t:t(17,19)=(17,18)pthread_rwlockattr_t:t(17,20)=(17,21)=s8__lockkind:(0,1),0,32;__pshared:(0,1),32,32;;pthread_t:t(17,22)=(0,5)wchar_t:t(19,1)=(0,3)wint_t:t(19,2)=(0,4)_G_int16_t:t(14,1)=(0,8)_G_int32_t:t(14,2)=(0,1)_G_uint16_t:t(14,3)=(0,9)_G_uint32_t:t(14,4)=(0,4)_IO_stdin_used:G(0,1)/home/wtaylor/AC/autoclass-c/prog/globals.c/usr/include/time.h/usr/include/features.h/usr/include/sys/cdefs.h/usr/include/gnu/stubs.h/usr/include/bits/time.h/usr/include/bits/types.h__u_char:t(7,1)=(0,11)__u_short:t(7,2)=(0,9)__u_int:t(7,3)=(0,4)__u_long:t(7,4)=(0,5)__u_quad_t:t(7,5)=(0,7)__quad_t:t(7,6)=(0,6)__int8_t:t(7,7)=(0,10)__uint8_t:t(7,8)=(0,11)__int16_t:t(7,9)=(0,8)__uint16_t:t(7,10)=(0,9)__int32_t:t(7,11)=(0,1)__uint32_t:t(7,12)=(0,4)__int64_t:t(7,13)=(0,6)__uint64_t:t(7,14)=(0,7)__qaddr_t:t(7,15)=(7,16)=*(7,6)__dev_t:t(7,17)=(7,5)__uid_t:t(7,18)=(7,3)__gid_t:t(7,19)=(7,3)__ino_t:t(7,20)=(7,4)__mode_t:t(7,21)=(7,3)__nlink_t:t(7,22)=(7,3)__off_t:t(7,23)=(0,3)__loff_t:t(7,24)=(7,6)__pid_t:t(7,25)=(0,1)__ssize_t:t(7,26)=(0,1)__rlim_t:t(7,27)=(0,3)__rlim64_t:t(7,28)=(7,6)__id_t:t(7,29)=(7,3)__fsid_t:t(7,30)=(7,31)=s8__val:(7,32)=ar(0,1);0;1;(0,1),0,64;;__daddr_t:t(7,33)=(0,1)__caddr_t:t(7,34)=(7,35)=*(0,2)__time_t:t(7,36)=(0,3)__swblk_t:t(7,37)=(0,3)__clock_t:t(7,38)=(0,3)__fd_mask:t(7,39)=(0,5)__fd_set:t(7,40)=(7,41)=s128__fds_bits:(7,42)=ar(0,1);0;31;(7,39),0,1024;;__key_t:t(7,43)=(0,1)__ipc_pid_t:t(7,44)=(0,9)__blkcnt_t:t(7,45)=(0,3)__blkcnt64_t:t(7,46)=(7,6)__fsblkcnt_t:t(7,47)=(7,4)__fsblkcnt64_t:t(7,48)=(7,5)__fsfilcnt_t:t(7,49)=(7,4)__fsfilcnt64_t:t(7,50)=(7,5)__ino64_t:t(7,51)=(7,4)__off64_t:t(7,52)=(7,24)__t_scalar_t:t(7,53)=(0,3)__t_uscalar_t:t(7,54)=(0,5)__intptr_t:t(7,55)=(0,1)clock_t:t(1,1)=(7,38)time_t:t(1,2)=(7,36)tm:T(1,3)=s44tm_sec:(0,1),0,32;tm_min:(0,1),32,32;tm_hour:(0,1),64,32;tm_mday:(0,1),96,32;tm_mon:(0,1),128,32;tm_year:(0,1),160,32;tm_wday:(0,1),192,32;tm_yday:(0,1),224,32;tm_isdst:(0,1),256,32;__tm_gmtoff:(0,3),288,32;__tm_zone:(1,4)=*(0,2),320,32;;/usr/include/sys/param.h/usr/lib/gcc-lib/i586-mandrake-linux/2.95.3/include/limits.h/usr/lib/gcc-lib/i586-mandrake-linux/2.95.3/include/syslimits.h/usr/include/limits.h/usr/include/bits/posix1_lim.h/usr/include/bits/local_lim.h/usr/include/linux/limits.h/usr/include/linux/param.h/usr/include/asm/param.h/usr/include/sys/types.hloff_t:t(19,1)=(7,24)ino_t:t(19,2)=(7,20)dev_t:t(19,3)=(7,17)gid_t:t(19,4)=(7,19)mode_t:t(19,5)=(7,21)nlink_t:t(19,6)=(7,22)uid_t:t(19,7)=(7,18)off_t:t(19,8)=(7,23)pid_t:t(19,9)=(7,25)ssize_t:t(19,10)=(7,26)int8_t:t(19,11)=(0,10)int16_t:t(19,12)=(0,8)int32_t:t(19,13)=(0,1)int64_t:t(19,14)=(0,6)u_int8_t:t(19,15)=(0,11)u_int16_t:t(19,16)=(0,9)u_int32_t:t(19,17)=(0,4)u_int64_t:t(19,18)=(0,7)register_t:t(19,19)=(0,1)blkcnt_t:t(19,20)=(7,45)fsblkcnt_t:t(19,21)=(7,47)fsfilcnt_t:t(19,22)=(7,49)autoclass.h/usr/include/stdio.h/usr/lib/gcc-lib/i586-mandrake-linux/2.95.3/include/stdarg.h__gnuc_va_list:t(24,1)=(24,2)=*(0,19)FILE:t(22,1)=(22,2)=xs_IO_FILE:/usr/include/libio.h/usr/include/_G_config.h_G_int16_t:t(26,1)=(0,8)_G_int32_t:t(26,2)=(0,1)_G_uint16_t:t(26,3)=(0,9)_G_uint32_t:t(26,4)=(0,4)_IO_lock_t:t(25,1)=(0,19)_IO_marker:T(25,2)=s12_next:(25,3)=*(25,2),0,32;_sbuf:(25,4)=*(22,2),32,32;_pos:(0,1),64,32;;_IO_FILE:T(22,2)=s148_flags:(0,1),0,32;_IO_read_ptr:(7,35),32,32;_IO_read_end:(7,35),64,32;_IO_read_base:(7,35),96,32;_IO_write_base:(7,35),128,32;_IO_write_ptr:(7,35),160,32;_IO_write_end:(7,35),192,32;_IO_buf_base:(7,35),224,32;_IO_buf_end:(7,35),256,32;_IO_save_base:(7,35),288,32;_IO_backup_base:(7,35),320,32;_IO_save_end:(7,35),352,32;_markers:(25,3),384,32;_chain:(25,4),416,32;_fileno:(0,1),448,32;_blksize:(0,1),480,32;_old_offset:(7,23),512,32;_cur_column:(0,9),544,16;_vtable_offset:(0,10),560,8;_shortbuf:(25,5)=ar(0,1);0;0;(0,2),568,8;_lock:(25,6)=*(25,1),576,32;_offset:(7,52),608,64;_unused2:(25,7)=ar(0,1);0;15;(0,1),672,512;;_IO_FILE:t(25,8)=(22,2)__io_read_fn:t(25,9)=(25,10)=f(7,26)__io_write_fn:t(25,11)=(25,12)=f(7,26)__io_seek_fn:t(25,13)=(25,14)=f(0,1)__io_close_fn:t(25,15)=(25,16)=f(0,1)fpos_t:t(22,3)=(7,23)/usr/include/bits/stdio_lim.h/usr/include/bits/stdio.h/usr/include/string.h/usr/include/bits/string.h/usr/include/bits/string2.h/usr/include/endian.h/usr/include/bits/endian.h/usr/include/math.h/usr/include/bits/huge_val.h/usr/include/bits/mathdef.h/usr/include/bits/mathcalls.h/usr/include/bits/mathinline.hgetparams.hBOOLEAN:t(42,1)=(0,4) :T(42,2)=eTSTRING:0,TBOOL:1,TINT:2,TFLOAT:3,TDOUBLE:4,TINT_LIST:5,;PARAMTYPE:t(42,3)=(42,2)PARAM:t(42,4)=(42,5)=s56paramtype:(42,3),0,32;paramname:(42,6)=ar(0,1);0;34;(0,2),32,280;paramptr:(24,2),320,32;paramptr_overridden:(24,2),352,32;overridden_p:(0,1),384,32;max_length:(0,1),416,32;;PARAMP:t(42,7)=(42,8)=*(42,5)results_data_types:T(21,1)=eINT_TYPE:0,CHAR_TYPE:1,FLOAT_TYPE:2,DOUBLE_TYPE:3,CLASS_TYPE:4,TERM_TYPE:5,WARN_ERR_TYPE:6,REAL_STATS_TYPE:7,DISCRETE_STATS_TYPE:8,DUMMY_STATS_TYPE:9,ATT_TYPE:10,DATABASE_TYPE:11,MODEL_TYPE:12,CLASSIFICATION_TYPE:13,CHECKPOINT_TYPE:14,TPARM_TYPE:15,;fptr:t(21,2)=(21,3)=*(0,12)fxlstr:t(21,4)=(21,5)=ar(0,1);0;159;(0,2)priors_DS:t(21,6)=(21,7)=*(21,8)=xspriors:class_DS:t(21,9)=(21,10)=*(21,11)=xsclass:term_DS:t(21,12)=(21,13)=*(21,14)=xsterm:warn_err_DS:t(21,15)=(21,16)=*(21,17)=xswarn_err:real_stats_DS:t(21,18)=(21,19)=*(21,20)=xsreal_stats:discrete_stats_DS:t(21,21)=(21,22)=*(21,23)=xsdiscrete_stats:att_DS:t(21,24)=(21,25)=*(21,26)=xsatt:database_DS:t(21,27)=(21,28)=*(21,29)=xsdatabase:model_DS:t(21,30)=(21,31)=*(21,32)=xsmodel:clsf_DS:t(21,33)=(21,34)=*(21,35)=xsclassification:search_try_DS:t(21,36)=(21,37)=*(21,38)=xssearch_try:search_DS:t(21,39)=(21,40)=*(21,41)=xssearch:shortstr:t(21,42)=(21,43)=ar(0,1);0;40;(0,2)very_long_str:t(21,44)=(21,45)=ar(0,1);0;19999;(0,2)chkpt_DS:t(21,46)=(21,47)=*(21,48)=xscheckpoint:rpt_DS:t(21,49)=(21,50)=*(21,51)=xsreports:sort_cell_DS:t(21,52)=(21,53)=*(21,54)=xssort_cell:invalid_value_errors_DS:t(21,55)=(21,56)=*(21,57)=xsinvalid_value_errors:incomplete_datum_DS:t(21,58)=(21,59)=*(21,60)=xsincomplete_datum:i_discrete_DS:t(21,61)=(21,62)=*(21,63)=xsi_discrete:i_integer_DS:t(21,64)=(21,65)=*(21,66)=xsi_integer:i_real_DS:t(21,67)=(21,68)=*(21,69)=xsi_real:xref_data_DS:t(21,70)=(21,71)=*(21,72)=xsxref_data:rpt_att_string_DS:t(21,73)=(21,74)=*(21,75)=xsreport_attribute_string:ordered_influ_vals_DS:t(21,76)=(21,77)=*(21,78)=xsordered_influence_values:formatted_p_p_star_DS:t(21,79)=(21,80)=*(21,81)=xsformatted_p_p_star:int_list:t(21,82)=(21,83)=*(0,1)params.htparm_DS:t(43,1)=(43,2)=*(43,3)=xsnew_term_params:MODEL_TYPES:T(43,4)=eUNKNOWN:0,TIGNORE:1,MM_D:2,MM_S:3,MN_CN:4,SM:5,SN_CM:6,SN_CN:7,;mm_d_param:T(43,5)=s28sizes:(21,83),0,32;wts:(43,6)=*(21,2),32,32;probs:(43,6),64,32;log_probs:(43,6),96,32;wts_vec:(21,3),128,32;probs_vec:(21,3),160,32;log_probs_vec:(21,3),192,32;;mm_s_param:T(43,7)=s16count:(0,1),0,32;wt:(0,12),32,32;prob:(0,12),64,32;log_prob:(0,12),96,32;;mn_cn_param:T(43,8)=s44ln_root:(0,12),0,32;log_ranges:(0,12),32,32;emp_means:(21,3),64,32;emp_covar:(43,6),96,32;means:(21,3),128,32;covariance:(43,6),160,32;factor:(43,6),192,32;values:(21,3),224,32;temp_v:(21,3),256,32;temp_m:(43,6),288,32;min_sigma_2s:(21,3),320,32;;sm_param:T(43,9)=s32gamma_term:(0,12),0,32;range:(0,1),32,32;range_m1:(0,12),64,32;inv_range:(0,12),96,32;range_factor:(0,12),128,32;val_wts:(21,2),160,32;val_probs:(21,2),192,32;val_log_probs:(21,2),224,32;;sn_cm_param:T(43,10)=s88known_wt:(0,12),0,32;known_prob:(0,12),32,32;known_log_prob:(0,12),64,32;unknown_log_prob:(0,12),96,32;weighted_mean:(0,12),128,32;weighted_var:(0,12),160,32;mean:(0,12),192,32;sigma:(0,12),224,32;log_sigma:(0,12),256,32;variance:(0,12),288,32;log_variance:(0,12),320,32;inv_variance:(0,12),352,32;ll_min_diff:(0,12),384,32;skewness:(0,12),416,32;kurtosis:(0,12),448,32;prior_sigma_min_2:(0,12),480,32;prior_mean_mean:(0,12),512,32;prior_mean_sigma:(0,12),544,32;prior_sigmas_term:(0,12),576,32;prior_sigma_max_2:(0,12),608,32;prior_mean_var:(0,12),640,32;prior_known_prior:(0,12),672,32;;sn_cn_param:T(43,11)=s68weighted_mean:(0,12),0,32;weighted_var:(0,12),32,32;mean:(0,12),64,32;sigma:(0,12),96,32;log_sigma:(0,12),128,32;variance:(0,12),160,32;log_variance:(0,12),192,32;inv_variance:(0,12),224,32;ll_min_diff:(0,12),256,32;skewness:(0,12),288,32;kurtosis:(0,12),320,32;prior_sigma_min_2:(0,12),352,32;prior_mean_mean:(0,12),384,32;prior_mean_sigma:(0,12),416,32;prior_sigmas_term:(0,12),448,32;prior_sigma_max_2:(0,12),480,32;prior_mean_var:(0,12),512,32;;new_term_params:T(43,3)=s172n_atts:(0,1),0,32;tppt:(43,4),32,32;ptype:(43,12)=u88mm_d:(43,5),0,224;mm_s:(43,7),0,128;mn_cn:(43,8),0,352;sm:(43,9),0,256;sn_cm:(43,10),0,704;sn_cn:(43,11),0,544;;,64,704;collect:(0,1),768,32;n_term:(0,1),800,32;n_att:(0,1),832,32;n_att_indices:(0,1),864,32;n_datum:(0,1),896,32;n_data:(0,1),928,32;w_j:(0,12),960,32;ranges:(0,12),992,32;class_wt:(0,12),1024,32;disc_scale:(0,12),1056,32;log_pi:(0,12),1088,32;log_att_delta:(0,12),1120,32;log_delta:(0,12),1152,32;wts:(21,3),1184,32;datum:(21,3),1216,32;att_indices:(21,3),1248,32;data:(43,13)=*(21,3),1280,32;wt_m:(0,12),1312,32;log_marginal:(0,12),1344,32;;priors:T(21,8)=s32known_prior:(0,12),0,32;sigma_min:(0,12),32,32;sigma_max:(0,12),64,32;mean_mean:(0,12),96,32;mean_sigma:(0,12),128,32;mean_var:(0,12),160,32;minus_log_log_sigmas_ratio:(0,12),192,32;minus_log_mean_sigma:(0,12),224,32;;class:T(21,11)=s76w_j:(0,12),0,32;log_w_j:(0,12),32,32;pi_j:(0,12),64,32;log_pi_j:(0,12),96,32;log_a_w_s_h_pi_theta:(0,13),128,64;log_a_w_s_h_j:(0,13),192,64;known_parms_p:(0,1),256,32;num_tparms:(0,1),288,32;tparms:(21,84)=*(43,1),320,32;num_i_values:(0,1),352,32;i_values:(21,85)=*(24,2),384,32;i_sum:(0,12),416,32;max_i_value:(0,12),448,32;num_wts:(0,1),480,32;wts:(21,3),512,32;model:(21,30),544,32;next:(21,9),576,32;;term:T(21,14)=s56type:(21,42),0,328;n_atts:(0,1),352,32;att_list:(21,3),384,32;tparm:(43,1),416,32;;warn_err:T(21,17)=s108unspecified_dummy_warning:(21,42),0,328;unused_translators_warning:(21,3),352,32;single_valued_warning:(21,42),384,328;num_expander_warnings:(0,1),736,32;model_expander_warnings:(21,86)=*(21,4),768,32;num_expander_errors:(0,1),800,32;model_expander_errors:(21,86),832,32;;real_stats:T(21,20)=s20count:(0,1),0,32;mx:(0,12),32,32;mn:(0,12),64,32;mean:(0,12),96,32;var:(0,12),128,32;;discrete_stats:T(21,23)=s12range:(0,1),0,32;n_observed:(0,1),32,32;observed:(21,83),64,32;;att:T(21,26)=s172type:(21,42),0,328;sub_type:(21,42),328,328;dscrp:(21,42),656,328;r_statistics:(21,18),992,32;d_statistics:(21,21),1024,32;n_props:(0,1),1056,32;range:(0,1),1088,32;zero_point:(0,12),1120,32;n_trans:(0,1),1152,32;translations:(21,87)=*(7,35),1184,32;rel_error:(0,12),1216,32;props:(21,88)=*(21,85),1248,32;warnings_and_errors:(21,15),1280,32;error:(0,12),1312,32;missing:(0,1),1344,32;;invalid_value_errors:T(21,57)=s52n_datum:(0,1),0,32;n_att:(0,1),32,32;value:(21,42),64,328;;incomplete_datum:T(21,60)=s8n_datum:(0,1),0,32;datum_length:(0,1),32,32;;database:T(21,29)=s380data_file:(21,4),0,1280;header_file:(21,4),1280,1280;n_data:(0,1),2560,32;n_atts:(0,1),2592,32;input_n_atts:(0,1),2624,32;allo_n_atts:(0,1),2656,32;compressed_p:(0,1),2688,32;att_info:(21,89)=*(21,24),2720,32;data:(43,13),2752,32;datum_length:(21,83),2784,32;separator_char:(0,2),2816,8;comment_char:(0,2),2824,8;unknown_token:(0,2),2832,8;num_tsp:(0,1),2848,32;translations_supplied_p:(21,83),2880,32;num_invalid_value_errors:(0,1),2912,32;invalid_value_errors:(21,90)=*(21,55),2944,32;num_incomplete_datum:(0,1),2976,32;incomplete_datum:(21,91)=*(21,58),3008,32;;model:T(21,32)=s588id:(21,42),0,328;expanded_terms:(0,1),352,32;model_file:(21,4),384,1280;file_index:(0,1),1664,32;database:(21,27),1696,32;data_file:(21,4),1728,1280;header_file:(21,4),3008,1280;n_data:(0,1),4288,32;compressed_p:(0,1),4320,32;n_terms:(0,1),4352,32;terms:(21,92)=*(21,12),4384,32;n_att_locs:(0,1),4416,32;att_locs:(21,93)=*(21,42),4448,32;n_att_ignore_ids:(0,1),4480,32;att_ignore_ids:(21,93),4512,32;num_priors:(0,1),4544,32;priors:(21,94)=*(21,6),4576,32;num_class_store:(0,1),4608,32;class_store:(21,9),4640,32;global_clsf:(21,33),4672,32;;checkpoint:T(21,48)=s12accumulated_try_time:(0,1),0,32;current_try_j_in:(0,1),32,32;current_cycle:(0,1),64,32;;reports:T(21,51)=s200current_results:(21,4),0,1280;n_class_wt_ordering:(0,1),1280,32;class_wt_ordering:(21,83),1312,32;att_model_term_types:(21,95)=*(21,87),1344,32;max_class_strength:(0,12),1376,32;class_strength:(21,3),1408,32;datum_class_assignment:(21,83),1440,32;att_i_sums:(21,3),1472,32;att_max_i_sum:(0,12),1504,32;att_max_i_values:(21,3),1536,32;max_i_value:(0,12),1568,32;;classification:T(21,35)=s52log_p_x_h_pi_theta:(0,13),0,64;log_a_x_h:(0,13),64,64;database:(21,27),128,32;num_models:(0,1),160,32;models:(21,96)=*(21,30),192,32;n_classes:(0,1),224,32;classes:(21,97)=*(21,9),256,32;min_class_wt:(0,12),288,32;reports:(21,49),320,32;next:(21,33),352,32;checkpoint:(21,46),384,32;;search_try:T(21,38)=s44n:(0,1),0,32;time:(0,1),32,32;j_in:(0,1),64,32;j_out:(0,1),96,32;ln_p:(0,13),128,64;n_duplicates:(0,1),192,32;duplicates:(21,98)=*(21,36),224,32;clsf:(21,33),256,32;num_cycles:(0,1),288,32;max_cycles:(0,1),320,32;;search:T(21,41)=s40n:(0,1),0,32;time:(0,1),32,32;n_dups:(0,1),64,32;n_dup_tries:(0,1),96,32;last_try_reported:(21,36),128,32;n_tries:(0,1),160,32;tries:(21,98),192,32;start_j_list:(21,82),224,32;n_final_summary:(0,1),256,32;n_save:(0,1),288,32;;sort_cell:T(21,54)=s8float_value:(0,12),0,32;int_value:(0,1),32,32;;i_discrete:T(21,63)=s12influence_value:(0,12),0,32;n_p_p_star_list:(0,1),32,32;p_p_star_list:(21,3),64,32;;i_integer:T(21,66)=s12influence_value:(0,12),0,32;n_mean_sigma_list:(0,1),32,32;mean_sigma_list:(21,3),64,32;;i_real:T(21,69)=s24influence_value:(0,12),0,32;n_mean_sigma_list:(0,1),32,32;mean_sigma_list:(21,3),64,32;n_term_att_list:(0,1),96,32;class_covar:(43,6),128,32;term_att_list:(21,2),160,32;;xref_data:T(21,72)=s28class_case_sort_key:(0,1),0,32;case_number:(0,1),32,32;n_attribute_data:(0,1),64,32;discrete_attribute_data:(21,93),96,32;real_attribute_data:(21,3),128,32;n_collector:(0,1),160,32;wt_class_pairs:(21,52),192,32;;report_attribute_string:T(21,75)=s52att_number:(0,1),0,32;att_dscrp:(21,42),32,328;dscrp_length:(0,1),384,32;;ordered_influence_values:T(21,78)=s20att_i_sum:(0,12),0,32;n_att:(0,1),32,32;att_dscrp_ptr:(7,35),64,32;model_term_type_ptr:(7,35),96,32;norm_att_i_sum:(0,12),128,32;;formatted_p_p_star:T(21,81)=s60discrete_string_name:(21,42),0,328;abs_att_value_influence:(0,12),352,32;att_value_influence:(0,12),384,32;local_prob:(0,12),416,32;global_prob:(0,12),448,32;;G_db_length:G(0,1)G_m_id:G(0,1)G_m_length:G(0,1)G_plength:G(0,1)G_clsf_store:G(21,33)G_db_list:G(0,20)=*(21,27)G_model_list:G(21,96)G_break_on_warnings:G(0,1)G_likelihood_tolerance_ratio:G(0,12)G_save_compact_p:G(0,4)G_ac_version:G(21,42)G_log_file_fp:G(0,21)=*(22,1)G_stream:G(0,21)G_line_cnt_max:G(0,1)G_safe_file_writing_p:G(0,1)G_data_file_format:G(0,22)=ar(0,1);0;9;(0,2)G_solaris:G(0,1)G_training_clsf:G(21,33)G_prediction_p:G(0,1)G_interactive_p:G(0,1)G_num_cycles:G(0,1)G_slash:G(0,2)G_clsf_storage_log_p:G(0,1)G_n_freed_classes:G(0,1)G_n_create_classes_after_free:G(0,1)G_plist:G(21,88)G_transforms:G(0,23)=ar(0,1);0;1;(21,42)G_att_type_data:G(0,24)=ar(0,1);0;4;(21,42)G_checkpoint_file:G(21,4)G_search_cycle_begin_time:G(1,2)G_last_checkpoint_written:G(1,2)G_min_checkpoint_period:G(1,2)G_input_data_base:G(21,27)G_absolute_pathname:G(0,25)=ar(0,1);0;4094;(0,2)G_rand_base_normalizer:G(0,13)FILE:t(1,1)=(1,2)=xs_IO_FILE:fpos_t:t(1,3)=(7,23)off_t:t(1,4)=(7,23)/usr/include/stdlib.hdiv_t:t(21,1)=(21,2)=s8quot:(0,1),0,32;rem:(0,1),32,32;;ldiv_t:t(21,3)=(21,4)=s8quot:(0,3),0,32;rem:(0,3),32,32;;__compar_fn_t:t(21,5)=(21,6)=*(21,7)=f(0,1)loff_t:t(38,1)=(7,24)ino_t:t(38,2)=(7,20)dev_t:t(38,3)=(7,17)gid_t:t(38,4)=(7,19)mode_t:t(38,5)=(7,21)nlink_t:t(38,6)=(7,22)uid_t:t(38,7)=(7,18)pid_t:t(38,8)=(7,25)ssize_t:t(38,9)=(7,26)time_t:t(39,1)=(7,36)int8_t:t(38,10)=(0,10)int16_t:t(38,11)=(0,8)int32_t:t(38,12)=(0,1)int64_t:t(38,13)=(0,6)u_int8_t:t(38,14)=(0,11)u_int16_t:t(38,15)=(0,9)u_int32_t:t(38,16)=(0,4)u_int64_t:t(38,17)=(0,7)register_t:t(38,18)=(0,1)blkcnt_t:t(38,19)=(7,45)fsblkcnt_t:t(38,20)=(7,47)fsfilcnt_t:t(38,21)=(7,49)/usr/include/unistd.h/usr/include/bits/posix_opt.h/usr/include/bits/confname.h :T(44,1)=e_PC_LINK_MAX:0,_PC_MAX_CANON:1,_PC_MAX_INPUT:2,_PC_NAME_MAX:3,_PC_PATH_MAX:4,_PC_PIPE_BUF:5,_PC_CHOWN_RESTRICTED:6,_PC_NO_TRUNC:7,_PC_VDISABLE:8,_PC_SYNC_IO:9,_PC_ASYNC_IO:10,_PC_PRIO_IO:11,_PC_SOCK_MAXBUF:12,_PC_FILESIZEBITS:13,; :T(44,2)=e_SC_ARG_MAX:0,_SC_CHILD_MAX:1,_SC_CLK_TCK:2,_SC_NGROUPS_MAX:3,_SC_OPEN_MAX:4,_SC_STREAM_MAX:5,_SC_TZNAME_MAX:6,_SC_JOB_CONTROL:7,_SC_SAVED_IDS:8,_SC_REALTIME_SIGNALS:9,_SC_PRIORITY_SCHEDULING:10,_SC_TIMERS:11,_SC_ASYNCHRONOUS_IO:12,_SC_PRIORITIZED_IO:13,_SC_SYNCHRONIZED_IO:14,_SC_FSYNC:15,_SC_MAPPED_FILES:16,_SC_MEMLOCK:17,_SC_MEMLOCK_RANGE:18,_SC_MEMORY_PROTECTION:19,_SC_MESSAGE_PASSING:20,_SC_SEMAPHORES:21,_SC_SHARED_MEMORY_OBJECTS:22,_SC_AIO_LISTIO_MAX:23,_SC_AIO_MAX:24,_SC_AIO_PRIO_DELTA_MAX:25,_SC_DELAYTIMER_MAX:26,_SC_MQ_OPEN_MAX:27,_SC_MQ_PRIO_MAX:28,_SC_VERSION:29,_SC_PAGESIZE:30,_SC_RTSIG_MAX:31,_SC_SEM_NSEMS_MAX:32,_SC_SEM_VALUE_MAX:33,_SC_SIGQUEUE_MAX:34,_SC_TIMER_MAX:35,_SC_BC_BASE_MAX:36,_SC_BC_DIM_MAX:37,_SC_BC_SCALE_MAX:38,_SC_BC_STRING_MAX:39,_SC_COLL_WEIGHTS_MAX:40,_SC_EQUIV_CLASS_MAX:41,_SC_EXPR_NEST_MAX:42,_SC_LINE_MAX:43,_SC_RE_DUP_MAX:44,_SC_CHARCLASS_NAME_MAX:45,_SC_2_VERSION:46,_SC_2_C_BIND:47,_SC_2_C_DEV:48,_SC_2_FORT_DEV:49,_SC_2_FORT_RUN:50,_SC_2_SW_DEV:51,_SC_2_LOCALEDEF:52,_SC_PII:53,_SC_PII_XTI:54,_SC_PII_SOCKET:55,_SC_PII_INTERNET:56,_SC_PII_OSI:57,_SC_POLL:58,_SC_SELECT:59,_SC_UIO_MAXIOV:60,_SC_PII_INTERNET_STREAM:61,_SC_PII_INTERNET_DGRAM:62,_SC_PII_OSI_COTS:63,_SC_PII_OSI_CLTS:64,_SC_PII_OSI_M:65,_SC_T_IOV_MAX:66,_SC_THREADS:67,_SC_THREAD_SAFE_FUNCTIONS:68,_SC_GETGR_R_SIZE_MAX:69,_SC_GETPW_R_SIZE_MAX:70,_SC_LOGIN_NAME_MAX:71,_SC_TTY_NAME_MAX:72,_SC_THREAD_DESTRUCTOR_ITERATIONS:73,_SC_THREAD_KEYS_MAX:74,_SC_THREAD_STACK_MIN:75,_SC_THREAD_THREADS_MAX:76,_SC_THREAD_ATTR_STACKADDR:77,_SC_THREAD_ATTR_STACKSIZE:78,_SC_THREAD_PRIORITY_SCHEDULING:79,_SC_THREAD_PRIO_INHERIT:80,_SC_THREAD_PRIO_PROTECT:81,_SC_THREAD_PROCESS_SHARED:82,_SC_NPROCESSORS_CONF:83,_SC_NPROCESSORS_ONLN:84,_SC_PHYS_PAGES:85,_SC_AVPHYS_PAGES:86,_SC_ATEXIT_MAX:87,_SC_PASS_MAX:88,_SC_XOPEN_VERSION:89,_SC_XOPEN_XCU_VERSION:90,_SC_XOPEN_UNIX:91,_SC_XOPEN_CRYPT:92,_SC_XOPEN_ENH_I18N:93,_SC_XOPEN_SHM:94,_SC_2_CHAR_TERM:95,_SC_2_C_VERSION:96,_SC_2_UPE:97,_SC_XOPEN_XPG2:98,_SC_XOPEN_XPG3:99,_SC_XOPEN_XPG4:100,_SC_CHAR_BIT:101,_SC_CHAR_MAX:102,_SC_CHAR_MIN:103,_SC_INT_MAX:104,_SC_INT_MIN:105,_SC_LONG_BIT:106,_SC_WORD_BIT:107,_SC_MB_LEN_MAX:108,_SC_NZERO:109,_SC_SSIZE_MAX:110,_SC_SCHAR_MAX:111,_SC_SCHAR_MIN:112,_SC_SHRT_MAX:113,_SC_SHRT_MIN:114,_SC_UCHAR_MAX:115,_SC_UINT_MAX:116,_SC_ULONG_MAX:117,_SC_USHRT_MAX:118,_SC_NL_ARGMAX:119,_SC_NL_LANGMAX:120,_SC_NL_MSGMAX:121,_SC_NL_NMAX:122,_SC_NL_SETMAX:123,_SC_NL_TEXTMAX:124,_SC_XBS5_ILP32_OFF32:125,_SC_XBS5_ILP32_OFFBIG:126,_SC_XBS5_LP64_OFF64:127,_SC_XBS5_LPBIG_OFFBIG:128,_SC_XOPEN_LEGACY:129,_SC_XOPEN_REALTIME:130,_SC_XOPEN_REALTIME_THREADS:131,;clock_t:t(46,1)=(7,38)tm:T(46,2)=s44tm_sec:(0,1),0,32;tm_min:(0,1),32,32;tm_hour:(0,1),64,32;tm_mday:(0,1),96,32;tm_mon:(0,1),128,32;tm_year:(0,1),160,32;tm_wday:(0,1),192,32;tm_yday:(0,1),224,32;tm_isdst:(0,1),256,32;__tm_gmtoff:(0,3),288,32;__tm_zone:(46,3)=*(0,2),320,32;;globals.hinit:F(0,19)fp:r(0,20)=*(1,1)rand_range:V(0,13)one:r(0,13)two:V(0,13)slash:r(7,35)__u:r(0,21)=*(0,22)=u4__ui:(7,12),0,32;__usi:(7,10),0,16;__uc:(0,11),0,8;;init_properties:F(0,19)t1:(45,88)t1temp:r(45,88)t1temptemp:r(45,88)i2:(45,83)val1:r(45,83)t2:(45,87)types:r(45,95)__dest:r(7,35)io-read-data.cminmax.hcheck_stop_processing:F(0,19)total_error_cnt:p(0,1)total_warning_cnt:p(0,1)log_file_fp:p(0,20)=*(1,1)stream:p(0,20)log_file_fp:r(0,20)stream:r(0,20)str:(41,4)define_data_file_format:F(0,19)header_file_fp:p(0,20)log_file_fp:p(0,20)i:r(0,1)num:(0,1)def_name_string:(41,4)data_base:(41,27)caller:(0,21)=ar(0,1);0;23;(0,2)process_data_header_model_files:F(0,19)regenerate_p:p(0,1)db:p(41,27)models:p(41,96)num_models:p(0,1)total_error_cnt_ptr:p(41,83)total_warning_cnt_ptr:p(41,83)num_models:r(0,1)output_msg_type:(0,22)=ar(0,1);0;7;(0,2)log_header:F(0,19)data_file_ptr:p(7,35)header_file_ptr:p(7,35)model_file_ptr:p(7,35)log_file_ptr:p(7,35)caller:(0,23)=ar(0,1);0;10;(0,2)read_database:F(41,27)max_data:p(0,1)reread_p:p(0,1)header_file_ptr:r(7,35)n_att:r(0,1)data_file_fp:r(0,20)att_info:r(41,89)d_base:r(41,27)errors:r(41,15)check_for_non_empty:F(0,1)atts:p(41,89)n_atts:p(0,1)atts:r(41,89)n_atts:r(0,1)check_data_base:F(0,19)d_base:p(41,27)n_data:p(0,1)n_atts:(0,1)n_datum:r(0,1)datum_length:(0,1)datum_length_list:(41,83)num_errors:r(0,1)output_warning_msgs:F(7,35)n_att:p(0,1)att:p(41,24)model:p(41,30)model:r(41,30)msg:r(7,35)caller:(0,24)=ar(0,1);0;19;(0,2)warning_msg:(41,4)att_ignore_ids:(41,93)msg_length:r(0,1)errors:(41,15)output_error_msgs:F(7,35)caller:(0,25)=ar(0,1);0;17;(0,2)i:(0,1)output_message_summary:F(0,19)unspecified_dummy_warning_cnt:p(0,1)ignore_model_term_warning_cnt:p(0,1)unused_translators_warning_cnt:p(0,1)incomplete_errors_cnt:p(0,1)single_valued_warnings_cnt:p(0,1)invalid_value_errors_cnt:p(0,1)model_expander_warning_cnt:p(0,1)model_expander_error_cnt:p(0,1)log_file:p(0,20)output_p:p(0,1)output_p:r(0,1)caller:(0,26)=ar(0,1);0;22;(0,2)output_messages:F(0,19)output_msg_type_ptr:p(7,35)total_error_cnt_ptr:r(41,83)total_warning_cnt_ptr:r(41,83)n_att:(0,1)msg_header_p:(0,1)output_p:(0,1)warning_msgs:r(7,35)error_msgs:r(7,35)caller:(0,27)=ar(0,1);0;15;(0,2)att_ignore_ids:r(41,93)att:r(41,24)model:(41,30)att_info:(41,89)unused_translators_warning_cnt:(0,1)unspecified_dummy_warning_cnt:(0,1)ignore_model_term_warning_cnt:(0,1)model_expander_warning_cnt:(0,1)incomplete_datum_cnt:(0,1)invalid_value_errors_cnt:(0,1)single_valued_warnings_cnt:(0,1)model_expander_error_cnt:(0,1)output_db_error_messages:F(0,19)caller:(0,28)=ar(0,1);0;24;(0,2)read_data:F(0,19)data_file_fp:p(0,20)n:(0,1)data_allocated:(0,1)instance_length:(0,1)datum_length:(41,83)binary_instance_length:(0,1)input_binary_instance_length:(0,1)instance:(41,87)db2_bin_header:(0,29)=ar(0,1);0;9;(0,2)data:(46,13)str:(7,35)comment_chars:(0,30)=ar(0,1);0;3;(0,2)caller:(0,31)=ar(0,1);0;9;(0,2)binary_instance:(41,3)define_attribute_definitions:F(0,19)header_file_fp:r(0,20)data_base:r(41,27)process_attribute_definitions:F(0,19)j:r(0,1)att_num:r(0,1)num_tokens:(0,1)input_error:(0,1)integer_p:(0,1)n_atts_read:(0,1)tokens:r(41,87)caller:(0,32)=ar(0,1);0;29;(0,2)comment_chars:(0,33)=ar(0,1);0;5;(0,2)process_attribute_def:F(41,24)att_num:p(0,1)input_error_ptr:p(41,83)tokens:p(41,87)num_tokens:p(0,1)num_tokens:r(0,1)range:(0,1)rel_error:(0,12)error:(0,12)zero_point:(0,12)float_p:(0,1)index_1:r(0,1)index_2:(0,1)type_ptr:(7,35)sub_type_ptr:(7,35)dscrp_ptr:(7,35)caller:(0,34)=ar(0,1);0;21;(0,2)create_att_DS:F(41,24)range:p(0,1)rel_error:p(0,13)error:p(0,13)zero_point:p(0,13)type_ptr:p(7,35)sub_type_ptr:p(7,35)dscrp_ptr:p(7,35)input_error_ptr:r(41,83)rel_error:r(0,13)error:r(0,13)zero_point:r(0,13)sub_type_ptr:r(7,35)dscrp_ptr:r(7,35)caller:(0,35)=ar(0,1);0;13;(0,2)create_warn_err_DS:F(41,15)weds:r(41,15)expand_att_list:F(41,95)att_list:p(41,95)num:p(0,1)nlength:p(41,83)att_list:r(41,95)find_str_in_list:F(0,1)str:p(7,35)translations:p(41,87)translations:r(41,87)num:r(0,1)process_translation_msgs:F(0,19)translations_not_provided:p(41,83)default_translation:p(7,35)att_info:p(41,89)process_translation:F(41,87)att_dscrp:p(41,24)nat:p(0,1)att_translation:p(41,87)read_data_doit:F(41,87)first_read:p(0,1)instance_length_ptr:p(41,83)n_comment_chars:p(0,1)comment_chars:p(7,35)binary_instance_length:p(0,1)binary_instance_ptr:p(46,13)first_read:r(0,1)n_comment_chars:r(0,1)comment_chars:r(7,35)binary_instance_length:r(0,1)binary_instance_ptr:r(46,13)read_return_value:r(0,1)translate_instance:F(41,3)instance:p(41,87)instance_length:p(0,1)n_datum:p(0,1)num_atts:(0,1)new_instance:r(41,3)attribute:r(41,24)translate_real:F(0,13)value:p(7,35)num:(0,13)translate_discrete:F(0,1)attribute:p(41,24)value:r(7,35)val:r(0,1)stats:r(41,21)long_str:(41,4)very_long_str:(41,44)caller:(0,36)=ar(0,1);0;18;(0,2)get_line_tokens:F(41,87)separator_char:p(0,1)position:r(0,1)length:(0,1)line_tokens:(41,87)form:(0,37)=ar(0,1);0;499;(0,2)datum_string:(41,45)datum_string_first_char:r(0,1)read_from_string:F(0,1)s1:p(7,35)s2:p(7,35)string_limit:p(0,1)position:p(0,1)str_len:r(0,1)n_char:r(0,1)comment_p:(0,1)in_string_p:(0,1)__dest:(7,35)read_line:F(0,1)s:p(7,35)s:r(7,35)c:r(0,1)find_att_statistics:F(0,19)find_real_stats:F(0,19)count:(0,1)missing:(0,1)percent_error:r(0,1)mn:(0,12)mx:(0,12)val:r(0,12)sum:r(0,13)sum_sq:(0,13)mean:(0,13)variance:(0,13)double_val:r(0,13)float_unknown:V(0,13)rel_error:V(0,13)att:(41,24)stats:(41,18)caller:(0,38)=ar(0,1);0;15;(0,2)store_real_stats:F(0,19)statistics:p(41,18)count:p(0,1)mean:p(0,13)variance:p(0,13)missing:p(0,1)mx:p(0,13)mn:p(0,13)statistics:r(41,18)count:r(0,1)mean:r(0,13)variance:r(0,13)mx:r(0,13)mn:r(0,13)find_discrete_stats:F(0,19)missing_value:r(0,1)missing_value_cnt:(0,1)accumulator:r(41,83)ulength:(0,1)unused_translators:r(41,3)datum:r(41,3)output_att_statistics:F(0,19)stats_to_output_p:r(0,1)output_real_att_statistics:F(0,19)r_statistics:r(41,18)caller:(0,39)=ar(0,1);0;26;(0,2)output_created_translations:F(0,19)n_trans:r(0,1)translations_to_output_p:(0,1)str:(0,40)=ar(0,1);0;499;(0,2)caller:(0,41)=ar(0,1);0;27;(0,2)check_errors_and_warnings:F(0,19)database:p(41,27)total_error_cnt:(0,1)total_warning_cnt:(0,1)output_msg_type:(41,42)default_translation:G(41,87)processed:G(41,95)io-read-model.cread_model_file:F(28,96)model_file_fp:p(0,20)=*(1,1)d_base:p(28,27)expand_p:p(0,1)newlength:p(28,83)d_base:r(28,27)regenerate_p:r(0,1)expand_p:r(0,1)newlength:r(28,83)model_groups:(0,21)=*(28,95)model_group:r(28,95)size:(28,83)sizes:(0,22)=*(28,83)num_groups:(28,83)k:r(0,1)models:(28,96)caller:(0,23)=ar(0,1);0;15;(0,2)read_model_doit:F(28,95)model_file_fp:p(0,20)sizes:p(0,22)num:p(28,83)model_index:p(0,1)size:(0,1)list:r(28,87)big_list:(28,95)caller:(0,24)=ar(0,1);0;15;(0,2)comment_chars:(0,25)=ar(0,1);0;5;(0,2)num_model_def_lines:r(0,1)model_index_read:r(0,1)model_line:r(0,1)str:(28,4)read_lists:F(28,95)num:r(28,83)big_list:r(28,95)read_list:F(28,87)temp:(0,26)=ar(0,1);0;254;(0,2)list:(28,87)needright:(0,1)define_models:F(28,96)model_groups:p(0,21)source:p(7,35)num_model_groups:p(0,1)newnum:p(28,83)num_groups:p(28,83)n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X_i_C_j_pi_theta_j:r(0,13)ln_p_X_i_C_j_pi_theta_max:r(0,13)p_pi_theta:(0,20)p_X_i_pi_theta_div_p_X_i_C_j_pi_theta_max:(0,13)p_X_i_C_j_pi_theta_div_p_X_i_C_j_pi_theta_max:r(0,13)log_cutoff:r(0,13)log_tolerance:(0,13)ln_p_X_i_pi_theta:r(0,13)ln_p_X_pi_theta:(0,13)most_probable_class_for_datum_i:F(0,1)i:p(0,1)max_j:r(0,1)maxval:r(0,12)wt_i_j:r(0,12)update_ln_p_x_pi_theta:F(0,19)no_change:p(0,1)x:(33,13)x_i:(28,3)ln_p_x_i_c_j_pi_theta:(0,20)ln_p_x_pi_theta:(0,13)p_div_max:(0,13)ln_p_div_max:r(0,13)ln_p_j:r(0,13)search-basic.cgenerate_clsf:F(28,33)header_file_fp:p(0,20)=*(1,1)restart_p:p(0,1)start_fn_type:p(7,35)initial_cycles_p:p(0,4)start_j_list_from_s_params:p(0,1)data_file_ptr:r(7,35)log_file_ptr:r(7,35)start_fn_type:r(7,35)initial_cycles_p:r(0,4)max_data:r(0,1)num_models:(0,1)db:r(28,27)random_set_clsf:F(0,1)delete_duplicates:p(0,1)display_wts:p(0,1)delete_duplicates:r(0,1)display_wts:r(0,1)index_0:r(0,1)used_list:(28,83)n_used_list:(0,1)num_atts:r(0,1)n_others:(0,1)used_cls_list:(28,83)n_used_cls_list:r(0,1)m:r(0,1)class:(28,9)proto_wt:(0,12)set_up_clsf:F(28,33)model_set:p(28,96)n_models:p(0,1)n_models:r(0,1)block_set_clsf:F(0,19)block_size:p(0,1)base:(0,1)limit:(0,1)num_wts:(0,1)initialize_parameters:F(0,1)delete_class_duplicates:F(28,97)newlength:r(0,1)search-control.cautoclass_search:F(0,1)search_params_file_ptr:p(7,35)search_file_ptr:p(7,35)start_j_list:V(0,20)=ar(0,1);0;25;(0,1)fixed_j:(0,1)min_report_period:(0,1)max_duration:(0,1)max_n_tries:(0,1)n_save:(0,1)log_file_p:(0,4)search_file_p:(0,4)results_file_p:(0,4)min_save_period:(0,1)max_n_store:(0,1)n_final_summary:(0,1)start_fn_type:(43,42)try_fn_type:(43,42)n_classes_fn_type:(43,42)initial_cycles_p:(0,4)save_compact_p:(0,4)read_compact_p:(0,4)randomize_random_p:(0,4)halt_range:(0,12)halt_factor:(0,12)rel_delta_range:(0,12)n_average:(0,1)cs4_delta_range:(0,12)sigma_beta_n_values:(0,1)max_cycles:(0,1)converge_print_p:(0,4)force_new_search_p:(0,4)checkpoint_p:(0,4)min_checkpoint_period:(0,1)reconverge_type:(43,42)screen_output_p:(0,4)interactive_p:(0,4)break_on_warnings_p:(0,4)free_storage_p:(0,4)log_file_fp:V(0,21)=*(1,1)search_file_fp:V(0,21)header_file_fp:V(0,21)model_file_fp:V(0,21)stream:V(0,21)search_params_file_fp:V(0,21)restart_search:r(43,39)search:r(43,39)clsf:(43,33)restart_p:(0,1)n_dup_tries:(0,1)s_parms_error_cnt:(0,1)begin:(28,2)now:(28,2)last_search_save:(28,2)last_report:(28,2)end_time:(28,2)last_results_save:(28,2)begin_try:r(28,2)j_in:(0,1)n_stored_clsf:r(0,1)dup_p:r(0,1)max_j:(0,1)n_start_j_list:(0,1)new_start_j_list:r(43,83)bclength:(0,1)last_bclength:(0,1)latest_try:(43,36)best:(43,36)ss:r(43,98)best_clsfs:V(0,22)=*(43,33)last_saved_clsfs:V(0,22)stop_reason:(43,42)temp_str:(0,23)=ar(0,1);0;4;(0,2)caller:(0,24)=ar(0,1);0;16;(0,2)params:(0,25)=ar(0,1);0;39;(44,4)start_j_list_from_s_params:(0,1)checkpoint_clsf_cnt:(0,1)results_file_found:r(0,1)clsf_n_list:V(0,26)=ar(0,1);0;10;(0,1)checkpoint_file:V(43,4)maybe_checkpoint_file:V(43,4)results_file:V(43,4)n_classes_explain:V(43,4)str:(43,4)silent_p:r(0,1)double_str:(7,35)compact_p:r(0,4)search-control-2.ccut_where_above_table:S(0,20)=ar(0,1);0;30;(0,21)=ar(0,1);0;1;(0,12)remove_too_big:F(43,83)limit:p(0,1)list:p(43,83)num:p(43,83)num:r(43,83)size:r(0,1)new:r(43,83)too_big:F(0,1)limit:r(0,1)list:r(43,83)member_exceeds_limit:r(0,1)within:F(0,13)min_val:p(0,13)x:p(0,13)max_val:p(0,13)min_val:r(0,13)x:r(0,13)max_val:r(0,13)safe_subseq_of_tries:F(43,98)seq:p(43,98)begin:p(0,1)n_to_save:p(0,1)n_tries:p(0,1)n_saved:p(43,83)seq:r(43,98)begin:r(0,1)n_tries:r(0,1)n_saved:r(43,83)new_seq:r(43,98)print_initial_report:F(0,19)stream:p(0,22)=*(1,1)log_file_fp:p(0,22)min_report_period:p(0,1)end_time:p(28,2)max_n_tries:p(0,1)min_save_period:p(0,1)n_save:p(0,1)log_file_fp:r(0,22)min_save_period:r(0,1)caller:(0,23)=ar(0,1);0;20;(0,2)str_length:r(0,1)print_report:F(0,19)stream:p(0,22)search:p(43,39)last_save:p(28,2)last_report:p(28,2)reconverge_p:p(0,1)n_classes_explain:p(7,35)reconverge_p:r(0,1)n_classes_explain:r(7,35)n_dups:(0,1)time_so_far:(0,1)n_peaks_seen:(0,1)n_not_reported:r(0,1)min_n_peak:(0,1)min_best_time:(0,1)now:r(28,2)delta_time:(28,2)ln_p_avg:(0,12)ln_p_sigma:(0,12)ln_p:(0,12)delta_ln_p:(0,12)avg_best_delta_ln_p:(0,12)min_best_delta_ln_p:(0,12)avg_better_ln_p:(0,12)avg_better_time:(0,12)time_overhead:(0,12)try:(43,36)tries:r(43,98)caller:(0,24)=ar(0,1);0;12;(0,2)print_final_report:F(0,19)begin:p(28,2)stop_reason:p(7,35)results_file_p:p(0,4)search_file_p:p(0,4)n_final_summary:p(0,1)clsf:p(43,33)last_trial:p(28,2)last_save:r(28,2)stop_reason:r(7,35)clsf:r(43,33)caller:(0,25)=ar(0,1);0;18;(0,2)search_try:r(43,36)print_search_try:F(0,19)try:p(43,36)saved_p:p(0,1)new_line_p:p(0,1)pad:p(7,35)comment_data_headers_p:p(0,4)try:r(43,36)pad:r(7,35)empty_search_try:F(0,19)total_try_time:F(0,1)tries:p(43,98)sum:r(0,1)try_variation:F(43,36)j_in:p(0,1)trial_n:p(0,1)reconverge_type:p(7,35)try_fn_type:p(7,35)begin_try:p(28,2)halt_range:p(0,13)halt_factor:p(0,13)rel_delta_range:p(0,13)max_cycles:p(0,1)n_averag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7,35)substr:r(7,35)getf:F(6,2)list:p(40,88)property:p(7,35)list:r(40,88)get:F(6,2)target:p(7,35)t1:(40,5)t2:(40,5)add_property:F(0,19)pname:p(7,35)value:p(6,2)target:r(7,35)pname:r(7,35)value:r(6,2)add_to_plist:F(0,19)att:p(40,24)att:r(40,24)write_vector_float:F(0,19)vector:p(40,3)caller:(0,27)=ar(0,1);0;18;(0,2)write_matrix_float:F(0,19)vector:p(42,13)m:p(0,1)first_row:(0,1)caller:(0,28)=ar(0,1);0;18;(0,2)write_matrix_integer:F(0,19)vector:p(0,29)=*(40,83)caller:(0,30)=ar(0,1);0;20;(0,2)read_vector_float:F(0,19)read_matrix_float:F(0,19)i_file:(0,1)read_matrix_integer:F(0,19)vector:p(0,29)discard_comment_lines:F(0,1)flush_line:F(0,19)read_char_from_single_quotes:F(0,1)param_name:p(7,35)param_name:r(7,35)strcontains:F(0,1)c:p(0,1)output_int_list:F(0,1)i_list:p(40,82)i_list:r(40,82)first_list_item:(0,1)cnt:r(0,1)str:(40,42)pop_int_list:F(0,1)n_list_ptr:p(40,83)value_ptr:p(40,83)n_list_ptr:r(40,83)value_ptr:r(40,83)found_j_in:r(0,1)push_int_list:F(0,19)value:p(0,1)max_n_list:p(0,1)value:r(0,1)member_int_list:F(0,1)list:p(40,82)list:r(40,82)float_sort_cell_compare_gtr:F(0,1)i_cell:p(40,52)j_cell:p(40,52)i_cell:r(40,52)j_cell:r(40,52)class_case_sort_compare_lsr:F(0,1)i_xref:p(40,70)j_xref:p(40,70)i_xref:r(40,70)j_xref:r(40,70)att_i_sum_sort_compare_gtr:F(0,1)i_influ_val:p(40,76)j_influ_val:p(40,76)i_influ_val:r(40,76)j_influ_val:r(40,76)float_p_p_star_compare_gtr:F(0,1)i_formatted_p_p_star:p(40,79)j_formatted_p_p_star:p(40,79)i_formatted_p_p_star:r(40,79)j_formatted_p_p_star:r(40,79)safe_fprintf:F(0,19)format:p(7,35)format:r(7,35)safe_sprintf:F(0,19)str_length:p(0,1)utils-math.clog_gamma:F(0,13)low_precision:p(0,1)low_precision:r(0,1)atoi_p:F(0,1)string_num:p(7,35)integer_p_ptr:p(31,83)string_num:r(7,35)integer_p_ptr:r(31,83)string_length:r(0,1)a_char:r(0,1)__nptr:r(28,4)atof_p:F(0,13)float_p_ptr:p(31,83)float_p_ptr:r(31,83)num:r(0,13)safe_exp:F(0,13)mean_and_variance:F(0,19)vector:p(0,20)=*(0,13)cnt:p(0,1)mean_ptr:p(0,20)variance_ptr:p(0,20)vector:r(0,20)mean_ptr:r(0,20)variance_ptr:r(0,20)sum_sq:r(0,13)double_mean:r(0,13)safe_log:F(0,13)intf-reports.cautoclass_reports:F(0,1)reports_params_file_ptr:p(7,35)influ_vals_file_ptr:p(7,35)xref_class_file_ptr:p(7,35)xref_case_file_ptr:p(7,35)test_data_file:p(7,35)n_clsfs:(0,1)clsf_n_list:V(0,20)=ar(0,1);0;10;(0,1)report_type:(43,42)report_mode:(43,42)comment_data_headers_p:(0,4)num_atts_to_list:(0,1)xref_class_report_att_list:V(0,21)=ar(0,1);0;20;(0,1)order_attributes_by_influence_p:(0,4)max_num_xref_class_probs:(0,1)sigma_contours_att_list:V(0,22)=ar(0,1);0;29;(0,1)r_parms_error_cnt:r(0,1)clsf_n_list_from_s_params:(0,1)num_clsf_n_list:(0,1)num_clsfs_found:(0,1)n_clsf:(0,1)clsf_num:r(0,1)prediction_p:(0,1)last_clsf_p:(0,1)sigma_contours_list_len:(0,1)search:(43,39)search_file_fp:r(0,23)=*(1,1)reports_params_file_fp:r(0,23)log_file_fp:V(0,23)params:(0,24)=ar(0,1);0;39;(44,4)clsf_seq:(0,25)=*(43,33)test_clsf:(43,33)xref_data:(43,70)autoclass_mode:(43,4)caller:(0,26)=ar(0,1);0;17;(0,2)clsf_search_validity_check:F(0,1)clsf_id:(0,13)influence_values_report_streams:F(0,19)num_atts_to_list:p(0,1)report_mode:p(7,35)order_attributes_by_influence_p:p(0,4)sigma_contours_att_list:p(43,82)influ_vals_file_ptr:r(7,35)comment_data_headers_p:r(0,4)sigma_contours_att_list:r(43,82)influence_report_pathname:V(43,4)clsf_num_string:(0,27)=ar(0,1);0;3;(0,2)influence_report_fp:r(0,23)caller:(0,28)=ar(0,1);0;31;(0,2)case_class_data_sharing:F(43,70)report_type:p(7,35)xref_class_report_att_list:p(43,82)last_clsf_p:p(0,1)prediction_p:p(0,1)max_num_xref_class_probs:p(0,1)report_type:r(7,35)xref_class_file_ptr:r(7,35)xref_case_file_ptr:r(7,35)prediction_p:r(0,1)data:(43,70)case_report_streams:F(43,70)xref_data:p(43,70)last_clsf_p:r(0,1)max_num_xref_class_probs:r(0,1)xref_case_report_pathname:V(43,4)clsf_num_string:(0,29)=ar(0,1);0;3;(0,2)xref_case_report_fp:r(0,23)caller:(0,30)=ar(0,1);0;19;(0,2)class_report_streams:F(43,70)xref_class_report_att_list:r(43,82)xref_class_report_pathname:V(43,4)clsf_num_string:(0,31)=ar(0,1);0;3;(0,2)xref_class_report_fp:r(0,23)caller:(0,32)=ar(0,1);0;20;(0,2)xref_get_data:F(43,70)report_attributes:p(43,82)case_num:(0,1)att_number:r(0,1)n_collector:r(0,1)n_discrete_att:r(0,1)n_real_att:(0,1)num_discrete_att:(0,1)num_real_att:(0,1)xref_data_allocated:(0,1)collector_length:r(0,1)data_array:(45,13)datum_array:(43,3)collector:(43,52)att_info:(43,89)classes:(43,97)discrete_attribute_data:(43,93)real_attribute_data:(43,3)map_class_num_clsf_to_report:F(0,1)clsf_n_class:p(0,1)clsf_n_class:r(0,1)report_n_class:r(0,1)map_class_num_report_to_clsf:F(0,1)report_n_class:p(0,1)autoclass_xref_by_case_report:F(0,19)xref_case_report_fp:p(0,23)xref_data:r(43,70)initial_line_cnt_max:(0,1)classification_header:F(0,19)report_mode:r(7,35)xref_paginate_by_case:F(0,19)initial_line_cnt_max:p(0,1)line_cnt_max:r(0,1)page_num:(0,1)column_1_index:(0,1)column_3_index:(0,1)page_1_p:r(0,1)current_data_index:(0,1)current_n_data:(0,1)line_cnt:(0,1)column_2_index:(0,1)elt1:(43,72)elt2:(43,72)elt3:(43,72)float_value:r(0,12)xref_output_page_headers:F(0,19)page_1_p:p(0,1)num_report_attribute_strings:p(0,1)report_attribute_strings:p(0,33)=*(43,73)xref_report_fp:p(0,23)dashed_line:(0,34)=ar(0,1);0;91;(0,2)attribute_labels:(43,4)blank_cnt:r(0,1)divider_format:(43,42)report_att_string:r(43,73)autoclass_xref_by_class_report:F(0,19)xref_class_report_fp:p(0,23)initial_line_cnt:(0,1)xref_paginate_by_class:F(0,19)initial_line_cnt:p(0,1)initial_line_cnt:r(0,1)cnt:(0,1)current_class:(0,1)line_cnt:r(0,1)n_datum:(0,1)num_wt_class_pairs:r(0,1)num_report_att_strings:(0,1)prob_tab:(0,1)attribute_formats:(43,93)report_attribute_strings:(0,33)xref_datum:(43,72)wt_class_pairs:r(43,52)xref_class_report_attributes:F(0,33)report_attribute_numbers:p(43,82)attribute_formats_ptr:p(0,35)=*(43,93)prob_tab_ptr:p(43,83)att_dscrp:r(7,35)translations:(43,87)all_att_info:(43,89)att_number:(0,1)dscrp_length:r(0,1)xref_paginate_by_class_hdrs:F(0,19)cnt_ptr:p(43,83)line_cnt:p(0,1)wt_class_pairs:p(43,52)init:p(0,1)report_attribute_strings:p(0,33)cnt_ptr:r(43,83)num_report_attribute_strings:r(0,1)report_attribute_strings:r(0,33)xref_output_line_by_class:F(0,19)attribute_formats_ptr:p(0,35)xref_datum_ptr:p(43,70)prob_tab:p(0,1)prob_tab:r(0,1)print_atts_p:(0,1)prob_tab_format:(43,4)question_mark:(0,36)=ar(0,1);0;1;(0,2)autoclass_influence_values_report:F(0,19)header_information_p:p(0,1)influence_report_fp:p(0,23)report_class_number:r(0,1)class_number_type:(0,37)=ar(0,1);0;4;(0,2)influence_values_header:F(0,19)populated_classes_cnt:(0,1)clsf_class_number:r(0,1)max_line_cnt:(0,1)class_number_type:(0,38)=ar(0,1);0;4;(0,2)output:(43,76)header:(43,4)__u:r(0,39)=*(0,40)=u4__ui:(7,12),0,32;__usi:(7,10),0,16;__uc:(0,11),0,8;;autoclass_class_influence_values_report:F(0,19)class_number_type:p(7,35)class_number:p(0,1)single_class_p:p(0,1)header_information_p:r(0,1)single_class_p:r(0,1)clsf_class_number:(0,1)num_term_types:(0,1)first_term_type:(0,1)real_atts_header_p:(0,1)discrete_atts_header_p:(0,1)report_class_num:r(0,1)title_line_1:(0,41)=ar(0,1);0;319;(0,2)title_line_2:(0,42)=ar(0,1);0;479;(0,2)class_model_source:(43,4)temp:(43,4)term_types:(43,93)a_term_type:(43,42)caller:(0,43)=ar(0,1);0;39;(0,2)populated_class_p:F(0,1)class_number:r(0,1)class_number_type:r(7,35)ordered_normalized_influence_values:F(43,76)output:r(43,76)reports:(43,49)max_i_sum:(0,12)att_i_sum:r(0,12)influence_values_explanation:F(0,19)search_summary:F(0,19)pad:(7,35)dashes:(43,4)class_weights_and_strengths:F(0,19)max_strength:(0,12)class_strength:(0,12)output_title:(43,4)class_divergences:F(0,19)class_divergence:(0,12)text_stream_header:F(0,19)title_line_1:p(7,35)title_line_2:p(7,35)title_line_1:r(7,35)title_line_2:r(7,35)pre_format_attributes:F(0,19)clsf_class_number:p(0,1)discrete_atts_header_p:p(0,1)real_atts_header_p:p(0,1)sort_list:(43,52)number_of_sorted_attributes:(0,1)term_count:(0,1)sort_index:r(0,1)n_model_term:(0,1)current_model_term:r(0,1)discrete_iv_struct:r(43,61)integer_iv_struct:r(43,64)real_iv_struct:r(43,67)model:(43,30)mean_sigma_list:r(43,3)print_attribute_header:F(0,19)format_attribute:F(0,1)header:V(0,44)=ar(0,1);0;59;(0,2)header_continued:V(0,45)=ar(0,1);0;59;(0,2)descrp_length:r(0,1)temp:(43,42)temp1:(43,42)discrete_influence_values:r(43,61)integer_influence_values:r(43,64)real_influence_values:r(43,67)type:(7,35)type_letter:(0,46)=ar(0,1);0;1;(0,2)description:(7,35)model_term_type_symbol:(0,47)=ar(0,1);0;10;(0,2)print_string:r(7,35)dot:(0,2)caller:(0,48)=ar(0,1);0;16;(0,2)model:r(43,30)format_discrete_attribute:F(0,1)d_base:p(43,27)header:p(7,35)header_continued:p(7,35)influence_values:p(43,61)line_length:p(0,1)description:p(7,35)header:r(7,35)name_length:r(0,1)line_cnt_max:(0,1)new_lines:(0,1)list_index:(0,1)p_p_star_list:r(43,3)local_prob:(0,12)global_prob:(0,12)att_value_influence:(0,12)discrete_string_name:(43,42)formatted_p_p_star_list:(43,79)warn_errs:r(43,15)e_format_string:(43,4)format_integer_attribute:F(0,1)integer_influence_values:p(43,64)model_term_type_symbol:p(7,35)format_real_attribute:F(0,1)influence_values:p(43,67)header_continued:r(7,35)description:r(7,35)new_lines:r(0,1)generate_mncn_correlation_matrices:F(0,19)n_term_list:r(0,1)term_list:(43,3)f_list:r(43,3)covar_matrix:r(45,13)correl_matrix:(45,13)real_influence_values:(43,67)model_term_type:r(7,35)correl_term_list:(0,49)=ar(0,1);0;49;(0,1)num_correl_term_list:(0,1)i_list:r(43,83)attribute_model_term_number:F(0,1)model:p(43,30)sort_mncn_attributes:F(0,19)sort_list:p(43,52)sort_index:p(0,1)term_count:p(0,1)sort_list:r(43,52)term_count:r(0,1)sort_list_temp:r(43,52)temp_j:r(0,1)filter_e_format_exponents:F(7,35)e_format_string:p(7,35)e_format_string:r(7,35)filtered_numeric_string:V(0,50)=ar(0,1);0;159;(7,35)intf-extensions.cinitialize_reports_from_results_pathname:F(0,20)=*(43,33)clsf_n_list:p(43,82)num_clsfs_found_ptr:p(43,83)clsf_list:(0,20)clsf_seq:(0,20)i_clsf:r(0,1)file_type:(43,42)init_clsf_for_reports:F(43,33)class_strength_list:(43,3)get_class_weight_ordering:F(43,83)class_weight_ordering:r(43,83)temp_sort_list:r(43,52)get_attribute_model_term_types:F(43,95)model_term_type_array:(43,95)att_model_term_type_array:r(43,87)report_att_type:F(7,35)rpt_att_model_term_type:F(7,35)get_models_source_info:F(0,19)models:p(43,96)xref_case_text_fp:p(0,21)=*(1,1)get_class_model_source_info:F(0,19)class:p(43,9)class_model_source:p(7,35)class_model_source:r(7,35)source:r(7,35)caller:(0,22)=ar(0,1);0;27;(0,2)intf-influence-values.ccompute_influence_values:F(0,19)class:(43,9)curr_influence_value:r(0,12)class_influence_value_max:(0,12)influence_sum:(0,12)global_influence_value_max:(0,12)influence_sums:(43,3)all_classes_influence_value_max:(43,3)influence_struct_DS:(6,2)attribute_array:r(43,85)influence_value:F(0,13)att_type:p(7,35)influence_struct_DS_ptr:p(43,85)n_att_prob_list:(0,1)n_term_att_list:(0,1)term_params:r(45,1)influence_value:(0,12)class_div_global_att_prob_list:(43,3)class_mean:(0,12)class_sigma:(0,12)class_known_prob:(0,12)global_mean:(0,12)global_sigma:(0,12)global_known_prob:(0,12)term_att_list:(43,3)class_covar:(45,13)i_discrete_struct:r(43,61)i_real_struct:r(43,67)att:r(43,24)find_attribute_modeling_class:F(0,1)class_ptr:p(43,97)class_ptr:r(43,97)intf-sigma-contours.cgenerate_sigma_contours:F(0,19)sigma_att_list:p(43,82)influence_report_fp:p(0,20)=*(1,1)comment_data_headers_p:p(0,1)mean_x:(0,12)sigma_x:(0,12)mean_y:(0,12)sigma_y:(0,12)rotation:(0,12)att_x:(0,1)att_y:r(0,1)error_p:(0,1)sigma_contours_list_len:r(0,1)trans_att_x:(0,1)trans_att_y:(0,1)report_class_number:(0,1)att_err_msg_p:r(43,83)att_info_x:(43,24)att_info_y:(43,24)term_index_x:(0,1)term_index_y:(0,1)terms:r(43,92)term_x:r(43,12)term_y:(43,12)ignore_str:(0,21)=ar(0,1);0;6;(0,2)compute_sigma_contour_for_2_atts:F(0,1)att_x:p(0,1)att_y:p(0,1)trans_att_x:p(0,1)trans_att_y:p(0,1)term_index_x:p(0,1)term_index_y:p(0,1)mean_x:p(43,3)sigma_x:p(43,3)mean_y:p(43,3)sigma_y:p(43,3)rotation:p(43,3)trans_att_y:r(0,1)mean_x:r(43,3)class_covar_x:r(45,13)term_list:r(43,3)sigma_x_y:r(0,12)sum:(0,12)sigma_x_sq:r(0,12)sigma_y_sq:r(0,12)diff_sigma_x_y_term:(0,12)rotation_increment:r(0,12)arg:r(0,12)class_att_loc:F(0,1)trans_att_index:p(43,83)trans_att_index:r(43,83)att_loc_string:(43,42)term_index:(0,1)str_index:r(7,35)get_sigma_x_y:F(0,12)n_term_list:p(0,1)term_list:p(43,3)covariance:p(45,13)covariance:r(45,13)covar_index_x:(0,1)covar_index_y:(0,1)prints.csum_vector_f:F(0,19)t:p(7,35)v:r(28,3)t:r(7,35)float_sum:r(0,12)double_sum:r(0,13)print_vector_f:F(0,19)print_matrix_f:F(0,19)v:p(33,13)print_matrix_i:F(0,19)v:p(0,20)=*(28,83)print_mm_d_params:F(0,19)p:p(33,5)print_mm_s_params:F(0,19)p:p(33,7)print_mn_cn_params:F(0,19)p:p(33,8)print_sm_params:F(0,19)p:p(33,9)print_sn_cm_params:F(0,19)p:p(33,10)print_sn_cn_params:F(0,19)p:p(33,11)print_tparm_DS:F(0,19)p:p(33,1)p:r(33,1)print_priors_DS:F(0,19)p:p(28,6)p:r(28,6)print_class_DS:F(0,19)p:p(28,9)p:r(28,9)print_term_DS:F(0,19)p:p(28,12)p:r(28,12)print_real_stats_DS:F(0,19)p:p(28,18)p:r(28,18)print_discrete_stats_DS:F(0,19)p:p(28,21)p:r(28,21)print_att_DS:F(0,19)p:p(28,24)p:r(28,24)print_database_DS:F(0,19)p:p(28,27)p:r(28,27)print_model_DS:F(0,19)p:p(28,30)p:r(28,30)print_clsf_DS:F(0,19)p:p(28,33)p:r(28,33)print_search_try_DS:F(0,19)p:p(28,36)p:r(28,36)print_search_DS:F(0,19)p:p(28,39)p:r(28,39)getparams.cputparams:F(0,19)fp:p(0,20)=*(1,1)pp:p(32,7)only_overridden_p:p(0,1)fp:r(0,20)pp:r(32,7)first_param:(0,1)params_cnt:(0,1)paramptr_output:r(6,2)getparams:F(0,1)fp:p(0,20)params:p(32,7)buff:(0,21)=ar(0,1);0;101;(0,2)bp:r(7,35)pp:(32,7)error_cnt:(0,1)int_list_paramptr:r(31,83)int_list_paramptr_overridden:r(31,83)string_char_paramptr:r(7,35)input_string:(31,42)__s:r(28,4)__result:r(5,1)defparam:F(0,19)nparams:p(0,1)name:p(7,35)type:p(32,3)ptr:p(6,2)max_length:p(0,1)params:r(32,7)nparams:r(0,1)name:r(7,35)type:r(32,3)ptr:r(6,2)max_length:r(0,1)autoclass.cmain:F(0,1)argc:p(0,1)argv:p(43,87)argc:r(0,1)argv:r(43,87)data_file_arg_ptr:r(7,35)header_file_arg_ptr:(7,35)model_file_arg_ptr:(7,35)search_params_file_arg_ptr:r(7,35)reports_params_file_arg_ptr:r(7,35)ac_option:r(7,35)results_file_arg_ptr:r(7,35)search_file_arg_ptr:(7,35)data_file:V(43,4)header_file:V(43,4)model_file:V(43,4)search_params_file:V(43,4)search_file:V(43,4)log_file:V(43,4)reports_params_file:V(43,4)influ_vals_file:V(43,4)xref_class_file:V(43,4)xref_case_file:V(43,4)test_data_file:V(43,4)valid_file_p:(0,1)autoclass_args:F(0,19)operate:(0,20)=ar(0,1);0;11;(0,2)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)GCC: (GNU) 2.95.3 19991030 (prerelease)01.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.0101.01.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rel.got.rel.bss.rel.plt.init.plt.text.fini.rodata.data.eh_frame.ctors.dtors.got.dynamic.sbss.bss.stab.stabstr.comment.note# 1((7 ?<Go~Toll Pc  l ĉ u ܉  ~|| 1 P66F  '  <+<@+@H+HP+P0,0,,( t |2C ?I L 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.+.initfini.cgcc2_compiled.init.cLetextcrtstuff.cp.3__DTOR_LIST__completed.4__do_global_dtors_aux__EH_FRAME_BEGIN__fini_dummyobject.11frame_dummyinit_dummyforce_to_data__CTOR_LIST____do_global_ctors_aux__CTOR_END____DTOR_END____FRAME_END__globals.cio-read-data.cio-read-model.cio-results.ctemp_save_file.96save_file.97file.149binary_file.150file.154binary_file.155io-results-bin.cmodel-expander-3.cmatrix-utilities.cmodel-single-multinomial.cmodel-single-normal-cm.cbuild_sn_cm_priorsmodel-single-normal-cn.cbuild_sn_cn_priorsmodel-multi-normal-cn.cmodel-transforms.cmodel-update.csearch-basic.csearch-control.cstart_j_list.84log_file_fp.85search_file_fp.86header_file_fp.87model_file_fp.88stream.89search_params_file_fp.90best_clsfs.91last_saved_clsfs.92clsf_n_list.93checkpoint_file.94maybe_checkpoint_file.95results_file.96n_classes_explain.97search-control-2.ccut_where_above_tabletemp_search_file.189id.193clsf_n_list.203results_file.204search-converge.cstruct-class.cstruct-clsf.cstatistics.cpredictions.cstruct-data.cdata_file.102header_file.103struct-matrix.cstruct-model.cmodel_file.90utils.ccurrent_time.93time_string.100utils-math.cintf-reports.cclsf_n_list.84xref_class_report_att_list.85sigma_contours_att_list.86log_file_fp.87influence_report_pathname.94xref_case_report_pathname.101xref_class_report_pathname.105header.181header_continued.182filtered_numeric_string.204intf-extensions.cintf-influence-values.cintf-sigma-contours.cprints.cgetparams.cautoclass.cdata_file.84header_file.85model_file.86search_params_file.87search_file.88results_file.89log_file.90reports_params_file.91influ_vals_file.92xref_class_file.93xref_case_file.94test_data_file.95extend_terms_singleavg_time_till_improveG_clsf_storeupdate_location_infoload_att_DSread_lineextend_terms_multiG_transformsG_num_cyclesprint_search_tryG_absolute_pathnamewrite_sn_cn_paramswrite_vector_floatstore_real_statssolveget_class_model_source_infodescribe_clsfsort_mncn_attributessetf_v_vfgetc@@GLIBC_2.0find_classdefault_translationsum_vector_ffree_class_DS__strtod_internal@@GLIBC_2.0atoi_pwrite_att_DSprint_sm_paramswrite_clsf_seqmulti_normal_cn_log_likelihoodwrite_priors_DSatt_info_equalmax_plustry_variationroundstrncat@@GLIBC_2.0vsprintf@@GLIBC_2.0strchr@@GLIBC_2.0invert_factored_square_matrixread_tparm_DSsingle_normal_cn_log_likelihoodsingle_normal_cm_class_equivalencesafe_subseq_of_triesread_model_filefloat_sort_cell_compare_gtrG_n_create_classes_after_freeint_compare_greatercompute_sigma_contour_for_2_attsG_model_listattribute_model_term_numbercopy_class_DSread_model_DScompress_databasedump_mm_d_paramsxref_class_report_attributesfind_attribute_modeling_classread_clsf_seqatan@@GLIBC_2.0expand_clsfsrand48@@GLIBC_2.0min_time_till_bestfree_model_DSungetc@@GLIBC_2.0_DYNAMICinfluence_values_report_streamsrandomize_listtranslate_realread_listsread_char_from_single_quotesupper_end_normal_fitdiscard_comment_linesupdate_wtsblock_set_clsfupdate_m_approx_fnmulti_normal_cn_class_merged_marginalfind_str_in_list__register_frame_info@@GLIBC_2.0ordered_normalized_influence_valuesG_data_file_formatG_streamwrite_mm_s_paramsputparamsupdate_approximationssearch_summarywrite_search_DSprint_sn_cm_paramsmake_matrixincf_m_msread_matrix_integeradd_to_plistG_safe_file_writing_pstrcmp@@GLIBC_2.0averagefind_databaseprint_sn_cn_paramsxref_output_line_by_class_fp_hwprint_search_DSextract_rhosread_liststrcontainsfprintf@@GLIBC_2.0G_clsf_storage_log_pG_db_listgetfatt_stats_equivalent_pmulti_normal_cn_update_paramslog_headerfind_singleton_transformpush_clsfconverge_search_4variancey_or_n_pcheck_errors_and_warningsfind_database_psafe_fprintfcompress_clsffree_clsf_DSG_n_freed_classesprint_mm_s_paramsstore_clsf_DSmake_and_validate_pathnamegenerate_singleton_transformpercent_equalsetf_v_vspop_clsfavg_improve_delta_ln_pmallocformat_real_attributeprocessedlist_clsf_storageload_sm_paramsread_mm_s_paramsprint_priors_DSerfc_polydot_mmconditional_expand_model_termsbuild_compressed_class_DSfind_str_in_tableprint_term_DSstore_clsf_DS_classesmean_and_varianceprocess_attribute_defreconstruct_searchfind_discrete_statsG_solarisdump_clsf_seqoutput_created_translationsfind_real_statsn_svexpand_model_resetclass_DS_testcreate_clsf_DSoutput_message_summaryinterpolatecopy_tparm_DSoutput_db_error_messagesclass_weights_and_strengthsgetparamsdeterminent_fread_matrix_floatclass_divergencesrandom_j_from_ln_normalln_avg_pvector_root_diagonal_matrixG_checkpoint_filecheck_model_termssingle_multinomial_class_merged_marginalclsf_search_validity_checkprint_reportdecf_v_vextend_databaseevery_db_DS_same_source_pcopy_clsf_DSG_last_checkpoint_writtenpow@@GLIBC_2.0write_search_try_DSmost_probable_class_for_datum_idb_DS_equal_plog_likelihood_fnsafe_expmember_int_listdefine_attribute_definitionsG_m_idwrite_clsf_DSG_save_compact_psafe_logoutput_warning_msgssingle_multinomial_class_equivalencesystem@@GLIBC_2.0get_line_tokensG_plengthautoclass_xref_by_class_reportprint_logatof_patt_i_sum_sort_compare_gtrvalidate_results_pathnametranslate_discrete_initsingle_multinomial_model_term_buildermember_intload_clsfread_sn_cm_paramsG_min_checkpoint_periodinverse_erfcadd_propertymodel_DS_equal_patt_DS_equal_pinfluence_values_headerfillget_clsf_DSclass_merged_marginal_fnclsf_DS_max_n_classesfscanf@@GLIBC_2.0check_termsn_cm_params_influence_fnupdate_means_and_covariancesigma_sqgenerate_mncn_correlation_matricespop_int_listG_likelihood_tolerance_ratioload_model_DScut_where_abovefind_similar_modelmap_class_num_report_to_clsfwrite_model_DSload_mm_d_paramscollect_indexed_valuesfread@@GLIBC_2.0write_class_DS_sxref_get_datagenerate_attribute_inforpt_att_model_term_typefilter_e_format_exponentsupdate_params_fnto_screen_and_log_fileprint_final_reportwithin__deregister_frame_info@@GLIBC_2.0arrange_model_function_termssame_model_and_attributesxref_paginate_by_class_hdrsG_prediction_psafe_fwritecase_class_data_sharinglimit_min_diagonal_valuessingle_normal_cn_class_merged_marginalwrite_tparm_DSexpand_databasemodel_global_tparmsvalidate_search_start_fnpop_class_DSflush_lineread_mm_d_paramsapply_search_try_fncompute_influence_valuessingle_multinomial_update_paramsstdout@@GLIBC_2.0dot_vvincf_v_vstderr@@GLIBC_2.0read_databaseoutput_error_msgslog_transformabort@@GLIBC_2.0defparamget_search_from_fileautoclass_predictinit_clsf_for_reportsfind_duplicateroot_diagonal_matrixclass_strength_measuresingle_normal_cm_log_likelihooddelete_null_classesnext_best_deltaincf_v_vsread_mn_cn_paramsfloat_p_p_star_compare_gtrprint_discrete_stats_DSfree_clsf_class_search_storageG_interactive_pn_smextract_diagonal_matrixinsert_new_trialget_class_weight_orderingdump_class_DS_sapply_n_classes_fnG_input_data_basevfprintf@@GLIBC_2.0load_clsf_seqsingle_normal_cn_update_paramssingle_normal_cn_model_term_builderautoclass_class_influence_values_reportG_m_lengthwrite_database_DSget_search_DSget_attribute_model_term_typestime@@GLIBC_2.0dump_clsf_DSsigmaread_sm_paramsgenerate_sigma_contoursdefine_models_startoutput_messagesdefine_data_file_formatbase_cycleload_mm_s_paramsclassification_headerclsf_att_typeint_compare_lessfgets@@GLIBC_2.0get_sigma_x_ydump_tparm_DScheckpoint_clsfG_ac_versionconverge_search_3copy_to_matrixprint_mn_cn_paramsG_plistformat_time_durationatt_props_equivalent_pload_mn_cn_paramscheck_data_baseread_vector_floatfind_termbuild_class_DSprocess_translation_msgsget_sources_listG_line_cnt_maxstrstr@@GLIBC_2.0single_normal_cn_class_equivalenceread_datadescribe_searchmulti_normal_cn_class_equivalencemodel_typeread_database_DSdelete_class_duplicatesreallocsingle_multinomial_update_l_approxfind_class_test2influence_value__strtol_internal@@GLIBC_2.0qsort@@GLIBC_2.0prefixpopulated_class_pmin_best_peakclsf_DS_w_jinitialize_reports_from_results_pathnamepre_format_attributesautoclass_searchmulti_normal_cn_update_m_approxrandom_set_clsfupdate_l_approx_fnprint_model_DSlog_odds_transform_cmap_class_num_clsf_to_reporttrace_star_mmprint_initial_reportstore_class_DSfind_att_statisticsinitapprox_inverse_erfcprint_att_DScheck_stop_processingget_models_source_infolrand48@@GLIBC_2.0print_database_DSexpand_clsf_modelsfloor@@GLIBC_2.0single_normal_cn_update_m_approxmulti_normal_cn_model_term_buildernew_randomsingle_normal_cm_model_term_builderprocess_attribute_definitionsexpand_model__bss_startclass_duplicatespmainvalidate_n_classes_fnmulti_normal_cn_update_l_approxinfluence_values_explanationG_slashcentral_measures_x__libc_start_main@@GLIBC_2.0format_attributewrite_mn_cn_paramswrite_sn_cm_paramsextend_default_termsG_break_on_warningsexp@@GLIBC_2.0read_model_resetxref_paginate_by_caselist_class_storagefind_transformset_ignore_att_infosn_cn_params_influence_fnempty_search_tryread_model_doitmin_n_peaksdump_sm_paramsstrcat@@GLIBC_2.0print_class_DSget_source_listwrite_matrix_floatdata_startreport_att_typeset_up_clsfprintf@@GLIBC_2.0converge_search_3aupdate_ln_p_x_pi_thetatypical_bestfree_tparm_DSdump_term_DS_finifcntl@@GLIBC_2.0memcpy@@GLIBC_2.0sqrt@@GLIBC_2.0expand_att_listformat_integer_attributefclose@@GLIBC_2.1read_sn_cn_paramsexist_intersectionstrrchr@@GLIBC_2.0ctime@@GLIBC_2.0generate_clsfwrite_sm_paramsload_database_DSsetf_m_msgetvalidate_data_pathnameread_att_DSinit_propertiesautoclass_argsoutput_int_listremove_too_bigxref_output_page_headersclass_equivalence_fnsingle_normal_cm_update_m_approxoutput_att_statisticsxref_paginate_by_classsingle_multinomial_update_m_approxwrite_mm_d_paramstext_stream_headerfind_modelread_data_doitdump_mm_s_paramsadjust_clsf_DS_classesstar_vmvG_att_type_dataexit@@GLIBC_2.0save_searchtotal_try_timeautoclass_xref_by_case_reportprint_search_try_DSmake_mn_cn_paramautoclass_reportsformat_universal_timedelete_duplicatesread_clsfconvergesingle_normal_cm_update_l_approxcanonicalize_model_groupsscanf@@GLIBC_2.0_edatavalidate_search_try_fn_GLOBAL_OFFSET_TABLE__endcreate_warn_err_DSload_tparm_DSinitialize_parametersread_from_stringdump_mn_cn_paramsdump_database_DScreate_databasecheck_for_non_emptyapply_search_start_fnG_rand_base_normalizergetcwd@@GLIBC_2.0print_matrix_ifind_model_pprint_real_stats_DSmemset@@GLIBC_2.0create_att_DSincf_m_vvsstdin@@GLIBC_2.0translate_instancestrncpy@@GLIBC_2.0too_bigread_class_DS_sG_training_clsfget_class_DSfopen@@GLIBC_2.1db_same_source_pwrite_term_DSlog_gammaG_search_cycle_begin_timeprint_vector_fsingle_normal_cn_update_l_approxsingle_normal_cm_class_merged_marginalcopy_to_class_DSG_db_length_IO_stdin_usedclass_case_sort_compare_lsrstrtok@@GLIBC_2.0print_tparm_DSformat_discrete_attributeprint_attribute_headerwrite_matrix_integerprint_clsf_DScompute_factorpush_int_listget_search_try_from_fileprint_matrix_fsafe_sprintfdump_att_DSsprintf@@GLIBC_2.0print_mm_d_paramsclass_report_streamsfwrite@@GLIBC_2.0get_universal_timerandom_from_normalautoclass_influence_values_report__data_startinvert_diagonal_matrixsave_clsf_seqcheck_load_headerdisplay_stepsingle_multinomial_log_likelihood_IO_getc@@GLIBC_2.0process_translationmn_cn_params_influence_fnexpand_clsf_wtsclsf_DS_testexpand_model_termsget_clsf_seqlog@@GLIBC_2.0print_att_locs_and_ignore_idsupdate_parameterscase_report_streamsdump_model_DSclass_att_locprocess_data_header_model_filesdiagonal_productoutput_real_att_statisticsstar_mmload_class_DS_ssm_params_influence_fneqstringG_log_file_fpchar_input_testsearch_durationfreesingle_normal_cm_update_params__gmon_start__ceil@@GLIBC_2.0strcpy@@GLIBC_2.0autoclass-3.3.6.dfsg.1/version-3-3-5.text0000644000175000017500000000446611247310756015757 0ustar areare AUTOCLASS C VERSION 3.3.5 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.3.5. 2. autoclass-c/load-ac, autoclass-c/prog/autoclass.make.freebsd.gcc - Add support for the FreeBSD unix variant operating system. 3. autoclass-c/prog/model-multi-normal-cn.c - Change all calls to log, with safe_log, which checks for a zero argument. Certain real valued data set values caused a double precision underflow (< e-308) which resulted in 0.0. 4. autoclass-c/prog/intf-reports.c, utils-math.c, search-control-2.c, model-update.c, model-transforms.c, model-single-normal-cm.c, model-single-normal-cn.c, model-single-multinomial.c, model-expander-3.c - Make the change in item 3. to all files referencing log. 5. autoclass-c/sample/read.me.c - Correct file name typo: scriptc.lisp => scriptc.text 6. autoclass-c/load-ac - To prevent bad default .cshrc files from crashing the build, change "#!/bin/csh" to "#!/bin/csh -f". 7. autoclass-c/prog/io-results.c - write_att_DS modified to output warnings_and_errors->num_expander_warnings and warnings_and_errors->num_expander_errors strings with embedded carriage returns removed. This corrects a problem which occurs when the user's data generates warning messages during input checking, which the user ignores, and the user has specified save_compact_p = false and read_compact_p = false in their .s-params file. When they attempt to create reports, "autoclass -reports ..." breaks with an unexpected data error. 8. autoclass-c/load-ac-macosx, autoclass-c/prog/autoclass.make.macosx.gcc - Add support for the Macintosh OSX 10.4 operating system utilizing gcc 4.0. (OSFLAGS=-DMACOSX) 9. autoclass-c/prog/utils.c, autoclass-c/prog/autoclass.h - Routine "int round(double)" replaced by "int iround(double)". References to "round" were changed to "iround" in all affected routines. 10. autoclass-c/prog/autoclass.h - For MacOSX, do not define INFINITY here -- conflicts with OSX math library. autoclass-3.3.6.dfsg.1/version-3-2-1.text0000644000175000017500000000161511247310756015743 0ustar areare AUTOCLASS C VERSION 3.2.1 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/checkpoint-c.text - bring up to date the usage of force_new_search_p in the examples. Programming: ------------------------------ 1. autoclass-c/prog/globals.c, globals.h, search-control.c, intf-reports.c, autoclass.c, io-results.c, io-results-bin.c - Update "G_ac_version" to 3.2.1, and change type from float to string. 2. autoclass-c/prog/autoclass.h, io-read-data.c - Comment out unused functions: DEFINE_DISCRETE_TRANSLATIONS, and PROCESS_DISCRETE_TRANSLATIONS. Unused #defines MAXINT and DBG_LL commented out. ====================================================================== autoclass-3.3.6.dfsg.1/version-3-2.text0000644000175000017500000002302611247310756015605 0ustar areare AUTOCLASS C VERSION 3.2 NOTES ====================================================================== ====================================================================== Documentation: ------------------------------ 1. autoclass-c/doc/search-c.text - Added a new section: 14.0 How to get AutoClass C to Produce Repeatable Results. Added information about running AutoClass C with more than 1000 attributes in sections: 10.0 Do I Have Enough Memory and Disk Space? Changed the behavior of search parameter force_new_search_p in order to prevent search trials from being inadvertently lost: if TRUE, will ignore any previous search results, discarding the existing .search & .results[-bin] files after confirmation by the user; if FALSE, will continue the search using the existing .search & .results[-bin] files. The default value of force_new_search_p is now true. 2. autoclass-c/doc/interpretation-c.text - Added section headings and a new section entitled: Comparing Influence Report Class Weights And Class/Case Report Assignments 3. autoclass-c/doc/preparation-c.text - Added more to section: 1.2.1 SINGLE_NORMAL_CN/CM and MULTI_NORMAL_CN Models 4. autoclass-c/doc/reports-c.text - Improved the last pargraph of Generating Sigma Contour Values. Replace parameters start_sigma_contours_att and stop_sigma_contours_att with sigma_contours_att_list, to allow non-contiguous groups of attributes to be specified. Programming: ------------------------------ 1. autoclass-c/prog/globals.c - Update "G_ac_version" to 3.2. 2. autoclass-c/prog/intf-reports.c - In INFLUENCE_VALUES_HEADER, change `fprintf( influence_report_fp, header);' to `fprintf( influence_report_fp, header, "");', and in CLASS_WEIGHTS_AND_STRENGTHS and CLASS_DIVERGENCES add args to output_title fprintf for new page -- this prevents segmentation faults, when the number of attributes exceeds one page, while in report_mode = "text". 3. autoclass-c/prog/intf-sigma-contours.c - In COMPUTE_SIGMA_CONTOUR_FOR_2_ATTS, corrected initialization of *rotation. This corrects erroneous values of the contour's rotation. 4. autoclass-c/prog/struct-class.c - Correct compiler warning "struct-class.c:239: warning: unused variable `database'". 5. autoclass-c/prog/struct-data.c, globals.h, globals.c, search-control.c - In EXPAND_DATABASE, use comp_database->n_data rather than G_s_params_n_data, since G_s_params_n_data does not do the right thing when expand_database is called during report generation (it reads the whole file, not just n_data cases). Remove references to G_s_params_n_data from the 2nd to 4th files. 6. autoclass-c/prog/intf-reports.c - In XREF_GET_DATA, allocate more storage for instance class probabilities if there are more than MAX_NUM_XREF_CLASS_PROBS, and only save for printing a maximum of MAX_NUM_XREF_CLASS_PROBS classes. IMPORTANT NOTE: This bug fix means that for any previous reports generated by AutoClass C, any data base instance which has five class probability entries in the class cross-reference report, and 1.0 minus the sum of the five probabilities is greater than the largest of them, is in the WRONG CLASS! Re-run the reports with this version! 7. autoclass-c/prog/autoclass.c - Print the AutoClass C version when the user invokes AutoClass with no arguments: % autoclass 8. autoclass-c/load-ac - Specified define flags for SunOS gcc and Solaris gcc compilations to prevent compiler warnings. Added IRIX 6.4 compatibility. 9. autoclass-c/prog/autoclass.h - For gcc under SunOS, include function prototypes for *rand48 functions, to prevent compiler warnings. 10. autoclass-c/prog/intf-reports.c - Add descriptive text for each influence value class parameter for reports with parameter report_mode = "text". 11. autoclass-c/prog/autoclass.make.solaris.cc - Corrected optimization flag. 12. autoclass-c/prog/intf-reports.c - In FORMAT_REAL_ATTRIBUTE, correct correlation matrices print-out for non-contiguous model term attributes, and print matrices only once, after all class attributes are listed. 13. autoclass-c/prog/search-control.c - In AUTOCLASS_SEARCH, if force_new_search_p is false, exit if there is no <...>.results[-bin] file. Make TRUE the default for force_new_search_p. 14. autoclass-c/prog/intf-reports.c - In PRINT_ATTRIBUTE_HEADER, remove references to INTEGER attribute type. 15. autoclass-c/prog/getparams.c - In GETPARAMS, correct logic so that missing "line feed" on last line of the file will be read properly, rather than getting: ERROR: line read exceeds 100 characters: <.....>. In GETPARAMS, correct logic so that an empty integer list (e.g. start_j_list =) may be entered in the .s-params file. This is needed for a restart search situation when it is necessay to peel off as many classes from the start_j_list as were already done by the previous run. If all of the start_j_list was done already, then an empty list is required. 16. autoclass-c/prog/io-read-data.c, io-results.c, io-results-bin.c - In READ_DATA, EXPAND_CLSF_WTS, and LOAD_CLASS_DS_S add checks for "out of memory" returns from malloc and realloc. 17. autoclass-c/prog/io-results.c - In MAKE_AND_VALIDATE_PATHNAME, VALIDATE_RESULTS_PATHNAME, VALIDATE_DATA_PATHNAME, and GET_CLSF_SEQ change strchr to strrchr to handle `../filename.extension' 18. autoclass-c/prog/autoclass.h, predictions.c, search-basic.c, & search-control.c - Notify the user with a warning messasge and an option to exit from an initial classification run, if the data set size is greater than 1000. The messasge is "WARNING: the default start_j_list may not find the correct number of classes in your data set!". 19. autoclass-c/prog/autoclass.h, autoclass.c, & intf-reports.c - Write -reports option screen output to log file. 20. autoclass-c/prog/io-read-data.c - In FIND_DISCRETE_STATS, when the number of discrete value translators is less than attribute definition range, reduce the range and output an advisory, rather than outputting warning message and asking the user whether to proceed or not. The above change was REMOVED, since it caused an incompatablility with previous results files: "ERROR: expand_database found unmatched common attributes defs in <.results[-bin] file> and ........ 21. autoclass-c/prog/global.h, global.c, search-control-2.c, & search-control.c - Warn user of search trials which do not converge, which means that their number of try cycles reached the value of the "max_cycles" search parameter. Do this by printing a warning message after the trial completes. Also after the "SUMMARY OF n BEST RESULTS" at the conclusion of each run, print "SUMMARY OF TRY CONVERGENCE" for the n best results. 22. autoclass-c/prog/model-multi-normal-cn.c - It was recently brought to our attention that the multi-normal model, with more than about 10 attributes and several thousand instances, would consistently run to the the max_duration or max_n_tries limit, regardless of how large those limits were. Suitably instrumented experiments showed that EM (expectation maximization) was actually oscillating. The problem was traced to a conceptual error in the underflow limiting code that constrains the estimation of empirical standard deviations. This has been corrected. However users should be alert for, and report, any further problems of this nature. 23. autoclass-c/prog/autoclass.h, intf-reports.c - For MNcn attributes, do not sort them within their model term when order_attributes_by_influence_p = false. The outputing of MNcn correlation matrices after last class attribute, instead of after each term, is now done by a call to GENERATE_MNCN_CORRELATION_MATRICES from AUTOCLASS_CLASS_INFLUENCE_VALUES_REPORT. 24. autoclass-c/prog/intf-reports.c, intf-sigma-contours.c - Replace report parameters start_sigma_contours_att and stop_sigma_contours_att with sigma_contours_att_list, to allow non-contiguous groups of attributes to be specified. Check for attribute indices of reports parameter sigma_contours_att_list which are declared "ignore" by the .model file. Prevents segmentation fault. Correct erroneous rotations for non-covariant pairs of attributes modeled in two different covariant normal terms (the rotations in these cases should be 0.0). 25. autoclass-c/prog/intf-reports.c - Previously when specifying report_type = "xref_case" or report_type = "xref_class" along with n_clsfs > 1 or clsf_n_list with more than 1 list element, the .case-text-n or .class-text-n data would be identical. Sometimes segmentation faults would occur. This has been corrected. This was not a problem for report_type = "all" (the default). Also when using the default for report_type ("all"), previously the memory allocated for each classification's cross reference was not deallocated after each classification was processed. It is now properly deallocated. ======================================================================