gbutils-5.7.1/0000755000175000017500000000000013246772655010217 500000000000000gbutils-5.7.1/gbtest.c0000644000175000017500000021667713246754720011607 00000000000000/* gbtest (ver. 5.6) -- Compute statistical tests on data Copyright (C) 2011-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include #include #define NAME(x) !strcmp(test_name,(x)) /* Global variable accessed by different routines */ /* ---------------------------------------------- */ /* options */ char Is_Data_Sorted = 0; char Is_Data_Denan = 0; char O_Verbose = 0; char O_Significance = 0; /* requirement of the test */ char Need_Sorted=0; /* data must be sorted */ char Need_Distrib=0; /* data are from a distribution function, between 0 and 1 */ /* variables related to "ties" counting */ size_t tgroups=0; /* number of tied groups */ size_t *tgroup=NULL; /* size of tied groups */ /* degree of freedoms of the T tests with hetero variances */ double TdiffV_dof; /* Utility functions and structures */ /* ------------------------------- */ /* common ordering of joint samples */ typedef struct indexed { size_t sample; double data; } Indexed; int sort_indexed_by_value(const void *a, const void *b){ const Indexed *da = (const Indexed *) a; const Indexed *db = (const Indexed *) b; return (da->data > db->data) - (da->data < db->data); } /* One sample tests */ /* ---------------- */ /* the following is special. the first column contains the observations in each bin and the second column the theoretical probability of each bin */ double chi2_onesample(double ** const data, const size_t size) { size_t i; double N=0.0; double res=0.0; /* compute total number of observations */ for(i=0;i0) res+=(data[0][i]-N*data[1][i])*(data[0][i]-N*data[1][i])/(N*data[1][i]); else if(data[1][i] == 0 && data[0][i] >0 ){ res=NAN; break; } } return res; } size_t WILCOsize=0; /* number of non-zero entries */ double WILCO_test(double * data, const size_t size){ size_t i,j; double res=0.0; Indexed *alldata = (Indexed *) my_alloc(sizeof(Indexed)*size); /* zero entries are discarded */ j=0; for(i=0;i0){ /* positive entry */ alldata[j].sample = 1; alldata[j].data = data[i]; j++; } } WILCOsize=j; alldata = (Indexed *) my_realloc(alldata,WILCOsize*sizeof(Indexed)); /* sort pooled data */ qsort(alldata,WILCOsize,sizeof(Indexed),sort_indexed_by_value); /* for(i=0;i0){ tgroups++; tgroup = (size_t *) my_realloc((void *) tgroup,tgroups*sizeof(size_t)); tgroup[tgroups-1] = equals+1; for(j=i;j1){ if(WILCOsize0) fprintf(stderr,"# Number of ties group(s) %zd\n",tgroups); fprintf(stderr,"# Observed Wilcoxon W %g\n", statistic); fprintf(stderr,"# Large sample mean %g\n",mean); fprintf(stderr,"# Large sample std. dev. %g\n",stdev); fprintf(stderr,"# Standardized W %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statW} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|W|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } double TRTP_test(double * data, const size_t size){ double res=0; size_t i; for(i=1;idata[i+1]) || (data[i-1]>data[i] && data[i]1){ fprintf(stderr,"# Observed Turning Points %g\n", statistic); fprintf(stderr,"# Expected mean %g\n",mean); fprintf(stderr,"# Expected std. dev. %g\n",stdev); fprintf(stderr,"# Standardized score %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statobs.} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|obs.|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } double TRDS_test(double * data, const size_t size){ double res=0; size_t i; for(i=1;idata[i-1]) res=res+1.; return res; } double TRDS_pscore(const double statistic, const size_t size){ const double mean=(size-1.)/2.; const double stdev=sqrt((size+1.)/12.); const double stat =(statistic-mean)/stdev; /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed Positive Differences %g\n", statistic); fprintf(stderr,"# Expected mean %g\n",mean); fprintf(stderr,"# Expected std. dev. %g\n",stdev); fprintf(stderr,"# Standardized score %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statobs.} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|obs.|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } double TRRT_test(double * data, const size_t size){ double res=0; size_t i,j; for(i=0;idata[i]) res=res+1.; return res; } double TRRT_pscore(const double statistic, const size_t size){ const double mean=size*(size-1.)/4.; /* const double stdev=sqrt(size*(size-1.)*(2.*size+5.)/8.); */ const double stdev=sqrt(size*(size-1.)*(2.*size+5.)/72.); const double stat =(statistic-mean)/stdev; /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed increasing couples %g\n", statistic); fprintf(stderr,"# Expected mean %g\n",mean); fprintf(stderr,"# Expected std. dev. %g\n",stdev); fprintf(stderr,"# Standardized score %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statobs.} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|obs.|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } double Dplus_test(double * data, const size_t size) { size_t i; double result=0; /* sort data if needed */ if(Is_Data_Sorted==0) qsort(data,size,sizeof(double),sort_by_value); /* check that data are from a distribution function */ if(data[0]<0 || data[size-1]>1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;iresult) result = dtmp1; } } return result; } double Dplus_pscore(const double statistic, const size_t size) { const size_t maxindex = (size_t) floor(size*(1-statistic)); const double N = (double) size; const double stat = statistic*sqrt(N); const double Gasy=1-exp(-2.*stat*stat); size_t i; double G=0; double G_smallcontrib=0; for(i=0;i1){ fprintf(stderr,"# Observed D+ %g\n",statistic); fprintf(stderr,"# Sample size %zd\n",size); fprintf(stderr,"# Standardized score %g\n",stat); fprintf(stderr,"# Exact inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>D+} %g\n",1-G); fprintf(stderr,"# One-sided P-value: Pr{statD+} %g\n",1-Gasy); fprintf(stderr,"# One-sided P-value: Pr{stat1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;iresult) result = dtmp1; } } return result; } double Dminus_pscore(const double statistic, const size_t size) { const size_t maxindex = (size_t) floor(size*(1-statistic)); const double N = (double) size; const double stat = statistic*sqrt(N); const double Gasy=1-exp(-2.*stat*stat); size_t i; double G=0; double G_smallcontrib=0; for(i=0;i1){ fprintf(stderr,"# Observed D- %g\n",statistic); fprintf(stderr,"# Sample size %zd\n",size); fprintf(stderr,"# Standardized score %g\n",stat); fprintf(stderr,"# Exact inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>D-} %g\n",1-G); fprintf(stderr,"# One-sided P-value: Pr{statD-} %g\n",1-Gasy); fprintf(stderr,"# One-sided P-value: Pr{stat1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;idtmp2 && dtmp1>result) result = dtmp1; else if(dtmp2>result) result = dtmp2; } } return result; } double Qks(double x){ /* computation of Qks(x) */ const double x2 = -2.0*x*x; double const eps1=10*DBL_EPSILON; double const eps2=DBL_EPSILON; int i=1; double fac=2.0,sum=0.0,term=0.0,absterm=0.0; do{ absterm=fabs(term); /* previous term absolute value */ term=fac*exp(x2*i*i); /* new term */ sum += term; /* terms sum */ fac = -fac; /* change sign of the next term */ i++; } while(fabs(term) > eps1*absterm && fabs(term) > eps2*sum); return sum; } double D_pscore(const double statistic, const size_t size) { const double eff_size =sqrt(size); const double stat = (eff_size+0.12+0.11/eff_size)*statistic ; const double statasy = eff_size*statistic ; const double qks = Qks(stat); const double qksasy = Qks(statasy); /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed D %g\n",statistic); fprintf(stderr,"# Sample size %zd\n",size); fprintf(stderr,"# Asymptotic standardized score %g\n",statasy); fprintf(stderr,"# Small-sample corrected score %g\n",stat); fprintf(stderr,"# Small-sample corrected inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>D} %g\n",qks); fprintf(stderr,"# One-sided P-value: Pr{statD} %g\n",qksasy); fprintf(stderr,"# One-sided P-value: Pr{stat1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;iDplus) Dplus = dtmp1; } for(i=0;iDminus) Dminus = dtmp1; } result=Dplus+Dminus; } return result; } double W2_test(double * data, const size_t size) { size_t i; double result=0; /* sort data if needed */ if(Is_Data_Sorted==0) qsort(data,size,sizeof(double),sort_by_value); /* check that data are from a distribution function */ if(data[0]<0 || data[size-1]>1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;i1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;i1){ fprintf(stderr,"WARNING (%s): data not from a distribution function, NAN is returned.\n",GB_PROGNAME); result=NAN; } else{ for(i=0;i1){ fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",size-1); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>T} %g\n", gsl_cdf_tdist_Q (statistic,size-1.0)); fprintf(stderr,"# One-sided P-value: Pr{stat|T|} %g\n", 2*gsl_cdf_tdist_Q (fabs(statistic),size-1.)); } /* +++++++++++++++++++++++++++++++++++++ */ return 2*gsl_cdf_tdist_Q (fabs(statistic),size-1.); } #else double Tsingle_pscore(const double statistic, const size_t size) { return NAN; } #endif /* Paired samples tests */ /* -------------------- */ double WILCO2_test(const double * const data1, const double * const data2, const size_t size) { size_t i,j; double res=0.0; Indexed *alldata = (Indexed *) my_alloc(sizeof(Indexed)*size); /* zero entries are discarded */ j=0; for(i=0;i0){ /* positive entry */ alldata[j].sample = 1; alldata[j].data = dtmp; j++; } } WILCOsize=j; alldata = (Indexed *) my_realloc(alldata,WILCOsize*sizeof(Indexed)); /* sort pooled data */ qsort(alldata,WILCOsize,sizeof(Indexed),sort_indexed_by_value); /* for(i=0;i0){ tgroups++; tgroup = (size_t *) my_realloc((void *) tgroup,tgroups*sizeof(size_t)); tgroup[tgroups-1] = equals+1; for(j=i;j1){ fprintf(stderr,"# Size of the sample %zd\n",size); fprintf(stderr,"# Observed Pearson's R %g\n",statistic); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# Negative correlation P-value: Pr{statR} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Any correlation P-value: Pr{|stat|>|R|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } #else double R_pscore(const double statistic, const size_t size) { return NAN; } #endif #if defined HAVE_LIBGSL double RHO_test(const double * const data1, const double * const data2, const size_t size) { size_t i,j; size_t *temp = (size_t *) my_alloc(sizeof(size_t)*size); size_t equal=0; double *rank1 = (double *) my_alloc(sizeof(double)*size); double *rank2 = (double *) my_alloc(sizeof(double)*size); double s1=0.0,s2=0.0,s12=0.0; const double dtmp1 = size*((size+1.0)/2.0)*((size+1.0)/2.0); /* used to collect statistics on ties */ tgroup = (size_t *) my_realloc((void *) tgroup,2*sizeof(size_t)); gsl_sort_index (temp,data1,1,size); /* correct for ties */ tgroup[0]=0; for (i=0;i1){ fprintf(stderr,"# Number of X ties group %zd\n",tgroup[0]); fprintf(stderr,"# Number of Y ties group %zd\n",tgroup[1]); fprintf(stderr,"# Observed Spearman's RHO %g\n",statistic); fprintf(stderr,"# Standardized RHO %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# Negative correlation P-value: Pr{statRHO} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Any correlation P-value: Pr{|stat|>|RHO|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } #else double RHO_test(const double * const data1, const double * const data2, const size_t size) { return NAN; } double RHO_pscore(const double statistic,const size_t size) { return NAN; } #endif double TAU_test(const double * const data1, const double * const data2, const size_t size) { size_t i,j; double con=0,dis=0; /* used to collect statistics on ties */ tgroup = (size_t *) my_realloc((void *) tgroup,2*sizeof(size_t)); tgroup[0]=tgroup[1]=0; for (i=0;i0) con+=1; else if (dtmp1<0) dis+=1; else if(data2[i]-data2[j] == 0){ con += 0.5; dis += 0.5; tgroup[1]++; } else tgroup[0]++; } /* correct for ties */ /* printf("####%f %f\n", con,dis); */ return (con-dis)/(con+dis); } #if defined HAVE_LIBGSL double TAU_pscore(const double statistic, const size_t size) { /* compute K coefficient */ /* const double K = statistic*(size*(size-1)/2-tgroup[0])-1; */ /* compute standardized statistics */ const double stat = 3*statistic*sqrt( (size*(size-1.))/(2*(2*size+5.)) ); /* const double statK = K/sqrt( size*(size-1.)*(2*size+5.)/18); */ /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Number of X ties group %zd\n",tgroup[0]); fprintf(stderr,"# Number of Y ties group %zd\n",tgroup[1]); fprintf(stderr,"# Observed Kendall's TAU %g\n",statistic); fprintf(stderr,"# Standardized TAU %g\n",stat); /* fprintf(stderr,"# Observed Kendall's K %g\n",K); */ /* fprintf(stderr,"# Standardized K %g\n",statK); */ fprintf(stderr,"# Asymptotic inference based on TAU:\n"); fprintf(stderr,"# Negative correlation P-value: Pr{statTAU} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Any correlation P-value: Pr{|stat|>|TAU|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); /* fprintf(stderr,"# Asymptotic inference based on K:\n"); */ /* fprintf(stderr,"# Negative correlation P-value: Pr{statW} %g\n", */ /* gsl_cdf_ugaussian_Q (statK)); */ /* fprintf(stderr,"# Any correlation P-value: Pr{|stat|>|W|} %g\n", */ /* 2*gsl_cdf_ugaussian_Q (fabs(statK))); */ } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } #else double TAU_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double CHI2_2_test(const double * const data1, const double * const data2, const size_t size) { size_t i; double tot1=0.0,tot2=0.0,res=0.0,dtmp1,dtmp2; for(i=0;i0){ const double dtmp3 = (dtmp1*data1[i]-dtmp2*data2[i]); res += dtmp3*dtmp3/(data1[i]+data2[i]); } return res; } #if defined HAVE_LIBGSL double CHI2_2_pscore(const double statistic, const size_t size) { /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",size-1); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# Two-sided P-value: Pr{stat>T} %g\n", gsl_cdf_chisq_Q (statistic,size-1)); } /* +++++++++++++++++++++++++++++++++++++ */ return gsl_cdf_chisq_Q (statistic,size-1); } #else double CHI2_2_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double Tpaired_test(const double * const data1, const double * const data2, const size_t size) { size_t i; double mean, var, error; /* compute the mean and standard deviation of the difference */ /* compute the statistics */ mean=0; for(i=0;i1){ fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",size-1); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>T} %g\n", gsl_cdf_tdist_Q (statistic,size-1.0)); fprintf(stderr,"# One-sided P-value: Pr{stat|T|} %g\n", 2*gsl_cdf_tdist_Q (fabs(statistic),size-1.0)); } /* +++++++++++++++++++++++++++++++++++++ */ return 2*gsl_cdf_tdist_Q (fabs(statistic),size-1.0); } #else double Tpaired_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif /* Two sample tests */ /* ---------------- */ double F_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i; double mean1,mean2,var1,var2,error; /* compute the statistics */ mean1=mean2=0; for(i=0;i1){ fprintf(stderr,"# Degrees of freedom: %zd / %zd\n",size1,size2); fprintf(stderr,"# Variances %g / %g\n",var1,var2); } /* +++++++++++++++++++++++++++++++++++++ */ return var1/var2; } #if defined HAVE_LIBGSL double F_pscore(const double statistic, const size_t size1, const size_t size2) { const double twotailed = ( statistic > (size2-1.)/(size2-3.) ? 2.*gsl_cdf_fdist_Q (statistic,size1-1.,size2-1.): 2.*gsl_cdf_fdist_P (statistic,size1-1.,size2-1.)); /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed (expected) F %g (%g)\n", statistic,(size2-1.)/(size2-3.)); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statF} %g\n", gsl_cdf_fdist_Q (statistic,size1-1.,size2-1.) ); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|F|} %g\n", twotailed); } /* +++++++++++++++++++++++++++++++++++++ */ return twotailed; } #else double F_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double Thetero_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i; double mean1,mean2,var1,var2,error; double res; /* compute the statistics */ mean1=mean2=0; for(i=0;i1){ fprintf(stderr,"# Size of the first sample %zd\n",size1); fprintf(stderr,"# Size of the second sample %zd\n",size2); fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %g\n",TdiffV_dof); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>T} %g\n", gsl_cdf_tdist_Q (statistic,TdiffV_dof)); fprintf(stderr,"# One-sided P-value: Pr{stat|T|} %g\n", 2*gsl_cdf_tdist_Q (fabs(statistic),TdiffV_dof)); } /* +++++++++++++++++++++++++++++++++++++ */ return 2*gsl_cdf_tdist_Q (fabs(statistic),TdiffV_dof); } #else double Thetero_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double Thomo_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i; double mean1,mean2,var1,var2,error; double res; /* compute the statistics */ mean1=mean2=0; for(i=0;i1){ fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",size1+size2-2); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{stat>T} %g\n", gsl_cdf_tdist_Q (statistic,size1+size2-2.0)); fprintf(stderr,"# One-sided P-value: Pr{stat|T|} %g\n", 2*gsl_cdf_tdist_Q (fabs(statistic),size1+size2-2.0)); } /* +++++++++++++++++++++++++++++++++++++ */ return 2*gsl_cdf_tdist_Q (fabs(statistic),size1+size2-2.0); } #else double Thomo_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif /* Wilcoxon-Mann-Whitney U */ double WMW_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i,j; double rank2=0.0; const size_t N = size1+size2; Indexed *alldata = (Indexed *) my_alloc(sizeof(Indexed)*N); /* contains pooled data */ /* set the pooled data */ for(i=0;i0){ tgroups++; tgroup = (size_t *) my_realloc((void *) tgroup,tgroups*sizeof(size_t)); tgroup[tgroups-1] = equals+1; for(j=i;j0){ double dtmp1=0.0; for(i=0;i1){ if(tgroups>0) fprintf(stderr,"# Number of ties group(s) %zd\n",tgroups); fprintf(stderr,"# Observed Mann-Whitney U %g\n", statistic); fprintf(stderr,"# Large sample mean %g\n",mean); fprintf(stderr,"# Large sample std. dev. %g\n",stdev); fprintf(stderr,"# Standardized U %g\n",stat); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statW} %g\n", gsl_cdf_ugaussian_Q (stat)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|W|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(stat))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(stat)); } #else double WMW_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double FP_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i,j; double *rank1 = (double *) my_alloc(sizeof(double)*size1); double *rank2 = (double *) my_alloc(sizeof(double)*size2); double mean1,mean2,var1,var2,error; double res; /* sort data if needed */ if(Is_Data_Sorted==0){ qsort(data1,size1,sizeof(double),sort_by_value); qsort(data2,size2,sizeof(double),sort_by_value); } for(i=0;i1){ if(tgroups>0) fprintf(stderr,"# Number of ties group(s) %zd\n",tgroups); fprintf(stderr,"# Observed Standardized U %g\n",statistic); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# One-sided P-value: Pr{statU} %g\n", gsl_cdf_ugaussian_Q (statistic)); fprintf(stderr,"# Two-sided P-value: Pr{|stat|>|U|} %g\n", 2*gsl_cdf_ugaussian_Q (fabs(statistic))); } /* +++++++++++++++++++++++++++++++++++++ */ return 2.0*gsl_cdf_ugaussian_Q (fabs(statistic)); } #else double FP_pscore(const double statistic, const size_t size1, const size_t size2) { return NAN; } #endif double KS_test(double * data1, const size_t size1, double * data2, const size_t size2) { size_t i1=0,i2=0; double d1,d2,adiff,F1=0.0,F2=0.0,res=0.0; /* sort data if needed */ if(Is_Data_Sorted==0){ qsort(data1,size1,sizeof(double),sort_by_value); qsort(data2,size2,sizeof(double),sort_by_value); } while (i1 < size1 && i2 < size2) { d1=data1[i1]; d2=data2[i2]; if (d1 <= d2) F1=i1++/((double) size1); if (d2 <= d1) F2=i2++/((double) size2); adiff = fabs(F1-F2); if (adiff > res) res=adiff; } return res; } double KS_pscore(const double statistic, const size_t size1, const size_t size2) { const double eff_size =sqrt(((double) size1*size2)/( (double) size1+size2)); const double x = (eff_size+0.12+0.11/eff_size)*statistic ; /* computation of Qks(x) */ const double x2 = -2.0*x*x; double const eps1=10*DBL_EPSILON; double const eps2=DBL_EPSILON; int i=1; double fac=2.0,sum=0.0,term=0.0,absterm=0.0; do{ absterm=fabs(term); /* previous term absolute value */ term=fac*exp(x2*i*i); /* new term */ sum += term; /* terms sum */ fac = -fac; /* change sign of the next term */ i++; } while(fabs(term) > eps1*absterm && fabs(term) > eps2*sum); /* +++++++++++++++++++++++++++++++++++++ */ if(O_Verbose>1){ fprintf(stderr,"# Observed KS %g\n",statistic); fprintf(stderr,"# Effective Size %g\n",eff_size*eff_size); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# Two-sided P-value: Pr{stat>KS} %g\n",sum); } /* +++++++++++++++++++++++++++++++++++++ */ return sum; } /* Many samples tests */ /* ------------------ */ double CHI2_N_test (double ** const data,const size_t *const length, const size_t smpls){ /* all columns have equal length */ const size_t ctgs=length[0]; size_t i,j; double C[ctgs]; /* */ double N[smpls]; double Ntot; double res=0.0; for(i=0;i1){ fprintf(stderr,"# Observed T %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",(smpls-1)*(length[0]-1)); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# Two-sided P-value: Pr{stat>T} %g\n", gsl_cdf_chisq_Q (statistic,(smpls-1)*(length[0]-1)) ); } /* +++++++++++++++++++++++++++++++++++++ */ return gsl_cdf_chisq_Q (statistic,(smpls-1)*(length[0]-1)); } #else double CHI2_N_pscore(const double statistic, const size_t * const length, const size_t K){ return NAN; } #endif double KW_test (double ** const data,const size_t *const length, const size_t K){ size_t i,j,h; size_t N=0; /* total number of observations */ Indexed *alldata; /* contains pooled data */ double *ranks = (double *) my_alloc(sizeof(double)*K); /* average rank of the different samples */ double res=0; /* allocate and set the pooled data */ for(i=0;i0){ tgroups++; tgroup = (size_t *) my_realloc((void *) tgroup,tgroups*sizeof(size_t)); tgroup[tgroups-1] = equals+1; for(j=i;j0){ double dtmp1=0.0; for(i=0;i1){ fprintf(stderr,"# Kruskal-Wallis multi-sample statistics\n"); fprintf(stderr,"# Size of the sample %zd\n",*length); if(tgroups>0) fprintf(stderr,"# Number of ties group(s) %zd\n",tgroups); fprintf(stderr,"# Observed Standardized H %g\n",statistic); fprintf(stderr,"# Degrees of freedom %zd\n",K-1); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# P-value: Pr{stat>W} %g\n", gsl_cdf_chisq_Q (statistic,K-1)); } /* +++++++++++++++++++++++++++++++++++++ */ return gsl_cdf_chisq_Q (statistic,K-1); } #else double KW_pscore(const double statistic, const size_t * const length, const size_t K){ return NAN; } #endif double LEVENE_test (double ** const data,const size_t *const length, const size_t K){ size_t i,j; double numerator,denominator; double *means = my_alloc(sizeof(double)*K); double mean; size_t totlen; /* compute the statistics */ mean=0.0; totlen=0; for(j=0;j1){ fprintf(stderr,"# Number of observations %zd\n",totlen); fprintf(stderr,"# Number of groups %zd\n",K); } /* +++++++++++++++++++++++++++++++++++++ */ /* de-allocate */ free(means); return ((totlen-K)*numerator)/((K-1)*denominator); } double LEVENE_pscore(const double statistic, const size_t * const length, const size_t K){ size_t i,totlen; totlen=0; for(i=0;i1){ fprintf(stderr,"# Observed (expected) W %g (%g)\n", statistic,(totlen-K)/(totlen-K-2.)); fprintf(stderr,"# Asymptotic inference:\n"); fprintf(stderr,"# P-value: Pr{stat>W} %g\n", gsl_cdf_fdist_Q (statistic,K-1,totlen-K)); } /* +++++++++++++++++++++++++++++++++++++ */ return gsl_cdf_fdist_Q (statistic,K-1,totlen-K); } /* Generic functions to run tests */ /* ------------------------------ */ void run_onesample( char *testname, double (*test) (double *, const size_t), double (*pscore) (const double, const size_t), double ** const data,const size_t * const length, const size_t columns) { double *res = (double *) my_alloc(2*columns*sizeof(double)); size_t i; size_t column; double statistic,significance; /* ++++++++++++++++++++++++++++++ */ if (O_Verbose>0){ fprintf(stderr,"# %s\n",testname); } /* ++++++++++++++++++++++++++++++ */ /* for all columns */ for(i=0;i1 && columns > 1) fprintf(stderr,"# columns %zd: %zd\n",i,len); /* ++++++++++++++++++++++++++++++++++++++++++++ */ /* compute statistics */ res[i] = test(data1,len); /* compute p-score */ if(O_Significance == 1){ if(pscore == NULL) res[columns+i] = NAN; else res[columns+i] = pscore(res[i],len); } free(data1); } /* print results */ if(O_Significance == 0){ if(O_Verbose>0) printf("#statistic\n"); for(i=0;i0) printf("#statistic\tp-score\n"); for(i=0;i0){ fprintf(stderr,"# %s\n",testname); } /* ++++++++++++++++++++++++++++++ */ /* for the couples of columns */ for(i1=0;i11 && columns >2){ fprintf(stderr,"# === Columns %zd %zd\n",i1+1,i2+1); } /* ++++++++++++++++++++++++++++++++++++++++++++ */ /* compute statistics */ res[i2*columns+i1] = test(data1,data2,len); /* compute p-score */ if(O_Significance == 1){ if(pscore == NULL) res[i1*columns+i2] = NAN; else res[i1*columns+i2] = pscore(res[i2*columns+i1],len); } free(data1); free(data2); } } /* print results */ if(O_Significance == 0 && columns==2){ if(O_Verbose>0) printf("#statistic\n"); printf(FLOAT_NL,res[2]); } else if(O_Significance == 0 && columns>2){ for(i1=1;i10) printf("#statistic\tp-score\n"); printf(FLOAT_SEP,res[2]); printf(FLOAT_NL,res[1]); } else if (O_Significance == 1 && columns>2){ for(i1=0;i10){ fprintf(stderr,"# %s\n",testname); } /* ++++++++++++++++++++++++++++++ */ /* for the couples of columns */ for(i1=0;i11 && columns >2) fprintf(stderr,"# === Columns %zd %zd\n",i1+1,i2+1); /* ++++++++++++++++++++++++++++++++++++++++++++ */ /* compute statistics */ res[i2*columns+i1] = test(data1,len1,data2,len2); /* compute p-score */ if(O_Significance == 1){ if(pscore == NULL) res[i1*columns+i2] = NAN; else res[i1*columns+i2] = pscore(res[i2*columns+i1],len1,len2); } free(data1); free(data2); } } /* print results */ if(O_Significance == 0 && columns==2){ if(O_Verbose>0) printf("#statistic\n"); printf(FLOAT_NL,res[2]); } else if(O_Significance == 0 && columns>2){ for(i1=1;i10) printf("#statistic\tp-score\n"); printf(FLOAT_SEP,res[2]); printf(FLOAT_NL,res[1]); } else if (O_Significance == 1 && columns>2){ for(i1=0;i11){ fprintf(stderr,"# %s\n",testname); } /* ++++++++++++++++++++++++++++++ */ statistic = test(data,length,columns); if(O_Significance == 1){ printf(FLOAT_SEP,statistic); if(pscore == NULL) significance = NAN; else significance = pscore(statistic,length,columns); printf(FLOAT_NL,significance); } else printf(FLOAT_NL,statistic); } int main(int argc,char* argv[]){ size_t rows=0,columns=0,column; size_t *length; double **data=NULL; /* binary options */ int o_binary_in=0; size_t stat,i; char *splitstring = strdup(" \t"); /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"hF:v:spb:o:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Compute statistical tests on data. Each test is specified by a unique\n"); fprintf(stdout,"name and optional parameters follows, separated by commas. Depending\n"); fprintf(stdout,"on the test, one, two or three columns of data are expected. '1 sample'\n"); fprintf(stdout,"tests expect a single columns. Test on 'pairs' expect two columns of\n"); fprintf(stdout,"equal length. Test on '2 samples' expect two columns of data, possibly\n"); fprintf(stdout,"of different length. Test on 2+ and 3+ samples expect a varying number of\n"); fprintf(stdout,"columns. If more columns are provided, the test is repeated for any\n"); fprintf(stdout,"column in the case of 1 sample test, and any couple of columns in the case\n"); fprintf(stdout,"of 2 sample tests. In the last case, the output is expressed in matrix\n"); fprintf(stdout,"format, with estimated statistics on the lower triangle and p-scores\n"); fprintf(stdout,"(if requested) on the upper. The removal of 'nan' entries is automatic,\n"); fprintf(stdout,"and is performed consistently with the nature of the test.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Tests typically assume 1, 2 or 3+ samples. Available tests are:\n\n"); fprintf(stdout," D+,D-,D,V 1 samp Kolmogorov-Smirnov tests on cumulated data \n"); fprintf(stdout," W2,A2,U2 1 samp Cramer-von Mises tests on cumulated data \n"); fprintf(stdout," CHI2-1 1 samp Chi-Sqrd, 1 samp. 2nd column: th. prob. \n"); fprintf(stdout," WILCO 1 samp Wilcoxon signed-rank test (mode=0) \n"); fprintf(stdout," TS 1 samp Student's T (mean=0) \n"); fprintf(stdout," TR-TP 1 samp Test of randomness: turning points \n"); fprintf(stdout," TR-DS 1 samp Test of randomness: difference sign \n"); fprintf(stdout," TR-RT 1 samp Test of randomness: rank test \n"); fprintf(stdout," R pairs Pearson's correlation coefficient \n"); fprintf(stdout," RHO pairs Spearman's Rho rank correlation \n"); fprintf(stdout," TAU pairs Kendall's Tau correlation \n"); fprintf(stdout," CHI2-2 pairs Chi-Sqrd, 2 samples \n"); fprintf(stdout," TP pairs Student's T with paired samples \n"); fprintf(stdout," WILCO2 pairs Wilcoxon test on paired samples \n"); fprintf(stdout," KS 2 samp Kolmogorov-Smirnov test \n"); fprintf(stdout," T 2 samp Student's T with same variances \n"); fprintf(stdout," TH 2 samp Student's T with different variances \n"); fprintf(stdout," F 2 samp F-Test for different variances \n"); fprintf(stdout," WMW 2 samp Wilcoxon-Mann-Whitney U \n"); fprintf(stdout," FP 2 samp Fligner-Policello standardized U^ \n"); fprintf(stdout," LEV-MEAN 2+ samp Levene equality of variances using means \n"); fprintf(stdout," LEV-MED 2+ samp Levene equality of variances using medians \n"); fprintf(stdout," KW 3+ samp Kruscal-Wallis test on 3+ samples \n"); fprintf(stdout," CHI2-N 3+ samp Multi-columns contingency table analysis \n"); fprintf(stdout,"Options:\n"); fprintf(stdout," -p compute associated significance\n"); fprintf(stdout," -s input data are already sorted in ascending order\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -o set the output format (default '%%12.6e') \n"); fprintf(stdout," -v verbosity: 0 none, 1 headings, 2+ description (default 0) \n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbtest FP < file compute the Fligner-Policello test on the columns\n"); fprintf(stdout," of 'file', considering all possible pairings.\n"); fprintf(stdout," gbtest KW -p < file compute the Kruscal-Wallis test and its p-score\n"); fprintf(stdout," on the first three columns of data in 'file' \n"); finalize_program(); return(0); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='v'){ O_Verbose=(unsigned) atoi(optarg); } else if(opt=='p'){ O_Significance=1; } else if(opt=='s'){ Is_Data_Sorted=1; } else if(opt=='b'){ /*set binary input or output*/ switch(atoi(optarg)){ case 0 : break; case 1 : o_binary_in=1; break; case 2 : fprintf(stderr,"no binary output implemented, option ignored.\n"); break; case 3 : o_binary_in=1; fprintf(stderr,"no binary output implemented, option ignored.\n"); break; default: fprintf(stderr,"unclear binary specification %d, option ignored.\n",atoi(optarg)); break; } } else if(opt=='o'){ /*set the ouptustring */ FLOAT = strdup(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data as a matrix: all column have the first rows. Missing data are replaced with NAN*/ if(o_binary_in==1) loadtable_bin(&data,&rows,&columns,0); else{ loadtable(&data,&rows,&columns,0,splitstring); free(splitstring); } length =(size_t *) my_alloc(sizeof(size_t)*columns); for(i=0;i1) fprintf(stderr,"# loaded %zdx%zd data table\n",rows,columns); /*+++++++++++++++++++++++++++++++++++++++*/ /*+++++++++++++++++++++++++++++++++++++++*/ /* if(O_Verbose>1){ */ /* for(column=0;column2){ fprintf(stderr,"ERROR (%s): need exactly two columns",GB_PROGNAME); exit(-1); } else if(length[0] != length[1]){ fprintf(stderr,"ERROR (%s): columns must contain the same number of rows",GB_PROGNAME); exit(-1); } /* pairs denan */ denan_pairs(&(data[0]),&(data[1]),&(length[0])); length[1]=length[0]; /* ++++++++++++++++++++++++++++++ */ if(O_Verbose==1){ printf("#CHI^2 one-sample statistic"); if(O_Significance == 1) printf("\tp-score"); printf("\n"); } /* ++++++++++++++++++++++++++++++ */ statistic=chi2_onesample(data,length[0]); if(O_Significance == 1){ printf(FLOAT_SEP,statistic); #if defined HAVE_LIBGSL significance = gsl_cdf_chisq_Q (statistic,length[0]-1); #else significance = NAN; #endif printf(FLOAT_NL,significance); } else printf(FLOAT_NL,statistic); } else if(NAME("R")){ run_pairs("Pearson's R 2-sample correlation", R_test,R_pscore,data,length,columns); } else if(NAME("RHO")){ run_pairs("Spearman's Rho 2-sample rank correlation (with ties)", RHO_test,RHO_pscore,data,length,columns); } else if(NAME("TAU")){ run_pairs("Kendall's Tau 2-sample rank correlation (with ties)", TAU_test,TAU_pscore,data,length,columns); } else if(NAME("CHI2-2")){ run_pairs("Chi^2 Test; 2-samples",CHI2_2_test,CHI2_2_pscore,data,length,columns); } else if(NAME("TP")){ run_pairs("Student's T test; paired observations", Tpaired_test,Tpaired_pscore,data,length,columns); } else if(NAME("WILCO2")){ run_pairs("Wilcoxon signed-rank test for paired observations", WILCO2_test,WILCO_pscore,data,length,columns); } else if(NAME("KS")) { run_twosamples("Kolmogorov-Smirnov Test; 2-samples", KS_test,KS_pscore,data,length,columns); } else if(NAME("WMW")){ run_twosamples("Wilcoxon-Mann-Whitney 2-sample rank sum statistics", WMW_test,WMW_pscore,data,length,columns); } else if(NAME("F")){ run_twosamples("F-Test for difference in variance",F_test,F_pscore,data,length,columns); } else if(NAME("T")){ run_twosamples("Student's T test; 2 samples with same variance", Thomo_test,Thomo_pscore,data,length,columns); } else if(NAME("TH")){ run_twosamples("Student's T test; 2 samples with different variance", Thetero_test,Thetero_pscore,data,length,columns); } else if(NAME("FP")){ run_twosamples("Fligner-Policello 2-sample rank sum statistics", FP_test,FP_pscore,data,length,columns); } else if(NAME("KW")){ size_t i,j; if(columns<3){ fprintf(stderr,"ERROR (%s): Kruskal-Wallis test applies only to 3 or more samples.\n", GB_PROGNAME); exit(-1); } /* denan */ for(i=0;i\/\fR .SH DESCRIPTION Generate a grid using function f(r,c) where r is the row index and c is the column index. Indeces start from 1. If more than one function are provided they are printed one after the other. .SH OPTIONS .TP \fB\-c\fR specify number of rows (default 10) .TP \fB\-r\fR specify number of columns (default 10) .TP \fB\-o\fR set the output format (default '%12.6e') .TP \fB\-v\fR verbose mode .TP \fB\-h\fR this help .SH EXAMPLES .TP gbgrid \-c 1 \-r 10 r+c generates a vector with elements equal to the sum of the row and column indexes .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbplot.10000644000175000017500000000564213147510355011502 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBPLOT "1" "August 2017" "gbplot ver. 5.6" "User Commands" .SH NAME gbplot \- gnuplot command line interface .SH SYNOPSIS .B gbplot [\fI\,options\/\fR] [\fI\,command\/\fR] [\fI\,specstring\/\fR] \fI\,< datafile\/\fR .SH DESCRIPTION This command produce simple plots from the command line using the \&'gnuplot' program. 'comand' is one of the gnuplot commands: 'plot' or \&'splot' while 'specstring' is a list of gnuplot specifications. In order to escape shell interpretation place 'specstring' between double quotes. It is possible to specify the type of terminal and output file name as command line options. It is also possible to start an interactive session, useful to adjust plot parameters by hand. The program can be used in a shell pipe, as in .PP cat datafile | gbplot [options] [command] [specstring] .SH OPTIONS .HP \fB\-h\fR print this help .HP \fB\-i\fR start interactive session after the plot .HP \fB\-T\fR set a different terminal .HP \fB\-o\fR set an ouptut file .HP \fB\-t\fR assign a title to the plot .HP \fB\-p\fR prepend expression to plot command .HP \fB\-l\fR set log; possible values x,y and xy .HP \fB\-C\fR command to be run before the plot .HP \fB\-v\fR verbose output: print commands to standard error .TP \fB\-\-help\fR same as '\-h' .HP \fB\-\-version\fR print the program version .SH EXAMPLES Two plots: one using line and one using points. .IP echo "1\en2\en3" | gbplot plot "u 0:1 w l title 'line', '' u 0:1 w p title 'point'" .PP Save the plot in PDF format in a file named 'test.pdf' .IP echo "1\en2\en3" | gbplot \-T pdf \-o test.pdf plot "u 0:1 w l title 'line'" .PP Set log on the y axis .IP echo "1\en2\en3" | gbplot \-l y plot "u 0:1 w l title 'line', '' u 0:1 w p title 'point'" .PP Set log on the x axis and a grid .IP echo "1\en2\en3" | gbplot \-l x \-C "set grid" plot "u 0:1 w l title 'line', '' u 0:1 w p title 'point'" .PP Set a grid and the position of the legend .IP echo "1\en2\en3" | gbplot \-C "set grid" \-C "set key bottom right" plot "u 0:1 w l title 'line', '' u 0:1 w p title 'point'" .PP Compute the value of the gamma function on a set of points; gawk is used to filer the relevant numbers .IP echo "1\en2\en3" | gbplot \-C "set table" plot 'u (gamma(\-\-help))' | gawk '/ i/{print }' .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .br Package home page .SH COPYRIGHT Copyright \(co 2009\-2014 Giulio Bottazzi .PP This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation. .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbquant.c0000644000175000017500000004117613246754720011746 00000000000000/* gbquant (ver. 5.6.1) -- Print quantiles of data distribution Copyright (C) 2004-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ double *xvals=NULL,*qvals=0;; size_t xvalnum=0,qvalnum=0; size_t n=10; double wqmin=0,wqmax=1.; char *splitstring = strdup(" \t"); /* options */ int o_xval = 0; int o_qval = 0; int o_err = 0; int o_qwindow = 0; int o_table = 0; int o_verbose=0; int o_position=1; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"W:w:vhtx:q:en:F:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Print distribution quantiles. Data are read from standard input. If no\n"); fprintf(stdout,"-x or -q options are provided, a quantiles table is plotted. The number\n"); fprintf(stdout,"of quantiles can be chosen with -n. The option -x #1 prints the quantile\n"); fprintf(stdout,"associated with the value #1, while -q #2 print the value associate with\n"); fprintf(stdout,"the quantile #2. Of course #2 must be between 0 and 1. Multiple values\n"); fprintf(stdout,"can be provided to -x or -q, separated with commas. With -w #1,#2 all\n"); fprintf(stdout,"the observations inside the quantile range [#1,#2) are printed. If more\n"); fprintf(stdout,"columns are provided with option -t, the specified action is repeated on\n"); fprintf(stdout,"each column. Without -t, columns are pooled unless -w is specified, in\n"); fprintf(stdout,"which case all rows whose first element is inside the range are printed.\n"); fprintf(stdout,"A different column for sorting can be specified with option -W. If 0 is\n"); fprintf(stdout,"specified, the cut is applied with respect to all columns\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of quantiles to print (default 10)\n"); fprintf(stdout," -x print the quantile (interpolated) associated with the value\n"); fprintf(stdout," -q print the value (interpolated) associated with the quantile\n"); fprintf(stdout," -e print the (asymptotic) error for -x or -q; doesn't work with -t\n"); fprintf(stdout," -w print observations inside a quantile windows\n"); fprintf(stdout," -W set the column to use (default 1)\n"); fprintf(stdout," -t consider separately each column of input\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); fprintf(stdout," -v verbose mode\n"); return(0); } else if(opt=='W'){ o_position=atoi(optarg); if(o_position <0){ fprintf(stderr, "ERROR (%s): Column number must be non-negative\n", GB_PROGNAME); exit(1); } } else if(opt=='w'){ /*set the quantile window */ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; stmp2=strsep (&stmp1,","); if(strlen(stmp2)>0) wqmin=atof(stmp2); if(stmp1 != NULL && strlen(stmp1)>0) wqmax=atof(stmp1); free(stmp3); if(wqmax0 ){ xvalnum++; xvals = (double *) my_realloc((void *) xvals,xvalnum*sizeof(double)); xvals[xvalnum-1]=atof(stmp2); } o_xval=1; free(stmp3); } else if(opt=='q'){ /*set the quantile whose value is required*/ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; while( (stmp2=strsep (&stmp1,",")) != NULL && strlen(stmp2)>0 ){ qvalnum++; qvals = (double *) my_realloc((void *) qvals,qvalnum*sizeof(double)); qvals[qvalnum-1]=atof(stmp2); } o_qval=1; free(stmp3); } else if(opt=='e'){ /*set the verbose mode*/ o_err=1; } else if(opt=='v'){ /*set the verbose mode*/ o_verbose=1; } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); if(o_xval==0 && o_qval==0 && o_qwindow==0){/* print the list of quantiles */ if(!o_table){ size_t size; double *vals=NULL; size_t quant; load(&vals,&size,0,splitstring); denan(&vals,&size); qsort(vals,size,sizeof(double),sort_by_value); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zd data\n",size); /*+++++++++++++++++++++++++++++++++++++++*/ if(n>size) n = size;/* for consistency */ /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose){ printf(EMPTY_SEP,"#quant."); printf(EMPTY_NL,"val"); } /*+++++++++++++++++++++++++++++++++++++++*/ for(quant=1;quant<=n;quant++){ double dtmp1 = quant/(n+1.); int index = floor((size+1.)*dtmp1); double delta = (size+1.)*dtmp1-index; printf(FLOAT_SEP,dtmp1); printf(FLOAT_NL,vals[index-1]*(1.-delta)+vals[index]*delta); } } else{ size_t rows=0,columns=0,i,j; double **vals=NULL; double **quantiles; loadtable(&vals,&rows,&columns,0,splitstring); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zdx%zd data table\n",rows,columns); /*+++++++++++++++++++++++++++++++++++++++*/ if(n>rows) n = rows;/* for consistency */ /* allocate the array for the result */ quantiles = (double **) my_alloc(n*sizeof(double*)); for(i=0;i1.-1./(size+1.)) quantiles[j-1][i]=vals[i][size-1]; else quantiles[j-1][i]=vals[i][index-1]*(1.-delta)+vals[i][index]*delta; } } /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose){ printf(EMPTY_SEP,"#quant."); printf(EMPTY_NL,"vals->"); } /*+++++++++++++++++++++++++++++++++++++++*/ printmatrixbyrows(stdout,quantiles,n,columns); } } else if(o_qwindow==1){ /* print the data inside the quantile windows */ if(!o_table){ size_t rows=0,columns=0,i,j; double **vals=NULL; size_t indexmin,indexmax; /* load data: data[column][row] */ loadtable(&vals,&rows,&columns,0,splitstring); if(o_position > columns){ fprintf(stderr, "ERROR (%s): Column position larger than columns number.\n", GB_PROGNAME); exit(1); } /* remove NAN entries */ denan_data(&vals,&rows,&columns); /* define the indexes boundaries */ indexmin = floor(rows*wqmin); indexmax = ceil(rows*wqmax); if(columns==1){ /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zd data\n",rows); /*+++++++++++++++++++++++++++++++++++++++*/ /* sort data */ qsort(vals[0],rows,sizeof(double),sort_by_value); for(j=0;j1){ /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zdx%zd data table\n",rows,columns); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_position>0){/* sort data accordong to given position*/ sortn(vals,columns,rows,o_position-1,2); for(j=0;j maxresnum) maxresnum = resnum[i]; for(j=0;jvals[size-1]) printf(FLOAT_NL,1.0); else { int i=0; double dtmp1,dtmp2; while(vals[i]vals[i][size-1]) results[j][i]=1.0; else { int index=0; double dtmp1,dtmp2; while(vals[i][index]"); /*+++++++++++++++++++++++++++++++++++++++*/ printmatrixbyrows(stdout,results,xvalnum,columns); } } else if(o_qval==1) { /* print the values associated with the quantile */ if(!o_table){ size_t size,index; double *vals=NULL; load(&vals,&size,0,splitstring); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zd data\n",size); /*+++++++++++++++++++++++++++++++++++++++*/ denan(&vals,&size); qsort(vals,size,sizeof(double),sort_by_value); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose){ if(o_err==0) printf(EMPTY_NL,"#quantiles"); else{ printf(EMPTY_SEP,"#quantiles"); printf(EMPTY_NL,"std.err."); } } /*+++++++++++++++++++++++++++++++++++++++*/ for(index=0;index1.-1./(size+1.)) printf(FLOAT_NL,vals[size-1]); else { int index = floor((size+1.)*qval); double delta = (size+1.)*qval-index; double quantile = vals[index-1]*(1.-delta)+vals[index]*delta; if(o_err==0) printf(FLOAT_NL,quantile); else{ int supindex = ceil((size+1.)*qval+sqrt(qval*(1-qval)*(size+2.))); int infindex = floor((size+1.)*qval-sqrt(qval*(1-qval)*(size+2.))); double supdist= vals[supindex-1]-quantile; double infdist = quantile-vals[infindex-1]; printf(FLOAT_SEP,vals[index-1]*(1.-delta)+vals[index]*delta); printf(FLOAT_NL,(supdist>infdist?supdist:infdist)); } } } } else{ size_t rows=0,columns=0,i,j; double **vals=NULL; double **results; loadtable(&vals,&rows,&columns,0,splitstring); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stderr,"loaded %zdx%zd data table\n",rows,columns); /*+++++++++++++++++++++++++++++++++++++++*/ /* allocate the array for the result */ results = (double **) my_alloc(qvalnum*sizeof(double *)); for(j=0;j1.-1./(size+1.)) results[j][i]=vals[i][size-1]; else { int index = floor((size+1.)*qval); double delta = (size+1.)*qval-index; results[j][i]=vals[i][index-1]*(1.-delta)+vals[i][index]*delta; } } } /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) printf(EMPTY_NL,"#quantiles->"); /*+++++++++++++++++++++++++++++++++++++++*/ printmatrixbyrows(stdout,results,qvalnum,columns); } } else{ fprintf(stderr,"you asked for something impossible! check %s -h\n",argv[0]); } return(0); } gbutils-5.7.1/config.guess0000755000175000017500000012475312612517377012465 00000000000000#! /bin/sh # Attempt to guess a canonical system name. # Copyright 1992-2015 Free Software Foundation, Inc. timestamp='2015-08-20' # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see . # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that # program. This Exception is an additional permission under section 7 # of the GNU General Public License, version 3 ("GPLv3"). # # Originally written by Per Bothner; maintained since 2000 by Ben Elliston. # # You can get the latest version of this script from: # http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD # # Please send patches to . me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] Output the configuration name of the system \`$me' is run on. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.guess ($timestamp) Originally written by Per Bothner. Copyright 1992-2015 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE." help=" Try \`$me --help' for more information." # Parse command line while test $# -gt 0 ; do case $1 in --time-stamp | --time* | -t ) echo "$timestamp" ; exit ;; --version | -v ) echo "$version" ; exit ;; --help | --h* | -h ) echo "$usage"; exit ;; -- ) # Stop option processing shift; break ;; - ) # Use stdin as input. break ;; -* ) echo "$me: invalid option $1$help" >&2 exit 1 ;; * ) break ;; esac done if test $# != 0; then echo "$me: too many arguments$help" >&2 exit 1 fi trap 'exit 1' 1 2 15 # CC_FOR_BUILD -- compiler used by this script. Note that the use of a # compiler to aid in system detection is discouraged as it requires # temporary files to be created and, as you can see below, it is a # headache to deal with in a portable fashion. # Historically, `CC_FOR_BUILD' used to be named `HOST_CC'. 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Hard to guess exactly what SunOS6 will be like, but # it's likely to be more like Solaris than SunOS4. echo sparc-sun-solaris3`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:*:*) case "`/usr/bin/arch -k`" in Series*|S4*) UNAME_RELEASE=`uname -v` ;; esac # Japanese Language versions have a version number like `4.1.3-JL'. echo sparc-sun-sunos`echo ${UNAME_RELEASE}|sed -e 's/-/_/'` exit ;; sun3*:SunOS:*:*) echo m68k-sun-sunos${UNAME_RELEASE} exit ;; sun*:*:4.2BSD:*) UNAME_RELEASE=`(sed 1q /etc/motd | awk '{print substr($5,1,3)}') 2>/dev/null` test "x${UNAME_RELEASE}" = "x" && UNAME_RELEASE=3 case "`/bin/arch`" in sun3) echo m68k-sun-sunos${UNAME_RELEASE} ;; sun4) echo sparc-sun-sunos${UNAME_RELEASE} ;; esac exit ;; aushp:SunOS:*:*) echo sparc-auspex-sunos${UNAME_RELEASE} exit ;; # The situation for MiNT is a little confusing. The machine name # can be virtually everything (everything which is not # "atarist" or "atariste" at least should have a processor # > m68000). The system name ranges from "MiNT" over "FreeMiNT" # to the lowercase version "mint" (or "freemint"). Finally # the system name "TOS" denotes a system which is actually not # MiNT. But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) echo m68k-milan-mint${UNAME_RELEASE} exit ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) echo m68k-hades-mint${UNAME_RELEASE} exit ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) echo m68k-unknown-mint${UNAME_RELEASE} exit ;; m68k:machten:*:*) echo m68k-apple-machten${UNAME_RELEASE} exit ;; powerpc:machten:*:*) echo powerpc-apple-machten${UNAME_RELEASE} exit ;; RISC*:Mach:*:*) echo mips-dec-mach_bsd4.3 exit ;; RISC*:ULTRIX:*:*) echo mips-dec-ultrix${UNAME_RELEASE} exit ;; VAX*:ULTRIX*:*:*) echo vax-dec-ultrix${UNAME_RELEASE} exit ;; 2020:CLIX:*:* | 2430:CLIX:*:*) echo clipper-intergraph-clix${UNAME_RELEASE} exit ;; mips:*:*:UMIPS | mips:*:*:RISCos) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && dummyarg=`echo "${UNAME_RELEASE}" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`$dummy $dummyarg` && { echo "$SYSTEM_NAME"; exit; } echo mips-mips-riscos${UNAME_RELEASE} exit ;; Motorola:PowerMAX_OS:*:*) echo powerpc-motorola-powermax exit ;; Motorola:*:4.3:PL8-*) echo powerpc-harris-powermax exit ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) echo powerpc-harris-powermax exit ;; Night_Hawk:Power_UNIX:*:*) echo powerpc-harris-powerunix exit ;; m88k:CX/UX:7*:*) echo m88k-harris-cxux7 exit ;; m88k:*:4*:R4*) echo m88k-motorola-sysv4 exit ;; m88k:*:3*:R3*) echo m88k-motorola-sysv3 exit ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if [ $UNAME_PROCESSOR = mc88100 ] || [ $UNAME_PROCESSOR = mc88110 ] then if [ ${TARGET_BINARY_INTERFACE}x = m88kdguxelfx ] || \ [ ${TARGET_BINARY_INTERFACE}x = x ] then echo m88k-dg-dgux${UNAME_RELEASE} else echo m88k-dg-dguxbcs${UNAME_RELEASE} fi else echo i586-dg-dgux${UNAME_RELEASE} fi exit ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) echo m88k-dolphin-sysv3 exit ;; M88*:*:R3*:*) # Delta 88k system running SVR3 echo m88k-motorola-sysv3 exit ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) echo m88k-tektronix-sysv3 exit ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) echo m68k-tektronix-bsd exit ;; *:IRIX*:*:*) echo mips-sgi-irix`echo ${UNAME_RELEASE}|sed -e 's/-/_/g'` exit ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. echo romp-ibm-aix # uname -m gives an 8 hex-code CPU id exit ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) echo i386-ibm-aix exit ;; ia64:AIX:*:*) if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${UNAME_MACHINE}-ibm-aix${IBM_REV} exit ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` then echo "$SYSTEM_NAME" else echo rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then echo rs6000-ibm-aix3.2.4 else echo rs6000-ibm-aix3.2 fi exit ;; *:AIX:*:[4567]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El ${IBM_CPU_ID} | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if [ -x /usr/bin/lslpp ] ; then IBM_REV=`/usr/bin/lslpp -Lqc bos.rte.libc | awk -F: '{ print $3 }' | sed s/[0-9]*$/0/` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${IBM_ARCH}-ibm-aix${IBM_REV} exit ;; *:AIX:*:*) echo rs6000-ibm-aix exit ;; ibmrt:4.4BSD:*|romp-ibm:BSD:*) echo romp-ibm-bsd4.4 exit ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and echo romp-ibm-bsd${UNAME_RELEASE} # 4.3 with uname added to exit ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) echo rs6000-bull-bosx exit ;; DPX/2?00:B.O.S.:*:*) echo m68k-bull-sysv3 exit ;; 9000/[34]??:4.3bsd:1.*:*) echo m68k-hp-bsd exit ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) echo m68k-hp-bsd4.4 exit ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` case "${UNAME_MACHINE}" in 9000/31? ) HP_ARCH=m68000 ;; 9000/[34]?? ) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if [ -x /usr/bin/getconf ]; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case "${sc_cpu_version}" in 523) HP_ARCH="hppa1.0" ;; # CPU_PA_RISC1_0 528) HP_ARCH="hppa1.1" ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case "${sc_kernel_bits}" in 32) HP_ARCH="hppa2.0n" ;; 64) HP_ARCH="hppa2.0w" ;; '') HP_ARCH="hppa2.0" ;; # HP-UX 10.20 esac ;; esac fi if [ "${HP_ARCH}" = "" ]; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS= $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null) && HP_ARCH=`$dummy` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if [ ${HP_ARCH} = "hppa2.0w" ] then eval $set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | grep -q __LP64__ then HP_ARCH="hppa2.0w" else HP_ARCH="hppa64" fi fi echo ${HP_ARCH}-hp-hpux${HPUX_REV} exit ;; ia64:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` echo ia64-hp-hpux${HPUX_REV} exit ;; 3050*:HI-UX:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } echo unknown-hitachi-hiuxwe2 exit ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:* ) echo hppa1.1-hp-bsd exit ;; 9000/8??:4.3bsd:*:*) echo hppa1.0-hp-bsd exit ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) echo hppa1.0-hp-mpeix exit ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:* ) echo hppa1.1-hp-osf exit ;; hp8??:OSF1:*:*) echo hppa1.0-hp-osf exit ;; i*86:OSF1:*:*) if [ -x /usr/sbin/sysversion ] ; then echo ${UNAME_MACHINE}-unknown-osf1mk else echo ${UNAME_MACHINE}-unknown-osf1 fi exit ;; parisc*:Lites*:*:*) echo hppa1.1-hp-lites exit ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) echo c1-convex-bsd exit ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) echo c34-convex-bsd exit ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) echo c38-convex-bsd exit ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) echo c4-convex-bsd exit ;; CRAY*Y-MP:*:*:*) echo ymp-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*[A-Z]90:*:*:*) echo ${UNAME_MACHINE}-cray-unicos${UNAME_RELEASE} \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) echo t90-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*T3E:*:*:*) echo alphaev5-cray-unicosmk${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*SV1:*:*:*) echo sv1-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; *:UNICOS/mp:*:*) echo craynv-cray-unicosmp${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | sed -e 's/ /_/'` echo "${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/ /_/'` echo "sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) echo ${UNAME_MACHINE}-pc-bsdi${UNAME_RELEASE} exit ;; sparc*:BSD/OS:*:*) echo sparc-unknown-bsdi${UNAME_RELEASE} exit ;; *:BSD/OS:*:*) echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE} exit ;; *:FreeBSD:*:*) UNAME_PROCESSOR=`/usr/bin/uname -p` case ${UNAME_PROCESSOR} in amd64) echo x86_64-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; *) echo ${UNAME_PROCESSOR}-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; esac exit ;; i*:CYGWIN*:*) echo ${UNAME_MACHINE}-pc-cygwin exit ;; *:MINGW64*:*) echo ${UNAME_MACHINE}-pc-mingw64 exit ;; *:MINGW*:*) echo ${UNAME_MACHINE}-pc-mingw32 exit ;; *:MSYS*:*) echo ${UNAME_MACHINE}-pc-msys exit ;; i*:windows32*:*) # uname -m includes "-pc" on this system. echo ${UNAME_MACHINE}-mingw32 exit ;; i*:PW*:*) echo ${UNAME_MACHINE}-pc-pw32 exit ;; *:Interix*:*) case ${UNAME_MACHINE} in x86) echo i586-pc-interix${UNAME_RELEASE} exit ;; authenticamd | genuineintel | EM64T) echo x86_64-unknown-interix${UNAME_RELEASE} exit ;; IA64) echo ia64-unknown-interix${UNAME_RELEASE} exit ;; esac ;; [345]86:Windows_95:* | [345]86:Windows_98:* | [345]86:Windows_NT:*) echo i${UNAME_MACHINE}-pc-mks exit ;; 8664:Windows_NT:*) echo x86_64-pc-mks exit ;; i*:Windows_NT*:* | Pentium*:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # It also conflicts with pre-2.0 versions of AT&T UWIN. Should we # UNAME_MACHINE based on the output of uname instead of i386? echo i586-pc-interix exit ;; i*:UWIN*:*) echo ${UNAME_MACHINE}-pc-uwin exit ;; amd64:CYGWIN*:*:* | x86_64:CYGWIN*:*:*) echo x86_64-unknown-cygwin exit ;; p*:CYGWIN*:*) echo powerpcle-unknown-cygwin exit ;; prep*:SunOS:5.*:*) echo powerpcle-unknown-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; *:GNU:*:*) # the GNU system echo `echo ${UNAME_MACHINE}|sed -e 's,[-/].*$,,'`-unknown-${LIBC}`echo ${UNAME_RELEASE}|sed -e 's,/.*$,,'` exit ;; *:GNU/*:*:*) # other systems with GNU libc and userland echo ${UNAME_MACHINE}-unknown-`echo ${UNAME_SYSTEM} | sed 's,^[^/]*/,,' | tr '[A-Z]' '[a-z]'``echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'`-${LIBC} exit ;; i*86:Minix:*:*) echo ${UNAME_MACHINE}-pc-minix exit ;; aarch64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; aarch64_be:Linux:*:*) UNAME_MACHINE=aarch64_be echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; alpha:Linux:*:*) case `sed -n '/^cpu model/s/^.*: \(.*\)/\1/p' < /proc/cpuinfo` in EV5) UNAME_MACHINE=alphaev5 ;; EV56) UNAME_MACHINE=alphaev56 ;; PCA56) UNAME_MACHINE=alphapca56 ;; PCA57) UNAME_MACHINE=alphapca56 ;; EV6) UNAME_MACHINE=alphaev6 ;; EV67) UNAME_MACHINE=alphaev67 ;; EV68*) UNAME_MACHINE=alphaev68 ;; esac objdump --private-headers /bin/sh | grep -q ld.so.1 if test "$?" = 0 ; then LIBC="gnulibc1" ; fi echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; arc:Linux:*:* | arceb:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; arm*:Linux:*:*) eval $set_cc_for_build if echo __ARM_EABI__ | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_EABI__ then echo ${UNAME_MACHINE}-unknown-linux-${LIBC} else if echo __ARM_PCS_VFP | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_PCS_VFP then echo ${UNAME_MACHINE}-unknown-linux-${LIBC}eabi else echo ${UNAME_MACHINE}-unknown-linux-${LIBC}eabihf fi fi exit ;; avr32*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; cris:Linux:*:*) echo ${UNAME_MACHINE}-axis-linux-${LIBC} exit ;; crisv32:Linux:*:*) echo ${UNAME_MACHINE}-axis-linux-${LIBC} exit ;; e2k:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; frv:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; hexagon:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; i*86:Linux:*:*) echo ${UNAME_MACHINE}-pc-linux-${LIBC} exit ;; ia64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; m32r*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; m68*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; mips:Linux:*:* | mips64:Linux:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #undef CPU #undef ${UNAME_MACHINE} #undef ${UNAME_MACHINE}el #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) CPU=${UNAME_MACHINE}el #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) CPU=${UNAME_MACHINE} #else CPU= #endif #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep '^CPU'` test x"${CPU}" != x && { echo "${CPU}-unknown-linux-${LIBC}"; exit; } ;; openrisc*:Linux:*:*) echo or1k-unknown-linux-${LIBC} exit ;; or32:Linux:*:* | or1k*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; padre:Linux:*:*) echo sparc-unknown-linux-${LIBC} exit ;; parisc64:Linux:*:* | hppa64:Linux:*:*) echo hppa64-unknown-linux-${LIBC} exit ;; parisc:Linux:*:* | hppa:Linux:*:*) # Look for CPU level case `grep '^cpu[^a-z]*:' /proc/cpuinfo 2>/dev/null | cut -d' ' -f2` in PA7*) echo hppa1.1-unknown-linux-${LIBC} ;; PA8*) echo hppa2.0-unknown-linux-${LIBC} ;; *) echo hppa-unknown-linux-${LIBC} ;; esac exit ;; ppc64:Linux:*:*) echo powerpc64-unknown-linux-${LIBC} exit ;; ppc:Linux:*:*) echo powerpc-unknown-linux-${LIBC} exit ;; ppc64le:Linux:*:*) echo powerpc64le-unknown-linux-${LIBC} exit ;; ppcle:Linux:*:*) echo powerpcle-unknown-linux-${LIBC} exit ;; s390:Linux:*:* | s390x:Linux:*:*) echo ${UNAME_MACHINE}-ibm-linux-${LIBC} exit ;; sh64*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; sh*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; sparc:Linux:*:* | sparc64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; tile*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; vax:Linux:*:*) echo ${UNAME_MACHINE}-dec-linux-${LIBC} exit ;; x86_64:Linux:*:*) echo ${UNAME_MACHINE}-pc-linux-${LIBC} exit ;; xtensa*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; i*86:DYNIX/ptx:4*:*) # ptx 4.0 does uname -s correctly, with DYNIX/ptx in there. # earlier versions are messed up and put the nodename in both # sysname and nodename. echo i386-sequent-sysv4 exit ;; i*86:UNIX_SV:4.2MP:2.*) # Unixware is an offshoot of SVR4, but it has its own version # number series starting with 2... # I am not positive that other SVR4 systems won't match this, # I just have to hope. -- rms. # Use sysv4.2uw... so that sysv4* matches it. echo ${UNAME_MACHINE}-pc-sysv4.2uw${UNAME_VERSION} exit ;; i*86:OS/2:*:*) # If we were able to find `uname', then EMX Unix compatibility # is probably installed. echo ${UNAME_MACHINE}-pc-os2-emx exit ;; i*86:XTS-300:*:STOP) echo ${UNAME_MACHINE}-unknown-stop exit ;; i*86:atheos:*:*) echo ${UNAME_MACHINE}-unknown-atheos exit ;; i*86:syllable:*:*) echo ${UNAME_MACHINE}-pc-syllable exit ;; i*86:LynxOS:2.*:* | i*86:LynxOS:3.[01]*:* | i*86:LynxOS:4.[02]*:*) echo i386-unknown-lynxos${UNAME_RELEASE} exit ;; i*86:*DOS:*:*) echo ${UNAME_MACHINE}-pc-msdosdjgpp exit ;; i*86:*:4.*:* | i*86:SYSTEM_V:4.*:*) UNAME_REL=`echo ${UNAME_RELEASE} | sed 's/\/MP$//'` if grep Novell /usr/include/link.h >/dev/null 2>/dev/null; then echo ${UNAME_MACHINE}-univel-sysv${UNAME_REL} else echo ${UNAME_MACHINE}-pc-sysv${UNAME_REL} fi exit ;; i*86:*:5:[678]*) # UnixWare 7.x, OpenUNIX and OpenServer 6. case `/bin/uname -X | grep "^Machine"` in *486*) UNAME_MACHINE=i486 ;; *Pentium) UNAME_MACHINE=i586 ;; *Pent*|*Celeron) UNAME_MACHINE=i686 ;; esac echo ${UNAME_MACHINE}-unknown-sysv${UNAME_RELEASE}${UNAME_SYSTEM}${UNAME_VERSION} exit ;; i*86:*:3.2:*) if test -f /usr/options/cb.name; then UNAME_REL=`sed -n 's/.*Version //p' /dev/null >/dev/null ; then UNAME_REL=`(/bin/uname -X|grep Release|sed -e 's/.*= //')` (/bin/uname -X|grep i80486 >/dev/null) && UNAME_MACHINE=i486 (/bin/uname -X|grep '^Machine.*Pentium' >/dev/null) \ && UNAME_MACHINE=i586 (/bin/uname -X|grep '^Machine.*Pent *II' >/dev/null) \ && UNAME_MACHINE=i686 (/bin/uname -X|grep '^Machine.*Pentium Pro' >/dev/null) \ && UNAME_MACHINE=i686 echo ${UNAME_MACHINE}-pc-sco$UNAME_REL else echo ${UNAME_MACHINE}-pc-sysv32 fi exit ;; pc:*:*:*) # Left here for compatibility: # uname -m prints for DJGPP always 'pc', but it prints nothing about # the processor, so we play safe by assuming i586. # Note: whatever this is, it MUST be the same as what config.sub # prints for the "djgpp" host, or else GDB configury will decide that # this is a cross-build. echo i586-pc-msdosdjgpp exit ;; Intel:Mach:3*:*) echo i386-pc-mach3 exit ;; paragon:*:*:*) echo i860-intel-osf1 exit ;; i860:*:4.*:*) # i860-SVR4 if grep Stardent /usr/include/sys/uadmin.h >/dev/null 2>&1 ; then echo i860-stardent-sysv${UNAME_RELEASE} # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. echo i860-unknown-sysv${UNAME_RELEASE} # Unknown i860-SVR4 fi exit ;; mini*:CTIX:SYS*5:*) # "miniframe" echo m68010-convergent-sysv exit ;; mc68k:UNIX:SYSTEM5:3.51m) echo m68k-convergent-sysv exit ;; M680?0:D-NIX:5.3:*) echo m68k-diab-dnix exit ;; M68*:*:R3V[5678]*:*) test -r /sysV68 && { echo 'm68k-motorola-sysv'; exit; } ;; 3[345]??:*:4.0:3.0 | 3[34]??A:*:4.0:3.0 | 3[34]??,*:*:4.0:3.0 | 3[34]??/*:*:4.0:3.0 | 4400:*:4.0:3.0 | 4850:*:4.0:3.0 | SKA40:*:4.0:3.0 | SDS2:*:4.0:3.0 | SHG2:*:4.0:3.0 | S7501*:*:4.0:3.0) OS_REL='' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; 3[34]??:*:4.0:* | 3[34]??,*:*:4.0:*) /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4; exit; } ;; NCR*:*:4.2:* | MPRAS*:*:4.2:*) OS_REL='.3' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep pteron >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; m68*:LynxOS:2.*:* | m68*:LynxOS:3.0*:*) echo m68k-unknown-lynxos${UNAME_RELEASE} exit ;; mc68030:UNIX_System_V:4.*:*) echo m68k-atari-sysv4 exit ;; TSUNAMI:LynxOS:2.*:*) echo sparc-unknown-lynxos${UNAME_RELEASE} exit ;; rs6000:LynxOS:2.*:*) echo rs6000-unknown-lynxos${UNAME_RELEASE} exit ;; PowerPC:LynxOS:2.*:* | PowerPC:LynxOS:3.[01]*:* | PowerPC:LynxOS:4.[02]*:*) echo powerpc-unknown-lynxos${UNAME_RELEASE} exit ;; SM[BE]S:UNIX_SV:*:*) echo mips-dde-sysv${UNAME_RELEASE} exit ;; RM*:ReliantUNIX-*:*:*) echo mips-sni-sysv4 exit ;; RM*:SINIX-*:*:*) echo mips-sni-sysv4 exit ;; *:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` echo ${UNAME_MACHINE}-sni-sysv4 else echo ns32k-sni-sysv fi exit ;; PENTIUM:*:4.0*:*) # Unisys `ClearPath HMP IX 4000' SVR4/MP effort # says echo i586-unisys-sysv4 exit ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm echo hppa1.1-stratus-sysv4 exit ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. echo i860-stratus-sysv4 exit ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. echo ${UNAME_MACHINE}-stratus-vos exit ;; *:VOS:*:*) # From Paul.Green@stratus.com. echo hppa1.1-stratus-vos exit ;; mc68*:A/UX:*:*) echo m68k-apple-aux${UNAME_RELEASE} exit ;; news*:NEWS-OS:6*:*) echo mips-sony-newsos6 exit ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if [ -d /usr/nec ]; then echo mips-nec-sysv${UNAME_RELEASE} else echo mips-unknown-sysv${UNAME_RELEASE} fi exit ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. echo powerpc-be-beos exit ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. echo powerpc-apple-beos exit ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. echo i586-pc-beos exit ;; BePC:Haiku:*:*) # Haiku running on Intel PC compatible. echo i586-pc-haiku exit ;; x86_64:Haiku:*:*) echo x86_64-unknown-haiku exit ;; SX-4:SUPER-UX:*:*) echo sx4-nec-superux${UNAME_RELEASE} exit ;; SX-5:SUPER-UX:*:*) echo sx5-nec-superux${UNAME_RELEASE} exit ;; SX-6:SUPER-UX:*:*) echo sx6-nec-superux${UNAME_RELEASE} exit ;; SX-7:SUPER-UX:*:*) echo sx7-nec-superux${UNAME_RELEASE} exit ;; SX-8:SUPER-UX:*:*) echo sx8-nec-superux${UNAME_RELEASE} exit ;; SX-8R:SUPER-UX:*:*) echo sx8r-nec-superux${UNAME_RELEASE} exit ;; Power*:Rhapsody:*:*) echo powerpc-apple-rhapsody${UNAME_RELEASE} exit ;; *:Rhapsody:*:*) echo ${UNAME_MACHINE}-apple-rhapsody${UNAME_RELEASE} exit ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` || UNAME_PROCESSOR=unknown eval $set_cc_for_build if test "$UNAME_PROCESSOR" = unknown ; then UNAME_PROCESSOR=powerpc fi if test `echo "$UNAME_RELEASE" | sed -e 's/\..*//'` -le 10 ; then if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __LP64__'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then case $UNAME_PROCESSOR in i386) UNAME_PROCESSOR=x86_64 ;; powerpc) UNAME_PROCESSOR=powerpc64 ;; esac fi fi elif test "$UNAME_PROCESSOR" = i386 ; then # Avoid executing cc on OS X 10.9, as it ships with a stub # that puts up a graphical alert prompting to install # developer tools. Any system running Mac OS X 10.7 or # later (Darwin 11 and later) is required to have a 64-bit # processor. This is not true of the ARM version of Darwin # that Apple uses in portable devices. UNAME_PROCESSOR=x86_64 fi echo ${UNAME_PROCESSOR}-apple-darwin${UNAME_RELEASE} exit ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = "x86"; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi echo ${UNAME_PROCESSOR}-${UNAME_MACHINE}-nto-qnx${UNAME_RELEASE} exit ;; *:QNX:*:4*) echo i386-pc-qnx exit ;; NEO-?:NONSTOP_KERNEL:*:*) echo neo-tandem-nsk${UNAME_RELEASE} exit ;; NSE-*:NONSTOP_KERNEL:*:*) echo nse-tandem-nsk${UNAME_RELEASE} exit ;; NSR-?:NONSTOP_KERNEL:*:*) echo nsr-tandem-nsk${UNAME_RELEASE} exit ;; *:NonStop-UX:*:*) echo mips-compaq-nonstopux exit ;; BS2000:POSIX*:*:*) echo bs2000-siemens-sysv exit ;; DS/*:UNIX_System_V:*:*) echo ${UNAME_MACHINE}-${UNAME_SYSTEM}-${UNAME_RELEASE} exit ;; *:Plan9:*:*) # "uname -m" is not consistent, so use $cputype instead. 386 # is converted to i386 for consistency with other x86 # operating systems. if test "$cputype" = "386"; then UNAME_MACHINE=i386 else UNAME_MACHINE="$cputype" fi echo ${UNAME_MACHINE}-unknown-plan9 exit ;; *:TOPS-10:*:*) echo pdp10-unknown-tops10 exit ;; *:TENEX:*:*) echo pdp10-unknown-tenex exit ;; KS10:TOPS-20:*:* | KL10:TOPS-20:*:* | TYPE4:TOPS-20:*:*) echo pdp10-dec-tops20 exit ;; XKL-1:TOPS-20:*:* | TYPE5:TOPS-20:*:*) echo pdp10-xkl-tops20 exit ;; *:TOPS-20:*:*) echo pdp10-unknown-tops20 exit ;; *:ITS:*:*) echo pdp10-unknown-its exit ;; SEI:*:*:SEIUX) echo mips-sei-seiux${UNAME_RELEASE} exit ;; *:DragonFly:*:*) echo ${UNAME_MACHINE}-unknown-dragonfly`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` exit ;; *:*VMS:*:*) UNAME_MACHINE=`(uname -p) 2>/dev/null` case "${UNAME_MACHINE}" in A*) echo alpha-dec-vms ; exit ;; I*) echo ia64-dec-vms ; exit ;; V*) echo vax-dec-vms ; exit ;; esac ;; *:XENIX:*:SysV) echo i386-pc-xenix exit ;; i*86:skyos:*:*) echo ${UNAME_MACHINE}-pc-skyos`echo ${UNAME_RELEASE}` | sed -e 's/ .*$//' exit ;; i*86:rdos:*:*) echo ${UNAME_MACHINE}-pc-rdos exit ;; i*86:AROS:*:*) echo ${UNAME_MACHINE}-pc-aros exit ;; x86_64:VMkernel:*:*) echo ${UNAME_MACHINE}-unknown-esx exit ;; esac cat >&2 < in order to provide the needed information to handle your system. config.guess timestamp = $timestamp uname -m = `(uname -m) 2>/dev/null || echo unknown` uname -r = `(uname -r) 2>/dev/null || echo unknown` uname -s = `(uname -s) 2>/dev/null || echo unknown` uname -v = `(uname -v) 2>/dev/null || echo unknown` /usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null` /bin/uname -X = `(/bin/uname -X) 2>/dev/null` hostinfo = `(hostinfo) 2>/dev/null` /bin/universe = `(/bin/universe) 2>/dev/null` /usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null` /bin/arch = `(/bin/arch) 2>/dev/null` /usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null` /usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null` UNAME_MACHINE = ${UNAME_MACHINE} UNAME_RELEASE = ${UNAME_RELEASE} UNAME_SYSTEM = ${UNAME_SYSTEM} UNAME_VERSION = ${UNAME_VERSION} EOF exit 1 # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: gbutils-5.7.1/gaussian_gbhill.c0000644000175000017500000002746313246754720013443 00000000000000#include "gbhill.h" /* Gaussian Distribution */ /* ------------------------ */ /* x[0]=m x[1]=s */ /* F[j] = */ /* -log(f[j]) = log(s) + (1.0/(2*pow(s,2)))*pow((x[j]-m),2) */ /* N: sample size k: number of observations used */ void gaussian_nll (const size_t n, const double *x,void *params,double *fval){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double s=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += pow((data[i]-m),2); *fval = log(s) + (*fval)/(2*k*s*s); switch(method){ case 0: if(N>k){ const double F =gsl_cdf_gaussian_P(data[min]-m, s) ; *fval += -(N/k-1.0)*log(F); } break; case 1: if(N>k){ const double F =gsl_cdf_gaussian_P(d-m, s) ; *fval += -(N/k-1.0)*log(F); } break; case 2: if(N>k){ const double F =gsl_cdf_gaussian_P(data[max]-m, s); *fval += -(N/k-1.0)*log(1.0-F); } break; case 3: if(N>k){ const double F =gsl_cdf_gaussian_P(d-m, s) ; *fval += -(N/k-1.0)*log(1.0-F); } break; } /* fprintf(stderr,"[ f ] x1=%g x2=%g f=%g\n",x[0],x[1],*fval); */ } void gaussian_dnll (const size_t n, const double *x,void *params, double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double s=x[1]; const double k = ((double) max-min+1); grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += data[i]; grad[0]= -(grad[0]/k-m)/(s*s); grad[1]=0.0; for(i=min;i<=max;i++) grad[1] += pow((data[i]-m),2); grad[1]= - grad[1]/(k*pow(s,3)) + 1/s; switch(method){ case 0: if(N>k) { const double F = gsl_cdf_gaussian_P(data[min]-m, s); const double f = gsl_ran_gaussian_pdf(data[min]-m,s); const double dFdm = - f; const double dFds = - f*(data[min]-m)/s; grad[0] += - (N/k-1.0)*dFdm/F; grad[1] += - (N/k-1.0)*dFds/F; } break; case 1: if(N>k) { const double F = gsl_cdf_gaussian_P(d-m, s); const double f = gsl_ran_gaussian_pdf(d-m,s); const double dFdm = - f; const double dFds = - f*(d-m)/s; grad[0] += - (N/k-1.0)*dFdm/F; grad[1] += - (N/k-1.0)*dFds/F; } break; case 2: if(N>k){ const double F = gsl_cdf_gaussian_P(data[max]-m,s); const double f = gsl_ran_gaussian_pdf(data[max]-m,s); const double dFdm = - f; const double dFds = - f*(data[max]-m)/s; grad[0] += (N/k-1.0)*dFdm/(1.0-F); grad[1] += (N/k-1.0)*dFds/(1.0-F); } break; case 3: if(N>k){ const double F = gsl_cdf_gaussian_P(d-m,s); const double f = gsl_ran_gaussian_pdf(d-m,s); const double dFdm = - f; const double dFds = - f*(d-m)/s; grad[0] += (N/k-1.0)*dFdm/(1.0-F); grad[1] += (N/k-1.0)*dFds/(1.0-F); } break; } /* fprintf(stderr,"[ df] x1=%g x2=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],grad[0],grad[1]); */ } void gaussian_nlldnll (const size_t n, const double *x,void *params,double *fval,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double s=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += pow((data[i]-m),2); *fval = log(s) + (*fval)/(2*k*s*s); grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += data[i]; grad[0]= -(grad[0]/k-m)/(s*s); grad[1]=0.0; for(i=min;i<=max;i++) grad[1] += pow((data[i]-m),2); grad[1]= - grad[1]/(k*pow(s,3)) + 1/s; switch(method){ case 0: if(N>k) { const double F = gsl_cdf_gaussian_P(data[min]-m,s); const double f = gsl_ran_gaussian_pdf(data[min]-m,s); const double dFdm = - f; const double dFds = - f*(data[min]-m)/s; *fval += -(N/k-1.0)*log(F); grad[0] += - (N/k-1.0)*dFdm/F; grad[1] += - (N/k-1.0)*dFds/F; } break; case 1: if(N>k) { const double F = gsl_cdf_gaussian_P(d-m,s); const double f = gsl_ran_gaussian_pdf(d-m,s); const double dFdm = - f; const double dFds = - f*(d-m)/s; *fval += -(N/k-1.0)*log(F); grad[0] += - (N/k-1.0)*dFdm/F; grad[1] += - (N/k-1.0)*dFds/F; } break; case 2: if(N>k){ const double F = gsl_cdf_gaussian_P(data[max]-m,s); const double f = gsl_ran_gaussian_pdf(data[max]-m,s); const double dFdm = - f; const double dFds = - f*(data[max]-m)/s; *fval += -(N/k-1.0)*log(1.0-(F)); grad[0] += -(N/k-1.0)*(-dFdm)/(1.0-F); grad[1] += -(N/k-1.0)*(-dFds)/(1.0-F); } break; case 3: if(N>k){ const double F = gsl_cdf_gaussian_P(d-m,s); const double f = gsl_ran_gaussian_pdf(d-m,s); const double dFdm = - f; const double dFds = - f*(d-m)/s; *fval += -(N/k-1.0)*log(1.0-(F)); grad[0] += -(N/k-1.0)*(-dFdm)/(1.0-F); grad[1] += -(N/k-1.0)*(-dFds)/(1.0-F); } break; } /* fprintf(stderr,"[fdf] x1=%g x2=%g f=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],*fval,grad[0],grad[1]); */ } double gaussian_f (const double x,const size_t n, const double *par){ return gsl_ran_gaussian_pdf(x-par[0], par[1]); } double gaussian_F (const double x,const size_t n, const double *par){ return gsl_cdf_gaussian_P(x-par[0], par[1]); } gsl_matrix *gaussian_varcovar(const int o_varcovar, double *x, void *params){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double s=x[1]; double k = ((double) max-min+1); double sum = 0.0; for(i=min;i<=max;i++) sum += data[i]-m; double sum2 = 0.0; for(i=min;i<=max;i++) sum2 += pow((data[i]-m),2); double dldm = 0.0; double d2ldm2 = 0.0; double d2ldmds = 0.0; double dlds = 0.0; double d2lds2 = 0.0; gsl_matrix *covar; covar = gsl_matrix_alloc (2,2); double F; double f; switch(method){ case 0: /* GSL allocation of memory for the covar matrix */ F = gsl_cdf_gaussian_P(data[min]-m, s) ; f = gsl_ran_gaussian_pdf(data[min]-m, s) ; /* Compute ll partial derivatives. */ dldm = (1./pow(s,2.))*sum-(N-k)*f/F; d2ldm2 = (-k/pow(s,2.))-(N-k)*(f/F)*(((data[min]-m)/pow(s,2.))+f/F); d2ldmds = (-2./pow(s,3.))*sum-(N-k)*(f/F)*(1./s)*(-1.+(data[min]-m)*(f/F)+pow((data[min]-m)/s,2.)); dlds = -(k/s)+(1./pow(s,3.))*sum2-(N-k)*(f/F)*((data[min]-m)/s); d2lds2 = (k/pow(s,2.0))-(3./pow(s,4.))*sum2-(N-k)*((data[min]-m)/(F*s))*(-(f/s)+(f/pow(s,3.))*pow(data[min]-m,2.))-(N-k)*pow(f/F,2.)*pow((data[min]-m)/s,2.)+(N-k)*(f/F)*((data[min]-m)/pow(s,2.)); break; case 1: /* GSL allocation of memory for the covar matrix */ F = gsl_cdf_gaussian_P(d-m, s) ; f = gsl_ran_gaussian_pdf(d-m, s) ; /* Compute ll partial derivatives. */ dldm = (1./pow(s,2.))*sum-(N-k)*f/F; d2ldm2 = (-k/pow(s,2.))-(N-k)*(f/F)*(((data[min]-m)/pow(s,2.))+f/F); d2ldmds = (-2./pow(s,3.))*sum-(N-k)*(f/F)*(1./s)*(-1.+(data[min]-m)*(f/F)+pow((data[min]-m)/s,2.)); dlds = -(k/s)+(1./pow(s,3.))*sum2-(N-k)*(f/F)*((data[min]-m)/s); d2lds2 = (k/pow(s,2.0))-(3./pow(s,4.))*sum2-(N-k)*((data[min]-m)/(F*s))*(-(f/s)+(f/pow(s,3.))*pow(data[min]-m,2.))-(N-k)*pow(f/F,2.)*pow((data[min]-m)/s,2.)+(N-k)*(f/F)*((data[min]-m)/pow(s,2.)); break; case 2: { F = gsl_cdf_gaussian_P(data[max]-m,s); f = gsl_ran_gaussian_pdf(data[max]-m,s); const double dFdm = - f; const double dFds = - f*(data[max]-m)/s; const double dfdm = f*((data[max]-m)/pow(s,2.0)); const double dfds = (f/s)*(pow((data[max]-m)/s,2.0)-1); dldm = (1./pow(s,2.0))*sum+(N-k)*f/(1-F); d2ldm2 = -k/pow(s,2.0)+(N-k)*dfdm*(1/(1-F))+(N-k)*(f/pow(1-F,2.0))*dFdm; d2ldmds = -(2./pow(s,3.0))*sum+(N-k)*(dfds/(1.-F))+(N-k)*(f/pow(1.-F,2.0))*dFds; dlds = -k/s+sum2/pow(s,3.)+(N-k)*(f/(1.-F))*((data[max]-m)/s); d2lds2 = k/pow(s,2.)-(3./pow(s,4.))*sum2+(N-k)*(dfds/(1.-F))*((data[max]-m)/s)+(N-k)*(f/pow(1.-F,2.))*dFds*((data[max]-m)/s)-(N-k)*(f/(1.-F))*((data[max]-m)/pow(s,2.)); break; } case 3: { F = gsl_cdf_gaussian_P(d-m,s); f = gsl_ran_gaussian_pdf(d-m,s); const double dFdm = - f; const double dFds = - f*(d-m)/s; const double dfdm = f*((d-m)/pow(s,2.0)); const double dfds = (f/s)*(pow((d-m)/s,2.0)-1); dldm = (1./pow(s,2.0))*sum+(N-k)*f/(1-F); d2ldm2 = -k/pow(s,2.0)+(N-k)*dfdm*(1/(1-F))+(N-k)*(f/pow(1-F,2.0))*dFdm; d2ldmds = -(2./pow(s,3.0))*sum+(N-k)*(dfds/(1.-F))+(N-k)*(f/pow(1.-F,2.0))*dFds; dlds = -k/s+sum2/pow(s,3.)+(N-k)*(f/(1.-F))*((d-m)/s); d2lds2 = k/pow(s,2.)-(3./pow(s,4.))*sum2+(N-k)*(dfds/(1.-F))*((d-m)/s)+(N-k)*(f/pow(1.-F,2.))*dFds*((d-m)/s)-(N-k)*(f/(1.-F))*((d-m)/pow(s,2.)); } break; } gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *J = gsl_matrix_calloc (2,2); gsl_matrix *H = gsl_matrix_calloc (2,2); gsl_matrix *tmp = gsl_matrix_calloc (2,2); switch(o_varcovar){ case 0: gsl_matrix_set (J,0,0,dldm*dldm); gsl_matrix_set (J,0,1,dldm*dlds); gsl_matrix_set (J,1,0,dlds*dldm); gsl_matrix_set (J,1,1,dlds*dlds); gsl_linalg_LU_decomp (J,P,&signum); gsl_linalg_LU_invert (J,P,covar); break; case 1: gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_set(H,0,1,d2ldmds); gsl_matrix_set(H,1,0,d2ldmds); gsl_matrix_set(H,1,1,d2lds2); gsl_matrix_scale (H,-1.); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,covar); break; case 2: gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_set(H,0,1,d2ldmds); gsl_matrix_set(H,1,0,d2ldmds); gsl_matrix_set(H,1,1,d2lds2); gsl_matrix_set(J,0,0,dldm*dldm); gsl_matrix_set(J,0,1,dldm*dlds); gsl_matrix_set(J,1,0,dlds*dldm); gsl_matrix_set(J,1,1,dlds*dlds); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,tmp); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,tmp,J,0.0,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,tmp,0.0,covar); break; } gsl_matrix_free(H); gsl_matrix_free(J); gsl_matrix_free(tmp); gsl_permutation_free(P); return covar; } gbutils-5.7.1/gbhisto.c0000644000175000017500000003405513246754720011742 00000000000000/* gbhisto (ver. 5.6.1) -- Produce histogram from data Copyright (C) 1998-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" void printhist(double const *data,int const size, int const steps,int const o_output, int const o_bintype,double ratio, double const refpoint,double xmin, double xmax) { int i=0; double stepsize; int *histo; double *midpoint,*width; int index=0; histo = (int *) malloc(steps*sizeof(int)); midpoint = (double *) malloc(steps*sizeof(double)); width = (double *) malloc(steps*sizeof(double)); for(i=0;i0 && xmin > data[i]) xmin = data[i]; } } lxmin=log(xmin); stepsize=(log(xmax)-log(xmin))/steps; for(i=0;i=xmin && val0) fprintf(stderr,"WARNING (%s): %d non positive values ignored\n",GB_PROGNAME,ignored); } else if(o_bintype==2){ /* geometric binning */ double lratio = log(ratio); stepsize=(xmax-xmin)/(exp(lratio*steps)-1.); for(i=0;i0 && stmp1 != NULL && strlen(stmp1)>0){ wmin=atof(stmp2); wmax=atof(stmp1); } else fprintf(stderr,"ERROR (%s): provide comma separated min and max. Try option -h.\n",GB_PROGNAME); free(stmp3); o_window=1; } else if(opt=='n'){ /*set the number of steps*/ steps = (unsigned) atoi(optarg); } else if(opt=='M'){ /*set the type of output*/ o_output = atoi(optarg); if(o_output<0 || o_output>6){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='B'){ /*set the type of binning*/ o_bintype = atoi(optarg); if(o_bintype <0 || o_bintype>2 ) { fprintf(stderr,"ERROR (%s): binning option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_bintype); exit(-1); } } else if(opt=='S'){ /*set the type of binning*/ refpoint = atof(optarg); if(refpoint <0 || refpoint >1 ) { fprintf(stderr,"ERROR (%s): unsupported reference point value '%f'. Try option -h.\n", GB_PROGNAME,refpoint); exit(-1); } } else if(opt=='r'){ /*set the ration in the geometric binning*/ ratio = atof(optarg); if(ratio <=1) { fprintf(stderr,"ERROR (%s): option -r requires a value larger than one. Try option -h.\n", GB_PROGNAME); exit(-1); } } else if(opt=='t'){ /*set the table form*/ o_table=1; } else if(opt=='v'){ /*set verbose output*/ o_verbose=1; } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Compute histogram counting the occurrences of each value (discrete data) or\n"); fprintf(stdout,"binning the values on a regular grid (continuous data). Data are read from \n"); fprintf(stdout,"standard input. For discrete data, values and their occurrences are printed;\n"); fprintf(stdout,"For binned data, the bin reference point and the relative statistics are\n"); fprintf(stdout,"printed. The type of variable and the statistics can be set with option -M.\n"); fprintf(stdout,"The binning grid can be linear, logarithmic or geometric. The bin reference\n"); fprintf(stdout,"point is the algebraic midpoint for linear binning and the geometric midpoint\n"); fprintf(stdout,"for log or geometric binning. The reference point can be moved using option\n"); fprintf(stdout,"-S. Set it to 0 for the lower bound or to 1 for the upper bound.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of equispaced bins where the histogram is computed (default 10)\n"); fprintf(stdout," -w min,max set manually the binning window \n"); fprintf(stdout," -M set the type of output (default 0) \n"); fprintf(stdout," 0 occurrences number (continuous binned variable)\n"); fprintf(stdout," 1 relative frequency (continuous binned variable)\n"); fprintf(stdout," 2 empirical density (continuous binned variable)\n"); fprintf(stdout," 3 occurrences number (discrete variable)\n"); fprintf(stdout," 4 relative frequency (discrete variable)\n"); fprintf(stdout," 5 CDF (left cumulated probability; continuous binned variable)\n"); fprintf(stdout," 6 inverse CDF (1-CDF; continuous binned variable)\n"); fprintf(stdout," -B set the type of binning (default 0) \n"); fprintf(stdout," 0 linear\n"); fprintf(stdout," 1 logarithmic\n"); fprintf(stdout," 2 geometric\n"); fprintf(stdout," -S set the bin reference point (default 0.5) \n"); fprintf(stdout," -r set the ratio for geometrical binning (default 1.618) \n"); fprintf(stdout," -t print the histogram for each imput column\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -v verbose mode\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* ++++++++++++++++++++++++++++ */ if(o_verbose){ fprintf(stdout,"#variable type:\t"); switch(o_output){ case 0: case 1: case 2: fprintf(stdout,"continuous"); break; case 3: fprintf(stdout,"discrete"); break; } fprintf(stdout,"\n"); fprintf(stdout,"#output type:\t"); switch(o_output){ case 0: fprintf(stdout,"occurencies number"); break; case 1: fprintf(stdout,"relative frequency"); break; case 2: fprintf(stdout,"empirical density"); break; case 3: fprintf(stdout,"occurencies number"); break; case 4: fprintf(stdout,"relative frequency"); break; case 5: fprintf(stdout,"left cumulated probability"); break; case 6: fprintf(stdout,"right cumulated probability"); break; } fprintf(stdout,"\n"); fprintf(stdout,"#binning:\t"); switch(o_bintype){ case 0: fprintf(stdout,"linear"); case 1: fprintf(stdout,"logarithmic"); case 2: fprintf(stdout,"geometric with ratio %f",ratio); break; } fprintf(stdout,"\n"); fprintf(stdout,"#reference point: %f\n",refpoint); fprintf(stdout,"#bins number:\t"); switch(o_output){ case 0: case 1: case 2: fprintf(stdout,"%d",steps); break; } fprintf(stdout,"\n"); fprintf(stdout,"#tabular data:\t"); switch(o_table){ case 0: fprintf(stdout,"off"); break; case 1: fprintf(stdout,"on"); break; } fprintf(stdout,"\n"); fprintf(stdout,"#data statistics:\n"); fprintf(stdout, "# mean stdev skewness kurtosis ave.dev. min max\n"); } /* ++++++++++++++++++++++++++++ */ if(o_table){ size_t rows=0,columns=0,i; double **data=NULL; loadtable(&data,&rows,&columns,0,splitstring); /* ++++++++++++++++++++++++++++ */ if(o_verbose){ for(i=0;i=3 && o_output<=4) for(i=0;i data[i][j]) xmin = data[i][j]; if( xmax < data[i][j]) xmax = data[i][j]; } /* ------------------------------ */ printhist(data[i],rows,steps,o_output,o_bintype,ratio,refpoint,xmin,xmax); if(i=3 && o_output<=4) printhist_discrete(data,size,o_output); else { size_t i; double xmin = DBL_MAX; /*set maximum double */ double xmax = -DBL_MAX; /*set to minimum double */ /* determination of xmax and xmin */ if(o_window){ xmin=wmin; xmax=wmax; } else for(i=0;i data[i]) xmin = data[i]; if( xmax < data[i]) xmax = data[i]; } /* ------------------------------ */ printhist(data,size,steps,o_output,o_bintype,ratio,refpoint,xmin,xmax); } } return(0); } gbutils-5.7.1/gbdummyfy.10000644000175000017500000000467613147510354012223 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBDUMMYFY "1" "August 2017" "gbdummyfy ver. 5.6" "User Commands" .SH NAME gbdummyfy \- Produce dummies from labels .SH SYNOPSIS .B gbdummyfy [\fI\,options\/\fR] .SH DESCRIPTION This command reads from standard input a text file with space separated columns. The entry in one column (the first by default) are considered labels and expanded into a matrix of dummies , i.e. of 0 and 1 values. The number of columns of the matrix is equal to the number of different labels. Each row contains '1' in the place of the associated labels in the sorted list of labels, and '0' everywehere else. Since in general one less dummy variable is required than the number of labels, you can remove one column of dummies using the option '\-d'. .SH OPTIONS .TP \fB\-h\fR print this help .TP \fB\-c\fR set the column of labels (default 1) .TP \fB\-d\fR which column to remove, counting from 1 (default none) .TP \fB\-v\fR print the labels and associated positions to standard error .SH EXAMPLES .TP echo "a 1\enb 2" | gbdummyfy create a 4x3 marix with dummy values relative to labels 'a' and 'b' .PP This program requires awk or gawk. Notice that it simply expands the data adding new columns. When using the resulting the resulting matrix in other utilities, the user should specify explicitly which dummies variable to use and how. .PP A simple linear dependency can be automatically generated for 'gblreg' by inserting the following expression in the functional specification .PP `seq 3 12 | sed 's/\e(.*\e)/\e+d\e1\e*x\e1/' | tr \-d '\en'` .PP and .PP `seq 3 12 | sed 's/\e(.*\e)/,\e1=0/' | tr \-d '\en'` .PP among the initial conditions. In this case there are 10 different values for the dummy. They occupy column positions from 3 to 12 and their initial value is zero. .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2009\-2015 Giulio Bottazzi .PP This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation. .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/INSTALL0000644000175000017500000003660011775245551011170 00000000000000Installation Instructions ************************* Copyright (C) 1994-1996, 1999-2002, 2004-2011 Free Software Foundation, Inc. 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Some packages pay attention to `--enable-FEATURE' options to `configure', where FEATURE indicates an optional part of the package. They may also pay attention to `--with-PACKAGE' options, where PACKAGE is something like `gnu-as' or `x' (for the X Window System). The `README' should mention any `--enable-' and `--with-' options that the package recognizes. For packages that use the X Window System, `configure' can usually find the X include and library files automatically, but if it doesn't, you can use the `configure' options `--x-includes=DIR' and `--x-libraries=DIR' to specify their locations. Some packages offer the ability to configure how verbose the execution of `make' will be. For these packages, running `./configure --enable-silent-rules' sets the default to minimal output, which can be overridden with `make V=1'; while running `./configure --disable-silent-rules' sets the default to verbose, which can be overridden with `make V=0'. Particular systems ================== On HP-UX, the default C compiler is not ANSI C compatible. If GNU CC is not installed, it is recommended to use the following options in order to use an ANSI C compiler: ./configure CC="cc -Ae -D_XOPEN_SOURCE=500" and if that doesn't work, install pre-built binaries of GCC for HP-UX. HP-UX `make' updates targets which have the same time stamps as their prerequisites, which makes it generally unusable when shipped generated files such as `configure' are involved. Use GNU `make' instead. On OSF/1 a.k.a. Tru64, some versions of the default C compiler cannot parse its `' header file. The option `-nodtk' can be used as a workaround. If GNU CC is not installed, it is therefore recommended to try ./configure CC="cc" and if that doesn't work, try ./configure CC="cc -nodtk" On Solaris, don't put `/usr/ucb' early in your `PATH'. This directory contains several dysfunctional programs; working variants of these programs are available in `/usr/bin'. So, if you need `/usr/ucb' in your `PATH', put it _after_ `/usr/bin'. On Haiku, software installed for all users goes in `/boot/common', not `/usr/local'. It is recommended to use the following options: ./configure --prefix=/boot/common Specifying the System Type ========================== There may be some features `configure' cannot figure out automatically, but needs to determine by the type of machine the package will run on. Usually, assuming the package is built to be run on the _same_ architectures, `configure' can figure that out, but if it prints a message saying it cannot guess the machine type, give it the `--build=TYPE' option. TYPE can either be a short name for the system type, such as `sun4', or a canonical name which has the form: CPU-COMPANY-SYSTEM where SYSTEM can have one of these forms: OS KERNEL-OS See the file `config.sub' for the possible values of each field. If `config.sub' isn't included in this package, then this package doesn't need to know the machine type. If you are _building_ compiler tools for cross-compiling, you should use the option `--target=TYPE' to select the type of system they will produce code for. If you want to _use_ a cross compiler, that generates code for a platform different from the build platform, you should specify the "host" platform (i.e., that on which the generated programs will eventually be run) with `--host=TYPE'. Sharing Defaults ================ If you want to set default values for `configure' scripts to share, you can create a site shell script called `config.site' that gives default values for variables like `CC', `cache_file', and `prefix'. `configure' looks for `PREFIX/share/config.site' if it exists, then `PREFIX/etc/config.site' if it exists. Or, you can set the `CONFIG_SITE' environment variable to the location of the site script. A warning: not all `configure' scripts look for a site script. Defining Variables ================== Variables not defined in a site shell script can be set in the environment passed to `configure'. However, some packages may run configure again during the build, and the customized values of these variables may be lost. In order to avoid this problem, you should set them in the `configure' command line, using `VAR=value'. For example: ./configure CC=/usr/local2/bin/gcc causes the specified `gcc' to be used as the C compiler (unless it is overridden in the site shell script). Unfortunately, this technique does not work for `CONFIG_SHELL' due to an Autoconf bug. Until the bug is fixed you can use this workaround: CONFIG_SHELL=/bin/bash /bin/bash ./configure CONFIG_SHELL=/bin/bash `configure' Invocation ====================== `configure' recognizes the following options to control how it operates. `--help' `-h' Print a summary of all of the options to `configure', and exit. `--help=short' `--help=recursive' Print a summary of the options unique to this package's `configure', and exit. The `short' variant lists options used only in the top level, while the `recursive' variant lists options also present in any nested packages. `--version' `-V' Print the version of Autoconf used to generate the `configure' script, and exit. `--cache-file=FILE' Enable the cache: use and save the results of the tests in FILE, traditionally `config.cache'. FILE defaults to `/dev/null' to disable caching. `--config-cache' `-C' Alias for `--cache-file=config.cache'. `--quiet' `--silent' `-q' Do not print messages saying which checks are being made. To suppress all normal output, redirect it to `/dev/null' (any error messages will still be shown). `--srcdir=DIR' Look for the package's source code in directory DIR. Usually `configure' can determine that directory automatically. `--prefix=DIR' Use DIR as the installation prefix. *note Installation Names:: for more details, including other options available for fine-tuning the installation locations. `--no-create' `-n' Run the configure checks, but stop before creating any output files. `configure' also accepts some other, not widely useful, options. Run `configure --help' for more details. gbutils-5.7.1/gblreg.c0000644000175000017500000002334013246754720011540 00000000000000/* gblreg (ver. 5.6) -- Estimate linear regression model Copyright (C) 2005-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include void print_linear_verbose(FILE *file,const int o_weight, const int o_model, const size_t size, const double c0,const double c1, const double cov00,const double cov01, const double cov11, const double sumsq){ const int ndf = size-(o_model==0?2:1); fprintf(file,"\n"); fprintf(file," ------------------------------------------------------------\n"); fprintf(stderr," number of observations = %zd\n",size); fprintf(stderr," number of indep. variables = %d\n",ndf); fprintf(file,"\n"); fprintf(stderr," model (C[i]: i-th column; eps: residual):\n\n"); fprintf(stderr," C[2] ~ "); if(o_model==0) fprintf(stderr," c0 + "); fprintf(stderr,"c1*C[1] + "); if(o_weight) fprintf(stderr,"eps*C[3]\n"); else fprintf(stderr,"eps\n"); fprintf(file,"\n"); fprintf(file," ------------------------------------------------------------\n"); if(!o_weight){ fprintf(file," sum of squared residual (SSR) = %f\n",sumsq); fprintf(file," number degrees of freedom (ndf) = %d\n",ndf); fprintf(file," sqrt(SSR/ndf) = %f\n",sqrt(sumsq/ndf)); fprintf(file," chi-square test P(>SSR | ndf) = %e\n", gsl_cdf_chisq_Q (sumsq,ndf)); } else{ fprintf(file," sum of weighted squared residual (WSSR) = %f\n",sumsq); fprintf(file," number degrees of freedom (ndf) = %d\n",ndf); fprintf(file," sqrt(WSSR/ndf) = %f\n",sqrt(sumsq/ndf)); fprintf(file," chi-square test P(>WSSR | ndf) = %e\n", gsl_cdf_chisq_Q (sumsq,ndf)); } fprintf(file," ------------------------------------------------------------\n"); fprintf(file,"\n"); /* Normal approximation to chi^2 */ /* fprintf(file," chisq. test Q(ndf/2,WSSR/2) = %e\n", */ /* gsl_cdf_gaussian_Q(sumsq-(size-cov->size1),sqrt(2*(size-cov->size1)))); */ if(o_model==0){ fprintf(file," c0= %+f +/- %f (%5.1f%%) | % f % f |\n",c0,sqrt(cov00),100*sqrt(cov00)/fabs(c0),1.,cov01/sqrt(cov00*cov11)); fprintf(file," c1= %+f +/- %f (%5.1f%%) | % f % f |\n",c1,sqrt(cov11),100*sqrt(cov11)/fabs(c1),cov01/sqrt(cov00*cov11),1.); } else { fprintf(file," c1= %+f +/- %f (%5.1f%%)\n",c1,sqrt(cov11),100*sqrt(cov11)/fabs(c1)); } fprintf(file," ------------------------------------------------------------\n"); fprintf(file,"\n"); } int main(int argc,char* argv[]){ double **data=NULL; /* array of values */ size_t size=0; /* length of array vals */ char *splitstring = strdup(" \t"); int o_verbose=0; int o_model=0; int o_output=0; int o_weight=0; /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"v:hF:M:O:w",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='F'){ /* set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='M'){ /* set the method to use*/ o_model = atoi(optarg); } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); } else if(opt=='w'){ /* set third columns as weight */ o_weight = 1; } else if(opt=='v'){ /* set verbosity level*/ o_verbose = atoi(optarg); } else if(opt=='h'){ /* print short help */ fprintf(stdout,"Linear regression. Data provided should have independent variable on the\n"); fprintf(stdout,"1st column and dependent variable on the 2nd. Standard error on dependent\n"); fprintf(stdout,"variable can be provided on the 3rd column.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -M the linear regression model (default 0)\n"); fprintf(stdout," 0 with estimated intercept \n"); fprintf(stdout," 1 with zero intercept \n"); fprintf(stdout," -O the type of output (default 0)\n"); fprintf(stdout," 0 regression coefficients \n"); fprintf(stdout," 1 regression coefficients and errors \n"); fprintf(stdout," 2 x, fitted y, error on y, residual \n"); fprintf(stdout," -w consider provided standard errors on y \n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just output \n"); fprintf(stdout," 1 commented headings \n"); fprintf(stdout," 2 model details \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h print this help\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); switch(o_model){ case 0 : { double c0,c1,cov00,cov01,cov11,sumsq; if(!o_weight){ load2(&data,&size,0,splitstring); gsl_fit_linear (data[0],1, data[1],1, size, &c0,&c1,&cov00,&cov01,&cov11,&sumsq); } else { size_t i; load3(&data,&size,0,splitstring); for(i=0;i1) print_linear_verbose(stdout,o_weight,o_model,size,c0,c1,cov00,cov01,cov11,sumsq); switch(o_output){ case 0: /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,EMPTY_SEP,"#c0"); fprintf(stdout,EMPTY_NL,"c1"); } /* +++++++++++++++++++++++++++++++ */ fprintf(stdout,FLOAT_SEP,c0); fprintf(stdout,FLOAT_NL,c1); break; case 1: /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,EMPTY_SEP,"#c0"); fprintf(stdout,EMPTY_SEP,"+/-"); fprintf(stdout,EMPTY_SEP,"c1"); fprintf(stdout,EMPTY_NL,"+/-"); } /* +++++++++++++++++++++++++++++++ */ fprintf(stdout,FLOAT_SEP,c0); fprintf(stdout,FLOAT_SEP,sqrt(cov00)); fprintf(stdout,FLOAT_SEP,c1); fprintf(stdout,FLOAT_NL,sqrt(cov11)); break; case 2: { size_t i; double y,y_err; /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,EMPTY_SEP,"#c0"); fprintf(stdout,EMPTY_SEP,"+/-"); fprintf(stdout,EMPTY_SEP,"c1"); fprintf(stdout,EMPTY_NL,"+/-"); fprintf(stdout,"#"); fprintf(stdout,FLOAT_SEP,c0); fprintf(stdout,FLOAT_SEP,sqrt(cov00)); fprintf(stdout,FLOAT_SEP,c1); fprintf(stdout,FLOAT_NL,sqrt(cov11)); } /* +++++++++++++++++++++++++++++++ */ for(i=0;i1) print_linear_verbose(stdout,o_weight,o_model,size,0.0,c1,0.0,0.0,cov11,sumsq); switch(o_output){ case 0: /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0) fprintf(stdout,EMPTY_NL,"#c1"); /* +++++++++++++++++++++++++++++++ */ fprintf(stdout,FLOAT_NL,c1); break; case 1: /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,EMPTY_SEP,"#c1"); fprintf(stdout,EMPTY_NL,"+/-"); } /* +++++++++++++++++++++++++++++++ */ fprintf(stdout,FLOAT_SEP,c1); fprintf(stdout,FLOAT_NL,sqrt(cov11)); break; case 2: { size_t i; double y,y_err; /* +++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,EMPTY_SEP,"#c1"); fprintf(stdout,EMPTY_NL,"+/-"); } /* +++++++++++++++++++++++++++++++ */ fprintf(stdout,FLOAT_SEP,c1); fprintf(stdout,FLOAT_NL,sqrt(cov11)); for(i=0;i #else /* Find the first occurrence of C in S or the final NUL byte. */ extern char *strchrnul (const char *s, int c_in); #endif gbutils-5.7.1/lib/getdelim.c0000644000175000017500000000565110327011632012624 00000000000000/* getdelim.c --- Implementation of replacement getdelim function. Copyright (C) 1994, 1996, 1997, 1998, 2001, 2003, 2005 Free Software Foundation, Inc. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* Ported from glibc by Simon Josefsson. */ #ifdef HAVE_CONFIG_H # include #endif #include "getdelim.h" #include #include #include #ifndef SIZE_MAX # define SIZE_MAX ((size_t) -1) #endif #ifndef SSIZE_MAX # define SSIZE_MAX ((ssize_t) (SIZE_MAX / 2)) #endif #if !HAVE_FLOCKFILE # undef flockfile # define flockfile(x) ((void) 0) #endif #if !HAVE_FUNLOCKFILE # undef funlockfile # define funlockfile(x) ((void) 0) #endif /* Read up to (and including) a DELIMITER from FP into *LINEPTR (and NUL-terminate it). *LINEPTR is a pointer returned from malloc (or NULL), pointing to *N characters of space. It is realloc'ed as necessary. Returns the number of characters read (not including the null terminator), or -1 on error or EOF. */ ssize_t getdelim (char **lineptr, size_t *n, int delimiter, FILE *fp) { ssize_t result; size_t cur_len = 0; if (lineptr == NULL || n == NULL || fp == NULL) { errno = EINVAL; return -1; } flockfile (fp); if (*lineptr == NULL || *n == 0) { *n = 120; *lineptr = (char *) malloc (*n); if (*lineptr == NULL) { result = -1; goto unlock_return; } } for (;;) { int i; i = getc (fp); if (i == EOF) { result = -1; break; } /* Make enough space for len+1 (for final NUL) bytes. */ if (cur_len + 1 >= *n) { size_t needed_max = SSIZE_MAX < SIZE_MAX ? (size_t) SSIZE_MAX + 1 : SIZE_MAX; size_t needed = 2 * *n + 1; /* Be generous. */ char *new_lineptr; if (needed_max < needed) needed = needed_max; if (cur_len + 1 >= needed) { result = -1; goto unlock_return; } new_lineptr = (char *) realloc (*lineptr, needed); if (new_lineptr == NULL) { result = -1; goto unlock_return; } *lineptr = new_lineptr; *n = needed; } (*lineptr)[cur_len] = i; cur_len++; if (i == delimiter) break; } (*lineptr)[cur_len] = '\0'; result = cur_len ? cur_len : result; unlock_return: funlockfile (fp); return result; } gbutils-5.7.1/lib/getsubopt.h0000644000175000017500000000331610327011632013047 00000000000000/* Parse comma separate list into words. Copyright (C) 2004-2005 Free Software Foundation, Inc. Contributed by Simon Josefsson , 2004. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* Assuming *OPTIONP is a comma separated list of elements of the form "token" or "token=value", getsubopt parses the first of these elements. If the first element refers to a "token" that is member of the given NULL-terminated array of tokens: - It replaces the comma with a NUL byte, updates *OPTIONP to point past the first option and the comma, sets *VALUEP to the value of the element (or NULL if it doesn't contain an "=" sign), - It returns the index of the "token" in the given array of tokens. Otherwise it returns -1, and *OPTIONP and *VALUEP are undefined. For more details see the POSIX:2001 specification. http://www.opengroup.org/susv3xsh/getsubopt.html */ #if HAVE_GETSUBOPT /* Get getsubopt declaration. */ #include #else extern int getsubopt (char **optionp, char *const *tokens, char **valuep); #endif gbutils-5.7.1/lib/getline.h0000644000175000017500000000210610327011632012456 00000000000000/* getline.h --- Prototype for replacement getline function. Copyright (C) 2005 Free Software Foundation, Inc. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* Written by Simon Josefsson. */ /* Get size_t, FILE, ssize_t. And getline, if available. */ # include # include # include #if !HAVE_DECL_GETLINE ssize_t getline (char **lineptr, size_t *n, FILE *stream); #endif /* !HAVE_GETLINE */ gbutils-5.7.1/lib/strchrnul.c0000644000175000017500000000177110327011632013055 00000000000000/* Searching in a string. Copyright (C) 2003 Free Software Foundation, Inc. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* Specification. */ #include "strchrnul.h" /* Find the first occurrence of C in S or the final NUL byte. */ char * strchrnul (const char *s, int c_in) { char c = c_in; while (*s && (*s != c)) s++; return (char *) s; } gbutils-5.7.1/lib/getsubopt.c0000644000175000017500000000477510327011632013054 00000000000000/* Parse comma separate list into words. Copyright (C) 1996, 1997, 1999, 2004 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper , 1996. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include #include #if !_LIBC /* This code is written for inclusion in gnu-libc, and uses names in the namespace reserved for libc. If we're compiling in gnulib, define those names to be the normal ones instead. */ # include "strchrnul.h" # undef __strchrnul # define __strchrnul strchrnul #endif /* Parse comma separated suboption from *OPTIONP and match against strings in TOKENS. If found return index and set *VALUEP to optional value introduced by an equal sign. If the suboption is not part of TOKENS return in *VALUEP beginning of unknown suboption. On exit *OPTIONP is set to the beginning of the next token or at the terminating NUL character. */ int getsubopt (char **optionp, char *const *tokens, char **valuep) { char *endp, *vstart; int cnt; if (**optionp == '\0') return -1; /* Find end of next token. */ endp = __strchrnul (*optionp, ','); /* Find start of value. */ vstart = memchr (*optionp, '=', endp - *optionp); if (vstart == NULL) vstart = endp; /* Try to match the characters between *OPTIONP and VSTART against one of the TOKENS. */ for (cnt = 0; tokens[cnt] != NULL; ++cnt) if (strncmp (*optionp, tokens[cnt], vstart - *optionp) == 0 && tokens[cnt][vstart - *optionp] == '\0') { /* We found the current option in TOKENS. */ *valuep = vstart != endp ? vstart + 1 : NULL; if (*endp != '\0') *endp++ = '\0'; *optionp = endp; return cnt; } /* The current suboption does not match any option. */ *valuep = *optionp; if (*endp != '\0') *endp++ = '\0'; *optionp = endp; return -1; } gbutils-5.7.1/lib/getdelim.h0000644000175000017500000000213310327011632012621 00000000000000/* getdelim.h --- Prototype for replacement getdelim function. Copyright (C) 2005 Free Software Foundation, Inc. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* Written by Simon Josefsson. */ /* Get size_t, FILE, ssize_t. And getdelim, if available. */ # include # include # include #if !HAVE_DECL_GETDELIM ssize_t getdelim (char **lineptr, size_t *n, int delimiter, FILE *stream); #endif /* !HAVE_GETDELIM */ gbutils-5.7.1/lib/Makefile.in0000644000175000017500000004157013246753207012751 00000000000000# Makefile.in generated by automake 1.15 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2014 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. @SET_MAKE@ # Copyright (C) 2004 Free Software Foundation, Inc. # # This file is free software, distributed under the terms of the GNU # General Public License. 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AC_INIT([gbutils],[5.7.1]) AC_CONFIG_SRCDIR([tools.c]) AM_INIT_AUTOMAKE AM_CONFIG_HEADER(config.h) # Checks for programs # ------------------- AC_PROG_INSTALL AC_PROG_LN_S AC_PROG_CC #this seems to be useful only on cygwin AM_PROG_LEX # First gnulib piece # ------------------ gl_EARLY # Check getline and getsubopt from gnulib gl_INIT # Checks for typedefs, structures, and compiler characteristics # ------------------------------------------------------------- AC_C_CONST AC_C_INLINE AC_TYPE_SIZE_T # Checks for libraries # -------------------- # Check ranlib (used in lib/ directory of gnulib) AC_PROG_RANLIB # Checks for math libraries AC_CHECK_LIB([m], [sin],,AC_MSG_ERROR(Library mlib not found!)) # Check for GSL AC_CHECK_LIB([gslcblas],[cblas_dgemm]) AC_CHECK_LIB([gsl],[gsl_blas_dgemm]) AC_CHECK_LIB([gsl],[gsl_multifit_fdfsolver_jac],AC_DEFINE([GSL_VER_2],[1],[GSL ver. >=2])) # Check for matheval AC_CHECK_LIB([matheval], [evaluator_create],,,$LEXLIB) #Check for zlib AC_CHECK_LIB([z], [gzopen]) # Checks for library functions #----------------------------- AC_CHECK_FUNCS([floor pow sqrt strchr strdup strspn memchr strcspn strtol strtod]) # Checks for header files # ----------------------- AC_HEADER_STDC AC_CHECK_HEADERS([float.h stdlib.h string.h unistd.h assert.h regex.h fcntl.h limits.h stddef.h]) # Check for the host to detect cygwin # ----------------------------------- AC_CANONICAL_HOST AM_CONDITIONAL([ISCYGWIN], [test x$host_os = xcygwin]) # Checks for help2man # ------------------- AC_PATH_PROG(HELP2MAN, help2man, false) AM_CONDITIONAL([H2M], [test x$HELP2MAN != xfalse]) # Define the utility to compile # ----------------------------- if test x$ac_cv_lib_gsl_gsl_blas_dgemm = xyes; then GBKER='gbker$(EXEEXT)' GBKREG='gbkreg$(EXEEXT)' GBMODES='gbmodes$(EXEEXT)' GBINTERP='gbinterp$(EXEEXT)' GBLREG='gblreg$(EXEEXT)' GBGLREG='gbglreg$(EXEEXT)' GBRAND='gbrand$(EXEEXT)' GBHILL='gbhill$(EXEEXT)' GBACORR='gbacorr$(EXEEXT)' GBKERMAN='gbker.1' GBKREGMAN='gbkreg.1' GBMODESMAN='gbmodes.1' GBINTERPMAN='gbinterp.1' GBLREGMAN='gblreg.1' GBGLREGMAN='gbglreg.1' GBRANDMAN='gbrand.1' GBHILLMAN='gbhill.1' GBACORRMAN='gbacorr.1' else LACKING="yes" fi if test x$ac_cv_lib_matheval_evaluator_create = xyes; then GBFUN='gbfun$(EXEEXT)' GBGRID='gbgrid$(EXEEXT)' GBFUNMAN='gbfun.1' GBGRIDMAN='gbgrid.1' else LACKING="yes" fi if test x$ac_cv_lib_matheval_evaluator_create = xyes && test x$ac_cv_lib_gsl_gsl_blas_dgemm = xyes; then GBGET='gbget$(EXEEXT)' GBNLREG='gbnlreg$(EXEEXT)' GBNLPANEL='gbnlpanel$(EXEEXT)' GBNLQREG='gbnlqreg$(EXEEXT)' GBNLMULT='gbnlmult$(EXEEXT)' GBNLPROBIT='gbnlprobit$(EXEEXT)' GBNLPOLYIT='gbnlpolyit$(EXEEXT)' GBGETMAN='gbget.1' GBNLREGMAN='gbnlreg.1' GBNLPANELMAN='gbnlpanel.1' GBNLQREGMAN='gbnlqreg.1' GBNLMULTMAN='gbnlmult.1' GBNLPROBITMAN='gbnlprobit.1' GBNLPOLYITMAN='gbnlpolyit.1' else LACKING="yes" fi # Add the optional utilities to Makefile # --------------------------------------- AC_SUBST(GBKER) AC_SUBST(GBMODES) AC_SUBST(GBKREG) AC_SUBST(GBINTERP) AC_SUBST(GBFUN) AC_SUBST(GBGET) AC_SUBST(GBGRID) AC_SUBST(GBLREG) AC_SUBST(GBGLREG) AC_SUBST(GBNLREG) AC_SUBST(GBNLPANEL) AC_SUBST(GBNLQREG) AC_SUBST(GBNLMULT) AC_SUBST(GBNLPROBIT) AC_SUBST(GBNLPOLYIT) AC_SUBST(GBRAND) AC_SUBST(GBHILL) AC_SUBST(GBACORR) AC_SUBST(GBKERMAN) AC_SUBST(GBMODESMAN) AC_SUBST(GBKREGMAN) AC_SUBST(GBINTERPMAN) AC_SUBST(GBFUNMAN) AC_SUBST(GBGETMAN) AC_SUBST(GBGRIDMAN) AC_SUBST(GBLREGMAN) AC_SUBST(GBGLREGMAN) AC_SUBST(GBNLREGMAN) AC_SUBST(GBNLPANELMAN) AC_SUBST(GBNLQREGMAN) AC_SUBST(GBNLMULTMAN) AC_SUBST(GBNLPROBITMAN) AC_SUBST(GBNLPOLYITMAN) AC_SUBST(GBRANDMAN) AC_SUBST(GBHILLMAN) AC_SUBST(GBACORRMAN) AC_CONFIG_FILES([Makefile lib/Makefile]) AC_OUTPUT echo "" echo " --------------------------------------------------------------------" echo "| Congratulations! Configuration of ver. $PACKAGE_VERSION is complete." echo "| Optional dependencies: " echo "|" echo -n "| Gnu Scientific Library (GSL) " if test x$ac_cv_lib_gsl_gsl_blas_dgemm = xyes; then echo " found" else echo " not found" fi echo -n "| GNU matheval library " if test x$ac_cv_lib_matheval_evaluator_create = xyes; then echo " found" else echo " not found" fi echo -n "| zlib general purpose compression library " if test x$ac_cv_lib_z_gzopen = xyes; then echo " found" else echo " not found" fi echo -n "| help2man automatic man pages generator " if test x$HELP2MAN != xfalse; then echo " found" else echo " not found" fi if test x$LACKING = xyes; then echo "|" echo "| The following utilities will not be installed due to lacking" echo "| libraries (check the README file):" echo -n "|" if test x$GBKER != x'gbker$(EXEEXT)'; then echo -n " gbker" fi if test x$GBKREG != x'gbkreg$(EXEEXT)'; then echo -n " gbkreg" fi if test x$GBLREG != x'gblreg$(EXEEXT)'; then echo -n " gblreg" fi if test x$GBGLREG != x'gbglreg$(EXEEXT)'; then echo -n " gbglreg" fi if test x$GBNLREG != x'gbnlreg$(EXEEXT)'; then echo -n " gbnlreg" fi if test x$GBNLPANEL != x'gbnlpanel$(EXEEXT)'; then echo -n " gbnlpanel" fi if test x$GBNLQREG != x'gbnlreg$(EXEEXT)'; then echo -n " gbnlqreg" fi if test x$GBNLMULT != x'gbnlmult$(EXEEXT)'; then echo -n " gbnlmult" fi if test x$GBNLPROBIT != x'gbnlprobit$(EXEEXT)'; then echo -n " gbnlprobit" fi if test x$GBNLPOLYIT != x'gbnlpolyit$(EXEEXT)'; then echo -n " gbnlpolyit" fi if test x$GBMODES != x'gbmodes$(EXEEXT)'; then echo -n " gbmodes" fi if test x$GBINTERP != x'gbinterp$(EXEEXT)'; then echo -n " gbinterp" fi if test x$GBGET != x'gbget$(EXEEXT)'; then echo -n " gbget" fi if test x$GBFUN != x'gbfun$(EXEEXT)'; then echo -n " gbfun" fi if test x$GBGRID != x'gbgrid$(EXEEXT)'; then echo -n " gbgrid" fi if test x$GBRAND != x'gbrand$(EXEEXT)'; then echo -n " gbrand" fi if test x$GBHILL != x'gbhill$(EXEEXT)'; then echo -n " gbhill" fi if test x$GBACORR != x'gbacorr$(EXEEXT)'; then echo -n " gbacorr" fi echo "" fi echo "| " echo "| The utilities will be installed under ${prefix}" echo "| To compile and install the utilities in a different directory" echo "| use the configure option --prefix, see the INSTALL file." echo "|" echo "| Now you can type 'make' to compile GBUTILS and 'make install'" echo "| to install it" echo "| enjoy!" echo " --------------------------------------------------------------------" gbutils-5.7.1/gbenv.10000644000175000017500000000320313246772646011320 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBENV "1" "March 2018" "gbenv 5.7.1" "User Commands" .SH NAME gbenv \- Floating point locale, and gbutils settings .SH DESCRIPTION Show floating point environment (for x86 family processors), locale and gbutils settings. .SS "Information about the floating point environment includes:" .IP if the environment is the default environment of the machine; if the precision is single, double or extended; if the rounding is nearest, upward, downward or toward zero; the list of exceptions whose interrupts are ignored (masked). .SS "Information about the locale includes:" .IP decimal point; thousand separator; the relative position of the thousand separator (grouping). .SS "Information about gbutils environment include:" .IP if the error handler is activated; the value of the environment variables dictating the output format; the output format of integers and floating point numbers. .SH OPTIONS .TP \fB\-h\fR print this help message .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/ChangeLog0000644000175000017500000023634613246772567011731 000000000000002018-03-04 Giulio Bottazzi * configure.ac: version updated to 5.7.1 * .c: updated copyright date range * .c (main): changed "opt" from char to int, native returned by getopt and getopt_long functions. 2017-09-01 Giulio Bottazzi * configure.ac: version updated to 5.7.0 * gbtest.c : added 'WILCO2', Wilcoxon signed rank test on paired samples. * tools.c (gbutils_header): updated date in copyright message. * gbfun.c (main): added option -T to change the computation from row-wise to column wise. * gbtest.c (main): further code cleaning; local copy of data is generated for one, two or paired sample tests; this is safest when multiple tests are performed. 2017-08-28 Giulio Bottazzi * gbtest.c, gbstat.c: added option -o to set output format 2017-08-24 Giulio Bottazzi * gbtest.c: some code cleaning. * gbtest.c (FP_test): changed the sign of the U statistics for consistency with other tests 2017-08-23 Giulio Bottazzi * gbtest.c (R_pscore): corrected the implementation of the Fisher's transformation. * gbtest.c (WILCO_test, WMW_test): check for memory leaks and removed a bug. * gbtest.c (Tpaired_test): added Spearman's T test for paired samples. 2016-06-04 Giulio Bottazzi * configure.ac: version updated to 5.6.9 * lib/Makefile.am, lib/dummy.c: added a dummy function to be included in the library 'libgnu.a' also when no other functions are included. This is done because Darwin (OS X) does not handle empty libraries file. 2016-05-31 Giulio Bottazzi * configure.ac: added AM_PROG_AR to check the syntax of ar. This seems required to compile static library on Mac 2015-11-22 Giulio Bottazzi * ALL: Updated version number to 5.6.8 Tagged with CVS using #cvs tag gbutils-5-6-8 * configure.ac: added check for newer version (>= 2.0) of GSL. If a version greater equal to 2 is found, GSL_VER_2 is defined to 1. This variable can be used in a preprocessor directive. * gbnlreg.c, gbnlmult.c: updated reference to GSL multifit function for GSL ver. 2.0 2015-09-08 Giulio Bottazzi * ALL: Tagged with CVS using #cvs tag gbutils-5-6-7 * README, doc/intro.pdf, doc/intro.txt: updated to reflect availability on official Debian system; removed obsolete list of files from the README. * configure.ac: Source package version updated to 5.6.7. * gbenv.c: the reporting of floating point properties is constrained to x68 family processors. 2015-07-17 Giulio Bottazzi * *.c: updated the address of the FSF. 2015-07-15 Giulio Bottazzi * *.c: replace "lenght" with "length" in the source code. 2015-06-12 Giulio Bottazzi * ALL: Tagged with CVS using #cvs tag gbutils-5-6-6 * configure.ac: Source package version updated to 5.6.6. * gbstat.c: added the computation of the mode using the Half Sample Method by Robertson and Cryer. 2015-06-06 Giulio Bottazzi * ALL: Tagged with CVS using #cvs tag gbutils-5-6-5 * gbkreg2d.c, gbkreg.c, gbker2d.c, gbhisto2d.c, gbacorr.c, gbstat.c : fixed typos 2015-05-30 Giulio Bottazzi * gbget.c: set the expression 'x0l1' to the row number of the previous row, consistently with general notation. * configure.ac: added automatic manual creation for gbget 2015-05-29 Giulio Bottazzi * tools.c: updated copyright dates * configure.ac: Source package version updated to 5.6.5. * Makefile.am: removed redundant script gbkeys2values: it gets replaced by gbconvtable. * gbconvtable, gbenv, Makefile.am: added man pages for gbconvtable and gbenv. 2015-05-12 Giulio Bottazzi * ALL: Tagged with CVS using #cvs tag gbutils-5-6-4 * gbhill.c: updated and improved the comment in the source code * gbget.c: added information about the size of the final matrix in verbose mode. 2015-05-06 Giulio Bottazzi * configure.ac: Source package version updated to 5.6.4. * Makefile.am: removed 'debian' directory from the distributed package. This is required for the inclusion in the official debian repositories. 2015-04-22 Giulio Bottazzi * gbhill.c: improved help and error messages 2015-03-29 Giulio Bottazzi * configure.ac: removed any reference to 'flex' apart the initial "AM_PROG_LEX" which seems to be useful on Cygwin (but not on linux systems). Flex should be in general checked by the libmatheval package. Corrected dependencies of the gbget program. Source package version updated to 5.6.3. Tagged with CVS using: #cvs tag gbutils-5-6-3 * Makefile.am: correct dependency for gbget; the explicit specification of flex library in the '_LDADD' declarations is now activated only for Cygwin. * gbkreg.c : corrected typo in help message 2015-03-25 Giulio Bottazzi * gbmave.c: added default value for option '-s' to help message. 2015-03-05 Giulio Bottazzi * gblreg.c (main): fixed bug in option '-O 2': the estimated coefficient were wrongly printed on the first line. 2015-01-08 Giulio Bottazzi * ALL: present version becomes 5-6-2 (tagged with CVS) using #cvs tag gbutils-5-6-2 This can be recovered using #cvs checkout -r gbutils-5-6-2 2015-01-07 Giulio Bottazzi * configure.ac, debian/changelog: version updated to 5.6.2; used 'dch -v 5.6.2-1' to update debian/changelog * Makefile.am: updated to reflect new documents structure * gbzscore, gbdenan, gbrobreg, gbrobareg: obsolete utilities removed from the package * gbdummyfy, Makefile.am: added man page * gbrand.c, gbker.c, gbinterp.c, gbhisto2d.c, gbboot.c, gbglreg.c: error messages are now correctly printed to stderr 2015-01-06 Giulio Bottazzi * gbinterp.c, gbrand.c: warning messages are now correctly printed to stderr 2014-12-27 Giulio Bottazzi * README, debian/docs, doc: added directory containing documentation files cygwin_install.pdf, cygwin_install.txt, gbget.pdf, gbget.txt, intro.pdf, intro.txt, overview.pdf, overview.txt 2014-09-26 Giulio Bottazzi * README: updated list of source files 2014-09-25 Giulio Bottazzi * tools.c: added 'finalize_program' to free all variables inizialized in 'inizialized_program' * tools.h: exported variable 'NL' * tools.c, tools.h, gbquant.c, gbget.c, gbtest.c: added the function "denan_pairs"; the function "data_denan" has been renamed "denan_data" for consistency 2014-09-23 Giulio Bottazzi * gbtest.c (run_twosamples): changed the output of two sample tests in the case of many columns. Now point estimates are returned in the lower triangle irrespective of option -p. 2014-09-10 Giulio Bottazzi * Makefile.am: the list of files to be included in the package has been updated 2014-09-03 Giulio Bottazzi * ALL: present version becomes 5-6-1 (tagged with CVS) using #cvs tag gbutils-5-6-1 This can be recovered using #cvs checkout -r gbutils-5-6-1 * debian: updated file in the debian dir * gbhisto.c: added option '-S' to tune the midpoint calculation; added two cases to otpion -M to print cumulated distribution; fixed the output of some warning messages 2014-09-02 Giulio Bottazzi * gbhisto.c: added the description of the bin midpoint in the help message * gbacorr.c: added to the CVS repository 2014-08-27 Giulio Bottazzi * gbacorr.c: added optiopn -p to compute confidence intervals using Fisher transform * configure.ac, Makefile.am: added 'gbacorrr' among the utilities depending on gsl for installation. Fixed mis-allignemt of the rules to create man pages. 2014-08-26 Giulio Bottazzi * tools.h: removed redundant initializaton of optarg ,optind, opterr and optopt; * Makefile.am: added explicit linking option for gbget 2014-08-20 Giulio Bottazzi * gbplot: added an example using the 'set table' option; removed uneccessary '\' in the help message 2014-06-25 Giulio Bottazzi * ALL: present version becomes 5-6 (tagged with CVS) using #cvs tag gbutils-5-6 This can be recovered using #cvs checkout -r gbutils-5-6 * debian/control (Suggest): added gnuplot to suggested packages 2014-06-19 Giulio Bottazzi * gbnlprobit.c (main): added a one line header to verbose messages 2014-06-18 Giulio Bottazzi * gbnlprobit.c (main): fixed argument of 'sizeof' in my_alloc statements 2014-06-17 Giulio Bottazzi * tools.c (readblock): fixed nasty bug in the procedure with wich missing columns are filled with NAN 2014-06-12 Giulio Bottazzi * ALL: version updated to 5.6 * gbplot: added man page 2014-06-07 Giulio Bottazzi * gbmave.c: compute moving average for each column of data; previously the data from all columns were pooled 2014-06-06 Giulio Bottazzi * README: added mention of gbacorr and gbxcorr * ALL: implemented the long option '--version' and '--help' for all the executable, but not yet for the scripts. Now the help messages are sent to sdtout, in compliance with GNU coding standards. * tools.c, tools.h: added structure for getopt_long 2014-06-05 Giulio Bottazzi * Makefile.am, configure.ac : added help2man support. The man pages are added to the list as the revision of the help messages progresses. * gbker.c, gbget.c (main): Updated the -h message to comply with help2man syntax 2014-06-03 Giulio Bottazzi * gbxcorr.c: updated gbxcorr to conmpute cross-covariance and cross-correlation coefficients with and without the removal of the mean 2014-05-18 Giulio Bottazzi * gbacorr.c : added program 'gbacorr' to compute the autocorrelogram (or the cross-autocorrelogram) of a series of observations, column-wise. 2014-05-16 Giulio Bottazzi * debian: added the 'debian' directory that contains all information necessary to create the package. After 'autoreconf', './configure' use 'make dist' to obtain a source tarball which can be used to create the deb packages. Remember to change the name of the source package from gbutils-version.tar.gz to gbutils_version.orig.tar.gz. Extract the archive, move inside the source tree and issue 'debuild -us -uc -b' * Makefile.am (EXTRA_DIST): directory 'debian' added to list of distributed files 2014-05-15 Giulio Bottazzi * ALL: version set to 5.5.1 * gbker.c (main): added option -w to select the boundaries of the gird. Modified method -M 2 for a more precise boundaries management. Using '-M 2 -w x,x -n 1' it is possible to compute the density in the point x. * configure.ac: updated syntax * tools.c (gbutils_header): updated header output * gbnlpolyit.c: improved help message 2014-03-31 Giulio Bottazzi * gbtest.c (WMW_pscore): changed the text of WMW verbose output 2014-03-20 Giulio Bottazzi * gbdist.c: added short description of the procedure 2014-02-26 Giulio Bottazzi * gbnlreg.c (main): corrected bug that printed two times the number of observations 2014-02-20 Giulio Bottazzi * ALL: version set to 5.5 (consolidation release) * gbnlreg.c: added R^2, F statistics and one-sided significance for the OLS model (-m 0). The baseline model is NOT the model with the coefficients (or parameters) equal to zero, but the model with the coefficients equal to the values specified in the initial condition. * gbnlpanel.c, gbnlmult.c, exponential_gbhill.c, paretoI_gbhill.c, gbhill.c, gbker.c, gbmstat.c, gbgcorr.c, gbboot.c: checked with -Wall; removed unused variables. * gbnlprobit.c: found potential bug in score minimization * multimin.c: Added two new GSL alogithms for minimization without derivatives. Updated version to 1.2 2014-01-17 Giulio Bottazzi * gbnlreg.c: corrected bug in the LL of gaussian model 2013-12-20 Giulio Bottazzi * gbnlreg.c : implemented the report of various information criteria in the verbose mode. The statistics reported are : AIC, AICc, BIC and HQCin the case of LS regression 2013-11-23 Giulio Bottazzi * ALL: version set to 5.4.5 (bugfix release) 2013-11-22 Giulio Bottazzi * gbdist.c: avoid to print multiple values for consecutive identical observations. Only the last one is printed. Added the option '-i' (for identical) to recover previous behaviour. in the function printdist the type of the counter has changed form int to size_t 2013-10-23 Giulio Bottazzi * gbnlmult.c, gbrand.c, gbnlqreg.c : fixed minor bugs * tools.c, tools.h, gbkreg2d.c, gbker2d.c, gbker.c, gbkreg.c, gbmodes.c, gbnear.c: the inline specification has been removed from the definition of the different types of kernel and the functions have been moved in tools.c/tools.h 2013-02-01 Giulio Bottazzi * ALL: version set to 5.4.4 (bugfix release) 2013-01-21 Giulio Bottazzi * tools.c (readblock): added separate warnings for underflow and overflow. 2012-11-10 Giulio Bottazzi * gbdummyfy: corrected bug in the selection of the column to delete. 2012-11-07 Giulio Bottazzi * tools.c, gbenv.c: added two new environment variables, GB_OUT_SEP and GB_OUT_NL to set the output field and record separators respectively. 2012-11-06 Giulio Bottazzi * gbnlreg.c: removed the condition |dF|>0 in the convergence condition of the OLS fitting. Indeed it could be |dF| == 0 which is perfectly fine! Notice that with linear functions, the second iteration of the minimizer fail automatically (no way of improving upon the first iteration!) but this does not generate any trouble in gbnlreg, apart the printing of a somehow cryptic message "cannot reach the specified tolerance in F" when verbosity is set to high. * configure.ac: updated the check for the gsl, according to the recommendation in the gsl manual 2012-10-29 Giulio Bottazzi * tools.h, tools.c: replaced the occurrences of "GBFILE *" with GBFILEP, which is already a pointer. It is substituted by 'gzFile' if the zlib are found on the system or by "FILE *" if they are not. This is actually the fixing of a bug, that went unnoticed in the old zlib versions but is now rightly spotted by the compiler. 2012-10-25 Giulio Bottazzi * tools.c (readblock): added different warnings for different errors when converting string to double. 2012-10-24 Giulio Bottazzi * gbnlreg.c: added two relevant statistics to the LAD estimation (-M 1), namely the D statistics for inference and the coefficient of determination R2. The asymptotic probability to observe a D statistics as large as observed under the null (that is with parameters equal to their initial conditions) is also reported. These quantities are reported if the option -v is greater than 1. 2012-10-02 Giulio Bottazzi * gbnlreg.c: fixed bug in the conditions implying var-covar matrix computation 2012-10-01 Giulio Bottazzi * gbnlreg.c (main): computation of var-covar matrix only when needed; print warning message if the maximum number of iterations is reached without optimization success; implemented a restart algorithm for simplex minimization, i.e. -M 1 and -M 2; new command line options -I and -E. 2012-09-25 Giulio Bottazzi * gbconvtable: fixed a HUGE bug: replacement should be performed also if the column considered is not the last one 2012-07-09 Giulio Bottazzi * gbnlpolyit.c (main): Cox R^2 correctly renamed Effron R^2. 2012-07-04 Giulio Bottazzi * ALL: version set to 5.4.3 (bugfix release) * gbconvtable, gbdummyfy, gbzscore, gbdenan, gbplot: improved script portability by substituting '=' for '==' and replacing double square brackets test operator '[[' with single square bracket '['. The option '-E' has been removed from 'echo' instances and the help message is printed using an here-doc syntax instead than an 'echo' command. The 'if-then' syntax has been replaced with a 'case' construct for the parsing of the command line. 2012-06-23 Giulio Bottazzi * gbinterp.c: in points outside the input range the interpolation of the curve is set equal to the nearest value while the derivatives are set to zero. A warning is printed. 2012-05-28 Giulio Bottazzi * ALL: version set to 5.4.2 (bugfix release) * configure.ac: added version number to the final greeting; updated checking for (f)lex 2012-05-15 Giulio Bottazzi * gbhill.c, gaussian_gbhill.c, paretoI_gbhill.c, paretoIII_gbhill.c, exponential_gbhill.c: removed the "unconditional" likelihood (previous options -M 1 and -M 4) 2012-05-14 Giulio Bottazzi * gbtest.c (main): updated names of the statistics according to Stehphens' 1974 paper on J. Am. Stat. Ass. 2012-03-23 Giulio Bottazzi * gbrand.c (main) : implemented binary output for the univariate continuous distributions * gbtest.c (main) : implemented binary input * gbhill.c (main) : implemented binary input/output; the binary output is for residuals * tools.c, tools.h: reorganized the list of functions and added function 'printcol' and 'printcol_bin' which print a single column of output * gbfun.c (main), gbget.c (main), gbmstat.c (main) : fixed the option -b for binary input/output 2012-02-27 Giulio Bottazzi * tools.c (readblock): properly produce NAN when a conversion from string to float fails; print a warning if some conversion failed. 2012-02-14 Giulio Bottazzi * multimin.c: corrected the count of iterations. Now when the maximum number of iterations is set to zero, the program correctly does not perform any minimization and returns the initial values. * gbhill.c: included the Renjy transformation based on subsequent spacings. Still to be tested. 2011-12-21 Giulio Bottazzi * ALL: version set to 5.4.1 (bugfix release) * gbhill.c: improved the treatment of Renjy transform for the goodness of fit, introducing "artificial" distribution with mass only on the range covered by the sub sample used in the estimation. 2011-10-24 Giulio Bottazzi * gbxcorr.c (main): added utility to compute the cross-correlation matrix. 2011-10-04 Giulio Bottazzi * ALL: present version becomes officially 5.4 and it is released * gbget.c, gbnlpolyit.c: removed unused variable * gbtest.c: added explicit de-allocation of vectors * gbhisto.c: corrected bug in rage calculation in the case of many samples * tools.c (my_alloc): changed any instance of alloc to my_alloc. 2011-10-03 Giulio Bottazzi * gbget.c (new_matrix): removed unused variable. * gbhisto2d.c (main): removed unused function 'printhist2d'. Variables 'xsteps' and 'ysteps' changed type from int to size_t. * gbtest.c (KW_test, FP_test, RHO_test): corrected bug in memory allocation. * tools.c (readblock): corrected bug in data loading: missing values weren't properly set to NAN. Print the warning for missing values only once. 2011-08-02 Giulio Bottazzi * gbnlpolyit.c: corrected a bug in the bootstrap calculation of standard deviation of marginal elasticities and marginal effects. Added the printing of p-scores together with standard deviations when the option -v is specified. Added three different pseudo R^2 printed with the verbose option. 2011-08-01 Giulio Bottazzi * gbhill.c: Improved option -O 4, now the uniform transformation of Renjy variables is printed for any kind of estimator. Removed option -O 5 because it is redundant. Now the number of parameters is kept fixed and NAN are added where appropriate. 2011-07-21 Giulio Bottazzi * gbglreg.c: added option -O 4 to print fraction of explained variance associated to each explanatory variables. 2011-07-19 Giulio Bottazzi * gbnlpolyit.c: corrected bug in the determination of the number of occupancy classes. Added explicit calls to free(). 2011-07-18 Giulio Bottazzi * configure.ac: removed residual references to FFTW; introduced check for lex/flex library necessary to have a working matheval installation. * NEWS: start to update the file. It was not updated after revision 5.0 * README: removed references to FFTW 2011-07-08 Davide Pirino * gbhill.c: Added a new warning in the case of an exponential or pareto1 distribution for the lower tail estimation. * exponential_gbhill.c: Added varcovar for the lower tail estimation. * pareto1_gbhill.c: Added varcovar for the lower tail estimation. * gaussian_gbhill.c: Added varcovar for the lower tail estimation. 2011-07-13 Giulio Bottazzi * ALL: present version becomes 5.4; but some bug fixes in gbhill are necessary * gbkreg.c, gbker.c, configure.ac : dropped dependence on FFTW library 2011-07-09 Giulio Bottazzi * multimin.c: added transformation number 9 for semi-open interval (-infty,xmax) to be used, for instance, in gbhill. * gbhill.c: moved var-covar matrices in the distribution-specific files; changed default options of the minimization routines to use BFGS-2; changed the transformation used for the exponential distribution. 2011-07-08 Davide Pirino * exponential_gbhill.c: bugs fixed. 2011-07-05 Giulio Bottazzi * gbplot: fixed a bug in option '-p' and added verbose output with option '-v'. 2011-05-13 Davide Pirino * gbhill.c: Added option -O 5 for printing # of obs used to stdout. 2011-05-11 Giulio Bottazzi * gbplot: added option -l to set log scale on the axes and option -C to issue generic commands before the plot 2011-03-24 Davide Pirino * gbnlreg.c: Added option -O 4 to compute s-scores. 2011-03-10 Giulio Bottazzi * gbbin.c: added option -w that allows for forced setting of the boundaries. When this option is set, the bins are defined accordingly to an uniformly spaced grid in the provided interval. Notice that using this option leads to bins that are no longer equally populated. The new option -w is incompatible with options -x,-y and -c. 2011-02-28 Davide Pirino * gbhill: warnings now only in verbose mode for better visibility when gbhill is called repeatedly in scripts. 2011-02-25 Giulio Bottazzi * Makefile.am: added script gbkeys2values 2011-02-11 Giulio Bottazzi * gbget.c: multiple instances of option -t are joined together. 2011-01-19 Davide Pirino * gbhill: added varcovar options for upper tail estimation for gaussian, pareto1 and exponential. Note that pareto3 is still missing and lower tail estimations are not implemented. Probably they should be both removed from the code. 2010-12-11 Giulio Bottazzi * gbplot: use 'wxt' as default terminal if available, otherwise use 'x11' 2010-12-10 Giulio Bottazzi * gbinterp.c: if number of points and range are not provided, print 10 equally spaced points * gbnlpolyit.c, gbtest.c, gbnlqreg.c, gbnlprobit.c, gbnlpanel.c, gbnlreg.c, gbglreg.c, gbhisto.c, gbhill.c, gbgcorr.c, gbbin.c, gbfilternear.c: moved 'initialize_program' after command line processing 2010-12-03 Federico Tamagni * gbhill.c: renamed estimated parameters, according to new functional specifications adopted in exponential_gbhill.c and paretoI_gbhill.c * exponential_gbhill.c, paretoI_gbhill.c: new functional specifications for distribution functions, F[j], and density functions, -log(f[j]) 2010-12-03 Giulio Bottazzi * tools.h, tools.c: removed function "moment_gbbin" because no longer necessary. * gbbin.c: implemented the "xmedian" output option. Now both xmedian and ymedian are computed only when needed (the ordering operation implied in the computation of the median is N*log(N) in time and better avoided if unnecessary). 2010-12-02 Giulio Bottazzi * Makefile.am: added 'multimin.h' and 'gbhill.h' among the distributed files 2010-10-11 Giulio Bottazzi * gbnlprobit.c: updated help message with -g default value 2010-09-10 Davide Pirino * tools.c, tools.h: added new function moment_gbbin called in gbbin.c * gbbin.c: new option "ymedian" is now available 2010-09-03 Giulio Bottazzi * gbhisto.c: added new option '-w' to manually set the binning window 2010-08-22 Giulio Bottazzi * gbkreg.c: corrected bug, added explicit cast to double type in the computation of kernel values in method 1 (convolution). * gbkreg.c, gbker.c: new boundaries for convolution summation (method 1) should be more readable. 2010-08-21 Giulio Bottazzi * gbkreg.c: corrected bug, added explicit cast to int type in boundary definition of method 2. 2010-08-20 Giulio Bottazzi * tools.c: the function 'load2' and 'load3' have been rewritten as direct calls to function 'readblock', skipping the intermediate call to function 'loadtable'. * gbkreg.c, gbkreg2d.c, gbker2d.c: corrected bug in data loading: the variable 'size' was not explicitly initialized to zero. This is now necessary because of the new input functions. 2010-07-29 Giulio Bottazzi * tools.c (loadblocks,loadblocks_bin): now these functions append read blocks to an already existing block list (if block>0). * gbget.c: explicitly set internal data storage to NULL after freeing. 2010-07-22 Giulio Bottazzi * gbnlpolyit.c: added computation of marginal effects in two ways: total and elasticities. Added option -V to compute standard deviations. 2010-07-20 Giulio Bottazzi * multimin.c: added warning when the maximum number of iterations is reached without finding a solution. * gbnlpolyit.c: Added output to plot class-occupancies and chi-square. Added log-likelihood among the output 2010-07-07 Giulio Bottazzi * gbnlpolyit.c: added the new utility to obtain ML estimation of the "polyit" model. Just a draft, it lacks several features. 2010-07-06 Giulio Bottazzi * README: updated the description of 'sequential' data reading and the fact that only the first block of data files is read. * tools.c: load and loadtable functions have been rewritten to use the readblock function. Now these functions only read the first block. 2010-07-05 Giulio Bottazzi * tools.c, tools.h: revision of data loading functions. New version of binary function dropping unnecessary parameters. More general management of blocks structure, in both ASCII and binary functions. N.B.: now load_bin and loadtable_bin IGNORE the blocks after the first. This is different from the behaviour of their ASCII version. * gbget.c, gbfun.c, gbmstat.c: updated management of binary files input 2010-05-22 Giulio Bottazzi * gbconvtable: added script to make substitution on a given column of standard input given key-value dictionary file. Useful to group data. 2010-05-20 Giulio Bottazzi * gbget.c: remove unecessary creation of new matrices in data_applyfuns and data_applyselection. Added function data_applyfuns_recursive to implement recursively generic transformation on data. The recursive interpretation is invoked when the functions string begins with '@'. 2010-05-18 Giulio Bottazzi * gbnlreg.c (main): print the number of observations in verbose output 2010-05-03 Giulio Bottazzi * gbdummyfy: corrected the use of '>' and '<' operator with -gt and -lt respectively. These are the appropriate operators for integer comparison. 2010-04-30 Giulio Bottazzi * gbdummyfy: the new version based on gawk provides much better performances. 2010-04-29 Giulio Bottazzi * gbget.c (main): debugged nasty bug affecting the interaction between option -t and transformation 'P' 2010-02-27 Giulio Bottazzi * gbstat.c (main): fixed inconsistencies in help message 2010-02-22 Giulio Bottazzi * gbstat.c: implemented option -O to select which statistics to print. Notice that the output format has slightly changed (no more dots but spaces and right indentation). 2010-02-19 Giulio Bottazzi * tools.c (printnomatrixbycols): fixed bug which made this function always print to standard output. * gbget.c (data_applyselection): changed the way in which checks are handled. Now comma separated conditions inside a couple of curly brackets are OR-ed, while of course the specification of several bracket couples serve as an AND specification. 2010-02-18 Giulio Bottazzi * gbnlqreg.c (main): fixed initialization condition of simplex size; added switch -M to select between minimization algorithms; added option -s to scale initial simplex size; added option -N to set the max number of iterations. 2010-02-16 Giulio Bottazzi * gbget.c (data_applyselection): changed check implementation to make it consistent with NAN 2010-02-03 Giulio Bottazzi * gbget.c : added new (undocumented and to be tested) feature to select rows according to rules: in the transformation space, specs like '{fun}' apply fun to the row and if the result is greater equal to zero print the line. More specs can be separated by comma, like '{fun1,fun2,...}'. 2009-12-17 Giulio Bottazzi * tools.c: used size_t to count the blocks in loadblocks(). In printnomatrixbycols_bin() and printmatrixbycols_bin() added check that the amount of required output is actually printed. 2009-12-12 Giulio Bottazzi * tools.c, gbbin.c, gbtest.c, gbnear.c ,gbker2d.c, gbkreg2d.c, gbboot.c, gbgcorr.c, gbfilternear.c, gbmstat.c, gbker.c, gbkreg.c,gbinterp.c,gbfun.c,gblreg.c,gbglreg.c,gbnlreg.c, gbnlpanel.c,gbnlqreg.c,gbrand.c,gbhill.c,gbnlmult.c, multimin.c: fixed 'printf' spec for size_t output * gbget.c, tools.c, tools.h: added a custom version of strndup for systems without it (most notably MAC OS). The code is courtesy of Stefan Soucek ssoucek at coactive.com 2009-09-11 Davide Pirino * gbnlpanel: A isnan check was missing in the option -V1 -M0. Help message has been updated. 2009-09-26 Davide Pirino * gbnlpanel: Minor corrections. 2009-08-26 Davide Pirino * gbnlpanel: Important NaN handling improvement: the program was faulty for data where an entire realization should be removed because of Nan's. Help message has been updated. 2009-08-26 Giulio Bottazzi * gbdummyfy: little modification to error messages 2009-08-25 Giulio Bottazzi * gbdummyfy: Minor corrections to help message. 2009-08-20 Giulio Bottazzi * README: added mention of the GSL error handling behaviour. * gbnlprobit.c: added option -O 6 to compute marginal effect using the average of the derivative rather than the derivative computed in the average. Notice that -O 5 is not implemented as should become option 4 with error estimates. 2009-08-19 Giulio Bottazzi * tools.c, tools.h, gbenv.c: GSL error handling is switched off by the function 'initialize_program' if the variable 'GB_ERROR_HANDLER_OFF' is set in the environment. This requires the insertion of a GSL header file in 'tools.h'. Hope this does not endanger portability. The status of the handler can be obtained using the 'gbenv' function. * gbnlprobit.c: the verbose option prints also the value of the score function at the optimal threshold. 2009-08-18 Giulio Bottazzi * gbnlprobit.c: added colons in verbose output; implemented output of marginal effects as option -O 4. 2009-08-04 Giulio Bottazzi * multimin.c: verbose output of function and gradients are now printed using scientific notation. The initial value of the function is also printed. * gbnlprobit.c: re-implemented the object function and derivatives using the complementary error function and its log via the GSL function 'gsl_sf_log_erfc'. Set the default minimizer to BFGS2. Removed the computation of probabilities from the object function. Modified variance-covariance matrix to use complementary error function. 2009-08-03 Giulio Bottazzi * gbnlprobit.c: improved the management of threshold computation. Now by default any statistics based on threshold levels is NOT computed. One has to explicitly ask for it using option '-t'. * multimin.c: print iteration number in verbose mode. 2009-07-30 Giulio Bottazzi * gbnlprobit.c: remove z-scoring of constant variates. 2009-07-24 Giulio Bottazzi * gbnlprobit.c: fixed wrong deallocation of var-covar matrix. Fixed the code to properly recognize constant columns as non-dummy. * Makefile.am (EXTRA_SCRIPTS): added script 'gbdummyfy' to the package 2009-07-23 Giulio Bottazzi * multimin.h: added gsl_blas.h and gsl_linalg.h header files. * gbnlprobit.c: build covariance matrix only if output option is greater than 1. Build Brier score and threshold values only if required. 2009-07-21 Giulio Bottazzi * gbnlprobit.c: verbose messages properly redirected to stderr. 2009-07-20 Giulio Bottazzi * gbnlprobit.c: added printing of the number of observations in verobose output. Added warning for the case of completely separated dummy variables (troublesome in linear models). 2009-07-16 Giulio Bottazzi * gbboot.c: added output method -O 5 to bootstrap rows maintaining the block structure * gbplot: replaced 'return' with 'exit' 2009-07-15 Davide Pirino * gbnlpanel.c: Computation of the variance-covariance has been simplified and fitted to the latest version of BODIPI. Standard output of panel statistics has been improved. Other minor beautifications. 2009-07-09 Davide Pirino * gbnlpanel.c: Final implementation of the variance-covariance matrix estimation. 2009-07-02 Davide Pirino * gbnlpanel.c: Corrections on the fixed effect case. "denan" procedure totally implemented for the fixed effect case. 2009-06-28 Giulio Bottazzi * gbhill.c: added option -a to consider entire data-set when printing distribution or density 2009-06-24 Davide Pirino * gbnlpanel.c: implemented new variance-covariance matrix based on last modification and computed on non-reduced log-likelihood. 2009-06-18 Giulio Bottazzi * gbnlpanel.c: implemented new object function with prescription of zero mean for fixed effect constant. Variance-covariance expression must be re-worked. 2009-06-17 Davide Pirino * multimin.c: minor beautifications. * gbnlpanel.c: data structure has been modified: blocks identify variables, row identify realizations, columns identify time. The output possibilities now contain fixed or random effects statistics. 2009-06-11 Giulio Bottazzi * multimin.c: added Vector Broyden-Fletcher-Goldfarb-Shanno version 2 method. Added printing of gradient modulus in verbose output. * gbnlreg.c: switched to the use of "multimin" interface. Output improvement but still work to do. 2009-06-09 Giulio Bottazzi * README: included extended description of the new 'gbplot' command. * Makefile.am (EXTRA_SCRIPTS): added 'gbplot' among the package's scripts. This version supersedes the original examples provided in the README file. 2009-05-30 Giulio Bottazzi * gbstat.c: corrected bug in the printing of headings with the -t option 2009-05-22 Giulio Bottazzi * tools.c, tools.h : replace FILE with GBFILE to maintain compatibility with compressed files in loadtableblocks and readblock. * gbnlpanel.c: Change name from " loadtableblocks_gb" to "loadtableblocks". Added copyright notice. 2009-05-19 Davide Pirino * gbnlpanel.c: Random effect has been implemented. 2009-05-11 Davide Pirino * tools.c, tools.h: added functions loadtableblocks_gb and readblock for acquiring panel data. * gbnlpanel.c: First draft of nonlinear panel estimation. Only fixed effect available. 2009-04-07 Giulio Bottazzi * tools.c, tools.h: modified function mystrtod to accept a single string pointer. Check for NAN is performed inside the function. * tools.c, gbfun.c: modified calls to mystrtod. 2009-02-17 Giulio Bottazzi * gbfun.c: added binary file management. * tools.c: modified moment_nonan and moment_short_nonan to store the number of finite entries in a variable of type double. Added proper handling of output format in printmatrixbyrows() and printmatrixbycols(). Added function printnomatrixbycols() to print data which are not in matrix form. The "binary" IO method has been implemented with the function printmatrixbycols_bin() and printnomatrixbycols_bin() to print binary data and the functions load_bin(), loadtable_bin() and loadtable_block_bin() to read them. * gbstat.c: moved initialize_program() after options parsing. * gbmstat.c: there are two versions of this utility gbmstat_slow.c and gbmstat_fast.c. The first has a straightforward design: a slice of the input is passed to a routine which computes the statistics. The second is carefully design to run faster: it updates moments with new information so as to require less computations. For some reason (?) their speed is comparable. So I'll retain the slow version, which is easier to read, and keep the fast for future development. Notice that the 'fast' version does not have option '-D'. 2009-02-11 Giulio Bottazzi * gbmstat.c: added new command to compute moving statistics. Modified accordingly Makefile.am. 2009-02-09 Giulio Bottazzi * gbmave.c: reverted to the old version. The modified new version is now named gbmstat.c 2009-02-02 Giulio Bottazzi * TODO: check the use of moment_nonan in gbstat and the policy for the warnings about not enough observations. * gbmave.c: new version with the ability to process column and to compute different kinds of statistics. * tools.c: added function 'moment_nan' to compute statistics ignoring NAN entries and modified the existing function 'moment_short_nan' to provide, consistently with he previous one, the number of not NAN entries. Notice that moment_nonan is silent on the number of observation needed and simply returns NAN for a variable if unable to compute it. 2009-01-30 Giulio Bottazzi * gbtest.c: added test 'R' to compute Pearson's correlation and p-score * gbget.c: added transformation 'w' to normalize a column by dividing each element by the sum of them. Barring NAN entries,. the sum of the elements of the column is equal to one. 2008-10-22 Giulio Bottazzi * gbquant.c: added option -e to print estimated error, using asymptotic normal approximation, for the quantile value and score (options -x and -1). Presently not implemented in table mode (option -t). 2008-08-09 Giulio Bottazzi * gbnlqreg.c: finally implemented computation of var-covar matrix. The calculation is done considering the ML approach to quantile estimation, i.e. starting from an asymmetric Laplace distribution. 2008-08-07 Giulio Bottazzi * gbquant.c: fixed bug: corrected the definition of min and max indexes for the window selection explicitly using 'floor' and 'ceil' functions. * gbnlqreg.c: corrected help output. 2008-07-23 Giulio Bottazzi * gbget.c (main): updated help message 2008-07-22 Giulio Bottazzi * gbget.c (new_matrix): fixed bug affecting the reversed count of rows and columns when the skip is negative. 2008-07-12 Giulio Bottazzi * scripts.txt: created a new file to collect useful scripts * README: added mention of gbfilternear. Added description of shell function 'gbploti'. 2008-06-04 Giulio Bottazzi * gbbin.c: modified call to function sortn. * gbquant.c: modified call to function sortn and added option -W. * tools.c, tools.h: changed sortn to accept the position of the column to use for sorting. Various modifications to the source code, with the possible introduction of some bug. 2008-05-26 Giulio Bottazzi * gbfun.c: fixed bug in the output of '-t' option. 2008-05-03 Giulio Bottazzi * gbfilternear.c, Makefile.am: added program to filter near points in Euclidean metrics. Useful to reduce the size of scatter plots. 2008-04-07 Giulio Bottazzi * README: Added mention of the new programs. 2008-04-06 Giulio Bottazzi * gbgrid.c: added option '-o' to set output format string. 2008-03-31 Giulio Bottazzi * gbnlreg.c: explicitly freed some allocated space at the end. * gbnlprobit.c: fixed bug in data storage allocation. Added option -z to zscore non-dummy data. Added printing of Type I and II errors. Added estimation based on score function and a long help that could be possibly improved. * tools.c: explicitly freed 'line' variable inside the data reading functions. 2008-03-28 Giulio Bottazzi * gbnlprobit.c: added computation of summary statistics. Output is pretty ugly and should be improved. 2008-03-26 Giulio Bottazzi * multimin.c (multimin): * Makefile.am, configure.ac, gbnlprobit.c: added gbnlprobit to the list of utilities. * multimin.c: a NULL type array is equivalent to no transformation on the variables. Simplex hedge size initialized to step_size+maxsize. 2008-03-15 Giulio Bottazzi * gbhisto2d.c: added options to handle discrete asymmetric data,   that is couples where the two elements belongs to different sets of values. 2008-03-14 Giulio Bottazzi * gbget.c: corrected nasty bug in transformation identification. 2008-03-13 Giulio Bottazzi * ALL: present version becomes 5.3 (beta) * gbfun.c: some code beautification. * Makefile.am: added proper linking requirements for gbnlmult. * README: updated README with new utilities and features. * gbget.c: improved parsing of spec, now the order of the block and slice specification can be reversed. Added new transformation '<..>' that substitutes the matrix of selected data with a set of function computed on it. Added separator ';' to specify different slices of rows and columns. 2008-03-12 Giulio Bottazzi * gbfun.c: added option '-o' and '-s' to set output format. * gbget.c: created print-data function to handle all the printing. The check to see if data are in matrix form is now performed only there. Added transformation 'P' to print the data collected so far followed by two empty lines. In this way it is possible to implement a block-wise output. Now retrieval specs are builded from the command line in an intelligent way. Improved check on retrieval spec structure. 2008-01-30 Giulio Bottazzi * tools.c, gbstat.c: corrected a mistake in "moment" function: with one observation all the statistics are (almost) perfectly defined. Updated the help message in gbstat. 2008-01-16 Giulio Bottazzi * ALL: present version becomes 5-2-2 (tagged with CVS) using #cvs tag gbutils-5-2-2 This can be recovered using #cvs checkout -r gbutils-5-2-2 * Makefile.am (EXTRA_PROGRAMS): removed fake entry for "gbnlmulti" * gbker.c: added option '-p' to print confidence interval (as an extra column) computed according to eq. 3.51 in Hardle W., Muller M., Sperlich S. and Werwatz A. "Nonparametric and Semiparametric Methods" (2004) Springer-Verlag, Berlin. Fixed a bug in method 1-covolution, adding an explicit cast before taking differences of variables of type size_t as double. Other bugs could in principle be present. 2007-12-19 Giulio Bottazzi * gbtest.c: corrected name of variables in verbose output 2007-11-29 Giulio Bottazzi * gbquant.c: fixed the order in which the function data_denan is applied when invoked with option "-w". 2007-08-14 Giulio Bottazzi * multimin.c: fixed output of multimin for simplex initial settings 2007-08-09 Giulio Bottazzi * gbboot.c : added option -O 4 to have data "displaced" by a fraction of the minimal non-zero distance between consecutive observations. 2007-08-07 Giulio Bottazzi * gbinterp.c: added option -I to read interpolated points from a file 2007-08-03 Giulio Bottazzi * gbnlqreg.c: fixed the help message * gbtest.c: added Spearman's rho and Kendall's tau for correlation measure between two samples. The implemented statistics are valid with or without ties and are defined according to W.J. Conover "Practical Nonparametric Statistics", Third Edition. 2007-07-20 Giulio Bottazzi * gbtest.c: added computation of Spearman's Rho correlation. 2007-05-10 Giulio Bottazzi * gbutils.css: new CSS file. 2007-05-02 Giulio Bottazzi * gbtest.c: corrected typos in help messages. 2007-04-25 Giulio Bottazzi * gbnlqreg.c, configure.ac, makefile.am: added a new utility: gbnlqreg * gbnlreg.c: added mention of information matrix computation in comments for var_covar option number 3. 2007-04-05 Federico Tamagni * gbnlmult.c: fixing the help displayed to users. 2007-04-03 Giulio Bottazzi *Makefile.am, configure.ac: inserted gbnlmult in the list of utilities * gbnlmult.c: added gbnlmult to fit systems of simultaneous equation with OLS, that is assuming normally distributed shocks. 2007-03-30 Giulio Bottazzi * ALL: present version becomes 5.2.1 * tools.c: updated copyright notice * gbfun.c: added 'lag' operator to specify previous values. For instance 'x3l4' stands for column 3, four steps before. Notice that the first row of output is build accordingly, so if some lag is specified, the output number of rows is reduced. 2007-03-27 Giulio Bottazzi * ALL: --- RELEASED VERSION 5.2 ------------------------------- * gbgcorr.c: added a short description of the program * gbutils.css: added a stylesheet file to generate html documentation * README: added mention of new files in the list. The html is generated with rst2html.py --stylesheet gbutils.css --embed-stylesheet 2007-03-26 Giulio Bottazzi * gbenv.c : removing output of floating point environment under Cygwin, where the ieeefp.h seems not to work. 2007-03-25 Giulio Bottazzi * gbenv.c (main): added floating point environment reporting for Cygwin, which is based on ieeefp.h not present under Linux. 2007-03-23 Giulio Bottazzi * tools.c: correctly named "THOUSEP" the thousand separator instead of THOUSANDS_SEP. * Makefile.am : added necessary source files to gbhill 2007-03-13 Giulio Bottazzi * README: added mention of "gbenv" and "gbhill" in the listing of programs and in the table of properties. 2007-02-09 Giulio Bottazzi * gbboot.c: corrected mention of default sample size in gbboot help. 2007-02-08 Giulio Bottazzi * gbhill.c (main): corrected bug: added check of parameters number and the loading of their values in the pareto1 case. * README, README.gbutils, INSTALL.CYGWIN: with the most recent version of docutils (ver.0.4) simply use rst2html.py --embed-stylesheet * README: added a brief section on locale management 2007-01-23 Giulio Bottazzi * gbfun.c: changed strtod instances to mystrtod. * tools.c, tools.h: added function mystrod to properly parse thousands separator defined by the locale. It only works with single-character separators like ',' or '.' This function uses the external variable LCTHSEP which is initialized with the setting in the environment. 2007-01-22 Giulio Bottazzi * tools.c, gbenv.c: added locale initialization from environment variables through function "setlocale". 2006-12-13 Federico Tamagni * gbhill.c: fixed bugs in initial values for pareto1 pareto3 and gaussian fitting 2006-12-05 Federico Tamagni * gbhill.c: added a switch to check initial parameter values. * paretoI_gbhill.c, paretoIII_gbhill.c, gaussian_gbhill.c: added two -M options to compute estimates conditioning on threshold levels. Notice that now -M 0,1,2 refer to upper tail inference, while -M 3,4,5 to lower tail estimation. 2006-11-28 Giulio Bottazzi * gbhill.c, exponential_gbhill.c: added two -M options to compute estimates conditioning on threshold levels. Notice that now -M 0,1,2 refer to upper tail inference, while -M 3,4,5 to lower estimation. 2006-11-22 Giulio Bottazzi * gbrand.c: Added Pareto type III distribution. 2006-11-20 Giulio Bottazzi * gaussian_gbhill.c: commented out printing of debug output. 2006-11-17 Giulio Bottazzi * gaussian_gbhill.c: fixed some mistakes in derivatives expressions. 2006-11-11 Federico Tamagni * gbhill.c: removed bug in initialization of s parameter for pareto3 fitting. 2006-11-10 Giulio Bottazzi * gbtest.c: fixed bug in expected variance of TR-RT test, the formula in Brockwell and Davis is actually wrong. 2006-11-10 Federico Tamagni * gbhill.c: removed bug in initialization of s parameter for gaussian fitting. 2006-11-09 Giulio Bottazzi * gbtest.c: fixed typos in doc strings. 2006-11-08 Giulio Bottazzi * multimin.c: fixed typos in doc strings. * gbhill.c: removed old "print true values" code. It was a remnant of a previous implementation. Added minimum value of the negative log-likelihood to the output. Added switch to properly select minimization method and boundaries for the different cases of the exponential. 2006-11-08 Federico Tamagni * gbhill.c: removed bug in initialization of b parameter for exponential fitting. 2006-11-06 Giulio Bottazzi * gbhill.c: replaced minimization type 5 with 2 in estimating exponential's b parameter; remove bug in initialization of k value for exponential. Replaced minimization type 1 with 4 in estimating Pareto's b parameter. 2006-10-17 Giulio Bottazzi * gbgcorr.c: added Cees Diks' gbgcorr to compute Gaussian kernel correlation dimension of time series. 2006-10-14 Giulio Bottazzi * ALL: present version becomes 5.1.4 2006-09-28 Giulio Bottazzi * gbenv.c : added display of locales information 2006-09-26 Giulio Bottazzi * gbhisto2d.c: added management of discrete variables; notice that discrete states can also be labeled by floating point numbers. All the computation has been integrated in the main body of the program. * gbhisto.c: improved some error message and general outline. * gbtest.c: improved some error messages. * tools.c: added function my_calloc to allocate memory initially set to zero; 2006-09-22 Giulio Bottazzi * gbtest.c: added Levene test for equality of variances in 2 or more groups, robust to non-normality, see Brown, M. B. and Forsythe, A. B. 1974, Journal of the American Statistical Association, 69, 364-367. Made the output of F test more verbose. Changed the way in which the name of the test is printed. * gbstat.c: fixed bug in the determination of the median with option "-t". 2006-09-18 Giulio Bottazzi * gbquant.c : enhanced option -w to handle multi-columns input; modified help description. * tools.c: moved here from gbget.c the definitions of "data_denan" which remove entire records, that is rows, containing NAN entries. * README : updated the table with the properties of the utilities 2006-09-15 Giulio Bottazzi * gbbin.c : added outtype=NULL in the -O option; before passing it to realloc, a pointer not initialized should be set to NULL. 2006-09-01 Giulio Bottazzi * README: changed table entry relative to the way in which gbbin handles input data: from sequential to tabular. Updated version to 5.1. TODO: add mention of gbhill. * gbbin.c: changed behavior of gbbin to handle multiple columns of y entries. Added options -c to split the elements of a list of columns. 2006-08-10 Giulio Bottazzi * tools.c : added function sortn to sort all the columns of a table with respect to the values on the first column. 2006-05-25 Federico Tamagni * paretoIII_gbhill.c: fixed bugs in computation of likelihood derivatives. 2006-05-25 Giulio Bottazzi * paretoIII_gbhill.c: fixed bugs. Probably some error still remain. * gbhill.c: added more verbose warnings; constrained density and distribution output (-O 1 and -O 2) to the used observations; fixed pareto3 initialization.. 2006-05-25 Federico Tamagni * paretoIII_gbhill.c: file added for Pareto Type III tail fitting with gbhill. Still a draft. * gbhill.h, gbhill.c: files have been modified to include Pareto Type III fitting. 2006-05-18 Giulio Bottazzi * gbhill.c: added output option '3' to print Renyi-transformed residuals. 2006-05-06 Giulio Bottazzi * gbtest.c: added three tests of randomness: TR-TP, TR-DS and TR-RT. Added exact and asymptotic distribution under the null hypothesis for D+ and D- tests. The name of Kolmogorov-Smirnov statistics has been changed to D. 2006-04-26 Giulio Bottazzi * gaussian_gbhill.c: fixed the expression for the derivative dFdm. 2006-04-25 Giulio Bottazzi * gbhill.c : minor beautifications. 2006-04-22 Federico Tamagni * gaussian_gbhill.c: fixed some errors in computation of likelihood's derivatives. Estimation using all the data seems ok, but that using a fraction of the sample still have problems. * gbhill.c: changes made in the initialization of the parameters for the Gaussian case. 2006-04-20 Federico Tamagni * gaussian_gbhill.c: file added for gaussian tail fitting with gbhill. Very preliminary, estimation has several problems. * gbhill.h, gbhill.c: files have been modified to include gaussian fitting. 2006-04-13 Giulio Bottazzi * exponential_gbhill.c, paretoI_gbhill.c, gbhill.h: definitions of different distributions have been split in different files to keep gbhill.c smaller (and more readable). * tools.h: defined GBTOOLS_H variable to load the tools.h header file only once. * gbhill.c: fixed error in computation of Pareto Type I derivatives. Case M=3 seems to have problems. 2006-04-12 Federico Tamagni * gbhill.c: added Pareto Type I fitting, fixing boundaries of minimization for this case. Very preliminary, minimization has problems. 2006-04-06 Giulio Bottazzi * gbhill.c: fixed estimation when k=N. New version with all four estimators, upper and lower, conditional and unconditional. 2006-04-04 Giulio Bottazzi * gbhill.c: new version of gbhill; still a draft. 2006-03-31 Giulio Bottazzi * gbnlreg.c: fixed typo. * gbtest.c: added check that data are from cumulated distribution(s) for the 1 sample Kolmogorov-Smirnov or Cramer-von Mises tests. Added verbose output to KS test. 2006-03-29 Giulio Bottazzi * gbtest.c: added KS two samples to the displayed list of tests. 2006-03-24 Giulio Bottazzi * gbhill.c: fixed boundaries of minimization in exponential fitting. 2006-03-22 Giulio Bottazzi * tools.h: fixed path of .h includes from lib/ subdirectory. * gbhill.c: implementation of exponential fitting seems ok. The utility is still a draft. * multimin.c: a bit of beautification. 2006-03-21 Giulio Bottazzi * ALL: present version becomes 5.1.3 * gbhill.c: added new program for the estimation of tail behaviour. Just a draft. 2006-03-20 Giulio Bottazzi * gbker.c: fixed bug in the initialization of variable "imin" in method 2, exact summation. 2006-03-16 Giulio Bottazzi * gbtest.c, gbenv.c, gbrand.c: removed typos and non-dangerous bugs. 2006-03-14 Giulio Bottazzi * gbtest.c : added Student's T test for single sample. * gbmave.c : changed option "-t" in "-s". Previous name conflicted with general usage of "-t" as indicating "tabular" behavior. 2006-03-13 Giulio Bottazzi * ALL: present version becomes 5.1.2 * gbget.c: added check at the end to skip printing if no data were loaded. * gbtest.c: removed option "-d" no longer used. Improved some output with option "-v 2". Tested CHI^2, Wilcoxon, Mann-Whitney, Kruskal-Wallis and Fligner-Policello. Added F-test and Student's T test in the homoscedastic and heteroscedastic cases. 2006-03-10 Giulio Bottazzi * gbtest.c: added chi^2 test for 1, 2 and 3+ samples, that is contingency table analysis. Added more verbose option "-v 2" which prints detailed output. 2006-03-08 Giulio Bottazzi * tools.c: fixed bug in the loading functions, like load, loadtable, etc. affecting the parsing of the last line of the input file which does not end with a '\n'. * gbtest.c: added Wilcoxon and Fligner-Policello tests, and the relative large-sample p-scores, for both 1 and 2 sided cases. * gbrand.c (main): added "discrete" trasformation to generate integer from 1 to M with generic probabilities. Probabilities are passed as comma separated list of values. 2006-03-05 Giulio Bottazzi * gbtest.c: added Kruskal-Wsllis multi-sample test and p-score. The latter introduced a dependence of gbtest on gsl which has been added to the table in README. 2006-03-04 Giulio Bottazzi * gbtest.c: fixed bug in run_onesample when pscore=NULL. Added function run_manysamples for tests which uses multiple samples. Added specifier const to data pointer used by run_... functions. 2006-03-01 Giulio Bottazzi * gbenv.c: added utility to print floating point and gbutils settings. 2006-02-28 Giulio Bottazzi * gbnlreg.c: reverted the stopping condition of the estimation loop of the simplex algorithm to simplex size-check. This is the only consistent method. Slightly modified verbose output. Print numerical precision at the beginning of the iterations (if verbose enough). 2006-02-27 Giulio Bottazzi * Makefile.am: "m4/CVS" subdir removed from distribution tar.gz * ALL: present version becomes 5.1.1 * gbnlreg.c: fixed bug related to stopping criteria: assignment instead of comparison in "estimate_status == GSL_CONTINUE". 2006-02-09 Giulio Bottazzi * gbnlreg.c: modified the stopping condition for the estimation loop. Now the reduction, absolute and relative, of both the object function and the parameter estimations are considered. The aim is to have a stopping condition which scales well with the increase of the number of observations. 2006-02-08 Giulio Bottazzi * gbnlreg.c: added option '-e' to set the minimization tolerance. The default tolerance has been decreased to 1e-5. 2005-12-13 Giulio Bottazzi * gbtest.c: added pscore function for D (one-sample Kolmogorov-Smirnov) test; corrected bug in run_onesample function. 2005-12-12 Giulio Bottazzi * README : gbget added to the list of NAN-transparent utilities * gbget.c: modified options z and Z to ignore NAN entries in the computation of mean and averages 2005-12-10 Giulio Bottazzi * gbget.c: added option "-t" to gbget to define FINAL transformations; improved output management: removed unnecessary checks when output has a matrix structure; added check at the beginning of data_denan to see if NAN values are actually present. 2005-12-04 Giulio Bottazzi * ALL: --- RELEASED VERSION 5.1 ------------------------------- * gbbin.c (main): now statistics are printed following the order in which they are provided with the option -O. 2005-12-03 Giulio Bottazzi * ALL: reverted to the simple gnulib-tool --import getline getsubopt * tools.h: added new gnulib headers * configure.ac: removed AC_FUNC_ALLOCA AC_FUNC_MALLOC AC_FUNC_REALLOC AC_FUNC_STRTOD 2005-12-02 Giulio Bottazzi * gbnlreg.c, tools.c: removed any instance of asprintf. * gbnlreg.c: added ERROR when no parameters to estimate are provided * gbfun.c, gbgrid.c: fixed bug: free pointer returned by libmatheval evaluator_get_string * gbhisto.c (printhist): fixed output type for option 0: set to int. 2005-12-01 Giulio Bottazzi * gbkreg2d.c: fixed bug in output generation. 2005-11-25 Giulio Bottazzi * tools.c (initialize_program) : removed bug in strings concatenation. * ALL : compilation with CFLAGS="-Wall -W -g -O4" 2005-11-22 Giulio Bottazzi * gbfun.c, gbnlreg.c: Added hack around calls of evaluator_get_variables from libmatheval: this function requires an int, not a size_t. * configure.ac: reorganized the order of tests * tools.c: remove unnecessary parentheses in return statements. * gbget.c (parse_slice): fixed bug in "skip" setting. 2005-11-21 Giulio Bottazzi * ALL: --- RELEASED VERSION 5.0 ------------------------------- * gbnlreg.c (mad_varcovar): corrected bugs in the computation of the variance-covariance matrix with methods H^{-1} and H^{-1} J H^{-1}. * tools.c (loadtable, loadtable_block): fixed the management of empty input files and of empty lines at the beginning of input files. * gbfun.c: corrected fatal bug in freeing memory at the end of main. 2005-11-19 Giulio Bottazzi * gbget.c: in a slice spec ini:fin:skip with ini>fin the order of printing is reversed. The transformation 'r' has been removed. * *.c, tools.h: moved the declaration of external variables for reading command line options in 'tools.h'. 2005-11-18 Giulio Bottazzi * gbget.c: removed unused variable 'outputformat'. * tools.s: variable length properly defined as 'int' in function 'initialize_program' * gbfun.c, gbker.c, gbquant.c, gbkreg2d.c, gbker2d.c, gbnear.c, gbtest.c, gbmave.c, gbbin.c: fixed some 'size_t' vs. 'int' inconsistency. * gbmodes.c: removed unused parameter 'ave' from function 'pscore' and other little fixes of type conflicts. * gblreg.c: removed unnecessary printf variables in 'print_linear_verbose'. * gbnlreg.c : removed unused parameter 'Param' from 'ols_varcovar', 'mad_varcovar', 'amad_varcovar'. 2005-11-17 Giulio Bottazzi * gbfun.c: added option -R to print intermediary results of the recursive computing. The function has been optimized a bit with the introduction of variable "values" and "maxindex" in the "Function" structure. * gbtest.c: fixed the name-function assignment in run_onesample calls. 2005-11-16 Giulio Bottazzi * gbrand.c : added messages for verbose option. 2005-11-15 Giulio Bottazzi * gbrand.c: implemented the use of GSL environment variables to control the type and default seed of the random number generator. * gbget.c: removed dependence on the non-portable header file. * tools.c: fixed the initialize_program function. 2005-11-04 Giulio Bottazzi * *.c: all the programs have been updated to new output management. Notice that, in general, the output to stderr generated by the verbose option has not been modified. * tools.c: modified the output management. The separator is fixed to " " and cannot be modified via environment variables. Only gbget can change it. This means that GB_OUT_SEPARATOR is no longer read. The format of the EMPTY string, if not explicitly specified by the environment variable, is set to a value which is reasonable for the float format in use. The format INT to print integer numbers has been added. It is automatically set based on the format FLOAT. The variable 2005-11-03 Giulio Bottazzi * README: updated to reflect addition of gbrand utility. * gbrand.c: added a new utility "gbrand" for the generation of pseudo-random variates. The main part of the program is copied from the program gsl-randist by James Theiler and Brian Gough (c). It requires the gsl library. * tools.c: set the default values to GB_OUT_FLOAT_FORMAT="% 12.6e" GB_OUT_EMPTY_FORMAT="%-13s" GB_OUT_SEPARATOR=" " * gbnlreg.c: updated output management and corrected bug in the printing of heading for "-O 2". 2005-11-02 Giulio Bottazzi * README: updated to reflect changes. New section on "Output format and precision" has been added. * gbks.c: removed. Implemented in gbtest. * gbtest.c: now one samples test are performed with a call to function run_onesample. Two samples tests have been added through function run_twosamples. For now only the "KS" test has been implemented. 2005-10-31 Giulio Bottazzi * gbfun.c: remove apparently unused label; implemented new output management. * gbget.c, gbquant.c, gbstat.c, gbtest.c: rewritten to implement new output management. * tools.c: added new output functions: printline, printmatrixbyrows and printmatrixbycols. New output management is based on global variables FLOAT, EMPTY and SEP, from which various format strings are derived to be used inside "printf" statements. The function "initialize_program" is called AFTER command line parsing and set both output format strings and program's name for warning/error reporting. 2005-10-29 Giulio Bottazzi * gbstat.c: rewritten to incorporate new output management based on GB_... environment variables. 2005-10-28 Giulio Bottazzi * gbtest.c: added "gbtest" program to perform various statistical tests on data; this preliminary version only performs 1 sample tests on each input column. * tools.c, tools.h: added definition of external variables GB_... to globally specify the output formats of programs. * gbget.c: re-designed the output format management. The three relevant variables are now 'outputformat', 'emptyformat' and 'sepstring'. The global setting has been moved to tools.c. 2005-10-26 Giulio Bottazzi * gbstat.c: changed the output string to " 12.6e" for both the normal and "short" (-s) output. * gbget.c: changed default output string to " 12.6e" and default empty field to " ". * gbfun.c: changed the output string to " 12.6e". 2005-10-26 Giulio Bottazzi * gbget.c: added transformation 'r' to reverse entries in each column 2005-10-24 Giulio Bottazzi * INSTALL: file removed; it is automatically installed by autoreconf -i * README.gbget: updated the README to reflect recent modifications to gbget syntax * gbget.c: removed option '-w' from help message and minor beautifications 2005-10-23 Giulio Bottazzi * configure, mkefile,...: all configuration and make files have been removed from the package. They are now generated with autoreconf -i. * lib, m4: updated to new version of gnulib. * README: modified to reflect the removal of gbzscore, gbdenan, gbrobreg and gbrobareg from the package. * gbrobreg, gbrobareg: these programs have been removed from the packages, they are now superseded by the new options implemented in gbnlreg. Two scripts with the same names have been added to maintain backward compatibility. They are simple wrappers around gbnlreg. * gbnlreg.c: modified help output. 2005-10-21 Giulio Bottazzi * gbzscore, gbdenan: these programs have been removed from the packages, they are now superseded by the new transformations implemented in gbget. Two scripts with the same names have been added to maintain backward compatibility. They are simple wrappers around gbget. * configure.ac: UPDATED VERSION NUMBER TO 5.0. From now on all the files which are modified will have their version number updated to 5.0. Update list of check following the suggestion of autoscan. * gbget.c: added transformation "z" and "Z" to remove the mean and compute the zscores on each column. 2005-10-19 Giulio Bottazzi * gbget.c: Modified the output and empty string specification. Now the program uses the libc utility provided in printf.h. 2005-10-18 Giulio Bottazzi * gbnlreg.c: added option -M to choose among different estimation procedures: OLS, MAD and asymmetric MAD. Notice that this makes gbrobreg and gbrobareg obsolete. * gbglreg.c: replaced "%f" with "%e" for the output of estimated quantities. 2005-10-17 Giulio Bottazzi * tools.c: fixed the variable type in various printf. * gbget.c: fixed bug in parse_slice due to improper size_t int conversion; fixed the variable type in various printf. 2005-10-13 Giulio Bottazzi * gbget.c: modified help message and removed unnecessary variables. * tools.c: removed unnecessary variables in tools.c. 2005-10-13 Giulio Bottazzi * gbget.c: removed unnecessary structures. Added a warning when using blocks with input from stdin so that there is no possibility to seek. 2005-10-12 Giulio Bottazzi * gbquant.c (main): fixed a bug in options -q and -x when used together with option -t ( multiple calls of function denan ). 2005-09-08 Giulio Bottazzi * gbget.c: removed annoying printing of parsing results (it was added for debugging purposes). 2005-08-29 Giulio Bottazzi * gbget.c: improved and shortened the code. Removed the "null" transformation introduced for testing. Added "denan" transformation. 2005-08-22 Giulio Bottazzi * gbnlreg.c, gbrobreg.c, gbrobareg.c: added error message if no functions to fit are provided. * gbglreg.c: fixed a bug in output option -O 2. 2005-08-20 Giulio Bottazzi * gbglreg.c, gbnlreg.c, gbrobreg.c, gbrobareg.c,: added option -O 3 to print estimated coefficients and variance-covariance matrix. Moved definition of GB_PROGNAME before command line processing. Updated check on variance and output types command line options. Uniform definition of variance-covariance matrix across all the routines. * gbglreg.c: added option -M for intercept estimation. Compact help message. 2005-08-19 Giulio Bottazzi * gbglreg.c: replaced explicit loops for the computation of estimates and residuals with matrix-oriented manipulations. This should speed up the code quite a lot. (hccs): added function for the heteroscedastic consistent covariance estimation, 4 different methods implemented. (print_glinear_verbose): fixed little bugs 2005-08-18 Giulio Bottazzi * gbfun.c: moved definition of GB_PROGNAME before command line processing. In this way this variable is available for error messages from the very beginning. Added check on the initial value for recursive computation. The variable 'x' is set to an alias of 'x1' in non-recursive mode (it was an alias of x0). Improved help message. * gbget.c: fixed bug in parse_spec which prevented negative column and row specifications. 2005-08-04 Giulio Bottazzi * gbget.c: added data array deallocation before loading (removed from loadtable and loadtable_block functions) * tools.c: removed initial deallocation from loadtable and loatable_block: conflict with the use of loadtable in load2 and load3. Added possibility to specify number of columns in loadtable_block (consistent with loadtable behavior). * gbget.c: removed debug output messages 2005-08-01 Giulio Bottazzi * gbget.c: improved gbget spec parsing with the addition of more tests on the identified strings. Large part of the code has been rewritten with the introduction of functions and the list of possible transformations has been extended with log transform and column-wise differencing. Help has been rewritten. 2005-07-31 Giulio Bottazzi * tools.c: added initial de-allocation in loadtable and loadtable_block. * README: increased needed version of gsl to 1.6 2005-07-22 Giulio Bottazzi * gbquant.c (main): added possibility to print more than one quantile (or "inverse" quantile) value 2005-06-27 Giulio Bottazzi * gbrobareg.c: modified output structure to be similar to gbrobreg * gbrobreg.c: corrected bug in the headings of output option 2 2005-06-26 Giulio Bottazzi * gbrobreg.c: modified output structure to be similar to gbnlreg 2005-06-25 Giulio Bottazzi * gbnlreg.c: modified output structure: inserted three output options and added more verbosity levels 2005-06-24 Giulio Bottazzi * gblreg.c, gbglreg.c: modified output structure. Inserted three levels of verbosity and the choices of three types of output 2005-06-15 Giulio Bottazzi * gbrobreg.c, gbrobareg.c: added help to methods for variance-covariance computation (-V option) * gbnlreg.c: fixed reference to methods for variance-covariance computation 2005-05-09 Giulio Bottazzi * gbmodes.c: fixed typos in help description of output options. spell-checked comments and strings * gbker.c: modified the initial bandwidth when the interquartile range is zero * gbmodes.c: modified the initial bandwidth when the interquartile range is zero 2005-04-26 Giulio Bottazzi * gbnlreg.c, gbrobreg.c, gbrobareg.c: added help to regression utilities * gbget.c: now blocks are counted starting from 1 * README, README.gbget: updated 2005-04-24 Giulio Bottazzi * ALL: added GB_PROGNAME variable to all utilities for the "customization" of ERROR or WARNING line * README: updated 2005-04-20 Giulio Bottazzi * gbbin.c: improved verbose output; added the option to print number of observations in each bin; added the option to print standard deviation of x values 2005-04-09 Giulio Bottazzi * ALL: removed occurrences of "//" C++ style comments 2005-03-28 Giulio Bottazzi * gbfun.c: added x0 as row number 2005-03-20 Giulio Bottazzi * gbrobareg.c: added utility for the asymmetric robust regression * gbquant.c: new option -w to filter quantiles data 2005-03-17 Giulio Bottazzi * gbnlreg.c: improved * gbrobreg.c: added utility to perform robust estimation (with Laplace distributed errors) of a non linear model. 2005-03-16 Giulio Bottazzi * gbnlreg.c: added utility to perform least square estimation of non-linear model 2005-03-12 Giulio Bottazzi * gbbin.c: new option to print observations in each bin; now use the load2 and sort2 functions from tools.c * gbglreg.c (NEW): added utility for regression of general linear models * gbgrid.c (NEW): added utility to generate grid of values * gbfun.c: modified the function variable parsing to use libmatheval "evaluator_get_variables"; corrected a bug: multiple functions in tabular mode are printed one by one consequently for each column. If one function cannot be printed, none is printed. * gbboot.c (NEW): utility for bootstrap via random sampling with replacement * gbget.c: the syntax has changed. Now slices are specified as min:max:skip for both columns and rows * gblreg.c (NEW): utility for linear regression * gbker2d.c, gbkreg2d.c: code improved and bugs corrected * gbmodes.c: added Hall-York correction to significance estimation * gbinterp.c: now properly handles case n=1 * gbstat.c, gbker.c: option -i (block-wise input) has been removed, it was a legacy from previous package versions without gbget; fixed help * tools.c: if zlib is found on the system, the data loading is made using gz-oriented functions so that gz-compressed files can be directly read. Functions load2 and load3 in tools.c have been rewritten as simple interfaces around loadtable. If a values for columns !=0 is passed to loadtable, only this number of columns is read; the initial value of variables rows and columns has been set to 0 in all the utility using loadtable. Delimiter specifications implemented in data load functions. Data loading functions have been redefined to take file descriptor rather than stream * README, README.gbutils, INSTALL.CYGWIN: rewritten in reStructuredText format (docutils) for automatic HTML and LaTeX conversion. The HTML version of the documents is obtained with command like rst2html.py --stylesheet-path=/usr/share/doc/docutils-0.3.5/html/tools/stylesheets/pep.css --embed-stylesheet 2003-08-18 Giulio Bottazzi * gbker.c: changed the numbers of the different kernels in gbker 2003-08-16 Giulio Bottazzi * gbker2d.c: created the file * tools.c: added moment_short2 to compute statistics for bivariate data 2003-08-15 Giulio Bottazzi * gbker.c: gbker_gsl.c and gbker_fftw.c have been merged in a unique file gbker.c whose compilation is regulated by #ifdef directives. added option -H to directly set the kernel bandwidth modified the calculation of "standard bandwidth" according to Silverman (p.48 of "Density Estimation...") introduced the option -v to print details about the kernel estimation to stderr * gbstat.c: added options -i. The output format strings in gbstat have been changed from %f to %e * tools.c: improved and FIXED the behaviour of the function loadtable and loadtable_block. fixed the reporting of error in functions moment_short and moment when the number of observations is less then 2. Modified accordinlgy the programs using the "table" option (-t). gbutils-5.7.1/gbgcorr.10000644000175000017500000000315413246755335011645 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBGCORR "1" "March 2018" "gbgcorr 5.7.1" "User Commands" .SH NAME gbgcorr \- Gaussian kernel correlation dimension .SH SYNOPSIS .B gbgcorr [\fI\,options\/\fR] .SH DESCRIPTION Compute the correlation dimension with a Gaussian kernel. One block of output is printed for each embedding dimension, the first column report the bandwidth, the second the associated correlation coefficient. Bandwidths are equispaced in a log scale from 1 (maximum) to 2^(\fB\-n\fR+1) (minimum). n can be set on the command line. .SH OPTIONS .TP \fB\-h\fR this help .TP \fB\-l\fR time lag (default 1) .TP \fB\-m\fR maximum embedding dimension (default 2) .TP \fB\-t\fR Theiler correction (default 1) .TP \fB\-R\fR seed (default 0) .TP \fB\-p\fR number of reference points (default 1) .TP \fB\-r\fR number of bandwidths (default 24) .TP \fB\-s\fR print standard errors .TP \fB\-F\fR specify the input fields separators (default " \et") .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnlmult.10000644000175000017500000000354513246755335012050 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBNLMULT "1" "March 2018" "gbnlmult 5.7.1" "User Commands" .SH NAME gbnlmult \- Solve systems of non linear simultaneous equations .SH SYNOPSIS .B gbnlmult [\fI\,options\/\fR] \fI\,\/\fR .SH DESCRIPTION Least square estimation of a system of j non linear equations. Read data in columns (X_1 .. X_N). The j\-th equation is specified by a function f_j(x1,x2...) using variables names x1,x2,..,xN for the first, second...N\-th column of data. The f_j(x1,x2,...)'s are assumed i.i.d. according to a multivariate gaussian distribution. .SH OPTIONS .TP \fB\-O\fR type of output (default 0) .TP 0 parameters .TP 1 parameters and errors .TP 2 and residuals .TP 3 parameters and variance matrix .TP \fB\-V\fR variance matrix estimation (default 0) .IP 0 1 < J^{\-1} > 2 < H^{\-1} > 3 < H^{\-1} J H^{\-1} > .TP \fB\-e\fR minimization tolerance (default 1e\-5) .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps .TP 5 model definition .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gblreg.10000644000175000017500000000312713246755335011462 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBLREG "1" "March 2018" "gblreg 5.7.1" "User Commands" .SH NAME gblreg \- Estimate linear regression model .SH SYNOPSIS .B gblreg [\fI\,options\/\fR] .SH DESCRIPTION Linear regression. Data provided should have independent variable on the 1st column and dependent variable on the 2nd. Standard error on dependent variable can be provided on the 3rd column. .SH OPTIONS .TP \fB\-M\fR the linear regression model (default 0) .TP 0 with estimated intercept .TP 1 with zero intercept .TP \fB\-O\fR the type of output (default 0) .TP 0 regression coefficients .TP 1 regression coefficients and errors .TP 2 x, fitted y, error on y, residual .TP \fB\-w\fR consider provided standard errors on y .TP \fB\-v\fR verbosity level (default 0) .TP 0 just output .TP 1 commented headings .TP 2 model details .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR print this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbxcorr.10000644000175000017500000000327213246755335011667 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBXCORR "1" "March 2018" "gbxcorr 5.7.1" "User Commands" .SH NAME gbxcorr \- Compute cross correlation matrix .SH SYNOPSIS .B gbxcorr [\fI\,options\/\fR] .SH DESCRIPTION Take as input a data matrix A with N rows and T columns .TP A = [ a_{t,i} ] with t=1,..,T i=1,...,N .PP and compute the NxN correlation matrix C [ c_{i,j} ] following the method specified by option '\-M': with method 0 it is .IP c_{i,j} = 1/(T\-1) \esum_t (a_{t,i}\-m_i) (a_{t,j}\-m_j) .PP where m_i is the i\-th mean and with method 1 it is .IP c_{i,j} = 1/T \esum_t a_{t,i} a_{t,j} . .PP Covariance is stored in the lower triangle while correlation coefficients are stored in the upper triangle. .PP WARNING: previous implementations were row\-wise instead of column\-wise .SH OPTIONS .TP \fB\-M\fR choose the method (default 0) .TP 0 covariance/correlation with mean removal .TP 1 covariance/correlation without mean removal .HP \fB\-F\fR specify the input fields separators (default " \et") .HP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnlprobit.c0000644000175000017500000016037313246754720012450 00000000000000/* gbnlprobit (ver. 5.6) -- Non linear probit regression Copyright (C) 2009-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include "matheval.h" #include "assert.h" #include "multimin.h" #include #include /* #include */ #include #include #include #include #include #include #include #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void ***ddf; } Function; struct objdata { Function * F; size_t rows0; size_t rows1; size_t columns; double **data0; double **data1; double *prob0; double *prob1; double weight0; double weight1; }; /* Maximum likelihood estimation */ /* ============================= */ /* the objective function is the negative log-likelihood. It can be written as: - \sum_{y=0} log ( 1-F(g(x)) ) - \sum_{y=1} log ( F(g(x)) ) where F is the Normal distribution function with mean zero and variance 1 F(x) = (1/\sqrt{2\pi}) \int_{-\infty}^x dt \exp(-t^2/2). and where g(x) is the function to be estimated. Notice that F(0)=1/2, F(+\infty)=1 and F(-x)=1-F(x) and Q(x)=1-F(x). In GSL: F(x) = 1-gsl_sf_erf_Q(x) f(x) = F'(x) = gsl_sf_erf_Z(x) or one can use instead: F(x) = ( 1+erf(x/sqrt{2}) )/2 = (2-erfc(x/sqrt{2})/2 = erfc(-x/sqrt{2})/2 Q(x) = ( 1-erf(x/sqrt{2}) )/2 = erfc(x/sqrt{2})/2 */ void obj_f (const size_t pnum, const double *x,void *params,double *fval){ const Function *F = ((struct objdata *)params)->F; const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; const size_t columns=((struct objdata *)params)->columns; double **data0=((struct objdata *)params)->data0; double **data1=((struct objdata *)params)->data1; double values[pnum+columns]; size_t i,j; double res=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values); /* cdfQ(x) = erfc(x/\sqrt(2))/2 */ /* res += -log( gsl_sf_erf_Q (tempg) ); */ res += log(2)-gsl_sf_log_erfc(tmpg/sqrt(2)); } /* cycle on one occurrences */ for(i=0;if,columns+pnum,F->argname,values); /* cdfP(x) = erfc(-x/\sqrt(2))/2 */ /* res += -log( 1-gsl_sf_erf_Q (tempg) ); */ res += log(2)-gsl_sf_log_erfc(-tmpg/sqrt(2)); } *fval=res; /* rescale log-likelihood */ /* *fval=res/(rows0+rows1); */ /* fprintf(stderr," f: %+12.6e\n",res); */ /* fprintf(stderr," x:"); */ /* for(i=0;iF; const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; const size_t columns=((struct objdata *)params)->columns; double **data0=((struct objdata *)params)->data0; double **data1=((struct objdata *)params)->data1; double values[pnum+columns]; size_t i,j; /* variables used in computation */ double tmpg; /* the value of the internal function */ /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); for(j=0;jdf)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/gsl_sf_erf_Q(tmpg); */ grad[j] += evaluator_evaluate ( (F->df)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/( exp( gsl_sf_log_erfc(tmpg/sqrt(2)) )/2 ); } } /* cycle on one occurrences */ for(i=0;if,columns+pnum,F->argname,values); for(j=0;jdf)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/(1-gsl_sf_erf_Q(tmpg)); */ grad[j] += -evaluator_evaluate ( (F->df)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/( exp( gsl_sf_log_erfc(-tmpg/sqrt(2)) )/2 ); } } /* rescale log-likelihood */ /* for (j=0;jF; const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; const size_t columns=((struct objdata *)params)->columns; double **data0=((struct objdata *)params)->data0; double **data1=((struct objdata *)params)->data1; double values[pnum+columns]; size_t i,j; double res=0; /* variables used in computation */ double tmpg; /* the value of the internal function */ /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); /* cdfQ(x) = erfc(x/\sqrt(2))/2 */ /* res += -log( gsl_sf_erf_Q (tmpg) ); */ res += log(2)-gsl_sf_log_erfc(tmpg/sqrt(2)); for(j=0;jdf)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/gsl_sf_erf_Q(tmpg); */ grad[j] += evaluator_evaluate ( (F->df)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/( exp( gsl_sf_log_erfc(tmpg/sqrt(2)) )/2 ); } } /* cycle on one occurrences */ for(i=0;if,columns+pnum,F->argname,values); /* cdfP(x) = erfc(-x/\sqrt(2))/2 */ /* res += -log( 1-gsl_sf_erf_Q (tmpg) ); */ res += log(2)-gsl_sf_log_erfc(-tmpg/sqrt(2)); for(j=0;jdf)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/(1-gsl_sf_erf_Q(tmpg)); */ grad[j] += -evaluator_evaluate ( (F->df)[j],columns+pnum,F->argname,values)*gsl_sf_erf_Z(tmpg)/( exp( gsl_sf_log_erfc(-tmpg/sqrt(2)) )/2 ); } } *fval=res; /* rescale log-likelihood */ /* for (j=0;jf,columns+Pnum,F->argname,values)); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h,tmpdg1*tmpdg2*dtmp1*dtmp1); } } gsl_matrix_add (J,dJ); } for(i=0;if,columns+Pnum,F->argname,values)); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h,tmpdg1*tmpdg2*dtmp1*dtmp1); } } gsl_matrix_add (J,dJ); } /* invert information matrix; dJ store temporary LU decomp. */ gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); /* gsl_matrix_scale (covar,a*a); */ gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 2: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); /* notice that for Gaussian density f it is f'=-x*f */ /* compute Hessian */ for(i=0;if,columns+Pnum,F->argname,values); const double dtmp2 = gsl_sf_hazard(dtmp1); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); const double tmpddg = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,tmpddg*dtmp2-tmpdg1*tmpdg2*dtmp1*dtmp2+ tmpdg1*tmpdg2*dtmp2*dtmp2); } } gsl_matrix_add (H,dH); } for(i=0;if,columns+Pnum,F->argname,values); const double dtmp2 = gsl_sf_hazard(-dtmp1); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); const double tmpddg = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,-tmpddg*dtmp2+tmpdg1*tmpdg2*dtmp1*dtmp2+ tmpdg1*tmpdg2*dtmp2*dtmp2); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); /* gsl_matrix_scale (covar,a*a); */ gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 3: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); /* compute Hessian and information matrix */ for(i=0;if,columns+Pnum,F->argname,values); const double dtmp2 = gsl_sf_hazard(dtmp1); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); const double tmpddg = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,tmpddg*dtmp2-tmpdg1*tmpdg2*dtmp1*dtmp2+ tmpdg1*tmpdg2*pow(dtmp2,2)); gsl_matrix_set (dJ,j,h,tmpdg1*tmpdg2*pow(dtmp2,2)); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } for(i=0;if,columns+Pnum,F->argname,values); const double dtmp2 = gsl_sf_hazard(-dtmp1); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); const double tmpddg = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,-tmpddg*dtmp2+tmpdg1*tmpdg2*dtmp1*dtmp2+ tmpdg1*tmpdg2*pow(dtmp2,2)); gsl_matrix_set (dJ,j,h,tmpdg1*tmpdg2*pow(dtmp2,2)); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); /* gsl_matrix_scale (covar,a*a); */ gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } /* Compute probabilities used in threshold statistics */ /* ================================================== */ void probabilities(const size_t pnum, const double *x,void *params){ const Function *F = ((struct objdata *)params)->F; const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; const size_t columns=((struct objdata *)params)->columns; double **data0=((struct objdata *)params)->data0; double **data1=((struct objdata *)params)->data1; double *prob0=((struct objdata *)params)->prob0; double *prob1=((struct objdata *)params)->prob1; double values[pnum+columns]; size_t i,j; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values)); } /* cycle on one occurrences */ for(i=0;if,columns+pnum,F->argname,values)); } } /* Score function for the determination of optimal threshold */ /* ========================================================= */ double score(const double t, void *params){ const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; double *prob0=((struct objdata *)params)->prob0; double *prob1=((struct objdata *)params)->prob1; size_t i; double errors0=0; double errors1=0; for(i=0;i t) errors0 +=1; for(i=0;iF; const size_t rows0=((struct objdata *)params)->rows0; const size_t rows1=((struct objdata *)params)->rows1; const size_t columns=((struct objdata *)params)->columns; double **data0=((struct objdata *)params)->data0; double **data1=((struct objdata *)params)->data1; /* double *prob0=((struct objdata *)params)->prob0; */ /* double *prob1=((struct objdata *)params)->prob1; */ double values[pnum-1+columns]; /* last parameter is the threshold */ const double weight0=((struct objdata *)params)->weight0; const double weight1=((struct objdata *)params)->weight1; size_t i,j; double res=0; /* variables used in computation */ double tmpg; /* the value of the internal function */ /* set the parameter values */ for(i=columns;if,columns+pnum-1,F->argname,values); /* prob0[i] = tmpF = gsl_cdf_ugaussian_P(tmpg); */ if( tmpg > x[pnum-1] ) res += weight0; } /* cycle on one occurrences */ for(i=0;if,columns+pnum-1,F->argname,values); /* prob1[i] = tmpF= gsl_cdf_ugaussian_P(tmpg); */ if(tmpg < x[pnum-1]) res += weight1; } *fval=res; } int main(int argc,char* argv[]){ int i; /* options and default values */ char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_varcovar=0; int o_zscore=0; int o_method=0; int o_threshold=0; /* =0 ignore threshold; in (0,1) take as given; =1 compute optimal only global; =2 compute optimal global and local */ int o_provided_t=0; /* threshold is provided */ int o_onlyglobal_t=0; /* threshold is computed only globally */ /* data from sdtin */ size_t rows0=0,rows1=0,columns=0; double **data0=NULL; double **data1=NULL; int *vartype=NULL; /* =0 reg. variate; =1 dummy; =2 dummy which cannot be estimated; =3 constant*/ Function F; /* definition of the function */ char **Param=NULL; /* parameter names */ double *Pval=NULL; /* parameter values */ gsl_matrix *Pcovar=NULL; /* covariance matrix */ size_t Pnum=0; /* parameters number */ double llmin; /* minimization parameters */ struct multimin_params mmparams={.01,.1,100,1e-6,1e-6,5,0}; /* estimated probabilities */ double *prob0=NULL; double *prob1=NULL; /* statistics */ double brier=0; /* brier score */ double threshold=0; /* threshold */ double thmin; /* value of the score function */ size_t thsteps=10; /* steps for threshold identification */ unsigned wrongs0=0; /* mistakes in pred. 0 */ unsigned wrongs1=0; /* mistakes in pred. 1 */ /* error handler */ gsl_error_handler_t old_handler; double scoremin=0; struct multimin_params scoremmparams={1,.01,100,1e-6,1e-4,4,0}; /* parameters for the likelihood expression */ struct objdata llparams; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"v:hF:O:V:A:t:g:zB:M:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Non linear probit estimation. Minimize the negative log-likelihood\n"); fprintf(stdout,"\n"); fprintf(stdout," sum_{i in N_0} log(1-F(g(X_i))) + sum_{i in N_1} log(F(g(X_i)))\n"); fprintf(stdout,"\n"); fprintf(stdout,"where N_0 and N_1 are the sets of 0 and 1 observations, g is a generic \n"); fprintf(stdout,"function of the independent variables and F is the normal CDF. It is also\n"); fprintf(stdout,"possible to minimize the score function\n"); fprintf(stdout,"\n"); fprintf(stdout,"w_0 sum_{i in N_0} theta(F(g(X_i))-t) + \n"); fprintf(stdout," w_1 sum_{i in N_1} theta(t-F(g(X_i))) \n"); fprintf(stdout,"\n"); fprintf(stdout,"where theta is the Heaviside function and t a threshold level. Weights \n"); fprintf(stdout,"w_0 and w_1 scale the contribution of the two subpopulations. The first \n"); fprintf(stdout,"column of data contains 0/1 entries. Successive columns are independent \n"); fprintf(stdout,"variables. The model is specified by a function g(x1,x2...) where x1,.. \n"); fprintf(stdout,"stands for the first,second .. N-th column independent variables.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"options:\n"); fprintf(stdout," -O type of output (default 0)\n"); fprintf(stdout," 0 parameters \n"); fprintf(stdout," 1 parameters and errors \n"); fprintf(stdout," 2 and probabilities \n"); fprintf(stdout," 3 parameters and variance matrix\n"); fprintf(stdout," 4 marginal effects \n"); fprintf(stdout," -V variance matrix estimation (default 0)\n"); fprintf(stdout," 0 \n"); fprintf(stdout," 1 < J^{-1} > \n"); fprintf(stdout," 2 < H^{-1} > \n"); fprintf(stdout," 3 < H^{-1} J H^{-1} > \n"); fprintf(stdout," -z take zscore (not of 0/1 dummies)\n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3 covariance matrix \n"); fprintf(stdout," 4 minimization steps (default 10)\n"); fprintf(stdout," 5 model definition \n"); fprintf(stdout," -g set number of point for global optimal threshold identification \n"); fprintf(stdout," -h this help \n"); fprintf(stdout," -t set threshold value (default 0)\n"); fprintf(stdout," 0 ignore threshold \n"); fprintf(stdout," (0,1) user provided threshold \n"); fprintf(stdout," 1 compute optimal only global \n"); fprintf(stdout," 2 compute optimal \n"); fprintf(stdout," -M estimation method \n"); fprintf(stdout," 0 maximum likelihood \n"); fprintf(stdout," 1 min. score (w0=w1=1) \n"); fprintf(stdout," 2 min. score (w0=1/N0, w1=1/N1) \n"); fprintf(stdout," -A MLL optimization options (default 0.01,0.1,100,1e-6,1e-6,5)\n"); fprintf(stdout," fields are step,tol,iter,eps,msize,algo. Empty fields for default\n"); fprintf(stdout," step initial step size of the searching algorithm \n"); fprintf(stdout," tol line search tolerance iter: maximum number of iterations \n"); fprintf(stdout," eps gradient tolerance : stopping criteria ||gradient||0 && threshold <1){ /* user provided threshold */ o_provided_t=1; } else if (threshold == 1){ /* compute global optimal threshold */ o_provided_t=0; o_onlyglobal_t=1; } else if (threshold == 2){ /* compute optimal threshold */ o_provided_t=0; } else { fprintf(stderr,"ERROR (%s): wrong threshold specification: '%f'\n", GB_PROGNAME,threshold); exit(-1); } } else if(opt=='M'){ /*set the estimation method*/ o_method = atoi(optarg); if(o_method<0 || o_method>2){ fprintf(stderr,"ERROR (%s): method option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_method); exit(-1); } } else if(opt=='z'){ /* z-score the variates */ o_zscore=1; } else if(opt=='g'){ /* set the number of steps for global threshold identification */ thsteps = (size_t) atoi(optarg); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); if(o_output<0 || o_output>6){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>3){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='A'){ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.step_size=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.tol=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxiter=(unsigned) atoi(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.epsabs=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxsize=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.method= (unsigned) atoi(stmp2); } free(stmp3); } else if(opt=='B'){ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) scoremmparams.step_size=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) scoremmparams.maxiter=(unsigned) atoi(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) scoremmparams.maxsize=atof(stmp2); } free(stmp3); } else if(opt=='v'){ o_verbose = atoi(optarg); mmparams.verbosity=(o_verbose>2?o_verbose-2:0); scoremmparams.verbosity=(o_verbose>2?o_verbose-2:0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* define the dataset */ { size_t i,i0,i1,j; size_t rows=0; double **data=NULL; /* load data */ loadtable(&data,&rows,&columns,0,splitstring); /* check if there are dummies. vartype: 0 reg. variate; 1 dummy; 2 dummy which cannot be estimated; 3 constant*/ vartype = (int *) my_alloc((columns-1)*sizeof(int)); for(i=1;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ F.f = evaluator_create (stmp3); assert(F.f); } free(stmp3); } free(piece); } /* check that everything is correct */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } { size_t i,j; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; char **storedname=NULL; size_t storednum=0; /* retrieve list of arguments and their number */ /* notice that storedname is not allocated */ { int argnum; evaluator_get_variables (F.f,&storedname,&argnum); storednum = (size_t) argnum; } /* check the definition of the function: column specifications refer to existing columns and parameters are properly initialized */ for(i=0;i0?atoi(stmp1+1):0); if(index>columns){ fprintf(stderr,"ERROR (%s): column %zu not present in data\n", GB_PROGNAME,index); exit(-1); } if(vartype[index-1]==2) fprintf(stderr,"WARNING (%s): dummy variable in column %zu is completely separated\n",GB_PROGNAME,index); } else { for(j=0;j1){ F.ddf = (void ***) my_alloc(Pnum*sizeof(void **)); for(i=0;i3){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf (stderr,"\n Parameters and initial conditions:\n"); for(i=0;i1){ fprintf (stderr,"\n Model second derivatives:\n"); for(i=0;i2){ fprintf(stderr,"##--- START score minimization ---\n"); fprintf(stderr,"# initial score %f initial threshold %f\n",scoremin,Pval[Pnum-1]); } multimin(Pnum,Pval,&scoremin,xtype,xmin,xmax,score_obj_f,NULL,NULL,(void *) &llparams,scoremmparams); if(o_verbose>2){ fprintf(stderr,"# final score %f final threshold %f\n",scoremin,Pval[Pnum-1]); fprintf(stderr,"##--- END score minimization ---\n"); } free(xmin); free(xmax); free(xtype); } /* threshold is found by the minimization */ o_threshold=1; o_provided_t=1; threshold=Pval[Pnum-1]; } /* Compute implied probabilities */ /* ----------------------------- */ /* necessary for threshold computation and Brier score */ if(o_threshold==1 || o_verbose >1){ prob0 = (double *) my_alloc(rows0*sizeof(double)); prob1 = (double *) my_alloc(rows1*sizeof(double)); llparams.prob0 = prob0; llparams.prob1 = prob1; probabilities(Pnum-1,Pval,&llparams); /* threshold value, which is the last parameter, is not used */ } /* compute optimal threshold */ /* ------------------------- */ if(o_threshold==1 && o_provided_t==0) { size_t i; if(o_verbose>2){ fprintf(stderr,"##--- START global optimal threshold computation ---\n"); fprintf(stderr,"#grid size: %zd\n",thsteps); } thmin=1; for(i=0;i1) */ /* fprintf(stderr,"%f %f\n",dtmp1,dtmp2); */ if(dtmp22) fprintf(stderr,"# threshold global minimum : t=%f f=%f\n",threshold,thmin); if(o_verbose>2) fprintf(stderr,"##--- END global optimal threshold computation ---\n"); } /* local refinement minimization */ if(o_threshold==1 && o_provided_t==0 && o_onlyglobal_t==0){ /* variables for the local minimization */ int status; int iter = 0, max_iter = 100; const gsl_min_fminimizer_type *T; gsl_min_fminimizer *s; gsl_function F; /* ------------------------------------ */ double tmin,tmax=0; /* boundaries to threshold minimization */ /* initialization */ F.function = &score; F.params = &llparams; tmin=threshold-1/(thsteps-1.); tmax=threshold+1/(thsteps-1.); T = gsl_min_fminimizer_brent; s = gsl_min_fminimizer_alloc (T); gsl_min_fminimizer_set (s, &F,threshold,tmin,tmax); do { iter++; status = gsl_min_fminimizer_iterate (s); threshold = gsl_min_fminimizer_x_minimum (s); thmin = gsl_min_fminimizer_f_minimum (s); tmin = gsl_min_fminimizer_x_lower (s); tmax = gsl_min_fminimizer_x_upper (s); status = gsl_min_test_interval (tmin, tmax, 0.001, 0.0); /* if (status == GSL_SUCCESS) */ /* printf ("Converged:\n"); */ /* printf ("%5d [%.7f, %.7f] f=%f\n", */ /* iter, tmin, tmax, fmin); */ } while (status == GSL_CONTINUE && iter < max_iter); gsl_min_fminimizer_free (s); if(o_verbose>2){ fprintf(stderr,"# threshold local refinement: t=%f f=%f\n",threshold,thmin); fprintf(stderr,"##--- END optimal threshold computation ---\n"); } } /* build the covariance matrix */ /* --------------------------- */ if(o_method == 0){ if(o_output > 0) Pcovar = varcovar(o_varcovar,Pnum,Pval,llparams); } else { /* errors are unknown */ Pcovar = gsl_matrix_alloc (Pnum,Pnum); gsl_matrix_set_all (Pcovar,NAN); } /* compute brier score */ /* ------------------- */ if(o_verbose>1){ size_t i; for(i=0;i1 && o_threshold==1){ size_t i; for(i=0;ithreshold) wrongs0 +=1; for(i=0;i1 && o_output > 0){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr,"%*s point est. st. err. se/pe P{pe=0}",(int) strlen(Param[0])+2,""); if(o_verbose>2){ fprintf(stderr," var-covar = "); switch(o_varcovar){ case 0: fprintf(stderr,"\n\n"); break; case 1: fprintf(stderr,"J^{-1}\n\n"); break; case 2: fprintf(stderr,"H^{-1}\n\n"); break; case 3: fprintf(stderr,"H^{-1} J H^{-1}\n\n"); break; } } else fprintf(stderr,"\n"); for(i=0;i2){ fprintf(stderr," | "); for(j=0;j1) fprintf(stderr," ------------------------------------------------------------\n"); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>1){ fprintf(stderr,"#\n"); fprintf(stderr,"#Method: "); switch(o_method){ case 0: fprintf(stderr,"Maximum Likelihood\n"); fprintf(stderr,"#log-likelihood : %f\n",-llmin); fprintf(stderr,"#number of observations : %zu\n",rows0+rows1); fprintf(stderr,"#log-likelihood/obs : %f\n",-llmin/(rows0+rows1)); if(o_threshold==1){ if(o_provided_t==0){ fprintf(stderr,"#optimal threshold : %f\n",threshold); fprintf(stderr,"#score function : %f\n",thmin); } else fprintf(stderr,"#provided threshold : %f\n",threshold); } break; case 1: case 2: fprintf(stderr,"Score minimization\n"); fprintf(stderr,"# weights w0=%f w1=%f\n", llparams.weight0,llparams.weight1); fprintf(stderr,"#score minimum %f\n",scoremin); fprintf(stderr,"#optimal threshold %f\n",Pval[Pnum-1]); break; } fprintf(stderr,"#\n"); fprintf(stderr,"#summary statistics \n"); if(o_threshold==1){ fprintf(stderr,"#errors of Type I : %d\n",wrongs1); fprintf(stderr,"#errors of Type II : %d\n",wrongs0); fprintf(stderr,"#correctly predicted 0 : %f\n",1.-((double) wrongs0)/rows0); fprintf(stderr,"#correctly predicted 1 : %f\n",1.-((double) wrongs1)/rows1); fprintf(stderr,"#total corr. pred. : %f\n",((double) rows0-wrongs0+rows1-wrongs1)/(rows0+rows1)); } fprintf(stderr,"#brier score : %f\n",brier); fprintf(stderr,"#\n"); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ switch(o_output){ case 0: /* point estimates */ { size_t i; /* +++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ */ /* fprintf(stderr,"#"); */ /* for(i=0;i0){ fprintf(stdout,"#"); fprintf(stdout,EMPTY_SEP,"obs."); for(i=0;i0){ fprintf(stdout,"#point estimates\n"); fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#var-covar matrix\n"); fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0) Pnum--; evaluator_destroy(F.f); for(i=0;imax then count is reversed and skip must be negative (\fB\-1\fR by default). Different specs are separated by semicolon ';' and considered sequentially. .TP trans is a list of transformations applied to selected data: 'd' take the diff of subsequent columns; 'D' remove all rows with at least one Not\-A\-Number (NAN) entry; 'f' flatten the output piling all columns; 'l' take log of all entries, 'P' print all entries collected as a data\-block; 't' transpose the matrix of data; 'z' subtract from the entries in each column their mean; 'Z' replace the entry in each column with their zscore; \&'w' divide the entry in each columns by their mean. .IP \&'<..;..>' functions separated by semicolons in angle brackets can be used for generic data transformation; the function is computed for each row of data. Variables names are 'x' followed by the number of the column and optionally by 'l' and the number of lags. For instance 'x2+x3l1' means the sum of the entries in the 2nd column plus the entries in the 3rd column in the previous row. 'x0' stands for the row number and 'x' is equal to 'x1' .IP \&'<@..;..>' if the functions specification starts with a '@' the functions are computed recursively along the columns. In this case the number after the 'x' is the relative column counted starting from the one considered at each step. .IP \&'{...}' a function in curly brackets can be use to select data: only rows that return a non\-negative value are retained .SH OPTIONS .TP \fB\-F\fR set the input fields separators (default ' \et') .TP \fB\-o\fR set the output format (default '%12.6e') .TP \fB\-e\fR set the output format for empty fields (default '%13s') .TP \fB\-s\fR set the output separation string (default ' ') .TP \fB\-t\fR define global transformations applied before each output (default '') .TP \fB\-v\fR verbose mode .SH EXAMPLES .TP gbget 'file(1:3)ld' select the first three columns in 'file', take the log and the difference of successive columns; .TP gbget 'file(2,\-10:\-1) select the last ten elements of the second' of 'file' and print their squares .TP gbget '[2]()' '[1]()' < ... select the second and first data block from the standard input. .TP gbget 'file(1:3)' select the first three columns in 'file' and in each row multiply the first two entries and. subtract the third. .TP gbget 'file()<@x1+x2>' print the sum of two subsequent columns .TP gbget 'file(1:3){x2\-2}' select the first three columns in 'file' for the rows whose second field is not lower then 2 .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbfun.c0000644000175000017500000003646713246754720011415 00000000000000/* gbfun (ver. 5.6) -- Apply functions to table of data Copyright (C) 2008-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #if defined HAVE_LIBGSL #include "matheval.h" #include "assert.h" #endif /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; /* char *args[] = { "x" }; */ char **args; /* list of arguments names */ size_t *indeces; /* the index (shift) for each variable */ size_t *lags; /* the lag for each variable */ double *values; /* values used in computation */ size_t maxindex; /* the largest index */ size_t maxlag; /* the largest lag */ } Function; void data_transpose(double *** vals,size_t *rows,size_t *columns){ size_t i,j; /* allocate space */ double **newvals = (double **) my_alloc((*rows)*sizeof(double *)); for(i=0;i<*rows;i++){ newvals[i] = (double *) my_alloc((*columns)*sizeof(double)); } /* fill space */ for(i=0;i<*rows;i++) for(j=0;j<*columns;j++) newvals[i][j] = (*vals)[j][i]; /* deallocate old space */ if(*columns>0) for(i=0;i<*columns;i++) free((*vals)[i]); free(*vals); *vals = newvals; /* swap axes */ j=*columns; *columns=*rows; *rows=j; } int main(int argc,char* argv[]){ size_t rows=0,columns=0; double **data=NULL; char *splitstring = strdup(" \t"); Function *F=NULL; size_t Fnum=0; /* function spec */ size_t spec; /* OPTIONS */ int o_table=0; int o_transpose=0; int o_recursive=0; int o_recursive_printall=0; int o_verbose=0; int o_binary_in=0; double recursive_start=0; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* START COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"htvr:R:F:o:s:b:T",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Compute specified functions on data read from standard input. The default\n"); fprintf(stdout,"is to perform the computation column-wise, on each row of data separately.\n"); fprintf(stdout,"Variable 'xi' stands for the i-th column while 'x0' stands for the row\n"); fprintf(stdout,"number, e.g. a function f(x1,x2) operates on the first and secod column.\n"); fprintf(stdout,"With the option -t the function is computed, in turn, on every column. In\n"); fprintf(stdout,"this case f(x1,x2) stands for a function of the column itself and of the \n"); fprintf(stdout,"following column (the index being a lead operator). In these cases 'x' is\n"); fprintf(stdout,"equivalent to 'x1'. With -r or -R the function is recursevely computed\n"); fprintf(stdout,"\"columwise\" on each row. In this case the variable 'x' identifies the\n"); fprintf(stdout,"result of the previous evaluation. A lag operator can be specified with the\n"); fprintf(stdout,"letter l, like in 'x1l2', which means the first column two steps before.\n"); fprintf(stdout,"More functions can be specified and will be considered in turn.\n"); fprintf(stdout,"\nUsage: %s [options] ...\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -T compute the function row-wise, on each column separately.\n"); fprintf(stdout," -t compute on each column\n"); fprintf(stdout," -r set initial value and compute recursively\n"); fprintf(stdout," -R set initial value, compute recursively and print intermediary results\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -o set the output format (default '%%12.6e') \n"); fprintf(stdout," -s set the output separation string (default ' ') \n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbfun 'x0+log(x2)' < file print the log of the second column of 'file'\n"); fprintf(stdout," adding the progressive number of the row \n"); fprintf(stdout," gbfun -T 'x0+log(x2)' < file print the log of the second row of 'file'\n"); fprintf(stdout," adding the progressive number of the column \n"); fprintf(stdout," gbfun -r 0 'x+sqrt(x1)' < file print the sum of the square root of the\n"); fprintf(stdout," elements of the first column of 'file'\n"); return(0); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='b'){ /*set binary input or output*/ switch(atoi(optarg)){ case 0 : break; case 1 : o_binary_in=1; break; case 2 : PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; case 3 : o_binary_in=1; PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; default: fprintf(stderr,"unclear binary specification %d, option ignored.\n",atoi(optarg)); break; } } else if(opt=='o'){ /*set the ouptustring */ FLOAT = strdup(optarg); } else if(opt=='s'){ /*set the separator*/ SEP = strdup(optarg); } else if(opt=='t'){ o_table=1; } else if(opt=='T'){ o_transpose=1; } else if(opt=='r'){ o_recursive=1; recursive_start=mystrtod(optarg); } else if(opt=='R'){ o_recursive_printall=1; recursive_start=mystrtod(optarg); } else if(opt=='v'){ o_verbose=1; } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ if(o_binary_in==1) loadtable_bin(&data,&rows,&columns,0); else{ loadtable(&data,&rows,&columns,0,splitstring); free(splitstring); } /* transpose if required */ if(o_transpose == 1) { data_transpose(&data,&rows,&columns); } /* parse specs for functions specification */ for(spec=(size_t) optind;spec< (size_t) argc;spec++){ char *piece=strdup (argv[spec]); char *stmp1=piece; char *stmp2; size_t j; while( (stmp2=strsep (&stmp1,",")) != NULL ){/*take a piece*/ char *stmp3 = strdup(stmp2); /* fprintf(stderr,"token:%s\n",stmp3); */ /* allocate new function */ Fnum++; F=(Function *) my_realloc((void *) F,Fnum*sizeof(Function)); /* generate the formal expression */ F[Fnum-1].f = evaluator_create (stmp3); assert(F[Fnum-1].f); free(stmp3); /* retrive list of argument */ { /* hack necessary due to libmatheval propotypes */ int argnum; evaluator_get_variables (F[Fnum-1].f, &(F[Fnum-1].args), &argnum); F[Fnum-1].argnum = (size_t) argnum; } /* compute list of indeces */ F[Fnum-1].indeces = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].lags = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].maxindex=0; F[Fnum-1].maxlag=0; for(j=0;j F[Fnum-1].maxindex) F[Fnum-1].maxindex = (F[Fnum-1].indeces)[j]; /* possibly update the value of the maximum lag */ if( (F[Fnum-1].lags)[j] > F[Fnum-1].maxlag) F[Fnum-1].maxlag = (F[Fnum-1].lags)[j]; /* check index */ if((F[Fnum-1].indeces)[j]>columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,(F[Fnum-1].indeces)[j]); exit(-1); } /* check lag */ if((F[Fnum-1].lags)[j]>rows){ fprintf(stderr,"ERROR (%s): lag %zd is longer then the data length\n", GB_PROGNAME,(F[Fnum-1].lags)[j]); exit(-1); } } /* allocate list of values */ F[Fnum-1].values = (double *) my_alloc(F[Fnum-1].argnum*sizeof(double)); } free(piece); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1){ /* check the builded functions */ size_t i,j; for(i=0;ifirstrow) firstrow=F[j].maxlag; /* compute the largest column shift */ for(j=0;jlargest) largest=F[j].maxindex; /* allocate output */ out = (double **) my_alloc((columns-largest+1)*Fnum*sizeof(double *)); for(i=0;i<(columns-largest+1)*Fnum;i++) out[i] = (double *) my_alloc((rows-firstrow)*sizeof(double)); /* compute output */ for(i=firstrow;i 0 ? data[c+F[j].indeces[h]-1][i-F[j].lags[h]] : i+1); out[c*Fnum+j][i-firstrow]=evaluator_evaluate (F[j].f,F[j].argnum,F[j].args,F[j].values); } /* print output */ if(o_transpose == 0) PRINTMATRIXBYCOLS(stdout,out,rows-firstrow,(columns-largest+1)*Fnum); else if (o_transpose == 1) PRINTMATRIXBYROWS(stdout,out,(columns-largest+1)*Fnum,rows-firstrow); for(i=0;i<(columns-largest+1)*Fnum;i++) free(out[i]); free(out); } else if(o_recursive_printall==1){/*recursive evaluation with intermediary results*/ size_t i,j,h,c; size_t firstrow=0; double **out; size_t cols,col; /* compute the first row to print */ for(j=0;jfirstrow) firstrow=F[j].maxlag; /* allocate output */ cols=0; for(j=0;jfirstrow) firstrow=F[j].maxlag; /* allocate output */ out = (double **) my_alloc(Fnum*sizeof(double *)); for(i=0;ifirstrow) firstrow=F[j].maxlag; /* allocate output */ out = (double **) my_alloc(Fnum*sizeof(double *)); for(i=0;i0 ? data[F[j].indeces[h]-1][i-F[j].lags[h]] : i+1); out[j][i-firstrow] = evaluator_evaluate (F[j].f,F[j].argnum,F[j].args,F[j].values); } /* print output */ if(o_transpose == 0) PRINTMATRIXBYCOLS(stdout,out,rows-firstrow,Fnum); else if (o_transpose == 1) PRINTMATRIXBYROWS(stdout,out,Fnum,rows-firstrow); for(i=0;i #include #include #include #endif int main(int argc,char* argv[]){ double *data=NULL; size_t size; char *splitstring = strdup(" \t"); /* OPTIONS */ int o_method=0; int o_kerneltype=0; int o_setbandwidth=0; int o_verbose=0; int o_window=0; double wmax,wmin; double ave,sdev,min,max; /* data statistics */ size_t M=64; /* number of points */ double scale=1; /* optional scaling of the smoothing parameter */ double h=1;/* the smoothing parameter */ double (*K) (double) = Kgauss_tilde; /* the kernel to use */ /* confidence interval */ int o_pconf = 0; double pconf,zconf,sconf; /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"H:K:n:S:hvM:F:p:w:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='S'){ /*set the scale parameter for smoothing*/ scale=atof(optarg); } else if(opt=='n'){ /*the number of bins*/ M=(int) atoi(optarg); } else if(opt=='w'){ /*set the support window */ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; stmp2=strsep (&stmp1,","); if(strlen(stmp2)>0) wmin=atof(stmp2); if(stmp1 != NULL && strlen(stmp1)>0) wmax=atof(stmp1); free(stmp3); if(wmax=1){ fprintf(stderr,"ERROR (%s): confidence interval %f out of boud\n", GB_PROGNAME,pconf); exit(1); } else zconf=gsl_cdf_ugaussian_Pinv(1-pconf/2); } else if(opt=='K'){ /*the Kernel to use*/ o_kerneltype = atoi(optarg); } else if(opt=='H'){ /*set the kernel bandwidth*/ o_setbandwidth=1; h = atof(optarg); } else if(opt=='M'){ /*set the method to use*/ o_method= atoi(optarg); } else if(opt=='v'){ /*increase verbosity*/ o_verbose=1; } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Compute the kernel density estimation on an equispaced grid. Data are read\n"); fprintf(stdout,"from standard input. The kernel bandwidth, if not provided with the option\n"); fprintf(stdout,"-H, is set automatically using simple heuristics. Normally the whole support\n"); fprintf(stdout,"is displayed. The option -w can be used to restrict the computation to specific\n"); fprintf(stdout,"range of values. The method used to compute the density is set by the option -M.\n"); fprintf(stdout,"The boundaries of the grid are slightly different in the different cases.\n\n"); fprintf(stdout,"Usage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of equispaced points/bins where the density is computed (default 64)\n"); fprintf(stdout," -w set the grid boundaries with min,max (default whole support)\n"); fprintf(stdout," -H set the kernel bandwidth\n"); fprintf(stdout," -S scale the automatic kernel bandwidth\n"); fprintf(stdout," -K choose the kernel to use (default 0) \n"); fprintf(stdout," 0 Epanenchnikov\n"); fprintf(stdout," 1 Rectangular\n"); fprintf(stdout," 2 Gaussian\n"); fprintf(stdout," 3 Laplacian\n"); fprintf(stdout," -M choose the method for density computation (default 0)\n"); fprintf(stdout," 0 use FFT (# grid points rounded to nearest power of 2) \n"); fprintf(stdout," 1 use discrete convolution (compact kernels only; bins>2) \n"); fprintf(stdout," 2 explicit summation (boundaries excluded)\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbker -n 128 -K 2 < file use a Gaussian kernel to compute the density on 128\n"); fprintf(stdout," equispaced points with the FFT approach. All the data\n"); fprintf(stdout," in 'file' are used, multiple columns are pooled.\n"); fprintf(stdout," gbker -K 3 -M 2 < file use a Laplacian kernel to compute the density on 64 points.\n"); fprintf(stdout," The method used is the explicit summation.\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load the data */ load(&data,&size,0,splitstring); /* sort the data */ qsort(data,size,sizeof(double),sort_by_value); /* compute the statistics */ moment_short(data,size,&ave,&sdev,&min,&max); /* correct if windows provided */ if(o_window==1){ min=wmin; max=wmax; } /* the parameter h is set for a Gaussian kernel [Silverman p.48] */ /* if not provided on the command line */ if(o_setbandwidth == 0){ const double inter = fabs(data[3*size/4]-data[size/4])/1.34; const double A = (sdev>inter && inter>0 ? inter : sdev); h=scale*0.9*A/pow(size,.2); } /* nearest power of two if FFT */ if(o_method==0) M = (size_t) pow(2,nearbyint(log((double) M)/M_LN2)); /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* bandwidth */ fprintf(stdout,"bandwidth %e ",h); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic"); if(scale != 1 ) fprintf(stdout," scaled by %f",scale); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); /* kernel type */ fprintf(stdout,"kernel "); switch(o_kerneltype){ case 0: fprintf(stdout,"Epanechnikov"); break; case 1: fprintf(stdout,"Rectangular"); break; case 2: fprintf(stdout,"Gaussian"); break; case 3: fprintf(stdout,"Laplacian"); break; } fprintf(stdout,"\n"); /* confidence interval */ if(o_pconf == 1){ fprintf(stdout,"confidence %g\n",pconf); } } /* ++++++++++++++++++++++++++++ */ /* set the confidence rescaling factor */ if(o_pconf==1) switch(o_kerneltype){ case 0: sconf = zconf*sqrt(Kepanechnikov_K2/(h*size)); break; case 1: sconf = zconf*sqrt(Krectangular_K2/(h*size)); break; case 2: sconf = zconf*sqrt(Kgauss_K2/(h*size)); break; case 3: sconf = zconf*sqrt(Klaplace_K2/(h*size)); break; default: fprintf(stderr,"ERROR (%s): unknown kernel; use option -h\n", GB_PROGNAME); exit(1); } if(o_method==0){/*=== FFT methods ===*/ size_t i; double *csi;/* the density */ /* the boundaries of the interval are set according... */ /* const double a = min-4*h; */ /* const double b = max+4*h; */ /* const double delta = (b-a)/(M-1); */ const double margin=.05; const double a = min - 4*h - (max-min+4*h)*margin; const double b = max + 4*h + (max-min+4*h)*margin; const double delta = (b-a)/(M-1); /* choose the kernel to use */ switch(o_kerneltype){ case 0: K=Kepanechnikov_tilde; break; case 1: K=Krectangular_tilde; break; case 2: K=Kgauss_tilde; break; case 3: K=Klaplace_tilde; break; default: fprintf(stderr,"ERROR (%s): unknown kernel; use option -h\n", GB_PROGNAME); exit(+1); } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* method */ fprintf(stdout,"method FFT\n"); /* binning */ fprintf(stdout,"# bins %zd\n",M); fprintf(stdout,"range [%.3e,%.3e]\n",a,b); } /* ++++++++++++++++++++++++++++ */ /* allocate the bins */ csi = (double *) my_alloc(M*sizeof(double)); /* prepare the binnings */ for(i=0;i= (int) M || index<0) continue; csi[index]+=1-epsilon; if(index+1 <(int) M) csi[index+1]+=epsilon; else csi[0]+=epsilon; } /* normalization */ for(i=0;i= M-1 ) fprintf(stderr,"ERROR (%s): problem in grid values; possible bug\n", GB_PROGNAME); csi[index]+=1-epsilon; csi[index+1]+=epsilon; } /* normalization */ for(i=0;i2*J?-J:J-i); const int jmax = (i2*J?-J:J-i); const int jmax = (i, 1996. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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Check the 'README' file, it" echo "often tells you about the needed prerequisites for installing" echo "this package. You may also peek at any GNU archive site, in" echo "case some other package contains this missing '$1' program." ;; esac } give_advice "$1" | sed -e '1s/^/WARNING: /' \ -e '2,$s/^/ /' >&2 # Propagate the correct exit status (expected to be 127 for a program # not found, 63 for a program that failed due to version mismatch). exit $st # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: gbutils-5.7.1/gbgrid.c0000644000175000017500000001162013246754720011532 00000000000000/* gbgrid (ver. 5.6) -- Produce grid of data Copyright (C) 2008-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include "matheval.h" #include "assert.h" /* handy structure used in computation */ typedef struct function { void *f; int argnum; /* char *args[] = { "x" }; */ char **args; } Function; int main(int argc,char* argv[]){ int i,j,h; int columns=10; int rows=10; int o_verbose=0; char *names[]={"c","r"}; double values[2]={1.,1.}; Function *F=NULL; int Fnum=0; /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ while((opt=getopt_long(argc,argv,"hvc:r:o:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Generate a grid using function f(r,c) where r is the row index and c is\n"); fprintf(stdout,"the column index. Indeces start from 1. If more than one function are\n"); fprintf(stdout,"provided they are printed one after the other.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -c specify number of rows (default 10)\n"); fprintf(stdout," -r specify number of columns (default 10)\n"); fprintf(stdout," -o set the output format (default '%%12.6e') \n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbgrid -c 1 -r 10 r+c generates a vector with elements equal to the\n"); fprintf(stdout," sum of the row and column indexes\n"); return(0); } else if(opt=='v'){ o_verbose=1; } else if(opt=='c'){ columns=atoi(optarg); } else if(opt=='r'){ rows=atoi(optarg); } else if(opt=='o'){ /*set the ouptustring */ FLOAT = strdup(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* parse line for functions specification */ for(i=optind;i #include #include #include #include /* log-likelihood params */ struct loglike_params { size_t size; double *data; size_t used; size_t min; size_t max; int method; double d; }; /* Exponential distribution in exponential_gbhill.c */ void exponential_nll (const size_t, const double *,void *,double *); void exponential_dnll (const size_t, const double *,void *,double *); void exponential_nlldnll (const size_t, const double *,void *,double *,double *); double exponential_f (const double,const size_t, const double *); double exponential_F (const double,const size_t, const double *); gsl_matrix *exponential_varcovar(const int, double *, void *); /* Pareto Type I distribution in paretoI_gbhill.c */ void pareto1_nll (const size_t, const double *,void *,double *); void pareto1_dnll (const size_t, const double *,void *,double *); void pareto1_nlldnll (const size_t, const double *,void *,double *,double *); double pareto1_f (const double,const size_t, const double *); double pareto1_F (const double,const size_t, const double *); gsl_matrix *pareto1_varcovar(const int, double *, void *); /* Pareto Type III distribution in paretoIII_gbhill.c */ void pareto3_nll (const size_t, const double *,void *,double *); void pareto3_dnll (const size_t, const double *,void *,double *); void pareto3_nlldnll (const size_t, const double *,void *,double *,double *); double pareto3_f (const double,const size_t, const double *); double pareto3_F (const double,const size_t, const double *); gsl_matrix *pareto3_varcovar(const int, double *, void *); /* Gaussian distribution in gaussian_gbhill.c */ void gaussian_nll (const size_t, const double *,void *,double *); void gaussian_dnll (const size_t, const double *,void *,double *); void gaussian_nlldnll (const size_t, const double *,void *,double *,double *); double gaussian_f (const double,const size_t, const double *); double gaussian_F (const double,const size_t, const double *); gsl_matrix *gaussian_varcovar(const int, double *, void *); #endif gbutils-5.7.1/install-sh0000755000175000017500000003325611775245551012147 00000000000000#!/bin/sh # install - install a program, script, or datafile scriptversion=2011-01-19.21; # UTC # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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[-T] SRCFILE DSTFILE or: $0 [OPTION]... SRCFILES... DIRECTORY or: $0 [OPTION]... -t DIRECTORY SRCFILES... or: $0 [OPTION]... -d DIRECTORIES... In the 1st form, copy SRCFILE to DSTFILE. In the 2nd and 3rd, copy all SRCFILES to DIRECTORY. In the 4th, create DIRECTORIES. Options: --help display this help and exit. --version display version info and exit. -c (ignored) -C install only if different (preserve the last data modification time) -d create directories instead of installing files. -g GROUP $chgrpprog installed files to GROUP. -m MODE $chmodprog installed files to MODE. -o USER $chownprog installed files to USER. -s $stripprog installed files. -t DIRECTORY install into DIRECTORY. -T report an error if DSTFILE is a directory. 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Create the # directory the slow way, step by step, checking for races as we go. case $dstdir in /*) prefix='/';; [-=\(\)!]*) prefix='./';; *) prefix='';; esac eval "$initialize_posix_glob" oIFS=$IFS IFS=/ $posix_glob set -f set fnord $dstdir shift $posix_glob set +f IFS=$oIFS prefixes= for d do test X"$d" = X && continue prefix=$prefix$d if test -d "$prefix"; then prefixes= else if $posix_mkdir; then (umask=$mkdir_umask && $doit_exec $mkdirprog $mkdir_mode -p -- "$dstdir") && break # Don't fail if two instances are running concurrently. test -d "$prefix" || exit 1 else case $prefix in *\'*) qprefix=`echo "$prefix" | sed "s/'/'\\\\\\\\''/g"`;; *) qprefix=$prefix;; esac prefixes="$prefixes '$qprefix'" fi fi prefix=$prefix/ done if test -n "$prefixes"; then # Don't fail if two instances are running concurrently. (umask $mkdir_umask && eval "\$doit_exec \$mkdirprog $prefixes") || test -d "$dstdir" || exit 1 obsolete_mkdir_used=true fi fi fi if test -n "$dir_arg"; then { test -z "$chowncmd" || $doit $chowncmd "$dst"; } && { test -z "$chgrpcmd" || $doit $chgrpcmd "$dst"; } && { test "$obsolete_mkdir_used$chowncmd$chgrpcmd" = false || test -z "$chmodcmd" || $doit $chmodcmd $mode "$dst"; } || exit 1 else # Make a couple of temp file names in the proper directory. dsttmp=$dstdir/_inst.$$_ rmtmp=$dstdir/_rm.$$_ # Trap to clean up those temp files at exit. trap 'ret=$?; rm -f "$dsttmp" "$rmtmp" && exit $ret' 0 # Copy the file name to the temp name. (umask $cp_umask && $doit_exec $cpprog "$src" "$dsttmp") && # and set any options; do chmod last to preserve setuid bits. # # If any of these fail, we abort the whole thing. If we want to # ignore errors from any of these, just make sure not to ignore # errors from the above "$doit $cpprog $src $dsttmp" command. # { test -z "$chowncmd" || $doit $chowncmd "$dsttmp"; } && { test -z "$chgrpcmd" || $doit $chgrpcmd "$dsttmp"; } && { test -z "$stripcmd" || $doit $stripcmd "$dsttmp"; } && { test -z "$chmodcmd" || $doit $chmodcmd $mode "$dsttmp"; } && # If -C, don't bother to copy if it wouldn't change the file. if $copy_on_change && old=`LC_ALL=C ls -dlL "$dst" 2>/dev/null` && new=`LC_ALL=C ls -dlL "$dsttmp" 2>/dev/null` && eval "$initialize_posix_glob" && $posix_glob set -f && set X $old && old=:$2:$4:$5:$6 && set X $new && new=:$2:$4:$5:$6 && $posix_glob set +f && test "$old" = "$new" && $cmpprog "$dst" "$dsttmp" >/dev/null 2>&1 then rm -f "$dsttmp" else # Rename the file to the real destination. $doit $mvcmd -f "$dsttmp" "$dst" 2>/dev/null || # The rename failed, perhaps because mv can't rename something else # to itself, or perhaps because mv is so ancient that it does not # support -f. { # Now remove or move aside any old file at destination location. # We try this two ways since rm can't unlink itself on some # systems and the destination file might be busy for other # reasons. In this case, the final cleanup might fail but the new # file should still install successfully. { test ! -f "$dst" || $doit $rmcmd -f "$dst" 2>/dev/null || { $doit $mvcmd -f "$dst" "$rmtmp" 2>/dev/null && { $doit $rmcmd -f "$rmtmp" 2>/dev/null; :; } } || { echo "$0: cannot unlink or rename $dst" >&2 (exit 1); exit 1 } } && # Now rename the file to the real destination. $doit $mvcmd "$dsttmp" "$dst" } fi || exit 1 trap '' 0 fi done # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: gbutils-5.7.1/gbenv.c0000644000175000017500000001461613246754720011405 00000000000000/* gbenv (ver. 5.6) -- Floating point locale, and gbutils settings Copyright (C) 2006-2015 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #ifdef linux #include #include #endif #include #include int main(int argc,char* argv[]){ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"h",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Show floating point environment (for x86 family processors), locale and\n"); fprintf(stdout,"gbutils settings.\n"); fprintf(stdout," \n"); fprintf(stdout,"Information about the floating point environment includes:\n"); fprintf(stdout," if the environment is the default environment of the machine;\n"); fprintf(stdout," if the precision is single, double or extended;\n"); fprintf(stdout," if the rounding is nearest, upward, downward or toward zero;\n"); fprintf(stdout," the list of exceptions whose interrupts are ignored (masked).\n"); fprintf(stdout,"Information about the locale includes: \n"); fprintf(stdout," decimal point;\n"); fprintf(stdout," thousand separator;\n"); fprintf(stdout," the relative position of the thousand separator (grouping).\n"); fprintf(stdout,"Information about gbutils environment include:\n"); fprintf(stdout," if the error handler is activated;\n"); fprintf(stdout," the value of the environment variables dictating the output format;\n"); fprintf(stdout," the output format of integers and floating point numbers.\n"); fprintf(stdout,"Options: \n"); fprintf(stdout," -h print this help message \n"); return(0); } } /* END OF COMMAND LINE PROCESSING */ /* ---- linux implementation and x86 processor ---- */ #if defined(linux) && ( defined(__x86_64__) || defined(__i386) ) unsigned short mode = 0 ; int rounding = fegetround (); printf(" floating point environment ------------------------\n"); #ifdef _FPU_GETCW _FPU_GETCW(mode); #define PREC_MASK _FPU_EXTENDED printf(" The environment is "); if( mode==_FPU_DEFAULT ) printf("the default\n"); else printf("not the default\n"); /* precision */ printf(" Precision is "); if ( (mode & PREC_MASK) == _FPU_EXTENDED) printf("extended"); else if ( (mode & PREC_MASK) == _FPU_DOUBLE) printf("double"); else if ( (mode & PREC_MASK) == _FPU_SINGLE) printf("single"); printf("\n"); #endif /* rounding */ printf(" Rounding is "); switch(rounding) { case FE_TONEAREST : printf("nearest\n"); break; case FE_UPWARD : printf("upward\n"); break; case FE_DOWNWARD : printf("downward\n"); break; case FE_TOWARDZERO : printf("toward zero\n"); break; } /* exception */ printf(" Masked exceptions (no interrupt):\n"); #ifdef FE_INVALID if(!fetestexcept(FE_INVALID)) printf(" Invalid operation\n"); #endif #ifdef FE_DIVBYZERO if(!fetestexcept(FE_DIVBYZERO)) printf(" Division by zero\n"); #endif #ifdef FE_UNDERFLOW if(!fetestexcept(FE_UNDERFLOW)) printf(" Underflow\n"); #endif #ifdef FE_OVERFLOW if(!fetestexcept(FE_OVERFLOW)) printf(" Overflow\n"); #endif #ifdef FE_INEXACT if(!fetestexcept(FE_INEXACT)) printf(" Precision\n"); #endif printf("\n"); #endif /* ------------------------------ */ printf(" locale definitions --------------------------------\n"); { if (setlocale(LC_NUMERIC,"") == NULL) fprintf(stderr,"WARNING:Cannot set LC_NUMERIC to default locale\n"); printf(" decimal point \"%s\"\n",nl_langinfo(RADIXCHAR)); printf(" thousands separator \"%s\"\n",nl_langinfo(THOUSEP)); printf(" grouping "); {/* code snippet stolen from locale.c in glibc */ struct lconv *locale = localeconv(); const char *val = locale->grouping; int cnt = val ? strlen (val) : 0; while (cnt > 1){ printf ("%d;", *val == '\177' ? -1 : *val); --cnt; ++val; } printf ("%d\n", cnt == 0 || *val == '\177' ? -1 : *val); } } printf("\n"); #if defined HAVE_LIBGSL printf(" gsl environment -----------------------------------\n"); printf(" GSL error handler is "); if(getenv("GB_ERROR_HANDLER_OFF")) printf(" off\n"); else printf(" on\n"); printf("\n"); #endif printf(" gbutils environment -------------------------------\n"); printf(" GB_OUT_FLOAT_FORMAT is "); if(FLOAT==NULL && getenv("GB_OUT_FLOAT_FORMAT")) printf("%s\n",getenv("GB_OUT_FLOAT_FORMAT")); else printf("not set\n"); printf(" GB_OUT_EMPTY_FORMAT is "); if(FLOAT==NULL && getenv("GB_OUT_EMPTY_FORMAT")) printf("%s\n",getenv("GB_OUT_EMPTY_FORMAT")); else printf("not set\n"); printf(" GB_OUT_SEP is "); if(FLOAT==NULL && getenv("GB_OUT_SEP")) printf("%s\n",getenv("GB_OUT_SEP")); else printf("not set\n"); printf(" GB_OUT_NL is "); if(FLOAT==NULL && getenv("GB_OUT_NL")) printf("%s\n",getenv("GB_OUT_NL")); else printf("not set\n"); printf("\n"); printf(" gbutils settings ----------------------------------\n"); initialize_program(argv[0]); printf(" floating point output \"%s\"\n",FLOAT); printf(" integer output \"%s\"\n",INT); printf(" empty field \"%s\"\n",EMPTY); printf(" separation string \"%s\"\n",SEP); return 0; } gbutils-5.7.1/gbbin.c0000644000175000017500000003143713246754720011365 00000000000000/* gbbin (ver. 5.6) -- A program to bin data Copyright (C) 1998-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ double **data=NULL; /* array of values */ size_t rows=0,columns=0; size_t steps=10; size_t binum; int numpres; char *splitstring = strdup(" \t"); double wmin,wmax; /* otpions */ char * const output_opts[] = {"xmean","xstd","xmin","xmax","xmedian","ymean","yadev","ystd","yvar","yskew","ykurt","ymin","ymax","ymedian","num",NULL}; size_t outnum; size_t *outtype; int o_verbose=0; int o_printbinned = 0; int o_window=0; /* if the windows to bin is provided */ /* variable to handle data splitting */ size_t printnum = 0; size_t *printcolumn=NULL; /* variables for reading command line options */ int opt; /* set default output */ outnum=2; outtype = (size_t *) my_alloc(2*sizeof(size_t)); outtype[0]=0; outtype[1]=5; /* COMMAND LINE PROCESSING */ /* read the command line */ while((opt=getopt_long(argc,argv,"vn:hF:O:xyc:w:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='n'){ /*set the number of steps*/ steps = (size_t) atoi(optarg); } else if(opt=='v'){ /*increase verbosity*/ o_verbose = 1; } else if(opt=='c'){ /* set the columns whose elements are required */ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; o_printbinned = 1; while( (stmp2=strsep (&stmp1,",")) != NULL && strlen(stmp2)>0 ){ printnum++; printcolumn = (size_t *) my_realloc((void *) printcolumn,printnum*sizeof(size_t)); printcolumn[printnum-1]= (size_t) atoi(stmp2); } free(stmp3); } else if(opt=='x'){ /* print binned x values */ o_printbinned = 1; printnum++; printcolumn = (size_t *) my_realloc((void *) printcolumn,printnum*sizeof(size_t)); printcolumn[printnum-1]=1; } else if(opt=='y'){ /* print binned y values */ o_printbinned = 1; printnum++; printcolumn = (size_t *) my_realloc((void *) printcolumn,printnum*sizeof(size_t)); printcolumn[printnum-1]=2; } else if(opt=='O'){ /* set the output type */ char *subopts,*subvalue=NULL; /* clear default settings */ outnum=0; free(outtype); outtype=NULL; /* define new output */ subopts = optarg; while (*subopts != '\0'){ int itmp1 = getsubopt(&subopts, output_opts, &subvalue); outnum++; outtype = (size_t *) my_realloc((void *)outtype,outnum*sizeof(size_t)); if(itmp1 == -1){/* Unknown suboption. */ fprintf (stderr,"ERROR (%s): Unknown output type `%s'\n", GB_PROGNAME,subvalue); exit(1); } else outtype[outnum-1]=(size_t) itmp1; } } else if(opt=='w'){ /*set the binning window */ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; stmp2=strsep (&stmp1,","); if(strlen(stmp2)>0 && stmp1 != NULL && strlen(stmp1)>0){ wmin=atof(stmp2); wmax=atof(stmp1); } else fprintf(stderr,"ERROR (%s): provide comma separated min and max. Try option -h.\n",GB_PROGNAME); free(stmp3); o_window=1; } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Compute binned statistics. Data are read from standard input as records \n"); fprintf(stdout,"(X,Y1,Y2,...). Equipopulated bins are built with respect to the first field\n"); fprintf(stdout,"X. Option -O decide which statistics are printed for each bin. With options\n"); fprintf(stdout,"-x, -y or -c elements in different bins are split in different columns. \n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n set the number of equipopulated bins (default 10) \n"); fprintf(stdout," -w min,max set manually the binning window. Ignored with -x,-y and -c \n"); fprintf(stdout," -O set the output with a comma separated list of variables: xmean, xmin, \n"); fprintf(stdout," xmax, xstd, xmedian, ymean, yadev, ystd, yvar, yskew, ykurt, ymin, \n"); fprintf(stdout," ymax, ymedian, num (default xmean, ymean) \n"); fprintf(stdout," -c# the values in column # are split in different columns; # can be a comma\n"); fprintf(stdout," separated list of columns \n"); fprintf(stdout," -x equivalent to -c 1 \n"); fprintf(stdout," -y equivalent to -c 2 \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbbin -n 20 < file split the records (line) in 20 bins according to first \n"); fprintf(stdout," field. Print the average value of the bin entries for \n"); fprintf(stdout," each column. If 'file' has 3 columns, the output has \n"); fprintf(stdout," twenty rows and three columns.\n"); fprintf(stdout," gbbin -O ymedian < file bin the records with respect to the first value and\n"); fprintf(stdout," print the median value of the other columns in each bin\n"); fprintf(stdout," gbbin -c 3 < file the binned values of the third columns are printed on \n"); fprintf(stdout," separate columns. The output has ten columns.\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data: data[column][row] */ loadtable(&data,&rows,&columns,0,splitstring); if(columns==2) sort2(data[0],data[1],rows,2); else if(columns>2) sortn(data,columns,rows,0,2); else{ fprintf(stdout, "ERROR (%s): Provide at least two columns of input\n",GB_PROGNAME); exit(1); } /* compute the size of the bins */ binum=rows/steps; /* number of points per bin */ numpres=rows%steps; /* number of residual points */ /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ size_t i; fprintf(stdout,"#read rows %zd\n",rows); fprintf(stdout,"#read columns %zd\n",columns); fprintf(stdout,"#number of bins %zd\n",steps); fprintf(stdout,"#x support [%f,%f]\n",data[0][0],data[0][rows-1]); fprintf(stdout,"#Output: "); if(outnum==0){ fprintf(stdout,"%s %s\n",output_opts[0],output_opts[4]); } else{ for(i=0;i= wmin) break; } if(j>startindex) startindex=j; } /* chose the limit index of the observations in the windows */ actualbinum=0; for(j=startindex;jcolumns){ fprintf(stdout, "ERROR (%s): Col spec %zd larger than number of col\n", GB_PROGNAME,printcolumn[h]); exit(1); } for(j=0;j1) for(h=0;h .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbkreg2d.10000644000175000017500000000407713246755335011714 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBKREG2D "1" "March 2018" "gbkreg2d 5.7.1" "User Commands" .SH NAME gbkreg2d \- Kernel non linear regression for bivariate data .SH SYNOPSIS .B gbkreg2d [\fI\,options\/\fR] .SH DESCRIPTION 2D kernel estimation of conditional moments. Data are read from standard input as triplet (x,y,z). The moments of z are computed on a regular grid in x and y The kernel bandwidth if not provided with the option \fB\-H\fR is set automatically. Different bandwidths can be specified for x and y. Different moments are given in different data block. Options \fB\-n\fR, \fB\-f\fR, \fB\-H\fR and \fB\-S\fR accept comma separated values to specify different values for x and y components .SH OPTIONS .TP \fB\-n\fR number of points where the estimation is computed (default 10) .TP \fB\-f\fR fraction of the support for which to print the result (default .9) .TP \fB\-H\fR set the kernel bandwidth explicitly .TP \fB\-S\fR scale the kernel bandwidth with respect to heuristic 'optimal' .TP \fB\-K\fR choose the kernel: 0 Epanenchnikov, 1 Rectangular, 2 Silverman type I 3 Silverman type II (default 0) .TP \fB\-O\fR set the output, comma separated list of m mean, v standard deviation, s skewness and k kurtosis (default m) .TP \fB\-D\fR switch off data de\-variation procedure .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbconvtable.10000644000175000017500000000366613147510354012504 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBCONVTABLE "1" "August 2017" "gbconvtable ver. 5.6" "User Commands" .SH NAME gbconvtable \- Replace keys with values .SH SYNOPSIS .B gbconvtable [\fI\,options\/\fR] .SH DESCRIPTION Replace keys with values at a given position of the input file. The name of the dictionary file is provided on the command line with the option 'dictfile'. It is a simple text file organized in two columns. The first column contains the keys and the second column the respective values. Obviously the values can be equal, but the keys should be all different. Data are read from standard input and all fields at position 'pos' are considered keys of the provided dictionary and replaced with the associated keys. If 'pos' is not specified it is assumed equal to 1. If 'pos' is larger than the number of fields, no replacement takes place. If the option 'force' is not set, only those fields which appears as keys in the dictionary file are replaced. .SH OPTIONS .HP \fB\-d\fR name of the dictionary file .HP \fB\-c\fR position of the column of keys to be replaced .HP \fB\-f\fR forced look\-up: substitute provided string for non defined keys .SH EXAMPLES .IP gbconvtable \-d dict_file \-c 3 \-f 'none' < input_file .PP This program requires awk or gawk. .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2010\-2015 Giulio Bottazzi .PP This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation. .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnlqreg.10000644000175000017500000000401613246755335012017 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBNLQREG "1" "March 2018" "gbnlqreg 5.7.1" "User Commands" .SH NAME gbnlqreg \- Non linear quantile regression .SH SYNOPSIS .B gbnlqreg [\fI\,options\/\fR] \fI\,\/\fR .SH DESCRIPTION Non linear quantile regression. Read data in columns (X_1 .. X_N). The model is specified by a function f(x1,x2...) using variables names x1,x2,..,xN for the first, second .. N\-th column of data. f(x1,x2,...) are assumed i.i.d. .SH OPTIONS .TP \fB\-O\fR type of output (default 0) .TP 0 parameters .TP 1 parameters and errors .TP 2 and residuals .TP 3 parameters and variance matrix .TP \fB\-V\fR variance matrix estimation (default 0) .IP 0 1 < J^{\-1} > 2 < H^{\-1} > 3 < H^{\-1} J H^{\-1} > .TP \fB\-q\fR set the quantile (default .5) .TP \fB\-M\fR choose optimization method (default 0) .TP 0 Nelder\-Mead Simplex o(N) .TP 1 Nelder\-Mead Simplex o(N^2) .TP \fB\-s\fR initial simplex scaling (default 1) .TP \fB\-e\fR minimization tolerance (default 1e\-5) .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-h\fR this help .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps .TP 5 model definition .TP \fB\-N\fR max minimization steps (default 500) .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbinterp.c0000644000175000017500000002105213246754720012106 00000000000000/* gbinterp (ver. 5.6) -- Compute equispaced curve from interpolated data or interpolated values from equispaced data Copyright (C) 2007-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include int main(int argc,char* argv[]){ double **data=NULL; /* array of values */ size_t size=0; /* length of array vals */ char *splitstring = strdup(" \t"); gsl_interp_accel *accelerator; gsl_interp * interpolator; size_t n=10; /* number of points */ double rmin=0,rmax=0; /* boundaries of the range */ double step; /* distance between the evaluations*/ double *x,*y; /* array of original x and y vals*/ double *z=NULL; /* list of point bwhere the interpolation is printed */ size_t i; int o_verbose=0; int o_method=0; int o_setrange=0; int o_setnum=0; int o_output=0; int o_outrange=0; int o_fromfile=0; char *infilename = strdup(""); int infile; /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"vn:hr:M:O:F:I:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='I'){ /*set the filename from which x-values are red*/ o_fromfile=1; free(infilename); infilename = strdup(optarg); } else if(opt=='n'){ /*set the number of points*/ n = atoi(optarg); o_setnum=1; } else if(opt=='M'){ /*set the method to use*/ o_method = atoi(optarg); } else if(opt=='O'){ /*set the type of output */ o_output = atoi(optarg); } else if(opt=='r'){ /*set the range */ if(!strchr (optarg,',')){ rmin=rmax=atof(optarg); } else{ char *stmp1=strdup (optarg); rmin = atof(strtok (stmp1,",")); rmax = atof(strtok (NULL,",")); free(stmp1); } o_setrange=1; } else if(opt=='v'){ /*increase verbosity*/ o_verbose = 1; } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Compute the interpolating function of user provided data on a regular mesh.\n"); fprintf(stdout,"Data are read from standard input as couples (X,Y). The interpolation (x,\n"); fprintf(stdout,"f(x)) is printed for x in [rmin,rmax]. When rmin=rmax print a single value.\n"); fprintf(stdout,"With option -I the points where the interpolation is computed are read from\n"); fprintf(stdout,"a file.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of equispaced points (default 10)\n"); fprintf(stdout," -r set the range 'rmin,rmax' (default full x range)\n"); fprintf(stdout," -I read interpolation points from a file \n"); fprintf(stdout," -M the interpolation method: (default 0)\n"); fprintf(stdout," 0 linear (requires at least 2 points)\n"); fprintf(stdout," 1 cspline (requires at least 3 points)\n"); fprintf(stdout," 2 Akima spline (requires at least 5 points)\n"); fprintf(stdout," -O the type of output: (default 0)\n"); fprintf(stdout," 0 interpolation \n"); fprintf(stdout," 1 first derivative \n"); fprintf(stdout," 2 second derivative \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -h print this help\n"); fprintf(stdout," -v verbose mode\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ load2(&data,&size,0,splitstring); /* sort with respect to first values */ sort2(data[0],data[1],size,2); /* set handy pointers */ x=data[0]; y=data[1]; /* set the grid where the function is computed */ if(o_fromfile == 0){ if(o_setrange){ if(rminx[size-1]) rmax=x[size-1]; } else{ rmin=x[0]; rmax=x[size-1]; } if(!o_setnum && rmin==rmax) n=1; if(n>1){ step=(rmax-rmin)/(n-1.); } else{ step=0; rmax=rmin = (rmax+rmin)/2; } /* define the list of output points */ z= (double *) my_alloc(sizeof(double)*n); for(i=0;i 0){ fprintf(stdout,"#read couples %zd\n",size); fprintf(stdout,"#x support [%f,%f]\n",x[0],x[size-1]); fprintf(stdout,"#grid size %zd\n",n); fprintf(stdout,"#grid range [%f,%f]\n",rmin,rmax); fprintf(stdout,"#interpolation type %s\n",gsl_interp_name (interpolator)); fprintf(stdout,"#interpolation output "); switch(o_output){ case 0: fprintf(stdout,"value\n"); break; case 1: fprintf(stdout,"1st derivative\n"); break; case 2: fprintf(stdout,"2nd derivative\n"); break; } } /* ++++++++++++++++++++++++++++ */ /* initialize the interpolator */ gsl_interp_init(interpolator,x,y,size); /* allocate the lookup accelerator */ accelerator=gsl_interp_accel_alloc(); /* perform evaluation */ switch(o_output){ case 0: for(i=0;i x[size-1]) { o_outrange=1; fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,y[size-1]); } else { fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,gsl_interp_eval (interpolator,x,y,z[i],accelerator)); } break; case 1: for(i=0;i x[size-1]) { o_outrange=1; fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,0); } else { fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,gsl_interp_eval_deriv (interpolator,x,y,z[i],accelerator)); } break; case 2: for(i=0;i x[size-1]) { o_outrange=1; fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,0); } else { fprintf(stdout,FLOAT_SEP,z[i]); fprintf(stdout,FLOAT_NL,gsl_interp_eval_deriv2 (interpolator,x,y,z[i],accelerator)); } break; default: fprintf(stderr,"ERROR (%s): unknown output type: %d; use %s -h\n", GB_PROGNAME,o_output,argv[0]); exit(-1); } if(o_outrange==1) fprintf(stderr,"WARNING (%s): some points are outside the input range\n", GB_PROGNAME); gsl_interp_accel_free (accelerator); gsl_interp_free (interpolator); free(x); free(y); free(z); return(0); } gbutils-5.7.1/gbfilternear.c0000644000175000017500000001016013246754720012736 00000000000000/* gbfilternear (ver. 5.6) -- Filter too near data point in Euclidean metric Copyright (C) 2008-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ /* data storage variables */ size_t rows=0,columns=0; double **vals=NULL; /* minimum distance */ double mindist = 1; char *deleted; /* options */ int o_verbose=0; char *splitstring = strdup(" \t"); /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"vhF:d:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Filter out too near points. Each row represents Cartesian coordinates\n"); fprintf(stdout,"of a point in an Euclidean space, whose dimension is set by the number\n"); fprintf(stdout,"of columns. Rows are removed which are nearer than a minimal distance\n"); fprintf(stdout,"set with the option '-d'. The order is relevant as first entries are\n"); fprintf(stdout,"the last to be removed.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -d minimal allowed distance (default 1)\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); fprintf(stdout," -v verbose mode\n"); return(0); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='d'){ /*set the number of quantiles*/ mindist=atof(optarg); } else if(opt=='v'){ /*set the verbose mode*/ o_verbose=1; } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* read data vals[col][row] */ loadtable(&vals,&rows,&columns,0,splitstring); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose){ fprintf(stderr,"loaded points: %zd\n",rows); fprintf(stderr,"dimension of space: %zd\n",columns); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* allocate space for array of deleted entries */ deleted = (char *) my_calloc(rows,sizeof(char)); /* compute deleted entries */ { size_t i,j,h; double distance; for(i=0;i. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that # program. This Exception is an additional permission under section 7 # of the GNU General Public License, version 3 ("GPLv3"). # Please send patches to . # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # You can get the latest version of this script from: # http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS $0 [OPTION] ALIAS Canonicalize a configuration name. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.sub ($timestamp) Copyright 1992-2015 Free Software Foundation, Inc. This is free software; see the source for copying conditions. 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-sco5) os=-sco3.2v5 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco4) os=-sco3.2v4 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2.[4-9]*) os=`echo $os | sed -e 's/sco3.2./sco3.2v/'` basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2v[4-9]*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco5v6*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco*) os=-sco3.2v2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -udk*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -isc) os=-isc2.2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -clix*) basic_machine=clipper-intergraph ;; -isc*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -lynx*178) os=-lynxos178 ;; -lynx*5) os=-lynxos5 ;; -lynx*) os=-lynxos ;; -ptx*) basic_machine=`echo $1 | sed -e 's/86-.*/86-sequent/'` ;; -windowsnt*) os=`echo $os | sed -e 's/windowsnt/winnt/'` ;; -psos*) os=-psos ;; -mint | -mint[0-9]*) basic_machine=m68k-atari os=-mint ;; esac # Decode aliases for certain CPU-COMPANY combinations. case $basic_machine in # Recognize the basic CPU types without company name. # Some are omitted here because they have special meanings below. 1750a | 580 \ | a29k \ | aarch64 | aarch64_be \ | alpha | alphaev[4-8] | alphaev56 | alphaev6[78] | alphapca5[67] \ | alpha64 | alpha64ev[4-8] | alpha64ev56 | alpha64ev6[78] | alpha64pca5[67] \ | am33_2.0 \ | arc | arceb \ | arm | arm[bl]e | arme[lb] | armv[2-8] | armv[3-8][lb] | armv7[arm] \ | avr | avr32 \ | ba \ | be32 | be64 \ | bfin \ | c4x | c8051 | clipper \ | d10v | d30v | dlx | dsp16xx \ | e2k | epiphany \ | fido | fr30 | frv | ft32 \ | h8300 | h8500 | hppa | hppa1.[01] | hppa2.0 | hppa2.0[nw] | hppa64 \ | hexagon \ | i370 | i860 | i960 | ia64 \ | ip2k | iq2000 \ | k1om \ | le32 | le64 \ | lm32 \ | m32c | m32r | m32rle | m68000 | m68k | m88k \ | maxq | mb | microblaze | microblazeel | mcore | mep | metag \ | mips | mipsbe | mipseb | mipsel | mipsle \ | mips16 \ | mips64 | mips64el \ | mips64octeon | mips64octeonel \ | mips64orion | mips64orionel \ | mips64r5900 | mips64r5900el \ | mips64vr | mips64vrel \ | mips64vr4100 | mips64vr4100el \ | mips64vr4300 | mips64vr4300el \ | mips64vr5000 | mips64vr5000el \ | mips64vr5900 | mips64vr5900el \ | mipsisa32 | mipsisa32el \ | mipsisa32r2 | mipsisa32r2el \ | mipsisa32r6 | mipsisa32r6el \ | mipsisa64 | mipsisa64el \ | mipsisa64r2 | mipsisa64r2el \ | mipsisa64r6 | mipsisa64r6el \ | mipsisa64sb1 | mipsisa64sb1el \ | mipsisa64sr71k | mipsisa64sr71kel \ | mipsr5900 | mipsr5900el \ | mipstx39 | mipstx39el \ | mn10200 | mn10300 \ | moxie \ | mt \ | msp430 \ | nds32 | nds32le | nds32be \ | nios | nios2 | nios2eb | nios2el \ | ns16k | ns32k \ | open8 | or1k | or1knd | or32 \ | pdp10 | pdp11 | pj | pjl \ | powerpc | powerpc64 | powerpc64le | powerpcle \ | pyramid \ | riscv32 | riscv64 \ | rl78 | rx \ | score \ | sh | sh[1234] | sh[24]a | sh[24]aeb | sh[23]e | sh[234]eb | sheb | shbe | shle | sh[1234]le | sh3ele \ | sh64 | sh64le \ | sparc | sparc64 | sparc64b | sparc64v | sparc86x | sparclet | sparclite \ | sparcv8 | sparcv9 | sparcv9b | sparcv9v \ | spu \ | tahoe | tic4x | tic54x | tic55x | tic6x | tic80 | tron \ | ubicom32 \ | v850 | v850e | v850e1 | v850e2 | v850es | v850e2v3 \ | visium \ | we32k \ | x86 | xc16x | xstormy16 | xtensa \ | z8k | z80) basic_machine=$basic_machine-unknown ;; c54x) basic_machine=tic54x-unknown ;; c55x) basic_machine=tic55x-unknown ;; c6x) basic_machine=tic6x-unknown ;; leon|leon[3-9]) basic_machine=sparc-$basic_machine ;; m6811 | m68hc11 | m6812 | m68hc12 | m68hcs12x | nvptx | picochip) basic_machine=$basic_machine-unknown os=-none ;; m88110 | m680[12346]0 | m683?2 | m68360 | m5200 | v70 | w65 | z8k) ;; ms1) basic_machine=mt-unknown ;; strongarm | thumb | xscale) basic_machine=arm-unknown ;; xgate) basic_machine=$basic_machine-unknown os=-none ;; xscaleeb) basic_machine=armeb-unknown ;; xscaleel) basic_machine=armel-unknown ;; # We use `pc' rather than `unknown' # because (1) that's what they normally are, and # (2) the word "unknown" tends to confuse beginning users. i*86 | x86_64) basic_machine=$basic_machine-pc ;; # Object if more than one company name word. *-*-*) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; # Recognize the basic CPU types with company name. 580-* \ | a29k-* \ | aarch64-* | aarch64_be-* \ | alpha-* | alphaev[4-8]-* | alphaev56-* | alphaev6[78]-* \ | alpha64-* | alpha64ev[4-8]-* | alpha64ev56-* | alpha64ev6[78]-* \ | alphapca5[67]-* | alpha64pca5[67]-* | arc-* | arceb-* \ | arm-* | armbe-* | armle-* | armeb-* | armv*-* \ | avr-* | avr32-* \ | ba-* \ | be32-* | be64-* \ | bfin-* | bs2000-* \ | c[123]* | c30-* | [cjt]90-* | c4x-* \ | c8051-* | clipper-* | craynv-* | cydra-* \ | d10v-* | d30v-* | dlx-* \ | e2k-* | elxsi-* \ | f30[01]-* | f700-* | fido-* | fr30-* | frv-* | fx80-* \ | h8300-* | h8500-* \ | hppa-* | hppa1.[01]-* | hppa2.0-* | hppa2.0[nw]-* | hppa64-* \ | hexagon-* \ | i*86-* | i860-* | i960-* | ia64-* \ | ip2k-* | iq2000-* \ | k1om-* \ | le32-* | le64-* \ | lm32-* \ | m32c-* | m32r-* | m32rle-* \ | m68000-* | m680[012346]0-* | m68360-* | m683?2-* | m68k-* \ | m88110-* | m88k-* | maxq-* | mcore-* | metag-* \ | microblaze-* | microblazeel-* \ | mips-* | mipsbe-* | mipseb-* | mipsel-* | mipsle-* \ | mips16-* \ | mips64-* | mips64el-* \ | mips64octeon-* | mips64octeonel-* \ | mips64orion-* | mips64orionel-* \ | mips64r5900-* | mips64r5900el-* \ | mips64vr-* | mips64vrel-* \ | mips64vr4100-* | mips64vr4100el-* \ | mips64vr4300-* | mips64vr4300el-* \ | mips64vr5000-* | mips64vr5000el-* \ | mips64vr5900-* | mips64vr5900el-* \ | mipsisa32-* | mipsisa32el-* \ | mipsisa32r2-* | mipsisa32r2el-* \ | mipsisa32r6-* | mipsisa32r6el-* \ | mipsisa64-* | mipsisa64el-* \ | mipsisa64r2-* | mipsisa64r2el-* \ | mipsisa64r6-* | mipsisa64r6el-* \ | mipsisa64sb1-* | mipsisa64sb1el-* \ | mipsisa64sr71k-* | mipsisa64sr71kel-* \ | mipsr5900-* | mipsr5900el-* \ | mipstx39-* | mipstx39el-* \ | mmix-* \ | mt-* \ | msp430-* \ | nds32-* | nds32le-* | nds32be-* \ | nios-* | nios2-* | nios2eb-* | nios2el-* \ | none-* | np1-* | ns16k-* | ns32k-* \ | open8-* \ | or1k*-* \ | orion-* \ | pdp10-* | pdp11-* | pj-* | pjl-* | pn-* | power-* \ | powerpc-* | powerpc64-* | powerpc64le-* | powerpcle-* \ | pyramid-* \ | riscv32-* | riscv64-* \ | rl78-* | romp-* | rs6000-* | rx-* \ | sh-* | sh[1234]-* | sh[24]a-* | sh[24]aeb-* | sh[23]e-* | sh[34]eb-* | sheb-* | shbe-* \ | shle-* | sh[1234]le-* | sh3ele-* | sh64-* | sh64le-* \ | sparc-* | sparc64-* | sparc64b-* | sparc64v-* | sparc86x-* | sparclet-* \ | sparclite-* \ | sparcv8-* | sparcv9-* | sparcv9b-* | sparcv9v-* | sv1-* | sx*-* \ | tahoe-* \ | tic30-* | tic4x-* | tic54x-* | tic55x-* | tic6x-* | tic80-* \ | tile*-* \ | tron-* \ | ubicom32-* \ | v850-* | v850e-* | v850e1-* | v850es-* | v850e2-* | v850e2v3-* \ | vax-* \ | visium-* \ | we32k-* \ | x86-* | x86_64-* | xc16x-* | xps100-* \ | xstormy16-* | xtensa*-* \ | ymp-* \ | z8k-* | z80-*) ;; # Recognize the basic CPU types without company name, with glob match. xtensa*) basic_machine=$basic_machine-unknown ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 386bsd) basic_machine=i386-unknown os=-bsd ;; 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) basic_machine=m68000-att ;; 3b*) basic_machine=we32k-att ;; a29khif) basic_machine=a29k-amd os=-udi ;; abacus) basic_machine=abacus-unknown ;; adobe68k) basic_machine=m68010-adobe os=-scout ;; alliant | fx80) basic_machine=fx80-alliant ;; altos | altos3068) basic_machine=m68k-altos ;; am29k) basic_machine=a29k-none os=-bsd ;; amd64) basic_machine=x86_64-pc ;; amd64-*) basic_machine=x86_64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; amdahl) basic_machine=580-amdahl os=-sysv ;; amiga | amiga-*) basic_machine=m68k-unknown ;; amigaos | amigados) basic_machine=m68k-unknown os=-amigaos ;; amigaunix | amix) basic_machine=m68k-unknown os=-sysv4 ;; apollo68) basic_machine=m68k-apollo os=-sysv ;; apollo68bsd) basic_machine=m68k-apollo os=-bsd ;; aros) basic_machine=i386-pc os=-aros ;; asmjs) basic_machine=asmjs-unknown ;; aux) basic_machine=m68k-apple os=-aux ;; balance) basic_machine=ns32k-sequent os=-dynix ;; blackfin) basic_machine=bfin-unknown os=-linux ;; blackfin-*) basic_machine=bfin-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; bluegene*) basic_machine=powerpc-ibm os=-cnk ;; c54x-*) basic_machine=tic54x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c55x-*) basic_machine=tic55x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c6x-*) basic_machine=tic6x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c90) basic_machine=c90-cray os=-unicos ;; cegcc) basic_machine=arm-unknown os=-cegcc ;; convex-c1) basic_machine=c1-convex os=-bsd ;; convex-c2) basic_machine=c2-convex os=-bsd ;; convex-c32) basic_machine=c32-convex os=-bsd ;; convex-c34) basic_machine=c34-convex os=-bsd ;; convex-c38) basic_machine=c38-convex os=-bsd ;; cray | j90) basic_machine=j90-cray os=-unicos ;; craynv) basic_machine=craynv-cray os=-unicosmp ;; cr16 | cr16-*) basic_machine=cr16-unknown os=-elf ;; crds | unos) basic_machine=m68k-crds ;; crisv32 | crisv32-* | etraxfs*) basic_machine=crisv32-axis ;; cris | cris-* | etrax*) basic_machine=cris-axis ;; crx) basic_machine=crx-unknown os=-elf ;; da30 | da30-*) basic_machine=m68k-da30 ;; decstation | decstation-3100 | pmax | pmax-* | pmin | dec3100 | decstatn) basic_machine=mips-dec ;; decsystem10* | dec10*) basic_machine=pdp10-dec os=-tops10 ;; decsystem20* | dec20*) basic_machine=pdp10-dec os=-tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) basic_machine=m68k-motorola ;; delta88) basic_machine=m88k-motorola os=-sysv3 ;; dicos) basic_machine=i686-pc os=-dicos ;; djgpp) basic_machine=i586-pc os=-msdosdjgpp ;; dpx20 | dpx20-*) basic_machine=rs6000-bull os=-bosx ;; dpx2* | dpx2*-bull) basic_machine=m68k-bull os=-sysv3 ;; ebmon29k) basic_machine=a29k-amd os=-ebmon ;; elxsi) basic_machine=elxsi-elxsi os=-bsd ;; encore | umax | mmax) basic_machine=ns32k-encore ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson os=-ose ;; fx2800) basic_machine=i860-alliant ;; genix) basic_machine=ns32k-ns ;; gmicro) basic_machine=tron-gmicro os=-sysv ;; go32) basic_machine=i386-pc os=-go32 ;; h3050r* | hiux*) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; h8300hms) basic_machine=h8300-hitachi os=-hms ;; h8300xray) basic_machine=h8300-hitachi os=-xray ;; h8500hms) basic_machine=h8500-hitachi os=-hms ;; harris) basic_machine=m88k-harris os=-sysv3 ;; hp300-*) basic_machine=m68k-hp ;; hp300bsd) basic_machine=m68k-hp os=-bsd ;; hp300hpux) basic_machine=m68k-hp os=-hpux ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) basic_machine=m68000-hp ;; hp9k3[2-9][0-9]) basic_machine=m68k-hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) basic_machine=hppa1.1-hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) basic_machine=hppa1.1-hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) basic_machine=hppa1.0-hp ;; hppa-next) os=-nextstep3 ;; hppaosf) basic_machine=hppa1.1-hp os=-osf ;; hppro) basic_machine=hppa1.1-hp os=-proelf ;; i370-ibm* | ibm*) basic_machine=i370-ibm ;; i*86v32) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv32 ;; i*86v4*) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv4 ;; i*86v) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv ;; i*86sol2) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-solaris2 ;; i386mach) basic_machine=i386-mach os=-mach ;; i386-vsta | vsta) basic_machine=i386-unknown os=-vsta ;; iris | iris4d) basic_machine=mips-sgi case $os in -irix*) ;; *) os=-irix4 ;; esac ;; isi68 | isi) basic_machine=m68k-isi os=-sysv ;; leon-*|leon[3-9]-*) basic_machine=sparc-`echo $basic_machine | sed 's/-.*//'` ;; m68knommu) basic_machine=m68k-unknown os=-linux ;; m68knommu-*) basic_machine=m68k-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; m88k-omron*) basic_machine=m88k-omron ;; magnum | m3230) basic_machine=mips-mips os=-sysv ;; merlin) basic_machine=ns32k-utek os=-sysv ;; microblaze*) basic_machine=microblaze-xilinx ;; mingw64) basic_machine=x86_64-pc os=-mingw64 ;; mingw32) basic_machine=i686-pc os=-mingw32 ;; mingw32ce) basic_machine=arm-unknown os=-mingw32ce ;; miniframe) basic_machine=m68000-convergent ;; *mint | -mint[0-9]* | *MiNT | *MiNT[0-9]*) basic_machine=m68k-atari os=-mint ;; mips3*-*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'` ;; mips3*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'`-unknown ;; monitor) basic_machine=m68k-rom68k os=-coff ;; morphos) basic_machine=powerpc-unknown os=-morphos ;; moxiebox) basic_machine=moxie-unknown os=-moxiebox ;; msdos) basic_machine=i386-pc os=-msdos ;; ms1-*) basic_machine=`echo $basic_machine | sed -e 's/ms1-/mt-/'` ;; msys) basic_machine=i686-pc os=-msys ;; mvs) basic_machine=i370-ibm os=-mvs ;; nacl) basic_machine=le32-unknown os=-nacl ;; ncr3000) basic_machine=i486-ncr os=-sysv4 ;; netbsd386) basic_machine=i386-unknown os=-netbsd ;; netwinder) basic_machine=armv4l-rebel os=-linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony os=-newsos ;; news1000) basic_machine=m68030-sony os=-newsos ;; news-3600 | risc-news) basic_machine=mips-sony os=-newsos ;; necv70) basic_machine=v70-nec os=-sysv ;; next | m*-next ) basic_machine=m68k-next case $os in -nextstep* ) ;; -ns2*) os=-nextstep2 ;; *) os=-nextstep3 ;; esac ;; nh3000) basic_machine=m68k-harris os=-cxux ;; nh[45]000) basic_machine=m88k-harris os=-cxux ;; nindy960) basic_machine=i960-intel os=-nindy ;; mon960) basic_machine=i960-intel os=-mon960 ;; nonstopux) basic_machine=mips-compaq os=-nonstopux ;; np1) basic_machine=np1-gould ;; neo-tandem) basic_machine=neo-tandem ;; nse-tandem) basic_machine=nse-tandem ;; nsr-tandem) basic_machine=nsr-tandem ;; op50n-* | op60c-*) basic_machine=hppa1.1-oki os=-proelf ;; openrisc | openrisc-*) basic_machine=or32-unknown ;; os400) basic_machine=powerpc-ibm os=-os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson os=-ose ;; os68k) basic_machine=m68k-none os=-os68k ;; pa-hitachi) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; paragon) basic_machine=i860-intel os=-osf ;; parisc) basic_machine=hppa-unknown os=-linux ;; parisc-*) basic_machine=hppa-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; pbd) basic_machine=sparc-tti ;; pbb) basic_machine=m68k-tti ;; pc532 | pc532-*) basic_machine=ns32k-pc532 ;; pc98) basic_machine=i386-pc ;; pc98-*) basic_machine=i386-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium | p5 | k5 | k6 | nexgen | viac3) basic_machine=i586-pc ;; pentiumpro | p6 | 6x86 | athlon | athlon_*) basic_machine=i686-pc ;; pentiumii | pentium2 | pentiumiii | pentium3) basic_machine=i686-pc ;; pentium4) basic_machine=i786-pc ;; pentium-* | p5-* | k5-* | k6-* | nexgen-* | viac3-*) basic_machine=i586-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumpro-* | p6-* | 6x86-* | athlon-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumii-* | pentium2-* | pentiumiii-* | pentium3-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium4-*) basic_machine=i786-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pn) basic_machine=pn-gould ;; power) basic_machine=power-ibm ;; ppc | ppcbe) basic_machine=powerpc-unknown ;; ppc-* | ppcbe-*) basic_machine=powerpc-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppcle | powerpclittle | ppc-le | powerpc-little) basic_machine=powerpcle-unknown ;; ppcle-* | powerpclittle-*) basic_machine=powerpcle-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64) basic_machine=powerpc64-unknown ;; ppc64-*) basic_machine=powerpc64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64le | powerpc64little | ppc64-le | powerpc64-little) basic_machine=powerpc64le-unknown ;; ppc64le-* | powerpc64little-*) basic_machine=powerpc64le-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ps2) basic_machine=i386-ibm ;; pw32) basic_machine=i586-unknown os=-pw32 ;; rdos | rdos64) basic_machine=x86_64-pc os=-rdos ;; rdos32) basic_machine=i386-pc os=-rdos ;; rom68k) basic_machine=m68k-rom68k os=-coff ;; rm[46]00) basic_machine=mips-siemens ;; rtpc | rtpc-*) basic_machine=romp-ibm ;; s390 | s390-*) basic_machine=s390-ibm ;; s390x | s390x-*) basic_machine=s390x-ibm ;; sa29200) basic_machine=a29k-amd os=-udi ;; sb1) basic_machine=mipsisa64sb1-unknown ;; sb1el) basic_machine=mipsisa64sb1el-unknown ;; sde) basic_machine=mipsisa32-sde os=-elf ;; sei) basic_machine=mips-sei os=-seiux ;; sequent) basic_machine=i386-sequent ;; sh) basic_machine=sh-hitachi os=-hms ;; sh5el) basic_machine=sh5le-unknown ;; sh64) basic_machine=sh64-unknown ;; sparclite-wrs | simso-wrs) basic_machine=sparclite-wrs os=-vxworks ;; sps7) basic_machine=m68k-bull os=-sysv2 ;; spur) basic_machine=spur-unknown ;; st2000) basic_machine=m68k-tandem ;; stratus) basic_machine=i860-stratus os=-sysv4 ;; strongarm-* | thumb-*) basic_machine=arm-`echo $basic_machine | sed 's/^[^-]*-//'` ;; sun2) basic_machine=m68000-sun ;; sun2os3) basic_machine=m68000-sun os=-sunos3 ;; sun2os4) basic_machine=m68000-sun os=-sunos4 ;; sun3os3) basic_machine=m68k-sun os=-sunos3 ;; sun3os4) basic_machine=m68k-sun os=-sunos4 ;; sun4os3) basic_machine=sparc-sun os=-sunos3 ;; sun4os4) basic_machine=sparc-sun os=-sunos4 ;; sun4sol2) basic_machine=sparc-sun os=-solaris2 ;; sun3 | sun3-*) basic_machine=m68k-sun ;; sun4) basic_machine=sparc-sun ;; sun386 | sun386i | roadrunner) basic_machine=i386-sun ;; sv1) basic_machine=sv1-cray os=-unicos ;; symmetry) basic_machine=i386-sequent os=-dynix ;; t3e) basic_machine=alphaev5-cray os=-unicos ;; t90) basic_machine=t90-cray os=-unicos ;; tile*) basic_machine=$basic_machine-unknown os=-linux-gnu ;; tx39) basic_machine=mipstx39-unknown ;; tx39el) basic_machine=mipstx39el-unknown ;; toad1) basic_machine=pdp10-xkl os=-tops20 ;; tower | tower-32) basic_machine=m68k-ncr ;; tpf) basic_machine=s390x-ibm os=-tpf ;; udi29k) basic_machine=a29k-amd os=-udi ;; ultra3) basic_machine=a29k-nyu os=-sym1 ;; v810 | necv810) basic_machine=v810-nec os=-none ;; vaxv) basic_machine=vax-dec os=-sysv ;; vms) basic_machine=vax-dec os=-vms ;; vpp*|vx|vx-*) basic_machine=f301-fujitsu ;; vxworks960) basic_machine=i960-wrs os=-vxworks ;; vxworks68) basic_machine=m68k-wrs os=-vxworks ;; vxworks29k) basic_machine=a29k-wrs os=-vxworks ;; w65*) basic_machine=w65-wdc os=-none ;; w89k-*) basic_machine=hppa1.1-winbond os=-proelf ;; xbox) basic_machine=i686-pc os=-mingw32 ;; xps | xps100) basic_machine=xps100-honeywell ;; xscale-* | xscalee[bl]-*) basic_machine=`echo $basic_machine | sed 's/^xscale/arm/'` ;; ymp) basic_machine=ymp-cray os=-unicos ;; z8k-*-coff) basic_machine=z8k-unknown os=-sim ;; z80-*-coff) basic_machine=z80-unknown os=-sim ;; none) basic_machine=none-none os=-none ;; # Here we handle the default manufacturer of certain CPU types. It is in # some cases the only manufacturer, in others, it is the most popular. w89k) basic_machine=hppa1.1-winbond ;; op50n) basic_machine=hppa1.1-oki ;; op60c) basic_machine=hppa1.1-oki ;; romp) basic_machine=romp-ibm ;; mmix) basic_machine=mmix-knuth ;; rs6000) basic_machine=rs6000-ibm ;; vax) basic_machine=vax-dec ;; pdp10) # there are many clones, so DEC is not a safe bet basic_machine=pdp10-unknown ;; pdp11) basic_machine=pdp11-dec ;; we32k) basic_machine=we32k-att ;; sh[1234] | sh[24]a | sh[24]aeb | sh[34]eb | sh[1234]le | sh[23]ele) basic_machine=sh-unknown ;; sparc | sparcv8 | sparcv9 | sparcv9b | sparcv9v) basic_machine=sparc-sun ;; cydra) basic_machine=cydra-cydrome ;; orion) basic_machine=orion-highlevel ;; orion105) basic_machine=clipper-highlevel ;; mac | mpw | mac-mpw) basic_machine=m68k-apple ;; pmac | pmac-mpw) basic_machine=powerpc-apple ;; *-unknown) # Make sure to match an already-canonicalized machine name. ;; *) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; esac # Here we canonicalize certain aliases for manufacturers. case $basic_machine in *-digital*) basic_machine=`echo $basic_machine | sed 's/digital.*/dec/'` ;; *-commodore*) basic_machine=`echo $basic_machine | sed 's/commodore.*/cbm/'` ;; *) ;; esac # Decode manufacturer-specific aliases for certain operating systems. if [ x"$os" != x"" ] then case $os in # First match some system type aliases # that might get confused with valid system types. # -solaris* is a basic system type, with this one exception. -auroraux) os=-auroraux ;; -solaris1 | -solaris1.*) os=`echo $os | sed -e 's|solaris1|sunos4|'` ;; -solaris) os=-solaris2 ;; -svr4*) os=-sysv4 ;; -unixware*) os=-sysv4.2uw ;; -gnu/linux*) os=`echo $os | sed -e 's|gnu/linux|linux-gnu|'` ;; # First accept the basic system types. # The portable systems comes first. # Each alternative MUST END IN A *, to match a version number. # -sysv* is not here because it comes later, after sysvr4. -gnu* | -bsd* | -mach* | -minix* | -genix* | -ultrix* | -irix* \ | -*vms* | -sco* | -esix* | -isc* | -aix* | -cnk* | -sunos | -sunos[34]*\ | -hpux* | -unos* | -osf* | -luna* | -dgux* | -auroraux* | -solaris* \ | -sym* | -kopensolaris* | -plan9* \ | -amigaos* | -amigados* | -msdos* | -newsos* | -unicos* | -aof* \ | -aos* | -aros* | -cloudabi* | -sortix* \ | -nindy* | -vxsim* | -vxworks* | -ebmon* | -hms* | -mvs* \ | -clix* | -riscos* | -uniplus* | -iris* | -rtu* | -xenix* \ | -hiux* | -386bsd* | -knetbsd* | -mirbsd* | -netbsd* \ | -bitrig* | -openbsd* | -solidbsd* \ | -ekkobsd* | -kfreebsd* | -freebsd* | -riscix* | -lynxos* \ | -bosx* | -nextstep* | -cxux* | -aout* | -elf* | -oabi* \ | -ptx* | -coff* | -ecoff* | -winnt* | -domain* | -vsta* \ | -udi* | -eabi* | -lites* | -ieee* | -go32* | -aux* \ | -chorusos* | -chorusrdb* | -cegcc* \ | -cygwin* | -msys* | -pe* | -psos* | -moss* | -proelf* | -rtems* \ | -mingw32* | -mingw64* | -linux-gnu* | -linux-android* \ | -linux-newlib* | -linux-musl* | -linux-uclibc* \ | -uxpv* | -beos* | -mpeix* | -udk* | -moxiebox* \ | -interix* | -uwin* | -mks* | -rhapsody* | -darwin* | -opened* \ | -openstep* | -oskit* | -conix* | -pw32* | -nonstopux* \ | -storm-chaos* | -tops10* | -tenex* | -tops20* | -its* \ | -os2* | -vos* | -palmos* | -uclinux* | -nucleus* \ | -morphos* | -superux* | -rtmk* | -rtmk-nova* | -windiss* \ | -powermax* | -dnix* | -nx6 | -nx7 | -sei* | -dragonfly* \ | -skyos* | -haiku* | -rdos* | -toppers* | -drops* | -es* | -tirtos*) # Remember, each alternative MUST END IN *, to match a version number. ;; -qnx*) case $basic_machine in x86-* | i*86-*) ;; *) os=-nto$os ;; esac ;; -nto-qnx*) ;; -nto*) os=`echo $os | sed -e 's|nto|nto-qnx|'` ;; -sim | -es1800* | -hms* | -xray | -os68k* | -none* | -v88r* \ | -windows* | -osx | -abug | -netware* | -os9* | -beos* | -haiku* \ | -macos* | -mpw* | -magic* | -mmixware* | -mon960* | -lnews*) ;; -mac*) os=`echo $os | sed -e 's|mac|macos|'` ;; -linux-dietlibc) os=-linux-dietlibc ;; -linux*) os=`echo $os | sed -e 's|linux|linux-gnu|'` ;; -sunos5*) os=`echo $os | sed -e 's|sunos5|solaris2|'` ;; -sunos6*) os=`echo $os | sed -e 's|sunos6|solaris3|'` ;; -opened*) os=-openedition ;; -os400*) os=-os400 ;; -wince*) os=-wince ;; -osfrose*) os=-osfrose ;; -osf*) os=-osf ;; -utek*) os=-bsd ;; -dynix*) os=-bsd ;; -acis*) os=-aos ;; -atheos*) os=-atheos ;; -syllable*) os=-syllable ;; -386bsd) os=-bsd ;; -ctix* | -uts*) os=-sysv ;; -nova*) os=-rtmk-nova ;; -ns2 ) os=-nextstep2 ;; -nsk*) os=-nsk ;; # Preserve the version number of sinix5. -sinix5.*) os=`echo $os | sed -e 's|sinix|sysv|'` ;; -sinix*) os=-sysv4 ;; -tpf*) os=-tpf ;; -triton*) os=-sysv3 ;; -oss*) os=-sysv3 ;; -svr4) os=-sysv4 ;; -svr3) os=-sysv3 ;; -sysvr4) os=-sysv4 ;; # This must come after -sysvr4. -sysv*) ;; -ose*) os=-ose ;; -es1800*) os=-ose ;; -xenix) os=-xenix ;; -*mint | -mint[0-9]* | -*MiNT | -MiNT[0-9]*) os=-mint ;; -aros*) os=-aros ;; -zvmoe) os=-zvmoe ;; -dicos*) os=-dicos ;; -nacl*) ;; -none) ;; *) # Get rid of the `-' at the beginning of $os. os=`echo $os | sed 's/[^-]*-//'` echo Invalid configuration \`$1\': system \`$os\' not recognized 1>&2 exit 1 ;; esac else # Here we handle the default operating systems that come with various machines. # The value should be what the vendor currently ships out the door with their # machine or put another way, the most popular os provided with the machine. # Note that if you're going to try to match "-MANUFACTURER" here (say, # "-sun"), then you have to tell the case statement up towards the top # that MANUFACTURER isn't an operating system. Otherwise, code above # will signal an error saying that MANUFACTURER isn't an operating # system, and we'll never get to this point. case $basic_machine in score-*) os=-elf ;; spu-*) os=-elf ;; *-acorn) os=-riscix1.2 ;; arm*-rebel) os=-linux ;; arm*-semi) os=-aout ;; c4x-* | tic4x-*) os=-coff ;; c8051-*) os=-elf ;; hexagon-*) os=-elf ;; tic54x-*) os=-coff ;; tic55x-*) os=-coff ;; tic6x-*) os=-coff ;; # This must come before the *-dec entry. pdp10-*) os=-tops20 ;; pdp11-*) os=-none ;; *-dec | vax-*) os=-ultrix4.2 ;; m68*-apollo) os=-domain ;; i386-sun) os=-sunos4.0.2 ;; m68000-sun) os=-sunos3 ;; m68*-cisco) os=-aout ;; mep-*) os=-elf ;; mips*-cisco) os=-elf ;; mips*-*) os=-elf ;; or32-*) os=-coff ;; *-tti) # must be before sparc entry or we get the wrong os. os=-sysv3 ;; sparc-* | *-sun) os=-sunos4.1.1 ;; *-be) os=-beos ;; *-haiku) os=-haiku ;; *-ibm) os=-aix ;; *-knuth) os=-mmixware ;; *-wec) os=-proelf ;; *-winbond) os=-proelf ;; *-oki) os=-proelf ;; *-hp) os=-hpux ;; *-hitachi) os=-hiux ;; i860-* | *-att | *-ncr | *-altos | *-motorola | *-convergent) os=-sysv ;; *-cbm) os=-amigaos ;; *-dg) os=-dgux ;; *-dolphin) os=-sysv3 ;; m68k-ccur) os=-rtu ;; m88k-omron*) os=-luna ;; *-next ) os=-nextstep ;; *-sequent) os=-ptx ;; *-crds) os=-unos ;; *-ns) os=-genix ;; i370-*) os=-mvs ;; *-next) os=-nextstep3 ;; *-gould) os=-sysv ;; *-highlevel) os=-bsd ;; *-encore) os=-bsd ;; *-sgi) os=-irix ;; *-siemens) os=-sysv4 ;; *-masscomp) os=-rtu ;; f30[01]-fujitsu | f700-fujitsu) os=-uxpv ;; *-rom68k) os=-coff ;; *-*bug) os=-coff ;; *-apple) os=-macos ;; *-atari*) os=-mint ;; *) os=-none ;; esac fi # Here we handle the case where we know the os, and the CPU type, but not the # manufacturer. We pick the logical manufacturer. vendor=unknown case $basic_machine in *-unknown) case $os in -riscix*) vendor=acorn ;; -sunos*) vendor=sun ;; -cnk*|-aix*) vendor=ibm ;; -beos*) vendor=be ;; -hpux*) vendor=hp ;; -mpeix*) vendor=hp ;; -hiux*) vendor=hitachi ;; -unos*) vendor=crds ;; -dgux*) vendor=dg ;; -luna*) vendor=omron ;; -genix*) vendor=ns ;; -mvs* | -opened*) vendor=ibm ;; -os400*) vendor=ibm ;; -ptx*) vendor=sequent ;; -tpf*) vendor=ibm ;; -vxsim* | -vxworks* | -windiss*) vendor=wrs ;; -aux*) vendor=apple ;; -hms*) vendor=hitachi ;; -mpw* | -macos*) vendor=apple ;; -*mint | -mint[0-9]* | -*MiNT | -MiNT[0-9]*) vendor=atari ;; -vos*) vendor=stratus ;; esac basic_machine=`echo $basic_machine | sed "s/unknown/$vendor/"` ;; esac echo $basic_machine$os exit # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: gbutils-5.7.1/gbhisto.10000644000175000017500000000474113246755334011661 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBHISTO "1" "March 2018" "gbhisto 5.7.1" "User Commands" .SH NAME gbhisto \- Produce histogram from data .SH SYNOPSIS .B gbhisto [\fI\,options\/\fR] .SH DESCRIPTION Compute histogram counting the occurrences of each value (discrete data) or binning the values on a regular grid (continuous data). Data are read from standard input. For discrete data, values and their occurrences are printed; For binned data, the bin reference point and the relative statistics are printed. The type of variable and the statistics can be set with option \fB\-M\fR. The binning grid can be linear, logarithmic or geometric. The bin reference point is the algebraic midpoint for linear binning and the geometric midpoint for log or geometric binning. The reference point can be moved using option \fB\-S\fR. Set it to 0 for the lower bound or to 1 for the upper bound. .SH OPTIONS .TP \fB\-n\fR number of equispaced bins where the histogram is computed (default 10) .TP \fB\-w\fR min,max set manually the binning window .TP \fB\-M\fR set the type of output (default 0) .TP 0 occurrences number (continuous binned variable) .TP 1 relative frequency (continuous binned variable) .TP 2 empirical density (continuous binned variable) .TP 3 occurrences number (discrete variable) .TP 4 relative frequency (discrete variable) .TP 5 CDF (left cumulated probability; continuous binned variable) .TP 6 inverse CDF (1\-CDF; continuous binned variable) .TP \fB\-B\fR set the type of binning (default 0) .TP 0 linear .TP 1 logarithmic .TP 2 geometric .TP \fB\-S\fR set the bin reference point (default 0.5) .TP \fB\-r\fR set the ratio for geometrical binning (default 1.618) .TP \fB\-t\fR print the histogram for each imput column .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-v\fR verbose mode .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbquant.10000644000175000017500000000460213246755335011660 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBQUANT "1" "March 2018" "gbquant 5.7.1" "User Commands" .SH NAME gbquant \- Print quantiles of data distribution .SH SYNOPSIS .B gbquant [\fI\,options\/\fR] .SH DESCRIPTION Print distribution quantiles. Data are read from standard input. If no \fB\-x\fR or \fB\-q\fR options are provided, a quantiles table is plotted. The number of quantiles can be chosen with \fB\-n\fR. The option \fB\-x\fR #1 prints the quantile associated with the value #1, while \fB\-q\fR #2 print the value associate with the quantile #2. Of course #2 must be between 0 and 1. Multiple values can be provided to \fB\-x\fR or \fB\-q\fR, separated with commas. With \fB\-w\fR #1,#2 all the observations inside the quantile range [#1,#2) are printed. If more columns are provided with option \fB\-t\fR, the specified action is repeated on each column. Without \fB\-t\fR, columns are pooled unless \fB\-w\fR is specified, in which case all rows whose first element is inside the range are printed. A different column for sorting can be specified with option \fB\-W\fR. If 0 is specified, the cut is applied with respect to all columns .SH OPTIONS .TP \fB\-n\fR number of quantiles to print (default 10) .TP \fB\-x\fR print the quantile (interpolated) associated with the value .TP \fB\-q\fR print the value (interpolated) associated with the quantile .TP \fB\-e\fR print the (asymptotic) error for \fB\-x\fR or \fB\-q\fR; doesn't work with \fB\-t\fR .TP \fB\-w\fR print observations inside a quantile windows .TP \fB\-W\fR set the column to use (default 1) .TP \fB\-t\fR consider separately each column of input .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .TP \fB\-v\fR verbose mode .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbmodes.10000644000175000017500000000414513246755335011641 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBMODES "1" "March 2018" "gbmodes 5.7.1" "User Commands" .SH NAME gbmodes \- Analyze multimodality in univariate data .SH SYNOPSIS .B gbmodes [\fI\,options\/\fR] .SH DESCRIPTION Multimodality analysis via kernel density estimation. Compute the maximal kernel bandwidth h_c(M) for which #(modes in the estimated density)>M.The significance of h_c is computed via smoothed bootstrap. Read data from std. input. Print the couple h_c p(h_c) and the modal values (set with \fB\-O\fR). .SH OPTIONS .TP \fB\-n\fR number of equispaced points where the density is computed (default 100) .TP \fB\-m\fR number of modes (default 1) .TP \fB\-r\fR search range for modes; comma separated couple 'data_min,data_max' .TP \fB\-s\fR scale the search range to [min*s+(1\-s)*Max,Max*s+(1\-s)*min] (default 1) .TP \fB\-e\fR relative tolerance on h_c value (default 1e\-6) .TP \fB\-S\fR significance level (for Hall\-York correction) (default .05) .TP \fB\-t\fR number of bootstrap trials used for significance computation (default 1000) .TP \fB\-R\fR RNG seed for bootstrap trials (default 0) .TP \fB\-K\fR choose the kernel to use: 0 Gaussian or 1 Laplacian (default 0) .TP \fB\-v\fR verbose mode (more verbose if provided 2 times) .TP \fB\-O\fR output type (default 0) .TP 0 h_c p(h_c) [modes locations] .TP 1 h_c [modes locations and heights] .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/tools.c0000644000175000017500000016540613246755055011452 00000000000000/* tools.c (ver. 5.4.5) -- Various utilities functions for gbutils Copyright (C) 2001-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" /* ======================= */ /* PROGRAMS INITIALIZATION */ /* ======================= */ char *GB_PROGNAME=NULL; char *FLOAT=NULL,*FLOAT_SEP=NULL,*FLOAT_NL=NULL; char *EMPTY=NULL,*EMPTY_SEP=NULL,*EMPTY_NL=NULL; char *INT=NULL,*INT_SEP=NULL,*INT_NL=NULL; char *SEP=NULL,*NL=NULL; /* locale parsing of thousand */ char LCTHSEP='\0'; void initialize_program(const char *progname){ GB_PROGNAME=strdup(progname); /* set locale according to environment variables and thousands separator according to locale*/ if (setlocale(LC_NUMERIC,"") == NULL) fprintf(stderr,"WARNING:Cannot set LC_NUMERIC to default locale\n"); else{ /* char *thsep=nl_langinfo (THOUSANDS_SEP); */ char *thsep=nl_langinfo (THOUSEP); if(strlen(thsep)>0) LCTHSEP=thsep[0]; } /* read the environment */ if(FLOAT==NULL && getenv("GB_OUT_FLOAT_FORMAT")) FLOAT = strdup(getenv("GB_OUT_FLOAT_FORMAT")); if(EMPTY==NULL && getenv("GB_OUT_EMPTY_FORMAT")) EMPTY = strdup(getenv("GB_OUT_EMPTY_FORMAT")); if(SEP==NULL && getenv("GB_OUT_SEP")) SEP = strdup(getenv("GB_OUT_SEP")); if(NL==NULL && getenv("GB_OUT_NL")) NL = strdup(getenv("GB_OUT_NL")); /* set the variables */ if(FLOAT==NULL && EMPTY==NULL){ FLOAT=strdup("% 12.6e"); EMPTY = strdup("%-13s"); INT = strdup("% 13d"); } else if(FLOAT!=NULL && EMPTY==NULL){ int length,itmp1; length=snprintf(NULL,0,FLOAT,M_PI); if(length < 0){ fprintf(stderr,"ERROR (%s): wrong output specification: %s\n", GB_PROGNAME,FLOAT); exit(-1); } itmp1 = snprintf(NULL,0,"%% %dd",length); if(itmp1<0){ fprintf(stderr,"ERROR (%s): wrong output specification: %s\n", GB_PROGNAME,FLOAT); exit(-1); } INT = (char *) my_alloc((itmp1+1)*sizeof(char)); snprintf(INT,itmp1+1,"%% %dd",length); itmp1 = snprintf(NULL,0,"%%-%ds",length); if(itmp1<0){ fprintf(stderr,"ERROR (%s): wrong output specification: %s\n", GB_PROGNAME,FLOAT); exit(-1); } EMPTY = (char *) my_alloc((itmp1+1)*sizeof(char)); snprintf(EMPTY,itmp1+1,"%%-%ds",length); } else if(FLOAT==NULL && EMPTY!=NULL){ FLOAT=strdup("% 12.6e"); INT = strdup("% 13d"); } else{ int length,itmp1; length=snprintf(NULL,0,FLOAT,M_PI); if(length < 0){ fprintf(stderr,"ERROR (%s): wrong output specification: %s\n", GB_PROGNAME,FLOAT); exit(-1); } itmp1 = snprintf(NULL,0,"%% %dd",length); if(itmp1<0){ fprintf(stderr,"ERROR (%s): wrong output specification: %s\n", GB_PROGNAME,FLOAT); exit(-1); } INT = (char *) my_alloc((itmp1+1)*sizeof(char)); snprintf(INT,itmp1+1,"%% %dd",length); } if(SEP==NULL) SEP = strdup(" "); if(NL==NULL) NL = strdup("\n"); /* fprintf(stderr,"FLOAT='%s' (%d) SEP='%s' (%d)\n", */ /* FLOAT,strlen(FLOAT),SEP,strlen(SEP)); */ FLOAT_SEP = (char *) my_alloc((strlen(FLOAT)+strlen(SEP)+1)*sizeof(char)); strcpy(FLOAT_SEP,FLOAT); strcat(FLOAT_SEP,SEP); FLOAT_NL=(char *) my_alloc((strlen(FLOAT)+3)*sizeof(char)); strcpy(FLOAT_NL,FLOAT); strcat(FLOAT_NL,NL); EMPTY_SEP=(char *) my_alloc((strlen(EMPTY)+strlen(SEP)+1)*sizeof(char)); strcpy(EMPTY_SEP,EMPTY); strcat(EMPTY_SEP,SEP); EMPTY_NL=(char *) my_alloc((strlen(EMPTY)+3)*sizeof(char)); strcpy(EMPTY_NL,EMPTY); strcat(EMPTY_NL,NL); INT_SEP = (char *) my_alloc((strlen(INT)+strlen(SEP)+1)*sizeof(char)); strcpy(INT_SEP,INT); strcat(INT_SEP,SEP); INT_NL=(char *) my_alloc((strlen(INT)+3)*sizeof(char)); strcpy(INT_NL,INT); strcat(INT_NL,NL); #if defined HAVE_LIBGSL /* suspend error handling */ if(getenv("GB_ERROR_HANDLER_OFF")) gsl_set_error_handler_off(); #endif } void finalize_program(){ free(GB_PROGNAME); free(FLOAT); free(EMPTY); free(SEP); free(NL); free(INT); free(FLOAT_SEP); free(EMPTY_SEP); free(INT_SEP); free(FLOAT_NL); free(EMPTY_NL); free(INT_NL); } /* long options */ struct option gb_long_options[] = { {"version", no_argument, NULL, 0 }, {"help", no_argument, NULL, 'h' }, {0, 0, 0, 0 } }; int gb_option_index = 0; /* ================= */ /* UTILITY FUNCTIONS */ /* ================= */ double mystrtod(const char *nptr){ /* pointer to end of interpreted string */ char *endptr; double res; if(LCTHSEP != '\0' && strchr(nptr,LCTHSEP) != NULL ){ char *new = strdup(nptr); char *src = new; char *dst = new; do { while (*src == LCTHSEP) ++src; } while ((*dst++ = *src++) != '\0'); res=strtod(new,&endptr); free(new); } else res = strtod(nptr,&endptr); if(*endptr != '\0'){ fprintf(stderr,"WARNING (%s) : error on conversion: %s -> %e; generate a NAN\n",GB_PROGNAME,nptr,res); res=NAN; } return res; } void *my_alloc(size_t size){ void *temp; if(!(temp = malloc(size))){ perror("malloc, memory allocation failed"); exit(1); } return temp; } void *my_calloc(size_t length,size_t size){ void *temp; if(!(temp = calloc(length,size))){ perror("calloc, memory allocation failed"); exit(1); } return temp; } void *my_realloc(void *ptr,size_t size){ void *temp=NULL; if(size == 0){ free(ptr); } else if(!(temp = realloc(ptr,size))){ perror("realloc, memory allocation failed"); exit(1); } return temp; } /* replacement for systems without a strndup function */ /* copyright by Stefan Soucek ssoucek at coactive.com */ char *my_strndup(const char *str, size_t len) { register size_t n; register char *dst; n = strlen(str); if (len < n) n = len; dst = (char *) my_alloc(n+1); if (dst) { memcpy(dst, str, n); dst[n] = '\0'; } return dst; } /* ================ */ /* OUTPUT FUNCTIONS */ /* ================ */ /* default ASCII (or ASCII.gz) functions */ void (* PRINTMATRIXBYCOLS) (FILE *,double **,const size_t,const size_t) = printmatrixbycols; void (* PRINTMATRIXBYROWS) (FILE *,double **,const size_t,const size_t) = printmatrixbyrows; void (* PRINTNOMATRIXBYCOLS) (FILE *,double **,size_t *,const size_t) = printnomatrixbycols; void (* PRINTCOL) (FILE *,double *,const size_t) = printcol; void printline(FILE *out,double * data,const size_t size){ size_t i; for(i=0;i maxrows) maxrows=rows[j]; /* print the result */ if( strcspn (FLOAT,"fge") < strlen(FLOAT) ){ for(i=0;i *columns) { fprintf(stderr,"Warning (%s) : Reading only the first %zd columns of %zd provided. \n",GB_PROGNAME,*columns,i); } else if( i < *columns) { fprintf(stderr,"ERROR (%s) : wrong block shape... exiting!\n",GB_PROGNAME); exit(-1); } /* ----------------------------------------------------- */ /* table initialization -------------------------------- */ data = (double **) my_alloc((*columns)*sizeof(double*)); nrow = Mem_block; for(i=0;i<*columns;i++){ (data)[i] = (double *) my_alloc(nrow*sizeof(double)); } /* ----------------------------------------------------- */ /* read the data --------------------------------------- */ seplines=0; row=0; (*linenum)--; do{ (*linenum)++; stmp1 = strdup (line); if(stmp1 [strlen(stmp1)-1]=='\n') stmp1 [strlen(stmp1)-1]='\0'; /* checking if empty or comment --------- */ if( strspn(stmp1," \t") == strlen(stmp1) ){ /*empty line*/ ++seplines; if(seplines==2){ free(stmp1); break; } else continue; } else{ seplines=0; } token = strtok (stmp1, delimiters); if(token[0] == '#') /*comment line*/ { free(stmp1); continue; } /* -------------------------------------- */ /* check if nrow must be increased --- */ if(row >= nrow){ nrow+=Mem_block; for(i=0;i<*columns;i++){ (data)[i] = (double *) my_realloc((data)[i],nrow*sizeof(double)); } } /* ----------------------------------- */ /* reading the line --------------------- */ i=0; do{ char *endptr; errno=0; (data)[i][row] = strtod(token,&endptr); if(errno!=0 || *endptr != '\0') /* something went wrong */ { if(errno == ERANGE) { /* overflow or underflow */ if( (data)[i][row] == 0 ) underflow=1; else overflow=1; } else if(endptr == token) { /* no conversion */ (data)[i][row] = NAN; failedconversion=1; } else if(*endptr != '\0') { /* partial conversion */ partialconversion=1; } } i++; } while( i< *columns && ((token = strtok (NULL, delimiters)) != NULL) ); if(i< *columns){ /* missing values */ do {(data)[i][row]=NAN; i++;} while(i< *columns); notenough=1; } /* -------------------------------------- */ row++; free(stmp1); } while(GBGETLINE (&line,&linedim,input) !=-1); /* ----------------------------------------------------- */ /* print message if missing values or failed conversion -- */ if(notenough==1) fprintf(stderr,"WARNING (%s): missing values in some columns; filling with NAN\n",GB_PROGNAME); if(failedconversion==1) fprintf(stderr,"WARNING (%s): conversion(s) failed; filling with NAN\n",GB_PROGNAME); if(partialconversion==1) fprintf(stderr,"WARNING (%s): conversion(s) failed; used part of the string\n",GB_PROGNAME); if(underflow==1) fprintf(stderr,"WARNING (%s): conversion(s) failed; underflow occurred\n",GB_PROGNAME); if(overflow==1) fprintf(stderr,"WARNING (%s): conversion(s) failed; overflow occurred\n",GB_PROGNAME); /* ------------------------------------------------------- */ /* set the final shape of the table -------------------- */ if(*rows == 0){ *rows=row; } if( row > *rows) { fprintf(stderr,"Warning (%s) : Reading only the first %zd rows of %zd provided. \n",GB_PROGNAME,*rows,row); } else if( row < *rows) { fprintf(stderr,"ERROR (%s) : wrong block shape... exiting!\n",GB_PROGNAME); exit(-1); } for(i=0;i<*columns;i++){ (data)[i] = (double *) my_realloc((data)[i],(*rows)*sizeof(double)); } /* ----------------------------------------------------- */ free(line); return data; } /* read a series of blocks with possibly different columns and rows Parameters: double ****data : address of a three dim. arry of data (output) size_t *blocks : pointer to the array of blocks size_t **rows : pointer to the array of number of rows size_t **columns : pointer to the array of number of columns int fildes : file descriptor const char *delimiters : delimiters to use Return value: number of read lines */ int loadblocks(double ****data,size_t *blocks,size_t **rows,size_t **columns,int fildes,const char *delimiters){ unsigned linenum=0; /* number of lines read */ size_t nblock=*blocks; /* number of blocks */ double ** block=NULL; /* pointer to block */ size_t newrows=0,newcolumns=0; /* rows and columns of the file read */ /* input stream opening -------------------------------- */ GBFILEP input = GBFDOPEN (fildes,"r"); /* ----------------------------------------------------- */ /* read blocks ----------------------------------------- */ while( (block = readblock(&newrows, &newcolumns, &linenum, input, delimiters)) != NULL) { nblock++; *data = (double ***) my_realloc(*data,nblock*sizeof(double**)); (*data)[nblock-1] = block; *rows = (size_t *) my_realloc(*rows,nblock*sizeof(size_t)); *columns = (size_t *) my_realloc(*columns,nblock*sizeof(size_t)); (*rows)[nblock-1]=newrows; (*columns)[nblock-1]=newcolumns; newrows=0; newcolumns=0; } /* ----------------------------------------------------- */ /* input stream closing -------------------------------- */ GBCLOSE (input); /* ----------------------------------------------------- */ *blocks=nblock; return linenum; } /* Load the first block of data in an single array. Parameters: double **data: address of arry of data (output) int *size : the size of the array (output) FILE * input : file stream (input) Return value: number of read lines */ int load(double **data,size_t *size,int fildes,const char *delimiters){ double **vals=NULL; size_t rows=0,columns=0,index,i,j; unsigned linenum=0; /*number of lines read*/ /* input stream opening -------------------------------- */ GBFILEP input = GBFDOPEN (fildes,"r"); /* ----------------------------------------------------- */ vals = readblock(&rows,&columns, &linenum,input, delimiters); /* input stream closing -------------------------------- */ GBCLOSE (input); /* ----------------------------------------------------- */ *size = columns*rows; *data = (double *) my_alloc((*size)*sizeof(double)); index=0; for(i=0;i*blocks){ fprintf(stderr,"Warning (%s): Reading block %zd-th after %zd.\n",GB_PROGNAME,nblock-*blocks,*blocks); } } /* ----------------------------------------------------- */ /* input stream closing -------------------------------- */ GBCLOSE (input); /* ----------------------------------------------------- */ *blocks=nblock; return linenum; } /* ----------------------------------------------------- */ /* utility function to read binary data and print error message if problems arise */ void myfread(void *ptr, size_t size, size_t nmemb, FILE *stream){ if(fread(ptr,size,nmemb,stream) != nmemb){ fprintf(stderr,"ERROR (%s): problems in binary file... exiting!\n", GB_PROGNAME); exit(-1); } } /* Load the whole data set in a single array, ignoring columns and blocks. Parameters: double **data: arry of data (output) int *size : the size of the array (output) FILE * input : file stream (input) Output: number of read lines */ int load_bin(double **data,size_t *size,int fildes){ size_t i,tempcols,temprows; /* temporary number of columns */ FILE *binput =fdopen (fildes,"r"); /* read the first block */ myfread(&tempcols,sizeof(size_t),1,binput); *size=0; for(i=0;i 0){ size_t tempsize=0; for(i=0;i newrows) newrows= temprows[i]; } (*rows)[nblock-1]=newrows; (*columns)[nblock-1]=newcolumns; rowsnum += newrows; /* initialize the block */ (*data)[nblock-1] = (double **) my_alloc(newcolumns*sizeof(double*)); for(i=0;i *columns) { fprintf(stderr,"WARNING (%s): read only the %zd first columns of %zd provided\n",GB_PROGNAME, *columns,tempcols); } /* check rows structure */ *rows=0; for(i=0;i<*columns;i++){ if(temprows[i] >*rows) *rows= temprows[i]; } /* initialize the array */ *data = (double **) my_alloc((*columns)*sizeof(double*)); for(i=0;i<*columns;i++) (*data)[i] = (double *) my_alloc((*rows)*sizeof(double)); /* load data */ for(i=0;i<*columns;i++){ myfread((*data)[i],sizeof(double),temprows[i],binput); if(temprows[i]<*rows){ size_t j; fprintf(stderr,"WARNING (%s): not enought data at column %zd; filling with NAN\n", GB_PROGNAME,i); for(j=temprows[i];j<*rows;j++) (*data)[i][j]=NAN; } } free(temprows); fclose(binput); return *rows; } int sort_by_value(const void *a, const void *b){ const double *da = (const double *) a; const double *db = (const double *) b; return (*da > *db) - (*da < *db); } void moment(const double data[], const size_t size, double *ave, double *adev, double *sdev, double *var, double *skew, double *curt, double *min,double *max){ size_t i; /*index*/ double dtmp1,dtmp2,error; double lmin,lmax,lave,ladev,lsdev,lvar,lskew,lcurt; /* local vars */ if (size == 0) { fprintf(stderr,"WARNING (%s): at least 1 observation is required\n", GB_PROGNAME); *min=NAN; *max=NAN; *ave=NAN; *adev=NAN; *var=NAN; *sdev=NAN; *skew=NAN; *curt=NAN; return ; } else if (size <= 1) { *min=data[0]; *max=data[0]; *ave=data[0]; *adev=0; *var=0; *sdev=0; *skew=0; *curt=0; return ; } /* find total sum; min max values */ lmin = lmax =data[0]; dtmp1=0.0;/*total sum*/ for (i=0;i lmax){ lmax = data[i]; } dtmp1 += data[i]; } lave=dtmp1/size; /* build statistics */ error=ladev=lvar=lskew=lcurt=0.0; for (i=0;i0) { lskew /= (size-1.)*lvar*lsdev; lcurt=lcurt/((size-1.)*lvar*lvar)-3.0; } else { fprintf(stderr,"WARNING (%s): No skew/kurtosis when variance=0\n",GB_PROGNAME); lskew=lcurt=NAN; } /* set the return values */ *min=lmin; *max=lmax; *ave=lave; *adev=ladev; *var=lvar; *sdev=lsdev; *skew=lskew; *curt=lcurt; } /* in variable 'nonan' the number of finite values is returned */ void moment_nonan(const double data[], const size_t size, double *ave, double *adev, double *sdev, double *var, double *skew, double *curt,double *min,double *max, double *nonan){ size_t i; /*index*/ double dtmp1,dtmp2,error; double lmin,lmax,lave,ladev,lsdev,lvar,lskew,lcurt; /* local vars */ size_t lsize; if (size == 0) { *min=NAN; *max=NAN; *ave=NAN; *adev=NAN; *var=NAN; *sdev=NAN; *skew=NAN; *curt=NAN; *nonan=0; return ; } else if (size <= 1) { *min=data[0]; *max=data[0]; *ave=data[0]; *adev=0; *var=0; *sdev=0; *skew=0; *curt=0; if( finite(data[0]) ){ *nonan=1; } else{ *nonan=0; } return ; } /* find total sum; min max values */ lmin = lmax =data[0]; dtmp1=0.0;/*total sum*/ lsize=0; for (i=0;i lmax){ lmax = data[i]; } dtmp1 += data[i]; } lave=dtmp1/lsize; /* build statistics */ error=ladev=lvar=lskew=lcurt=0.0; for (i=0;i0) { lskew /= (lsize-1.)*lvar*lsdev; lcurt=lcurt/((lsize-1.)*lvar*lvar)-3.0; } else { lskew=lcurt=NAN; } /* set the return values */ *min=lmin; *max=lmax; *ave=lave; *adev=ladev; *var=lvar; *sdev=lsdev; *skew=lskew; *curt=lcurt; *nonan= (double) lsize; } void moment_short(const double data[], const size_t size, double *ave, double *sdev,double *min,double *max){ size_t i; /*index*/ double dtmp1,error; double lmin,lmax,lave,lsdev; /* local vars */ if (size <= 1) { fprintf(stderr,"ERROR (%s): at least 2 observations are required\n", GB_PROGNAME); exit(+1); } /* find total sum; min max values */ lmin = lmax =data[0]; dtmp1=0.0;/*total sum*/ for (i=0;i lmax){ lmax = data[i]; } dtmp1 += data[i]; } lave=dtmp1/size; /* build statistics */ error=lsdev=0.0; for (i=0;i max[0]){ max[0] = dtmp0; } if(dtmp1< min[1]){ min[1] = dtmp1; } else if(dtmp1> max[1]){ max[1] = dtmp1; } s0 += dtmp0; s1 += dtmp1; } ave[0]=s0/n; ave[1]=s1/n; *cov=sdev[0]=sdev[1]=0.0; error0=error1=0.0; for (j=0;j max[0]){ max[0] = dtmp0; } if(dtmp1< min[1]){ min[1] = dtmp1; } else if(dtmp1> max[1]){ max[1] = dtmp1; } if(dtmp2< min[2]){ min[2] = dtmp2; } else if(dtmp2> max[2]){ max[2] = dtmp2; } s0 += dtmp0; s1 += dtmp1; s2 += dtmp2; } ave[0]=s0/n; ave[1]=s1/n; ave[2]=s2/n; *covxy=sdev[0]=sdev[1]=0.0; error0=error1=ep2=0.0; for (j=0;j\n\n"); fprintf(out,"Package home page \n"); } void moment_short_nonan(const double data[], const size_t size, double *ave, double *sdev,double *min,double *max,double *nonan){ size_t i,realsize; /*index*/ double dtmp1,error; double lmin,lmax,lave,lsdev; /* local vars */ if (size <= 1) { fprintf(stderr,"ERROR (%s): at least 2 observations are required\n", GB_PROGNAME); exit(+1); } /* find total sum; min max values */ lmin = lmax =data[0]; dtmp1=0.0;/*total sum*/ realsize=0; for (i=0;i lmax){ lmax = data[i]; } dtmp1 += data[i]; } lave=dtmp1/realsize; /* build statistics */ error=lsdev=0.0; for (i=0;i0) for(i=0;i<*columns;i++) free((*vals)[i]); free(*vals); /* new dimensions */ *rows=newrows; /* reallocate space */ for(i=0;i<*columns;i++) newvals[i] = (double *) my_realloc((void *) (newvals[i]), (*rows)*sizeof(double)); *vals = newvals; } /* translated by f2c (version 20030320) from dsort.c. */ #define abs(x) ((x) >= 0 ? (x) : -(x)) /* ***BEGIN PROLOGUE DSORT */ /* ***PURPOSE Sort an array and optionally make the same interchanges in */ /* an auxiliary array. The array may be sorted in increasing */ /* or decreasing order. A slightly modified QUICKSORT */ /* algorithm is used. */ /* ***LIBRARY SLATEC */ /* ***CATEGORY N6A2B */ /* ***TYPE DOUBLE PRECISION (SSORT-S, DSORT-D, ISORT-I) */ /* ***KEYWORDS SINGLETON QUICKSORT, SORT, SORTING */ /* ***AUTHOR Jones, R. E., (SNLA) */ /* Wisniewski, J. A., (SNLA) */ /* ***DESCRIPTION */ /* DSORT sorts array DX and optionally makes the same interchanges in */ /* array DY. The array DX may be sorted in increasing order or */ /* decreasing order. A slightly modified quicksort algorithm is used. */ /* Description of Parameters */ /* DX - array of values to be sorted (usually abscissas) */ /* DY - array to be (optionally) carried along */ /* N - number of values in array DX to be sorted */ /* KFLAG - control parameter */ /* = 2 means sort DX in increasing order and carry DY along. */ /* = 1 means sort DX in increasing order (ignoring DY) */ /* = -1 means sort DX in decreasing order (ignoring DY) */ /* = -2 means sort DX in decreasing order and carry DY along. */ /* ***REFERENCES R. C. Singleton, Algorithm 347, An efficient algorithm */ /* for sorting with minimal storage, Communications of */ /* the ACM, 12, 3 (1969), pp. 185-187. */ int sort2(double *dx, double *dy, long int n, long int kflag) { /* System generated locals */ long int i__1; /* Local variables */ static long int i__, j, k, l, m; static double r__, t; static long int ij, il[21], kk, nn, iu[21]; static double tt, ty, tty; /* Parameter adjustments */ --dy; --dx; /* Function Body */ nn = n; if (nn < 1) { fprintf(stderr,"SLATEC DSORT: The number of values to be sorted is not positive."); return 0; } kk = abs(kflag); if (kk != 1 && kk != 2) { fprintf(stderr,"SLATEC DSORT: The sort control parameter, K, is not 2, 1, -1, or -2."); return 0; } /* Alter array DX to get decreasing order if needed */ if (kflag <= -1) { i__1 = nn; for (i__ = 1; i__ <= i__1; ++i__) { dx[i__] = -dx[i__]; /* L10: */ } } if (kk == 2) { goto L100; } /* Sort DX only */ m = 1; i__ = 1; j = nn; r__ = .375; L20: if (i__ == j) { goto L60; } if (r__ <= .5898437) { r__ += .0390625; } else { r__ += -.21875; } L30: k = i__; /* Select a central element of the array and save it in location T */ ij = i__ + (long int) ((j - i__) * r__); t = dx[ij]; /* If first element of array is greater than T, interchange with T */ if (dx[i__] > t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; } l = j; /* If last element of array is less than than T, interchange with T */ if (dx[j] < t) { dx[ij] = dx[j]; dx[j] = t; t = dx[ij]; /* If first element of array is greater than T, interchange with T */ if (dx[i__] > t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; } } /* Find an element in the second half of the array which is smaller */ /* than T */ L40: --l; if (dx[l] > t) { goto L40; } /* Find an element in the first half of the array which is greater */ /* than T */ L50: ++k; if (dx[k] < t) { goto L50; } /* Interchange these elements */ if (k <= l) { tt = dx[l]; dx[l] = dx[k]; dx[k] = tt; goto L40; } /* Save upper and lower subscripts of the array yet to be sorted */ if (l - i__ > j - k) { il[m - 1] = i__; iu[m - 1] = l; i__ = k; ++m; } else { il[m - 1] = k; iu[m - 1] = j; j = l; ++m; } goto L70; /* Begin again on another portion of the unsorted array */ L60: --m; if (m == 0) { goto L190; } i__ = il[m - 1]; j = iu[m - 1]; L70: if (j - i__ >= 1) { goto L30; } if (i__ == 1) { goto L20; } --i__; L80: ++i__; if (i__ == j) { goto L60; } t = dx[i__ + 1]; if (dx[i__] <= t) { goto L80; } k = i__; L90: dx[k + 1] = dx[k]; --k; if (t < dx[k]) { goto L90; } dx[k + 1] = t; goto L80; /* Sort DX and carry DY along */ L100: m = 1; i__ = 1; j = nn; r__ = .375; L110: if (i__ == j) { goto L150; } if (r__ <= .5898437) { r__ += .0390625; } else { r__ += -.21875; } L120: k = i__; /* Select a central element of the array and save it in location T */ ij = i__ + (long int) ((j - i__) * r__); /* GB Fri Nov 25 10:50:36 CET 2005 */ /* fprintf(stderr,"%d\n",ij); */ t = dx[ij]; ty = dy[ij]; /* If first element of array is greater than T, interchange with T */ if (dx[i__] > t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; dy[ij] = dy[i__]; dy[i__] = ty; ty = dy[ij]; } l = j; /* If last element of array is less than T, interchange with T */ if (dx[j] < t) { dx[ij] = dx[j]; dx[j] = t; t = dx[ij]; dy[ij] = dy[j]; dy[j] = ty; ty = dy[ij]; /* If first element of array is greater than T, interchange with T */ if (dx[i__] > t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; dy[ij] = dy[i__]; dy[i__] = ty; ty = dy[ij]; } } /* Find an element in the second half of the array which is smaller */ /* than T */ L130: --l; if (dx[l] > t) { goto L130; } /* Find an element in the first half of the array which is greater */ /* than T */ L140: ++k; if (dx[k] < t) { goto L140; } /* Interchange these elements */ if (k <= l) { tt = dx[l]; dx[l] = dx[k]; dx[k] = tt; tty = dy[l]; dy[l] = dy[k]; dy[k] = tty; goto L130; } /* Save upper and lower subscripts of the array yet to be sorted */ if (l - i__ > j - k) { il[m - 1] = i__; iu[m - 1] = l; i__ = k; ++m; } else { il[m - 1] = k; iu[m - 1] = j; j = l; ++m; } goto L160; /* Begin again on another portion of the unsorted array */ L150: --m; if (m == 0) { goto L190; } i__ = il[m - 1]; j = iu[m - 1]; L160: if (j - i__ >= 1) { goto L120; } if (i__ == 1) { goto L110; } --i__; L170: ++i__; if (i__ == j) { goto L150; } t = dx[i__ + 1]; ty = dy[i__ + 1]; if (dx[i__] <= t) { goto L170; } k = i__; L180: dx[k + 1] = dx[k]; dy[k + 1] = dy[k]; --k; if (t < dx[k]) { goto L180; } dx[k + 1] = t; dy[k + 1] = ty; goto L170; /* Clean up */ L190: if (kflag <= -1) { i__1 = nn; for (i__ = 1; i__ <= i__1; ++i__) { dx[i__] = -dx[i__]; /* L200: */ } } return 0; } /* sort2 */ int sortn(double **data, const long int len, const long int n, const long int pos, const long int kflag) { /* System generated locals */ long int i__1; /* Local variables */ static long int i__, j, k, l, m,h; static double r__, t; static long int ij, il[21], kk, nn, iu[21]; static double tt; double *ty,*tty, *dx; if(pos t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; } l = j; /* If last element of array is less than than T, interchange with T */ if (dx[j] < t) { dx[ij] = dx[j]; dx[j] = t; t = dx[ij]; /* If first element of array is greater than T, interchange with T */ if (dx[i__] > t) { dx[ij] = dx[i__]; dx[i__] = t; t = dx[ij]; } } /* Find an element in the second half of the array which is smaller */ /* than T */ L40: --l; if (dx[l] > t) { goto L40; } /* Find an element in the first half of the array which is greater */ /* than T */ L50: ++k; if (dx[k] < t) { goto L50; } /* Interchange these elements */ if (k <= l) { tt = dx[l]; dx[l] = dx[k]; dx[k] = tt; goto L40; } /* Save upper and lower subscripts of the array yet to be sorted */ if (l - i__ > j - k) { il[m - 1] = i__; iu[m - 1] = l; i__ = k; ++m; } else { il[m - 1] = k; iu[m - 1] = j; j = l; ++m; } goto L70; /* Begin again on another portion of the unsorted array */ L60: --m; if (m == 0) { goto L190; } i__ = il[m - 1]; j = iu[m - 1]; L70: if (j - i__ >= 1) { goto L30; } if (i__ == 1) { goto L20; } --i__; L80: ++i__; if (i__ == j) { goto L60; } t = dx[i__ + 1]; if (dx[i__] <= t) { goto L80; } k = i__; L90: dx[k + 1] = dx[k]; --k; if (t < dx[k]) { goto L90; } dx[k + 1] = t; goto L80; /* Sort DX and carry DY along */ L100: m = 1; i__ = 1; j = nn; r__ = .375; L110: if (i__ == j) { goto L150; } if (r__ <= .5898437) { r__ += .0390625; } else { r__ += -.21875; } L120: k = i__; /* Select a central element of the array and save it in location T */ ij = i__ + (long int) ((j - i__) * r__); /* GB Fri Nov 25 10:50:36 CET 2005 */ /* fprintf(stderr,"%d\n",ij); */ t = dx[ij]; for(h=0;h t) { for(h=0;h t) { for(h=0;h t) { goto L130; } /* Find an element in the first half of the array which is greater */ /* than T */ L140: ++k; if (dx[k] < t) { goto L140; } /* Interchange these elements */ if (k <= l) { for(h=0;h j - k) { il[m - 1] = i__; iu[m - 1] = l; i__ = k; ++m; } else { il[m - 1] = k; iu[m - 1] = j; j = l; ++m; } goto L160; /* Begin again on another portion of the unsorted array */ L150: --m; if (m == 0) { goto L190; } i__ = il[m - 1]; j = iu[m - 1]; L160: if (j - i__ >= 1) { goto L120; } if (i__ == 1) { goto L110; } --i__; L170: ++i__; if (i__ == j) { goto L150; } t = dx[i__ + 1]; for(h=0;h #if HAVE_INTTYPES_H # include #endif #if HAVE_STDINT_H # include #endif #ifndef PTRDIFF_MAX # define PTRDIFF_MAX ((ptrdiff_t) (SIZE_MAX / 2)) #endif #ifndef SIZE_MAX # define SIZE_MAX ((size_t) -1) #endif #ifndef SSIZE_MAX # define SSIZE_MAX ((ssize_t) (SIZE_MAX / 2)) #endif /* The maximum value that getndelim2 can return without suffering from overflow problems, either internally (because of pointer subtraction overflow) or due to the API (because of ssize_t). */ #define GETNDELIM2_MAXIMUM (PTRDIFF_MAX < SSIZE_MAX ? PTRDIFF_MAX : SSIZE_MAX) /* Try to add at least this many bytes when extending the buffer. MIN_CHUNK must be no greater than GETNDELIM2_MAXIMUM. */ #define MIN_CHUNK 64 ssize_t gzgetndelim2 (char **lineptr, size_t *linesize, size_t offset, size_t nmax, int delim1, int delim2, gzFile stream) { size_t nbytes_avail; /* Allocated but unused bytes in *LINEPTR. */ char *read_pos; /* Where we're reading into *LINEPTR. */ ssize_t bytes_stored = -1; char *ptr = *lineptr; size_t size = *linesize; if (!ptr) { size = nmax < MIN_CHUNK ? nmax : MIN_CHUNK; ptr = my_alloc (size); if (!ptr) return -1; } if (size < offset) goto done; nbytes_avail = size - offset; read_pos = ptr + offset; if (nbytes_avail == 0 && nmax <= size) goto done; for (;;) { /* Here always ptr + size == read_pos + nbytes_avail. */ int c; /* We always want at least one byte left in the buffer, since we always (unless we get an error while reading the first byte) NUL-terminate the line buffer. */ if (nbytes_avail < 2 && size < nmax) { size_t newsize = size < MIN_CHUNK ? size + MIN_CHUNK : 2 * size; char *newptr; if (! (size < newsize && newsize <= nmax)) newsize = nmax; if (GETNDELIM2_MAXIMUM < newsize - offset) { size_t newsizemax = offset + GETNDELIM2_MAXIMUM + 1; if (size == newsizemax) goto done; newsize = newsizemax; } nbytes_avail = newsize - (read_pos - ptr); newptr = realloc (ptr, newsize); if (!newptr) goto done; ptr = newptr; size = newsize; read_pos = size - nbytes_avail + ptr; } c = gzgetc (stream); if (c == EOF) { /* Return partial line, if any. */ if (read_pos == ptr) goto done; else break; } if (nbytes_avail >= 2) { *read_pos++ = c; nbytes_avail--; } if (c == delim1 || c == delim2) /* Return the line. */ break; } /* Done - NUL terminate and return the number of bytes read. At this point we know that nbytes_avail >= 1. */ *read_pos = '\0'; bytes_stored = read_pos - (ptr + offset); done: *lineptr = ptr; *linesize = size; return bytes_stored; } ssize_t gzgetdelim (char **lineptr, size_t *linesize, int delimiter, gzFile stream) { return gzgetndelim2 (lineptr, linesize, 0, GETNLINE_NO_LIMIT, delimiter, EOF, stream); } ssize_t gzgetline (char **lineptr, size_t *linesize, gzFile stream) { return gzgetdelim (lineptr, linesize, '\n', stream); } #endif /* ===================================== */ /* UTILITY FUNCTIONS FOR KERNEL ANALYSIS */ /* ===================================== */ /* 1-dimensional */ const double Krectangular_K2= .5; const double Klaplace_K2= 1/(2*M_SQRT2); const double Kgauss_K2= 1/(2*M_PI); const double Kepanechnikov_K2= 3./(5*2.23606797749978969641); double Krectangular(double x) { return(.5); } double Krectangular_tilde(double x) { if(x==0){ return(1); } else{ return(sin(x)/x); } } double Klaplace(double x) { return(exp(-M_SQRT2*fabs(x))/M_SQRT2); } double Klaplace_tilde(double x) { return(1./(1.+x*x*0.5)); } double Kgauss(double x) { return(exp(-x*x/2.)/(sqrt(2.*M_PI))); } double Kgauss_tilde(double x) { return(exp(-x*x/2)); } double Kepanechnikov(double x) { return 3.*(1-x*x/5.)/(4.*sqrt(5)); } double Kepanechnikov_tilde(double x) { if(x==0){ return(1); } else{ double dtmp1=x*sqrt(5); return(3*(sin(dtmp1)-dtmp1*cos(dtmp1))/(dtmp1*dtmp1*dtmp1)); } } /* 2-dimensional */ double Krectangular2d(double x) { return( (x<1. ? 1./M_PI : 0.0) ); } double Kepanechnikov2d(double x) { return( (x<1. ? 2.*(1-x)/M_PI : 0.0) ); } double Ksilverman2d_1(double x) { const double dtmp1= 1-x; return( (x<1. ? 3.*dtmp1*dtmp1/M_PI : 0.0) ); } double Ksilverman2d_2(double x) { const double dtmp1= 1-x; return( (x<1. ? 4.*dtmp1*dtmp1*dtmp1/M_PI : 0.0) ); } gbutils-5.7.1/gbstat.c0000644000175000017500000002024713246754720011565 00000000000000/* gbstat (ver. 5.6) -- Basic descriptive statistics of data Copyright (C) 2000-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" double HSM(double *vals,size_t size){ double mode; size_t n=size; size_t i=0; while(n>3){ size_t h; size_t j=i; size_t N=(size_t) ceil(n/2.0); double w=vals[i+n-1]-vals[i]; /* printf("from %d to %d\n",i,i+n-N); */ for(h=i; h < i+n-N+1; h++){ double dtmp1=vals[h+N-1]-vals[h]; /* printf("w[%d]=%f ",h,dtmp1); */ if(dtmp1idx1) idx1=idx2; *transform = strdup(pointer+idx1+1); } /* fprintf(stderr,"pointer:%s len:%d idx1:%d idx2:%d trasf:%s\n", */ /* pointer,strlen(pointer),idx1,idx2,*transform); */ free(piece); /* exit(0); */ /* final checks */ if(strspn(*blockslice,"-1234567890:;")0 ) fin = ini = atoi(stmp5); if( (stmp5=strsep (&stmp4,":")) != NULL){ if(strlen(stmp5)>0) fin = atoi(stmp5); else fin=max; } /* treat negative elements */ if(ini<0) ini=max+ini+1; if(fin<0) fin=max+fin+1; /* eventually set skip */ if( (stmp5=strsep (&stmp4,",")) != NULL && strlen(stmp5)>0 ) skip= atoi(stmp5); else if(ini>fin) skip=-1; free(stmp3); /*check the boundaries and skip*/ if(ini<1){ fprintf(stderr, "WARNING (%s): requested %s %d out of range: set to %d\n",GB_PROGNAME,typename,ini,1); ini=1; } else if(ini>max){ fprintf(stderr,"WARNING (%s): requested %s %d out of range: set to %d\n",GB_PROGNAME,typename,ini,max); ini=max; } if(fin<1){ fprintf(stderr,"WARNING (%s): requested %s %d out of range: set to %d\n",GB_PROGNAME,typename,fin,1); fin=1; } else if(fin>max){ fprintf(stderr,"WARNING (%s): requested %s %d out of range: set to %d\n",GB_PROGNAME,typename,fin,max); fin=max; } if( ( (ini > fin) && (skip>0) ) || ((ini < fin) && (skip<0)) ){ fprintf(stderr,"WARNING (%s): requested %s out of range: increment reversed\n", GB_PROGNAME,typename); skip=-skip; } } /* assign values */ *indexini = (size_t *) my_realloc((void *) *indexini,(*piecenum)*sizeof(size_t)); *indexfin = (size_t *) my_realloc((void *) *indexfin,(*piecenum)*sizeof(size_t)); *indexskip = (int *) my_realloc((void *) *indexskip,(*piecenum)*sizeof(int)); (*indexini)[(*piecenum)-1]=ini; (*indexfin)[(*piecenum)-1]=fin; (*indexskip)[(*piecenum)-1]=skip; } free(pieces); } void new_matrix(double ***newdata,size_t *columnlength,size_t *rowlength, double ** const vals,const size_t columns,const size_t rows, const char *columnslice,const char *rowslice){ size_t i; size_t cslicenum=0; size_t *cini=NULL; size_t *cfin=NULL; int *cskip=NULL; size_t rslicenum=0; size_t *rini=NULL; size_t *rfin=NULL; int *rskip=NULL; /* read the specs */ parse_slice(columnslice,"column",columns,&cslicenum,&cini,&cfin,&cskip); parse_slice(rowslice,"row",rows,&rslicenum,&rini,&rfin,&rskip); /* decide new matrix dimensions */ *columnlength=0; for(i=0;icini[i] ? cfin[i]-cini[i] : cini[i]-cfin[i])/(cskip[i]>0?cskip[i]:-cskip[i])+1; *rowlength=0; for(i=0;irini[i] ? rfin[i]-rini[i] : rini[i]-rfin[i])/(rskip[i]>0?rskip[i]:-rskip[i])+1; /* for(i=0;i0 ? c < cfin[cs] : c+2 > cfin[cs] ) ;c+=cskip[cs]){ row=0; for(rs=0;rs0 ? r < rfin[rs] : r+2 > rfin[rs] ) ;r+=rskip[rs]){ /* fprintf(stderr,"val[%zd][%zd]=%f -> ", */ /* c,r,vals[c][r]); */ data[column][row]=vals[c][r]; /* fprintf(stderr,"data[%zd][%zd]=%f\n", */ /* column,row,data[column][row]); */ row++; } } column++; } } *newdata=data; } free(cini); free(cfin); free(cskip); free(rini); free(rfin); free(rskip); } void data_transpose(double *** vals,size_t *rows,size_t *columns){ size_t i,j; /* allocate space */ double **newvals = (double **) my_alloc((*rows)*sizeof(double *)); for(i=0;i<*rows;i++){ newvals[i] = (double *) my_alloc((*columns)*sizeof(double)); } /* fill space */ for(i=0;i<*rows;i++) for(j=0;j<*columns;j++) newvals[i][j] = (*vals)[j][i]; /* deallocate old space */ if(*columns>0) for(i=0;i<*columns;i++) free((*vals)[i]); free(*vals); *vals = newvals; /* swap axes */ j=*columns; *columns=*rows; *rows=j; } void data_flat(double *** vals,size_t *rows,size_t *columns){ size_t i,j,index; /* allocate space */ double **newvals = (double **) my_alloc(sizeof(double *)); newvals[0] = (double *) my_alloc((*columns)*(*rows)*sizeof(double)); /* fill space */ index=0; for(i=0;i<*columns;i++) for(j=0;j<*rows;j++) newvals[0][index++] = (*vals)[i][j]; /* deallocate old space */ for(i=0;i<*columns;i++) free((*vals)[i]); free(*vals); *vals = newvals; /* new axes */ *rows=(*columns)*(*rows); *columns=1; } void data_diff(double *** vals,size_t *rows,size_t *columns){ size_t i,j; /* allocate space */ double **newvals = (double **) my_alloc((*columns-1)*sizeof(double *)); for(i=0;i<*columns-1;i++) newvals[i] = (double *) my_alloc((*rows)*sizeof(double)); /* fill space */ for(i=0;i<*columns-1;i++) for(j=0;j<*rows;j++) newvals[i][j] = (*vals)[i+1][j]-(*vals)[i][j]; /* deallocate old space */ if(*columns>0) for(i=0;i<*columns;i++) free((*vals)[i]); free(*vals); *vals = newvals; /* new dimensions */ *columns=*columns-1; } #if defined HAVE_LIBGSL /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; /* char *args[] = { "x" }; */ char **args; /* list of arguments names */ size_t *indeces; /* the index (shift) for each variable */ size_t *lags; /* the lag for each variable */ double *values; /* values used in computation */ size_t maxindex; /* the largest index */ size_t maxlag; /* the largest lag */ } Function; void data_applyfuns(double *** myvals,size_t *myrows,size_t *mycolumns,const char *funslist){ Function *F=NULL; size_t Fnum=0; char *funspecs=strdup(funslist); char *specstart=funspecs,*funspec; /* traslate to more handy names */ double **data=*myvals; size_t columns=*mycolumns; size_t rows=*myrows; /* fprintf(stderr,"func spec=%s\n",funspecs); */ /* parse line for functions specification */ while( (funspec=strsep (&funspecs,";")) != NULL ){ char *piece=strdup (funspec); char *stmp1=piece; char *stmp2; while( (stmp2=strsep (&stmp1,",")) != NULL ){/*take a piece*/ size_t j; char *stmp3 = strdup(stmp2); /* fprintf(stderr,"token:%s\n",stmp3); */ /* allocate new function */ Fnum++; F=(Function *) my_realloc((void *) F,Fnum*sizeof(Function)); /* generate the formal expression */ F[Fnum-1].f = evaluator_create (stmp3); assert(F[Fnum-1].f); free(stmp3); /* retrive list of argument */ { /* hack necessary due to libmatheval propotypes */ int argnum; evaluator_get_variables (F[Fnum-1].f, &(F[Fnum-1].args), &argnum); F[Fnum-1].argnum = (size_t) argnum; } /* compute list of indeces */ F[Fnum-1].indeces = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].lags = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].maxindex=0; F[Fnum-1].maxlag=0; for(j=0;j F[Fnum-1].maxindex) F[Fnum-1].maxindex = (F[Fnum-1].indeces)[j]; /* possibly update the value of the maximum lag */ if( (F[Fnum-1].lags)[j] > F[Fnum-1].maxlag) F[Fnum-1].maxlag = (F[Fnum-1].lags)[j]; /* check index */ if((F[Fnum-1].indeces)[j]> columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,(F[Fnum-1].indeces)[j]); exit(-1); } /* check lag */ if((F[Fnum-1].lags)[j]> rows){ fprintf(stderr,"ERROR (%s): lag %zd is longer then the data length\n", GB_PROGNAME,(F[Fnum-1].lags)[j]); exit(-1); } } /* allocate list of values */ F[Fnum-1].values = (double *) my_alloc(F[Fnum-1].argnum*sizeof(double)); } free(piece); } free(specstart); /* generate new matrix */ { /* placeholder for new data */ double **newvals; size_t i,j,h; /* double **output = (double **) my_alloc(Fnum*sizeof(double *)); */ /* for(i=0;ifirstrow) firstrow=F[j].maxlag; /* allocate space */ newvals = (double **) my_alloc(Fnum*sizeof(double *)); for(i=0;i0 ? data[F[j].indeces[h]-1][i-F[j].lags[h]] : i+1-F[j].lags[h]); newvals[j][i-firstrow]=evaluator_evaluate (F[j].f,F[j].argnum,F[j].args,F[j].values); } } /* free previous allocated space */ for(i=0;i<*mycolumns;i++) free( (*myvals)[i]); free((*myvals)); /* return newly allocated matrix */ *mycolumns=Fnum; *myrows=rows-firstrow; *myvals = newvals; } } void data_applyfuns_recursive(double *** myvals,size_t *myrows,size_t *mycolumns,const char *funslist){ Function *F=NULL; size_t Fnum=0; char *funspecs=strdup(funslist); char *specstart=funspecs,*funspec; /* traslate to more handy names */ double **data=*myvals; size_t columns=*mycolumns; size_t rows=*myrows; /* fprintf(stderr,"func spec=%s\n",funspecs); */ /* parse line for functions specification */ while( (funspec=strsep (&funspecs,";")) != NULL ){ char *piece=strdup (funspec); char *stmp1=piece; char *stmp2; while( (stmp2=strsep (&stmp1,",")) != NULL ){/*take a piece*/ size_t j; char *stmp3 = strdup(stmp2); /* fprintf(stderr,"token:%s\n",stmp3); */ /* allocate new function */ Fnum++; F=(Function *) my_realloc((void *) F,Fnum*sizeof(Function)); /* generate the formal expression */ F[Fnum-1].f = evaluator_create (stmp3); assert(F[Fnum-1].f); free(stmp3); /* retrive list of argument */ { /* hack necessary due to libmatheval propotypes */ int argnum; evaluator_get_variables (F[Fnum-1].f, &(F[Fnum-1].args), &argnum); F[Fnum-1].argnum = (size_t) argnum; } /* compute list of indeces */ F[Fnum-1].indeces = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].lags = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].maxindex=0; F[Fnum-1].maxlag=0; for(j=0;j F[Fnum-1].maxindex) F[Fnum-1].maxindex = (F[Fnum-1].indeces)[j]; /* possibly update the value of the maximum lag */ if( (F[Fnum-1].lags)[j] > F[Fnum-1].maxlag) F[Fnum-1].maxlag = (F[Fnum-1].lags)[j]; /* check index */ if((F[Fnum-1].indeces)[j]> columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,(F[Fnum-1].indeces)[j]); exit(-1); } /* check lag */ if((F[Fnum-1].lags)[j]> rows){ fprintf(stderr,"ERROR (%s): lag %zd is longer then the data length\n", GB_PROGNAME,(F[Fnum-1].lags)[j]); exit(-1); } } /* allocate list of values */ F[Fnum-1].values = (double *) my_alloc(F[Fnum-1].argnum*sizeof(double)); } free(piece); } free(specstart); /* generate new matrix */ { /* placeholder for new data */ double **newvals; size_t i,j,l,h; /* double **output = (double **) my_alloc(Fnum*sizeof(double *)); */ /* for(i=0;ifirstrow) firstrow=F[j].maxlag; /* compute the total number of columns */ for(j=0;j0 ? data[l+F[j].indeces[h]-1][i-F[j].lags[h]] : i+1-F[j].lags[h]); newvals[col+l][i-firstrow]=evaluator_evaluate (F[j].f,F[j].argnum,F[j].args,F[j].values); } col+=columns-F[j].maxindex+1; } } /* free previous allocated space */ for(i=0;i<*mycolumns;i++) free( (*myvals)[i]); free((*myvals)); /* allocate new space */ *mycolumns=totalcols; *myrows=rows-firstrow; *myvals=newvals; } } void data_applyselection(double *** myvals,size_t *myrows,size_t *mycolumns,const char *funslist){ Function *F=NULL; size_t Fnum=0; char *funspecs=strdup(funslist); char *specstart=funspecs,*funspec; /* traslate to more handy names */ double **data=*myvals; size_t columns=*mycolumns; size_t rows=*myrows; /* fprintf(stderr,"func spec=%s\n",funspecs); */ /* parse line for functions specification */ while( (funspec=strsep (&funspecs,";")) != NULL ){ char *piece=strdup (funspec); char *stmp1=piece; char *stmp2; while( (stmp2=strsep (&stmp1,",")) != NULL ){/*take a piece*/ size_t j; char *stmp3 = strdup(stmp2); /* fprintf(stderr,"token:%s\n",stmp3); */ /* allocate new function */ Fnum++; F=(Function *) my_realloc((void *) F,Fnum*sizeof(Function)); /* generate the formal expression */ F[Fnum-1].f = evaluator_create (stmp3); assert(F[Fnum-1].f); free(stmp3); /* retrive list of argument */ { /* hack necessary due to libmatheval propotypes */ int argnum; evaluator_get_variables (F[Fnum-1].f, &(F[Fnum-1].args), &argnum); F[Fnum-1].argnum = (size_t) argnum; } /* compute list of indeces */ F[Fnum-1].indeces = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].lags = (size_t *) my_alloc(F[Fnum-1].argnum*sizeof(size_t)); F[Fnum-1].maxindex=0; F[Fnum-1].maxlag=0; for(j=0;j F[Fnum-1].maxindex) F[Fnum-1].maxindex = (F[Fnum-1].indeces)[j]; /* possibly update the value of the maximum lag */ if( (F[Fnum-1].lags)[j] > F[Fnum-1].maxlag) F[Fnum-1].maxlag = (F[Fnum-1].lags)[j]; /* check index */ if((F[Fnum-1].indeces)[j]> columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,(F[Fnum-1].indeces)[j]); exit(-1); } /* check lag */ if((F[Fnum-1].lags)[j]> rows){ fprintf(stderr,"ERROR (%s): lag %zd is longer then the data length\n", GB_PROGNAME,(F[Fnum-1].lags)[j]); exit(-1); } } /* allocate list of values */ F[Fnum-1].values = (double *) my_alloc(F[Fnum-1].argnum*sizeof(double)); } free(piece); } free(specstart); /* generate new matrix */ { /* placeholder for new data */ double **newvals; size_t newrows; size_t i,j,h; /* double **output = (double **) my_alloc(Fnum*sizeof(double *)); */ /* for(i=0;ifirstrow) firstrow=F[j].maxlag; /* allocate space */ newvals = (double **) my_alloc(columns*sizeof(double *)); for(i=0;i0 ? data[F[j].indeces[h]-1][i-F[j].lags[h]] : i+1); if(evaluator_evaluate (F[j].f,F[j].argnum,F[j].args,F[j].values) >= 0) valid=1; } /* if passed all checks, add it */ if(valid==1){ for(j=0;jtran]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ data_transpose(matrix,rowlength,columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'f': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->flat]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ data_flat(matrix,rowlength,columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'l': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[-> log]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ { size_t i,j; for(i=0;i<*columnlength;i++) for(j=0;j<*rowlength;j++) (*matrix)[i][j]=log((*matrix)[i][j]); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'd': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->diff]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ data_diff(matrix,rowlength,columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'D': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->denan]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ denan_data(matrix,rowlength,columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'w': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->norm]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ { size_t i,j; for(i=0;i<*columnlength;i++){ double sum=0; for(j=0;j<*rowlength;j++) if(finite( (*matrix)[i][j] )) sum += (*matrix)[i][j]; for(j=0;j<*rowlength;j++) (*matrix)[i][j]/=sum; } } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'z': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->detr]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ { size_t i,j; for(i=0;i<*columnlength;i++){ double mean=0; size_t num=0; for(j=0;j<*rowlength;j++) if(finite( (*matrix)[i][j] )){ mean += (*matrix)[i][j]; num++; } mean /= num; for(j=0;j<*rowlength;j++) (*matrix)[i][j]-=mean; } } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'Z': /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[->zscr]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ { size_t i,j; for(i=0;i<*columnlength;i++){ double mean=0,stdev=0,error=0,dtmp1; size_t num=0; for(j=0;j<*rowlength;j++) if(finite( (*matrix)[i][j] )) { mean += (*matrix)[i][j]; num++; } mean /= num; for(j=0;j<*rowlength;j++) if(finite( (*matrix)[i][j] )) { error += ( dtmp1= (*matrix)[i][j] - mean); stdev += dtmp1*dtmp1; } stdev = sqrt( (stdev-error*error/num)/(num-1)); for(j=0;j<*rowlength;j++) (*matrix)[i][j]=((*matrix)[i][j]-mean)/stdev; } } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; case 'P': /* ignored; the data will be printed at the end of this slice */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr," %zdx%zd\t[print ]\t",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"%zdx%zd\n",*rowlength,*columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ break; #if defined HAVE_LIBGSL case '<': { size_t i; char *funslist; /* select the list of functions specification */ for (i=itmp1;imax) max = length[i]; if(length[i]0) matrix_transform(&data,&datanum,&rowlength, finaltransform,o_verbose); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"output => %zdx%zd\n",rowlength,datanum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print the result */ PRINTMATRIXBYCOLS(stdout,data,rowlength,datanum); } else{/* final output has columns of different lengths */ /* can't apply final transformations */ if(strlen(finaltransform)>0) fprintf(stderr, "WARNING (%s): cannot transform final output; request ignored\n",GB_PROGNAME); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1){ fprintf(stderr,"output (%zd columns of size) =>",datanum); for(i=0;i0) for(i=0;imax then count is reversed \n"); fprintf(stdout," and skip must be negative (-1 by default). Different specs \n"); fprintf(stdout," are separated by semicolon ';' and considered sequentially. \n"); fprintf(stdout," trans is a list of transformations applied to selected data: 'd' \n"); fprintf(stdout," take the diff of subsequent columns; 'D' remove all rows with\n"); fprintf(stdout," at least one Not-A-Number (NAN) entry; 'f' flatten the output\n"); fprintf(stdout," piling all columns; 'l' take log of all entries, 'P' print all\n"); fprintf(stdout," entries collected as a data-block; 't' transpose the matrix \n"); fprintf(stdout," of data; 'z' subtract from the entries in each column their \n"); fprintf(stdout," mean; 'Z' replace the entry in each column with their zscore;\n"); fprintf(stdout," 'w' divide the entry in each columns by their mean. \n\n"); fprintf(stdout," '<..;..>' functions separated by semicolons in angle brackets\n"); fprintf(stdout," can be used for generic data transformation; the function is \n"); fprintf(stdout," computed for each row of data. Variables names are 'x' followed\n"); fprintf(stdout," by the number of the column and optionally by 'l' and the number\n"); fprintf(stdout," of lags. For instance 'x2+x3l1' means the sum of the entries in\n"); fprintf(stdout," the 2nd column plus the entries in the 3rd column in the previous\n"); fprintf(stdout," row. 'x0' stands for the row number and 'x' is equal to 'x1'\n\n"); fprintf(stdout," '<@..;..>' if the functions specification starts with a '@' the\n"); fprintf(stdout," functions are computed recursively along the columns. In this\n"); fprintf(stdout," case the number after the 'x' is the relative column counted \n"); fprintf(stdout," starting from the one considered at each step. \n\n"); fprintf(stdout," '{...}' a function in curly brackets can be use to select data:\n"); fprintf(stdout," only rows that return a non-negative value are retained \n"); fprintf(stdout,"Options: \n"); fprintf(stdout," -F set the input fields separators (default ' \\t') \n"); fprintf(stdout," -o set the output format (default '%%12.6e') \n"); fprintf(stdout," -e set the output format for empty fields (default '%%13s') \n"); fprintf(stdout," -s set the output separation string (default ' ') \n"); fprintf(stdout," -t define global transformations applied before each output (default '')\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbget 'file(1:3)ld' select the first three columns in 'file', take the\n"); fprintf(stdout," log and the difference of successive columns;\n"); fprintf(stdout," gbget 'file(2,-10:-1) select the last ten elements of the second'\n"); fprintf(stdout," of 'file' and print their squares\n"); fprintf(stdout," gbget '[2]()' '[1]()' < ... select the second and first data block from the\n"); fprintf(stdout," standard input.\n"); fprintf(stdout," gbget 'file(1:3)' select the first three columns in 'file' and\n"); fprintf(stdout," in each row multiply the first two entries and.\n"); fprintf(stdout," subtract the third.\n"); fprintf(stdout," gbget 'file()<@x1+x2>' print the sum of two subsequent columns\n"); fprintf(stdout," gbget 'file(1:3){x2-2}' select the first three columns in 'file' for the\n"); fprintf(stdout," rows whose second field is not lower then 2 \n"); return(0); } else if(opt=='F'){ /*set the input fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='e'){ /*set the format for empty fields*/ EMPTY = strdup(optarg); } else if(opt=='o'){ /*set the output string */ FLOAT = strdup(optarg); } else if(opt=='s'){ /*set the separator*/ SEP = strdup(optarg); } else if(opt=='t'){ /* set the final transformations */ size_t len1 = strlen(optarg); size_t len2 = strlen(finaltransform); finaltransform = (char *) my_realloc((void *) finaltransform ,(len1+len2+1)*sizeof(char)); strcpy(finaltransform+len2,optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* build the list of space and tabs separated specs */ specsnum=0; for(spec=optind;spec<(size_t) argc;spec++){ char *piece=strdup (argv[spec]); char *stmp1=piece,*stmp2; while( (stmp2=strsep (&stmp1," \t")) != NULL) if(strlen(stmp2) >0){ specsnum++; specs = (char **) my_realloc((void *) specs,specsnum*sizeof(char *)); specs[specsnum-1] = strdup(stmp2); } free(piece); } /* +++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose){ fprintf(stderr,"Output format='%s' Separator='%s' Empty format='%s' Empty string='%s' ", FLOAT,SEP,EMPTY," "); if(o_binary_in || o_binary_out){ fprintf(stderr,"Binary:"); if(o_binary_in) fprintf(stderr," in"); if(o_binary_out) fprintf(stderr," out"); } fprintf(stderr,"\n"); } /* +++++++++++++++++++++++++++++++++++++++++++++ */ /* parse line for data specification */ /* --------------------------------- */ for(spec=0;spec0 ? b < bfin[bs] : b+2 > bfin[bs] );b+=bskip[bs]){ /* create the new matrix */ new_matrix(&newdata,&columnlength,&rowlength, vals[b],columns[b],rows[b],columnslice,rowslice); /* apply transformations to the new matrix */ if(strlen(transform)>0 && strcmp(transform,"P") ) matrix_transform(&newdata,&columnlength,&rowlength, transform,o_verbose); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose==1) fprintf(stderr,"-> %zdx%zd\n",rowlength,columnlength); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* add the new matrix to the data */ add_matrix(&data,&length,&datanum, newdata,columnlength,rowlength); /* if P transformation was specified then print data collected so far and restart to collect them */ if(index(transform,'P') != NULL){ /* final transformation and print */ print_data(&datanum, &length, &data, finaltransform,o_verbose); /* add two empty lines in ASCII output */ if(o_binary_out==0) printf("\n\n"); } } } free(bini); free(bfin); free(bskip); } /* free everything */ free(columnslice); free(rowslice); free(blockslice); free(transform); free(newdata); }/* END OF DATA CREATION */ /* free some space */ free(old_filename); free(filename); free(splitstring); if(specsnum>0){ size_t i; for(i=0;i #include #include #include #include #include #include #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void ***ddf; } Function; struct objdata { Function * F; size_t rows; size_t columns; double **data; }; /* Ordinary Least Squares estimation */ /* --------------------------------- */ /* The objective function sumsq= \sum_{i=1}^n f(x_i,\theta )^2, the sum of the squared residuals, is minimized. Under the hypothesis of normally distributed errors the log-likelihood is LL=-n/2-n*log(sumsq/n)-n*log(sqrt(2*pi)) */ int ols_obj_f (const gsl_vector * x, void *params, gsl_vector * f){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values)); } return GSL_SUCCESS; } int ols_obj_df (const gsl_vector * x, void *params, gsl_matrix * J){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; for(i=columns;idf)[j],columns+pnum,F->argname,values)); } } return GSL_SUCCESS; } int ols_obj_fdf (const gsl_vector * x, void *params, gsl_vector * f, gsl_matrix * J){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; for(i=columns;if,columns+pnum,F->argname,values)); /* for(j=0;jargname[j],values[j]); */ /* printf("-> %f\n",evaluator_evaluate (F->f,columns+pnum,F->argname,values)); */ for(j=0;jdf)[j],columns+pnum,F->argname,values)); } } return GSL_SUCCESS; } /* compute variance-covariance matrix */ gsl_matrix * ols_varcovar(const int o_varcovar, gsl_multifit_fdfsolver *s, const struct objdata obj, const size_t Pnum){ size_t i,j,h; const size_t rows = obj.rows; const size_t columns = obj.columns; double **data = obj.data; const Function *F = obj.F; const double sumsq = pow(gsl_blas_dnrm2(s->f),2.); const double sigma2 = sumsq/(rows-Pnum); gsl_matrix *covar = gsl_matrix_alloc (Pnum,Pnum); switch(o_varcovar){ case 0: /* inverse "reduced Hessian", as in Numerical Recipes */ { #ifdef GSL_VER_2 gsl_matrix *J = gsl_matrix_alloc(rows, Pnum); gsl_multifit_fdfsolver_jac(s, J); #else gsl_matrix *J = s->J; #endif gsl_multifit_covar (J, 1e-6, covar); gsl_matrix_scale (covar,sigma2); #ifdef GSL_VER_2 gsl_matrix_free (J); #endif } break; case 1: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h,df1*df2*f*f); } } gsl_matrix_add (J,dJ); } /* invert information matrix; dJ store temporary LU decomp. */ gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2*sigma2); } break; case 2: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2+f*ddf); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2); } break; case 3: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian and information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2+f*ddf); gsl_matrix_set (dJ,j,h,df1*df2*f*f); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } /* Minimum Absolute Deviation (MAD) estimation */ /* ------------------------------------------- */ /* The distribution considered is f(x) = 1/a exp( (x-m)/a ) the objective function becomes obj = 1/n \sum_i |x_i-m| . The log-likelihood reads LL= -n*( 1+ log(2*obj) ). */ double mad_obj_f (const gsl_vector * x, void *params){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; double res=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values)); } res/=rows; return res; } /* compute variance-covariance matrix */ gsl_matrix *mad_varcovar(const int o_varcovar, const gsl_multimin_fminimizer *s, const struct objdata params, const size_t Pnum) { size_t i,j,h; const size_t rows=((struct objdata )params).rows; const size_t columns=((struct objdata )params).columns; double **data=((struct objdata )params).data; const Function *F = ((struct objdata )params).F; const double a = s->fval; gsl_matrix *covar = gsl_matrix_alloc (Pnum,Pnum); switch(o_varcovar){ case 0: /* inverse "reduced Hessian", as in Numerical Recipes */ case 1: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double df1,df2; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute information matrix */ for(i=0;idf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h,df1*df2); } } gsl_matrix_add (J,dJ); } /* invert information matrix; dJ store temporary LU decomp. */ gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 2: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2+(f>0 ? 1. : -1.)*a*ddf); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 3: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian and information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2+(f>0 ? 1. : -1.)*a*ddf); gsl_matrix_set (dJ,j,h,df1*df2); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } /* Asymmetric Minimum Absolute Deviation (AMAD) estimation */ /* ------------------------------------------------------- */ /* The distribution considered is / exp( (m-x)/al) if x<0 f(x) = 1/(al+ar) | \ exp( (x-m)/ar ) if x>0 the objective function becomes obj = \sqrt{sumL/nL}+\sqrt{sumR/nR} with sumL = \sum{x_i<0} -x_i ; nL = \sum{x_i<0} 1 sumL = \sum{x_i>0} x_i ; nL = \sum{x_i>0} 1 The log-likelihood reads LL= -n*( 1+2 log(obj) ). */ double amad_obj_f (const gsl_vector * x, void *params){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; double sumL=0,sumR=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values); if(dtmp1>0) sumR += dtmp1; else sumL += -dtmp1; } sumL/=rows; sumR/=rows; return sqrt(sumL)+sqrt(sumR); } /* compute variance-covariance matrix */ gsl_matrix *amad_varcovar(const int o_varcovar, const gsl_multimin_fminimizer *s, const struct objdata params, const size_t Pnum) { size_t i,j,h; const size_t rows=((struct objdata )params).rows; const size_t columns=((struct objdata )params).columns; double **data=((struct objdata )params).data; const Function *F = ((struct objdata )params).F; double values[columns+Pnum]; double al,ar; gsl_matrix *covar = gsl_matrix_alloc (Pnum,Pnum); /* compute al,ar at the minimum */ { double sumL=0,sumR=0; /* set the parameter values */ for(i=columns;ix,i-columns); /* set the variables values */ for(i=0;if,columns+Pnum,F->argname,values); if(dtmp1>0) sumR += dtmp1; else sumL += -dtmp1; } sumL/=rows; sumR/=rows; al = sumL+sqrt(sumL*sumR); ar = sumR+sqrt(sumL*sumR); } switch(o_varcovar){ case 0: /* inverse "reduced Hessian", adapted following Numerical Recipes */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double df1,df2; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;idf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_scale (covar,al*ar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 1: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h,df1*df2/(f>0 ? ar*ar : al*al)); } } gsl_matrix_add (J,dJ); } /* invert information matrix; dJ store temporary LU decomp. */ gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 2: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2+(f>0 ? al : -ar)*ddf); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_scale (covar,al*ar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 3: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian and information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,df1*df2/(al*ar)+ddf/(f>0 ? ar : -al)); gsl_matrix_set (dJ,j,h,df1*df2/(f>0 ? ar*ar : al*al)); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } int main(int argc,char* argv[]){ int i; char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_varcovar=0; int o_method=0; /* data from sdtin */ size_t rows=0,columns=0; double **data=NULL; /* definition of the function */ Function F; char **Param=NULL; double *Pval=NULL; gsl_matrix *Pcovar=NULL; /* covariance matrix */ size_t Pnum=0; /* minimization tolerance */ double eps=1e-5; size_t maxiter=500; double restart_accuracy=1e-5; size_t restart_maxiter=2000; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* initialize global variables */ initialize_program(argv[0]); /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"v:hF:O:V:M:e:E:I:i:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Non linear least square regression. Read data in columns (X_1 .. X_N).\n"); fprintf(stdout,"The model is specified by a function f(x1,x2...) using variables names\n"); fprintf(stdout,"x1,x2,..,xN for the first, second .. N-th column of data. f(x1,x2,...)\n"); fprintf(stdout,"are assumed i.i.d.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -O type of output (default 0)\n"); fprintf(stdout," 0 parameters \n"); fprintf(stdout," 1 parameters and errors \n"); fprintf(stdout," 2 and residuals \n"); fprintf(stdout," 3 parameters and variance matrix \n"); fprintf(stdout," 4 parameters, errors and s-scores\n"); fprintf(stdout," -V variance matrix estimation (default 0)\n"); fprintf(stdout," 0 \n"); fprintf(stdout," 1 < J^{-1} > \n"); fprintf(stdout," 2 < H^{-1} > \n"); fprintf(stdout," 3 < H^{-1} J H^{-1} > \n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3 covariance matrix \n"); fprintf(stdout," 4 minimization steps \n"); fprintf(stdout," 5 model definition \n"); fprintf(stdout," -M estimation method (default 0)\n"); fprintf(stdout," 0 ordinary least square (OLS) \n"); fprintf(stdout," 1 minimum absolute deviation (MAD)\n"); fprintf(stdout," 2 asymmetric MAD \n"); fprintf(stdout," -e minimization tolerance (default 1e-5)\n"); fprintf(stdout," -i minimization max iterations (default 500) \n"); fprintf(stdout," -E restart accuracy (default 1e-5, ignored with option -M 0)\n"); fprintf(stdout," -I restart max iterations (default 2000, ignored with option -M 0)\n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help \n"); return(0); } else if(opt=='e'){ /* set the minimization tolerance */ eps = fabs(atof(optarg)); } else if(opt=='E'){ /* set the accuracy of the simplex (method 1 and 2) */ restart_accuracy = fabs(atof(optarg)); } else if(opt=='i'){ /* maximum number of iterations for the optimizer */ maxiter = atoi(optarg); } else if(opt=='I'){ /* maximum number of iterations for restarting */ restart_maxiter = atoi(optarg); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='M'){ /*set the estimation method*/ o_method = atoi(optarg); if(o_method<0 || o_method>2){ fprintf(stderr,"ERROR (%s): method option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_method); exit(-1); } } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); if(o_output<0 || o_output>4){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>3){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='v'){ o_verbose = atoi(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* load data */ loadtable(&data,&rows,&columns,0,splitstring); /* parse line for functions and variables specification */ if(optind == argc){ fprintf(stderr,"ERROR (%s): please provide a function to fit.\n", GB_PROGNAME); exit(-1); } for(i=optind;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ F.f = evaluator_create (stmp3); assert(F.f); } free(stmp3); } free(piece); } /* check that everything is correct */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } { size_t i,j; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; char **storedname=NULL; size_t storednum=0; /* retrive list of arguments and their number */ /* notice that storedname is not allocated */ { int argnum; evaluator_get_variables (F.f,&storedname,&argnum); storednum = (size_t) argnum; } /* check the definition of the function */ for(i=0;i0?atoi(stmp1+1):0); if(index>columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,index); exit(-1); } } else { for(j=0;j1){ F.ddf = (void ***) my_alloc(Pnum*sizeof(void **)); for(i=0;i4){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Model [xi: i-th data column]:\n"); switch(o_method){ case 0: fprintf(stderr," %s ~ i.i.d. N(0,stdev) \n", evaluator_get_string (F.f) ); break; case 1: fprintf(stderr," %s ~ i.i.d. L(0,sdev) \n", evaluator_get_string (F.f) ); break; case 2: fprintf(stderr," %s ~ i.i.d. L(0,a_l,a_r) \n", evaluator_get_string (F.f) ); break; } fprintf (stderr,"\n Parameters and initial conditions:\n"); for(i=0;i1){ fprintf (stderr,"\n Model second derivatives:\n"); for(i=0;i3){ fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Iter "); for(i=0;if); do { double f,g,df,dg; solver_status = gsl_multifit_fdfsolver_iterate (s); /* compute increment in object and estimates */ f=gsl_blas_dnrm2 (s->f); df=fabs(oldf-f); oldf=f; g=gsl_blas_dnrm2 (s->x); dg=gsl_blas_dnrm2 (s->dx); iter++; /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr,"%3zd ",iter); for(i=0;ix, i)); fprintf (stderr," %8.5e %8.5e %8.5e %8.5e %s\n", f,df,g,dg,gsl_strerror (solver_status)); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* if (status) */ /* break; */ /* check condition on estimates precision */ if( estimate_status == GSL_CONTINUE && dg < eps && dg < eps*g ) estimate_status = GSL_SUCCESS; /* check condition of object convergence */ if( object_status == GSL_CONTINUE && df < eps && df < eps*fabs(f) ) object_status = GSL_SUCCESS; } while ( (estimate_status == GSL_CONTINUE || object_status == GSL_CONTINUE) && iter < maxiter); if(iter == maxiter && (estimate_status == GSL_CONTINUE || object_status == GSL_CONTINUE) ) fprintf(stderr,"WARNING (%s): maximum number of iterations without convergence.\n", GB_PROGNAME); /* store final values */ for(i=0;ix,i); /* build the covariance matrix */ if(o_output>0 || o_verbose>2) Pcovar= ols_varcovar(o_varcovar,s,param,Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>1){ const double sumsq = pow(gsl_blas_dnrm2(s->f),2); const double sigma = sqrt(sumsq/(rows-Pnum)); const double LL= -(1+log(sumsq/rows)+log(2*M_PI) )*0.5*rows; /* n.b.: the baseline model is NOT params=0 BUT params=initial condition */ const double R2=1-sumsq/pow(sumrootsq0,2); const double R2adj=1-(sumsq/(rows-Pnum-1))/(pow(sumrootsq0,2.)/(rows-1)); const double F=((rows-Pnum-1)/Pnum)*R2/(1-R2); fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," number of observations = %zd\n",rows); fprintf(stderr," sum of squared residual (SSR) = %g\n",sumsq); fprintf(stderr," number degrees of freedom (ndf) = %zd\n",rows-Pnum); fprintf(stderr," residuals stdev sqrt(SSR/ndf) = %g\n",sigma); fprintf(stderr," R^2 (sum sq. initial /sum sq. estimated) = %g\n",R2); fprintf(stderr," adjusted R^2 = %g\n",R2adj); fprintf(stderr," F statistics = %g\n",F); fprintf(stderr," One-sided P-value: Pr{stat>obs.} = %g\n",gsl_cdf_chisq_Q(Pnum*F,Pnum)); fprintf(stderr," chi-square test P(>SSR | ndf) = %g\n", gsl_cdf_chisq_Q (sumsq,rows-Pnum)); fprintf(stderr," log-likelihood log(L) = %g\n",LL); fprintf(stderr," Akaike information criterion AIC = %g\n",2*Pnum-2*LL ); fprintf(stderr," corrected Akaike criterion AICc = %g\n", (2*Pnum*(Pnum+1))/(rows-Pnum-1)+2*Pnum-2*LL); fprintf(stderr," Bayesian information criterion BIC = %g\n",Pnum*log(rows)-2*LL); fprintf(stderr," Hannan–Quinn criterion HQC = %g\n", 2*Pnum*log(log(rows))+rows*log(sumsq/rows) ); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* free space */ gsl_multifit_fdfsolver_free (s); } else if (o_method == 1){ const gsl_multimin_fminimizer_type *T = gsl_multimin_fminimizer_nmsimplex2; gsl_multimin_fminimizer *s = NULL; gsl_vector *x = gsl_vector_alloc (Pnum); gsl_vector *x0 = gsl_vector_alloc (Pnum); gsl_vector *ss = gsl_vector_calloc(Pnum); int converged; size_t totiter=0; gsl_multimin_function obj; /* set minimizer initial condition */ struct objdata param; /* set the parameter for the function */ param.F = &F; param.rows = rows; param.columns = columns; param.data =data ; /* set the object structure */ obj.f = &mad_obj_f; obj.n = Pnum; obj.params = ¶m; /* allocate the solver */ s = gsl_multimin_fminimizer_alloc (T, Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>3){ size_t i; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Stopping criteria: max{simplex size} < %g\n\n",eps); fprintf(stderr," Iter "); for(i=0;idata)[i] = 0.05*fabs(gsl_vector_get(x,i))+0.0075; /* initialize the solver */ gsl_multimin_fminimizer_set (s, &obj,x, ss); /* not converged yet */ converged=0; do { status = gsl_multimin_fminimizer_iterate(s); const double size = gsl_multimin_fminimizer_size (s); iter++; /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr,"%3zd ",iter); for(i=0;ix, i)); fprintf (stderr," %8.5e %8.5e %s\n", s->fval,size,gsl_strerror (status)); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ status=gsl_multimin_test_size (size,eps); } while (status == GSL_CONTINUE && iter < maxiter); /* x = x-s->x */ gsl_blas_daxpy (-1,s->x,x); /* norm_diff = ||x-s->x|| */ norm_diff=gsl_blas_dnrm2 (x); if( norm_diff < restart_accuracy ){ converged = 1; /* store final values */ for(i=0;ix,i); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr," min=%8.10e ||x-x_old||=%8.5e =>",s->fval,norm_diff); if(converged==0) fprintf (stderr," restart\n"); else fprintf (stderr," converged\n"); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* update the initial point: x = s->x */ if(converged==0){ gsl_vector_memcpy (x,s->x); totiter += iter; } if(totiter >= restart_maxiter) fprintf(stderr,"WARNING (%s): maximum number of iterations without convergence. final tolerance = %f\n", GB_PROGNAME,norm_diff); } while (converged == 0 && totiter < restart_maxiter); /* store final values */ for(i=0;ix,i); /* build the covariance matrix */ if(o_output>0 || o_verbose>2) Pcovar = mad_varcovar(o_varcovar,s,param,Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>1){ double values[Pnum+columns]; size_t i,j; double residuals[rows]; double ll0,D,SC95; const double LL= -(1+log(2*s->fval))*rows; /* compute the absolute deviation with initial parameters; remember that the objective function is already divided by the number of observations */ ll0=obj.f(x0,¶m); /* compute the residual with estimated parameters */ for(i=columns;ifval)*rows/SC95; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," number of observations = %zd\n",rows); fprintf(stderr," average absolute residuals a = %g\n",s->fval); /* fprintf(stderr," conf. int. median of res. (p=0.95) SC95 = %f\n",SC95); */ fprintf(stderr," Shrader-McKean statistics D = %g\n",D); fprintf(stderr," One-sided P-value: Pr{stat>obs.} = %g\n",gsl_cdf_chisq_Q(D,Pnum)); fprintf(stderr," McKean-Sievers coeff. of determination R2 = %g\n",(ll0-s->fval)/(ll0-s->fval+(rows-Pnum-1)*SC95/(2*rows))); fprintf(stderr," log-likelihood log(L) = %g\n",LL); fprintf(stderr," Akaike information criterion AIC = %g\n",2*Pnum-2*LL ); fprintf(stderr," corrected Akaike criterion AICc = %g\n", (2*Pnum*(Pnum+1))/(rows-Pnum-1)+2*Pnum-2*LL); fprintf(stderr," Bayesian information criterion BIC = %g\n",Pnum*log(rows)-2*LL); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* free space */ gsl_vector_free(ss); gsl_multimin_fminimizer_free (s); } else if (o_method == 2){ const gsl_multimin_fminimizer_type *T = gsl_multimin_fminimizer_nmsimplex2; gsl_multimin_fminimizer *s = NULL; gsl_vector *x = gsl_vector_alloc (Pnum); gsl_vector *ss = gsl_vector_calloc(Pnum); int converged; size_t totiter=0; gsl_multimin_function obj; /* set minimizer initial condition */ struct objdata param; /* set the parameter for the function */ param.F = &F; param.rows = rows; param.columns = columns; param.data =data ; /* set the object structure */ obj.f = &amad_obj_f; obj.n = Pnum; obj.params = ¶m; /* allocate the solver */ s = gsl_multimin_fminimizer_alloc (T, Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>3){ size_t i; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Stopping criteria: max{simplex size} < %g\n\n",eps); fprintf(stderr," Iter "); for(i=0;idata)[i] = 0.05*fabs(gsl_vector_get(x,i))+0.0075; /* initialize the solver */ gsl_multimin_fminimizer_set (s, &obj,x, ss); /* not converged yet */ converged=0; do { status = gsl_multimin_fminimizer_iterate(s); const double size = gsl_multimin_fminimizer_size (s); iter++; /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr,"%3zd ",iter); for(i=0;ix, i)); fprintf (stderr," %8.5e %8.5e %s\n", s->fval,size,gsl_strerror (status)); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ status=gsl_multimin_test_size (size,eps); } while (status == GSL_CONTINUE && iter < maxiter); /* x = x-s->x */ gsl_blas_daxpy (-1,s->x,x); /* norm_diff = ||x-s->x|| */ norm_diff=gsl_blas_dnrm2 (x); if( norm_diff < restart_accuracy ){ converged = 1; /* store final values */ for(i=0;ix,i); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr," min=%8.10e ||x-x_old||=%8.5e =>",s->fval,norm_diff); if(converged==0) fprintf (stderr," restart\n"); else fprintf (stderr," converged\n"); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* update the initial point: x = s->x */ if(converged==0){ gsl_vector_memcpy (x,s->x); totiter += iter; } if(totiter >= restart_maxiter) fprintf(stderr,"WARNING (%s): maximum number of iterations without convergence. final tolerance = %f\n", GB_PROGNAME,norm_diff); } while (converged == 0 && totiter < restart_maxiter); /* build the covariance matrix, if needed */ if(o_output>0 || o_verbose>2) Pcovar = amad_varcovar(o_varcovar,s,param,Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>1){ double values[Pnum+columns]; size_t i,j; double sumL=0,sumR=0,al,ar,dtmp1; const double LL= -(1+2*log(s->fval))*rows; fprintf(stderr," ------------------------------------------------------------\n"); /* compute sumL and sumR at the minimum */ for(i=columns;i0) sumR += dtmp1; else sumL += -dtmp1; } sumL/=rows; sumR/=rows; al = sumL+sqrt(sumL*sumR); ar = sumR+sqrt(sumL*sumR); fprintf(stderr," number of observations = %zd\n",rows); fprintf(stderr," square root sum of left residuals srqrt(S_L) = %g\n",sqrt(sumL)); fprintf(stderr," square root sum of right residuals srqrt(S_R) = %g\n",sqrt(sumR)); fprintf(stderr," sqrt(S_L)+ srqrt(S_R) = %g\n",s->fval); fprintf(stderr," a_L = %g\n",al); fprintf(stderr," a_R = %g\n",ar); fprintf(stderr," log-likelihood log(L) = %g\n",LL); fprintf(stderr," Akaike information criterion AIC = %g\n",2*Pnum-2*LL ); fprintf(stderr," corrected Akaike criterion AICc = %g\n", (2*Pnum*(Pnum+1))/(rows-Pnum-1)+2*Pnum-2*LL); fprintf(stderr," Bayesian information criterion BIC = %g\n",Pnum*log(rows)-2*LL); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* free space */ gsl_multimin_fminimizer_free (s); gsl_vector_free(ss); gsl_vector_free(x); } /* output */ /* ------ */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>2){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," variance matrix = "); switch(o_varcovar){ case 0: fprintf(stderr,"\n"); break; case 1: fprintf(stderr,"J^{-1}\n"); break; case 2: fprintf(stderr,"H^{-1}\n"); break; case 3: fprintf(stderr,"H^{-1} J H^{-1}\n"); break; } fprintf(stderr,"\n"); for(i=0;i1) fprintf(stderr," ------------------------------------------------------------\n"); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ switch(o_output){ case 0: { size_t i; /* +++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stderr,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i max[0]){ max[0] = dtmp0; } if(dtmp1 < min[1]){ min[1] = dtmp1; } else if(dtmp1 > max[1]){ max[1] = dtmp1; } } } /* choose the kernel to use */ switch(o_kerneltype){ case 0: K=Kepanechnikov2d; Kscale=2.40; break; case 1: K=Krectangular2d; break; case 2: K=Ksilverman2d_1; Kscale=2.78; break; case 3: K=Ksilverman2d_2; Kscale=3.12; break; default: fprintf(stdout,"unknown kernel; use %s -h\n",argv[0]); exit(+1); } /* the parameter h is not provided on command line it is set by an automatic procedure [Silverman p.86] */ if(o_setbandwidth == 0){ if(o_devariate == 1){ h[0]=scale[0]*Kscale/pow(size,1./6); h[1]=scale[1]*Kscale/pow(size,1./6); } else{ h[0]=sdev[0]*scale[0]*Kscale/pow(size,1./6); h[1]=sdev[1]*scale[1]*Kscale/pow(size,1./6); } } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stdout,"---- Density Parameters ------------\n"); /* data treatment */ fprintf(stdout,"de-variation "); if(o_devariate == 1) fprintf(stdout,"yes (detJ=%+.3e)",detJ); else fprintf(stdout,"no"); fprintf(stdout,"\n"); /* kernel type */ fprintf(stdout,"kernel type "); switch(o_kerneltype){ case 0: fprintf(stdout,"Epanechnikov"); break; case 1: fprintf(stdout,"Rectangular"); break; case 2: fprintf(stdout,"Silverman type 1"); break; case 3: fprintf(stdout,"Silverman type 2"); break; } fprintf(stdout,"\n"); /* bandwidth */ fprintf(stdout,"x bandwidth %.3e ",h[0]); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic with A=%.2f ",Kscale); if(scale[0] != 1 ) fprintf(stdout,"scaled by %f",scale[0]); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); fprintf(stdout,"y bandwidth %.3e ",h[1]); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic with A=%.2f ",Kscale); if(scale[1] != 1 ) fprintf(stdout,"scaled by %f",scale[1]); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ /* the boundaries of the interval are set to have a=xmin b=xmax */ /* delta[0] = eta[0]*(max[0]-min[0])/( M[0] -1. ); */ /* delta[1] = eta[1]*(max[1]-min[1])/( M[1] -1. ); */ /* a[0] = min[0]+.5*(1.-eta[0])*(max[0]-min[0]); */ /* a[1] = min[1]+.5*(1.-eta[1])*(max[1]-min[1]); */ /* the boundaries of the interval are set to have a=xmin-delta/2 b=xmax+delta/2 */ delta[0] = (max[0]-min[0])/( M[0] -2. ); delta[1] = (max[1]-min[1])/( M[1] -2. ); a[0] = min[0]-.5*delta[0]; a[1] = min[1]-.5*delta[1]; J[0] = (size_t) floor(h[0]/delta[0]); J[1] = (size_t) floor(h[1]/delta[1]); /* define the range on which to print */ infidx[0] = (int) nearbyint(.5*(1.-eta[0])*M[0]); supidx[0] = M[0]-(int) nearbyint(.5*(1.-eta[0])*M[0])-1; infidx[1] = (int) nearbyint(.5*(1.-eta[1])*M[1]); supidx[1] = M[1]-(int) nearbyint(.5*(1.-eta[1])*M[1])-1; /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* binning */ fprintf(stdout,"---- Binning Structure ------------\n"); fprintf(stdout," #bins bins range delta J printed range\n"); fprintf(stdout,"x: %zd [%+.2e,%+.2e] %+.2e %zd [%+.2e,%+.2e]\n", M[0],a[0],a[0]+(M[0]-1)*delta[0],delta[0],J[0], a[0]+infidx[0]*delta[0],a[0]+supidx[0]*delta[0]); fprintf(stdout,"y: %zd [%+.2e,%+.2e] %+.2e %zd [%+.2e,%+.2e]\n", M[1],a[1],a[1]+(M[1]-1)*delta[1],delta[1],J[1], a[1]+infidx[1]*delta[1],a[1]+supidx[1]*delta[1]); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ /* set the values of the kernel */ { double check=0.0; Kvals = (double *) my_alloc((2*J[0]+1)*(2*J[1]+1)*sizeof(double)); for(i=0;i<=2*J[0];i++){ const double dtmp0 = (int) i- (int) J[0]; for(j=0;j<=2*J[1];j++){ const double dtmp1 = (int) j- (int) J[1]; const double dtmp2 = dtmp0*dtmp0*delta[0]*delta[0]/(h[0]*h[0])+ dtmp1*dtmp1*delta[1]*delta[1]/(h[1]*h[1]) ; check += Kvals[i*(2*J[1]+1)+j] = K(dtmp2)/(h[0]*h[1]); } } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* binning */ fprintf(stdout,"---- Final Checks -----------------\n"); fprintf(stdout,"sum of kernel coeff. (~1): %e\n",check*(delta[0]*delta[1])); } /* ++++++++++++++++++++++++++++ */ } /* allocate the bins */ csi = (double *) my_alloc(M[0]*M[1]*sizeof(double)); csiz = (double *) my_alloc(M[0]*M[1]*sizeof(double)); if(o_sdev||o_skew||o_kurt) csiz2 = (double *) my_alloc(M[0]*M[1]*sizeof(double)); if(o_skew) csiz3 = (double *) my_alloc(M[0]*M[1]*sizeof(double)); if(o_kurt) csiz4 = (double *) my_alloc(M[0]*M[1]*sizeof(double)); /* prepare the binning */ for(i=0;i= M[0]-1 || index1 >= M[1]-1){ fprintf(stdout,"%zd %zd skipped!\n",index0,index1); continue; } csi[index0*M[1]+index1]+=(1.-epsilon0)*(1.-epsilon1); csi[index0*M[1]+index1+1]+=(1.-epsilon0)*epsilon1; csi[(index0+1)*M[1]+index1]+=epsilon0*(1.-epsilon1); csi[(index0+1)*M[1]+index1+1]+=epsilon0*epsilon1; csiz[index0*M[1]+index1]+=dtmp2*(1.-epsilon0)*(1.-epsilon1); csiz[index0*M[1]+index1+1]+=dtmp2*(1.-epsilon0)*epsilon1; csiz[(index0+1)*M[1]+index1]+=dtmp2*epsilon0*(1.-epsilon1); csiz[(index0+1)*M[1]+index1+1]+=dtmp2*epsilon0*epsilon1; if(o_sdev || o_skew || o_kurt){ dtmp3 = dtmp2*dtmp2; csiz2[index0*M[1]+index1]+=dtmp3*(1.-epsilon0)*(1.-epsilon1); csiz2[index0*M[1]+index1+1]+=dtmp3*(1.-epsilon0)*epsilon1; csiz2[(index0+1)*M[1]+index1]+=dtmp3*epsilon0*(1.-epsilon1); csiz2[(index0+1)*M[1]+index1+1]+=dtmp3*epsilon0*epsilon1; } if(o_skew){ csiz3[index0*M[1]+index1]+=dtmp2*dtmp3*(1.-epsilon0)*(1.-epsilon1); csiz3[index0*M[1]+index1+1]+=dtmp2*dtmp3*(1.-epsilon0)*epsilon1; csiz3[(index0+1)*M[1]+index1]+=dtmp2*dtmp3*epsilon0*(1.-epsilon1); csiz3[(index0+1)*M[1]+index1+1]+=dtmp2*dtmp3*epsilon0*epsilon1; } if(o_kurt){ csiz4[index0*M[1]+index1]+=dtmp3*dtmp3*(1.-epsilon0)*(1.-epsilon1); csiz4[index0*M[1]+index1+1]+=dtmp3*dtmp3*(1.-epsilon0)*epsilon1; csiz4[(index0+1)*M[1]+index1]+=dtmp3*dtmp3*epsilon0*(1.-epsilon1); csiz4[(index0+1)*M[1]+index1+1]+=dtmp3*dtmp3*epsilon0*epsilon1; } } /* normalization */ for(i=0;i0?i-Jx:0); const int max0 = (i+Jx>Mx-1?Mx-1:i+Jx); for(j=infidx[1];j<=supidx[1];j++){ const double tmpy = a[1]+j*delta[1]; const int min1 = (j-Jy>0?j-Jy:0); const int max1 = (j+Jy>My-1?My-1:j+Jy); double x,y,z=0,z2=0,z3=0,z4=0,ker=0; for(l=min0;l<=max0;l++){ for(m=min1;m<=max1;m++){ const double kval= Kvals[(i-l+Jx)*(2*Jy+1)+(j-m+Jy)]; ker+=kval*csi[l*My+m]; z += kval*csiz[l*My+m]; if(o_sdev || o_skew || o_kurt) z2+=kval*csiz2[l*My+m]; if(o_skew) z3+=kval*csiz3[l*My+m]; if(o_kurt) z4+=kval*csiz4[l*My+m]; } } check+=ker; /* compute and print x,y coordinates */ if(o_devariate == 1){ x = (tmpx*cphi-tmpy*sphi)*sdev[0]+ave[0]; y = (tmpy*cphi-tmpx*sphi)*sdev[1]+ave[1]; } else{ x = tmpx; y = tmpy; } printf(FLOAT_SEP,x); printf(FLOAT_SEP,y); if(o_mean) printf(FLOAT_SEP,(ker>0 ? z/ker : NAN)); if(o_sdev) printf(FLOAT_SEP,(ker>0?sqrt(fabs(z2/ker-z*z/(ker*ker))):NAN)); if(o_skew){ if(ker>0){ const double mz= z/ker; const double vz= fabs(z2/ker-mz*mz); printf(FLOAT_SEP,(z3/ker-3.*mz*z2/ker+2.*mz*mz*mz)/pow(vz,1.5)); } else printf(EMPTY_SEP,"NAN"); } if(o_kurt){ if(ker>0){ const double mz= z/ker; const double vz= fabs(z2/ker-mz*mz); printf(FLOAT_SEP,(z4/ker-4.*mz*z3/ker+6.*mz*mz*z2/ker-3.*mz*mz*mz*mz)/(vz*vz)-3.); } else printf(EMPTY_SEP,"NAN"); } printf("\n"); } printf("\n"); } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stdout,"sum of discrete density (~1): %e\n",check*delta[0]*delta[1]); } /* ++++++++++++++++++++++++++++ */ } exit(0); } gbutils-5.7.1/gbkreg.10000644000175000017500000000436413246755335011465 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBKREG "1" "March 2018" "gbkreg 5.7.1" "User Commands" .SH NAME gbkreg \- Kernel non linear regression function .SH SYNOPSIS .B gbkreg [\fI\,options\/\fR] .SH DESCRIPTION Kernel estimation of conditional moments. Data are read from standard input as couple (x,y). The moments of y are computed on a regular grid in x. The kernel bandwidth, if not provided with the option \fB\-H\fR, is set automatically. .SH OPTIONS .TP \fB\-n\fR number of equispaced points where moments are computed (default 64) .TP \fB\-H\fR set the kernel bandwidth .TP \fB\-S\fR scale the automatic kernel bandwidth .TP \fB\-K\fR choose the kernel to use (default 0) .TP 0 Epanenchnikov .TP 1 Rectangular .TP 2 Gaussian .TP \fB\-M\fR choose the method to compute the density (default 1) .TP 0 FFT (number of points rounded to nearest power of 2) .TP 1 discrete convolution (only with compact kernels) .TP 2 explicit summation (can be long) .TP \fB\-O\fR set the output, comma separated list of m mean, v standard deviation, s skewness and k kurtosis (default m) .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH EXAMPLES .TP gbkreg \-M 2 < file compute the kernel regression of the entries in the second column of 'file' vs. the entries in the first column. If more data columns exist in file they are ignored. Explicit summation method (slower) is used. .TP gbkreg \-02 < file compute the kernel regression of the standard deviation of the entries in the second column of 'file' vs. the entries in the first colum .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbkreg.c0000644000175000017500000004535413246754720011550 00000000000000/* gbkreg (ver. 5.6) -- Kernel non linear regression function Copyright (C) 2003-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #if defined HAVE_LIBGSL #include #include #endif /* required for sorting */ /* handy structure used in computation */ typedef struct couple { double x,y; } Couple; /* compare the couple according to first value */ int couplecompare(Couple *a, Couple *b){ if(a->x > b->x){ return 1; } else if(a->x < b->x){ return -1; } else{ return 0; } } int main(int argc,char* argv[]){ double **data=NULL; size_t size=0; char *splitstring = strdup(" \t"); /* OPTIONS */ int o_method=1; int o_kerneltype=0; int o_setbandwidth=0; int o_verbose=0; int o_mean=1; int o_sdev=0; int o_skew=0; int o_kurt=0; double ave,sdev,min,max; /* data statistics */ size_t M=64; /* number of bins */ double scale=1; /* optional scaling of the smoothing parameter */ double h=1;/* the smoothing parameter */ double (*K) (double) ; /* the kernel to use */ /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"H:K:n:S:hvM:O:F:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='S'){ /*set the scale parameter for smoothing*/ scale=atof(optarg); } else if(opt=='n'){ /*the number of bins*/ M=(size_t) atoi(optarg); } else if(opt=='K'){ /*the Kernel to use*/ o_kerneltype = atoi(optarg); } else if(opt=='H'){ /*set the kernel bandwidth*/ o_setbandwidth=1; h = atof(optarg); } else if(opt=='M'){ /*set the method to use*/ o_method= atoi(optarg); } else if(opt=='O'){ /*set the output type*/ char *output_opts[] = {"m","v","s","k",NULL}; char *subopts,*subvalue=NULL; /* remove default */ o_mean=0; /* define new output */ subopts = optarg; while (*subopts != '\0') switch (getsubopt(&subopts, output_opts, &subvalue)) { case 0: o_mean=1; break; case 1: o_sdev=1; break; case 2: o_skew=1; break; case 3: o_kurt=1; break; default:/* Unknown suboption. */ fprintf (stderr,"Unknown suboption `%s'\n", subvalue); exit(1); } } else if(opt=='v'){ /*increase verbosity*/ o_verbose=1; } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Kernel estimation of conditional moments. Data are read from standard input\n"); fprintf(stdout,"as couple (x,y). The moments of y are computed on a regular grid in x. The\n"); fprintf(stdout,"kernel bandwidth, if not provided with the option -H, is set automatically.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of equispaced points where moments are computed (default 64)\n"); fprintf(stdout," -H set the kernel bandwidth\n"); fprintf(stdout," -S scale the automatic kernel bandwidth\n"); fprintf(stdout," -K choose the kernel to use (default 0)\n"); fprintf(stdout," 0 Epanenchnikov\n"); fprintf(stdout," 1 Rectangular\n"); fprintf(stdout," 2 Gaussian\n"); fprintf(stdout," -M choose the method to compute the density (default 1)\n"); fprintf(stdout," 0 FFT (number of points rounded to nearest power of 2)\n"); fprintf(stdout," 1 discrete convolution (only with compact kernels)\n"); fprintf(stdout," 2 explicit summation (can be long)\n"); fprintf(stdout," -O set the output, comma separated list of m mean, v standard deviation,\n"); fprintf(stdout," s skewness and k kurtosis (default m)\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbkreg -M 2 < file compute the kernel regression of the entries in the\n"); fprintf(stdout," second column of 'file' vs. the entries in the first\n"); fprintf(stdout," column. If more data columns exist in file they are \n"); fprintf(stdout," ignored. Explicit summation method (slower) is used. \n"); fprintf(stdout," gbkreg -02 < file compute the kernel regression of the standard deviation\n"); fprintf(stdout," of the entries in the second column of 'file' vs. the \n"); fprintf(stdout," entries in the first colum\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load the data */ load2(&data,&size,0,splitstring); /* sort with respect to the first array */ sort2(data[0],data[1],size,2); /* compute the statistics */ moment_short(data[0],size,&ave,&sdev,&min,&max); /* the parameter h is set for a Gaussian kernel [Silverman p.48] */ /* if not provided on command line */ if(o_setbandwidth == 0){ const unsigned index1 = size/4; const unsigned index2 = 3*size/4; const double inter4 = fabs(data[0][index1]-data[0][index2])/1.34; const double A = (sdev>inter4?inter4:sdev); h=scale*0.9*A/pow(size,.2); } /* nearest power of two */ if(o_method==0) M=(int) pow(2,nearbyint(log((double) M)/M_LN2)); /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* bandwidth */ fprintf(stdout,"bandwidth %e ",h); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic"); if(scale != 1 ) fprintf(stdout,"scaled by %e",scale); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); /* kernel type */ fprintf(stdout,"kernel "); switch(o_kerneltype){ case 0: fprintf(stdout,"Epanechnikov"); break; case 1: fprintf(stdout,"Rectangular"); break; case 2: fprintf(stdout,"Gaussian"); break; } fprintf(stdout,"\n"); fprintf(stdout,"output "); if(o_mean) fprintf(stdout,"mean "); if(o_sdev) fprintf(stdout,"st.dev. "); if(o_skew) fprintf(stdout,"skewness "); if(o_kurt) fprintf(stdout,"kurtosis "); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ if(o_method==0){/*=== FFT methods ===*/ size_t i; double *f;/* the density */ double *mf;/* the estimation of f*y */ double *m2f=0;/* the estimation of f*y*y */ double *m3f=0;/* the estimation of f*y**3 */ double *m4f=0;/* the estimation of f*y**4 */ /* the boundaries of the interval are set according... */ const double a = min-2*h; const double b = max+2*h; const double delta = (b-a)/(M-1); /* const double margin=.05; */ /* const double a = min - 4*h - (max-min+4*h)*margin; */ /* const double b = max + 4*h + (max-min+4*h)*margin; */ /* const double delta = (b-a)/(M-1); */ /* choose the kernel to use */ switch(o_kerneltype){ case 0: K=Kepanechnikov_tilde; break; case 1: K=Krectangular_tilde; break; case 2: K=Kgauss_tilde; break; default: fprintf(stdout,"unknown kernel; use %s -h\n",argv[0]); exit(+1); } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* method */ fprintf(stdout,"method FFT\n"); /* binning */ fprintf(stdout,"# bins %zd\n",M); fprintf(stdout,"range [%.3e,%.3e]\n",a,b); } /* ++++++++++++++++++++++++++++ */ /* allocate the bins */ f = (double *) my_alloc(M*sizeof(double)); mf = (double *) my_alloc(M*sizeof(double)); if(o_sdev || o_skew || o_kurt) m2f = (double *) my_alloc(M*sizeof(double)); if(o_skew == 1) m3f = (double *) my_alloc(M*sizeof(double)); if(o_kurt == 1) m4f = (double *) my_alloc(M*sizeof(double)); /* prepare the binning */ for(i=0;i=M || dtmp1<0) continue; f[index]+=1-epsilon; mf[index]+= (dtmp1=(1-epsilon)*dtmp2); if(o_sdev || o_skew || o_kurt) m2f[index]+= (dtmp1*=dtmp2); if(o_skew) m3f[index]+=dtmp1*dtmp2; if(o_kurt) m4f[index]+=dtmp1*dtmp2*dtmp2; if(index0){ const double m = mf[i]/f[i]; const double v =( o_sdev||o_skew||o_kurt ? fabs(m2f[i]/f[i]-m*m) : 0.0); if(o_mean) printf(FLOAT_SEP,m); if(o_sdev) printf(FLOAT_SEP,sqrt(v)); if(o_skew) printf(FLOAT_SEP, (m3f[i]/f[i]-3.*m*m2f[i]/f[i]+2.*m*m*m)/pow(v,1.5)); if(o_kurt) printf(FLOAT_SEP, (m4f[i]/f[i]-4.*m*m3f[i]/f[i]+ 6.*m*m*m2f[i]/f[i]-3.*pow(m,4))/(v*v) -3.); } else{ if(o_mean) printf(FLOAT_SEP,0.0); if(o_sdev) printf(FLOAT_SEP,0.0); if(o_skew) printf(FLOAT_SEP,0.0); if(o_kurt) printf(FLOAT_SEP,0.0); } printf("\n"); } } else if(o_method==2){ /*=== Exact Summation ===*/ int i; /* the boundaries of the interval are set according... */ /* const double delta = (max-min)/((double) M-1.5); */ /* const double a = min-.5*delta; */ const double delta = (max-min)/((double) M-3.0); const double a = min-.5*delta; /* boundaries of the sum */ const int imin = -( (int) M)/20; const int imax = M-1+( (int) M)/20; /* const double a = min-2*h; */ /* const double b = max+2*h; */ /* const double delta = (b-a)/M; */ /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* method */ fprintf(stdout,"method exact summation\n"); /* binning */ fprintf(stdout,"# bins %zd\n",M); fprintf(stdout,"range [%.3e,%.3e]\n",a,a+(M-1)*delta); } /* ++++++++++++++++++++++++++++ */ switch(o_kerneltype){ case 0: K=Kepanechnikov; break; case 1: K=Krectangular; break; case 2: K=Kgauss; break; default: fprintf(stdout,"unknown kernel; use %s -h\n",argv[0]); exit(+1); } for(i=imin;izmax) break; dtmp1 = K((z-data[0][j])/h); x+= dtmp1; y+= (dtmp1*=data[1][j]); if(o_sdev||o_skew||o_kurt) y2+= (dtmp1*=data[1][j]);; if(o_skew) y3+= dtmp1*data[1][j]; if(o_kurt) y4+= dtmp1*data[1][j]*data[1][j]; } printf(FLOAT_SEP,z); m=(x>0 ? y/x : 0.0); v=((o_sdev||o_skew||o_kurt) && x>0?fabs(y2/x-m*m):0.0); if(o_mean) printf(FLOAT_SEP,m); if(o_sdev) printf(FLOAT_SEP,sqrt(v)); if(o_skew) printf(FLOAT_SEP, (x>0? (y3/x-3.*m*y2/x+2.*m*m*m)/pow(v,1.5) : 0.0 )); if(o_kurt) printf(FLOAT_SEP, (x>0? (y4/x-4.*m*y3/x+6.*m*m*y2/x-3.*m*m*m*m)/(v*v)-3. : 0.0)); printf("\n"); } } else if(o_method==1){ /*=== convolution ===*/ double *f;/* the density */ double *mf;/* the estimation of f*y */ double *m2f=0;/* the estimation of f*y*y */ double *m3f=0;/* the estimation of f*y**3 */ double *m4f=0;/* the estimation of f*y**4 */ size_t i; size_t J;/* maximum index for the kernel */ double *Kvals;/* values of the kernel */ double *Kj;/* pointer to values of the kernel */ /* the boundaries of the interval are set in order to span the interval [xmin-delta, xmax+delta] */ const double delta = (max-min)/((double) M-3); const double a = min-.5*delta; /* choose the kernel to use */ switch(o_kerneltype){ case 0: K=Kepanechnikov; J = (size_t) floor(sqrt(5)*h/delta); break; case 1: K=Krectangular; J = (size_t) floor(h/delta); break; default: fprintf(stdout,"kernel unknown or not compact ; use %s -h\n",argv[0]); exit(+1); } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* method */ fprintf(stdout,"method discrete convolution\n"); /* binning */ fprintf(stdout,"# bins %zd\n",M); fprintf(stdout,"range [%.3e,%.3e]\n",a,a+(M-1)*delta); fprintf(stdout,"# K vals %zd\n",J); } /* ++++++++++++++++++++++++++++ */ /* set the values of the kernel */ Kvals = (double *) my_alloc((2*J+1)*sizeof(double)); for(i=0;i<=2*J;i++){ Kvals[i]=K((i-(double) J)*delta/h)/h; } Kj = Kvals+J; /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* check */ double sum=0; for(i=0;i<=2*J;i++) sum+=Kvals[i]; fprintf(stdout,"sum K vals %.3e\n",sum*delta); } /* ++++++++++++++++++++++++++++ */ /* allocate the bins */ f = (double *) my_alloc(M*sizeof(double)); mf = (double *) my_alloc(M*sizeof(double)); if(o_sdev||o_skew||o_kurt) m2f = (double *) my_alloc(M*sizeof(double)); if(o_skew) m3f = (double *) my_alloc(M*sizeof(double)); if(o_kurt) m4f = (double *) my_alloc(M*sizeof(double)); /* prepare the binning */ for(i=0;i=M || dtmp1<0) continue; f[index]+=1-epsilon; mf[index]+= (dtmp1=(1-epsilon)*dtmp2); if(o_sdev || o_skew || o_kurt) m2f[index]+= (dtmp1*=dtmp2); if(o_skew) m3f[index]+=dtmp1*dtmp2; if(o_kurt) m4f[index]+=dtmp1*dtmp2*dtmp2; if(indexJ?-J:-i); const int jmax = (i0 ? y/x : 0.0); v=((o_sdev||o_skew||o_kurt) && x>0?fabs(y2/x-m*m):0.0); if(o_mean) printf(FLOAT_SEP,m); if(o_sdev) printf(FLOAT_SEP,sqrt(v)); if(o_skew) printf(FLOAT_SEP, (x>0? (y3/x-3.*m*y2/x+2.*m*m*m)/pow(v,1.5) : 0.0 )); if(o_kurt) printf(FLOAT_SEP, (x>0? (y4/x-4.*m*y3/x+6.*m*m*y2/x-3.*m*m*m*m)/(v*v)-3. : 0.0)); printf("\n"); } } else{/*=== unknown method ===*/ fprintf(stdout,"unknown method; use %s -h\n",argv[0]); exit(+1); } exit(0); } gbutils-5.7.1/exponential_gbhill.c0000644000175000017500000003230313246754720014144 00000000000000#include "gbhill.h" /* Exponential distribution */ /* ------------------------ */ /* x[0]=mu x[1]=b */ /* F[k] = 1-exp(-(1/mu)*(x[k]-b)) */ /* -log(f[k]) = -log(1/mu)+(1/mu)*(x[k]-b) */ /* N: sample size k: number of observations used */ void exponential_nll (const size_t n, const double *x,void *params,double *fval){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double b=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += data[i]; *fval = (1./m)*(*fval)/k -log(1./m) - (1./m)*b; switch(method){ case 0: if(N>k) *fval += -(N/k-1.0)*log(1.-exp(-(1./m)*(data[min]-b))); break; case 1: if(N>k) *fval += -(N/k-1.0)*log(1.-exp(-(1./m)*(d-b))); break; case 2: if(N>k) *fval += (N/k-1.0)*(1./m)*(data[max]-b); break; case 3: //if(N>k) *fval += -(N/k-1.0)*(1./m)*(d-b) ; if(N>k) *fval += (N/k-1.0)*(1./m)*(d-b) ; break; } /* fprintf(stderr,"[ f ] x1=%g x2=%g f=%g \n",x[0],x[1],*fval); */ } void exponential_dnll (const size_t n, const double *x,void *params,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double b=x[1]; const double k = ((double) max-min+1); grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += data[i]; grad[0]=(grad[0]/k -m -b)*(-1./pow(m,2)); grad[1]=-(1./m); switch(method){ case 0: if(N>k) { const double F = 1.-exp(-(1./m)*(data[min]-b)); const double dFdm = exp(-(1./m)*(data[min]-b))*(data[min]-b); const double dFdb = -exp(-(1./m)*(data[min]-b))*(1./m); grad[0] += (-(N/k-1.0)*dFdm/F)*(-1./pow(m,2)); //grad[1] += (-(N/k-1.0)*dFdb/F)*(-1./pow(m,2)); // <=== Double-Check!! grad[1] += (-(N/k-1.0)*dFdb/F); } break; case 1: if(N>k) { const double F = 1.-exp(-(1./m)*(d-b)); const double dFdm = exp(-(1./m)*(d-b))*(d-b); const double dFdb = -exp(-(1./m)*(d-b))*(1./m); grad[0] += (-(N/k-1.0)*dFdm/F)*(-1./pow(m,2)); grad[1] += (-(N/k-1.0)*dFdb/F); } break; case 2: if(N>k){ grad[0] += (N/k-1.0)*(data[max]-b)*(-1./pow(m,2)); grad[1] += -(1./m)*(N/k-1.0); } break; case 3: if(N>k){ grad[0] += (N/k-1.0)*(d-b)*(-1./pow(m,2)); grad[1] += -(1./m)*(N/k-1.0); } break; } /* fprintf(stderr,"[ df] x1=%g x2=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],grad[0],grad[1]); */ } void exponential_nlldnll (const size_t n, const double *x,void *params,double *fval,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double b=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += data[i]; grad[0]=((*fval)/k -m -b)*(-1./pow(m,2)); *fval = (1./m)*(*fval)/k -log(1./m) - (1./m)*b; grad[1]=-1./m; switch(method){ case 0: if(N>k) { const double F = 1.-exp(-(1./m)*(data[min]-b)); const double dFdm = exp(-(1./m)*(data[min]-b))*(data[min]-b); const double dFdb = -exp(-(1./m)*(data[min]-b))*(1./m); *fval += -(N/k-1.0)*log(1.-exp(-(1./m)*(data[min]-b))); grad[0] += (-(N/k-1.0)*dFdm/F)*(-1./pow(m,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 1: { *fval += -(1./m)*(data[min-1]-b); grad[0] += (-data[min-1]+b)*(-1./pow(m,2)); grad[1] += 1./m; } break; case 2: if(N>k) { const double F = 1.-exp(-(1./m)*(d-b)); const double dFdm = exp(-(1./m)*(d-b))*(d-b); const double dFdb = -exp(-(1./m)*(d-b))*(1./m); *fval += -(N/k-1.0)*log(1.-exp(-(1./m)*(d-b))); grad[0] += (-(N/k-1.0)*dFdm/F)*(-1./pow(m,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 3: if(N>k){ *fval += (N/k-1.0)*(1./m)*(data[max]-b); grad[0] += (N/k-1.0)*(data[max]-b)*(-1./pow(m,2)); grad[1] += -(1./m)*(N/k-1.0); } break; case 4: { const double F = 1.-exp(-(1./m)*(data[max+1]-b)); const double dFdm = exp(-(1./m)*(data[max+1]-b))*(data[max+1]-b); const double dFdb = -exp(-(1./m)*(data[max+1]-b))*(1./m); *fval += log(F); grad[0] += (dFdm/F)*(-1./pow(m,2)); grad[1] += dFdb/F; } break; case 5: if(N>k){ *fval += (N/k-1.0)*(1./m)*(d-b); grad[0] += (N/k-1.0)*(d-b)*(-1./pow(m,2)); grad[1] += -(1./m)*(N/k-1.0); } break; } /* fprintf(stderr,"[fdf] x1=%g x2=%g f=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],*fval,grad[0],grad[1]); */ } double exponential_f (const double x,const size_t n, const double *par){ return gsl_ran_exponential_pdf(x-par[1],par[0]); } double exponential_F (const double x,const size_t n, const double *par){ return gsl_cdf_exponential_P (x-par[1],par[0]); } gsl_matrix *exponential_varcovar(const int o_varcovar, double *x, void *params){ /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double m=x[0]; const double b=x[1]; const double k = ((double) max-min+1); double dldm = 0.0; double d2ldm2 = 0.0; double d2ldmdb = 0.0; double dldb = 0.0; double d2ldb2 = 0.0; gsl_matrix *covar; double sum=0.0; double ZN = data[0]; double Zk = data[min]; double ZNk = data[max]; /* compute the sum of observations */ { size_t i; for(i=min;i<=max;i++) sum += data[i]; } switch(method){ case 0: /* GSL allocation of memory for the covar matrix */ covar = gsl_matrix_alloc (2,2); /* Compute ll partial derivatives. */ dldm = -((exp(b/m)*Zk-b*exp(b/m))*N+(-sum+k*m+b*k)*exp(Zk/m)-k*exp(b/m)*Zk+exp(b/m)*sum-k*m*exp(b/m))/(pow(m,2.)*exp(Zk/m)-pow(m,2.)*exp(b/m)); d2ldm2 = -(((exp(b/m)*pow(Zk,2.)+(-2.*m-2.*b)*exp(b/m)*Zk+(2.*b*m+pow(b,2.))*exp(b/m))*exp(Zk/m)+2.*m*exp((2.*b)/m)*Zk-2.*b*m*exp((2.*b)/m))*N+(2.*m*sum-k*pow(m,2.)-2.*b*k*m)*exp((2.*Zk)/m)+(-k*exp(b/m)*pow(Zk,2.)+(2.*k*m+2.*b*k)*exp(b/m)*Zk-4*m*exp(b/m)*sum+(2.*k*pow(m,2.)+2.*b*k*m-pow(b,2.)*k)*exp(b/m))*exp(Zk/m)-2.*k*m*exp((2.*b)/m)*Zk+2.*m*exp((2.*b)/m)*sum-k*pow(m,2.)*exp((2.*b)/m))/(-2.*pow(m,4.)*exp(Zk/m+b/m)+pow(m,4.)*exp((2.*Zk)/m)+pow(m,4.)*exp((2.*b)/m)); d2ldmdb = -(((exp(b/m)*Zk+(-m-b)*exp(b/m))*exp(Zk/m)+m*exp((2.*b)/m))*N+k*m*exp((2.*Zk)/m)+((b*k-k*m)*exp(b/m)-k*exp(b/m)*Zk)*exp(Zk/m))/(-2.*pow(m,3.)*exp(Zk/m+b/m)+pow(m,3.)*exp((2.*Zk)/m)+pow(m,3.)*exp((2.*b)/m)); dldb = -(exp(b/m)*N-k*exp(Zk/m))/(m*exp(Zk/m)-m*exp(b/m)); d2ldb2 = -(exp(Zk/m+b/m)*N-k*exp(Zk/m+b/m))/(-2.*pow(m,2.)*exp(Zk/m+b/m)+pow(m,2.)*exp((2.*Zk)/m)+pow(m,2.)*exp(2.*b/m)); break; case 1: /* GSL allocation of memory for the covar matrix */ covar = gsl_matrix_alloc (2,2); dldm = (-k)*(((d-b)*exp(b/m)*N+(exp(b/m)-exp(d/m))*sum+(k*m+b*k)*exp(d/m)+(-k*m-d*k)*exp(b/m))/(k*pow(m,2.0)*exp(d/m)-k*pow(m,2.0)*exp(b/m))); dldb =(-k)*( (exp(b/m)*N-k*exp(d/m))/(k*m*exp(d/m)-k*m*exp(b/m))); d2ldm2=(-k)*(-((((2*d-2*b)*m-pow(d,2.0)+2*b*d-pow(b,2.0))*exp(d/m+b/m)+(2*b-2*d)*m*exp((2*b)/m))*N+(4*m*exp(d/m+b/m)-2*m*exp((2*d)/m)-2*m*exp((2*b)/m))*sum+(-2*k*pow(m,2.0)+(-2*d-2*b)*k*m+(pow(d,2.0)-2*b*d+pow(b,2.0))*k)*exp(d/m+b/m)+(k*pow(m,2.0)+2*b*k*m)*exp((2*d)/m)+(k*pow(m,2.0)+2*d*k*m)*exp((2*b)/m))/(-2*k*pow(m,4.0)*exp(d/m+b/m)+k*pow(m,4.0)*exp((2*d)/m)+k*pow(m,4.0)*exp((2*b)/m))); d2ldmdb=(-k)*(-(((m-d+b)*exp(d/m+b/m)-m*exp((2*b)/m))*N+(k*m+(d-b)*k)*exp(d/m+b/m)-k*m*exp((2*d)/m))/(-2*k*pow(m,3)*exp(d/m+b/m)+k*pow(m,3)*exp((2*d)/m)+k*pow(m,3)*exp((2*b)/m))); d2ldb2=(-k)*((exp(d/m+b/m)*N-k*exp(d/m+b/m))/(-2*k*pow(m,2.0)*exp(d/m+b/m)+k*pow(m,2.0)*exp((2*d)/m)+k*pow(m,2.0)*exp((2*b)/m))); break; case 2: /* GSL allocation of memory for the covar matrix */ covar = gsl_matrix_alloc (1,1); dldm = -(N*ZN-ZNk*N+k*ZNk-sum+k*m)/pow(m,2.0); d2ldm2 = (2*N*ZN-2*ZNk*N+2*k*ZNk-2*sum+k*m)/pow(m,3.0); break; case 3: covar = gsl_matrix_alloc (1,1); dldm = -(N*ZN-d*N-sum+k*m+d*k)/pow(m,2.0); d2ldm2 = (2*N*ZN-2*d*N-2*sum+k*m+2*d*k)/pow(m,3.0); break; } switch(o_varcovar){ case 0: if (method < 2 ){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *J = gsl_matrix_calloc (2,2); gsl_matrix_set (J,0,0,dldm*dldm); gsl_matrix_set (J,0,1,dldm*dldb); gsl_matrix_set (J,1,0,dldb*dldm); gsl_matrix_set (J,1,1,dldb*dldb); gsl_linalg_LU_decomp (J,P,&signum); gsl_linalg_LU_invert (J,P,covar); gsl_matrix_free(J); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *J = gsl_matrix_calloc (1,1); gsl_matrix_set(J,0,0,dldm*dldm); gsl_linalg_LU_decomp (J,P,&signum); gsl_linalg_LU_invert (J,P,covar); gsl_matrix_free(J); gsl_permutation_free(P); } break; case 1: if (method < 2 ){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *H = gsl_matrix_calloc (2,2); gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_set(H,0,1,d2ldmdb); gsl_matrix_set(H,1,0,d2ldmdb); gsl_matrix_set(H,1,1,d2ldb2); gsl_matrix_scale (H,-1.); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,covar); gsl_matrix_free(H); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *H = gsl_matrix_calloc (1,1); gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_scale (H,-1.); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,covar); gsl_matrix_free(H); gsl_permutation_free(P); } break; case 2: if (method < 2 ){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *H = gsl_matrix_calloc (2,2); gsl_matrix *J = gsl_matrix_calloc (2,2); gsl_matrix *tmp = gsl_matrix_calloc (2,2); gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_set(H,0,1,d2ldmdb); gsl_matrix_set(H,1,0,d2ldmdb); gsl_matrix_set(H,1,1,d2ldb2); gsl_matrix_set(J,0,0,dldm*dldm); gsl_matrix_set(J,0,1,dldm*dldb); gsl_matrix_set(J,1,0,dldb*dldm); gsl_matrix_set(J,1,1,dldb*dldb); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,tmp); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,tmp,J,0.0,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,tmp,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(J); gsl_matrix_free(tmp); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *H = gsl_matrix_calloc (1,1); gsl_matrix *J = gsl_matrix_calloc (1,1); gsl_matrix *tmp = gsl_matrix_calloc (1,1); gsl_matrix_set(H,0,0,d2ldm2); gsl_matrix_set(J,0,0,dldm*dldm); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,tmp); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,tmp,J,0.0,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,tmp,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(J); gsl_matrix_free(tmp); gsl_permutation_free(P); } break; } /* scale the covariance matrix to the appropriate dimension */ if(covar->size1 == 1) { const double dtmp1 = gsl_matrix_get(covar,0,0); gsl_matrix_free (covar); covar = gsl_matrix_alloc (2,2); gsl_matrix_set(covar,0,0,dtmp1); gsl_matrix_set(covar,0,1,NAN); gsl_matrix_set(covar,1,0,NAN); gsl_matrix_set(covar,1,1,NAN); } return covar; } gbutils-5.7.1/gbmodes.c0000644000175000017500000004131613246754720011721 00000000000000/* gbmodes (ver. 5.6) -- Analyze multimodality in univariate data data using direct (and slow) kernel computation with Silverman method and, when applicable, Hall York corrections. Copyright (C) 2004-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include /* =========================================== */ /* 1 - Kernels */ /* =========================================== */ /* K(x) = exp(-x^2/(2 \pi))/sqrt(2 \pi) */ /* ------------------------------------ */ double Kgauss_rng(const gsl_rng * rng) { return(gsl_ran_ugaussian (rng)); } /* K(x) = exp(-sqrt(2) |x|)/sqrt(2) */ /* -------------------------------- */ double Klaplace_rng(const gsl_rng * rng) { return(gsl_ran_laplace (rng,M_SQRT1_2)); } /* =========================================== */ /* =========================================== */ /* 2 - Functions */ /* =========================================== */ /* Counts the modes and Find the modal values */ /* ------------------------------------------- */ /* Input: data list of observations size number of observations min lower bound of the range where to search max upper bound of the range where to search N number of equispaced points h bandwidth K kernel Output: modes_pos if not NULL position of modes modes_height if not NULL height of modes modes number of modes */ unsigned findmodes(const double *data, size_t size, const double min, const double max, const unsigned N, const double h, double (*K) (double), double **modes_pos, double **modes_height) { unsigned i,modes; const double delta = (max-min)/((double) N-1.0); /* the kernel estimate */ double * x = my_alloc(N*sizeof(double)); /* compute the kernel density estimates on the equispaced points */ for(i=0;i=x[1]){ modes++; if(modes_pos != NULL && modes_height != NULL){ *modes_pos = (double *) my_realloc(*modes_pos,modes*sizeof(double)); (*modes_pos)[modes-1]=min; *modes_height = (double *) my_realloc(*modes_height,modes*sizeof(double)); (*modes_height)[modes-1]=x[0]; } } for(i=1;ix[i-1] && x[i]>x[i+1]){ modes++; if(modes_pos != NULL && modes_height != NULL){ *modes_pos = (double *) my_realloc(*modes_pos,modes*sizeof(double)); (*modes_pos)[modes-1]=min+i*delta; *modes_height = (double *) my_realloc(*modes_height,modes*sizeof(double)); (*modes_height)[modes-1]=x[i]; } } } if(x[N-1]>=x[N-2]){ modes++; if(modes_pos != NULL && modes_height != NULL){ *modes_pos = (double *) my_realloc(*modes_pos,modes*sizeof(double)); (*modes_pos)[modes-1]=max; *modes_height = (double *) my_realloc(*modes_height,modes*sizeof(double)); (*modes_height)[modes-1]=x[N-1]; } } /* free allocated space */ free(x); return(modes); } /* Find the critical bandwidth */ /* ------------------------------------------- */ /* Input: data list of observations size number of observations min lower bound of the range where to search max upper bound of the range where to search N number of equispaced points K kernel M number of modes o_verbose verbosity level epsrel relative tolerance on critical bandwidth Output: hmin lower bound on critical bandwidth hmax upper bound on critical bandwidth modes number of modes */ void findhcrit(const double *data,const unsigned size, const double min, const double max, const unsigned N, double (*K) (double),const unsigned M, const unsigned o_verbose,const double epsrel,double *hmin,double *hmax) { double epsabs; double h=.5*(*hmin+*hmax); /* find initial boundary [hmin,hmax] */ if( findmodes(data,size,min,max,N,h,K,NULL,NULL) <=M ){ *hmax=h; *hmin=h/1.5; while(findmodes(data,size,min,max,N,*hmin,K,NULL,NULL)<=M) *hmin/=1.5; } else{ *hmin=h; *hmax=1.5*h; while(findmodes(data,size,min,max,N,*hmax,K,NULL,NULL)>M) *hmax*=1.5; } /* re-scale the absolute value by the problem's scale */ epsabs=epsrel*0.5*(*hmin+*hmax); /* ++++++++++++++++++++++++++++ */ if(o_verbose > 1){ fprintf(stderr," initial interval [%e,%e]\n",*hmin,*hmax); fprintf(stderr," absolute error on h %e\n",epsabs); } /* ++++++++++++++++++++++++++++ */ /* find an estimate for h_c */ do{ double *modes_pos=NULL,*modes_height=NULL; const double h = .5*(*hmin+*hmax); const unsigned modes = findmodes(data,size,min,max,N,h,K,&modes_pos,&modes_height); /* ++++++++++++++++++++++++++++ */ if(o_verbose>1) { unsigned i; fprintf(stderr," h=%.3e modes: ",h); for(i=0;i M) *hmin=h; else *hmax=h; free(modes_pos); free(modes_height); } while(fabs(*hmax-*hmin)>epsabs); /* ++++++++++++++++++++++++++++ */ if(o_verbose > 0){ fprintf(stderr," bandwidth final interval [%e,%e]\n",*hmin,*hmax); } /* ++++++++++++++++++++++++++++ */ } /* Compute the significance of h_c using Silverman */ /* ---------------------------------------------------------- */ /* Input: data list of observations size number of observations ave data average sdev data standard deviation min lower bound of the range where to search max upper bound of the range where to search N number of bins h bandwidth M number of modes trials number of sample trials seed RNG seed K kernel K_rng random number generator alpha significance Output: ratio score/trials */ double pscore(const double *data,const unsigned size,const double sdev, const double min,const double max,const unsigned N, const double h, const unsigned M, const unsigned trials, const unsigned long seed,double (*K) (double), double (*K_rng) (const gsl_rng *), const double alpha) { unsigned t,i; double score=0.0; const double samplescale = 1./sqrt(1.+h*h/(sdev*sdev)); double *sample = (double *) my_alloc(size*sizeof(double)); double lambda=1.; const gsl_rng_type * T = gsl_rng_default ; gsl_rng * rng = gsl_rng_alloc (T); gsl_rng_set (rng,seed); /* set the lambda value of the Hall-York correction if: - the number of modes is 1 - the kernel is Gaussian - a proper value of alpha is provided Hall and York, Statistica Sinica 11(2001), 515-536 */ if(M==1 && alpha > 0 && alpha < 1 && K == Kgauss){ /* Hall-York coefficient */ const double c[] = {.94029,-1.59914,.17695,.48971,-1.77793,.36162,.42423}; lambda = (c[0]*pow(alpha,3.)+c[1]*pow(alpha,2.)+c[2]*alpha+c[3])/(pow(alpha,3.)+c[4]*pow(alpha,2.)+c[5]*alpha+c[6]); } for(t=0;tM ) score+=1; } free(sample); gsl_rng_free (rng); return(score/trials); } /* =========================================== */ /* =========================================== */ /* 3 - Main */ /* =========================================== */ int main(int argc,char* argv[]){ double *data=NULL; size_t size; char *splitstring = strdup(" \t"); double data_ave,data_sdev,data_min,data_max; /* OPTIONS */ int o_verbose=0; int o_kerneltype=0; int o_setxregion=0; int o_output=0; int o_setxscale=0; double xscale=1; /* the scale factor defining the range where to look for modes*/ int N=100; /* number of bins */ double alpha=0.0; /*significance level*/ double xmin=0,xmax=0; /* range where to look for modes */ double hmin,hmax; unsigned modes; double epsrel=1e-6; unsigned long seed=0; unsigned M=1;/* number of modes */ unsigned trials=1000;/* trials number for pscore*/ double (*K) (double); /* the kernel to use */ double (*K_rng) (const gsl_rng *); /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"t:n:m:he:vR:K:r:O:s:F:S:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='n'){ /*the number of bins*/ N=(int) atoi(optarg); } else if(opt=='v'){ /*increase verbosity*/ o_verbose ++; } else if(opt=='m'){ /*number of modes*/ M = (unsigned) atoi(optarg); } else if(opt=='r'){ /*where to look for modes*/ if(!strchr (optarg,',')){ fprintf(stderr, "option -r requires comma separated couple of values\n"); return(-1); } else{ char *stmp1=strdup (optarg); xmin = atof(strtok (stmp1,",")); xmax = atof(strtok (NULL,",")); free(stmp1); } o_setxregion=1; } else if(opt=='s'){ /*where to look for modes: scaling factor*/ xscale=atof(optarg); o_setxscale=1; } else if(opt=='R'){ /*RNG seed*/ seed = (unsigned) atol(optarg); } else if(opt=='t'){ /*number of trials*/ trials = (unsigned) atoi(optarg); } else if(opt=='K'){ /*the Kernel to use*/ o_kerneltype = atoi(optarg); } else if(opt=='O'){ /*the type of output*/ o_output = atoi(optarg); } else if(opt=='e'){ /*set the absolute error on h_c*/ epsrel = atof(optarg); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='S'){ /*set the significance level*/ alpha = atof(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Multimodality analysis via kernel density estimation. Compute the maximal\n"); fprintf(stdout,"kernel bandwidth h_c(M) for which #(modes in the estimated density)>M.The\n"); fprintf(stdout,"significance of h_c is computed via smoothed bootstrap. Read data from std.\n"); fprintf(stdout,"input. Print the couple h_c p(h_c) and the modal values (set with -O).\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of equispaced points where the density is computed (default 100)\n"); fprintf(stdout," -m number of modes (default 1)\n"); fprintf(stdout," -r search range for modes; comma separated couple 'data_min,data_max'\n"); fprintf(stdout," -s scale the search range to [min*s+(1-s)*Max,Max*s+(1-s)*min] (default 1)\n"); fprintf(stdout," -e relative tolerance on h_c value (default 1e-6)\n"); fprintf(stdout," -S significance level (for Hall-York correction) (default .05)\n"); fprintf(stdout," -t number of bootstrap trials used for significance computation (default 1000)\n"); fprintf(stdout," -R RNG seed for bootstrap trials (default 0)\n"); fprintf(stdout," -K choose the kernel to use: 0 Gaussian or 1 Laplacian (default 0)\n"); fprintf(stdout," -v verbose mode (more verbose if provided 2 times) \n"); fprintf(stdout," -O output type (default 0)\n"); fprintf(stdout," 0 h_c p(h_c) [modes locations] \n"); fprintf(stdout," 1 h_c [modes locations and heights] \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); switch(o_kerneltype){ case 0: K=Kgauss; K_rng=Kgauss_rng; break; case 1: K=Klaplace; K_rng=Klaplace_rng; break; default: fprintf(stdout,"unknown kernel; use %s -h\n",argv[0]); exit(+1); } /* load data */ load(&data,&size,0,splitstring); /* sort the data */ qsort(data,size,sizeof(double),sort_by_value); /* compute relevant statistics */ moment_short(data,size,&data_ave,&data_sdev,&data_min,&data_max); /* the parameter h is initially set according to [Silverman p.48] */ { const double inter = fabs(data[3*size/4]-data[size/4])/1.34; const double A = (data_sdev>inter && inter>0 ? inter : data_sdev); hmin=hmax=0.9*A/pow(size,.2); } /* set the boundaries of region where to check for modes*/ if(o_setxregion){ /* user provided boundaries */ if(xmin>=xmax){ /* swap if wrongly provided by user */ double dtmp=xmin; xmin=xmax; xmax=dtmp; } } else{ /* automatic setting */ xmin=data_min; xmax=data_max; } /* apply the scaling factor */ if(o_setxscale){ const double delta= .5*(xmax-xmin)*(1.-xscale); xmax-=delta; xmin+=delta; } /* cut excessive range */ if(xmax>data_max) xmax= data_max; if(xmin 0){ /* data */ fprintf(stdout," number of data %zd\n",size); fprintf(stdout," sample mean %f\n",data_ave); fprintf(stdout," sample std. dev. %f\n",data_sdev); /* binning */ fprintf(stdout," number of bins %d\n",N); fprintf(stdout," modes search region [%f,%f]\n",xmin,xmax); fprintf(stdout," number of modes %d\n",M); /* kernel type */ fprintf(stdout," kernel type "); switch(o_kerneltype){ case 0: fprintf(stdout,"Gaussian"); break; case 1: fprintf(stdout,"Laplacian"); break; } fprintf(stdout,"\n"); /* bandwidth */ fprintf(stdout," initial bandwidth %e\n",hmin); } /* ++++++++++++++++++++++++++++ */ /* find the critical bandwidth */ findhcrit(data,size,xmin,xmax,N,K,M,o_verbose,epsrel,&hmin,&hmax); switch (o_output){ case 0: { double *modes_pos=NULL,*modes_height=NULL; unsigned i; modes = findmodes(data,size,xmin,xmax,N,hmax,K,&modes_pos,&modes_height); /* ++++++++++++++++++++++++++++ */ if(o_verbose > 0){ printf("#h_c(%d)\t\tpscore",M); if(M==1 && alpha > 0 && alpha < 1 && K == Kgauss && o_output==0) printf(" (HY %.3f)",alpha); else printf("\t"); printf("\tmodes locations ->\n"); } /* ++++++++++++++++++++++++++++ */ printf(FLOAT_SEP,.5*(hmax+hmin)); printf(FLOAT_SEP, pscore(data,size,data_sdev,xmin,xmax,N, .5*(hmin+hmax),M,trials,seed,K,K_rng,alpha)); for(i=0;i 0) printf("#h_c(%d)\t\tmodes (location,height) ->\n",M); /* ++++++++++++++++++++++++++++ */ printf(FLOAT_SEP,.5*(hmax+hmin)); for(i=0;i\/\fR .SH DESCRIPTION Non linear polyit estimation. Minimize the negative log\-likelihood .IP sum_{h=0}^{L\-1} log(A+h) \- sum_{l=1}^L sum_{h=0}^{n_l\-1} log(a_l+h) .PP for the Polya specification or .IP L log(A) \- sum_{l=1}^L n_l log(a_l) .PP for the multinomial specification, where A = sum_{l=1}^L a_l and L is the number of alternatives. The input data file should contain L rows, one for each alternative, of the type n x1 ... XN. The first column contains the dependent variable (# of observations) and the other columns the independent variables. The model is specified by a function a_l=g(x1,x2...) where x1,.. XN stands for the first, second .. N\-th column of independent variables. .SH OPTIONS .TP \fB\-O\fR type of output (default 0) .TP 0 parameters and log\-like (ll) .TP 1 marginal effects .TP 2 marginal elasticities .TP 3 n_l n*_l a*_l *=estimated .TP 4 occupancies classes .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-V\fR standard errors and p\-scores of diff. from zero using bootstrap .TP \fB\-r\fR number of replicas (default 20) .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps .TP 5 model definition .TP \fB\-R\fR set the rng seed (default 0) .TP \fB\-M\fR set the model to use (default 0) | .TP 0 Polya .TP 1 multinomial .TP \fB\-A\fR MLL optimization options (default 0.01,0.1,100,1e\-6,1e\-6,5) fields are step,tol,iter,eps,msize,algo. Empty fields for default .TP step initial step size of the searching algorithm .TP tol line search tolerance iter: maximum number of iterations .TP eps gradient tolerance : stopping criteria ||gradient|| .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/paretoI_gbhill.c0000644000175000017500000003225513246754720013227 00000000000000#include "gbhill.h" /* Pareto 1 distribution */ /* ------------------------ */ /* x[0]=g x[1]=b */ /* F[j] = 1-pow(b*x[j],-1./g)} */ /* -log(f[j]) = -log(1./g) + (1./g)*log(b) + (1./g+1)log(x[j]) */ /* N: sample size k: number of observations used */ void pareto1_nll (const size_t n, const double *x,void *params,double *fval){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double g=x[0]; const double b=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += log(data[i]); *fval = (1./g+1.0)*(*fval)/k -log(1./g) + (1./g)*log(b); switch(method){ case 0: if(N>k) *fval += -(N/k-1.0)*log(1.0-pow(b*data[min],-1./g)); break; case 1: if(N>k) *fval += -(N/k-1.0)*log(1.0-pow(b*d,-1./g)); break; case 2: if(N>k) *fval += (N/k-1.0)*((1./g)*log(b) + (1./g)*log(data[max])); break; case 3: if(N>k) *fval += (N/k-1.0)*((1./g)*log(b) + (1./g)*log(d)); break; } /* fprintf(stderr,"[ f ] x1=%g x2=%g f=%g\n",x[0],x[1],*fval); */ } void pareto1_dnll (const size_t n, const double *x,void *params,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double g=x[0]; const double b=x[1]; const double k = ((double) max-min+1); grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += log(data[i]); grad[0]=(grad[0]/k -g +log(b))*(-1./pow(g,2)); grad[1]=1./(b*g); switch(method){ case 0: if(N>k) { const double F = 1.0- pow(b*data[min],-1./g); const double dFdg = log(b*data[min])*pow(b*data[min],-1./g); const double dFdb = (1./g)*pow(b*(data[min]),-1./g)/b; grad[0] += (-(N/k-1.0)*dFdg/F)*(-1./pow(g,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 1: if(N>k){ const double F = 1.0- pow(b*d,-1./g); const double dFdg = log(b*d)*pow(b*d,-1./g); const double dFdb = (1./g)*pow(b*d,-1./g)/b; grad[0] += (-(N/k-1.0)*dFdg/F)*(-1./pow(g,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 2: if(N>k){ grad[0] += ((N/k-1.0)*(log(b)+log(data[max])))*(-1./pow(g,2)); grad[1] += (N/k-1.0)*(1./(b*g)); } break; case 3: if(N>k){ grad[0] += ((N/k-1.0)*(log(b)+log(d)))*(-1./pow(g,2)); grad[1] += (N/k-1.0)*(1./(g*b)); } break; } /* fprintf(stderr,"[ df] x1=%g x2=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],grad[0],grad[1]); */ } void pareto1_nlldnll (const size_t n, const double *x,void *params,double *fval,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double g=x[0]; const double b=x[1]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += log(data[i]); grad[0]=((*fval)/k -g +log(b))*(-1./pow(g,2)); *fval = ((1./g)+1.0)*(*fval)/k -log(1./g) + (1./g)*log(b); grad[1]=1./(b*g); switch(method){ case 0: if(N>k) { const double F = 1.0- pow(b*data[min],-1./g); const double dFdg = log(b*data[min])*pow(b*data[min],-1./g); const double dFdb = (1./g)*pow(b*(data[min]),-1./g)/b; *fval += -(N/k-1.0)*log(F); grad[0] += (-(N/k-1.0)*dFdg/F)*(-1./pow(g,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 1: if(N>k) { const double F = 1.0- pow(b*d,-1./g); const double dFdg = log(b*d)*pow(b*d,-1./g); const double dFdb = (1./g)*pow(b*d,-1./g)/b; *fval += -(N/k-1.0)*log(F); grad[0] += (-(N/k-1.0)*dFdg/F)*(-1./pow(g,2)); grad[1] += -(N/k-1.0)*dFdb/F; } break; case 2: if(N>k){ *fval += (N/k-1.0)*((1./g)*log(b) + (1./g)*log(data[max])); grad[0] += ((N/k-1.0)*(log(b)+log(data[max])))*(-1./pow(g,2)); grad[1] += (N/k-1.0)*(1./(b*g)); } break; case 3: if(N>k){ *fval += (N/k-1.0)*((1./g)*log(b) + (1./g)*log(d)); grad[0] += ((N/k-1.0)*(log(b)+log(d)))*(-1./pow(g,2)); grad[1] += (N/k-1.0)*(1./(b*g)); } break; } /* fprintf(stderr,"[fdf] x1=%g x2=%g f=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],*fval,grad[0],grad[1]); */ } double pareto1_f (const double x,const size_t n, const double *par){ return gsl_ran_pareto_pdf(x,1./par[0],1./par[1]); } double pareto1_F (const double x,const size_t n, const double *par){ return gsl_cdf_pareto_P (x,1./par[0],1./par[1]); } gsl_matrix *pareto1_varcovar(const int o_varcovar, double *x, void *params){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double g=x[0]; const double b=x[1]; const double k = ((double) max-min+1); double dldg = 0.0; double d2ldg2 = 0.0; double d2ldgdb = 0.0; double dldb = 0.0; double d2ldb2 = 0.0; gsl_matrix *covar; double sum = 0.0; for(i=min;i<=max;i++) sum += log(data[i]); double ZN = data[0]; double Zk = data[min]; double ZNk = data[max]; switch(method){ case 0: /* GSL allocation of memory for the covar matrix */ covar = gsl_matrix_alloc (2,2); /* Compute ll partial derivatives. */ dldg = -(log(b*Zk)*N-k*log(b*Zk)+((g-log(b))*k-sum)*pow(b*Zk,1./g)+sum+(log(b)-g)*k)/(pow(g,2.)*pow(b*Zk,1./g)-pow(g,2.)); d2ldg2 = -((pow(b*Zk,1./g)*pow(log(b*Zk),2.)+(2.*g-2.*g*pow(b*Zk,1./g))*log(b*Zk))*N-k*pow(b*Zk,1./g)*pow(log(b*Zk),2.)+(2.*g*k*pow(b*Zk,1./g)-2.*g*k)*log(b*Zk)+(2.*g*sum+(2.*log(b)*g-pow(g,2.))*k)*pow(b*Zk,2./g)+((2.*pow(g,2.)-4*log(b)*g)*k-4*g*sum)*pow(b*Zk,1./g)+2.*g*sum+(2.*log(b)*g-pow(g,2.))*k)/(pow(g,4.)*pow(b*Zk,2./g)-2.*pow(g,4.)*pow(b*Zk,1./g)+pow(g,4.)); d2ldgdb = (g*pow(b*Zk,1./g)*(log(b*Zk)*N-k*log(b*Zk)+((g-log(b))*k-sum)*pow(b*Zk,1./g)+sum+(log(b)-g)*k))/(b*pow(pow(g,2.)*pow(b*Zk,1./g)-pow(g,2.),2.))-(N/b+(((g-log(b))*k-sum)*pow(b*Zk,1./g))/(b*g)-(k*pow(b*Zk,1./g))/b)/(pow(g,2.)*pow(b*Zk,1./g)-pow(g,2.)); dldb = (N-k)/(b*g*pow(b*Zk,1./g)*(1-1/pow(b*Zk,1./g)))-k/(b*g); d2ldb2=-(N-k)/(pow(b,2.)*g*pow(b*Zk,1./g)*(1-1/pow(b*Zk,1./g)))-(N-k)/(pow(b,2.)*pow(g,2.)*pow(b*Zk,1./g)*(1-1/pow(b*Zk,1./g)))-(N-k)/(pow(b,2.)*pow(g,2.)*pow(b*Zk,2./g)*pow(1-1/pow(b*Zk,1./g),2.))+k/(pow(b,2.)*g); break; case 1: /* GSL allocation of memory for the covar matrix */ covar = gsl_matrix_alloc (2,2); /* Compute ll partial derivatives (Wolfram Mathematica or equivalent). */ dldg = -(log(b*d)*N+(1-pow(b*d,1./g))*sum+((pow(b*d,1./g)-1)*g-log(b)*pow(b*d,1./g)-log(b*d)+log(b))*k)/((pow(b*d,1./g)-1)*pow(g,2.0)); d2ldg2 = (((2*pow(b*d,1./g)*log(b*d)-2*log(b*d))*g-pow(b*d,1./g)*pow(log(b*d),2.0))*N+(-2*pow(b*d,2.0/g)+4*pow(b*d,1./g)-2)*g*sum+((pow(b*d,2.0/g)-2*pow(b*d,1./g)+1)*pow(g,2.0)+(-2*log(b)*pow(b*d,2.0/g)+2*log(b*d)+pow(b*d,1./g)*(4*log(b)-2*log(b*d))-2*log(b))*g+pow(b*d,1./g)*pow(log(b*d),2.0))*k)/((pow(b*d,2.0/g)-2*pow(b*d,1./g)+1)*pow(g,4.0)); d2ldgdb = -(((pow(b*d,1./g)-1)*g-pow(b*d,1./g)*log(b*d))*N+((pow(b*d,1./g)-pow(b*d,2.0/g))*g+pow(b*d,1./g)*log(b*d))*k)/((b*pow(b*d,2.0/g)-2*b*pow(b*d,1./g)+b)*pow(g,3.0)); dldb = (N-pow(b*d,1./g)*k)/((b*pow(b*d,1./g)-b)*g); d2ldb2 = -(((pow(b*d,1./g)-1)*g+pow(b*d,1./g))*N+((pow(b*d,1./g)-pow(b*d,2.0/g))*g-pow(b*d,1./g))*k)/((pow(b,2.0)*pow(b*d,2.0/g)-2*pow(b,2.0)*pow(b*d,1./g)+pow(b,2.0))*pow(g,2.0)); break; case 2: /* GSL allocation of memory for the covar matrix */ /* Nota that the ll does not depend on b in this case.*/ /* As a consequence the covar matrix is 1x1 . */ covar = gsl_matrix_alloc (1,1); /* Compute ll partial derivatives. */ dldg = -(N*log(ZN)-log(ZNk)*N+k*log(ZNk)-sum+g*k)/pow(g,2.0); d2ldg2 = (2*N*log(ZN)-2*log(ZNk)*N+2*k*log(ZNk)-2*sum+g*k)/pow(g,3.0); break; case 3: /* GSL allocation of memory for the covar matrix */ /* Nota that the ll does not depend on b in this case.*/ /* As a consequence the covar matrix is 1x1 . */ covar = gsl_matrix_alloc (1,1); /* Compute ll partial derivatives. */ dldg = ((log(d)+log(1/ZN))*N+sum+(-g-log(d))*k)/pow(g,2.0); d2ldg2 = -((2*log(d)+2*log(1/ZN))*N+2*sum+(-g-2*log(d))*k)/pow(g,3.0); break; } switch(o_varcovar){ case 0: if (method < 2 ){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *J = gsl_matrix_calloc (2,2); gsl_matrix_set (J,0,0,dldg*dldg); gsl_matrix_set (J,0,1,dldg*dldb); gsl_matrix_set (J,1,0,dldb*dldg); gsl_matrix_set (J,1,1,dldg*dldb); gsl_linalg_LU_decomp (J,P,&signum); gsl_linalg_LU_invert (J,P,covar); gsl_matrix_free(J); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *J = gsl_matrix_calloc (1,1); gsl_matrix_set(J,0,0,dldg*dldg); gsl_linalg_LU_decomp (J,P,&signum); gsl_linalg_LU_invert (J,P,covar); gsl_matrix_free(J); gsl_permutation_free(P); } break; case 1: if (method < 2){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *H = gsl_matrix_calloc (2,2); gsl_matrix_set(H,0,0,d2ldg2); gsl_matrix_set(H,0,1,d2ldgdb); gsl_matrix_set(H,1,0,d2ldgdb); gsl_matrix_set(H,1,1,d2ldb2); gsl_matrix_scale (H,-1.); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,covar); gsl_matrix_free(H); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *H = gsl_matrix_calloc (1,1); gsl_matrix_set(H,0,0,d2ldg2); gsl_matrix_scale (H,-1.); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,covar); gsl_matrix_free(H); gsl_permutation_free(P); } break; case 2: if (method < 2){ gsl_permutation * P = gsl_permutation_alloc (2); int signum; gsl_matrix *H = gsl_matrix_calloc (2,2); gsl_matrix *J = gsl_matrix_calloc (2,2); gsl_matrix *tmp = gsl_matrix_calloc (2,2); gsl_matrix_set(H,0,0,d2ldg2); gsl_matrix_set(H,0,1,d2ldgdb); gsl_matrix_set(H,1,0,d2ldgdb); gsl_matrix_set(H,1,1,d2ldb2); gsl_matrix_set(J,0,0,dldg*dldg); gsl_matrix_set(J,0,1,dldg*dldb); gsl_matrix_set(J,1,0,dldb*dldg); gsl_matrix_set(J,1,1,dldb*dldb); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,tmp); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,tmp,J,0.0,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,tmp,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(J); gsl_matrix_free(tmp); gsl_permutation_free(P); }else{ gsl_permutation * P = gsl_permutation_alloc (1); int signum; gsl_matrix *H = gsl_matrix_calloc (1,1); gsl_matrix *J = gsl_matrix_calloc (1,1); gsl_matrix *tmp = gsl_matrix_calloc (1,1); gsl_matrix_set(H,0,0,d2ldg2); gsl_matrix_set(J,0,0,dldg*dldg); gsl_linalg_LU_decomp (H,P,&signum); gsl_linalg_LU_invert (H,P,tmp); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,tmp,J,0.0,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,tmp,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(J); gsl_matrix_free(tmp); gsl_permutation_free(P); } break; } /* scale the covariance matrix to the appropriate dimension */ if(covar->size1 == 1) { const double dtmp1 = gsl_matrix_get(covar,0,0); gsl_matrix_free (covar); covar = gsl_matrix_alloc (2,2); gsl_matrix_set(covar,0,0,dtmp1); gsl_matrix_set(covar,0,1,NAN); gsl_matrix_set(covar,1,0,NAN); gsl_matrix_set(covar,1,1,NAN); } return covar; } gbutils-5.7.1/TODO0000644000175000017500000000016512411117114010601 00000000000000TODO: + write texinfo documentation + improve getline for gzip files + add 'finalize_program()' to all the utilitiesgbutils-5.7.1/gbnlpolyit.c0000644000175000017500000013257613246754720012475 00000000000000/* gbnlpolyit (ver. 5.6) -- Non linear polyit regression Copyright (C) 2010-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include "matheval.h" #include "assert.h" #include "multimin.h" #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void **dfdx; } Function; struct objdata { Function * F; size_t rows; size_t columns; size_t *datan; double **datax; size_t N; }; /* ========================== */ /* Zeroth-order approximation */ /* ========================== */ /* This is the negative log-likelihood per observation (that is, divided by the total number of observations) computed with no independent variables: the agglomeration parameter is set to zero and no dependence on regressors is introduced. Under this hypothesis ALL locations have the same attractiveness, equal to 1/L where L is the number of locations: NLL = -log(N!) + N*log(L) + \sum_l log(n_l!) This function is used for the computation of the generalized R^2. */ double zeromll (size_t *datan,const size_t rows,const size_t N){ size_t i; double res= -lgamma(N+1) + N*log(rows); for(i=0;iF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double res=0; /* negative log likelihood */ double a,A=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values); A += a; if( a < 0 ){ fprintf(stderr,">> x:"); for(i=0;i> n=%zd a=%f col=%f\n",datan[i],a,datax[0][i]); fprintf(stderr,">> f(x) = %s\n",evaluator_get_string (F->f)); } res += lgamma(datan[i]+1) - datan[i]*log(a); } res += N*log(A)-lgamma(N+1); *fval=res/rows; /* fprintf(stderr," f: %+12.6e\n",*fval); */ /* fprintf(stderr," x:"); */ /* for(i=0;iF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double a,A=0; /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); A += a; } /* cycle on rows */ for(i=0;if,columns+pnum,F->argname,values); for(j=0;jdf)[j],columns+pnum,F->argname,values); } for(j=0;jF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double res=0; /* negative log likelihood */ double a,A=0; /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); A += a; res += lgamma(datan[i]+1)-datan[i]*log(a); } res += N*log(A)-lgamma(N); /* cycle on rows */ for(i=0;if,columns+pnum,F->argname,values); for(j=0;jdf)[j],columns+pnum,F->argname,values); } for(j=0;j 0, a+x > 0. */ double marginal_polya(const size_t n, const double a, const double A, const size_t N){ return exp( gsl_sf_lnpoch(a,n) + gsl_sf_lnpoch(A-a,N-n)- gsl_sf_lnpoch(A,N) + gsl_sf_lnchoose(N,n)); } /* Likelihood and gradients */ /* ======================== */ void objpolya_f (const size_t pnum, const double *x,void *params,double *fval){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double res=0; /* negative log likelihood */ double a,A=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values); A += a; if( a < 0 ){ fprintf(stderr,">> x:"); for(i=0;i> n=%zd a=%f col=%f\n",datan[i],a,datax[0][i]); fprintf(stderr,">> f(x) = %s\n",evaluator_get_string (F->f)); } if(datan[i]>0) for(j=0;jF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double a,A=0,sumA=0; /* variables used in computation */ double y[rows]; /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); A += a; if(datan[i]>0) for(j=0;jdf)[j],columns+pnum,F->argname,values); } for(j=0;jF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t N=((struct objdata *)params)->N; size_t *datan=((struct objdata *)params)->datan; double **datax=((struct objdata *)params)->datax; double values[pnum+columns]; size_t i,j; double res=0; /* negative log likelihood */ double a,A=0,sumA=0; /* variables used in computation */ double y[rows]; /* initialize gradient */ for(i=0;if,columns+pnum,F->argname,values); A += a; if(datan[i]>0) for(j=0;jdf)[j],columns+pnum,F->argname,values); } for(j=0;j \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -O type of output (default 0)\n"); fprintf(stdout," 0 parameters and log-like (ll) \n"); fprintf(stdout," 1 marginal effects \n"); fprintf(stdout," 2 marginal elasticities \n"); fprintf(stdout," 3 n_l n*_l a*_l *=estimated \n"); fprintf(stdout," 4 occupancies classes \n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -V standard errors and p-scores of diff. from zero using bootstrap \n"); fprintf(stdout," -r number of replicas (default 20)\n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3 covariance matrix \n"); fprintf(stdout," 4 minimization steps \n"); fprintf(stdout," 5 model definition \n"); fprintf(stdout," -R set the rng seed (default 0)\n"); fprintf(stdout," -M set the model to use (default 0) |\n"); fprintf(stdout," 0 Polya\n"); fprintf(stdout," 1 multinomial\n"); fprintf(stdout," -A MLL optimization options (default 0.01,0.1,100,1e-6,1e-6,5)\n"); fprintf(stdout," fields are step,tol,iter,eps,msize,algo. Empty fields for default\n"); fprintf(stdout," step initial step size of the searching algorithm \n"); fprintf(stdout," tol line search tolerance iter: maximum number of iterations \n"); fprintf(stdout," eps gradient tolerance : stopping criteria ||gradient||5){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* compute standard errors */ o_stderr=1; } else if(opt=='R'){ /* RNG seed */ seed = (unsigned) atol(optarg); } else if(opt=='r'){ /* number of replicas */ replicas = (size_t) atoi(optarg); } else if(opt=='A'){ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.step_size=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.tol=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxiter=(unsigned) atoi(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.epsabs=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxsize=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.method= (unsigned) atoi(stmp2); } free(stmp3); } else if(opt=='v'){ o_verbose = atoi(optarg); mmparams.verbosity=(o_verbose>2?o_verbose-2:0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* define the dataset */ { size_t i,j; double **data=NULL; /* load data; the first column contains occupancy numbers */ loadtable(&data,&rows,&columns,0,splitstring); /* split the occupancies from the variables */ datan = (size_t *) my_alloc(rows*sizeof(size_t)); datax = (double **) my_alloc((columns-1)*sizeof(double *)); for(i=0;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ F.f = evaluator_create (stmp3); assert(F.f); } free(stmp3); } free(piece); } /* check that everything is correct */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } { size_t i,j; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; char **storedname=NULL; size_t storednum=0; /* retrieve list of arguments and their number */ /* notice that storedname is not allocated */ { int argnum; evaluator_get_variables (F.f,&storedname,&argnum); storednum = (size_t) argnum; } /* check the definition of the function: column specifications refer to existing columns and parameters are properly initialized */ for(i=0;i0?atoi(stmp1+1):0); if(index>columns){ fprintf(stderr,"ERROR (%s): column %zu not present in data\n", GB_PROGNAME,index); exit(-1); } } else { for(j=0;j3){ size_t i; fprintf(stderr," ------------------------------------------------------------\n"); fprintf (stderr,"\n Parameters and initial conditions:\n"); for(i=0;i1){ size_t i,j; double A=0; double MFpR2=0; /* McFadden adjusted pseudo R^2 */ double CpR2=0; /* Effron pseudo R^2 */ double CSpR2=0; /* Cox&Snell rescaled pseudo R^2 */ double values[Pnum+columns]; fprintf(stderr,"#\n"); fprintf(stderr,"# Summary statistics \n"); fprintf(stderr,"# number of rows : %zd\n",rows); fprintf(stderr,"# number of variables : %zd\n",Pnum); fprintf(stderr,"# number of regressors : %zd\n",columns); fprintf(stderr,"# number of events : %zd\n",llparams.N); /* set the parameter values */ for(i=columns;i 0 && o_output > 0){ size_t i; fprintf(stderr,"# Estimated parameters:\n"); fprintf(stderr,"#"); if(o_stderr==0){ for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ if(o_output==1) fprintf(stdout,"# Marginal effects\n"); else if (o_output==2 ) fprintf(stdout,"# Marginal elasticities\n"); } /* +++++++++++++++++++++++++++++++++++ */ if(o_stderr==0){ /* +++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;icolumns) fprintf(stdout,EMPTY_NL,"n"); else fprintf(stdout,"\n"); } /* +++++++++++++++++++++++++++++++++++ */ for(i=0;i0){ fprintf(stdout,"#"); for(i=0;icolumns){ fprintf(stdout,EMPTY_SEP,"n"); fprintf(stdout,EMPTY_SEP,"+/-"); fprintf(stdout,EMPTY_NL,"p-scr"); } else fprintf(stdout,"\n"); } /* +++++++++++++++++++++++++++++++++++ */ for(i=0;i0){ fprintf(stdout,"#observed vs. predicted occupancy\n"); fprintf(stdout,"#"); fprintf(stdout,EMPTY_SEP,"obs."); fprintf(stdout,EMPTY_SEP,"pred."); fprintf(stdout,EMPTY_NL,"a/b"); } /* +++++++++++++++++++++++++++++++++++ */ for(i=0;ibins) bins=itmp1; } bins+=1; /* allocate necessary space */ emp = (double *) my_calloc(bins,sizeof(double)); the = (double *) my_calloc(bins,sizeof(double)); /* set the parameter values */ for(i=columns;i0){ fprintf(stderr,"# chi-square: %f\n",chisq); fprintf(stdout,"# occupancy by classes\n"); fprintf(stdout,"#"); fprintf(stdout,EMPTY_SEP,"cmin"); fprintf(stdout,EMPTY_SEP,"cmax"); fprintf(stdout,EMPTY_SEP,"emp"); fprintf(stdout,EMPTY_NL,"theo"); } /* +++++++++++++++++++++++++++++++++++ */ /* print the classes occupancy */ for(i=0;i \/\fR... .SH DESCRIPTION Compute specified functions on data read from standard input. The default is to perform the computation column\-wise, on each row of data separately. Variable 'xi' stands for the i\-th column while 'x0' stands for the row number, e.g. a function f(x1,x2) operates on the first and secod column. With the option \fB\-t\fR the function is computed, in turn, on every column. In this case f(x1,x2) stands for a function of the column itself and of the following column (the index being a lead operator). In these cases 'x' is equivalent to 'x1'. With \fB\-r\fR or \fB\-R\fR the function is recursevely computed "columwise" on each row. In this case the variable 'x' identifies the result of the previous evaluation. A lag operator can be specified with the letter l, like in 'x1l2', which means the first column two steps before. More functions can be specified and will be considered in turn. .SH OPTIONS .TP \fB\-T\fR compute the function row\-wise, on each column separately. .TP \fB\-t\fR compute on each column .TP \fB\-r\fR set initial value and compute recursively .TP \fB\-R\fR set initial value, compute recursively and print intermediary results .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-o\fR set the output format (default '%12.6e') .TP \fB\-s\fR set the output separation string (default ' ') .TP \fB\-h\fR this help .SH EXAMPLES .TP gbfun 'x0+log(x2)' < file print the log of the second column of 'file' adding the progressive number of the row .TP gbfun \-T 'x0+log(x2)' < file print the log of the second row of 'file' adding the progressive number of the column .TP gbfun \-r 0 'x+sqrt(x1)' < file print the sum of the square root of the elements of the first column of 'file' .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/README0000644000175000017500000000473112573765607011025 00000000000000gbutils -- Utilities Overview Giulio Bottazzi (bottazzi@sssup.it), September 2015 gbutils is a set of command line utilities for the manipulation and statistical analysis of data. Data fare usually read from standard input in ASCII format and results are printed in ASCII format to standard output. These utilities are completely written in C. They have been tested (and used many many times) on different Linux distributions and should compile and run, in principle, on any Unix platform. No other OSes have been tested. Please notice that these programs have been written for personal use, and are distributed under the GPL license (see the file "COPYING") in the hope they could be of help to other people, but without any implied warranty. Please report bugs to . The package home page is http://cafim.sssup.it/~giulio/software/gbutils/index.html The most up-to-date information and documentation about the gbutils package can be found in its home page. A few documents are however distributed with the package. For Linux installation instruction and a brief introduction to the library, see "intro". More technical details are provided in "overview". The document "cygwin_install" describes how to install the package on Windows machines, using the cygwin emulation framework. The file 'gbget' is a brief tutorial on using the eponymous program, a sort of Swiss knife for the manipulation of data file, which is often essential for an effective usage of the utilities in the package. These documents are distributed in ASCII (.txt extension) and PDF format. The package has several dependencies, see the online documentation or 'intro" for further information. Installation instruction ======================== I recommend to install directly the binary packages from the debian or Ubuntu repositories. See the online documentation or the file "intro". In the case this is not possible, the installation from source should be rather straightforward. Download the package and unpack it tar xvzf gbutils-{version}.tar.gz move inside the source directory cd gbutils-{version} run the configure script ./configure then build the files make become root su and install them make install If you can't install the file as root you have to provide a different directory for the binaries using the option ~--prefix~ of the configure script. See "INSTALL" for details. For more detailed instructions see the "INSTALL" file in the distributed package. gbutils-5.7.1/gbker2d.c0000644000175000017500000003211713246754720011620 00000000000000/* gbker2d (ver. 5.6) -- Kernel density estimate for bivariate data Copyright (C) 2003-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ double **data=NULL; size_t size=0; char *splitstring = strdup(" \t"); /* OPTIONS */ int o_kerneltype=0; int o_setbandwidth=0; int o_verbose=0; int o_devariate=1; /* data statistics */ double ave[2],sdev[2],min[2],max[2],cov; /* variables for "de-covariation" */ double corr,cphi,sphi,rho,detJ; /* variables for binning */ size_t M[2]={10,10}; /* number of bins */ double *csi; /* the bins */ /* kernel variables */ double (*K) (double); /* kernel */ double Kscale=1; /* automatic bandwidth factor */ double scale[2]={1.,1.}; /* optional scaling of the bandwidth */ double h[2]={1.,1.}; /* bandwidth */ double delta[2]; /* step sizes */ double a[2]; /* initial points */ size_t J[2]; /* integer radius */ double * Kvals; /* kernel values */ /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"H:K:n:S:hvDF:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='S'){ /*set the scale parameter for smoothing*/ if(!strchr (optarg,',')){ scale[0]=scale[1]=atof(optarg); } else{ char *stmp1=strdup (optarg); scale[0] = atof(strtok (stmp1,",")); scale[1] = atof(strtok (NULL,",")); free(stmp1); } } else if(opt=='n'){ /*the number of bins*/ if(!strchr (optarg,',')){ M[0]=M[1]=atoi(optarg); } else{ char *stmp1=strdup (optarg); M[0] = atoi(strtok (stmp1,",")); M[1] = atoi(strtok (NULL,",")); free(stmp1); } } else if(opt=='K'){ /*the Kernel to use*/ o_kerneltype = atoi(optarg); } else if(opt=='H'){ /*set the kernel bandwidth*/ o_setbandwidth=1; if(!strchr (optarg,',')){ h[0]=h[1]=atof(optarg); } else{ char *stmp1=strdup (optarg); h[0] = atof(strtok (stmp1,",")); h[1] = atof(strtok (NULL,",")); free(stmp1); } } else if(opt=='v'){ /*increase verbosity*/ o_verbose=1; } else if(opt=='D'){ /*skip data de-(co)varation*/ o_devariate=0; } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"2D kernel density estimation on an equispaced grid. Data are read from standard\n"); fprintf(stdout,"input as couple (x,y). The kernel bandwidth if not provided with the option -H\n"); fprintf(stdout,"is set automatically. Different bandwidths can be specified for x and y. \n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of points where the density is computed (default 10) use comma (,)\n"); fprintf(stdout," to specify different values for x and y components\n"); fprintf(stdout," -H set the kernel bandwidth explicitly; use comma (,) to specify different\n"); fprintf(stdout," values for x and y components\n"); fprintf(stdout," -S scale the kernel bandwidth with respect to euristic 'optimal' value; use\n"); fprintf(stdout," comma (,) to specify different values for x and y components\n"); fprintf(stdout," -K choose the kernel to use: 0: Epanenchnikov 1: Rectangular 2: Silverman\n"); fprintf(stdout," type 1 3: Silverman type 2 (default 0)\n"); fprintf(stdout," -D switch off data de-variation procedure\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbker2d -n 10,20 -K 1 < file compute the kernel on an equispaced grid 10x20\n"); fprintf(stdout," using a rectangular kernel\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load the data */ load2(&data,&size,0,splitstring); /* compute the statistics */ moment_short2(data,size,ave,sdev,&cov,min,max); corr = cov/(sdev[0]*sdev[1]); if(corr == 1 || corr == -1){ fprintf(stdout,"deterministic relation between x and y\n"); exit(+1); } rho = 1./sqrt(1-corr*corr); cphi = cos(.5*asin(-corr)); sphi = sin(.5*asin(-corr)); detJ = rho/(sdev[0]*sdev[1]); /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stdout,"---- Data Statistics ------------\n"); fprintf(stdout," ave sdev min max\n"); fprintf(stdout,"x: %+.2e %+.2e %+.2e %+.2e\n", ave[0],sdev[0],min[0],max[0]); fprintf(stdout,"y: %+.2e %+.2e %+.2e %+.2e\n", ave[1],sdev[1],min[1],max[1]); fprintf(stdout,"r=%e rho=%e phi=%e\n",corr,rho,.5*asin(-corr)); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ /* possibly eliminate variance and covariance */ if(o_devariate == 1){ size_t i; for(i=0;i max[0]){ max[0] = dtmp0; } if(dtmp1 < min[1]){ min[1] = dtmp1; } else if(dtmp1 > max[1]){ max[1] = dtmp1; } } } /* choose the kernel to use */ switch(o_kerneltype){ case 0: K=Kepanechnikov2d; Kscale=2.40; break; case 1: K=Krectangular2d; break; case 2: K=Ksilverman2d_1; Kscale=2.78; break; case 3: K=Ksilverman2d_2; Kscale=3.12; break; default: fprintf(stdout,"unknown kernel; use %s -h\n",argv[0]); exit(+1); } /* the parameter h is not provided on command line it is set by an automatic procedure [Silverman p.86] */ if(o_setbandwidth == 0){ if(o_devariate == 1){ h[0]=scale[0]*Kscale/pow(size,1./6); h[1]=scale[1]*Kscale/pow(size,1./6); } else{ h[0]=sdev[0]*scale[0]*Kscale/pow(size,1./6); h[1]=sdev[1]*scale[1]*Kscale/pow(size,1./6); } } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stdout,"---- Density Parameters ------------\n"); /* data treatment */ fprintf(stdout,"de-variation "); if(o_devariate == 1) fprintf(stdout,"yes (detJ=%+.3e)",detJ); else fprintf(stdout,"no"); fprintf(stdout,"\n"); /* kernel type */ fprintf(stdout,"kernel type "); switch(o_kerneltype){ case 0: fprintf(stdout,"Epanechnikov"); break; case 1: fprintf(stdout,"Rectangular"); break; case 2: fprintf(stdout,"Silverman type 1"); break; case 3: fprintf(stdout,"Silverman type 2"); break; } fprintf(stdout,"\n"); /* bandwidth */ fprintf(stdout,"x bandwidth %.3e ",h[0]); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic with A=%.2f ",Kscale); if(scale[0] != 1 ) fprintf(stdout,"scaled by %f",scale[0]); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); fprintf(stdout,"y bandwidth %.3e ",h[1]); if(o_setbandwidth == 0){ fprintf(stdout,"(automatic with A=%.2f ",Kscale); if(scale[1] != 1 ) fprintf(stdout,"scaled by %f",scale[1]); fprintf(stdout,")"); } else{ fprintf(stdout,"(provided)"); } fprintf(stdout,"\n"); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ /* the boundaries of the interval are set to have a = xmin-delta b=xmax+delta */ delta[0] = (max[0]-min[0])/(M[0] -2.0); delta[1] = (max[1]-min[1])/(M[1] -2.0); a[0] = min[0]-.5*delta[0]; a[1] = min[1]-.5*delta[1]; J[0] = (size_t) floor(h[0]/delta[0]); J[1] = (size_t) floor(h[1]/delta[1]); /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* binning */ fprintf(stdout,"---- Binning Structure ------------\n"); fprintf(stdout," M data range delta J bins range\n"); fprintf(stdout,"x: %zd [%+.2e,%+.2e] %+.2e %zd [%+.2e,%+.2e]\n", M[0],min[0],max[0],delta[0],J[0],a[0],a[0]+(M[0]-1)*delta[0]); fprintf(stdout,"y: %zd [%+.2e,%+.2e] %+.2e %zd [%+.2e,%+.2e]\n", M[1],min[1],max[1],delta[1],J[1],a[1],a[1]+(M[1]-1)*delta[1]); fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++ */ /* set the values of the kernel */ { size_t i,j; Kvals = (double *) my_alloc((2*J[0]+1)*(2*J[1]+1)*sizeof(double)); for(i=0;i<=2*J[0];i++){ const double dtmp0 = (int) i- (int) J[0]; for(j=0;j<=2*J[1];j++){ const double dtmp1 = (int) j- (int) J[1]; const double dtmp2 = dtmp0*dtmp0*delta[0]*delta[0]/(h[0]*h[0])+ dtmp1*dtmp1*delta[1]*delta[1]/(h[1]*h[1]) ; Kvals[i*(2*J[1]+1)+j] = K(dtmp2)/(h[0]*h[1]); } } } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ size_t i,j; double sum=0.0; fprintf(stdout,"---- Final Checks -----------------\n"); for(i=0;i<=2*J[0];i++){ for(j=0;j<=2*J[1];j++){ sum += Kvals[i*(2*J[1]+1)+j]; /* fprintf(stdout,"%f ",Kvals[i*(2*J[1]+1)+j]); */ } /* fprintf(stdout,"\n"); */ } fprintf(stdout,"sum of kernel coeff. (~1): %e\n",sum*delta[0]*delta[1]); } /* ++++++++++++++++++++++++++++ */ /* binning */ { size_t i,j; /* double sum=0.0; */ /* allocate the bins */ csi = (double *) my_alloc(M[0]*M[1]*sizeof(double)); /* prepare the binning */ for(i=0;i= M[0]-1 || index1 >= M[1]-1 ) continue; csi[index0*M[1]+index1]+=(1.-epsilon0)*(1.-epsilon1); csi[index0*M[1]+index1+1]+=(1.-epsilon0)*epsilon1; csi[(index0+1)*M[1]+index1]+=epsilon0*(1.-epsilon1); csi[(index0+1)*M[1]+index1+1]+=epsilon0*epsilon1; } /* normalization */ for(i=0;i0?i-Jx:0); const int max0 = (i+Jx>Mx-1?Mx-1:i+Jx); for(j=-Jy;j0?j-Jy:0); const int max1 = (j+Jy>My-1?My-1:j+Jy); double sum=0.0; int l,m; for(l=min0;l<=max0;l++){ for(m=min1;m<=max1;m++){ sum+=Kvals[(i-l+Jx)*(2*Jy+1)+(j-m+Jy)]*csi[l*My+m]; } } if(o_devariate == 1){ const double x = (tmpx*cphi-tmpy*sphi)*sdev[0]+ave[0]; const double y = (tmpy*cphi-tmpx*sphi)*sdev[1]+ave[1]; printf(FLOAT_SEP,x); printf(FLOAT_SEP,y); printf(FLOAT_NL,sum*detJ); dtmp1+=sum; } else{ printf(FLOAT_SEP,tmpx); printf(FLOAT_SEP,tmpy); printf(FLOAT_NL,sum); dtmp1+=sum; } } printf("\n"); } /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stdout,"sum of discrete density (~1): %e\n",dtmp1*delta[0]*delta[1]); } /* ++++++++++++++++++++++++++++ */ } exit(0); } gbutils-5.7.1/depcomp0000755000175000017500000004755611775245551011530 00000000000000#! /bin/sh # depcomp - compile a program generating dependencies as side-effects scriptversion=2011-12-04.11; # UTC # Copyright (C) 1999, 2000, 2003, 2004, 2005, 2006, 2007, 2009, 2010, # 2011 Free Software Foundation, Inc. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Alexandre Oliva . case $1 in '') echo "$0: No command. Try \`$0 --help' for more information." 1>&2 exit 1; ;; -h | --h*) cat <<\EOF Usage: depcomp [--help] [--version] PROGRAM [ARGS] Run PROGRAMS ARGS to compile a file, generating dependencies as side-effects. Environment variables: depmode Dependency tracking mode. source Source file read by `PROGRAMS ARGS'. object Object file output by `PROGRAMS ARGS'. DEPDIR directory where to store dependencies. depfile Dependency file to output. tmpdepfile Temporary file to use when outputting dependencies. libtool Whether libtool is used (yes/no). Report bugs to . EOF exit $? ;; -v | --v*) echo "depcomp $scriptversion" exit $? ;; esac if test -z "$depmode" || test -z "$source" || test -z "$object"; then echo "depcomp: Variables source, object and depmode must be set" 1>&2 exit 1 fi # Dependencies for sub/bar.o or sub/bar.obj go into sub/.deps/bar.Po. depfile=${depfile-`echo "$object" | sed 's|[^\\/]*$|'${DEPDIR-.deps}'/&|;s|\.\([^.]*\)$|.P\1|;s|Pobj$|Po|'`} tmpdepfile=${tmpdepfile-`echo "$depfile" | sed 's/\.\([^.]*\)$/.T\1/'`} rm -f "$tmpdepfile" # Some modes work just like other modes, but use different flags. We # parameterize here, but still list the modes in the big case below, # to make depend.m4 easier to write. Note that we *cannot* use a case # here, because this file can only contain one case statement. if test "$depmode" = hp; then # HP compiler uses -M and no extra arg. gccflag=-M depmode=gcc fi if test "$depmode" = dashXmstdout; then # This is just like dashmstdout with a different argument. dashmflag=-xM depmode=dashmstdout fi cygpath_u="cygpath -u -f -" if test "$depmode" = msvcmsys; then # This is just like msvisualcpp but w/o cygpath translation. # Just convert the backslash-escaped backslashes to single forward # slashes to satisfy depend.m4 cygpath_u='sed s,\\\\,/,g' depmode=msvisualcpp fi if test "$depmode" = msvc7msys; then # This is just like msvc7 but w/o cygpath translation. # Just convert the backslash-escaped backslashes to single forward # slashes to satisfy depend.m4 cygpath_u='sed s,\\\\,/,g' depmode=msvc7 fi case "$depmode" in gcc3) ## gcc 3 implements dependency tracking that does exactly what ## we want. Yay! Note: for some reason libtool 1.4 doesn't like ## it if -MD -MP comes after the -MF stuff. Hmm. ## Unfortunately, FreeBSD c89 acceptance of flags depends upon ## the command line argument order; so add the flags where they ## appear in depend2.am. Note that the slowdown incurred here ## affects only configure: in makefiles, %FASTDEP% shortcuts this. for arg do case $arg in -c) set fnord "$@" -MT "$object" -MD -MP -MF "$tmpdepfile" "$arg" ;; *) set fnord "$@" "$arg" ;; esac shift # fnord shift # $arg done "$@" stat=$? if test $stat -eq 0; then : else rm -f "$tmpdepfile" exit $stat fi mv "$tmpdepfile" "$depfile" ;; gcc) ## There are various ways to get dependency output from gcc. Here's ## why we pick this rather obscure method: ## - Don't want to use -MD because we'd like the dependencies to end ## up in a subdir. Having to rename by hand is ugly. ## (We might end up doing this anyway to support other compilers.) ## - The DEPENDENCIES_OUTPUT environment variable makes gcc act like ## -MM, not -M (despite what the docs say). ## - Using -M directly means running the compiler twice (even worse ## than renaming). if test -z "$gccflag"; then gccflag=-MD, fi "$@" -Wp,"$gccflag$tmpdepfile" stat=$? if test $stat -eq 0; then : else rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" echo "$object : \\" > "$depfile" alpha=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz ## The second -e expression handles DOS-style file names with drive letters. sed -e 's/^[^:]*: / /' \ -e 's/^['$alpha']:\/[^:]*: / /' < "$tmpdepfile" >> "$depfile" ## This next piece of magic avoids the `deleted header file' problem. ## The problem is that when a header file which appears in a .P file ## is deleted, the dependency causes make to die (because there is ## typically no way to rebuild the header). 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This case will never be run, # since it is checked for above. exit 1 ;; none) exec "$@" ;; *) echo "Unknown depmode $depmode" 1>&2 exit 1 ;; esac exit 0 # Local Variables: # mode: shell-script # sh-indentation: 2 # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: gbutils-5.7.1/gbplot0000755000175000017500000001527712377156723011364 00000000000000#!/bin/sh # gbplot ver. 1.1 Copyright (C) 2009-2010 Giulio Bottazzi #read command line options; the position of the last option is saved #in OPTIND while getopts "T:o:t:p:ihl:C:v-:" opt do case $opt in -) case "${OPTARG}" in help) help=yes;; version) version=yes;; esac;; T) terminal=$OPTARG;; o) outfile=$OPTARG;; t) title=$OPTARG;; p) prestring=$OPTARG;; i) interactive=yes;; h) help=yes;; l) setlog=$OPTARG;; C) precommand="${precommand}; $OPTARG";; v) verbose=yes;; \?) help=yes;; esac done if [ "$version" = "yes" ]; then cat - < Package home page EOF exit fi if [ "$help" = "yes" ]; then cat - < .gb.data #check the syntax and define plotting parameters #plotcmd=${@:$OPTIND:1} <- bashism shift `expr $OPTIND - 1` plotcmd=$1 if [ "$plotcmd" != "plot" ] && [ "$plotcmd" != "splot" ]; then echo "provide a plot or splot command" exit fi #firstpar=${@:$(( OPTIND+1 )):1} <- bashism #plotpar=${@:$(( OPTIND+2 ))} <- bashism shift firstpar=$1 shift plotpar=$@ #identify range specification if echo "$firstpar" | grep '\[*\]' > /dev/null; then plotcmd="$plotcmd $firstpar" else plotpar="$firstpar $plotpar" fi #set the default terminal if echo "set term" | gnuplot 2>&1 | grep wx > /dev/null; then DEFTERM=wxt else DEFTERM=x11 fi #----------------------------------------------- if [ "$interactive" = "yes" ]; then #interactive session #create temporary files CMDFILE=`tempfile` ENDFILE=`tempfile` # --- initial gnuplot commands #set the default terminal echo "set term $DEFTERM noraise" >> $CMDFILE #next line useful for screen to start in present directory echo "cd \"$PWD\"" >> $CMDFILE #no need of irrelevant titles echo "set key noautotitle" >> $CMDFILE #prepend expression if [ "$prestring" != "" ]; then echo "$prestring" >> $CMDFILE fi #add title if [ "$title" != "" ]; then echo "set title \"$title\"" >> $CMDFILE fi #set log scale if [ "$setlog" != "" ]; then echo "set log $setlog" >> $CMDFILE fi #add pre-plot command if [ "$precommand" != "" ]; then echo "$precommand" >> $CMDFILE fi echo "$plotcmd \".gb.data\" $plotpar" >> $CMDFILE echo "show plot" >> $CMDFILE # ---------------------------- # --- final gnuplot commands #prepare for final output if 'terminal' or 'outfile' is specified if [ "$terminal" != "" ]; then echo "set term $terminal" >> $ENDFILE fi if [ "$outfile" != "" ]; then echo "set out \"$outfile\"" >> $ENDFILE echo "replot" >> $ENDFILE echo "set out" >> $ENDFILE fi #remove temporary files echo "!rm .gb.data" >> $ENDFILE echo "!rm $CMDFILE $ENDFILE" >> $ENDFILE # ---------------------------- if [ "$verbose" = "yes" ]; then cat $CMDFILE > /dev/stderr echo "-- session -- " > /dev/stderr cat $ENDFILE > /dev/stderr fi if [ "$TERM" = "screen" ]; then #start gnuplot in a new screen window screen -X screen gnuplot $CMDFILE - $ENDFILE else #start gnuplot in a new X terminal aterm -e gnuplot $CMDFILE - $ENDFILE fi else #non-interactive session #create temporary files CMDFILE=`tempfile` #next line useful for screen to start in present directory echo "cd \"$PWD\"" >> $CMDFILE #no need of irrelevant titles echo "set key noautotitle" >> $CMDFILE if [ "$terminal" != "" ]; then echo "set term $terminal" >> $CMDFILE else echo "set term $DEFTERM noraise" >> $CMDFILE fi if [ "$outfile" != "" ]; then echo "set out \"$outfile\"" >> $CMDFILE fi #prepend expression if [ "$prestring" != "" ]; then echo "$prestring" >> $CMDFILE echo "$prestring" fi #add title if [ "$title" != "" ]; then echo "set title \"$title\"" >> $CMDFILE fi #set log scale if [ "$setlog" != "" ]; then echo "set log $setlog" >> $CMDFILE fi #add pre-plot command if [ "$precommand" != "" ]; then echo "$precommand" >> $CMDFILE fi echo "$plotcmd \".gb.data\" $plotpar" >> $CMDFILE if [ "$verbose" = "yes" ]; then cat $CMDFILE > /dev/stderr fi gnuplot -persist ${CMDFILE} rm .gb.data rm "$CMDFILE" fi gbutils-5.7.1/gbconvtable0000755000175000017500000000537412532112602012335 00000000000000#!/bin/sh # gbconvtable ver. 5.6 Copyright (C) 2010-2015 Giulio Bottazzi #default settings pos=1 force="no" dictfile="" #read command line options; the position of the last option is saved #in OPTIND while getopts "d:c:f:h-:" opt do case $opt in -) case "${OPTARG}" in help) help=yes;; version) version=yes;; esac;; d) dictfile=$OPTARG;; c) pos=$OPTARG;; f) force=yes ; fstring=$OPTARG ;; h) help=yes;; \?) help=yes;; esac done if [ "$version" = "yes" ]; then cat - < Package home page EOF exit fi if [ "$help" = "yes" ]; then cat - < 0){ split(line,pair) dict[pair[1]]=pair[2] } } { #replace the field and print it if(NF>=pos){ if($pos in dict) $pos=dict[$pos] else{ if(force=="yes") $pos=fstring } } print $0 } ' gbutils-5.7.1/AUTHORS0000644000175000017500000000020710206604747011173 00000000000000Author of GBUTILS is Giulio Bottazzi (bottazzi@sssup.it) Contributors: Angelo Secchi (secchi@sssup.it) : bugfixes and documentation gbutils-5.7.1/gbker2d.10000644000175000017500000000370013246755334011534 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBKER2D "1" "March 2018" "gbker2d 5.7.1" "User Commands" .SH NAME gbker2d \- Kernel density estimate for bivariate data .SH SYNOPSIS .B gbker2d [\fI\,options\/\fR] .SH DESCRIPTION 2D kernel density estimation on an equispaced grid. Data are read from standard input as couple (x,y). The kernel bandwidth if not provided with the option \fB\-H\fR is set automatically. Different bandwidths can be specified for x and y. .SH OPTIONS .TP \fB\-n\fR number of points where the density is computed (default 10) use comma (,) to specify different values for x and y components .TP \fB\-H\fR set the kernel bandwidth explicitly; use comma (,) to specify different values for x and y components .TP \fB\-S\fR scale the kernel bandwidth with respect to euristic 'optimal' value; use comma (,) to specify different values for x and y components .TP \fB\-K\fR choose the kernel to use: 0: Epanenchnikov 1: Rectangular 2: Silverman type 1 3: Silverman type 2 (default 0) .TP \fB\-D\fR switch off data de\-variation procedure .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH EXAMPLES .TP gbker2d \-n 10,20 \-K 1 < file compute the kernel on an equispaced grid 10x20 using a rectangular kernel .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/multimin.c0000644000175000017500000006435213246754720012144 00000000000000/* multimin.c (ver. 1.2) -- Interface to GSL multidim. minimization Copyright (C) 2002-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ /* multimin is an interface to the various GSL minimization routines. When invoked, all the information necessary to perform the minimization are passed as formal parameters. This generate a pretty long, FORTRAN-like, list of parameters. This approach allows, however, to black-box, as far as possible, the interior functioning of the routine from the rest of the program. Let's analyse the calling convention in details: multimin(size_t n,double *x,double *fun, unsigned *type,double *xmin,double *xmax, void (*f) (const size_t,const double *,void *,double *), void (* df) (const size_t,const double *, void *,double *), void (* fdf) (const size_t,const double *, void *,double *,double *), void *fparams, const struct multimin_params oparams) where -------------------------------------------------------------------- n INPUT: dimension of the problem, number of independent variables of the function. -------------------------------------------------------------------- x INPUT: pointer to an array of n values x[0],...x[n-1] containing the initial estimate of the minimum point OUTPUT: contains the final estimation of the minimum position -------------------------------------------------------------------- type a pointer to an array of integer type[1],...,type[n-1] describing the boundary conditions for the different variables. The problem is solved as an unconstrained one on a suitably transformed variable y. Possible values are: Interval: Transformation: 0 unconstrained x=y 1 semi-closed right half line [ xmin,+infty ) x=xmin+y^2 2 semi-closed left half line ( -infty,xmax ] x=xmax-y^2 3 closed interval [ xmin,xmax ] x=SS+SD*sin(y) 4 open right half line ( xmin,+infty ) x=xmin+exp(y) 5 open left half line ( -infty,xmax ) x=xmax-exp(y) 6 open interval ( xmin,xmax ) x=SS+SD*tanh(y) where SS=.5(xmin+xmax) SD=.5(xmax-xmin) There are also other UNSUPPORTED transformations used in various test 7 open interval ( xmin,xmax ) x=SS+SD*(1+y/sqrt(1+y^2)) 8 open right half line ( xmin,+infty ) x=xmin+.5*(y+sqrt(1+y^2)) 9 open left half line ( -infty,xmax ) x=xmax+.5*(y-sqrt(1+y^2)) -------------------------------------------------------------------- xmin xmax pointers to arrays of double containing respectively the lower and upper boundaries of the different variables. For a given variable, only the values that are implied by the type of constraints, defined as in *type, are actually inspected. -------------------------------------------------------------------- f f calculates the objective function at a specified point x. Its specification is void (*f) (const size_t n, const double *x,void *fparams,double *fval) n INPUT: the number of variables x INPUT:the point at which the function is required fparams pointer to a structure containing parameters required by the function. If no external parameter are required it can be set to NULL. fval OUTPUT: the value of the objective function at the current point x. -------------------------------------------------------------------- df df calculates the gradient of the objective function at a specified point x. Its specification is void (*df) (const size_t n, const double *x,void *fparams,double *grad) n INPUT: the number of variables x INPUT:the point at which the function is required fparams pointer to a structure containing parameters required by the function. If no external parameter are required it can be set to NULL. grad OUTPUT: the values of the gradient of the objective function at the current point x are stored in grad[0],...,grad[n-1]. -------------------------------------------------------------------- fdf fdf calculates the value and the gradient of the objective function at a specified point x. Its specification is void (*fdf) (const size_t n, const double *x,void *fparams,double *fval,double *grad) n INPUT: the number of variables x INPUT:the point at which the function is required fparams pointer to a structure containing parameters required by the function. If no external parameter are required it can be set to NULL. fval OUTPUT: the value of the objective function at the current point x. grad OUTPUT: the values of the gradient of the objective function at the current point x are stored in grad[0],...,grad[n-1]. -------------------------------------------------------------------- fparams pointer to a structure containing parameters required by the function. If no external parameter are required it can be set to NULL. -------------------------------------------------------------------- oparams structure of the type "multimin_params" containing the optimization parameters. The members are double step_size size of the first trial step double tol accuracy of the line minimization unsigned maxiter maximum number of iterations double epsabs accuracy of the minimization double maxsize; final size of the simplex unsigned method method to use. Possible values are: 0: Fletcher-Reeves conjugate gradient 1: Polak-Ribiere conjugate gradient 2: Vector Broyden-Fletcher-Goldfarb-Shanno method 3: Steepest descent algorithm 4: Nelder-Mead simplex 5: Vector Broyden-Fletcher-Goldfarb-Shanno method ver. 2 6: Simplex algorithm of Nelder and Mead ver. 2 7: Simplex algorithm of Nelder and Mead: random initialization unsigned verbosity if greater then 0 print info on intermediate steps */ #include "multimin.h" struct g_params { size_t n; const unsigned *type; const double *xmin; const double *xmax; void (*f) (const size_t,const double *,void *,double *); void (* df) (const size_t,const double *, void *,double *); void (* fdf) (const size_t,const double *, void *,double *,double *); void *fparams; }; void *multimin_alloc(size_t size){ void *temp; if(!(temp = malloc(size))){ perror("malloc, memory allocation failed"); exit(1); } return temp; } static double g(const gsl_vector *y,void *gparams){ struct g_params *p= (struct g_params *) gparams; size_t i; double dtmp1; double res=GSL_NAN;/* the function is forced to return a value */ /* dereference useful stuff */ const size_t n = p->n; const unsigned *type = p->type; const double * xmin = p->xmin; const double * xmax = p->xmax; double *x = (double *) multimin_alloc(sizeof(double)*n); /* compute values of x and of dx/dy */ for(i=0;if(n,x,p->fparams,&res) ; free (x); return(res); } static void dg(const gsl_vector *y,void *gparams,gsl_vector *dg){ struct g_params *p= (struct g_params *) gparams; size_t i; double dtmp1,dtmp2; /* dereference useful stuff */ const size_t n = p->n; const unsigned *type = p->type; const double * xmin = p->xmin; const double * xmax = p->xmax; double *x = (double *) multimin_alloc(sizeof(double)*n); double *dx = (double *) multimin_alloc(sizeof(double)*n); double *df = (double *) multimin_alloc(sizeof(double)*n); /* compute values of x and of dx/dy */ for(i=0;idf(n,x,p->fparams,df); /* debug output; comment out if necessary */ /* fprintf(stderr,"#dg: x=( "); */ /* for(i=0;in; const unsigned *type = p->type; const double * xmin = p->xmin; const double * xmax = p->xmax; double *x = (double *) multimin_alloc(sizeof(double)*n); double *dx = (double *) multimin_alloc(sizeof(double)*n); double *df = (double *) multimin_alloc(sizeof(double)*n); /* compute values of x and of dx/dy */ for(i=0;ifdf(n,x,p->fparams,g,df); /* debug output; comment out if necessary */ /* fprintf(stderr,"#gdg: f=%f x=( ",g); */ /* for(i=0;iname; break; case 1:/* Polak-Ribiere conjugate gradient */ Tfdf = gsl_multimin_fdfminimizer_conjugate_pr; Tname = Tfdf->name; break; case 2:/* Vector Broyden-Fletcher-Goldfarb-Shanno method */ Tfdf = gsl_multimin_fdfminimizer_vector_bfgs; Tname = Tfdf->name; break; case 3:/* Steepest descent algorithm */ Tfdf =gsl_multimin_fdfminimizer_steepest_descent; Tname = Tfdf->name; break; case 4:/* Simplex algorithm of Nelder and Mead */ Tf = gsl_multimin_fminimizer_nmsimplex; Tname = Tf->name; break; case 5:/* Vector Broyden-Fletcher-Goldfarb-Shanno2 method */ Tfdf = gsl_multimin_fdfminimizer_vector_bfgs2; Tname = Tfdf->name; break; case 6:/* Simplex algorithm of Nelder and Mead version 2 */ Tf = gsl_multimin_fminimizer_nmsimplex2; Tname = Tf->name; break; case 7:/* Simplex algorithm of Nelder and Mead: random initialization */ Tf = gsl_multimin_fminimizer_nmsimplex2rand; Tname = Tf->name; break; default: fprintf(stderr,"Optimization method not recognized. Specify one of the following:\n\n"); fprintf(stderr,"0: Fletcher-Reeves conjugate gradient\n"); fprintf(stderr,"1: Polak-Ribiere conjugate gradient\n"); fprintf(stderr,"2: Vector Broyden-Fletcher-Goldfarb-Shanno method\n"); fprintf(stderr,"3: Steepest descent algorithm\n"); fprintf(stderr,"4: Nelder-Mead simplex\n"); fprintf(stderr,"5: Vector Broyden-Fletcher-Goldfarb-Shanno method ver. 2\n"); fprintf(stderr,"6: Simplex algorithm of Nelder and Mead ver. 2\n"); fprintf(stderr,"7: Simplex algorithm of Nelder and Mead: random initialization\n"); fprintf(stderr,"or try -h\n"); exit(EXIT_FAILURE); } /* --- OUPUT ---------------------------------- */ if(oparams.verbosity>0){ fprintf(stderr,"#--- MULTIMIN START\n"); fprintf(stderr,"# method %s\n",Tname); if(oparams.method<4 || oparams.method==5){ fprintf(stderr,"# initial step size %g\n", oparams.step_size); fprintf(stderr,"# line minimization tolerance %g\n",oparams.tol); fprintf(stderr,"# maximum number of iterations %u\n",oparams.maxiter); fprintf(stderr,"# precision %g\n",oparams.epsabs); } else{ fprintf(stderr,"# maximum number of iterations %u\n",oparams.maxiter); fprintf(stderr,"# maximum simplex size %g\n",oparams.maxsize); } } /* -------------------------------------------- */ /* compute values of y for initial condition */ for(i=0;ixmin[i]? (2.*x[i]-xmax[i]-xmin[i])/(xmax[i]-xmin[i]) : 0); /* dtmp1 = (2.*x[i]-xmax[i]-xmin[i])/(xmax[i]-xmin[i]); */ SET(y,i,asin( dtmp1 )); break; case 4:/* (a,+inf) */ SET(y,i,log( x[i]-xmin[i] )); break; case 5:/* (-inf,a) */ SET(y,i,log( xmax[i]-x[i] )); break; case 6:/* (a,b) */ dtmp1 = (2.*x[i]-xmax[i]-xmin[i])/(xmax[i]-xmin[i]); SET(y,i,gsl_atanh ( dtmp1 )); break; case 7:/* (a,b) second approach */ dtmp1 = (2.*x[i]-xmax[i]-xmin[i])/(xmax[i]-xmin[i]); SET(y,i, dtmp1/sqrt(1-dtmp1*dtmp1)); break; case 8:/* (a,+inf) second approach */ dtmp1 = x[i]-xmin[i]; SET(y,i, dtmp1-1./(4.*dtmp1)); break; case 9:/* (-inf,a) second approach */ dtmp1 = xmax[i]-x[i]; SET(y,i, 1./(4.*dtmp1)-dtmp1); break; } } /* --- OUPUT ---------------------------------- */ if(oparams.verbosity>1){ fprintf(stderr,"# - variables initial value and boundaries\n"); for(i=0;i %e\n",(int) i,x[i],GET(y,i)); else switch(type[i]){ case 0:/* (-inf,+inf) */ fprintf(stderr,"# x[%d]=%e (-inf,+inf) trans 0 -> %e\n",(int) i,x[i],GET(y,i)); break; case 1:/* [a,+inf) */ fprintf(stderr,"# x[%d]=%e [%g,+inf) trans 1 -> %e\n",(int) i,x[i],xmin[i],GET(y,i)); break; case 2:/* (-inf,a] */ fprintf(stderr,"# x[%d]=%e (-inf,%g] trans 2 -> %e\n",(int) i,x[i],xmax[i],GET(y,i)); break; case 3:/* [a,b] */ fprintf(stderr,"# x[%d]=%e [%g,%g] trans 3 -> %e\n",(int) i,x[i],xmin[i],xmax[i],GET(y,i)); break; case 4:/* (a,+inf) */ fprintf(stderr,"# x[%d]=%e (%g,+inf) trans 4 -> %e\n",(int) i,x[i],xmin[i],GET(y,i)); break; case 5:/* (-inf,a) */ fprintf(stderr,"# x[%d]=%e (-inf,%g) trans 5 -> %e\n",(int) i,x[i],xmax[i],GET(y,i)); break; case 6:/* (a,b) */ fprintf(stderr,"# x[%d]=%e (%g,%g) trans 6 -> %e\n",(int) i,x[i],xmin[i],xmax[i],GET(y,i)); break; case 7: fprintf(stderr,"# x[%d]=%e (%g,%g) trans 7 -> %e\n",(int) i,x[i],xmin[i],xmax[i],GET(y,i)); break; case 8:/* [a,+inf) */ fprintf(stderr,"# x[%d]=%e (%g,+inf) trans 8 -> %e\n",(int) i,x[i],xmin[i],GET(y,i)); break; case 9:/* [a,+inf) */ fprintf(stderr,"# x[%d]=%e (-inf,%g) trans 9 -> %e\n",(int) i,x[i],xmax[i],GET(y,i)); break; } } { double res; fprintf(stderr,"# - function initial value\n"); f(n,x,fparams,&res); fprintf(stderr,"# f=%e\n",res); } } /* -------------------------------------------- */ if(oparams.method<4 || oparams.method==5){/* methods with derivatives */ unsigned iter=0; int status; struct g_params gparams; gsl_multimin_function_fdf GdG; gsl_multimin_fdfminimizer *s = gsl_multimin_fdfminimizer_alloc (Tfdf,n); /* set the parameters of the new function */ gparams.n = n; gparams.type = type; gparams.xmin = xmin; gparams.xmax = xmax; gparams.f = f; gparams.df = df; gparams.fdf = fdf; gparams.fparams = fparams; /* set the function to solve */ GdG.f=g; GdG.df=dg; GdG.fdf=gdg; GdG.n=n; GdG.params=(void *) &gparams; /* initialize minimizer */ status=gsl_multimin_fdfminimizer_set(s,&GdG,y,oparams.step_size,oparams.tol); if(status) { fprintf(stderr,"#ERROR: %s\n",gsl_strerror (status)); exit(EXIT_FAILURE); } /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(oparams.verbosity>2) fprintf(stderr,"# - start minimization \n"); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ do { if( ++iter > oparams.maxiter) break; status = gsl_multimin_fdfminimizer_iterate (s); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(oparams.verbosity>2){ fprintf(stderr,"# [%d]",iter); fprintf(stderr," g=%+12.6e y=( ",s->f); for(i=0;ix,i)); fprintf(stderr,") dg=( "); for(i=0;igradient,i)); fprintf(stderr,") |dg|=%12.6e ",gsl_blas_dnrm2 (s->gradient)); fprintf(stderr,"|dx|=%12.6e\n",gsl_blas_dnrm2 (s->dx)); } /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(status == GSL_ENOPROG){ fprintf(stderr,"# status: %s\n",gsl_strerror (status)); break; } if(status){ fprintf(stderr,"#WARNING: %s\n", gsl_strerror (status)); break; } status = gsl_multimin_test_gradient (s->gradient,oparams.epsabs); } while (status == GSL_CONTINUE); gsl_vector_memcpy (y,s->x); *fun=s->f; gsl_multimin_fdfminimizer_free (s); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(oparams.verbosity>2){ fprintf(stderr,"# - end minimization\n"); fprintf(stderr,"# iterations %u\n",iter-1); } /* +++++++++++++++++++++++++++++++++++++++++++++++ */ } else{ /* methods without derivatives */ unsigned iter=0; int status; double size; gsl_vector *ss = gsl_vector_alloc (n); struct g_params gparams; gsl_multimin_function G; gsl_multimin_fminimizer *s=gsl_multimin_fminimizer_alloc (Tf,n); /* set the parameters of the new function */ gparams.n = n; gparams.type = type; gparams.xmin = xmin; gparams.xmax = xmax; gparams.f = f; gparams.fparams = fparams; /* set the function to solve */ G.f=g; G.n=n; G.params=(void *) &gparams; /* Initial vertex size vector */ gsl_vector_set_all (ss,oparams.step_size+oparams.maxsize); /* --- OUPUT ---------------------------------- */ if(oparams.verbosity>0){ size_t i; fprintf(stderr,"# initial simplex sizes\n"); fprintf(stderr,"# "); for(i=0;i2) fprintf(stderr,"# - start minimization \n"); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ do { if( ++iter > oparams.maxiter) break; status = gsl_multimin_fminimizer_iterate(s); size = gsl_multimin_fminimizer_size (s); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(oparams.verbosity>2){ fprintf(stderr,"# g=%g y=( ",s->fval); for(i=0;ix,i)); fprintf(stderr,") "); fprintf(stderr," simplex size=%g ",size); fprintf(stderr,"\n"); } /* +++++++++++++++++++++++++++++++++++++++++++++++ */ status=gsl_multimin_test_size (size,oparams.maxsize); } while (status == GSL_CONTINUE); gsl_vector_memcpy (y, s->x); *fun=s->fval; gsl_multimin_fminimizer_free (s); /* +++++++++++++++++++++++++++++++++++++++++++++++ */ if(oparams.verbosity>2){ fprintf(stderr,"# - end minimization\n"); fprintf(stderr,"# iterations %u\n",iter-1); } /* +++++++++++++++++++++++++++++++++++++++++++++++ */ } /* compute values of x */ for(i=0;i0){ for(i=0;i x[%zd]=%e\n",GET(y,i),i,x[i]); fprintf(stderr,"#--- MULTIMIN END --- \n"); } /* -------------------------------------------- */ gsl_vector_free (y); } gbutils-5.7.1/paretoIII_gbhill.c0000644000175000017500000002162713246754720013452 00000000000000#include "gbhill.h" /* Pareto 3 distribution */ /* ------------------------ */ /* x[0]=a x[1]=b x[2]=s */ /* F[j] = 1-pow(x[j]/s +1 ,-a)*exp(-b*x[j]/s) */ /* -log(f[j]) = -log(1/s) + a*log(x[j]/s +1) - b*x[j]/s +log( b+a*pow((x[j]/s +1),-1) ) */ /* N: sample size k: number of observations used */ void pareto3_nll (const size_t n, const double *x,void *params,double *fval){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const double N = (double) ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double a=x[0]; const double b=x[1]; const double s=x[2]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += a*log(data[i]/s+1) + b*data[i]/s -log(b+a/(data[i]/s+1)); *fval = log(s) + (*fval)/k; switch(method){ case 0: if(N>k){ const double z=data[min]; const double F = 1-pow(z/s +1 ,-a)*exp(-b*z/s); *fval += -(N/k-1.0)*log(F); } break; case 1: if(N>k){ const double F = 1-pow(d/s +1 ,-a)*exp(-b*d/s); *fval += -(N/k-1.0)*log(F); } break; case 2: if(N>k){ const double z=data[max]; const double F = 1-pow(z/s +1 ,-a)*exp(-b*z/s); *fval += -(N/k-1.0)*log(1-F); } break; case 3: if(N>k){ const double F = 1-pow(d/s +1 ,-a)*exp(-b*d/s); *fval += -(N/k-1.0)*log(1-F); } break; } /* fprintf(stderr,"[ f ] x1=%g x2=%g x2=%g f=%g\n",x[0],x[1],x[2],*fval); */ } void pareto3_dnll (const size_t n, const double *x,void *params,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const size_t N = ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double a=x[0]; const double b=x[1]; const double s=x[2]; const double k = ((double) max-min+1); grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += log(data[i]/s +1) - 1/((data[i]/s +1)*(b+a/(data[i]/s +1))); grad[0]/=k; grad[1]=0.0; for(i=min;i<=max;i++) grad[1] += data[i]/s - 1/(b+a/(data[i]/s +1)); grad[1]/=k; grad[2]=0.0; for(i=min;i<=max;i++) grad[2] += a*data[i]/(s*(data[i]+s)) + b*data[i]/(s*s) + a*data[i]/(pow(data[i]+s,2)*(b+a/(data[i]/s +1))); grad[2]= 1/s-grad[2]/k; switch(method){ case 0: if(N>k) { const double z=data[min]; const double dtmp1=z/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*z/s); const double dFda = exp(-b*z/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a)*exp(-b*z/s)*z/s; const double dFds = - exp(-b*z/s)*pow(dtmp1,-a)*(b+a/dtmp1)*z/(s*s); grad[0] += -(N/k-1.0)*dFda/F; grad[1] += -(N/k-1.0)*dFdb/F; grad[2] += -(N/k-1.0)*dFds/F; } break; case 1: if(N>k) { const double dtmp1=d/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*d/s); const double dFda = exp(-b*d/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*d/s)*d/s; const double dFds = - exp(-b*d/s)*pow(dtmp1,-a)*(b+a/dtmp1)*d/(s*s); grad[0] += -(N/k-1.0)*dFda/F; grad[1] += -(N/k-1.0)*dFdb/F; grad[2] += -(N/k-1.0)*dFds/F; } break; case 2: if(N>k){ const double z=data[max]; const double dtmp1=z/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*z/s); const double dFda = exp(-b*z/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*z/s)*z/s; const double dFds = - exp(-b*z/s)*pow(dtmp1,-a)*(b+a/dtmp1)*z/(s*s); grad[0] += (N/k-1.0)*dFda/(1-F); grad[1] += (N/k-1.0)*dFdb/(1-F); grad[2] += (N/k-1.0)*dFds/(1-F); } break; case 3: if(N>k){ const double dtmp1=d/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*d/s); const double dFda = exp(-b*d/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*d/s)*d/s; const double dFds = - exp(-b*d/s)*pow(dtmp1,-a)*(b+a/dtmp1)*d/(s*s); grad[0] += (N/k-1.0)*dFda/(1-F); grad[1] += (N/k-1.0)*dFdb/(1-F); grad[2] += (N/k-1.0)*dFds/(1-F); } break; } /* fprintf(stderr,"[ df] x1=%g x2=%g x3=%g df/dx1=%g df/dx2=%g df/dx3=%g\n", */ /* x[0],x[1],x[2],grad[0],grad[1],grad[2]); */ } void pareto3_nlldnll (const size_t n, const double *x,void *params,double *fval,double *grad){ size_t i; /* log-likelihood parameters */ const size_t min = ((struct loglike_params *) params)->min; const size_t max = ((struct loglike_params *) params)->max; const size_t N = ((struct loglike_params *) params)->size; const double d = (double) ((struct loglike_params *) params)->d; const int method = ((struct loglike_params *) params)->method; const double *data = ((struct loglike_params *) params)->data; /* distribution parameters */ const double a=x[0]; const double b=x[1]; const double s=x[2]; const double k = ((double) max-min+1); *fval=0.0; for(i=min;i<=max;i++) *fval += a*log(data[i]/s+1) + b*data[i]/s -log(b+a/(data[i]/s+1)); *fval = log(s) + (*fval)/k; grad[0]=0.0; for(i=min;i<=max;i++) grad[0] += log(data[i]/s +1) - 1/((data[i]/s +1)*(b+a/(data[i]/s +1))); grad[0]/=k; grad[1]=0.0; for(i=min;i<=max;i++) grad[1] += data[i]/s - 1/(b+a/(data[i]/s +1)); grad[1]/=k; grad[2]=0.0; for(i=min;i<=max;i++) grad[2] += a*data[i]/(s*(data[i]+s)) + b*data[i]/(s*s) + a*data[i]/(pow(data[i]+s,2)*(b+a/(data[i]/s +1))); grad[2]= 1/s-grad[2]/k; switch(method){ case 0: if(N>k) { const double z=data[min]; const double dtmp1=z/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*z/s); const double dFda = exp(-b*z/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*z/s)*z/s; const double dFds = - exp(-b*z/s)*pow(dtmp1,-a)*(b+a/dtmp1)*z/(s*s); *fval += -(N/k-1.0)*log(F); grad[0] += -(N/k-1.0)*dFda/F; grad[1] += -(N/k-1.0)*dFdb/F; grad[2] += -(N/k-1.0)*dFds/F; } break; case 1: if(N>k) { const double dtmp1=d/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*d/s); const double dFda = exp(-b*d/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*d/s)*d/s; const double dFds = - exp(-b*d/s)*pow(dtmp1,-a)*(b+a/dtmp1)*d/(s*s); *fval += -(N/k-1.0)*log(F); grad[0] += -(N/k-1.0)*dFda/F; grad[1] += -(N/k-1.0)*dFdb/F; grad[2] += -(N/k-1.0)*dFds/F; } break; case 2: if(N>k){ //const double z=data[min]; const double z=data[max]; const double dtmp1=z/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*z/s); const double dFda = exp(-b*z/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*z/s)*z/s; const double dFds = - exp(-b*z/s)*pow(dtmp1,-a)*(b+a/dtmp1)*z/(s*s); *fval += -(N/k-1.0)*log(1-F); grad[0] += (N/k-1.0)*dFda/(1-F); grad[1] += (N/k-1.0)*dFdb/(1-F); grad[2] += (N/k-1.0)*dFds/(1-F); } break; case 3: if(N>k){ const double dtmp1=d/s+1; const double F = 1-pow(dtmp1,-a)*exp(-b*d/s); const double dFda = exp(-b*d/s)*pow(dtmp1, -a)*log(dtmp1); const double dFdb = pow(dtmp1,-a )*exp(-b*d/s)*d/s; const double dFds = - exp(-b*d/s)*pow(dtmp1,-a)*(b+a/dtmp1)*d/(s*s); *fval += -(N/k-1.0)*log(1-F); grad[0] += (N/k-1.0)*dFda/(1-F); grad[1] += (N/k-1.0)*dFdb/(1-F); grad[2] += (N/k-1.0)*dFds/(1-F); } break; } /* fprintf(stderr,"[fdf] x1=%g x2=%g f=%g df/dx1=%g df/dx2=%g\n", */ /* x[0],x[1],*fval,grad[0],grad[1]); */ } double pareto3_f (const double x,const size_t n, const double *par){ const double a=par[0]; const double b=par[1]; const double s=par[2]; return exp(-b*x/s)*pow(x/s +1.,-a)*(b+a/(x/s +1.))/s; } double pareto3_F (const double x,const size_t n, const double *par){ const double a=par[0]; const double b=par[1]; const double s=par[2]; return 1-pow((x/s +1) ,-a)*exp(-b*x/s); } /* !!!!! NOT YET IMPLEMENTED. RETURN NANs !!!!! */ gsl_matrix *pareto3_varcovar(const int o_varcovar, double *x, void *params){ size_t i,j; gsl_matrix *covar; covar = gsl_matrix_alloc (3,3); for(i=0;i<3;i++) for(j=0;j<3;j++) gsl_matrix_set (covar,i,j,NAN); return covar; } gbutils-5.7.1/multimin.h0000644000175000017500000000154711232303521012124 00000000000000/*insert GNU extensions*/ #define _GNU_SOURCE /*in particular, use of NAN extension*/ /* used by */ extern int errno; /* GSL ----------- */ #include #include #include #include #include /* --------------- */ #define GET(x,i) gsl_vector_get(x,i) #define SET(x,i,y) gsl_vector_set(x,i,y) struct multimin_params { double step_size; double tol; unsigned maxiter; double epsabs; double maxsize; unsigned method; unsigned verbosity; }; void multimin(size_t,double *,double *, const unsigned *,const double *,const double *, void (*) (const size_t,const double *,void *,double *), void (*) (const size_t,const double *, void *,double *), void (*) (const size_t,const double *, void *,double *,double *), void *, const struct multimin_params); gbutils-5.7.1/gbdist.c0000644000175000017500000001331713246754720011555 00000000000000/* gbdist (ver. 5.6) -- Produce cumulative distribution from data Copyright (C) 2000-20014 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" /* this is the unbiased version of the empirical distribution function. Indeed if x_k is the k-th largest observation, that is k-1 are smaller and n-k larger, then E[F(x_k)]=k/(N+1) */ void printdist(double const *data,int const size,int const o_reverse, int const o_identical){ size_t i; if(o_identical){ /* print one point for any observed value */ if(!o_reverse){ for(i=0;i Package home page EOF exit fi if [ "$help" = "yes" ]; then cat - < $dataorig #count the number of columns colnum=`head -n 1 $dataorig | gawk '{print NF}'` #separate the input file in three parts: before the column of dummies if [ $pos -gt 1 ]; then cut -d ' ' -f 1-$(( pos-1 )) < $dataorig > $initial fi #the column of dummies itself cut -d ' ' -f $pos < $dataorig > $dummies #after the column of dummies if [ $pos -lt $colnum ]; then cut -d ' ' -f $(( pos+1 ))- < $dataorig > $final fi awk -v verbose=$verbose ' { #store the dummy of the line line[NR]=$1 # prepare the list of labels label[$1]=1 } END { #sort and collect labels labnum=asorti(label,labsort) #print labels if verbose if(verbose ~ "yes") for(i=1;i<=labnum;i++) print labsort[i],i | "cat 1>&2" #prepare the array for(i=1;i<=labnum;i++){ #create the string string="" for(j=1;j $newdummies #delete a column if [ "$del" != "no" ]; then labnum=`head -n 1 $newdummies | gawk '{print NF}'` if [ $del = 1 ]; then storage=`tempfile` cat $newdummies > $storage cut -d ' ' -f 2- < $storage > $newdummies rm $storage elif [ $del = $labnum ]; then storage=`tempfile` cat $newdummies > $storage cut -d ' ' -f 1-$(( labnum-1 )) < $storage > $newdummies rm $storage elif test $del -gt 1 && test $del -lt $labnum; then storage1=`tempfile` storage2=`tempfile` cut -d ' ' -f 1-$(( del-1 )) < $newdummies > $storage1 cut -d ' ' -f $(( del+1 ))- < $newdummies > $storage2 paste -d ' ' $storage1 $storage2 > $newdummies rm $storage1 $storage2 else echo "gbdummyfy: wrong column spec in option -d; request ignored" > /dev/stderr fi fi #rebuild the file if [ "$colnum" = 1 ]; then cat $newdummies elif [ "$pos" = 1 ]; then paste -d ' ' $newdummies $final elif [ "$pos" = "$colnum" ]; then paste -d ' ' $initial $newdummies else paste -d ' ' $initial $newdummies $final fi #remove temporary files rm $dataorig $initial $final $dummies $newdummies gbutils-5.7.1/COPYING0000644000175000017500000004311010206604751011151 00000000000000 GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. 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If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. 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If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. gbutils-5.7.1/gbmave.10000644000175000017500000000223613246755334011460 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBMAVE "1" "March 2018" "gbmave 5.7.1" "User Commands" .SH NAME gbmave \- Produce moving average from data .SH SYNOPSIS .B gbmave [\fI\,options\/\fR] .SH DESCRIPTION Compute moving average along each column. Data are read from standard input. .SH OPTIONS .TP \fB\-s\fR number of steps to average (default 10) .TP \fB\-n\fR print a progressive tailing integer .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/config.h.in0000644000175000017500000001160613246753213012153 00000000000000/* config.h.in. Generated from configure.ac by autoheader. */ /* GSL ver. >=2 */ #undef GSL_VER_2 /* Define to 1 if you have the header file. */ #undef HAVE_ASSERT_H /* Define to 1 if you have the declaration of `getdelim', and to 0 if you don't. */ #undef HAVE_DECL_GETDELIM /* Define to 1 if you have the declaration of `getline', and to 0 if you don't. */ #undef HAVE_DECL_GETLINE /* Define to 1 if you have the header file. */ #undef HAVE_FCNTL_H /* Define to 1 if you have the header file. */ #undef HAVE_FLOAT_H /* Define to 1 if you have the `flockfile' function. */ #undef HAVE_FLOCKFILE /* Define to 1 if you have the `floor' function. */ #undef HAVE_FLOOR /* Define to 1 if you have the `funlockfile' function. */ #undef HAVE_FUNLOCKFILE /* Define to 1 if you have the `getdelim' function. */ #undef HAVE_GETDELIM /* Define to 1 if you have the `getsubopt' function. */ #undef HAVE_GETSUBOPT /* Define to 1 if you have the header file. */ #undef HAVE_INTTYPES_H /* Define to 1 if you have the `gsl' library (-lgsl). */ #undef HAVE_LIBGSL /* Define to 1 if you have the `gslcblas' library (-lgslcblas). */ #undef HAVE_LIBGSLCBLAS /* Define to 1 if you have the `m' library (-lm). */ #undef HAVE_LIBM /* Define to 1 if you have the `matheval' library (-lmatheval). */ #undef HAVE_LIBMATHEVAL /* Define to 1 if you have the `z' library (-lz). */ #undef HAVE_LIBZ /* Define to 1 if you have the header file. */ #undef HAVE_LIMITS_H /* Define to 1 if you have the `memchr' function. */ #undef HAVE_MEMCHR /* Define to 1 if you have the header file. */ #undef HAVE_MEMORY_H /* Define to 1 if you have the `pow' function. */ #undef HAVE_POW /* Define to 1 if you have the header file. */ #undef HAVE_REGEX_H /* Define to 1 if you have the `sqrt' function. */ #undef HAVE_SQRT /* Define to 1 if you have the header file. */ #undef HAVE_STDDEF_H /* Define to 1 if you have the header file. */ #undef HAVE_STDINT_H /* Define to 1 if you have the header file. */ #undef HAVE_STDLIB_H /* Define to 1 if you have the `strchr' function. */ #undef HAVE_STRCHR /* Define to 1 if you have the `strchrnul' function. */ #undef HAVE_STRCHRNUL /* Define to 1 if you have the `strcspn' function. */ #undef HAVE_STRCSPN /* Define to 1 if you have the `strdup' function. */ #undef HAVE_STRDUP /* Define to 1 if you have the header file. */ #undef HAVE_STRINGS_H /* Define to 1 if you have the header file. */ #undef HAVE_STRING_H /* Define to 1 if you have the `strspn' function. */ #undef HAVE_STRSPN /* Define to 1 if you have the `strtod' function. */ #undef HAVE_STRTOD /* Define to 1 if you have the `strtol' function. */ #undef HAVE_STRTOL /* Define to 1 if you have the header file. */ #undef HAVE_SYS_STAT_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_TYPES_H /* Define to 1 if you have the header file. */ #undef HAVE_UNISTD_H /* Name of package */ #undef PACKAGE /* Define to the address where bug reports for this package should be sent. */ #undef PACKAGE_BUGREPORT /* Define to the full name of this package. */ #undef PACKAGE_NAME /* Define to the full name and version of this package. */ #undef PACKAGE_STRING /* Define to the one symbol short name of this package. */ #undef PACKAGE_TARNAME /* Define to the home page for this package. */ #undef PACKAGE_URL /* Define to the version of this package. */ #undef PACKAGE_VERSION /* Define to 1 if you have the ANSI C header files. */ #undef STDC_HEADERS /* Enable extensions on AIX 3, Interix. */ #ifndef _ALL_SOURCE # undef _ALL_SOURCE #endif /* Enable GNU extensions on systems that have them. */ #ifndef _GNU_SOURCE # undef _GNU_SOURCE #endif /* Enable threading extensions on Solaris. */ #ifndef _POSIX_PTHREAD_SEMANTICS # undef _POSIX_PTHREAD_SEMANTICS #endif /* Enable extensions on HP NonStop. */ #ifndef _TANDEM_SOURCE # undef _TANDEM_SOURCE #endif /* Enable general extensions on Solaris. */ #ifndef __EXTENSIONS__ # undef __EXTENSIONS__ #endif /* Version number of package */ #undef VERSION /* Define to 1 if `lex' declares `yytext' as a `char *' by default, not a `char[]'. */ #undef YYTEXT_POINTER /* Define to 1 if on MINIX. */ #undef _MINIX /* Define to 2 if the system does not provide POSIX.1 features except with this defined. */ #undef _POSIX_1_SOURCE /* Define to 1 if you need to in order for `stat' and other things to work. */ #undef _POSIX_SOURCE /* Define to empty if `const' does not conform to ANSI C. */ #undef const /* Define to a replacement function name for getline(). */ #undef getline /* Define to `__inline__' or `__inline' if that's what the C compiler calls it, or to nothing if 'inline' is not supported under any name. */ #ifndef __cplusplus #undef inline #endif /* Define to `unsigned int' if does not define. */ #undef size_t gbutils-5.7.1/gbmstat.c0000644000175000017500000001565213246754720011746 00000000000000/* gbmstat (ver. 5.6) -- Computing statistics in a moving windows Copyright (C) 1998-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ size_t lag=10; char *splitstring = strdup(" \t"); /* options */ int o_binary_in=0; int o_table = 0; int o_denan=0; char * const output_opts[] = {"mean","adev","std","var","skew","kurt","min","max","nonan",NULL}; size_t outnum; size_t *outtype; /* set default output */ outnum=1; outtype = (size_t *) my_alloc(1*sizeof(size_t)); outtype[0]=0; /* mean */ /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"s:hF:tO:Db:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='D'){ /*ignore NAN data*/ o_denan=1; } else if(opt=='s'){ /*set the lag*/ lag=(size_t) atoi(optarg); } else if(opt=='t'){ /*set the table form*/ o_table=1; } else if(opt=='b'){ /*set binary input or output*/ switch(atoi(optarg)){ case 0 : break; case 1 : o_binary_in=1; break; case 2 : PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; case 3 : o_binary_in=1; PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; default: fprintf(stderr,"unclear binary specification %d, option ignored.\n",atoi(optarg)); break; } } else if(opt=='O'){ /* set the output type */ char *subopts,*subvalue=NULL; /* clear default settings */ outnum=0; free(outtype); outtype=NULL; /* define new output */ subopts = optarg; while (*subopts != '\0'){ int itmp1 = getsubopt(&subopts, output_opts, &subvalue); outnum++; outtype = (size_t *) my_realloc((void *)outtype,outnum*sizeof(size_t)); if(itmp1 == -1){/* Unknown suboption. */ fprintf (stderr,"ERROR (%s): Unknown output type `%s'\n", GB_PROGNAME,subvalue); exit(1); } else outtype[outnum-1]=(size_t) itmp1; } } else if(opt=='h'){ /*print short help*/ fprintf(stdout, "Compute moving average. Data are read from standard input.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -s number of steps to average (default 10)\n"); fprintf(stdout," -t compute average for each column of input \n"); fprintf(stdout," -D ignore NAN entries in computation \n"); fprintf(stdout," -O set the output, comma separated list of \n"); fprintf(stdout," mean, adev, std, var, skew, kurt, min ,max \n"); fprintf(stdout," and nonan (default mean). nonan works only with\n"); fprintf(stdout," option -D \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); return(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); if(!o_table){ double *data=NULL; size_t size,i,j; double **out; /* 1 2 3 4 5 6 7 8 9 */ /* mean adev std var skew kurt min max nonan */ double * stats = (double *) my_alloc(9*sizeof(double)); /* load data */ if(o_binary_in==1) load_bin(&data,&size,0); else load(&data,&size,0,splitstring); /* check there are enough entries */ if(size\/\fR .SH DESCRIPTION Non linear least square regression. Read data in columns (X_1 .. X_N). The model is specified by a function f(x1,x2...) using variables names x1,x2,..,xN for the first, second .. N\-th column of data. f(x1,x2,...) are assumed i.i.d. .SH OPTIONS .TP \fB\-O\fR type of output (default 0) .TP 0 parameters .TP 1 parameters and errors .TP 2 and residuals .TP 3 parameters and variance matrix .TP 4 parameters, errors and s\-scores .TP \fB\-V\fR variance matrix estimation (default 0) .TP 0 .TP 1 < J^{\-1} > .TP 2 < H^{\-1} > .TP 3 < H^{\-1} J H^{\-1} > .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps .TP 5 model definition .TP \fB\-M\fR estimation method (default 0) .TP 0 ordinary least square (OLS) .TP 1 minimum absolute deviation (MAD) .TP 2 asymmetric MAD .TP \fB\-e\fR minimization tolerance (default 1e\-5) .TP \fB\-i\fR minimization max iterations (default 500) .TP \fB\-E\fR restart accuracy (default 1e\-5, ignored with option \fB\-M\fR 0) .TP \fB\-I\fR restart max iterations (default 2000, ignored with option \fB\-M\fR 0) .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbgcorr.c0000644000175000017500000001777613246754720011743 00000000000000/* gbgcorr (ver. 5.6) -- Gaussian kernel correlation dimension. Copyright (C) 2006-2018 Cees Diks and Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. */ #include "tools.h" int main(int argc,char* argv[]){ /* OPTIONS */ int o_verbose=0; int o_nref=0; int o_sigma=0; /* variables for range and num. steps */ unsigned nres=24; unsigned ntau=1; /* delay time */ unsigned ndim=2; /* maximum embedding dimension */ unsigned ntc=1; /* Theiler correction */ unsigned nref; /* number of reference points */ unsigned npt; /* number of delay vectors */ /* random generator variables */ unsigned seed=0; double *r2; /* bandwidths */ double **mm; /* correlation integrals */ double **var; /* variance of correlation integrals */ double **epsilon; /* variance of correlation integrals */ double *index; /* array of indices */ size_t i,ii,m,k,j,d; /* data storage */ size_t ntot; double *x=NULL; char *splitstring = strdup(" \t"); /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"hvF:r:l:m:t:R:p:s",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout, "Compute the correlation dimension with a Gaussian kernel.\n"); fprintf(stdout, "One block of output is printed for each embedding dimension,\n"); fprintf(stdout, "the first column report the bandwidth, the second the associated\n"); fprintf(stdout, "correlation coefficient. Bandwidths are equispaced in a log scale\n"); fprintf(stdout, "from 1 (maximum) to 2^(-n+1) (minimum). n can be set on the command \n"); fprintf(stdout, "line.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options: \n"); fprintf(stdout," -h this help\n"); fprintf(stdout," -l time lag (default 1)\n"); fprintf(stdout," -m maximum embedding dimension (default 2)\n"); fprintf(stdout," -t Theiler correction (default 1)\n"); fprintf(stdout," -R seed (default 0)\n"); fprintf(stdout," -p number of reference points (default 1)\n"); fprintf(stdout," -r number of bandwidths (default 24)\n"); fprintf(stdout," -s print standard errors \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); return(0); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='s'){ o_sigma = 1; } else if(opt=='l'){ ntau = (unsigned) atoi(optarg); } else if(opt=='m'){ ndim = (unsigned) atoi(optarg); } else if(opt=='t'){ ntc = (unsigned) atoi(optarg); } else if(opt=='R'){ /*RNG seed*/ seed= (unsigned) atoi(optarg); } else if(opt=='p'){ o_nref=1; nref= (unsigned) atoi(optarg); } else if(opt=='v'){ /*increase verbosity*/ o_verbose=1; } else if(opt=='r'){ nres= (unsigned) atoi(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* initialize RNG */ srand(seed); /* load data */ load(&x,&ntot,0,splitstring); /* remove NaN */ denan(&x,&ntot); /* normalize to 1/2 variance */ { double mean=0.0, meansq=0.0, fac; for (i=0;inpt) nref=npt; /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ fprintf(stderr,"delay : %d\n",ntau); fprintf(stderr,"max embedding dimension : %d\n",ndim); fprintf(stderr,"Theiler correction : %d\n",ntc); fprintf(stderr,"RNG seed : %d\n",seed); fprintf(stderr,"Max. num. ref. points : %d\n",npt); fprintf(stderr,"Num. of ref. points : %d\n",nref); } /* ++++++++++++++++++++++++++++ */ /* allocation and initialization of output variables */ r2 = my_alloc(nres*sizeof(double)); for (k=0;k= ntc) { double dis2 = 0.0; ncount++; for (m=0;m!=ndim;m++) { double tmp; dis2 += (x[i+m*ntau]-x[j+m*ntau])*(x[i+m*ntau]-x[j+m*ntau]); tmp = exp(-dis2); /* for (k=0;(k!=nres && tmp >= 1.E-24) ;k++) { */ for (k=0;k!=nres;k++) { mm[m][k] += tmp; tmp *= tmp; /* var[m][k] += tmp; */ } } } } for (m=0;m!=ndim;m++) for (k=0;k!=nres;k++) mm[m][k] /= (double) ncount; if(o_sigma) { for (ii=0;ii!=nref;ii++) { /* +++++++++++++++++++++++++++ */ if (o_verbose == 1 && ii%10==0) fprintf(stderr,"\rrp %zd", ii); /* +++++++++++++++++++++++++++ */ i = index[ii]; for (j=0;j!=npt;j++) if (abs(i-j) >= ntc) { double dis2 = 0.0; ncount++; for (m=0;m!=ndim;m++) { double tmp; dis2 += (x[i+m*ntau]-x[j+m*ntau])*(x[i+m*ntau]-x[j+m*ntau]); tmp = exp(-dis2); /* for (k=0;(k!=nres && tmp >= 1.E-24) ;k++) { */ for (k=0;k!=nres;k++) { epsilon[m][k] += tmp-mm[m][k]; var[m][k] += epsilon[m][k]*epsilon[m][k]; tmp *= tmp; } } } } for (m=0;m!=ndim;m++) for (k=0;k!=nres;k++) var[m][k] = (var[m][k]-epsilon[m][k]*epsilon[m][k]/ncount)/(ncount-1.); } /* ++++++++++++++++++++++++++++++++ */ if(o_verbose) printf("\n#ncount = %zd\n", ncount); /* ++++++++++++++++++++++++++++++++ */ } /* output */ for (d=0;d!=ndim;d++){ for (i=nres-1;i!=-1;i--){ if (mm[d][i]!=0){ printf(INT_SEP,d+1); printf(FLOAT_SEP,sqrt(r2[i])); printf(FLOAT_SEP,mm[d][i]); if(o_sigma) printf(FLOAT_SEP,sqrt(var[d][i])); printf("\n"); } } printf("\n\n"); } return 0; } gbutils-5.7.1/gbstat.10000644000175000017500000000372013246755334011502 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBSTAT "1" "March 2018" "gbstat 5.7.1" "User Commands" .SH NAME gbstat \- Basic descriptive statistics of data .SH SYNOPSIS .B gbstat [\fI\,options\/\fR] .SH DESCRIPTION Sample statistics. Compute mean, standard deviation, skewness, kurtosis, average deviation, minimum, maximum, median (if option \fB\-m\fR is specified) and number of valid observations. Option \fB\-O\fR allows one to select specific statistics. With option \fB\-t\fR statistics are printed for each column of data separately. A header line is added with the meaning of the different columns. The mode is computed using the Half\-Sample method. .SH OPTIONS .TP \fB\-m\fR add the sample median to the estimated statistics .TP \fB\-t\fR print statistics for each column of input .TP \fB\-s\fR short: mean std skew kurt adev min max (median) num .TP \fB\-O\fR select output: mean,std,skew,kurt,adev,min,max,median,num,mode .TP \fB\-o\fR set the output format (default '%12.6e') .TP \fB\-F\fR specify the input fields separators (default " \et") .HP \fB\-h\fR this help .SH EXAMPLES .TP gbstat \-O median,kurt < file compute the median and the kurtosis of all the data in 'file' .TP gbstat \-ts < file compute statistics for each columns separately. Since option \-s is provided, the header is omitted. .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbrand.c0000644000175000017500000003340313246754720011534 00000000000000/* gbrand (ver. 5.6) -- Sampling from random distributions Copyright (C) 2008-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include #include #include double pareto3(gsl_rng *r, double a, double b, double s){ const double q = gsl_rng_uniform (r); const double dtmp1= b/a; return s*(gsl_sf_lambert_W0 (dtmp1*exp(dtmp1)*pow(1-q,-1/a))/dtmp1-1); } int main(int argc,char* argv[]){ size_t columns=1,rows=1; const gsl_rng_type * r_T; gsl_rng * r; char o_verbose=0; char o_seed =0; char *name; unsigned long int seed=0; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* initialize random number generator */ gsl_rng_env_setup(); r_T = gsl_rng_default; r = gsl_rng_alloc (r_T); /* initialize global variables */ initialize_program(argv[0]); /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"hvc:r:R:b:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Generates a grid of i.i.d. samples from a distribution. The name of the\n"); fprintf(stdout,"distribution and its parameters are specified on the command line.\n"); fprintf(stdout,"the column index. Indeces start from 1. If more than one function are\n"); fprintf(stdout,"provided they are printed one after the other. The type of random number\n"); fprintf(stdout,"generator and the default seed can be set with the environment variables\n"); fprintf(stdout,"GSL_RNG_TYPE and GSL_RNG_SEED, respectively, as documented in GSL manual.\n"); fprintf(stdout,"\nValid distributions are:\n"); fprintf(stdout,"beta, binomial, bivariate-gaussian, cauchy, chisq, dir-2d, dir-3d, dir-nd,\n"); fprintf(stdout,"erlang, exponential, exppow, fdist, flat, gamma, gaussian-tail, gaussian,\n"); fprintf(stdout,"geometric, gumbel1, gumbel2, hypergeometric, laplace, landau, levy,\n"); fprintf(stdout,"levy-skew, logarithmic,logistic, lognormal, negative-binomial,pareto,\n"); fprintf(stdout,"pascal, poisson, rayleigh-tail, rayleigh,tdist,ugaussian-tail, ugaussian,\n"); fprintf(stdout,"weibull,paret03.\n"); fprintf(stdout,"\nUsage: %s [options] NAME PARAMETERS... \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -r specify number of rows (default 1)\n"); fprintf(stdout," -c specify number of columns (default 1)\n"); fprintf(stdout," -R set the seed (default 1)\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -h this help\n"); finalize_program(); return 0; } else if(opt=='R'){ /*RNG seed*/ o_seed=1; seed=atol(optarg); } else if(opt=='v'){ o_verbose=1; } else if(opt=='c'){ columns=atoi(optarg); } else if(opt=='r'){ rows=atoi(optarg); } else if(opt=='b'){ /*set binary output*/ switch(atoi(optarg)){ case 0 : break; case 1 : fprintf(stdout,"no binary input implemented, option ignored.\n"); break; case 2 : PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; case 3 : fprintf(stdout,"no binary input implemented, option ignored.\n"); PRINTMATRIXBYCOLS=printmatrixbycols_bin; break; default: fprintf(stdout,"unclear binary specification %d, option ignored.\n",atoi(optarg)); break; } } } if(optind0 ){ pnum++; plist = (double *) my_realloc((void *) plist,pnum*sizeof(double)); plist[pnum-1]=atof(stmp2); } free(stmp3); /* check */ dtmp1=0.0; for(i=0;i 1e-06) fprintf(stderr,"WARNING: probabilities do not sum to one!\n"); } else { PERROR("plist"); } table=gsl_ran_discrete_preproc (pnum,plist); INTOUTPUT( (gsl_ran_discrete (r,table)+1) ); free(plist); } else { fprintf(stderr,"ERROR (%s): unrecognized distribution: %s\n",GB_PROGNAME,name); exit(-1); } /* END OF MAIN PART */ } gsl_rng_free (r); finalize_program(); return 0; } gbutils-5.7.1/gbhill.c0000644000175000017500000007405513246754720011550 00000000000000/* gbhill (ver. 5.6) -- Hill Maximum Likelihhod estimation Copyright (C) 2012-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "gbhill.h" #include "multimin.h" /* In C ordering x[0] <= x[1] <= ... <= X[N-1] so the (relative) negative log-likelihood is written method: def: 0 -LL[up,unc]/k = C[up,unc] -1/k\sum_{j=1}^{k} log f(x[j]) - (N/k-1) log(F(x[k])) 1 -LL[up,con]/(k+1) = C[up,con2] -1/(k+1)\sum_{j=1}^{k+1} log f(x[j]) -(N/(k+1) -1) log(F(d)) 2 -LL[lo,unc]/k = C[lo,unc] -1/k\sum_{j=N-k+1}^{N} log f(x[j]) - (N/k-1) log(1-F(x[N-k-1])) 3 -LL[lo,con]/(k+1) = C[lo,con2] -1/(k+1)\sum_{j=N-k}^{N} log f(x[j]) - (N/(k+1) -1) log(1-F(d)) */ int main(int argc,char* argv[]){ char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_printall=0; int o_varcovar=1; /* binary options */ int o_binary_in=0; /* name of the distribution to fit */ char *name; /* store the amount of data to use */ double used=1; /* minimization parameters */ size_t xnum; double llmin; double *x; unsigned *xtype; double *xmin; double *xmax; char **xname; /* definition of the covariance matrix */ gsl_matrix *Pcovar=NULL; /* covariance matrix */ /* pointer to functions to use */ double (*f) (const double,const size_t, const double *); double (*F) (const double,const size_t, const double *); void (*obj_f) (const size_t, const double *,void *,double *); void (*obj_df) (const size_t, const double *,void *,double *); void (*obj_fdf) (const size_t, const double *,void *,double *,double *); gsl_matrix * (*varcovar) (const int, double *, void *); /* log-likelihood parameters */ struct loglike_params llparams={0,NULL,0,0,0,0}; /* minimization parameters */ struct multimin_params mmparams={.01,.01,100,1e-6,1e-6,5,0}; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"v:hF:O:A:u:M:V:ab:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Maximum Likelihood estimation of distribution based on extremal\n"); fprintf(stdout,"(tail) observations. The distributions included are: exponential,\n"); fprintf(stdout,"pareto1, pareto3, gaussian. Provide the name of the distribution\n"); fprintf(stdout,"and the initial values of the parameters in the command line.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -O type of output (default 0) \n"); fprintf(stdout," 0 parameters and min NLL \n"); fprintf(stdout," 1 parameters and errors \n"); fprintf(stdout," 2 the distribution function \n"); fprintf(stdout," 3 the density function \n"); fprintf(stdout," 4 transformed observations: uniform in [0.1] under the null\n"); fprintf(stdout," 5 Renyi residuals: iid uniform in [0.1] under the null\n"); fprintf(stdout," -M method used (default 0) \n"); fprintf(stdout," 0 unconditional, upper tail \n"); fprintf(stdout," 1 threshold, upper tail \n"); fprintf(stdout," 2 unconditional, lower tail \n"); fprintf(stdout," 3 threshold, lower tail \n"); fprintf(stdout," -V variance matrix estimation (default2) \n"); fprintf(stdout," 0 < J^{-1} > \n"); fprintf(stdout," 1 < H^{-1} > \n"); fprintf(stdout," 2 < H^{-1} J H^{-1} > \n"); fprintf(stdout," -v verbosity level (default0) \n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3+ minimization steps \n"); fprintf(stdout," -a print entire set for -O 1,2 \n"); fprintf(stdout," -u observations or threshold (default 1)\n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); fprintf(stdout," -A comma separated MLL optimization options step,tol,iter,eps,msize,\n"); fprintf(stdout," algo. Use empty fields for default (default 0.01,0.01,100,1e-6,1e-6,5)\n"); fprintf(stdout," step initial step size of the searching algorithm \n"); fprintf(stdout," tol line search tolerance iter: maximum number of iterations \n"); fprintf(stdout," eps gradient tolerance : stopping criteria ||gradient||5){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>2){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='M'){ /* set the type of method */ llparams.method = atoi(optarg); if(o_output<0 || o_output>3){ fprintf(stderr,"ERROR (%s): method '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='A'){ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.step_size=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.tol=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxiter=(unsigned) atoi(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.epsabs=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxsize=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.method= (unsigned) atoi(stmp2); } free(stmp3); } else if(opt=='v'){ o_verbose = atoi(optarg); mmparams.verbosity=(o_verbose>2?o_verbose-2:0); } else if(opt=='b'){ /*set binary input or output*/ switch(atoi(optarg)){ case 0 : break; case 1 : /* binary input */ o_binary_in=1; break; case 2 : /* binary output */ PRINTCOL=printcol_bin; break; case 3 : /* binary input and output */ o_binary_in=1; PRINTCOL=printcol_bin; break; default: fprintf(stderr,"unclear binary specification %d, option ignored.\n",atoi(optarg)); break; } } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ /* ========= */ if(o_binary_in==1) load_bin(&llparams.data,&llparams.size,0); else load(&llparams.data,&llparams.size,0,splitstring); /* sort data in ascending order */ /* ============================ */ qsort(llparams.data,llparams.size,sizeof(double),sort_by_value); /* decide the number of observations used */ /* ====================================== */ if(llparams.method == 1) { size_t i; if(llparams.data[llparams.size-1]<=used){ fprintf(stderr,"ERROR (%s): wrong value, too large threshold: %g\n", GB_PROGNAME,used); exit(-1); } llparams.d=used; for(i=0;iused) break; llparams.used=llparams.size-i; } else if(llparams.method == 3) { size_t i; if(llparams.data[0]>=used){ fprintf(stderr,"ERROR (%s): wrong value, too small threshold: %g\n", GB_PROGNAME,used); exit(-1); } llparams.d= used; for(i=0;iused) break; llparams.used=i; } else if (used>0 && used<=1) llparams.used=llparams.size*used; else if( used>0 && floor(used) == used && used <= llparams.size ) llparams.used=(size_t) used; else{ fprintf(stderr,"ERROR (%s): wrong value, used observations: %g\n", GB_PROGNAME,used); exit(-1); } /* set minimum and maximum index */ /* ============================= */ switch(llparams.method){ case 0: llparams.min=llparams.size-llparams.used; llparams.max=llparams.size-1; break; case 1: llparams.min=llparams.size-llparams.used; llparams.max=llparams.size-1; break; case 2: llparams.min=0; llparams.max=llparams.used-1; break; case 3: llparams.min=0; llparams.max=llparams.used-1; break; } /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose){ fprintf(stderr,"# total observations %zd; xmin=%g xmax=%g\n", llparams.size,llparams.data[0],llparams.data[llparams.size-1]); switch(llparams.method){ case 0: fprintf(stderr,"# tail: upper\n"); fprintf(stderr,"# condition: unconditional\n"); fprintf(stderr,"# num obs: %zd\n",llparams.used); break; case 1: fprintf(stderr,"# tail: upper\n"); fprintf(stderr,"# condition: x[%zd]>%g\n", llparams.min,llparams.d); fprintf(stderr,"# num obs: %zd\n",llparams.used); break; case 2: fprintf(stderr,"# tail: lower\n"); fprintf(stderr,"# condition: unconditional\n"); fprintf(stderr,"# num obs: %zd\n",llparams.used); break; case 3: fprintf(stderr,"# tail: lower\n"); fprintf(stderr,"# condition: x[%zd]<%g\n", llparams.max,llparams.d); fprintf(stderr,"# num obs: %zd\n",llparams.used); break; } fprintf(stderr,"# indexes: from %zd to %zd\n", llparams.min,llparams.max); fprintf(stderr,"# values: from %g to %g\n", llparams.data[llparams.min],llparams.data[llparams.max]); } /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* estimate parameters */ /* =================== */ #define NAME(x) !strcmp(name,(x)) #define PERROR(x) {fprintf(stderr,"ERROR (%s): parameters needed: %s\n",GB_PROGNAME,x); exit(-1);} #define DBL_ARG(x) if (argc-optind) { x=atof(argv[optind]);optind++;} else {PERROR( #x);}; if(optind xmax[1] ){ x[1] = xmax[1] - mmparams.step_size; if(o_verbose){ fprintf(stderr,"WARNING (%s): parameter b out of range; set to %g\n", GB_PROGNAME,x[1]); fprintf(stderr,"WARNING (%s): b must be smaller equal than the smallest observation used\n", GB_PROGNAME); } } /* set the functions to use */ F = exponential_F; f = exponential_f; obj_f = exponential_nll; obj_df = exponential_dnll; obj_fdf = exponential_nlldnll; varcovar = exponential_varcovar; /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose){ if (llparams.method>1) fprintf(stderr,"WARNING (%s): likelihood is monotonic in b: boundary solution\n", GB_PROGNAME); } /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ } else if (NAME("pareto1")) { xnum=2; x = (double *) my_alloc(xnum*sizeof(double)); xmin = (double *) my_alloc(xnum*sizeof(double)); xmax = (double *) my_alloc(xnum*sizeof(double)); xtype = (unsigned *) my_alloc(xnum*sizeof(unsigned)); xname = (char **) my_alloc(xnum*sizeof(char *)); /* gamma */ xname[0]=strdup("gamma (tail index)"); x[0]=1.0; xmin[0]=0.0; xmax[0]=NAN; xtype[0]=4; /* b */ xname[1]=strdup("b (position)"); x[1]=0.0; xmax[1]=NAN; /* lower bound on b */ switch(llparams.method){ /* upper tail */ case 0: xmin[1]=1./llparams.data[llparams.size-llparams.used]; break; case 1: xmin[1]=1./llparams.d; break; /* lower tail */ case 2: case 3: xmin[1]=1./llparams.data[0]; break; } /* minimization method for b */ switch(llparams.method){ /* upper tail */ case 0: case 1: /* for method=0,1 solution is internal */ xtype[1]=4; break; /* lower tail */ case 2: case 3: /* for method=2,3 solution is on the boundary */ xtype[1]=4; break; } /* check number of parameters and load their values */ if (argc < optind+2){ fprintf(stderr,"ERROR (%s): Pareto Type 1 distribution F(x)=1-(b x[j])^(-1/gamma)\n",GB_PROGNAME); fprintf(stderr,"ERROR (%s): provide initial values of gamma (tail index) and b (position)\n",GB_PROGNAME); exit(-1); } DBL_ARG(*x); DBL_ARG(*(x+1)); /* check parameters initial values */ if(x[0]<= 0){ x[0]=1.0; if(o_verbose){ fprintf(stderr, "WARNING (%s): parameter gamma out of range; set to %g\n", GB_PROGNAME,x[0]); fprintf(stderr, "WARNING (%s): gamma must be positive\n", GB_PROGNAME); } } if(x[1] <= xmin[1] ){ /* N.B.: when llparams.method==1 the likelihood DOES NOT depend on b; so in principle one could set x[1] to xmin[1]. For consistencey with other cases, we retain this setting. */ x[1] = xmin[1]+ mmparams.step_size; if(o_verbose){ fprintf(stderr,"WARNING (%s): parameter b out of range; set to %g\n", GB_PROGNAME,x[1]); fprintf(stderr,"WARNING (%s): b must be larger than the inverse of the smallest observation used\n", GB_PROGNAME); } } /* set the functions to use */ F = pareto1_F; f = pareto1_f; obj_f = pareto1_nll; obj_df = pareto1_dnll; obj_fdf = pareto1_nlldnll; varcovar = pareto1_varcovar; /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose){ if (llparams.method>1) fprintf(stderr,"WARNING (%s): likelihood is monotonic in b: boundary solution\n", GB_PROGNAME); } /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ } else if (NAME("pareto3")) { xnum=3; x = (double *) my_alloc(xnum*sizeof(double)); xmin = (double *) my_alloc(xnum*sizeof(double)); xmax = (double *) my_alloc(xnum*sizeof(double)); xtype = (unsigned *) my_alloc(xnum*sizeof(unsigned)); xname = (char **) my_alloc(xnum*sizeof(char *)); /* alpha */ xname[0]=strdup("alpha (power decay index)"); x[0]=1.0; xmin[0]=0.0; xmax[0]=NAN; xtype[0]=4; /* beta */ xname[1]=strdup("beta (exponential decay index)"); x[1]=1.0; xmin[1]=0.0; xmax[1]=NAN; xtype[1]=4; /* s */ xname[2]=strdup("s (scale)"); x[2]=1.0; xmin[2]=0.0; xmax[2]=NAN; xtype[2]=4; if (argc < optind+3){ fprintf(stderr,"ERROR (%s): Pareto Type III distribution F(x)=1-(x/s +1)^(-alpha) exp(-beta x/s)\n",GB_PROGNAME); fprintf(stderr,"ERROR (%s): provide initial values of alpha (power decay index), beta (exponential decay index), s (scale)\n",GB_PROGNAME); exit(-1); } DBL_ARG(*x); DBL_ARG(*(x+1)); DBL_ARG(*(x+2)); /* check parameters initial values */ if(x[0]<=0){ x[0]=1.0; if(o_verbose){ fprintf(stderr, "WARNING (%s): parameter alpha out of range; set to %g\n", GB_PROGNAME,x[0]); fprintf(stderr,"WARNING (%s): alpha must be positive\n",GB_PROGNAME); } } if(x[1] <=0 ){ x[1] = 1.0 ; if(o_verbose){ fprintf(stderr,"WARNING (%s): parameter beta out of range; set to %g\n", GB_PROGNAME,x[1]); fprintf(stderr,"WARNING (%s): beta must be positive\n",GB_PROGNAME); } } if(x[2] <= 0 ){ x[2] = 1.0 ; if(o_verbose){ fprintf(stderr,"WARNING (%s): parameter s out of range; set to %g\n", GB_PROGNAME,x[2]); fprintf(stderr,"WARNING (%s): s must be positive\n",GB_PROGNAME); } } /* set the functions to use */ F = pareto3_F; f = pareto3_f; obj_f = pareto3_nll; obj_df = pareto3_dnll; obj_fdf = pareto3_nlldnll; varcovar = pareto3_varcovar; } else if(NAME("gaussian")) { xnum=2; x = (double *) my_alloc(xnum*sizeof(double)); xmin = (double *) my_alloc(xnum*sizeof(double)); xmax = (double *) my_alloc(xnum*sizeof(double)); xtype = (unsigned *) my_alloc(xnum*sizeof(unsigned)); xname = (char **) my_alloc(xnum*sizeof(char *)); /* Mean */ xname[0]=strdup("m (mean)"); x[0]=0.0; xmin[0]=NAN; xmax[0]=NAN; xtype[0]=0; /* Standard Deviation */ xname[1]=strdup("s (standard deviation)"); x[0]=1.0; xmin[1]=0.0; xmax[1]=NAN; xtype[1]=4; if (argc < optind+2){ fprintf(stderr,"ERROR (%s): Gaussian density f(x)=exp(-1/2 (x-m)^2/s^2)/sqrt(2 pi s^2)\n",GB_PROGNAME); fprintf(stderr,"ERROR (%s): provide initial values of m (mean) and s (standard deviation)\n",GB_PROGNAME); exit(-1); } DBL_ARG(*x); DBL_ARG(*(x+1)); /* check parameters initial values */ if(x[1] <= 0 ){ x[1] = 1.0 ; if(o_verbose){ fprintf(stderr,"WARNING (%s): parameter s out of range; set to %g\n", GB_PROGNAME,x[1]); fprintf(stderr,"WARNING (%s): s must be positive\n", GB_PROGNAME); } } /* set the functions to use */ F = gaussian_F; f = gaussian_f; obj_f = gaussian_nll; obj_df = gaussian_dnll; obj_fdf = gaussian_nlldnll; varcovar = gaussian_varcovar; } else { fprintf(stderr,"ERROR (%s): unrecognized distribution: %s\n",GB_PROGNAME,name); fprintf(stderr,"ERROR (%s): available choices:\n",GB_PROGNAME); fprintf(stderr,"ERROR (%s): exponential pareto1 gaussian pareto3\n",GB_PROGNAME); exit(-1); } /* minimize negative log likelihood */ /* ================================ */ multimin(xnum,x,&llmin,xtype,xmin,xmax,obj_f,obj_df,obj_fdf,(void *) &llparams,mmparams); /* compute variance-covariance matrix */ /* ================================== */ if(o_output==1) Pcovar = varcovar(o_varcovar,x,&llparams); /* output */ /* ===== */ switch(o_output){ case 0: /* print parameters*/ { size_t i; /* ++++++++++++++++++++++++d+++++++++++ */ if(o_verbose>=1){ for(i=0;i=1){ for(i=0;i=1){ for(i=0;i=1){ for(i=0;i=1){ for(i=0;i=1){ for(i=0;i .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/configure0000755000175000017500000062412613246753207012051 00000000000000#! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.69 for gbutils 5.7.1. # # # Copyright (C) 1992-1996, 1998-2012 Free Software Foundation, Inc. # # # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## -------------------- ## ## M4sh Initialization. ## ## -------------------- ## # Be more Bourne compatible DUALCASE=1; export DUALCASE # for MKS sh if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then : emulate sh NULLCMD=: # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which # is contrary to our usage. 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"$LINENO" 5 fi # Check for GSL { $as_echo "$as_me:${as_lineno-$LINENO}: checking for cblas_dgemm in -lgslcblas" >&5 $as_echo_n "checking for cblas_dgemm in -lgslcblas... " >&6; } if ${ac_cv_lib_gslcblas_cblas_dgemm+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-lgslcblas $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. 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Configuration of ver. $PACKAGE_VERSION is complete." echo "| Optional dependencies: " echo "|" echo -n "| Gnu Scientific Library (GSL) " if test x$ac_cv_lib_gsl_gsl_blas_dgemm = xyes; then echo " found" else echo " not found" fi echo -n "| GNU matheval library " if test x$ac_cv_lib_matheval_evaluator_create = xyes; then echo " found" else echo " not found" fi echo -n "| zlib general purpose compression library " if test x$ac_cv_lib_z_gzopen = xyes; then echo " found" else echo " not found" fi echo -n "| help2man automatic man pages generator " if test x$HELP2MAN != xfalse; then echo " found" else echo " not found" fi if test x$LACKING = xyes; then echo "|" echo "| The following utilities will not be installed due to lacking" echo "| libraries (check the README file):" echo -n "|" if test x$GBKER != x'gbker$(EXEEXT)'; then echo -n " gbker" fi if test x$GBKREG != x'gbkreg$(EXEEXT)'; then echo -n " gbkreg" fi if test x$GBLREG != x'gblreg$(EXEEXT)'; then echo -n " gblreg" fi if test x$GBGLREG != x'gbglreg$(EXEEXT)'; then echo -n " gbglreg" fi if test x$GBNLREG != x'gbnlreg$(EXEEXT)'; then echo -n " gbnlreg" fi if test x$GBNLPANEL != x'gbnlpanel$(EXEEXT)'; then echo -n " gbnlpanel" fi if test x$GBNLQREG != x'gbnlreg$(EXEEXT)'; then echo -n " gbnlqreg" fi if test x$GBNLMULT != x'gbnlmult$(EXEEXT)'; then echo -n " gbnlmult" fi if test x$GBNLPROBIT != x'gbnlprobit$(EXEEXT)'; then echo -n " gbnlprobit" fi if test x$GBNLPOLYIT != x'gbnlpolyit$(EXEEXT)'; then echo -n " gbnlpolyit" fi if test x$GBMODES != x'gbmodes$(EXEEXT)'; then echo -n " gbmodes" fi if test x$GBINTERP != x'gbinterp$(EXEEXT)'; then echo -n " gbinterp" fi if test x$GBGET != x'gbget$(EXEEXT)'; then echo -n " gbget" fi if test x$GBFUN != x'gbfun$(EXEEXT)'; then echo -n " gbfun" fi if test x$GBGRID != x'gbgrid$(EXEEXT)'; then echo -n " gbgrid" fi if test x$GBRAND != x'gbrand$(EXEEXT)'; then echo -n " gbrand" fi if test x$GBHILL != x'gbhill$(EXEEXT)'; then echo -n " gbhill" fi if test x$GBACORR != x'gbacorr$(EXEEXT)'; then echo -n " gbacorr" fi echo "" fi echo "| " echo "| The utilities will be installed under ${prefix}" echo "| To compile and install the utilities in a different directory" echo "| use the configure option --prefix, see the INSTALL file." echo "|" echo "| Now you can type 'make' to compile GBUTILS and 'make install'" echo "| to install it" echo "| enjoy!" echo " --------------------------------------------------------------------" gbutils-5.7.1/gbhisto2d.c0000644000175000017500000003130013246754720012156 00000000000000/* gbhisto2d (ver. 5.6) -- Produce 2D histogram from data Copyright (C) 1998-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ size_t xsteps=10; size_t ysteps=10; int o_output=0;/*output type*/ char *splitstring = strdup(" \t"); int o_verbose=0; double **data=NULL; size_t size=0; /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* initialize global variables */ initialize_program(argv[0]); /* read the command line */ while((opt=getopt_long(argc,argv,"n:M:vhF:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='n'){ /*the number of bins*/ if(!strchr (optarg,',')){ xsteps=ysteps=atoi(optarg); } else{ char *stmp1=strdup (optarg); xsteps = atoi(strtok (stmp1,",")); ysteps = atoi(strtok (NULL,",")); free(stmp1); } } else if(opt=='M'){ /*set the type of output*/ o_output = atoi(optarg); if(o_output<0 || o_output>7){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='v'){ /*set verbose output*/ o_verbose=1; } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"2D histogram on a regular grid. Data are read from standard input as couples \n"); fprintf(stdout,"(X,Y). If data are treated as continuous, equispaced bins are built and their \n"); fprintf(stdout,"center coordinates together with the required statistics are printed. For\n"); fprintf(stdout,"discrete variables, bins are built directly from the given variables.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options :\n"); fprintf(stdout," -M choose the statistics to print for each bin (default 0)\n"); fprintf(stdout," 0 occurrences number for continuous binned variables (*)\n"); fprintf(stdout," 1 relative frequency for continuous binned variables (*)\n"); fprintf(stdout," 2 empirical density for continuous binned variables (*)\n"); fprintf(stdout," 3 occurrences number for discrete symmetric variables (bins{X}=bins{Y})\n"); fprintf(stdout," 4 relative frequency for discrete symmetric variables (bins{X}=bins{Y})\n"); fprintf(stdout," 5 transition matrix (X=>Y)\n"); fprintf(stdout," 6 occurrences number for discrete asymmetric variables\n"); fprintf(stdout," 7 relative frequency for discrete asymmetric variables\n"); fprintf(stdout," -n number of equispaced bins where the histogram is computed. For output marked\n"); fprintf(stdout," with (*), use comma ',' to specify different values for x and y coordinates.\n"); fprintf(stdout," (default 10) \n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* load the data */ load2(&data,&size,0,splitstring); /* ++++++++++++++++++++++++++++ */ if(o_verbose){ double xmean,xvar,xsd,xskew,xkurt,xadev,xmin,xmax; fprintf(stdout,"#output type:\t"); switch(o_output){ case 0: fprintf(stdout,"absolute frequency"); break; case 1: fprintf(stdout,"relative frequency"); break; case 2: fprintf(stdout,"empirical density"); break; case 3: fprintf(stdout,"occurencies number"); break; case 4: fprintf(stdout,"relative frequency"); break; case 5: fprintf(stdout,"transition matrix"); break; case 6: fprintf(stdout,"occurencies number (asym)"); break; case 7: fprintf(stdout,"relative frequency (asym)"); break; } fprintf(stdout,"\n"); fprintf(stdout,"#data statistics:\n"); fprintf(stdout, "# mean stdev skewness kurtosis ave.dev. min max obs.\n"); moment(data[0],size,&xmean,&xadev,&xsd,&xvar,&xskew,&xkurt,&xmin,&xmax); fprintf(stdout,"#X %+.2e %+.2e %+.2e %+.2e %+.2e %+.2e %+.2e %zd\n", xmean,xsd,xskew,xkurt,xadev,xmin,xmax,size); moment(data[1],size,&xmean,&xadev,&xsd,&xvar,&xskew,&xkurt,&xmin,&xmax); fprintf(stdout,"#Y %+.2e %+.2e %+.2e %+.2e %+.2e %+.2e %+.2e %zd\n", xmean,xsd,xskew,xkurt,xadev,xmin,xmax,size); } /* ++++++++++++++++++++++++++++ */ /* continuous variables */ if(o_output < 3){ size_t steps=xsteps*ysteps; size_t i,j; double xstepsize,ystepsize; int *histo; double xmin = DBL_MAX; /*set maximum double */ double xmax = -DBL_MAX; /*set to minimum double */ double ymin = DBL_MAX; /*set maximum double */ double ymax = -DBL_MAX; /*set to minimum double */ /* ++++++++++++++++++++++++++++ */ if(o_verbose){ fprintf(stdout,"#x bins number:\t%zd\n",xsteps); fprintf(stdout,"#y bins number:\t%zd\n",ysteps); } /* ++++++++++++++++++++++++++++ */ /* determination of xmax and xmin */ for(i=0;i dtmp0) xmin = dtmp0; else if( xmax < dtmp0) xmax = dtmp0; if( ymin > dtmp1) ymin = dtmp1; else if( ymax < dtmp1) ymax = dtmp1; } /* ------------------------------ */ histo = (int *) malloc(steps*sizeof(int)); for(i=0;i\/\fR .SH DESCRIPTION Each row of a block represents a single process realization. Blocks are separated by two white spaces and represent model variables, columns represent the time variable. Input is read as (X1_[r,t],...,Xj_[r,t],... XN_[r,t]), where r is the row, t the column and j the block. The model assumes that .PP FUN(X1_[r,t],...,XN_[t,t]) \- c_r = e_{r,t} .PP with e i.i.d and 'c' arow specific element, which can be fixed (fixed effect) or a normal random variable (random effect). .SH OPTIONS .TP \fB\-M\fR type of model (default 0) .TP 0 fixed effects .TP 1 random effects .TP \fB\-O\fR type of output (default 0) .TP 0 parameters .TP 1 parameters and errors .TP 2 and panel statistics .TP 3 parameters and variance matrix .TP \fB\-V\fR variance matrix estimation (default 0) .TP 0 < J^{\-1} >, computed via fully\-reduced log\-likelihood .TP 1 < H^{\-1} >, computed via fully\-reduced log\-likelihood .TP 2 < H^{\-1} J H^{\-1} >, computed via fully\-reduced log\-likelihood .TP 3 < J^{\-1} >, computed via non\-reduced log\-likelihood .TP 4 < H^{\-1} >, computed via non\-reduced log\-likelihood .TP 5 < H^{\-1} J H^{\-1} >, computed via non\-reduced log\-likelihood .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps .TP 5 model definition .TP \fB\-e\fR minimization tolerance (default 1e\-6) .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-h\fR this help .TP \fB\-A\fR comma separated MLL optimization options: step,tol,iter,eps,msize,algo. Use empty fields for default. (default 0.1,0.01,500,1e\-6,1e\-6,0) .TP step initial step size of the searching algorithm .TP tol line search tolerance iter: maximum number of iterations .TP eps gradient tolerance : stopping criteria ||gradient|| .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbxcorr.c0000644000175000017500000001124213246754720011742 00000000000000/* gbxcorr (ver. 5.6) -- Compute cross correlation matrix Copyright (C) 2011-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ char *splitstring = strdup(" \t"); size_t rows=0,columns=0; double **vals=NULL,**res=NULL; size_t i,j,k; /* OPTIONS */ int o_method=0; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"hF:M:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Take as input a data matrix A with N rows and T columns \n"); fprintf(stdout," A = [ a_{t,i} ] with t=1,..,T i=1,...,N \n"); fprintf(stdout,"and compute the NxN correlation matrix C [ c_{i,j} ] following \n"); fprintf(stdout,"the method specified by option '-M': with method 0 it is \n"); fprintf(stdout," c_{i,j} = 1/(T-1) \\sum_t (a_{t,i}-m_i) (a_{t,j}-m_j) \n"); fprintf(stdout,"where m_i is the i-th mean and with method 1 it is \n"); fprintf(stdout," c_{i,j} = 1/T \\sum_t a_{t,i} a_{t,j} . \n"); fprintf(stdout,"Covariance is stored in the lower triangle while correlation \n"); fprintf(stdout,"coefficients are stored in the upper triangle. \n\n"); fprintf(stdout,"WARNING: previous implementations were row-wise instead of column-wise\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options: \n"); fprintf(stdout," -M choose the method (default 0) \n"); fprintf(stdout," 0 covariance/correlation with mean removal \n"); fprintf(stdout," 1 covariance/correlation without mean removal \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -h this help\n"); return(0); } else if(opt=='M'){ /*set the method to use*/ o_method= atoi(optarg); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtable(&vals,&rows,&columns,0,splitstring); /* allocate space for the result */ res = (double **) my_alloc(columns*sizeof(double *)); for(i=0;i .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnear.c0000644000175000017500000002035713246754720011541 00000000000000/* gbnear (ver. 5.6) -- Produce nearest neighborhood density estimate Copyright (C) 2001-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" double dk2(const double *data, const int size, const double x, const int k) { int left,right,i; double toleft,toright,res; left=0; right=size-1; do{ const int index = (right+left)/2 ; const double dtmp1 = data[ index ]; if(x>dtmp1) left=index; else right=index; } while (right-left>1); toright = data[right]-x; toleft = x-data[left]; res =( torighttoleft) res = toleft; else if(toright0)left--; else break; } } else if(toleft0){ /* possible */ left--; toleft = x-data[left]; if(toright>toleft) res = toleft; else if(toright 0){ /* move in both direction */ const double tmp_toright = data[right+1]-x; const double tmp_toleft = x-data[left-1]; if(tmp_toleft>tmp_toright){ right++; toright=tmp_toright; res=(toright>toleft?toleft:toright); } else if(tmp_toright>tmp_toleft){ left--; toleft=tmp_toleft; res=(toright>toleft?toleft:toright); } else{ right++; left--; res=toright=tmp_toright; toleft=tmp_toleft; i++; } } else if (right>=size-1 && left>0){/* move to left */ left--; res = toleft = x-data[left]; } else if (left==0 && right res=%f\n",res); */ /*============*/ /* fprintf(stderr,"#----- [%zd]=%f x[%zd]=%f res= %f \n", */ /* left,data[left],right,data[right],res); */ /*============*/ return(res); } /* NO LONGER USED... TO BE REMOVED!!! */ double dk(const double *data, const int size, const int k, const int ind){ const double x = data[ind]; int i; int left=ind; int right=ind; double toright=0; double toleft=0; double res=0; for(i=0;i0){ /* move in both direction */ if(toright==toleft){ const double tmp_toright = data[right+1]-x; const double tmp_toleft = x-data[left-1]; if(tmp_toleft>tmp_toright){ res=toright=tmp_toright; right++; } else if(tmp_toright>tmp_toleft){ res=toleft=tmp_toleft; left++; } else{ res=toright=tmp_toright; right++; toleft=tmp_toleft; left++; i++; } } else if(toright0){ /* move to left */ left--; res = toleft = x-data[left]; } else if (left==0 && right x[%zd]=%f ] => %f \n", */ /* left,data[left],ind,data[ind],right,data[right], */ /* res); */ return(res ); } int main(int argc,char* argv[]){ double *data=NULL; size_t size,i; /*int left,right;*/ char *splitstring = strdup(" \t"); /* OPTIONS */ int o_kerneltype=1; int o_verbose=0; /* PARAMETERS */ size_t n=10; /* number of points */ int k=2; /* number of neighbors */ /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"vn:K:k:hF:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='k'){ /*the number of neighbors*/ k= atoi(optarg); } else if(opt=='n'){ /*the number of points*/ n= (size_t) atoi(optarg); } else if(opt=='K'){ /*the Kernel to use*/ o_kerneltype = atoi(optarg); } else if(opt=='v'){ /*increase verbosity*/ o_verbose=1; } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='h'){ fprintf(stdout, "Nearest neighbors density estimation. Data are read from standard input. The\n"); fprintf(stdout, "density is computed on a regular grid. \n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n number of points where the density is computed (default 10)\n"); fprintf(stdout," -k number of neighbors points considered (default 2)\n"); fprintf(stdout," -K choose the kernel to use: 0 Epanenchnikov, 1 Rectangular\n"); fprintf(stdout," (default 0)\n"); fprintf(stdout," -v verbose mode\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -h this help\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ load(&data,&size,0,splitstring); /*order the values*/ qsort(data,size,sizeof(double),sort_by_value); /* ++++++++++++++++++++++++++++ */ if(o_verbose == 1){ /* bandwidth */ fprintf(stdout,"# neighbors %d\n",k); /* kernel type */ fprintf(stdout,"kernel "); switch(o_kerneltype){ case 0: fprintf(stdout,"Epanechnikov"); break; case 1: fprintf(stdout,"Rectangular"); break; } fprintf(stdout,"\n"); fprintf(stdout,"# bins %zd\n",n); fprintf(stdout,"# observations %zd\n",size); fprintf(stdout,"range [%.3e,%.3e]\n",data[0],data[size-1]); } /* ++++++++++++++++++++++++++++ */ if(o_kerneltype == 1){ /* rectangular */ const double xlow=data[0]; const double xhigh=data[size-1]; const double xstep=(xhigh-xlow)/(n-1); for(i=0;i to declare getsubopt(). AC_REQUIRE([AC_GNU_SOURCE]) AC_REPLACE_FUNCS(getsubopt) if test $ac_cv_func_getsubopt = no; then gl_PREREQ_GETSUBOPT fi ]) # Prerequisites of lib/getsubopt.c. AC_DEFUN([gl_PREREQ_GETSUBOPT], [:]) gbutils-5.7.1/m4/gnulib-comp.m40000644000175000017500000000307310344402774013124 00000000000000# Copyright (C) 2004 Free Software Foundation, Inc. # This file is free software, distributed under the terms of the GNU # General Public License. As a special exception to the GNU General # Public License, this file may be distributed as part of a program # that contains a configuration script generated by Autoconf, under # the same distribution terms as the rest of that program. # # Generated by gnulib-tool. # # This file represents the compiled summary of the specification in # gnulib-cache.m4. It lists the computed macro invocations that need # to be invoked from configure.ac. # In projects using CVS, this file can be treated like other built files. # This macro should be invoked from ./configure.ac, in the section # "Checks for programs", right after AC_PROG_CC, and certainly before # any checks for libraries, header files, types and library functions. AC_DEFUN([gl_EARLY], [ AC_REQUIRE([AC_GNU_SOURCE]) ]) # This macro should be invoked from ./configure.ac, in the section # "Check for header files, types and library functions". AC_DEFUN([gl_INIT], [ AM_CONDITIONAL([GL_COND_LIBTOOL], [false]) gl_FUNC_GETDELIM gl_FUNC_GETLINE gl_FUNC_GETSUBOPT gl_FUNC_STRCHRNUL ]) # This macro records the list of files which have been installed by # gnulib-tool and may be removed by future gnulib-tool invocations. AC_DEFUN([gl_FILE_LIST], [ lib/getdelim.c lib/getdelim.h lib/getline.c lib/getline.h lib/getsubopt.c lib/getsubopt.h lib/strchrnul.c lib/strchrnul.h m4/getdelim.m4 m4/getline.m4 m4/getsubopt.m4 m4/onceonly_2_57.m4 m4/strchrnul.m4 ]) gbutils-5.7.1/m4/getdelim.m40000644000175000017500000000130210327011632012461 00000000000000# getdelim.m4 serial 1 dnl Copyright (C) 2005 Free Software dnl Foundation, Inc. dnl dnl This file is free software; the Free Software Foundation dnl gives unlimited permission to copy and/or distribute it, dnl with or without modifications, as long as this notice is preserved. AC_PREREQ(2.52) AC_DEFUN([gl_FUNC_GETDELIM], [ AC_LIBSOURCES([getdelim.c, getdelim.h]) dnl Persuade glibc to declare getdelim(). AC_REQUIRE([AC_GNU_SOURCE]) AC_REPLACE_FUNCS(getdelim) AC_CHECK_DECLS_ONCE(getdelim) if test $ac_cv_func_getdelim = no; then gl_PREREQ_GETDELIM fi ]) # Prerequisites of lib/getdelim.c. AC_DEFUN([gl_PREREQ_GETDELIM], [ AC_CHECK_FUNCS([flockfile funlockfile]) ]) gbutils-5.7.1/m4/getline.m40000644000175000017500000000431610327011632012326 00000000000000# getline.m4 serial 13 dnl Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2005 Free Software dnl Foundation, Inc. dnl dnl This file is free software; the Free Software Foundation dnl gives unlimited permission to copy and/or distribute it, dnl with or without modifications, as long as this notice is preserved. AC_PREREQ(2.52) dnl See if there's a working, system-supplied version of the getline function. dnl We can't just do AC_REPLACE_FUNCS(getline) because some systems dnl have a function by that name in -linet that doesn't have anything dnl to do with the function we need. AC_DEFUN([gl_FUNC_GETLINE], [ AC_LIBSOURCES([getline.c, getline.h]) dnl Persuade glibc to declare getline(). AC_REQUIRE([AC_GNU_SOURCE]) AC_CHECK_DECLS([getline]) gl_getline_needs_run_time_check=no AC_CHECK_FUNC(getline, dnl Found it in some library. 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AC_DEFINE([getline], [gnu_getline], [Define to a replacement function name for getline().]) AC_LIBOBJ(getline) gl_PREREQ_GETLINE fi ]) # Prerequisites of lib/getline.c. AC_DEFUN([gl_PREREQ_GETLINE], [ gl_FUNC_GETDELIM ]) gbutils-5.7.1/m4/strchrnul.m40000644000175000017500000000107310327011632012720 00000000000000# strchrnul.m4 serial 3 dnl Copyright (C) 2003 Free Software Foundation, Inc. dnl This file is free software; the Free Software Foundation dnl gives unlimited permission to copy and/or distribute it, dnl with or without modifications, as long as this notice is preserved. AC_DEFUN([gl_FUNC_STRCHRNUL], [ dnl Persuade glibc to declare strchrnul(). AC_REQUIRE([AC_GNU_SOURCE]) AC_REPLACE_FUNCS(strchrnul) if test $ac_cv_func_strchrnul = no; then gl_PREREQ_STRCHRNUL fi ]) # Prerequisites of lib/strchrnul.c. AC_DEFUN([gl_PREREQ_STRCHRNUL], [:]) gbutils-5.7.1/m4/onceonly_2_57.m40000644000175000017500000000700110327011632013253 00000000000000# onceonly_2_57.m4 serial 3 dnl Copyright (C) 2002-2003, 2005 Free Software Foundation, Inc. dnl This file is free software, distributed under the terms of the GNU dnl General Public License. As a special exception to the GNU General dnl Public License, this file may be distributed as part of a program dnl that contains a configuration script generated by Autoconf, under dnl the same distribution terms as the rest of that program. dnl This file defines some "once only" variants of standard autoconf macros. dnl AC_CHECK_HEADERS_ONCE like AC_CHECK_HEADERS dnl AC_CHECK_FUNCS_ONCE like AC_CHECK_FUNCS dnl AC_CHECK_DECLS_ONCE like AC_CHECK_DECLS dnl AC_REQUIRE([AC_HEADER_STDC]) like AC_HEADER_STDC dnl The advantage is that the check for each of the headers/functions/decls dnl will be put only once into the 'configure' file. It keeps the size of dnl the 'configure' file down, and avoids redundant output when 'configure' dnl is run. dnl The drawback is that the checks cannot be conditionalized. If you write dnl if some_condition; then gl_CHECK_HEADERS(stdlib.h); fi dnl inside an AC_DEFUNed function, the gl_CHECK_HEADERS macro call expands to dnl empty, and the check will be inserted before the body of the AC_DEFUNed dnl function. dnl This is like onceonly.m4, except that it uses diversions to named sections dnl DEFAULTS and INIT_PREPARE in order to check all requested headers at once, dnl thus reducing the size of 'configure'. Works with autoconf-2.57. The dnl size reduction is ca. 9%. dnl Autoconf version 2.57 or newer is recommended. AC_PREREQ(2.57) # AC_CHECK_HEADERS_ONCE(HEADER1 HEADER2 ...) is a once-only variant of # AC_CHECK_HEADERS(HEADER1 HEADER2 ...). AC_DEFUN([AC_CHECK_HEADERS_ONCE], [ : AC_FOREACH([gl_HEADER_NAME], [$1], [ AC_DEFUN([gl_CHECK_HEADER_]m4_quote(translit(gl_HEADER_NAME, [./-], [___])), [ m4_divert_text([INIT_PREPARE], [gl_header_list="$gl_header_list gl_HEADER_NAME"]) gl_HEADERS_EXPANSION AH_TEMPLATE(AS_TR_CPP([HAVE_]m4_defn([gl_HEADER_NAME])), [Define to 1 if you have the <]m4_defn([gl_HEADER_NAME])[> header file.]) ]) AC_REQUIRE([gl_CHECK_HEADER_]m4_quote(translit(gl_HEADER_NAME, [./-], [___]))) ]) ]) m4_define([gl_HEADERS_EXPANSION], [ m4_divert_text([DEFAULTS], [gl_header_list=]) AC_CHECK_HEADERS([$gl_header_list]) m4_define([gl_HEADERS_EXPANSION], []) ]) # AC_CHECK_FUNCS_ONCE(FUNC1 FUNC2 ...) is a once-only variant of # AC_CHECK_FUNCS(FUNC1 FUNC2 ...). AC_DEFUN([AC_CHECK_FUNCS_ONCE], [ : AC_FOREACH([gl_FUNC_NAME], [$1], [ AC_DEFUN([gl_CHECK_FUNC_]m4_defn([gl_FUNC_NAME]), [ m4_divert_text([INIT_PREPARE], [gl_func_list="$gl_func_list gl_FUNC_NAME"]) gl_FUNCS_EXPANSION AH_TEMPLATE(AS_TR_CPP([HAVE_]m4_defn([gl_FUNC_NAME])), [Define to 1 if you have the `]m4_defn([gl_FUNC_NAME])[' function.]) ]) AC_REQUIRE([gl_CHECK_FUNC_]m4_defn([gl_FUNC_NAME])) ]) ]) m4_define([gl_FUNCS_EXPANSION], [ m4_divert_text([DEFAULTS], [gl_func_list=]) AC_CHECK_FUNCS([$gl_func_list]) m4_define([gl_FUNCS_EXPANSION], []) ]) # AC_CHECK_DECLS_ONCE(DECL1 DECL2 ...) is a once-only variant of # AC_CHECK_DECLS(DECL1, DECL2, ...). AC_DEFUN([AC_CHECK_DECLS_ONCE], [ : AC_FOREACH([gl_DECL_NAME], [$1], [ AC_DEFUN([gl_CHECK_DECL_]m4_defn([gl_DECL_NAME]), [ AC_CHECK_DECLS(m4_defn([gl_DECL_NAME])) ]) AC_REQUIRE([gl_CHECK_DECL_]m4_defn([gl_DECL_NAME])) ]) ]) gbutils-5.7.1/compile0000755000175000017500000001624512676613141011513 00000000000000#! /bin/sh # Wrapper for compilers which do not understand '-c -o'. scriptversion=2012-10-14.11; # UTC # Copyright (C) 1999-2014 Free Software Foundation, Inc. # Written by Tom Tromey . # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # This file is maintained in Automake, please report # bugs to or send patches to # . nl=' ' # We need space, tab and new line, in precisely that order. Quoting is # there to prevent tools from complaining about whitespace usage. IFS=" "" $nl" file_conv= # func_file_conv build_file lazy # Convert a $build file to $host form and store it in $file # Currently only supports Windows hosts. If the determined conversion # type is listed in (the comma separated) LAZY, no conversion will # take place. func_file_conv () { file=$1 case $file in / | /[!/]*) # absolute file, and not a UNC file if test -z "$file_conv"; then # lazily determine how to convert abs files case `uname -s` in MINGW*) file_conv=mingw ;; CYGWIN*) file_conv=cygwin ;; *) file_conv=wine ;; esac fi case $file_conv/,$2, in *,$file_conv,*) ;; mingw/*) file=`cmd //C echo "$file " | sed -e 's/"\(.*\) " *$/\1/'` ;; cygwin/*) file=`cygpath -m "$file" || echo "$file"` ;; wine/*) file=`winepath -w "$file" || echo "$file"` ;; esac ;; esac } # func_cl_dashL linkdir # Make cl look for libraries in LINKDIR func_cl_dashL () { func_file_conv "$1" if test -z "$lib_path"; then lib_path=$file else lib_path="$lib_path;$file" fi linker_opts="$linker_opts -LIBPATH:$file" } # func_cl_dashl library # Do a library search-path lookup for cl func_cl_dashl () { lib=$1 found=no save_IFS=$IFS IFS=';' for dir in $lib_path $LIB do IFS=$save_IFS if $shared && test -f "$dir/$lib.dll.lib"; then found=yes lib=$dir/$lib.dll.lib break fi if test -f "$dir/$lib.lib"; then found=yes lib=$dir/$lib.lib break fi if test -f "$dir/lib$lib.a"; then found=yes lib=$dir/lib$lib.a break fi done IFS=$save_IFS if test "$found" != yes; then lib=$lib.lib fi } # func_cl_wrapper cl arg... # Adjust compile command to suit cl func_cl_wrapper () { # Assume a capable shell lib_path= shared=: linker_opts= for arg do if test -n "$eat"; then eat= else case $1 in -o) # configure might choose to run compile as 'compile cc -o foo foo.c'. eat=1 case $2 in *.o | *.[oO][bB][jJ]) func_file_conv "$2" set x "$@" -Fo"$file" shift ;; *) func_file_conv "$2" set x "$@" -Fe"$file" shift ;; esac ;; -I) eat=1 func_file_conv "$2" mingw set x "$@" -I"$file" shift ;; -I*) func_file_conv "${1#-I}" mingw set x "$@" -I"$file" shift ;; -l) eat=1 func_cl_dashl "$2" set x "$@" "$lib" shift ;; -l*) func_cl_dashl "${1#-l}" set x "$@" "$lib" shift ;; -L) eat=1 func_cl_dashL "$2" ;; -L*) func_cl_dashL "${1#-L}" ;; -static) shared=false ;; -Wl,*) arg=${1#-Wl,} save_ifs="$IFS"; IFS=',' for flag in $arg; do IFS="$save_ifs" linker_opts="$linker_opts $flag" done IFS="$save_ifs" ;; -Xlinker) eat=1 linker_opts="$linker_opts $2" ;; -*) set x "$@" "$1" shift ;; *.cc | *.CC | *.cxx | *.CXX | *.[cC]++) func_file_conv "$1" set x "$@" -Tp"$file" shift ;; *.c | *.cpp | *.CPP | *.lib | *.LIB | *.Lib | *.OBJ | *.obj | *.[oO]) func_file_conv "$1" mingw set x "$@" "$file" shift ;; *) set x "$@" "$1" shift ;; esac fi shift done if test -n "$linker_opts"; then linker_opts="-link$linker_opts" fi exec "$@" $linker_opts exit 1 } eat= case $1 in '') echo "$0: No command. Try '$0 --help' for more information." 1>&2 exit 1; ;; -h | --h*) cat <<\EOF Usage: compile [--help] [--version] PROGRAM [ARGS] Wrapper for compilers which do not understand '-c -o'. Remove '-o dest.o' from ARGS, run PROGRAM with the remaining arguments, and rename the output as expected. If you are trying to build a whole package this is not the right script to run: please start by reading the file 'INSTALL'. Report bugs to . EOF exit $? ;; -v | --v*) echo "compile $scriptversion" exit $? ;; cl | *[/\\]cl | cl.exe | *[/\\]cl.exe ) func_cl_wrapper "$@" # Doesn't return... ;; esac ofile= cfile= for arg do if test -n "$eat"; then eat= else case $1 in -o) # configure might choose to run compile as 'compile cc -o foo foo.c'. # So we strip '-o arg' only if arg is an object. eat=1 case $2 in *.o | *.obj) ofile=$2 ;; *) set x "$@" -o "$2" shift ;; esac ;; *.c) cfile=$1 set x "$@" "$1" shift ;; *) set x "$@" "$1" shift ;; esac fi shift done if test -z "$ofile" || test -z "$cfile"; then # If no '-o' option was seen then we might have been invoked from a # pattern rule where we don't need one. That is ok -- this is a # normal compilation that the losing compiler can handle. If no # '.c' file was seen then we are probably linking. That is also # ok. exec "$@" fi # Name of file we expect compiler to create. cofile=`echo "$cfile" | sed 's|^.*[\\/]||; s|^[a-zA-Z]:||; s/\.c$/.o/'` # Create the lock directory. # Note: use '[/\\:.-]' here to ensure that we don't use the same name # that we are using for the .o file. Also, base the name on the expected # object file name, since that is what matters with a parallel build. lockdir=`echo "$cofile" | sed -e 's|[/\\:.-]|_|g'`.d while true; do if mkdir "$lockdir" >/dev/null 2>&1; then break fi sleep 1 done # FIXME: race condition here if user kills between mkdir and trap. trap "rmdir '$lockdir'; exit 1" 1 2 15 # Run the compile. "$@" ret=$? if test -f "$cofile"; then test "$cofile" = "$ofile" || mv "$cofile" "$ofile" elif test -f "${cofile}bj"; then test "${cofile}bj" = "$ofile" || mv "${cofile}bj" "$ofile" fi rmdir "$lockdir" exit $ret # Local Variables: # mode: shell-script # sh-indentation: 2 # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: gbutils-5.7.1/gbtest.10000644000175000017500000000713113246755334011506 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBTEST "1" "March 2018" "gbtest 5.7.1" "User Commands" .SH NAME gbtest \- Compute statistical tests on data .SH SYNOPSIS .B gbtest [\fI\,options\/\fR] \fI\,\/\fR .SH DESCRIPTION Compute statistical tests on data. Each test is specified by a unique name and optional parameters follows, separated by commas. Depending on the test, one, two or three columns of data are expected. '1 sample' tests expect a single columns. Test on 'pairs' expect two columns of equal length. Test on '2 samples' expect two columns of data, possibly of different length. Test on 2+ and 3+ samples expect a varying number of columns. If more columns are provided, the test is repeated for any column in the case of 1 sample test, and any couple of columns in the case of 2 sample tests. In the last case, the output is expressed in matrix format, with estimated statistics on the lower triangle and p\-scores (if requested) on the upper. The removal of 'nan' entries is automatic, and is performed consistently with the nature of the test. .PP Tests typically assume 1, 2 or 3+ samples. Available tests are: .TP D+,D\-,D,V 1 samp Kolmogorov\-Smirnov tests on cumulated data .TP W2,A2,U2 1 samp Cramer\-von Mises tests on cumulated data .TP CHI2\-1 1 samp Chi\-Sqrd, 1 samp. 2nd column: th. prob. .TP WILCO 1 samp Wilcoxon signed\-rank test (mode=0) .TP TS 1 samp Student's T (mean=0) .TP TR\-TP 1 samp Test of randomness: turning points .TP TR\-DS 1 samp Test of randomness: difference sign .TP TR\-RT 1 samp Test of randomness: rank test .TP R pairs Pearson's correlation coefficient .TP RHO pairs Spearman's Rho rank correlation .TP TAU pairs Kendall's Tau correlation .TP CHI2\-2 pairs Chi\-Sqrd, 2 samples .TP TP pairs Student's T with paired samples .TP WILCO2 pairs Wilcoxon test on paired samples .TP KS 2 samp Kolmogorov\-Smirnov test .TP T 2 samp Student's T with same variances .TP TH 2 samp Student's T with different variances .TP F 2 samp F\-Test for different variances .TP WMW 2 samp Wilcoxon\-Mann\-Whitney U .TP FP 2 samp Fligner\-Policello standardized U^ .TP LEV\-MEAN 2+ samp Levene equality of variances using means .TP LEV\-MED 2+ samp Levene equality of variances using medians .TP KW 3+ samp Kruscal\-Wallis test on 3+ samples .TP CHI2\-N 3+ samp Multi\-columns contingency table analysis .SH OPTIONS .TP \fB\-p\fR compute associated significance .TP \fB\-s\fR input data are already sorted in ascending order .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-o\fR set the output format (default '%12.6e') .TP \fB\-v\fR verbosity: 0 none, 1 headings, 2+ description (default 0) .TP \fB\-h\fR this help .SH EXAMPLES .TP gbtest FP < file compute the Fligner\-Policello test on the columns of 'file', considering all possible pairings. .TP gbtest KW \-p < file compute the Kruscal\-Wallis test and its p\-score on the first three columns of data in 'file' .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbglreg.10000644000175000017500000000375313246755335011636 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBGLREG "1" "March 2018" "gbglreg 5.7.1" "User Commands" .SH NAME gbglreg \- Estimate general linear regression model .SH SYNOPSIS .B gbglreg [\fI\,options\/\fR] .SH DESCRIPTION General Linear regression. Data are read in columns (X_1 .. X_N Y). The last column contains the dependent observations. With option \fB\-w\fR standard errors associated with the observations can be provided. In this case data are read as (X_1...X_N,Y,std(Y)). .SH OPTIONS .TP \fB\-M\fR the regression model (default 1) .TP 0 with estimated intercept .TP 1 with zero intercept .TP \fB\-w\fR consider standard errors .TP \fB\-O\fR the type of output (default 0) .TP 0 regression coefficients .TP 1 regression coefficients and errors .TP 2 x, fitted y, error on y, residual .TP 3 coefficients and variance matrix .TP 4 coefficients and explained variance .TP \fB\-V\fR method to estimate variance matrix (default 0) .TP 0 ordinary least square estimator .TP 1 heteroscedastic consistent White estimator .TP 2 Hinkley adjusted White estimator .TP 3 Horn\-Horn\-Duncan adjusted White estimator .TP 4 jacknife estimator .TP \fB\-v\fR verbosity level (default 0) .TP 0 just output .TP 1 commented headings .TP 2 model details .TP \fB\-h\fR print this help .TP \fB\-F\fR specify the input fields separators (default " \et") .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbbin.10000644000175000017500000000445513246755334011305 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBBIN "1" "March 2018" "gbbin 5.7.1" "User Commands" .SH NAME gbbin \- A program to bin data .SH SYNOPSIS .B gbbin [\fI\,options\/\fR] .SH DESCRIPTION Compute binned statistics. Data are read from standard input as records (X,Y1,Y2,...). Equipopulated bins are built with respect to the first field X. Option \fB\-O\fR decide which statistics are printed for each bin. With options \fB\-x\fR, \fB\-y\fR or \fB\-c\fR elements in different bins are split in different columns. .SH OPTIONS .TP \fB\-n\fR set the number of equipopulated bins (default 10) .TP \fB\-w\fR min,max set manually the binning window. Ignored with \fB\-x\fR,\-y and \fB\-c\fR .TP \fB\-O\fR set the output with a comma separated list of variables: xmean, xmin, xmax, xstd, xmedian, ymean, yadev, ystd, yvar, yskew, ykurt, ymin, ymax, ymedian, num (default xmean, ymean) .TP \fB\-c\fR# the values in column # are split in different columns; # can be a comma separated list of columns .TP \fB\-x\fR equivalent to \fB\-c\fR 1 .TP \fB\-y\fR equivalent to \fB\-c\fR 2 .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-v\fR verbose mode .SH EXAMPLES .TP gbbin \-n 20 < file split the records (line) in 20 bins according to first field. Print the average value of the bin entries for each column. If 'file' has 3 columns, the output has twenty rows and three columns. .TP gbbin \-O ymedian < file bin the records with respect to the first value and print the median value of the other columns in each bin .TP gbbin \-c 3 < file the binned values of the third columns are printed on separate columns. The output has ten columns. .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbdist.10000644000175000017500000000265013246755334011473 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBDIST "1" "March 2018" "gbdist 5.7.1" "User Commands" .SH NAME gbdist \- Produce cumulative distribution from data .SH SYNOPSIS .B gbdist [\fI\,options\/\fR] .SH DESCRIPTION Compute the empirical probability distribution function as F(x_k) = k/(N+1) where x_k is the k\-th largest observation, that is k\-1 observations are smaller then x_k and n\-k are larger, and N is the sample size. .SH OPTIONS .TP \fB\-r\fR print right cumulated distribution function, i.e. 1\-F(x) .TP \fB\-i\fR print multiple points for identical observations .TP \fB\-t\fR print the distribution for each column of input .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-v\fR verbose mode .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbacorr.10000644000175000017500000000437413246755336011645 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBACORR "1" "March 2018" "gbacorr 5.7.1" "User Commands" .SH NAME gbacorr \- Compute auto/cross-correlation coefficients .SH SYNOPSIS .B gbacorr [\fI\,options\/\fR] .SH DESCRIPTION Compute auto/cross\-correlation coefficients .PP If the input is a single columns x_1...X_T, the autocorrelation function c(t) is printed, defined as .IP c_{t} = 1/(T\-t\-1) \esum_i (x_i\-m) (x_{i+t}\-m) /s^2 .PP where m is the sample average and s the standard deviation. With a second column y_1...y_T, the cross\-correlation .IP c_{t} = 1/(T\-t\-1) \esum_i (x_i\-mx) (y_{i+t}\-my) /(sx sy) .PP is printed where mx and my are the average values of the two columns and sx and sy their standard deviations. With \fB\-M\fR 1 it is mx=my=0, the st.dev. is computed accordingly and in the previous formula T\-t\-1 is replaced by T\-t. The range of t is set by option \fB\-t\fR. Options .TP \fB\-M\fR choose the method (default '0'): .TP 0 auto/cross\-correlation with mean removal, .TP 1 auto/cross\-correlation without mean removal .TP \fB\-t\fR set range of t (default '0,10'), accept negative integers .TP \fB\-p\fR specify the confidence level in (0,1). Interval ac_low,ac_hi has a probability 1\-confidence to contain the true value. With this option the output becomes: lag ac ac_low ac_hi. .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH EXAMPLES .TP gbacorr \-t 0,2 'file(1)' first three a.c. coeff. of the first data column .TP gbacorr \-p 0.05 'file(1:2)' x\-corr of the first two columns together with their 5% confidence intervals .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnear.10000644000175000017500000000254213246755334011455 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBNEAR "1" "March 2018" "gbnear 5.7.1" "User Commands" .SH NAME gbnear \- Produce nearest neighborhood density estimate .SH SYNOPSIS .B gbnear [\fI\,options\/\fR] .SH DESCRIPTION Nearest neighbors density estimation. Data are read from standard input. The density is computed on a regular grid. .SH OPTIONS .TP \fB\-n\fR number of points where the density is computed (default 10) .TP \fB\-k\fR number of neighbors points considered (default 2) .TP \fB\-K\fR choose the kernel to use: 0 Epanenchnikov, 1 Rectangular (default 0) .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbmave.c0000644000175000017500000000640613246754720011543 00000000000000/* gbmave (ver. 5.6) -- Produce moving average from data Copyright (C) 1998-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" int main(int argc,char* argv[]){ double **data=NULL; /* array of values */ size_t rows=0,columns=0; double *average; size_t i,col; size_t lag=10; int o_printnum=0; char *splitstring = strdup(" \t"); /* COMMAND LINE PROCESSING */ /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* read the command line */ while((opt=getopt_long(argc,argv,"s:hnF:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='n'){ /*print the time steps numbers*/ o_printnum=1; } else if(opt=='s'){ /*set the lag*/ lag=(size_t) atoi(optarg); } else if(opt=='h'){ /*print short help*/ fprintf(stdout,"Compute moving average along each column. Data are read \n"); fprintf(stdout,"from standard input.\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -s number of steps to average (default 10)\n"); fprintf(stdout," -n print a progressive tailing integer\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help\n"); return(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data: data[column][row] */ loadtable(&data,&rows,&columns,0,splitstring); if(rows> stream xÚÍWK“ä4 ¾Ï¯È¤j“µüL8»LAÕÛ{žnO‹<'`=rìdº›PÕTÁЗ8/Iþ$}’|»¹yû5” ¨‚‚àÉæ1¡¼,U‰$ª\&›]òcº[÷Y΀¥]Feúì/Æ=[ó[öóæ[ÔR%E%õZ ,€%9#¨· :îìXÛU àm7 úóç,‡Ôzñ$¯ Vª$§¼`U4úÎlM“Q•>d9^ ²Ty±7ñö9O…¹œì瀦+E“œË‚Q4~Õµƒt0¸t裤H&xIrº ÈòŠ’ôÖYóˆ&KHw¦ß:{,*›^tñÃÁu{—AªtH JßÈc ªÀo±<: 4dúNõ1š~Э=Œµ–DÅÒBÂ'O׳€P–)¨ä#ý ÆÁé¶ì\Q*!¯×1> stream xÚ­XKÛ6¾çWø(±¢÷£·´I€-H|Kz %®—¨,©$µ»úã;/ÙZ¯‚"@/Öpø˜áp¾yø×ý›wŸòlGaÕñf¿‰³$ŒëtSäq˜äÕfßn¾ß“8Úþ¹ÿýݧ¸x±8 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From its website "Cygwin is a large collection of GNU and Open Source tools which provide functionality similar to a Linux distribution on Windows". The Cygwin DLL currently works with all recent, commercially released x86 32 bit and 64 bit versions of Windows, starting with Windows XP SP3. [Cygwin] https://www.cygwin.com/ 2 Install Cygwin ================ You can skip this part if you use Cygwin already. Otherwise follow the instructions. From the [Cygwin] website download the installation program on the windows machine. There is both a 32 bit and a 64 bit flavor. Launch the executable. It will require to set a few parameters, but I usually accept the default setting. Then you are presented with a page that ask you to select extra packages for installation, similar to the picture below. [file:cygwin_install.png] [http://cafim.sssup.it/~giulio/software/gbutils/cygwin_install.png] From this window select the packages (by clicking on the "circular arrows" symbol on the left) `gsl' and `gsl-devel'. Moreover check that the packages `crypt' and `make' are selected. They should be already, but you never know. If you plan to install the libmatheval support (highly recommended) install also `flex', `guile', `guile-devel', `libgmp' and `libgmp-devel'. [Cygwin] https://www.cygwin.com/ 3 Install GNU matheval library ============================== Download the last package from the [libmatheval website]. It should have extension `.tar.gz'. Move the package in `c:\cigwin\usrl\local' and launch the `Cygwin desktop' (an icon should have been created in your Desktop). Inside the shell move to `/usr/local/' and unpack the source code ,---- | tar xvzf libmatheval-{version}.tar.gz `---- move inside the source directory ,---- | cd libmatheval-{version} `---- run the configure script specifying the prefix `/usr' ,---- | ./configure --prefix=/usr `---- then build the files ,---- | make `---- and install them ,---- | make install `---- If more detailed instructions are necessary, see the file `INSTALL' in the source directory (you can use the command `less INSTALL'). [libmatheval website] https://www.gnu.org/software/libmatheval/ 4 Install gbutils ================= From the [cafed repository] download the last `tar.gz' package and move it in the `/usr/local/' directory. In the `Cygwin desktop' unpack the package: ,---- | tar xvzf gbutils-{version}.tar.gz `---- move inside the source directory ,---- | cd gbutils-{version} `---- run the configure script ,---- | ./configure `---- then build the files ,---- | make `---- and install them ,---- | make install `---- If more detailed instructions are necessary, see the file `INSTALL' in the source directory (you can use the command `less INSTALL'). This is the END, enjoy your gbutils :-) [cafed repository] ftp://cafed.sssup.it/packages/ gbutils-5.7.1/doc/overview.txt0000644000175000017500000005153112447776433013320 00000000000000 __________________ GBUTILS OVERVIEW Giulio Bottazzi __________________ Table of Contents _________________ 1 Brief description of programs .. 1.1 Data Manipulation .. 1.2 Data transformation .. 1.3 Descriptive statistics .. 1.4 Statistical tests and models 2 Understanding Input/Output .. 2.1 Sequential, tabular and compounded input .. 2.2 Missing values and NaN management .. 2.3 Radix and thousands separator .. 2.4 Output format and precision 3 Numerical Error handling 4 Binary format 5 Graphic output .. 5.1 GNU plotutils package .. 5.2 Gnuplot interactive session .. 5.3 Gnuplot's plot from command line 6 Programs summary table 1 Brief description of programs =============================== The programs in `gbutils' can be divided in four broad classes: - Data Manipulation - Data Transformation - Descriptive Statistics - Statistical Tests and Models The basic operation is essentially the same for all programs: you feed the standard input of the program with data in ASCII format separated by spaces, tabs or newline character. In general, each input line is considered a record and the blank separated entries in each line are considered different fields. The exact way in which different records and fields are treated depends on the program and can vary accordingly to the options specified in the command line (see below). After the program has read the data from standard input, it performs the required manipulations/analyses and prints the result to standard output, in the form of an ASCII file of newline separated records. Inside each output record, the fields are separated by spaces. Obviously, the meaning of the records and fields depend on the program. 1.1 Data Manipulation ~~~~~~~~~~~~~~~~~~~~~ These programs do not perform any analysis by themselves. Rather, they are provided as an help to prepare data for subsequent analysis. In particular, gbget is the only program that reads data from file and not from standard input. It can be used to extract data, according to a given pattern, from one or more files and send them, through a pipe `|', to other utilities. This program possesses a rather complex set of options. See README.gbget for a tutorial on its use. gbget: extract data from a tabular input according to a specified pattern. It is possible to access more files at the same time, merge their contents and transpose or flatten the resulting table. gbfun: compute generic functions on data in a column-wise manner. The function can be applied to all the columns or defined in a recursive way. gbgrid: generate a grid (i.e. a matrix) of values according to a user specified function gbboot: generate bootstrapped sequences from data provided sample gbrand: generated i.i.d. pseudo random variates gbenv: provide information about the numeric environment and the internal settings of the package 1.2 Data transformation ~~~~~~~~~~~~~~~~~~~~~~~ These programs perform basic transformation on input data, which are often considered preliminary to further statistical analysis. gbmave: print moving statistics (average, variance, etc.) of input data gbinterp: compute the interpolation on a regular mesh of user provided points. It can also print first and second derivative of the interpolation. gbfilternear: filter near points in Ecuclidean metrics. Point whose distance is below a given threshold are removed. 1.3 Descriptive statistics ~~~~~~~~~~~~~~~~~~~~~~~~~~ These utilities are useful in the representation and description of data. They encompass simple statistics and more "advanced" non parametric methods. gbdist: cumulative distribution of input data gbstat: simple descriptive statistics of input data gbbin: compute binned statistics gbquant: quantiles of the empirical distribution of input data gbhisto: histogram for univariate data. Choose between absolute frequencies, relative frequencies and empirical density gbker: kernel density estimate for univariate data. The type of kernel, the bandwidth and the computation method can be specified at the command line gbnear: density estimate via nearest neighbors method gbker2d: kernel density estimate for bivariate data gbhisto2d: histograms for bivariate data gbgcorr: Compute the correlation dimension of a time series with a Gaussian kernel. gbacorr: It computes the autocorrelogram or the cross-autocorrelogram of a series of observations. It reads the data column-wise. gbxcorr: Compute the cross-covariance and cross-correlation coefficients with and without the removal of the mean of two samples. It reads the data column-wise. 1.4 Statistical tests and models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The utilities provide statistical tests to compare different samples and non-parametric method to investigate relationship between paired (or in general compounded) observations. gbtest: various one and two samples statistical tests. When available, p-score significance is also provided. gbmodes: find the critical bandwidth for a kernel density estimate to generate a given number of modes and compute the associated p-value using smoothed bootstrap technique gbbin: the program takes couples of values X Y (separated by spaces), bins them with respect to the first variable and prints statistics of the second variables gbkreg: compute the kernel non-linear regression function gbkreg2d: compute the kernel non-linear regression function on three dimensional data gblreg: compute linear OLS regression gbglreg: compute generalized linear OLS regression gbnlreg: compute non linear regression using OLS, MAD or asymmetric MAD estimators gbnlqreg: compute non linear quantile regression gbnlmult: contemporaneous least square estimation of a system of non linear equations. gbnlprobit: estimate a non linear probit model on binary data gbhill: estimate different families of probability distribution on the extremal data using maximum likelihood. For more information on a specific command, use the -h command line option. Please, notice that all the programs work by loading the whole set of data in memory before computing the relevant statistics. In this respect, they are probably not suitable to be used on very large datasets. 2 Understanding Input/Output ============================ All the commands of this package read input in ASCII format. The data should be separated by white characters (spaces or tabs) or newlines. Lines beginning with a fence symbol `#' are ignored. They are simply skipped by the input routine. If support for the zlib has been included at compile time (see above) the input ASCII file can be gz-compressed. A file can contain several blocks of data. Blocks are separated by two consecutive blank lines. In general, all operations are performed on the first block found in the datafile. The program 'gbget' can be used to extract one particular block (or set of blocks) from one file. 2.1 Sequential, tabular and compounded input ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The utilities in this package use three different ways of reading data from input: sequential: In 'sequential' format a single dataset is internally build from the data input file. All the entries found on one column of input are read sequentially and put in the same dataset. Notice that the different lines must contain the same number of entries or NAN values are generated. tabular: In 'tabular' format, each column of the input is treated as a different dataset; the program will internally create a list of datasets, one for each column of input. The different entries on one line are then put inside different sets. Notice that, in this case, the number of fields in the first non-comment non-empty line decides the number of datasets. All subsequent input lines should contain the same number of fields (but see below). compounded: In 'compounded' format the program reads a fixed number of fields from each line. Each line is internally stored as an n-tuple (a couple or a triplet) and treated accordingly. Notice that if some line contains more fields than needed, the extra fields are ignored. An example can clarify the difference between the 'sequential', 'tabular' and 'compounded' format. Suppose to have the following input datafile ,---- | 1.0 2.0 3.0 | 4.0 5.0 6.0 | 7.0 8.0 9.0 `---- using the 'sequential' input the unique dataset `{1.0,2.0,3.0,4.0,...,9.0}' is internally generated by the program. Notice the ordering: all the entries of one line are inserted in the internal dataset before the next line is red. In 'tabular' format, the program builds instead three different datasets: `{1.0,4.0,7.0}', `{2.0,5.0,8.0}' and `{3.0,6.0,9.0}' and use each set separately for its subsequent duties (topically reproducing the same statistical analysis for each set). In 'compounded' format, assuming that the program accepts couples, the following array of ordered couples is generated `{(1.0,2.0),(4.0,5.0),(7.0,8.0)}'. Notice that this is a single dataset, made of couples of associated values. When available, the "sequential" format is the default while the "tabular" format is activated with the option '-t'. See the 6 for the list of input format accepted by the different utilities. 2.2 Missing values and NaN management ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When the conversion of an input entry to an internal floating point number cannot be performed, or when, on an input line, there are not enough values for the required "tabular" or "compounded" format, a NAN (not-a-number) value is generated. This approach is introduced to make possible the manipulation of files with an uneven number of entries in different columns or with "non numerical" values. The following utilities automatically remove the `NaN' values from their input: `gbstat', `gbdist' and `gbquant'. The other utilities do handle `NaN' values as expected: if `NaN' values are present they typically return `NaN' output. In this case, the option `D' of the `gbget' utility is provided to remove all the lines containing `NaN' entries. This program can be used in a pipe like ,---- | ...| gbget '()D' | .... `---- to treat the data before passing them to other `NaN'-sensitive utilities. 2.3 Radix and thousands separator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In addition to the radix symbol which separates the fractional and the integer part of the number, sometimes data are reported with a thousand separator symbol. For instance "one million" could be written "1,000,000.00". The character used to separate thousands and the fractional part are defined inside the C locale. Programs in the gbutils package can automatically recognize the locale settings and process these entries accordingly. Please use "gbenv" to see the definitions in use. Changing the locale typically amount simply to the redefinition of the LANG environment variable ,---- | # export LANG="en_US" `---- A list of the available locale can be obtained with the locale program ,---- | # locale -a `---- and the actual setting verified with ,---- | # locale `---- For more details refer to the locale documentation. 2.4 Output format and precision ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In general, the output from the different programs is made of newline separated records of space separated fields of standard ASCII characters, which represent floating point numbers. The default format is scientific notation with a precision of six digits. The format and the precision can be changed using the environment variable `GB_OUT_FLOAT_FORMAT'. This variable can be set to any `printf' (the standard library C function) meaningful string. For instance with ,---- | # export GB_OUT_FLOAT_FORMAT="%.8e" `---- the precision is extended to eight digit. While with ,---- | # export GB_OUT_FLOAT_FORMAT="%.fe" `---- the scientific notation is replaced with a fixed-point notation. Please, refer to the `printf' documentation for further details. There is also a second variable, `GB_OUT_EMPTY_FORMAT', which can be used to tune the comment headings that many programs generate with the verbose option `-v'. Notice that it is automatically set to a value which is consistent with the float format chosen, so in general it is a good idea not to change it explicitly. 3 Numerical Error handling ========================== The default behaviour of Gnu Scientific Library functions is to abort the execution of the program if a numeric error is produced. Some of these errors, especially underflow errors, are tolerable inside a computation. The 'gbutils' package provides a way of switching off the GSL error handling. It is sufficient to set the environment variable `GB_ERROR_HANDLER_OFF' using ,---- | # export GB_ERROR_HANDLER_OFF= `---- and all the programs will ignore numerical errors. This feature must be used carefully, after checking that the loss of precision implied by the presence of these errors can be considered tolerable for the actual computation one wants to perform. The default behaviour can be recovered using ,---- | # unset GB_ERROR_HANDLER_OFF `---- 4 Binary format =============== /THIS IS AN EXPERIMENTAL FEATURE/ Like ASCII files, the binary files are structured as sequences of separate blocks. Each block is made of - one `size_t' with number of columns C - C `size_t' with the length of the rows, R_1 ... R_C - the data stored sequentially column by column, for a total number equal to R_1+R_2+...+R_C This structure allows the storage of non matrices structures in binary format. If lengths are different, the missing values are replaced with NANs. This mimic the behaviour of ASCII data handling. Notice that blocks are simply written one after the other. No particular separators are inserted between them. Implementation: the option `-b' redefines the function used to read and/or write data. This feature has been implemented for `gbget', `gbmstat' and `gbfun'. 5 Graphic output ================ As previously mentioned, the output of many programs in the gbutils package, like gbhisto or gbker, is intended to be plotted and not directly read from the terminal. It is generally composed of records and fields of standard ASCII characters. This type of output can be displayed using the various plotting utilities commonly available in Unix systems. We shortly review below three possibilities. 5.1 GNU plotutils package ~~~~~~~~~~~~~~~~~~~~~~~~~ The plotutils package can be found [here]. It contains the program `graph' which generate a plot starting from input data. For example to obtain a plot of the kernel density of the data in file `datafile.dat' one can use ,---- | gbker < datafile.dat | graph -T x `---- where `-T x' choose an xwindow as output device. [here] http://www.gnu.org/software/plotutils/ 5.2 Gnuplot interactive session ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ An alternative is to use the powerful plotting environment provided by gnuplot. The program can be found [here]. From inside a gnuplot session, the previous kernel density can be obtained with ,---- | plot "< gbker < datafile.dat " `---- see Gnuplot documentations for details. [here] http://www.gnuplot.info/ 5.3 Gnuplot's plot from command line ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As the example above shows, in order to directly plot the output of a command, inside gnuplot you need to put it inside a special string delimited by ~"<~ and ~"~. Moreover, all double quotes symbols ~"~ have to be escaped. These requirements can lead to cumbersome expressions when complicated commands are necessary. In any case, starting an interactive gnuplot session and writing the expression whose output should be plotted doesn't seem so attractive when one needs fast, simple plotting, for exploratory purposes. For these case the command `gbplot' is provided. This is a shell script that accept the data to be plotted as input and the directive on how to plot it on the command line. The basic usage is as follows ,---- | gbplot [options] [plot|splot] < datafile `---- or ,---- | command pipe | gbplot [options] [plot|splot] `---- The command `plot' or `splot' are required. One can provide further plotting options by inserting them after these command. For example one can plot the kernel density estimate using ,---- | gbker < datafile.dat | gbplot plot `---- In this way the density is plotted using simple points. To use the fancier gnuplot's 'histeps' style use instead ,---- | gbker < datafile.dat | gbplot plot with histeps `---- The syntax of the plotting options is exactly the same that would be used inside gnuplot, after a the `plot' or `splot' command. For instance to specify a range for the x values use ,---- | gbker < datafile.dat | gbplot plot '[-1:1]' with histeps `---- It is also possible to obtain multiple plots of the data using the gnuplot special file name '""', as in ,---- | gbker < datafile.dat | gbplot plot 'w p , "" w l' `---- This command draws the kernel estimate two times: the first with points, the second with a line (as specified by the `w l' expression). `gbplot' also possesses several options. They must be specified before the `plot' or `splot' command. To insert a title in the plot use the option `-t' ,---- | gbker < datafile.dat | gbplot -t Title plot with histeps `---- Terminal type and output file can be specified with the `-T' and `-o' options respectively. The command ,---- | gbker < datafile.dat | gbplot -T pdf -o output.pdf plot with histeps `---- produce a pdf version fo the plot and save it in 'output.pdf'. Finally, if an interactive manipulation of plot parameters or data is required, you can use the option `-i'. This option opens an interactive gnuplot session, allowing for direct manipulation of plot settings and parameters ,---- | gbker < datafile.dat | gbplot -i plot with histeps `---- Once the session is closed, the output is saved in a file using a specific terminal if options `-o' and `-T' have been specified. 6 Programs summary table ======================== Name Input Type External lib NAN ----------------------------------------------- gbget c+ (matheval) * gbfun c+ matheval gbgrid no matheval gbrand no gsl gbboot s,t,c+ (gsl) gbenv no gbmave s,t * gbinterp c,2 gsl gbfilternear c+ gbdist s,t * gbstat s,t * gbquant s,t,c+ * gbhisto s,t gbker s gsl gbnear s gbhisto2d c2 gbgcorr s gbacorr c1,c2 gbxcorr c2 gbker2d c2 gbbin c+ gbtest c+ (gsl) * gbmodes s gsl gbbin t gbkreg c2 gsl gbkreg2d c3 gblreg c2 gsl gbglreg c+ gsl gbnlreg c+ gsl,matheval gbnlqreg c+ gsl,matheval gbhill s gsl gbnlmult c+ gsl,matheval gbnlprobit c+ gsl,matheval gbnlpanel c+ gsl,matheval * gbnlpolyit c+ gsl,matheval *Input Type*: 's' sequential; 't' tabular; 'c' compounded c2 read couples, c3 triplets, c+ a variable number of columns; 'no' no input required *External libs*: gsl: Gnu Scientific Library matheval: GNU matheval library () means optional dependence (special features are available only if the library is found) *NAN*: program automatically ignores NAN values in computations gbutils-5.7.1/doc/intro.txt0000644000175000017500000002002212573765610012567 00000000000000 â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” GBUTILS: COMMAND LINE ECONOMETRICS Giulio Bottazzi â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” Table of Contents ───────────────── 1 Getting Started .. 1.1 Requirements .. 1.2 Installation instructions 2 Documentation 3 Contributors 1 Getting Started â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â• This is a brief description of `gbutils' (version 5.6), a set of command line utilities for the manipulation and statistical analysis of data. These utilities read data from standard input in an ASCII format and print the result in ASCII format to standard output. See the [overview] for more details and join [gbutils google group] for discussion and news. In many cases, the output of the utilities in the `gbutils' package is designed in a format suitable to be sent to other program, like the graph program in the [plotutils] package, for plotting. Alternatively, these utilities can be used inside an interactive [gnuplot] session, or inside a gnuplot script, with the help of the special datafile identifier `<' (see Gnuplot documentations for details). [file:./example1.png] [http://cafim.sssup.it/~giulio/software/gbutils/example1.png] Let us see some examples. Figure 1 reports empirical estimates of the density of $200$ realizations of a exponential power random variable, obtained using both a binned distribution and a kernel estimate. This picture can be obtained in terminal with the following list of commands: ╭──── │ gbrand -c 1 -r 200 gaussian 1 > data.txt │ │ gbker < data.txt >kernel.txt │ │ gbhisto -n 20 -M 2 histogram.txt │ │ gbplot -T 'png enhan crop' -o example1.png plot 'w histeps title "binned",\ │ "kernel.txt" w l title "kernel" ' < histogram.txt │ │ rm data.dat kernel.txt histogram.txt ╰──── In the first line, a sample of 200 observations is independently drawn form a Gaussian distribution with unit variance and saved in the file `data.txt'. The second line builds a kernel estimate of the density and the third an histograms. Results are saved in `kernel.txt' and `histogram.txt', respectively. The command in the fourth line, which continues on the fifth, generates the plot and save the result in `example1.png' and the last line remove all the intermediary files. [file:./example2.png] [http://cafim.sssup.it/~giulio/software/gbutils/example2.png] The second example, Figure 2, is a scatter plot of couples of points together with a non-linear kernel regression. ╭──── │ gbrand -c 2 -r 200 gaussian 1 | gbfun 'x1' 'x1+.5*x2' > data.txt │ │ gbkreg < data.txt > kernel_reg.txt │ │ gbplot -T 'png enhan crop' -o example2.png plot 'w p pt 5,\ │ "kernel_reg.txt" w l ' < data.txt │ │ rm kernel_reg.txt data.txt ╰──── The data generated in the first line and saved in `data.txt' are independent couples of correlated random variables. A kernel regression is performed in the second line. The third (and fourth) lines produce the plot. What these commands do is to generate random samples, somehow manipulate them, perform some model estimation and finally plot the result. All this in one line of code. Both pictures are generated using `gbplot', which is an interface to the [gnuplot] graphic program. To reproduce these pictures the latter must be installed in your system. Grasping all the details of the lines above is probably complicated. The baseline message, whoever, should be clear: even complicated analysis can be split in a sequence of very simple steps. The programs in `gbutils' are completely written in C. Some of them can make use of external libraries (see below). These programs have been tested (and repeatedly used for many years) on different Linux distributions and should compile and run, in principle, on any Unix platform. Other operating systems, most notably Windows, have been scarcely tested. These programs have been originally written for personal use. Even if today they are used by several persons, please consider that I maintain and develop them in my spare time. They are distributed under the [GPL license] in the hope they could be of help to other people, but without any implied warranty. Please report bugs to [mailto:gbutils@googlegroups.com] [overview] http://cafim.sssup.it/~giulio/software/gbutils/overview.html [gbutils google group] http://groups.google.com/group/gbutils [plotutils] https://www.gnu.org/software/plotutils/ [gnuplot] http://www.gnuplot.info/ [GPL license] file:COPYING 1.1 Requirements ──────────────── To be installed from source, the package requires a C compiler and the standard C library. Unix-like environment in which GNU auto-tools can work is required for automatic installation. Many programs in gbutils do not require ANY external library apart the standard ones. There are however exceptions. Several programs use the GNU Scientific Library (GSL) (version >= 1.6) and other the GNU matheval library (version >=1.0.1). See the "Programs summary table" for the list of dependencies of the different utilities. At install time, if a library is not found, the programs requiring it are omitted from the list of programs to be installed. If the zlib library is found on the system, all the utilities are compiled with the capability of reading gz-compressed input. For more information about GSL, including installation procedures on various platforms, check [here]. GNU matheval library is hosted [here] and zlib home page is [here]. Notice that, in principle, using programs linked with different libraries can produce slightly different outputs. [here] http://www.gnu.org/software/gsl/ [here] http://savannah.gnu.org [here] http://www.gzip.org/zlib/ 1.2 Installation instructions ───────────────────────────── On `Linux', the `gbutils' package can be either installed using a .deb package or from source. The first method is recommended. You can download the binaries executable for your system from the [Debian] or the [Ubuntu] repositories. Alternatively, the latest source code is available from the [cafed] repository. Check the `README' file in the distributed package for instruction about the installation from source. In `Windows', the `gbutils' package is installed in the `Cygwin' environment. Follow the instructions in the [cygwin installation] page. [Debian] https://packages.debian.org/unstable/main/gbutils [Ubuntu] http://packages.ubuntu.com/wily/gbutils [cafed] ftp://cafed.sssup.it/packages/ [cygwin installation] http://cafim.sssup.it/~giulio/software/gbutils/cygwin_install.html 2 Documentation â•â•â•â•â•â•â•â•â•â•â•â•â•â•â• For a brief survey of the package features see the [overview]. The `gbget' utility is the Swiss knife of the package, the main tool to perform data manipulation. Its special role commands a specific [gbget tutorial]. A brief summary of options and modes of operation can be obtained from all the programs using the command line option `-h' or `--help'. All the programs are accompanied by automatically generated manual pages which can be isnpected with the `man' Unix utility. [overview] http://cafim.sssup.it/~giulio/software/gbutils/overview.html [gbget tutorial] http://cafim.sssup.it/~giulio/software/gbutils/gbget.html 3 Contributors â•â•â•â•â•â•â•â•â•â•â•â•â•â• [Cees Diks], [Federico Tamagni], [Angelo Secchi] and Davide Pirino provided helpful suggestions and coding. 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Notice that comment lines (beginning with a "#" symbol) DO NOT count as empty lines for this purpose. col-range: the range of required columns, specified as `begin:end:skip'. Negative values are counted from the end. Default is `1:-1:1' i.e. all columns. If begin>end the columns are read in reversed order. rows-range: the range of required rows, specified as `begin:end:skip'. Negative values are counted from the end. Default is `1:-1:1' i.e. all rows. If begin>end the rows are read in reversed order. : is a list of single letter options that identify successive transformations to be applied to data. The list of options includes t: transpose the matrix f: flatten the data column-wise, reducing them to a single column l: take the log of all fields d: take the column-wise difference: substract column 1 from 2, column 2 from 3, etc. D: remove all lines containing at least one NAN entry z: remove the mean to each column Z: reduce each column to zscores, that is remove the mean and divide by the standard deviation 2 Examples ========== A few examples can help to understand the gbget syntax. Consider the file `test.dat' with the following content ,---- | 10 20 30 | 11 21 31 | 12 22 32 | 13 23 33 | 14 24 34 | | | 15 25 35 | 16 26 36 | 17 27 37 | 18 28 38 | 19 29 39 `---- i.e. two data blocks, separated by two blank lines, each made of three columns and 5 rows. This file should already exists in the `gbutils' source directory. If the support for [zlib] has been found on your system, in the examples below you can equivalently use the compressed file `test.dat.gz'. Now if you type: ,---- | # gbget 'test.dat[1](1:2)' `---- (the ~'~ are normally required to protect the content of the string from shell expansion) you obtain ,---- | 1.000000e+01 2.000000e+01 | 1.100000e+01 2.100000e+01 | 1.200000e+01 2.200000e+01 | 1.300000e+01 2.300000e+01 | 1.400000e+01 2.400000e+01 `---- i.e. the first two column `(1:2)' of the first datablock `[1]'. By default, the output is in scientific notation, but more on this below. If instead you type ,---- | # gbget 'test.dat[2](-2:,2:3)' `---- you obtain ,---- | 2.600000e+01 3.600000e+01 | 2.700000e+01 3.700000e+01 `---- i.e. the rows from 2 to 3 (included) of the last two columns of the second data block `[2]'. Notice that a negative entry in column or row specification means "count from the end". You can also print /each second column/ of the second block with ,---- | # gbget 'test.dat[2](::2)' `---- that gives you ,---- | 1.500000e+01 3.500000e+01 | 1.600000e+01 3.600000e+01 | 1.700000e+01 3.700000e+01 | 1.800000e+01 3.800000e+01 | 1.900000e+01 3.900000e+01 `---- or /each third row/ of the whole file ,---- | # gbget 'test.dat(,::3)' `---- to have ,---- | 1.000000e+01 2.000000e+01 3.000000e+01 | 1.300000e+01 2.300000e+01 3.300000e+01 | 1.600000e+01 2.600000e+01 3.600000e+01 | 1.900000e+01 2.900000e+01 3.900000e+01 `---- If the initial and final positions in a slice specification are reversed, the columns or the rows are printed in reverse order. For instance ,---- | # gbget 'test.dat(,3:1)' `---- gives ,---- | 1.200000e+01 2.200000e+01 3.200000e+01 | 1.100000e+01 2.100000e+01 3.100000e+01 | 1.000000e+01 2.000000e+01 3.000000e+01 `---- Several different transformations can be applied to the chosen /matrix/ of data. For instance, the matrix can be flattened column-wise, i.e. each column can be put after the previous one in a single, long, column using the flag `f', or you can transpose it using the option `t'. Let see some examples. Consider ,---- | # gbget 'test.dat[1](,2:3)' `---- which gives ,---- | 1.100000e+01 2.100000e+01 3.100000e+01 | 1.200000e+01 2.200000e+01 3.200000e+01 `---- now you can flatten the output ,---- | # gbget 'test.dat[1](,2:3)f' `---- ,---- | 1.100000e+01 | 1.200000e+01 | 2.100000e+01 | 2.200000e+01 | 3.100000e+01 | 3.200000e+01 `---- or transpose it ,---- | # gbget 'test.dat[1](,2:3)t' `---- ,---- | 1.100000e+01 1.200000e+01 | 2.100000e+01 2.200000e+01 | 3.100000e+01 3.200000e+01 `---- Finally, the output format can be customized using options `-o' and `-e'. To fully exploit these options you need to know the syntax of the `printf' command implemented in the standard C libraries. However, the /type/ of output can be easily chosen with a single flag. If instead of the scientific notation you prefer a fixed point notation for your output, you have to use the option `-o' with the value `%f' ,---- | # gbget 'test.dat[1](,2:3)t' -o ' %f' `---- ,---- | 11.000000 12.000000 | 21.000000 22.000000 | 31.000000 32.000000 `---- while in order to have an /integer/ output you need `-o' with the value `%d' ,---- | # gbget 'test.dat[1](,2:3)t' -o ' %d' `---- ,---- | 11 12 | 21 22 | 31 32 `---- in this case, however, be aware of the truncations: gbget rounds a non-integer value down to the nearest integer! 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Å¡0EužÊäØ´;0Y³}aæ$˜ÅÀÙGÃ`v$ÌÉ€¹oÃ<Å2?DÃÂtXT‹UÄ%SºÑ{n‘#rÍ–ðÒÙÇL§}r>e¼¶´Ê5p°LÇ/_+nÂʧð…¾üÙ;´£è$òEÑ]¼)zˆž¢—Ùš)ܶ%Óý0ÄÔ endstream endobj startxref 105519 %%EOF gbutils-5.7.1/gbker.10000644000175000017500000000454113246755335011313 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBKER "1" "March 2018" "gbker 5.7.1" "User Commands" .SH NAME gbker \- Produce kernel density estimation .SH SYNOPSIS .B gbker [\fI\,options\/\fR] .SH DESCRIPTION Compute the kernel density estimation on an equispaced grid. Data are read from standard input. The kernel bandwidth, if not provided with the option \fB\-H\fR, is set automatically using simple heuristics. Normally the whole support is displayed. The option \fB\-w\fR can be used to restrict the computation to specific range of values. The method used to compute the density is set by the option \fB\-M\fR. The boundaries of the grid are slightly different in the different cases. .SH OPTIONS .TP \fB\-n\fR number of equispaced points/bins where the density is computed (default 64) .TP \fB\-w\fR set the grid boundaries with min,max (default whole support) .TP \fB\-H\fR set the kernel bandwidth .TP \fB\-S\fR scale the automatic kernel bandwidth .TP \fB\-K\fR choose the kernel to use (default 0) .TP 0 Epanenchnikov .TP 1 Rectangular .TP 2 Gaussian .TP 3 Laplacian .TP \fB\-M\fR choose the method for density computation (default 0) .TP 0 use FFT (# grid points rounded to nearest power of 2) .TP 1 use discrete convolution (compact kernels only; bins>2) .TP 2 explicit summation (boundaries excluded) .TP \fB\-F\fR specify the input fields separators (default " \et") .SH EXAMPLES .TP gbker \-n 128 \-K 2 < file use a Gaussian kernel to compute the density on 128 equispaced points with the FFT approach. All the data in 'file' are used, multiple columns are pooled. .TP gbker \-K 3 \-M 2 < file use a Laplacian kernel to compute the density on 64 points. The method used is the explicit summation. .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbhisto2d.10000644000175000017500000000403613246755334012104 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBHISTO2D "1" "March 2018" "gbhisto2d 5.7.1" "User Commands" .SH NAME gbhisto2d \- Produce 2D histogram from data .SH SYNOPSIS .B gbhisto2d [\fI\,options\/\fR] .SH DESCRIPTION 2D histogram on a regular grid. Data are read from standard input as couples (X,Y). If data are treated as continuous, equispaced bins are built and their center coordinates together with the required statistics are printed. For discrete variables, bins are built directly from the given variables. .SS "Options :" .TP \fB\-M\fR choose the statistics to print for each bin (default 0) .TP 0 occurrences number for continuous binned variables (*) .TP 1 relative frequency for continuous binned variables (*) .TP 2 empirical density for continuous binned variables (*) .TP 3 occurrences number for discrete symmetric variables (bins{X}=bins{Y}) .TP 4 relative frequency for discrete symmetric variables (bins{X}=bins{Y}) .TP 5 transition matrix (X=>Y) .TP 6 occurrences number for discrete asymmetric variables .TP 7 relative frequency for discrete asymmetric variables .TP \fB\-n\fR number of equispaced bins where the histogram is computed. For output marked with (*), use comma ',' to specify different values for x and y coordinates. (default 10) .TP \fB\-v\fR verbose mode .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbnlprobit.10000644000175000017500000000664313246755335012370 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBNLPROBIT "1" "March 2018" "gbnlprobit 5.7.1" "User Commands" .SH NAME gbnlprobit \- Non linear probit regression .SH SYNOPSIS .B gbnlprobit [\fI\,options\/\fR] \fI\,\/\fR .SH DESCRIPTION Non linear probit estimation. Minimize the negative log\-likelihood .IP sum_{i in N_0} log(1\-F(g(X_i))) + sum_{i in N_1} log(F(g(X_i))) .PP where N_0 and N_1 are the sets of 0 and 1 observations, g is a generic function of the independent variables and F is the normal CDF. It is also possible to minimize the score function .PP w_0 sum_{i in N_0} theta(F(g(X_i))\-t) + .IP w_1 sum_{i in N_1} theta(t\-F(g(X_i))) .PP where theta is the Heaviside function and t a threshold level. Weights w_0 and w_1 scale the contribution of the two subpopulations. The first column of data contains 0/1 entries. Successive columns are independent variables. The model is specified by a function g(x1,x2...) where x1,.. stands for the first,second .. N\-th column independent variables. .SS "options:" .TP \fB\-O\fR type of output (default 0) .TP 0 parameters .TP 1 parameters and errors .TP 2 and probabilities .TP 3 parameters and variance matrix .TP 4 marginal effects .TP \fB\-V\fR variance matrix estimation (default 0) .TP 0 .TP 1 < J^{\-1} > .TP 2 < H^{\-1} > .TP 3 < H^{\-1} J H^{\-1} > .TP \fB\-z\fR take zscore (not of 0/1 dummies) .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-v\fR verbosity level (default 0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .TP 3 covariance matrix .TP 4 minimization steps (default 10) .TP 5 model definition .TP \fB\-g\fR set number of point for global optimal threshold identification .TP \fB\-h\fR this help .TP \fB\-t\fR set threshold value (default 0) .TP 0 ignore threshold .TP (0,1) user provided threshold .TP 1 compute optimal only global .TP 2 compute optimal .TP \fB\-M\fR estimation method .TP 0 maximum likelihood .TP 1 min. score (w0=w1=1) .TP 2 min. score (w0=1/N0, w1=1/N1) .TP \fB\-A\fR MLL optimization options (default 0.01,0.1,100,1e\-6,1e\-6,5) fields are step,tol,iter,eps,msize,algo. Empty fields for default .TP step initial step size of the searching algorithm .TP tol line search tolerance iter: maximum number of iterations .TP eps gradient tolerance : stopping criteria ||gradient|| .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbboot.10000644000175000017500000000347613246755335011503 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBBOOT "1" "March 2018" "gbboot 5.7.1" "User Commands" .SH NAME gbboot \- Bootstrap user provided data .SH SYNOPSIS .B gbboot [\fI\,options\/\fR] .SH DESCRIPTION Data bootstrap. Random sampling with replacement or addition of noise. In the last case the option \fB\-n\fR set the number of times the entire sample is printed with a random noise added. The noise is uniformly distributed around zero with semi\-width equal to the minimal positive distance between consecutives observations times a fraction set with option .SH OPTIONS .TP \fB\-n\fR set the number of deviates (default =sample size) .TP \fB\-O\fR type of output (default 0): .TP 0 flatten input in one distribution .TP 1 multivariate output: rows with independent columns .TP 2 multivariate output: rows with fixed columns .TP 3 flatten and smoothed (EDF linear interpolation) .TP 4 add noise to data .TP 5 multivariate output: rows with fixed blocks .TP \fB\-e\fR noise term ( default .5) .TP \fB\-R\fR set the rng seed (default 0) .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR print this help .TP \fB\-v\fR verbose mode .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbmstat.10000644000175000017500000000256013246755335011661 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBMSTAT "1" "March 2018" "gbmstat 5.7.1" "User Commands" .SH NAME gbmstat \- Computing statistics in a moving windows .SH SYNOPSIS .B gbmstat [\fI\,options\/\fR] .SH DESCRIPTION Compute moving average. Data are read from standard input. .SH OPTIONS .TP \fB\-s\fR number of steps to average (default 10) .TP \fB\-t\fR compute average for each column of input .TP \fB\-D\fR ignore NAN entries in computation .TP \fB\-O\fR set the output, comma separated list of mean, adev, std, var, skew, kurt, min ,max and nonan (default mean). nonan works only with option \fB\-D\fR .TP \fB\-F\fR specify the input fields separators (default " \et") .TP \fB\-h\fR this help .SH AUTHOR Written by Giulio Bottazzi .SH "REPORTING BUGS" Report bugs to .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/tools.h0000644000175000017500000001454713152231511011434 00000000000000#ifndef GBUTILS_H #define GBUTILS_H 1 /*insert GNU extensions*/ #define _GNU_SOURCE /*in particular, use of NAN extension*/ /* gnulib headers */ #include "lib/getdelim.h" #include "lib/getline.h" #include "lib/getsubopt.h" #include "lib/strchrnul.h" /* -------------- */ #include #include #include #include #include #include #include #include #include #include #include #include #include "config.h" #if defined HAVE_LIBGSL #include #endif #if defined HAVE_LIBZ #include #include #include #endif #if defined HAVE_LIBZ #define GBFILEP gzFile #define GBGETLINE gzgetline #define GBFDOPEN gzdopen #define GBCLOSE gzclose #define GBTELL gztell #else #define GBFILEP FILE * #define GBGETLINE getline #define GBFDOPEN fdopen #define GBCLOSE fclose #define GBTELL ftell #endif /* used by */ /* extern int errno; */ extern char *GB_PROGNAME; void initialize_program(const char *); void finalize_program(); /* variables for reading command line options */ /* ------------------------------------------ */ /* extern char *optarg; */ /* extern int optind, opterr, optopt; */ /* ------------------------------------------ */ /* long options */ extern struct option gb_long_options[]; extern int gb_option_index; /* Output Management */ extern char *FLOAT,*FLOAT_SEP,*FLOAT_NL; extern char *EMPTY,*EMPTY_SEP,*EMPTY_NL; extern char *INT,*INT_SEP,*INT_NL; extern char *SEP,*NL; void printline(FILE *,double *,const size_t); void printmatrixbyrows(FILE *,double **,const size_t,const size_t); void printmatrixbycols(FILE *,double **,const size_t,const size_t); void printnomatrixbycols(FILE *,double **,size_t *,const size_t); void printcol(FILE *,double *,const size_t); void printmatrixbycols_bin(FILE *,double **,const size_t,const size_t); void printnomatrixbycols_bin(FILE *,double **,size_t *,const size_t); void printcol_bin(FILE *,double *,const size_t); /* ----------------- */ /* Input Management */ extern char GBTHSEP; extern char GBRADIX; double mystrtod(const char *); /* ---------------- */ void *my_alloc(size_t); void *my_calloc(size_t,size_t); void *my_realloc(void *,size_t); char *my_strndup(const char *, size_t); int blocks_count(long **,size_t *,int); /* Data loading functions */ int load(double **,size_t *,int,const char*); int loadtable(double ***,size_t *,size_t *,int,const char*); int loadblocks(double ****,size_t *,size_t **,size_t **,int,const char *); int loadtableblocks(double ****,size_t *,size_t *,size_t *,int, const char *); double ** readblock(size_t *,size_t *,unsigned *,GBFILEP, const char *); int load2(double ***,size_t *,int,const char*); int load3(double ***,size_t *,int,const char*); int load_bin(double **,size_t *,int); int loadtable_bin(double ***,size_t *,size_t *,int); int loadblocks_bin(double ****,size_t *,size_t **,size_t **,int); /* ---------------------- */ /* pointer to data printing function */ extern void (* PRINTMATRIXBYCOLS) (FILE *,double **,const size_t,const size_t); extern void (* PRINTMATRIXBYROWS) (FILE *,double **,const size_t,const size_t); extern void (* PRINTNOMATRIXBYCOLS) (FILE *,double **,size_t *,const size_t); extern void (* PRINTCOL) (FILE *,double *,const size_t); /* -------------------------------- */ int sort_by_value(const void *, const void *); void moment(const double [], const size_t, double *, double *, double *, double *, double *, double *, double *,double *); void moment_nonan(const double [], const size_t, double *, double *, double *, double *, double *, double *, double *, double *, double *); void moment_short(const double [], const size_t, double *, double *,double *,double *); void moment_short2(double **, const size_t, double *, double *,double *, double *,double *); void moment_short3(double **, const size_t, double *, double *,double *, double *,double *); void gbutils_header(char const *,FILE *); void moment_short_nonan(const double [], const size_t, double *, double *,double *,double *, double *); void denan(double **,size_t *); void denan_pairs(double **,double **,size_t *); void denan_data(double ***,size_t *,size_t *); int sort2(double *, double *, long int, long int); int sortn(double **, const long int, const long int, const long int, const long int); /* -------------------------------------- */ /* for cygwin portability */ #ifndef NAN #define NAN nan("") #endif /* -------------------------------------- */ /* getline if zlib present */ #if defined HAVE_LIBZ #define GETNLINE_NO_LIMIT ((size_t) -1) /* Read into a buffer *LINEPTR returned from malloc (or NULL), pointing to *LINESIZE bytes of space. Store the input bytes starting at *LINEPTR + OFFSET, and null-terminate them. Reallocate the buffer as necessary, but if NMAX is not GETNLINE_NO_LIMIT then do not allocate more than NMAX bytes; if the line is longer than that, read and discard the extra bytes. Stop reading after after the first occurrence of DELIM1 or DELIM2, whichever comes first; a delimiter equal to EOF stands for no delimiter. Read the input bytes from STREAM. Return the number of bytes read and stored at *LINEPTR + OFFSET (not including the NUL terminator), or -1 on error or EOF. */ extern ssize_t gzgetndelim2 (char **lineptr, size_t *linesize, size_t offset, size_t nmax, int delim1, int delim2, gzFile stream); extern ssize_t gzgetline (char **_lineptr, size_t *_linesize, gzFile _stream); extern ssize_t gzgetdelim (char **_lineptr, size_t *_linesize, int _delimiter, gzFile _stream); #endif /* utility functions for kernel analysis */ extern const double Krectangular_K2; extern const double Klaplace_K2; extern const double Kgauss_K2; extern const double Kepanechnikov_K2; double nearbyint(double); /* for some strange reason this seems required */ double Krectangular(double); double Krectangular_tilde(double); double Klaplace(double); double Klaplace_tilde(double); double Kgauss(double); double Kgauss_tilde(double); double Kepanechnikov(double); double Kepanechnikov_tilde(double); double Krectangular2d(double); double Kepanechnikov2d(double); double Ksilverman2d_1(double); double Ksilverman2d_2(double); /* end GBUTILS_H */ #endif gbutils-5.7.1/gbglreg.c0000644000175000017500000004465513246754720011723 00000000000000/* gbglreg (ver. 5.6) -- Estimate general linear regression model Copyright (C) 2005-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include #include #include #include #include #include #include void print_glinear_verbose(FILE *file,const int o_weight,const int o_model, const size_t N, const gsl_vector * coeffs, const gsl_matrix * cov,const double sumsq) { size_t i,j; const size_t K = coeffs->size; fprintf(file,"\n"); fprintf(file," ------------------------------------------------------------\n"); fprintf(stderr," number of observations = %zd\n",N); fprintf(stderr," number of indep. variables = %zd\n",K); fprintf(file,"\n"); fprintf(stderr," model (C[i]: i-th column; ci: i-th coefficient; eps: residual;):\n\n"); fprintf(stderr," C[%zd] ~ ",K+1); for(i=0;iSSR | ndf) = %e\n", gsl_cdf_chisq_Q (sumsq,(N-K))); } else{ fprintf(file," sum of weighted squared residual (WSSR) = %f\n",sumsq); fprintf(file," number degrees of freedom (ndf) = %zd\n",N-K); fprintf(file," sqrt(WSSR/ndf) = %f\n",sqrt(sumsq/(N-K))); fprintf(file," chi-square test P(>WSSR | ndf) = %e\n", gsl_cdf_chisq_Q (sumsq,(N-K))); } fprintf(file," ------------------------------------------------------------\n"); /* Normal approximation to chi^2 */ /* fprintf(file," chisq. test Q(ndf/2,WSSR/2) = %e\n", */ /* gsl_cdf_gaussian_Q(sumsq-(size-cov->size1),sqrt(2*(size-cov->size1)))); */ fprintf(file,"\n"); fprintf(file," ------------------------------------------------------------\n"); fprintf(file,"\n"); for(i=0;isize;j++) fprintf(file,"%+f ", gsl_matrix_get(cov,i,j)/sqrt(gsl_matrix_get(cov,i,i)*gsl_matrix_get(cov,j,j))); fprintf(file,"|\n"); } fprintf(file," ------------------------------------------------------------\n"); fprintf(file,"\n"); } void hccs(gsl_vector *Y,gsl_matrix *X,gsl_vector *C,gsl_matrix *COV,int o_covtype) { size_t i,j; const size_t rows = Y->size; const size_t columns = COV->size1+1; /* iS = (X^t X)^{-1} */ gsl_matrix * iS = gsl_matrix_alloc (columns-1,columns-1); /* RESidual */ gsl_vector * RES = gsl_vector_alloc (rows); /* HCCV - diagonal */ gsl_vector * PHI = gsl_vector_alloc (rows); {/* iS = (X^t X)^{-1} */ gsl_matrix * S = gsl_matrix_alloc (columns-1,columns-1); gsl_permutation * P = gsl_permutation_alloc (columns-1); int signum; /* S = X^t X */ gsl_blas_dgemm (CblasTrans,CblasNoTrans,1.0,X,X,0.0,S); /* iS = S^{-1} */ gsl_linalg_LU_decomp (S,P,&signum); gsl_linalg_LU_invert (S,P,iS); gsl_permutation_free(P); gsl_matrix_free(S); } /* RES=Y-XC */ gsl_vector_memcpy (RES,Y); gsl_blas_dgemv (CblasNoTrans,-1.0,X,C,1.0,RES); switch(o_covtype ){ case 1: /* PHI_i = RES_i^2 */ { gsl_vector_memcpy (PHI,RES); gsl_vector_mul (PHI,RES); } break; case 2: /* PHI_i = N/(N-K) RES_i^2 */ { gsl_vector_memcpy (PHI,RES); gsl_vector_mul (PHI,RES); gsl_vector_scale (PHI,rows/(rows-columns+1)); } break; case 3: /* PHI_i = RES_i^2/(1-H_i) */ { for(i=0;i4){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='w'){ /* set third columns as weight */ o_weight = 1; } else if(opt=='V'){ /* set the type of covariance */ o_covtype = atoi(optarg); if(o_covtype<0 || o_covtype>4){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_covtype); exit(-1); } } else if(opt=='v'){ /* set verbosity level*/ o_verbose = atoi(optarg); } else if(opt=='h'){ /* print short help */ fprintf(stdout,"General Linear regression. Data are read in columns (X_1 .. X_N Y). The\n"); fprintf(stdout,"last column contains the dependent observations. With option -w standard\n"); fprintf(stdout,"errors associated with the observations can be provided. In this case data\n"); fprintf(stdout,"are read as (X_1...X_N,Y,std(Y)).\n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -M the regression model (default 1)\n"); fprintf(stdout," 0 with estimated intercept\n"); fprintf(stdout," 1 with zero intercept \n"); fprintf(stdout," -w consider standard errors \n"); fprintf(stdout," -O the type of output (default 0) \n"); fprintf(stdout," 0 regression coefficients \n"); fprintf(stdout," 1 regression coefficients and errors\n"); fprintf(stdout," 2 x, fitted y, error on y, residual\n"); fprintf(stdout," 3 coefficients and variance matrix\n"); fprintf(stdout," 4 coefficients and explained variance\n"); fprintf(stdout," -V method to estimate variance matrix (default 0)\n"); fprintf(stdout," 0 ordinary least square estimator \n"); fprintf(stdout," 1 heteroscedastic consistent White estimator \n"); fprintf(stdout," 2 Hinkley adjusted White estimator \n"); fprintf(stdout," 3 Horn-Horn-Duncan adjusted White estimator \n"); fprintf(stdout," 4 jacknife estimator \n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just output \n"); fprintf(stdout," 1 commented headings \n"); fprintf(stdout," 2 model details \n"); fprintf(stdout," -h print this help \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); exit(0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtable(&data,&rows,&columns,0,splitstring); /* set the method to use and initialize variables */ N = rows; switch(o_model){ case 0: if(o_weight) K = columns-1; else K = columns; break; case 1: if(o_weight) K = columns-2; else K = columns-1; break; default: fprintf(stderr,"ERROR (%s): unrecognized model:%d\n", GB_PROGNAME,o_model); exit(-1); break; } /* allocate workspace */ WORK = gsl_multifit_linear_alloc (N,K); /* allocate imput variables */ X = gsl_matrix_alloc (N,K); Y = gsl_vector_alloc (N); /* allocate output variable */ C = gsl_vector_alloc (K); COV = gsl_matrix_alloc (K,K); /* allocate weights */ if(o_weight) W = gsl_vector_alloc (N); /* initialize input variables */ if(o_weight){ size_t i,j; for(i=0;i 0) hccs(Y,X,C,COV,o_covtype); /* ++++++++++++++++++++++++++++++++++++++++ */ /* in case, print verbose output */ if(o_verbose>1) print_glinear_verbose(stdout,o_weight,o_model,N,C,COV,CHISQ); /* ++++++++++++++++++++++++++++++++++++++++ */ /* output */ switch(o_output){ case 0: { size_t i; /* ++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;isize;i++){ char *label; int length; length = snprintf(NULL,0,"c%zd",i+1); label = (char *) my_alloc((length+1)*sizeof(char)); snprintf(label,length+1,"c%zd",i+1); fprintf(stdout,EMPTY_SEP,label); free(label); } fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++++++++++++++ */ for(i=0;isize;i++) fprintf(stdout,FLOAT_SEP,gsl_vector_get(C,i)); fprintf (stdout,"\n"); } break; case 1: { size_t i; /* ++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;isize;i++){ char *label; int length; length = snprintf(NULL,0,"c%zd",i+1); label = (char *) my_alloc((length+1)*sizeof(char)); snprintf(label,length+1,"c%zd",i+1); fprintf(stdout,EMPTY_SEP,label); free(label); fprintf(stdout,EMPTY_SEP,"+/-"); } fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++++++++++++++ */ for(i=0;isize;i++){ fprintf(stdout,FLOAT_SEP,gsl_vector_get(C,i)); fprintf(stdout,FLOAT_SEP,sqrt(gsl_matrix_get(COV,i,i))); } fprintf(stdout,"\n"); } break; case 2: { size_t i,j; gsl_vector * RES = gsl_vector_alloc (N); gsl_vector * YERR = gsl_vector_alloc (N); /* compute residuals: RES=Y-XC */ gsl_vector_memcpy (RES,Y); gsl_blas_dgemv (CblasNoTrans,-1.0,X,C,1.0,RES); /* compute errors YERR_n = \sum_{h,k} X_{n,h} X_{n,k} COV_{h,k} */ { gsl_matrix * MTMP = gsl_matrix_alloc (N,K); /* MTMP=X COV */ gsl_blas_dgemm (CblasNoTrans,CblasNoTrans,1.0,X,COV,0.0,MTMP); /* YERR_n = \sum_h MTMP_{n,h} X_{n,h} */ for(i=0;i0){ fprintf(stdout,"#"); for(i=0;isize;i++){ char *label; int length; length = snprintf(NULL,0,"c%zd",i+1); label = (char *) my_alloc((length+1)*sizeof(char)); snprintf(label,length+1,"c%zd",i+1); fprintf(stdout,EMPTY_SEP,label); free(label); fprintf(stdout,EMPTY_SEP,"+/-"); } fprintf(stdout,"\n"); for(i=0;isize;i++){ fprintf(stdout,FLOAT_SEP,gsl_vector_get(C,i)); fprintf(stdout,FLOAT_SEP,sqrt(gsl_matrix_get(COV,i,i))); } fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++++++++++++++ */ for(i=0;i0){ fprintf(stdout,"#"); for(i=0;isize;i++){ char *label; int length; length = snprintf(NULL,0,"c%zd",i+1); label = (char *) my_alloc((length+1)*sizeof(char)); snprintf(label,length+1,"c%zd",i+1); fprintf(stdout,EMPTY_SEP,label); free(label); } fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++++++++++++++ */ for(i=0;isize;i++) fprintf(stdout,FLOAT_SEP,gsl_vector_get(C,i)); fprintf(stdout,"\n\n\n"); for(i=0;isize;i++){ for(j=0;jsize;j++) fprintf(stdout,FLOAT_SEP,gsl_matrix_get(COV,i,j)); fprintf(stdout,"\n"); } } break; case 4: { size_t i,j; const double YVAR= gsl_stats_tss(Y->data,Y->stride,Y->size)/N; /* variance of dependent variable */ const double RESVAR=CHISQ/N; /* variance of residuals */ /* compute the varcovar matrix of regressors */ /* XVAR_{i,j} = 1/(N-1) \sum_n (X_{n,i}-X_i) (X_{n,j}-X_j) with X_i = 1/N \sum_n X_{n,i} */ gsl_matrix *XVAR = gsl_matrix_alloc (K,K); for (i = 0; i < XVAR->size1; i++) { for (j = 0; j <= i; j++) { const gsl_vector_view tmp1 = gsl_matrix_column (X, i); const gsl_vector_view tmp2 = gsl_matrix_column (X, j); const double dtmp1 = gsl_stats_covariance (tmp1.vector.data, tmp1.vector.stride, tmp2.vector.data, tmp2.vector.stride, tmp1.vector.size); gsl_matrix_set (XVAR, i, j, dtmp1); gsl_matrix_set (XVAR, j, i, dtmp1); } } /* ++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;isize;i++){ char *label; int length; length = snprintf(NULL,0,"c%zd",i+1); label = (char *) my_alloc((length+1)*sizeof(char)); snprintf(label,length+1,"c%zd",i+1); fprintf(stdout,EMPTY_SEP,label); free(label); fprintf(stdout,EMPTY_SEP,"expl.var"); } fprintf(stdout,"\n"); } /* ++++++++++++++++++++++++++++++++++++++++ */ for(i=0;isize;i++){ fprintf(stdout,FLOAT_SEP,gsl_vector_get(C,i)); fprintf(stdout,FLOAT_SEP,gsl_matrix_get(XVAR,i,i)*gsl_vector_get(C,i)*gsl_vector_get(C,i)/(YVAR-RESVAR)); } fprintf(stdout,"\n\n\n"); /* print the matrix of s-scores S_{i,j}= C_i C_j XVAR_{i,j}/(YVAR-RESVAR) */ for(i=0;isize1;i++){ for(j=0;jsize2;j++) fprintf(stdout,FLOAT_SEP,gsl_matrix_get(XVAR,i,j)*gsl_vector_get(C,i)*gsl_vector_get(C,j)/(YVAR-RESVAR)); fprintf(stdout,"\n"); } } break; } return 0; } gbutils-5.7.1/gbnlpanel.c0000644000175000017500000016051013246754720012241 00000000000000/* gbnlpanel (ver. 5.6) -- Non-linear panel regression Copyright (C) 2009-2018 Giulio Bottazzi and Davide Pirino This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include "matheval.h" #include "assert.h" #include "multimin.h" #include #include #include #include #include #include #include #include #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void ***ddf; } Function; struct objdata { Function * F; size_t rows; size_t columns; size_t blocks; double ***data; }; /* Fixed effect object function and variance-covariance */ /* ---------------------------------------------------- */ void obj_fixed_f (const size_t Pnum, const double *x,void *params,double *fval){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double values[Pnum+blocks]; size_t b,c,r; /* counter for blocks, columns and rows */ size_t nan=0; /* total number of nan */ double trend_f; // sum f over columns (time) and rows (realizations). /* set the parameters */ for(b=blocks;bf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ trend_f +=dtmp1; } else rnan++; } nan+=rnan; } trend_f/=(rows*columns-nan); *fval=0; for(r=0;rf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf+=dtmp1; } else rnan++; } for(c=0;cf,blocks+Pnum,F->argname,values)+trend_f-(rsf/(columns-rnan)); if(isfinite(dtmp1)) *fval+=pow(dtmp1,2); } } *fval/=(rows*columns-nan); } void obj_fixed_df (const size_t Pnum, const double *x,void *params,double *grad){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double values[Pnum+blocks]; size_t h,b,c,r; /* counter for parameters, blocks, columns and rows */ size_t nan=0; /* total number of nan */ double trend_f; // sum f over columns (time) and rows (realizations). double trend_df[Pnum]; // sum df over columns (time) and rows (realizations). /* set the parameters */ for(b=blocks;bf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ trend_f +=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); trend_df[h]+=dtmp2; } }else rnan++; } nan+=rnan; } trend_f/=(rows*columns-nan); for(h=0;hf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf+=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); rsdf[h]+=dtmp2; } } else rnan++; } for(c=0;cf,blocks+Pnum,F->argname,values)+trend_f-(rsf/(columns-rnan)); if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values)+trend_df[h]-(rsdf[h]/(columns-rnan)); grad[h]+=dtmp1*dtmp2; } } } } for(h=0;hF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double values[Pnum+blocks]; size_t h,b,c,r; /* counter for parameters, blocks, columns and rows */ double trend_f; // sum f over columns (time) and rows (realizations). double trend_df[Pnum]; // sum df over columns (time) and rows (realizations). size_t nan=0; /* initializing grad and trends */ trend_f=0; for(h=0;hf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ trend_f +=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); trend_df[h]+=dtmp2; } } else rnan++; } nan+=rnan; } trend_f/=(rows*columns-nan); for(h=0;hf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf+=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); rsdf[h]+=dtmp2; } } else rnan++; } for(c=0;cf,blocks+Pnum,F->argname,values)+trend_f-(rsf/(columns-rnan)); if(isfinite(dtmp1)){ *f+=pow(dtmp1,2); for(h=0;hdf)[h],blocks+Pnum,F->argname,values)+trend_df[h]-(rsdf[h]/(columns-rnan)); grad[h]+=dtmp1*dtmp2; } } } } *f/=(rows*columns-nan); for(h=0;hf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ trend_f +=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); trend_df[h]+=dtmp2; if(o_varcovar>0){ for(k=0;kddf)[h][k],blocks+Pnum,F->argname,values); trend_ddf[h][k]+=dtmp3; } } } } else rnan++; } nan+=rnan; } if(rows*columns-nan!=0){ trend_f/=(rows*columns-nan); for(h=0;hf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf += dtmp1; rsf2 += pow(dtmp1,2); } else{ rnan++; } } if(columns-rnan!=0) rsf /= (columns-rnan); fixed[r]=rsf-trend_f; for(c=0;cf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ sigma2 += pow(dtmp1-fixed[r],2); } } } if(rows*columns-nan!=0){ sigma2 /= (rows*columns-nan); sigma4 = pow(sigma2,2); } /* ----------------------------------------------------- */ switch(o_varcovar){ case 0: { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); for(r=0;rf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf+=dtmp1; for(k=0;kdf)[k],blocks+Pnum,F->argname,values); rsdf[k]+=dtmp2; } } else rnan++; } for(c=0;cf,blocks+Pnum,F->argname,values)+trend_f-(rsf/(columns-rnan)),2); if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values)+trend_df[h]-(rsdf[h]/(columns-rnan)); for(k=0;kdf)[k],blocks+Pnum,F->argname,values)+trend_df[k]-(rsdf[k]/(columns-rnan)); gsl_matrix_set(dJ,h,k,dtmp2*dtmp3); } } gsl_matrix_scale (dJ,dtmp1); gsl_matrix_add (J,dJ); } } } gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma4); } break; case 1: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); int signum; /* compute hessian */ for(r=0;rdf)[h],blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsdf[h]+=dtmp1; for(k=0;kddf)[h][k],blocks+Pnum,F->argname,values); rsddf[h][k]+=dtmp2; } } } } for(c=0;cf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values); for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double res=dtmp2*dtmp3; res+=dtmp1*dtmp4; res+=-dtmp2*rsdf[k]/(columns-rnan); res+=-2.0*dtmp1*rsddf[h][k]/(columns-rnan); res+=dtmp2*trend_df[k]; res+=2.0*dtmp1*trend_ddf[h][k]; gsl_matrix_set(dH,k,h,res); } } gsl_matrix_add (H,dH); } } } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2); } break; case 2: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ rsf+=dtmp1; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); rsdf[h]+=dtmp2; for(k=0;kddf)[h][k],blocks+Pnum,F->argname,values); rsddf[h][k]+=dtmp3; } } } else rnan++; } for(c=0;cf,blocks+Pnum,F->argname,values); if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values); double dtmph=evaluator_evaluate ((F->df)[h],blocks+Pnum,F->argname,values)+trend_df[h]-(rsdf[h]/(columns-rnan)); for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double dtmpk=dtmp3+trend_df[k]-(rsdf[k]/(columns-rnan)); double res=dtmp2*dtmp3; res+=dtmp1*dtmp4; res+=-dtmp2*rsdf[k]/(columns-rnan); res+=-2.0*dtmp1*rsddf[h][k]/(columns-rnan); res+=dtmp2*trend_df[k]; res+=2.0*dtmp1*trend_ddf[h][k]; gsl_matrix_set(dH,k,h,res); gsl_matrix_set(dJ,k,h,dtmph*dtmpk); } } gsl_matrix_scale(dJ,pow(dtmp1+trend_f-(rsf/(columns-rnan)),2.0)); gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } } } gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 3: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); /* compute information matrix */ for(r=0;rf,blocks+Pnum,F->argname,values)-fixed[r],2); if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values); for(k=0;kdf)[k],blocks+Pnum,F->argname,values); gsl_matrix_set(dJ,k,h,dtmp1*dtmp2*dtmp3); } } gsl_matrix_add (J,dJ); } } } gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma4); } break; case 4: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); int signum; /* compute hessian */ for(r=0;rf,blocks+Pnum,F->argname,values)-fixed[r]; if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values); for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate((F->ddf)[h][k],blocks+Pnum,F->argname,values); gsl_matrix_set(dH,k,h,dtmp2*dtmp3+dtmp1*dtmp4); } } gsl_matrix_add (H,dH); } } } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2); } break; case 5: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); /* compute Hessian and information matrix */ for(r=0;rf,blocks+Pnum,F->argname,values)-fixed[r]; if(isfinite(dtmp1)){ for(h=0;hdf)[h],blocks+Pnum,F->argname,values); for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate((F->ddf)[h][k],blocks+Pnum,F->argname,values); gsl_matrix_set(dH,k,h,dtmp2*dtmp3+dtmp1*dtmp4); gsl_matrix_set(dJ,k,h,pow(dtmp1,2)*dtmp2*dtmp3); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } } } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } /* ----------------------------------------------------- */ /* Random effect object function and variance-covariance */ /* ----------------------------------------------------- */ void obj_rand_f (const size_t Pnum, const double *x,void *params,double *fval){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double res=0; double values[Pnum+blocks]; size_t r,b,c; for(b=blocks;bf,blocks+Pnum,F->argname,values); if(isfinite(dtmp5)){ dtmp3 +=dtmp5; dtmp4 +=pow(dtmp5,2); } else{ rnan++; } } nan +=rnan; if (columns-rnan!=0) dtmp1+=dtmp4-pow(dtmp3,2)/(columns-rnan); dtmp2+=pow(dtmp3,2); } dtmp1/=(rows*(columns-1)-nan); dtmp2/=(rows*columns-nan); res=(columns-(double)(nan/rows)-1)*log(dtmp1)+log(dtmp2); res /=(2*(columns-(double)(nan/rows))); *fval=res; } void obj_rand_df (const size_t Pnum, const double *x,void *params,double *grad){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double values[Pnum+blocks]; double df1[Pnum],df2[Pnum]; size_t k,b,r,c; /* set the parameters */ for(b=blocks;bf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ rsf += dtmp1; rsf2 += dtmp1*dtmp1; for(k=0;kdf)[k],blocks+Pnum,F->argname,values); rsdf[k] += dtmp2; rsfdf[k] += dtmp1*dtmp2; } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kF; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; const size_t blocks=((struct objdata *)params)->blocks; double ***data=((struct objdata *)params)->data; double values[Pnum+blocks]; double df1[Pnum],df2[Pnum]; size_t k,r,b,c; double res; for(b=blocks;bf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ rsf += dtmp1; rsf2 += dtmp1*dtmp1; for(k=0;kdf)[k],blocks+Pnum,F->argname,values); rsdf[k] += dtmp2; rsfdf[k] += dtmp1*dtmp2; } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ rsf+=dtmp1; rsf2+=pow(dtmp1,2); } else rnan++; } nan+=rnan; if(columns-rnan>1){ sigma2c+=(pow(rsf,2)-rsf2)/(columns-1-rnan); sigma2+=rsf2-(pow(rsf,2)/(columns-rnan)); } } sigma2c/=(rows*columns-nan); sigma2/=(rows*(columns-1)-nan); size_t Tcorr=columns-(double)(nan/rows); double sigma4=pow(sigma2,2); double ratio=sigma2c/(Tcorr*sigma2c+sigma2); switch(o_varcovar){ case 0: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values)+ratio*rsdf[k]; for(h=0;hdf)[h],blocks+Pnum,F->argname,values)+ratio*rsdf[h]; double res=dtmp2*dtmp3; gsl_matrix_set(dJ,k,h,res); } } gsl_matrix_scale(dJ,pow(dtmp1,2)); gsl_matrix_add (J,dJ); } } } gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma4); } break; case 1: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); int signum; size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hddf)[h][k],blocks+Pnum,F->argname,values); } } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hdf)[h],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double res=dtmp2*dtmp3; res+=dtmp1*dtmp4; res+=ratio*dtmp3*rsdf[k]; res+=ratio*dtmp1*rsddf[h][k]; gsl_matrix_set (dH,k,h,res); } } gsl_matrix_add (H,dH); } } } gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2); } break; case 2: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hddf)[h][k],blocks+Pnum,F->argname,values); } } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp3=dtmp2+ratio*rsdf[k]; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); double dtmp5=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double dtmp6=dtmp4+ratio*rsdf[h]; double res=dtmp2*dtmp4; res+=dtmp1*dtmp5; res+=ratio*dtmp4*rsdf[k]; res+=ratio*dtmp1*rsddf[h][k]; gsl_matrix_set (dJ,k,h,dtmp3*dtmp6); gsl_matrix_set (dH,k,h,res); } } gsl_matrix_scale(dJ,pow(dtmp1,2)); gsl_matrix_add (J,dJ); gsl_matrix_add (H,dH); } } } gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 3: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values)-ratio*rsdf[k]; for(h=0;hdf)[h],blocks+Pnum,F->argname,values)-ratio*rsdf[h]; double res=dtmp2*dtmp3; gsl_matrix_set(dJ,k,h,res); } } gsl_matrix_scale(dJ,pow(dtmp1,2)); gsl_matrix_add (J,dJ); } } } gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma4); } break; case 4: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); int signum; size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hddf)[h][k],blocks+Pnum,F->argname,values); } } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hdf)[h],blocks+Pnum,F->argname,values); double dtmp4=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double res=dtmp2*dtmp3; res+=dtmp1*dtmp4; res+=-ratio*dtmp3*rsdf[k]; res+=-ratio*dtmp1*rsddf[h][k]; gsl_matrix_set (dH,k,h,res); } } gsl_matrix_add (H,dH); } } } gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); gsl_matrix_scale (covar,sigma2); } break; case 5: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); size_t h,k; for(r=0;rf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); for(h=0;hddf)[h][k],blocks+Pnum,F->argname,values); } } } } for(c=0;cf,blocks+Pnum,F->argname,values); if (isfinite(dtmp1)){ for(k=0;kdf)[k],blocks+Pnum,F->argname,values); double dtmp3=dtmp2-ratio*rsdf[k]; for(h=0;hdf)[h],blocks+Pnum,F->argname,values); double dtmp5=evaluator_evaluate ((F->ddf)[h][k],blocks+Pnum,F->argname,values); double dtmp6=dtmp4-ratio*rsdf[h]; double res=dtmp2*dtmp4; res+=dtmp1*dtmp5; res+=-ratio*dtmp4*rsdf[k]; res+=-ratio*dtmp1*rsddf[h][k]; gsl_matrix_set (dJ,k,h,dtmp3*dtmp6); gsl_matrix_set (dH,k,h,res); } } gsl_matrix_scale(dJ,pow(dtmp1,2)); gsl_matrix_add (J,dJ); gsl_matrix_add (H,dH); } } } gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } int main(int argc,char* argv[]){ int i; char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_varcovar=0; int o_model=0; /* data from sdtin */ size_t rows=0,columns=0,blocks=0; double ***vals=NULL; /* definition of the function */ Function F; char **Param=NULL; double *Pval=NULL; gsl_matrix *Pcovar=NULL; /* covariance matrix */ size_t Pnum=0; double llmin; /* object function parameters */ struct objdata param; /* minimization parameters */ /* step_size,tol,maxiter,epsabs,maxsize,method,verbosity */ struct multimin_params mmparams={0.1,0.1,500,1e-6,1e-6,0}; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"M:v:hF:O:V:e:A:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"\ Each row of a block represents a single process realization. Blocks\n\ are separated by two white spaces and represent model variables,\n\ columns represent the time variable. Input is read as\n\ (X1_[r,t],...,Xj_[r,t],... XN_[r,t]), where r is the row, t the\n\ column and j the block. The model assumes that\n\ \n\ FUN(X1_[r,t],...,XN_[t,t]) - c_r = e_{r,t}\n\ \n\ with e i.i.d and 'c' arow specific element, which can be fixed (fixed\n\ effect) or a normal random variable (random effect).\n\ \n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"\ Options:\n\ -M type of model (default 0)\n\ 0 fixed effects\n\ 1 random effects\n\ -O type of output (default 0)\n\ 0 parameters\n\ 1 parameters and errors\n\ 2 and panel statistics\n\ 3 parameters and variance matrix\n\ -V variance matrix estimation (default 0)\n\ 0 < J^{-1} >, computed via fully-reduced log-likelihood\n\ 1 < H^{-1} >, computed via fully-reduced log-likelihood\n\ 2 < H^{-1} J H^{-1} >, computed via fully-reduced log-likelihood\n\ 3 < J^{-1} >, computed via non-reduced log-likelihood\n\ 4 < H^{-1} >, computed via non-reduced log-likelihood\n\ 5 < H^{-1} J H^{-1} >, computed via non-reduced log-likelihood\n\ -v verbosity level (default 0)\n\ 0 just results\n\ 1 comment headers\n\ 2 summary statistics\n\ 3 covariance matrix\n\ 4 minimization steps\n\ 5 model definition\n\ -e minimization tolerance (default 1e-6)\n\ -F input fields separators (default \" \\t\")\n\ -h this help\n\ -A comma separated MLL optimization options:\n\ step,tol,iter,eps,msize,algo. Use empty fields for default.\n\ (default 0.1,0.01,500,1e-6,1e-6,0)\n\ step initial step size of the searching algorithm\n\ tol line search tolerance iter: maximum number of iterations\n\ eps gradient tolerance : stopping criteria ||gradient||0) mmparams.step_size=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.tol=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxiter=(unsigned) atoi(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.epsabs=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.maxsize=atof(stmp2); } if( (stmp2=strsep (&stmp1,",")) != NULL){ if(strlen(stmp2)>0) mmparams.method= (unsigned) atoi(stmp2); } free(stmp3); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='M'){ /* set the type of model */ o_model = atoi(optarg); if(o_model<0 || o_model>1){ fprintf(stderr,"ERROR (%s): model option '%d' not recognized. Try option -h.\n",GB_PROGNAME,o_model); exit(-1); } } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); if(o_output<0 || o_output>3){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>5){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='v'){ o_verbose = atoi(optarg); mmparams.verbosity=(o_verbose>2?o_verbose-2:0); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtableblocks(&vals,&blocks,&rows,&columns,0,splitstring); /* parse line for functions and variables specification */ if(optind == argc){ fprintf(stderr,"ERROR (%s): please provide a function to fit.\n", GB_PROGNAME); exit(-1); } for(i=optind;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ F.f = evaluator_create (stmp3); assert(F.f); } free(stmp3); } free(piece); } /* check that everything is correct */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } { size_t i,j; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; char **storedname=NULL; size_t storednum=0; /* retrive list of arguments and their number */ /* notice that storedname is not allocated */ { int argnum; evaluator_get_variables (F.f,&storedname,&argnum); storednum = (size_t) argnum; } /* check the definition of the function */ for(i=0;i0?atoi(stmp1+1):0); if(index>blocks){ fprintf(stderr,"ERROR (%s): block %zd not present in data\n", GB_PROGNAME,index); exit(-1); } } else { for(j=0;j0){ F.ddf = (void ***) my_alloc(Pnum*sizeof(void **)); for(i=0;i4){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Model [xi: i-th data row]:\n"); fprintf(stderr," %s-c ~ i.i.d. N(0,stdev) \n", evaluator_get_string (F.f) ); fprintf (stderr,"\n Parameters and initial conditions:\n"); for(i=0;i0){ fprintf (stderr,"\n Model second derivatives:\n"); for(i=0;i2){ size_t i,j; fprintf(stderr," variance matrix = "); switch(o_varcovar){ case 0: fprintf(stderr,"J^{-1} reduced\n"); break; case 1: fprintf(stderr,"H^{-1} reduced\n"); break; case 2: fprintf(stderr,"H^{-1} J H^{-1} reduced\n"); break; case 3: fprintf(stderr,"J^{-1} non-reduced\n"); break; case 4: fprintf(stderr,"H^{-1} non-reduced\n"); break; case 5: fprintf(stderr,"H^{-1} J H^{-1} non-reduced\n"); break; } fprintf(stderr,"\n"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stderr,"#"); for(i=0;i1){ sigma2c+=(pow(rsf,2)-rsf2)/(columns-1-rnan); sigma2+=rsf2-(pow(rsf,2)/(columns-rnan)); } } sigma2c/=(rows*columns-nan); sigma2/=(rows*(columns-1)-nan); if (o_verbose>0){ fprintf(stderr,EMPTY_SEP,"#sigma2c"); fprintf(stderr,EMPTY_NL,"#sigma2"); fprintf(stderr,FLOAT_SEP,sigma2c); fprintf(stderr,FLOAT_NL,sigma2); } fprintf(stdout,FLOAT_SEP,sigma2c); fprintf(stdout,FLOAT_NL,sigma2); } } break; case 3: { size_t i; /* +++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;i #endif int main(int argc,char* argv[]){ /* default options */ unsigned o_verbose=0; unsigned o_output=0; unsigned o_setn=0; /* default parameters values */ char *splitstring = strdup(" \t"); unsigned n=0; /* number of extractions */ unsigned seed=0; double noise=0.5; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ while((opt=getopt_long(argc,argv,"vhF:M:O:R:n:e:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ /* print short help */ fprintf(stdout,"Data bootstrap. Random sampling with replacement or addition\n"); fprintf(stdout,"of noise. In the last case the option -n set the number of\n"); fprintf(stdout,"times the entire sample is printed with a random noise added.\n"); fprintf(stdout,"The noise is uniformly distributed around zero with semi-width\n"); fprintf(stdout,"equal to the minimal positive distance between consecutives\n"); fprintf(stdout,"observations times a fraction set with option \n"); fprintf(stdout,"\nUsage: %s [options]\n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -n set the number of deviates (default =sample size) \n"); fprintf(stdout," -O type of output (default 0): \n"); fprintf(stdout," 0 flatten input in one distribution \n"); fprintf(stdout," 1 multivariate output: rows with independent columns \n"); fprintf(stdout," 2 multivariate output: rows with fixed columns \n"); fprintf(stdout," 3 flatten and smoothed (EDF linear interpolation) \n"); fprintf(stdout," 4 add noise to data \n"); fprintf(stdout," 5 multivariate output: rows with fixed blocks \n"); fprintf(stdout," -e noise term ( default .5) \n"); fprintf(stdout," -R set the rng seed (default 0) \n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\")\n"); fprintf(stdout," -h print this help\n"); fprintf(stdout," -v verbose mode\n"); exit(0); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='R'){ /*RNG seed*/ seed = (unsigned) atol(optarg); } else if(opt=='e'){ /*displacement length*/ noise = atof(optarg); } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); } else if(opt=='n'){ /*set the number of generated deviates*/ n=atoi(optarg); o_setn=1; } else if(opt=='v'){ /*set the verbose mode*/ o_verbose=1; } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* set the RNG */ srandom(seed); switch(o_output){ case 0: { unsigned j; double *data=NULL; size_t size; load(&data,&size,0,splitstring); /*+++++++++++++++++++++++++++++++++++++++*/ if(o_verbose) fprintf(stdout,"loaded %zd data\n",size); /*+++++++++++++++++++++++++++++++++++++++*/ if(!o_setn) n=size; for(j=0;j0 && dtmp1 < minskip[j] ) minskip[j] = dtmp1; } free(x); } if(!o_setn) n=1; for(i=0;i #include #include #include #include #include #include #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void ***ddf; } Function; struct objdata { Function * F; size_t rows; size_t columns; double theta; double **data; }; /* Quantile estimation */ /* ------------------- */ double qreg_obj_f (const gsl_vector * x, void *params){ const Function *F = ((struct objdata *)params)->F; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const double theta=((struct objdata *)params)->theta; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j; double sumL=0,sumR=0; /* set the parameter values */ for(i=columns;if,columns+pnum,F->argname,values); if(dtmp1>0) sumR += dtmp1; else sumL += -dtmp1; } sumL/=rows; sumR/=rows; return (1-theta)*sumL+theta*sumR; } /* compute variance-covariance matrix */ gsl_matrix *qreg_varcovar(const int o_varcovar, const gsl_multimin_fminimizer *s, const struct objdata params, const size_t Pnum) { size_t i,j,h; const size_t rows=((struct objdata )params).rows; const size_t columns=((struct objdata )params).columns; double **data=((struct objdata )params).data; const Function *F = ((struct objdata )params).F; const double theta=((struct objdata )params).theta; /* the minimized value */ const double a = s->fval; double values[columns+Pnum]; gsl_matrix *covar = gsl_matrix_alloc (Pnum,Pnum); switch(o_varcovar){ case 0: /* inverse "reduced Hessian", adapted following Numerical Recipes */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double df1,df2; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;idf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,theta*(1-theta)*df1*df2); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 1: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); gsl_matrix_set (dJ,j,h, df1*df2*(f>0 ? theta*theta : (1-theta)*(1-theta))); } } gsl_matrix_add (J,dJ); } /* invert information matrix; dJ store temporary LU decomp. */ gsl_matrix_memcpy (dJ,J); gsl_linalg_LU_decomp (dJ,P,&signum); gsl_linalg_LU_invert (dJ,P,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; case 2: /* H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,(f>0 ? theta: -(1-theta))*a*ddf +theta*(1-theta)*df1*df2); } } gsl_matrix_add (H,dH); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_permutation_free(P); } break; case 3: /* H^{-1} J H^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *H = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dH = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2,ddf; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute Hessian and information matrix */ for(i=0;if,columns+Pnum,F->argname,values); for(j=0;jdf)[j],columns+Pnum,F->argname,values); for(h=0;hdf)[h],columns+Pnum,F->argname,values); ddf = evaluator_evaluate ((F->ddf)[j][h],columns+Pnum,F->argname,values); gsl_matrix_set (dH,j,h,(f>0 ? theta: -(1-theta))*a*ddf +theta*(1-theta)*df1*df2); gsl_matrix_set (dJ,j,h, df1*df2*(f>0 ? theta*theta : (1-theta)*(1-theta))); } } gsl_matrix_add (H,dH); gsl_matrix_add (J,dJ); } /* invert Hessian; dH store temporary LU decomp. */ gsl_matrix_memcpy (dH,H); gsl_linalg_LU_decomp (dH,P,&signum); gsl_linalg_LU_invert (dH,P,H); /* dJ = H^{-1} J ; covar = dJ H^{-1} */ gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,H,J,0.0,dJ); gsl_blas_dgemm (CblasNoTrans, CblasNoTrans,1.0,dJ,H,0.0,covar); gsl_matrix_scale (covar,a*a); gsl_matrix_free(H); gsl_matrix_free(dH); gsl_matrix_free(J); gsl_matrix_free(dJ); gsl_permutation_free(P); } break; } return covar; } int main(int argc,char* argv[]){ int i; char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_varcovar=0; int o_method=0; /* data from sdtin */ size_t rows=0,columns=0; double **data=NULL; /* definition of the function */ Function F; char **Param=NULL; double *Pval=NULL; gsl_matrix *Pcovar=NULL; /* covariance matrix */ size_t Pnum=0; /* minimization tolerance */ double eps=1e-5; /* initial scaling of simplex size */ double init_scale =1.0; /* max number of steps */ size_t maxsteps = 500; /* quantile */ double theta=0.5; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"v:hF:O:V:e:q:M:s:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Non linear quantile regression. Read data in columns (X_1 .. X_N).\n"); fprintf(stdout,"The model is specified by a function f(x1,x2...) using variables names\n"); fprintf(stdout,"x1,x2,..,xN for the first, second .. N-th column of data. f(x1,x2,...)\n"); fprintf(stdout,"are assumed i.i.d.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -O type of output (default 0) \n"); fprintf(stdout," 0 parameters \n"); fprintf(stdout," 1 parameters and errors \n"); fprintf(stdout," 2 and residuals \n"); fprintf(stdout," 3 parameters and variance matrix \n"); fprintf(stdout," -V variance matrix estimation (default 0)\n"); fprintf(stdout," 0 \n"); fprintf(stdout," 1 < J^{-1} > \n"); fprintf(stdout," 2 < H^{-1} > \n"); fprintf(stdout," 3 < H^{-1} J H^{-1} > \n"); fprintf(stdout," -q set the quantile (default .5)\n"); fprintf(stdout," -M choose optimization method (default 0) \n"); fprintf(stdout," 0 Nelder-Mead Simplex o(N)\n"); fprintf(stdout," 1 Nelder-Mead Simplex o(N^2)\n"); fprintf(stdout," -s initial simplex scaling (default 1)\n"); fprintf(stdout," -e minimization tolerance (default 1e-5)\n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help \n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3 covariance matrix \n"); fprintf(stdout," 4 minimization steps \n"); fprintf(stdout," 5 model definition \n"); fprintf(stdout," -N max minimization steps (default 500) \n"); return(0); } else if(opt=='e'){ /* set the minimization tolerance */ eps = fabs(atof(optarg)); } else if(opt=='s'){ /* set the minimization tolerance */ init_scale = fabs(atof(optarg)); } else if(opt=='q'){ /* set the minimization tolerance */ theta = fabs(atof(optarg)); if(theta <= 0 || theta >=1){ fprintf(stderr,"ERROR (%s): quantile '%f' must be in (0.1). Try option -h.\n", GB_PROGNAME,theta); exit(-1); } } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='M'){ /*set the estimation method*/ o_method = atoi(optarg); if(o_method<0 || o_method>2){ fprintf(stderr,"ERROR (%s): method option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_method); exit(-1); } } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); if(o_output<0 || o_output>3){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>3){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='v'){ o_verbose = atoi(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtable(&data,&rows,&columns,0,splitstring); /* parse line for functions and variables specification */ if(optind == argc){ fprintf(stderr,"ERROR (%s): please provide a function to fit.\n", GB_PROGNAME); exit(-1); } for(i=optind;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ F.f = evaluator_create (stmp3); assert(F.f); } free(stmp3); } free(piece); } /* check that everything is correct */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } { size_t i,j; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; char **storedname=NULL; size_t storednum=0; /* retrive list of arguments and their number */ /* notice that storedname is not allocated */ { int argnum; evaluator_get_variables (F.f,&storedname,&argnum); storednum = (size_t) argnum; } /* check the definition of the function */ for(i=0;i0?atoi(stmp1+1):0); if(index>columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,index); exit(-1); } } else { for(j=0;j1){ F.ddf = (void ***) my_alloc(Pnum*sizeof(void **)); for(i=0;i4){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Model [xi: i-th data column]:\n"); fprintf(stderr," %s\n", evaluator_get_string (F.f) ); fprintf (stderr,"\n Parameters and initial conditions:\n"); for(i=0;i1){ fprintf (stderr,"\n Model second derivatives:\n"); for(i=0;idata)[j]=0.0; for(i=0;idata)[j] += (f>0 ? evaluator_evaluate (F.df[j],F.argnum,F.argname,values) : -evaluator_evaluate (F.df[j],F.argnum,F.argname,values))*init_scale; } } for(j=0;jdata)[j] = fabs((ss->data)[j]/rows); } /* initialize the solver */ s = gsl_multimin_fminimizer_alloc (T, Pnum); gsl_multimin_fminimizer_set (s, &obj, &x.vector, ss); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>3){ fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Stopping criteria: max{simplex size} < %g\n\n",eps); fprintf(stderr," Iter "); for(i=0;i3){ fprintf (stderr,"%3zd ",iter); for(i=0;ix, i)); fprintf (stderr," %8.5e %8.5e %s\n", s->fval,size,gsl_strerror (status)); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ status=gsl_multimin_test_size (size,eps); } while (status == GSL_CONTINUE && iter < maxsteps); if(iter == maxsteps) fprintf(stderr,"WARNING (%s): failed to converge after %zd iterations\n", GB_PROGNAME,maxsteps); /* store final values */ for(i=0;ix,i); /* build the covariance matrix */ Pcovar = qreg_varcovar(o_varcovar,s,param,Pnum); /* free space */ gsl_vector_free(ss); gsl_multimin_fminimizer_free (s); } /* output */ /* ------ */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>2){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," variance matrix = "); switch(o_varcovar){ case 0: fprintf(stderr,"\n"); break; case 1: fprintf(stderr,"J^{-1}\n"); break; case 2: fprintf(stderr,"H^{-1}\n"); break; case 3: fprintf(stderr,"H^{-1} J H^{-1}\n"); break; } fprintf(stderr,"\n"); for(i=0;i1) fprintf(stderr," ------------------------------------------------------------\n"); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ switch(o_output){ case 0: { size_t i; /* +++++++++++++++++++++++++++++++++++ */ if(o_verbose>0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stderr,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i0){ fprintf(stdout,"#"); for(i=0;i #include #include #include #include #include #include #include /* handy structure used in computation */ typedef struct function { void *f; size_t argnum; char **argname; void **df; void ***ddf; } Function; typedef struct system { Function *fun; size_t fnum; } System; struct objdata { System * S; size_t rows; size_t columns; double **data; }; /* Ordinary Least Squares estimation */ /* --------------------------------- */ int ols_obj_f (const gsl_vector * x, void *params, gsl_vector * f){ const System *S = ((struct objdata *)params)->S; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j,h; const size_t fnum =S->fnum; /* set the parameter values */ for(i=columns;ifun)[h]; for(i=0;iS; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j,h; const size_t fnum =S->fnum; for(i=columns;ifun)[h]; for(i=0;iS; const size_t rows=((struct objdata *)params)->rows; const size_t columns=((struct objdata *)params)->columns; double **data=((struct objdata *)params)->data; const size_t pnum = x->size; double values[pnum+columns]; size_t i,j,h; const size_t fnum =S->fnum; for(i=columns;ifnum ;h++){ Function F=(S->fun)[h]; for(i=0;iargname[j],values[j]); */ /* printf("-> %f\n",evaluator_evaluate (F->f,columns+pnum,F->argname,values)); */ for(j=0;jf),2.); const double sigma2 = sumsq/(rows-Pnum); gsl_matrix *covar = gsl_matrix_alloc (Pnum,Pnum); switch(o_varcovar){ case 0: /* inverse "reduced Hessian", as in Numerical Recipes */ { #ifdef GSL_VER_2 gsl_matrix *J = gsl_matrix_alloc(rows, Pnum); gsl_multifit_fdfsolver_jac(s, J); #else gsl_matrix *J = s->J; #endif gsl_multifit_covar (J, 1e-6, covar); gsl_matrix_scale (covar,sigma2); #ifdef GSL_VER_2 gsl_matrix_free (J); #endif } break; case 1: /* J^{-1} */ { gsl_permutation * P = gsl_permutation_alloc (Pnum); int signum; gsl_matrix *J = gsl_matrix_calloc (Pnum,Pnum); gsl_matrix *dJ = gsl_matrix_calloc (Pnum,Pnum); double f,df1,df2; double values[columns+Pnum]; /* fill with optimal parameter */ for(i=columns;ix,i-columns); /* compute information matrix */ for(k=0 ; kfnum ;k++){ Function F=(S->fun)[k]; for(i=0;ix,i-columns); /* compute Hessian */ for(k=0 ; kfnum ;k++){ Function F=(S->fun)[k]; for(i=0;ix,i-columns); /* compute Hessian and Jacobian */ for(k=0 ; kfnum ;k++){ Function F=(S->fun)[k]; for(i=0;ifnum; double values[columns+Pnum]; gsl_matrix *covar = gsl_matrix_calloc (fnum,fnum); gsl_matrix *dy = gsl_matrix_calloc (fnum,fnum); /* fill with optimal parameters */ for(i=columns;ix,i-columns); /* compute covariance matrix */ for(i=0;ifun)[h]; double f1 = evaluator_evaluate (F1.f,columns+Pnum,F1.argname,values); for(k=0 ; kfun)[k]; double f2 = evaluator_evaluate (F2.f,columns+Pnum,F2.argname,values); gsl_matrix_set (dy,h,k,f1*f2); } } gsl_matrix_add (covar,dy); } gsl_matrix_free(dy); gsl_matrix_scale (covar,1./sqrt(rows)); return covar; } int main(int argc,char* argv[]){ int i; char *splitstring = strdup(" \t"); int o_verbose=0; int o_output=0; int o_varcovar=0; /* data from sdtin */ size_t rows=0,columns=0; double **data=NULL; /* parameter of the function to minimize */ char **Param=NULL; double *Pval=NULL; gsl_matrix *Pcovar=NULL; /* parameters covariance matrix */ gsl_matrix *Rcovar=NULL; /* residuals covariance matrix */ size_t Pnum=0; /* definition of the system */ System S; /* minimization tolerance */ double eps=1e-5; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"v:hF:O:V:M:e:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Least square estimation of a system of j non linear equations. Read data \n"); fprintf(stdout,"in columns (X_1 .. X_N). The j-th equation is specified by a function f_j(x1,x2...)\n"); fprintf(stdout,"using variables names x1,x2,..,xN for the first, second...N-th column of data.\n"); fprintf(stdout,"The f_j(x1,x2,...)'s are assumed i.i.d. according to a multivariate gaussian\n"); fprintf(stdout,"distribution.\n"); fprintf(stdout,"\nUsage: %s [options] \n\n",argv[0]); fprintf(stdout,"Options:\n"); fprintf(stdout," -O type of output (default 0)\n"); fprintf(stdout," 0 parameters \n"); fprintf(stdout," 1 parameters and errors \n"); fprintf(stdout," 2 and residuals \n"); fprintf(stdout," 3 parameters and variance matrix\n"); fprintf(stdout," -V variance matrix estimation (default 0)\n"); fprintf(stdout," 0 \n"); fprintf(stdout," 1 < J^{-1} > \n"); fprintf(stdout," 2 < H^{-1} > \n"); fprintf(stdout," 3 < H^{-1} J H^{-1} > \n"); fprintf(stdout," -e minimization tolerance (default 1e-5)\n"); fprintf(stdout," -v verbosity level (default 0)\n"); fprintf(stdout," 0 just results \n"); fprintf(stdout," 1 comment headers \n"); fprintf(stdout," 2 summary statistics \n"); fprintf(stdout," 3 covariance matrix \n"); fprintf(stdout," 4 minimization steps \n"); fprintf(stdout," 5 model definition \n"); fprintf(stdout," -F input fields separators (default \" \\t\")\n"); fprintf(stdout," -h this help \n"); return(0); } else if(opt=='e'){ /* set the minimization tolerance */ eps = fabs(atof(optarg)); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } else if(opt=='O'){ /* set the type of output */ o_output = atoi(optarg); if(o_output<0 || o_output>3){ fprintf(stderr,"ERROR (%s): output option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_output); exit(-1); } } else if(opt=='V'){ /* set the type of covariance */ o_varcovar = atoi(optarg); if(o_varcovar<0 || o_varcovar>3){ fprintf(stderr,"ERROR (%s): variance option '%d' not recognized. Try option -h.\n", GB_PROGNAME,o_varcovar); exit(-1); } } else if(opt=='v'){ o_verbose = atoi(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtable(&data,&rows,&columns,0,splitstring); /* parse line for functions and variables specification */ /* ---------------------------------------------------- */ if(optind == argc){ fprintf(stderr,"ERROR (%s): please provide a system to fit.\n", GB_PROGNAME); exit(-1); } S.fnum=0; S.fun=NULL; for(i=optind;i0 && strlen(stmp4)>0){ Pnum++; Param=(char **) my_realloc((void *) Param,Pnum*sizeof(char *)); Pval=(double *) my_realloc((void *) Pval,Pnum*sizeof(double)); Param[Pnum-1] = strdup(stmp5); Pval[Pnum-1] = atof(stmp4); } } else{ /* allocate new function */ S.fnum++; S.fun = (Function *) my_realloc((void *) S.fun,S.fnum*sizeof(Function)); (S.fun[S.fnum-1]).f = evaluator_create (stmp3); assert((S.fun[S.fnum-1]).f); } free(stmp3); } free(piece); } /* ---------------------------------------------------- */ /* check functions definition and build new list */ /* -------------------------------------------- */ /* check that something to fit is provided */ if(Pnum==0){ fprintf(stderr,"ERROR (%s): please provide a parameter to estimate.\n", GB_PROGNAME); exit(-1); } /* check the definition of function */ { size_t i,j,h; char **NewParam=NULL; double *NewPval=NULL; size_t NewPnum=0; size_t totstorednum=0; char ***storedname= (char ***) my_alloc(S.fnum*sizeof(char **)); size_t *storednum = (size_t *) my_alloc(S.fnum*sizeof(size_t)); /* retrive list of arguments and their number */ /* notice that storedname is not allocated */ for(h=0;h0?atoi(stmp1+1):0); if(index>columns){ fprintf(stderr,"ERROR (%s): column %zd not present in data\n", GB_PROGNAME,index); exit(-1); } } else { for(j=0;jargnum=columns+Pnum; F->argname = (char **) my_alloc((columns+Pnum)*sizeof(char *)); for(i=0;iargname[i] = (char *) my_alloc((length+1)*sizeof(char)); snprintf(F->argname[i],length+1,"x%zd",i+1); } for(i=columns;iargname[i] = (char *) my_alloc((length+1)*sizeof(char)); snprintf(F->argname[i],length+1,"%s",Param[i-columns]); } /* define first order derivatives */ F->df = (void **) my_alloc(Pnum*sizeof(void *)); for(i=0;idf[i] = evaluator_derivative (F->f,Param[i]); assert(F->df[i]); } /* define second order derivatives */ if(o_varcovar>1){ F->ddf = (void ***) my_alloc(Pnum*sizeof(void **)); for(i=0;iddf[i] = (void **) my_alloc(Pnum*sizeof(void *)); for(i=0;iddf[i][j] = evaluator_derivative ((F->df)[i],Param[j]); assert((F->ddf)[i][j]); } } } } /* -------------------------------------------- */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print builded functions and variables */ if(o_verbose>4){ size_t i,j,h; fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Model [xi: i-th data column]:\n"); for(h=0;h1){ fprintf (stderr,"\n Model second derivatives:\n"); for(h=0;h3){ fprintf(stderr," ------------------------------------------------------------\n"); fprintf(stderr," Iter "); for(i=0;if); do { double f,g,df,dg; solver_status = gsl_multifit_fdfsolver_iterate (s); /* compute increment in object and estimates */ f=gsl_blas_dnrm2 (s->f); df=fabs(oldf-f); oldf=f; g=gsl_blas_dnrm2 (s->x); dg=gsl_blas_dnrm2 (s->dx); iter++; /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* print status */ if(o_verbose>3){ fprintf (stderr,"%3zd ",iter); for(i=0;ix, i)); fprintf (stderr," %8.5e %8.5e %8.5e %s\n", f,df,dg,gsl_strerror (solver_status)); } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* if (status) */ /* break; */ /* check condition on estimates precision */ if( estimate_status == GSL_CONTINUE && dg < eps && dg < eps*g ) estimate_status = GSL_SUCCESS; /* check condition of object convergence */ if( object_status == GSL_CONTINUE && df>0 && df < eps && df < eps*fabs(f) ) object_status = GSL_SUCCESS; } while ( (estimate_status == GSL_CONTINUE || object_status == GSL_CONTINUE) && iter < 500); /* store final values */ for(i=0;ix,i); /* build the parameters covariance matrix */ Pcovar = ols_varcovar(o_varcovar,s,param,Pnum); /* build the residuals covariance matrix */ Rcovar = res_varcovar(s,param,Pnum); /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>1){ size_t i,j,h; fprintf(stderr, " ------------------------------------------------------------\n"); fprintf(stderr," residuals variance-covariance structure \n\n"); for(i=0;isize1;i++){ fprintf(stderr," e_%zd = %+f | ", i+1, sqrt(gsl_matrix_get(Rcovar,i,i))); for(j=0;jsize2;j++) if(j != i) fprintf(stderr,"%+f ", gsl_matrix_get(Rcovar,i,j)/sqrt(gsl_matrix_get(Rcovar,i,i)*gsl_matrix_get(Rcovar,j,j))); else fprintf(stderr,"%+f ",1.0); fprintf(stderr,"|\n"); } for(i=0;isize1;i++){ const double sumsq=gsl_matrix_get(Rcovar,i,i)*sqrt(rows); size_t ndf = 0; char **storedname=NULL; int storednum=0; /* notice that storedname is not allocated */ evaluator_get_variables (S.fun[i].f,&storedname,&storednum); for(j=0;jSSR | ndf) = %e\n", gsl_cdf_chisq_Q (sumsq,ndf)); } } /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ /* free space */ gsl_multifit_fdfsolver_free (s); } /* output */ /* ------ */ /* ++++++++++++++++++++++++++++++++++++++++++++++++++++++ */ if(o_verbose>2){ size_t i,j; fprintf(stderr," ------------------------------------------------------------\n"); 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$(HELP2MAN) -n "Floating point locale, and gbutils settings" -N --output=$@ ./gbenv @H2M_TRUE@gbconvtable.1: gbconvtable @H2M_TRUE@ $(HELP2MAN) -n "Replace keys with values" -N --output=$@ ./gbconvtable #-------------------- # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: gbutils-5.7.1/gbhill.10000644000175000017500000000534413246755335011464 00000000000000.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3. .TH GBHILL "1" "March 2018" "gbhill 5.7.1" "User Commands" .SH NAME gbhill \- Hill Maximum Likelihhod estimation .SH SYNOPSIS .B gbhill [\fI\,options\/\fR] \fI\,\/\fR .SH DESCRIPTION Maximum Likelihood estimation of distribution based on extremal (tail) observations. The distributions included are: exponential, pareto1, pareto3, gaussian. Provide the name of the distribution and the initial values of the parameters in the command line. .SH OPTIONS .TP \fB\-O\fR type of output (default 0) .TP 0 parameters and min NLL .TP 1 parameters and errors .TP 2 the distribution function .TP 3 the density function .TP 4 transformed observations: uniform in [0.1] under the null .TP 5 Renyi residuals: iid uniform in [0.1] under the null .TP \fB\-M\fR method used (default 0) .TP 0 unconditional, upper tail .TP 1 threshold, upper tail .TP 2 unconditional, lower tail .TP 3 threshold, lower tail .TP \fB\-V\fR variance matrix estimation (default2) .TP 0 < J^{\-1} > .TP 1 < H^{\-1} > .TP 2 < H^{\-1} J H^{\-1} > .TP \fB\-v\fR verbosity level (default0) .TP 0 just results .TP 1 comment headers .TP 2 summary statistics .IP 3+ minimization steps .TP \fB\-a\fR print entire set for \fB\-O\fR 1,2 .TP \fB\-u\fR observations or threshold (default 1) .TP \fB\-F\fR input fields separators (default " \et") .TP \fB\-h\fR this help .TP \fB\-A\fR comma separated MLL optimization options step,tol,iter,eps,msize, algo. Use empty fields for default (default 0.01,0.01,100,1e\-6,1e\-6,5) .TP step initial step size of the searching algorithm .TP tol line search tolerance iter: maximum number of iterations .TP eps gradient tolerance : stopping criteria ||gradient|| .PP .br Package home page .SH COPYRIGHT Copyright \(co 2001\-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; .PP This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. gbutils-5.7.1/gbacorr.c0000644000175000017500000001724713246755012011721 00000000000000/* gbacorr (ver. 5.6) -- Compute auto/cross-correlation coefficients Copyright (C) 2014-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation; This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include "tools.h" #include int main(int argc,char* argv[]){ char *splitstring = strdup(" \t"); size_t rows=0,columns=0; double **vals=NULL,*res=NULL; size_t i; int tini=0,tfin=10; int t; /* OPTIONS */ int o_method=0; int o_confidence=0; double confidence=0.01; /* variables for reading command line options */ /* ------------------------------------------ */ int opt; /* ------------------------------------------ */ /* COMMAND LINE PROCESSING */ while((opt=getopt_long(argc,argv,"hF:M:t:p:",gb_long_options, &gb_option_index))!=EOF){ if(opt==0){ gbutils_header(argv[0],stdout); exit(0); } else if(opt=='?'){ fprintf(stderr,"option %c not recognized\n",optopt); return(-1); } else if(opt=='h'){ /*print help*/ fprintf(stdout,"Compute auto/cross-correlation coefficients\n\n"); fprintf(stdout,"Usage: %s [options]\n",argv[0]); fprintf(stdout," \n"); fprintf(stdout,"If the input is a single columns x_1...X_T, the autocorrelation function \n"); fprintf(stdout,"c(t) is printed, defined as \n"); fprintf(stdout," c_{t} = 1/(T-t-1) \\sum_i (x_i-m) (x_{i+t}-m) /s^2 \n"); fprintf(stdout,"where m is the sample average and s the standard deviation. \n"); fprintf(stdout,"With a second column y_1...y_T, the cross-correlation \n"); fprintf(stdout," c_{t} = 1/(T-t-1) \\sum_i (x_i-mx) (y_{i+t}-my) /(sx sy) \n"); fprintf(stdout,"is printed where mx and my are the average values of the two columns and sx\n"); fprintf(stdout,"and sy their standard deviations. With -M 1 it is mx=my=0, the st.dev. is\n"); fprintf(stdout,"computed accordingly and in the previous formula T-t-1 is replaced by T-t. \n"); fprintf(stdout,"The range of t is set by option -t. \n"); fprintf(stdout,"Options\n"); fprintf(stdout," -M choose the method (default '0'): \n"); fprintf(stdout," 0 auto/cross-correlation with mean removal, \n"); fprintf(stdout," 1 auto/cross-correlation without mean removal \n"); fprintf(stdout," -t set range of t (default '0,10'), accept negative integers\n"); fprintf(stdout," -p specify the confidence level in (0,1). Interval ac_low,ac_hi has a \n"); fprintf(stdout," probability 1-confidence to contain the true value. With this option\n"); fprintf(stdout," the output becomes: lag ac ac_low ac_hi.\n"); fprintf(stdout," -F specify the input fields separators (default \" \\t\") \n"); fprintf(stdout," -h this help\n"); fprintf(stdout,"Examples:\n"); fprintf(stdout," gbacorr -t 0,2 'file(1)' first three a.c. coeff. of the first data column\n"); fprintf(stdout," gbacorr -p 0.05 'file(1:2)' x-corr of the first two columns together with\n"); fprintf(stdout," their 5%% confidence intervals\n"); return(0); } else if(opt=='t'){ /*set the lead-lag range */ char *stmp1=strdup (optarg); char *stmp2; char *stmp3=stmp1; stmp2=strsep (&stmp1,","); if(strlen(stmp2)>0) tini=atoi(stmp2); if(stmp1 != NULL && strlen(stmp1)>0) tfin=atof(stmp1); free(stmp3); if(tini>tfin){ const int dtmp1 = tini; tini=tfin; tfin=dtmp1; } } else if(opt=='M'){ /*set the method to use*/ o_method = atoi(optarg); } else if(opt=='p'){ /*set the method to use*/ o_confidence =1; confidence = atof(optarg); } else if(opt=='F'){ /*set the fields separator string*/ free(splitstring); splitstring = strdup(optarg); } } /* END OF COMMAND LINE PROCESSING */ /* initialize global variables */ initialize_program(argv[0]); /* load data */ loadtable(&vals,&rows,&columns,0,splitstring); /* allocate space for the result */ res = (double *) my_alloc((tfin-tini+1)*sizeof(double)); /* compute the result */ if(o_method==0){/* with mean removal */ if(columns==1){ double ave,sdev,min,max; moment_short(vals[0],rows,&ave,&sdev,&min,&max); for(t=tini;t<=tfin;t++){ size_t startindex = (t<0 ? -t : 0); size_t endindex = (t>0 ? rows-t : rows); res[t-tini]=0.0; for(i=startindex;i < endindex ;i++) res[t-tini] += (vals[0][i]-ave)*(vals[0][i+t]-ave); res[t-tini] /= (endindex-startindex-1)*sdev*sdev; } } else{ double ave1,sdev1,min1,max1; double ave2,sdev2,min2,max2; moment_short(vals[0],rows,&ave1,&sdev1,&min1,&max1); moment_short(vals[1],rows,&ave2,&sdev2,&min2,&max2); for(t=tini;t<=tfin;t++){ size_t startindex = (t<0 ? -t : 0); size_t endindex = (t>0 ? rows-t : rows); res[t-tini]=0.0; for(i=startindex;i < endindex ;i++) res[t-tini] += (vals[0][i]-ave1)*(vals[1][i+t]-ave2); res[t-tini] /= (endindex-startindex-1)*sdev1*sdev2; } } } else if(o_method==1){/* without mean removal */ /* the mean is assumed known and the denominator of the unbiased estimator of standard deviation is #obs. instead of #obs. -1 */ if(columns==1){ double sdev=0; for(i=0;i < rows ;i++) sdev += vals[0][i]*vals[0][i]; sdev /= rows; for(t=tini;t<=tfin;t++){ size_t startindex = (t<0 ? -t : 0); size_t endindex = (t>0 ? rows-t : rows); res[t-tini]=0.0; for(i=startindex;i < endindex ;i++) res[t-tini] += vals[0][i]*vals[0][i+t]; res[t-tini] /= (endindex-startindex)*sdev*sdev; } } else{ double sdev1=0, sdev2=0; for(i=0;i < rows ;i++){ sdev1 += vals[0][i]*vals[0][i]; sdev2 += vals[1][i]*vals[1][i]; } sdev1 /= rows; sdev2 /= rows; for(t=tini;t<=tfin;t++){ size_t startindex = (t<0 ? -t : 0); size_t endindex = (t>0 ? rows-t : rows); res[t-tini]=0.0; for(i=startindex;i < endindex ;i++) res[t-tini] += vals[0][i]*vals[1][i+t]; res[t-tini] /= (endindex-startindex)*sdev1*sdev2; } } } else{/* unknown method */ fprintf(stderr,"ERROR (%s): unknown method; use option -h\n", GB_PROGNAME); exit(+1); } /* print the result */ if(o_confidence==0){ for(t=tini;t<=tfin;t++){ printf(INT_SEP,t); printf(FLOAT_NL,res[t-tini]); } } else if(o_confidence==1){ for(t=tini;t<=tfin;t++){ const double r = res[t-tini]; if(-1 < r && r <1 ){ const double z = 0.5*log((1+r)/(1-r)); /* Fisher transformation */ const double zlow=z-gsl_cdf_ugaussian_Qinv(confidence/2)/sqrt(rows-fabs(t)-4); const double zhi=z+gsl_cdf_ugaussian_Qinv(confidence/2)/sqrt(rows-fabs(t)-4); printf(INT_SEP,t); printf(FLOAT_SEP,(exp(2*zlow)-1)/(exp(2*zlow)+1)); printf(FLOAT_SEP,r); printf(FLOAT_NL,(exp(2*zhi)-1)/(exp(2*zhi)+1)); } else{ printf(INT_SEP,t); printf(FLOAT_SEP,r); printf(FLOAT_SEP,r); printf(FLOAT_NL,r); } } } return(0); }