nnet/0000755000175000017500000000000011475775705010500 5ustar mikemikennet/DESCRIPTION0000644000175000017500000000046611475772656012216 0ustar mikemikeName: nnet Version: 0.1.13 Date: 2010-12-02 Author: Michael Schmid Maintainer: Michael Schmid Title: Neural Networks Description: A feed forward multi-layer neural network. Depends: octave (>= 3.0.0) Autoload: yes License: GPL version 2 or later Url: http://octave.sf.net Url: http://octnnettb.sourceforge.net nnet/INDEX0000644000175000017500000000042611341022510011237 0ustar mikemikennet >> Neural Networks Creation newff Data preprocessing & postprocessing mapstd prestd poststd trastd Simulation sim Training train Transfer functions logsig purelin radbas tansig Utility dividerand ind2vec isposint min_max saveMLPStruct subset vec2indnnet/doc/0000755000175000017500000000000011475775705011245 5ustar mikemikennet/doc/latex/0000755000175000017500000000000011475775705012362 5ustar mikemikennet/doc/latex/users/0000755000175000017500000000000011475775705013523 5ustar mikemikennet/doc/latex/users/bibliography.tex0000644000175000017500000000150710566046463016713 0ustar mikemike% Preamble %\documentclass[a4paper]{report} %\usepackage[ngerman]{babel} %\usepackage[T1]{fontenc} %\usepackage[ansinew]{inputenc} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start text here!! %\begin{document} \begin{thebibliography}{XXXXXXX} \bibitem [1]{1} John W. Eaton GNU Octave Manual, Edition 3, PDF-Version, February 1997 \bibitem [2]{2} The MathWorks, Inc. MATLAB Help, MATLAB Version 7.1 (R14SP3), Neural Network Toolbox Version 4.0.6 (R14SP3) \bibitem [3]{3} Christopher M. Bishop Neural Networks for Pattern Recognition, OXFORD University Press, Great Clarendon Streed, Oxford OX2 6DP, ISBN 0-19-853864-2, 2002 \bibitem [4]{4} Martin T. Hagen, Howard B. Demuth, Mark H. Beale NEURAL NETWORK DESIGN, PWS Publishing Company, 20 Park Plaza, Boston, MA 02116-4324, ISBN 053494332-2, 1996 \end{thebibliography} %\end{document}nnet/doc/latex/users/examples/0000755000175000017500000000000011475775705015341 5ustar mikemikennet/doc/latex/users/examples/1/0000755000175000017500000000000011475775705015501 5ustar mikemikennet/doc/latex/users/examples/1/MLP9_1_1.m_template0000644000175000017500000000624011054444505020715 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## Author: Michel D. Schmid ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. # The first 12 columns are the inputs # the last column is the output, # remove column 4, 8 and 12! # 89 rows. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 1; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\ {"tansig","purelin"},"trainlm","","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(:) = 1.5; MLPnet.b{2,1}(:) = 0.5; saveMLPStruct(MLPnet,"MLP3test.txt"); ## define validation data new, for matlab compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); [simOut] = sim(net,mTestInputN); simOut nnet/doc/latex/users/examples/1/1.tex0000644000175000017500000000764011073645625016361 0ustar mikemike\section{Example 1} You can find this example in the \textit{tests/MLP} directory of each release or from the subversion repository. I will do (more or less) a line by line walkthrough, so after this should be everything clear. I assume that you have some experience with multilayer perceptrons. \subsection{Introduction} Our problem can be solved with a monotonically increasing or decreasing surface. An input vector \textbf{p} (with 9 values) should be mapped onto one output value. Because we know that it can be solved with a monotonically increasing or decreasing surface, we can choose a 9-1-1 multi-layer perceptron (short: MLP). This means an MLP with 9 input neurons, only 1 hidden neuron and with 1 output neuron. \subsection{Code m-file} \input{examples/1/MLP9_1_1} \subsection{Walkthrough} Till line number 0023 there is realy nothing interesting.\\ On line 0023 \& 0024 data will be loaded. This data matrix contains 13 columns. Column 4, 8 and 12 won't be used (this is because the datas are of a real world problem). Column 13 contains the target values. So on the lines 0049 till 0051 this will be splittet into the corresponding peaces. A short repetition about the datas: Each line is a data set with 9 input values and one target value. On line 0038 and 0039 the datas are transposed. So we have now in each column one data set.\\ Now let's split the data matrix again in 3 pieces. The biggest part is for training the network. The second part for testing the trained network to be sure it's still possible to generalize with the net. And the third part, and the smallest one, for validate during training. This splitting happens on the lines 0041 till 0061.\\ Line 0063 is the first special command from this toolbox. This command will be used to pre-standardize the input datas. Do it ever! Non linear transfer functions will squash the whole input range to an small second range e.g. the transfer function \textit{tansig} will squash the datas between -1 and +1.\\ On line 0069 the next toolbox command will be used. This command \textit{min\_max} creates a $Rx2$ matrix of the complete input matrix. Don't ask me for what MATLAB(TM) this is using. I couldn't figure out it. One part is the number of input neurons, but for this, the range would not be needed. Who cares ;-)\\ Now it's time to create a structure which holds the informations about the neural network. The command \textbf{newff} can do it for us. See the complete line and actually, please use it only on this way, each other try will fail! This means, you can change the number of input neurons, the number of hidden neurons and the number of output neurons of course. But don't change the train algorithm or the performance function.\\ \textbf{saveMLPStruct} on line 0083 is a command which doesn't exist in MATLAB(TM). This will save the structure with the same informations you can see in MATLAB(TM) if you try to open the net-type.\\ The validation part on line 0086 \& 0087 is important. The naming convention is for MATLAB(TM) compatibility. For validate, you have to define a structure with the name \textbf{VV}. Inside this structure you have to define actually \textbf{VV.P} \& \textbf{VV.T} for validate inputs and validate targets. Bye the way, you have to pre-standardize them like the training input matrix. Use for this the command \textbf{trastd} like on line 0090.\\ \textbf{train} is the next toolbox command and of course one of the most important. Please also use this command like on line 0092. Nothing else will work.\\ The second last step is to standardize again datas. This time the test datas. See line 0096 for this and the last step. Simulate the network. This can be done with the command \textbf{sim}. This will be a critical part if someone else will write a toolbox with this command name!\\ I hope this short walkthrough will help for first steps. In next time, I will try to improve this documentation and of course, the toolbox commands. But time is realy rare. nnet/doc/latex/users/examples/1/mData.txt0000644000175000017500000000603710566046463017266 0ustar mikemike# Created by Octave 2.9.5, Wed May 24 10:33:36 2006 # name: mData # type: matrix # rows: 89 # columns: 13 306 286 2 0 12 61 2 0 3 28 4 0 2 368 188 1 0 6 49 0 0 3 37 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 492 98 0 0 7 23 0 0 13 36 0 0 2 506 64 0 0 32 23 0 0 13 29 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3 429 44 0 0 37 19 0 0 23 19 0 0 1 521 137 0 0 16 17 0 0 7 24 0 0 2 340 163 1 0 16 72 3 0 3 31 1 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 344 240 2 0 4 43 1 0 4 39 6 0 2 459 22 0 0 46 14 0 0 27 17 0 0 1 487 36 0 0 34 10 0 0 19 27 0 0 3 331 169 0 0 19 66 1 0 0 34 1 0 1 541 269 1 0 12 5 0 0 6 20 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 475 186 1 0 15 28 0 0 5 35 0 0 2 496 319 0 0 2 4 0 0 2 14 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 484 37 0 0 50 13 0 0 29 18 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 429 433 3 0 8 11 0 0 5 15 0 0 3 471 8 0 0 58 2 0 0 25 13 0 0 1 303 269 3 0 10 66 2 0 1 32 7 0 2 445 74 0 0 19 30 0 0 4 43 0 0 1 488 83 0 0 34 18 0 0 15 34 0 0 3 264 298 4 0 7 68 10 0 1 30 7 0 2 489 44 0 0 21 12 0 0 29 21 0 0 1 475 34 0 0 38 13 0 0 28 18 0 0 3 492 14 0 0 44 4 0 0 40 7 0 0 1 454 173 1 0 14 21 0 0 2 42 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 349 224 1 0 3 66 3 0 2 41 1 0 1 447 189 0 0 11 34 0 0 5 45 0 0 2 496 34 0 0 49 6 0 0 31 11 0 0 3 484 222 0 0 16 12 0 0 9 24 0 0 2 412 50 0 0 25 47 0 0 11 29 0 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 495 34 0 0 39 4 0 0 21 21 0 0 3 387 294 1 0 10 37 0 0 4 30 5 0 2 258 236 2 0 2 72 7 0 0 26 8 0 1 423 25 0 0 26 16 0 0 16 29 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 511 48 0 0 35 7 0 0 27 15 0 0 2 295 271 3 0 6 71 5 0 3 29 4 0 1 502 34 0 0 25 9 0 0 23 11 0 0 3 458 36 0 0 12 7 0 0 24 19 0 0 2 470 273 1 0 9 17 0 0 2 32 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 406 77 1 0 15 24 2 0 6 37 1 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 390 47 0 0 17 45 1 0 3 33 2 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 487 35 0 0 34 12 0 0 29 24 0 0 3 532 57 0 0 19 6 0 0 28 15 0 0 2 286 207 1 0 5 74 2 0 1 38 4 0 1 405 199 0 0 13 37 0 0 6 45 0 0 2 337 177 0 0 11 47 1 0 4 39 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 506 24 0 0 34 12 0 0 26 17 0 0 3 396 217 0 0 16 43 2 0 6 30 0 0 2 457 15 0 0 44 1 0 0 30 13 0 0 1 472 139 0 0 16 25 0 0 11 38 0 0 2 493 21 0 0 33 16 0 0 22 19 0 0 3 311 236 0 0 6 66 4 0 2 24 9 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 485 29 0 0 33 7 0 0 14 20 0 0 3 481 43 0 0 38 11 0 0 21 24 0 0 2 nnet/doc/latex/users/examples/1/MLP9_1_1.tex0000644000175000017500000002234310651413412017363 0ustar mikemike\noindent \ttfamily \hlstd{}\hlline{00001\ }\hlslc{\#\# Copyright (C) 2006 Michel D. Schmid}\hlstd{\ \ }\hlslc{$<$michaelschmid@users.sourceforge.net$>$}\\ \hlline{00002\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00003\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00004\ }\hlstd{}\hlslc{\#\# This program is free software; you can redistribute it and/or modify it}\\ \hlline{00005\ }\hlstd{}\hlslc{\#\# under the terms of the GNU General Public License as published by}\\ \hlline{00006\ }\hlstd{}\hlslc{\#\# the Free Software Foundation; either version 2, or (at your option)}\\ \hlline{00007\ }\hlstd{}\hlslc{\#\# any later version.}\\ \hlline{00008\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00009\ }\hlstd{}\hlslc{\#\# This program is distributed in the hope that it will be useful, but}\\ \hlline{00010\ }\hlstd{}\hlslc{\#\# WITHOUT ANY WARRANTY; without even the implied warranty of}\\ \hlline{00011\ }\hlstd{}\hlslc{\#\# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.}\hlstd{\ \ }\hlslc{See the GNU}\\ \hlline{00012\ }\hlstd{}\hlslc{\#\# General Public License for more details.}\\ \hlline{00013\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00014\ }\hlstd{}\hlslc{\#\# You should have received a copy of the GNU General Public License}\\ \hlline{00015\ }\hlstd{}\hlslc{\#\# along with this program; see the file COPYING.}\hlstd{\ \ }\hlslc{If not, write to the Free}\\ \hlline{00016\ }\hlstd{}\hlslc{\#\# Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA}\\ \hlline{00017\ }\hlstd{}\hlslc{\#\# 02110{-}1301, USA.}\\ \hlline{00018\ }\hlstd{}\\ \hlline{00019\ }\hlslc{\#\# Author: Michel D. Schmid}\\ \hlline{00020\ }\hlstd{}\\ \hlline{00021\ }\\ \hlline{00022\ }\hlslc{\#\# load data}\\ \hlline{00023\ }\hlstd{mData }\hlsym{= }\hlstd{}\hlkwc{load}\hlstd{}\hlsym{(}\hlstd{}\hlstr{"mData.txt"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mData"}\hlstd{}\hlsym{);}\\ \hlline{00024\ }\hlstd{mData }\hlsym{= }\hlstd{mData.mData}\hlsym{;}\\ \hlline{00025\ }\hlstd{}\hlsym{{[}}\hlstd{nRows}\hlsym{, }\hlstd{nColumns}\hlsym{{]} = }\hlstd{}\hlkwc{size}\hlstd{}\hlsym{(}\hlstd{mData}\hlsym{);}\\ \hlline{00026\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# this file contains 13 columns.}\\ \hlline{00027\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# The first 12 columns are the inputs}\\ \hlline{00028\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# the last column is the output,}\\ \hlline{00029\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# remove column 4, 8 and 12!}\\ \hlline{00030\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# 89 rows.}\\ \hlline{00031\ }\hlstd{\\ \hlline{00032\ }\\ \hlline{00033\ }mOutput }\hlsym{= }\hlstd{}\hlstd{mData}\hlstd{}\hlsym{(:,}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{);}\\ \hlline{00034\ }\hlstd{mInput }\hlsym{= }\hlstd{}\hlstd{mData}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{{-}}\hlstd{}\hlnum{1}\hlstd{}\hlsym{);}\\ \hlline{00035\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,{[}}\hlstd{}\hlnum{4 8 12}\hlstd{}\hlsym{{]}) = {[}{]}; }\hlstd{}\hlslc{\# delete column 4, 8 and 12}\\ \hlline{00036\ }\hlstd{}\\ \hlline{00037\ }\hlslc{\#\# now prepare data}\\ \hlline{00038\ }\hlstd{mInput }\hlsym{= }\hlstd{mInput}\hlstd{';}\\ \hlline{00039\ }\hlstd{mOutput = mOutput'}\hlstd{}\hlsym{;}\\ \hlline{00040\ }\hlstd{}\\ \hlline{00041\ }\hlslc{\# now split the data matrix in 3 pieces, train data, test data and validate data}\\ \hlline{00042\ }\hlstd{}\hlslc{\# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data}\\ \hlline{00043\ }\hlstd{}\hlslc{\# in this neural network we have 12 weights, for each weight at least 3 train sets..}\\ \hlline{00044\ }\hlstd{}\hlslc{\# (that's a rule of thumb like 1/2, 1/3 and 1/6)}\\ \hlline{00045\ }\hlstd{}\hlslc{\# 1/2 of 89 = 44.5; let's take 44 for training}\\ \hlline{00046\ }\hlstd{nTrainSets }\hlsym{= }\hlstd{}\hlkwc{floor}\hlstd{}\hlsym{(}\hlstd{nRows}\hlsym{/}\hlstd{}\hlnum{2}\hlstd{}\hlsym{);}\\ \hlline{00047\ }\hlstd{}\hlslc{\# now the rest of the sets are again 100\%}\\ \hlline{00048\ }\hlstd{}\hlslc{\# ==$>$ 2/3 for test sets and 1/3 for validate sets}\\ \hlline{00049\ }\hlstd{nTestSets }\hlsym{= (}\hlstd{nRows}\hlsym{{-}}\hlstd{nTrainSets}\hlsym{)/}\hlstd{}\hlnum{3}\hlstd{}\hlsym{{*}}\hlstd{}\hlnum{2}\hlstd{}\hlsym{;}\\ \hlline{00050\ }\hlstd{nValiSets }\hlsym{= }\hlstd{nRows}\hlsym{{-}}\hlstd{nTrainSets}\hlsym{{-}}\hlstd{nTestSets}\hlsym{;}\\ \hlline{00051\ }\hlstd{\\ \hlline{00052\ }mValiInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{);}\\ \hlline{00053\ }\hlstd{mValliOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{);}\\ \hlline{00054\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{) = {[}{]};}\\ \hlline{00055\ }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{) = {[}{]};}\\ \hlline{00056\ }\hlstd{mTestInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{);}\\ \hlline{00057\ }\hlstd{mTestOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{);}\\ \hlline{00058\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{) = {[}{]};}\\ \hlline{00059\ }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{) = {[}{]};}\\ \hlline{00060\ }\hlstd{mTrainInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTrainSets}\hlsym{);}\\ \hlline{00061\ }\hlstd{mTrainOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTrainSets}\hlsym{);}\\ \hlline{00062\ }\hlstd{}\\ \hlline{00063\ }\hlsym{{[}}\hlstd{mTrainInputN}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{{]} = }\hlstd{}\hlkwc{prestd}\hlstd{}\hlsym{(}\hlstd{mTrainInput}\hlsym{);}\hlstd{}\hlslc{\# standardize inputs}\\ \hlline{00064\ }\hlstd{}\\ \hlline{00065\ }\hlslc{\#\# comments: there is no reason to standardize the outputs because we have only}\\ \hlline{00066\ }\hlstd{}\hlslc{\# one output ...}\\ \hlline{00067\ }\hlstd{}\\ \hlline{00068\ }\hlslc{\# define the max and min inputs for each row}\\ \hlline{00069\ }\hlstd{mMinMaxElements }\hlsym{= }\hlstd{}\hlkwc{min\textunderscore max}\hlstd{}\hlsym{(}\hlstd{mTrainInputN}\hlsym{); }\hlstd{}\hlslc{\# input matrix with (R x 2)...}\\ \hlline{00070\ }\hlstd{}\\ \hlline{00071\ }\hlslc{\#\# define network}\\ \hlline{00072\ }\hlstd{nHiddenNeurons }\hlsym{= }\hlstd{}\hlnum{1}\hlstd{}\hlsym{;}\\ \hlline{00073\ }\hlstd{nOutputNeurons }\hlsym{= }\hlstd{}\hlnum{1}\hlstd{}\hlsym{;}\\ \hlline{00074\ }\hlstd{\\ \hlline{00075\ }MLPnet }\hlsym{= }\hlstd{}\hlkwc{newff}\hlstd{}\hlsym{(}\hlstd{mMinMaxElements}\hlsym{,{[}}\hlstd{nHiddenNeurons nOutputNeurons}\hlsym{{]},}\hlstd{$\backslash$\\ \hlline{00076\ }}\hlstd{\ \ \ \ \ \ \ \ }\hlstd{}\hlsym{\{}\hlstd{}\hlstr{"tansig"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"purelin"}\hlstd{}\hlsym{\},}\hlstd{}\hlstr{"trainlm"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{""}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mse"}\hlstd{}\hlsym{);}\\ \hlline{00077\ }\hlstd{}\hlslc{\#\# for test purpose, define weights by hand}\\ \hlline{00078\ }\hlstd{MLPnet.IW}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{1.5}\hlstd{}\hlsym{;}\\ \hlline{00079\ }\hlstd{MLPnet.LW}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{0.5}\hlstd{}\hlsym{;}\\ \hlline{00080\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{1.5}\hlstd{}\hlsym{;}\\ \hlline{00081\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{0.5}\hlstd{}\hlsym{;}\\ \hlline{00082\ }\hlstd{}\\ \hlline{00083\ }\hlkwc{saveMLPStruct}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{}\hlstr{"MLP3test.txt"}\hlstd{}\hlsym{);}\\ \hlline{00084\ }\hlstd{}\\ \hlline{00085\ }\hlslc{\#\# define validation data new, for matlab compatibility}\\ \hlline{00086\ }\hlstd{VV.P }\hlsym{= }\hlstd{mValiInput}\hlsym{;}\\ \hlline{00087\ }\hlstd{VV.T }\hlsym{= }\hlstd{mValliOutput}\hlsym{;}\\ \hlline{00088\ }\hlstd{}\\ \hlline{00089\ }\hlslc{\#\# standardize also the validate data}\\ \hlline{00090\ }\hlstd{VV.P }\hlsym{= }\hlstd{}\hlkwc{trastd}\hlstd{}\hlsym{(}\hlstd{VV.P}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{);}\\ \hlline{00091\ }\hlstd{}\\ \hlline{00092\ }\hlsym{{[}}\hlstd{net}\hlsym{{]} = }\hlstd{}\hlkwc{train}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{mTrainInputN}\hlsym{,}\hlstd{mTrainOutput}\hlsym{,{[}{]},{[}{]},}\hlstd{VV}\hlsym{);}\\ \hlline{00093\ }\hlstd{}\\ \hlline{00094\ }\hlslc{\# make preparations for net test and test MLPnet}\\ \hlline{00095\ }\hlstd{}\hlslc{\#}\hlstd{\ \ }\hlslc{standardise input \& output test data}\\ \hlline{00096\ }\hlstd{}\hlsym{{[}}\hlstd{mTestInputN}\hlsym{{]} = }\hlstd{}\hlkwc{trastd}\hlstd{}\hlsym{(}\hlstd{mTestInput}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{);}\\ \hlline{00097\ }\hlstd{}\\ \hlline{00098\ }\hlsym{{[}}\hlstd{simOut}\hlsym{{]} = }\hlstd{}\hlkwc{sim}\hlstd{}\hlsym{(}\hlstd{net}\hlsym{,}\hlstd{mTestInputN}\hlsym{);}\\ \hlline{00099\ }\hlstd{simOut}\\ \mbox{} \normalfontnnet/doc/latex/users/examples/examples.tex0000644000175000017500000000010011104334046017642 0ustar mikemike\chapter{Examples} \input{examples/1/1} \input{examples/2/2}nnet/doc/latex/users/examples/2/0000755000175000017500000000000011475775705015502 5ustar mikemikennet/doc/latex/users/examples/2/MLP9_1_1.m_template0000644000175000017500000000521711104334046020713 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## Author: Michel D. Schmid ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. # The first 12 columns are the inputs # the last column is the output, # remove column 4, 8 and 12! # 89 rows. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 mData = [mInput mOutput]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training [mTrain,mTest,mVali] = subset(mData',1); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrain(1:end-1,:));# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 1; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\ {"tansig","purelin"},"trainlm","","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(:) = 1.5; MLPnet.b{2,1}(:) = 0.5; saveMLPStruct(MLPnet,"MLP3test.txt"); ## define validation data new, for matlab compatibility VV.P = mVali(1:end-1,:); VV.T = mVali(end,:); ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); [net] = train(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV); # make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTest(1:end-1,:),cMeanInput,cStdInput); [simOut] = sim(net,mTestInputN); simOut nnet/doc/latex/users/examples/2/highlight.sty0000644000175000017500000000173411104334046020171 0ustar mikemike% Style definition file generated by highlight 2.6.0, http://www.andre-simon.de/ % Highlighting theme definition: \newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}} \newcommand{\hlnum}[1]{\textcolor[rgb]{0.5,0,0.5}{\bf{#1}}} \newcommand{\hlesc}[1]{\textcolor[rgb]{1,0,1}{\bf{#1}}} \newcommand{\hlstr}[1]{\textcolor[rgb]{0.65,0.52,0}{#1}} \newcommand{\hldstr}[1]{\textcolor[rgb]{0,0,1}{#1}} \newcommand{\hlslc}[1]{\textcolor[rgb]{0.95,0.47,0}{#1}} \newcommand{\hlcom}[1]{\textcolor[rgb]{1,0.5,0}{#1}} \newcommand{\hldir}[1]{\textcolor[rgb]{0,0.5,0.75}{\bf{#1}}} \newcommand{\hlsym}[1]{\textcolor[rgb]{1,0,0.5}{\bf{#1}}} \newcommand{\hlline}[1]{\textcolor[rgb]{0.19,0.19,0.19}{#1}} \newcommand{\hlkwa}[1]{\textcolor[rgb]{0.73,0.47,0.47}{\bf{#1}}} \newcommand{\hlkwb}[1]{\textcolor[rgb]{0.5,0.5,0.75}{\bf{#1}}} \newcommand{\hlkwc}[1]{\textcolor[rgb]{0,0.5,0.75}{#1}} \newcommand{\hlkwd}[1]{\textcolor[rgb]{0,0.27,0.4}{#1}} \definecolor{bgcolor}{rgb}{0.93,0.93,0.93} nnet/doc/latex/users/examples/2/2.tex0000644000175000017500000000320611104334046016340 0ustar mikemike\section{Example 2} You can find this example in this directory but renamed to \textit{MLP9\_1\_1.m\_template}. I will explain only differencies to the example1, so please read it first, if you haven't. \subsection{Introduction} Our problem can be solved with a monotonically increasing or decreasing surface. An input vector \textbf{p} (with 9 values) should be mapped onto one output value. Because we know that it can be solved with a monotonically increasing or decreasing surface, we can choose a 9-1-1 multi-layer perceptron (short: MLP). This means an MLP with 9 input neurons, only 1 hidden neuron and with 1 output neuron. \subsection{Code m-file} \input{examples/2/MLP9_1_1} \subsection{Walkthrough} The difference to the example1 starts below the line number 0035.\\ The difference concerns only the pre-processing section where the data set is splittet into the three subsets. This time, the command \textbf{subset} is used, which makes the complete example about 19 lines shorter! On line 0023 \& 0024 data will be loaded. This data matrix contains 13 columns. Column 4, 8 and 12 won't be used (this is because the datas are of a real world problem). Column 13 contains the target values.\\ Now on line 35 we have to merge the input and output targets again. Subset will take the complete matrix as argument! On line 42 happens the complete magic :-). Subset will return three subsets containing each time the input and output arguments. So this part must be splitet once more! But this is very easy and happens at some specific positions below.\\ That's it, \textbf{subset} will help you to write short scripts! nnet/doc/latex/users/examples/2/mData.txt0000644000175000017500000000617511104334046017254 0ustar mikemike# Created by Octave 2.9.5, Wed May 24 10:33:36 2006 # name: mData # type: matrix # rows: 89 # columns: 13 306 286 2 0 12 61 2 0 3 28 4 0 2 368 188 1 0 6 49 0 0 3 37 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 492 98 0 0 7 23 0 0 13 36 0 0 2 506 64 0 0 32 23 0 0 13 29 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3 429 44 0 0 37 19 0 0 23 19 0 0 1 521 137 0 0 16 17 0 0 7 24 0 0 2 340 163 1 0 16 72 3 0 3 31 1 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 344 240 2 0 4 43 1 0 4 39 6 0 2 459 22 0 0 46 14 0 0 27 17 0 0 1 487 36 0 0 34 10 0 0 19 27 0 0 3 331 169 0 0 19 66 1 0 0 34 1 0 1 541 269 1 0 12 5 0 0 6 20 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 475 186 1 0 15 28 0 0 5 35 0 0 2 496 319 0 0 2 4 0 0 2 14 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 484 37 0 0 50 13 0 0 29 18 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 429 433 3 0 8 11 0 0 5 15 0 0 3 471 8 0 0 58 2 0 0 25 13 0 0 1 303 269 3 0 10 66 2 0 1 32 7 0 2 445 74 0 0 19 30 0 0 4 43 0 0 1 488 83 0 0 34 18 0 0 15 34 0 0 3 264 298 4 0 7 68 10 0 1 30 7 0 2 489 44 0 0 21 12 0 0 29 21 0 0 1 475 34 0 0 38 13 0 0 28 18 0 0 3 492 14 0 0 44 4 0 0 40 7 0 0 1 454 173 1 0 14 21 0 0 2 42 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 349 224 1 0 3 66 3 0 2 41 1 0 1 447 189 0 0 11 34 0 0 5 45 0 0 2 496 34 0 0 49 6 0 0 31 11 0 0 3 484 222 0 0 16 12 0 0 9 24 0 0 2 412 50 0 0 25 47 0 0 11 29 0 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 495 34 0 0 39 4 0 0 21 21 0 0 3 387 294 1 0 10 37 0 0 4 30 5 0 2 258 236 2 0 2 72 7 0 0 26 8 0 1 423 25 0 0 26 16 0 0 16 29 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 511 48 0 0 35 7 0 0 27 15 0 0 2 295 271 3 0 6 71 5 0 3 29 4 0 1 502 34 0 0 25 9 0 0 23 11 0 0 3 458 36 0 0 12 7 0 0 24 19 0 0 2 470 273 1 0 9 17 0 0 2 32 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 406 77 1 0 15 24 2 0 6 37 1 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 390 47 0 0 17 45 1 0 3 33 2 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 487 35 0 0 34 12 0 0 29 24 0 0 3 532 57 0 0 19 6 0 0 28 15 0 0 2 286 207 1 0 5 74 2 0 1 38 4 0 1 405 199 0 0 13 37 0 0 6 45 0 0 2 337 177 0 0 11 47 1 0 4 39 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 506 24 0 0 34 12 0 0 26 17 0 0 3 396 217 0 0 16 43 2 0 6 30 0 0 2 457 15 0 0 44 1 0 0 30 13 0 0 1 472 139 0 0 16 25 0 0 11 38 0 0 2 493 21 0 0 33 16 0 0 22 19 0 0 3 311 236 0 0 6 66 4 0 2 24 9 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 485 29 0 0 33 7 0 0 14 20 0 0 3 481 43 0 0 38 11 0 0 21 24 0 0 2 nnet/doc/latex/users/examples/2/MLP9_1_1.tex0000644000175000017500000001415711104334046017367 0ustar mikemike\noindent \ttfamily \hlstd{}\hlline{00001\ }\hlslc{\#\# Copyright (C) 2008 Michel D. Schmid}\hlstd{\ \ }\hlslc{$<$michaelschmid@users.sourceforge.net$>$}\\ \hlline{00002\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00003\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00004\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00005\ }\hlstd{}\hlslc{\#\# This program is free software;you can redistribute it and/or modify it}\\ \hlline{00006\ }\hlstd{}\hlslc{\#\# under the terms of the GNU General Public License as published by}\\ \hlline{00007\ }\hlstd{}\hlslc{\#\# the Free Software Foundation; either version 2, or (at your option)}\\ \hlline{00008\ }\hlstd{}\hlslc{\#\# any later version.}\\ \hlline{00009\ }\hlstd{}\hlslc{\#\#}\\ \hlline{00010\ }\hlstd{}\hlslc{\#\# This program is distributed in the hope that it will be useful, but}\\ \hlline{00011\ }\hlstd{}\hlslc{\#\# WITHOUT ANY WARRANTY; without even the implied warranty of}\\ \hlline{00012\ }\hlstd{}\hlslc{\#\# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.}\hlstd{\ \ }\hlslc{See the GNU}\\ \hlline{00013\ }\hlstd{}\hlslc{\#\# General Public License for more details.}\\ \hlline{00014\ }\hlstd{}\hlslc{\#\#\\} \hlline{00015\ }\hlstd{}\hlslc{\#\# You should have received a copy of the GNU General Public License}\\ \hlline{00016\ }\hlstd{}\hlslc{\#\# along with this program; see the file COPYING.}\hlstd{\ \ }\hlslc{If not, see}\\ \hlline{00017\ }\hlstd{}\hlslc{\#\# http://www.gnu.org/licenses.}\\ \hlline{00018\ }\hlstd{}\\ \hlline{00019\ }\hlstd{}\hlslc{\#\# Author: Michel D. Schmid}\\ \hlline{00020\ }\hlstd{}\\ \hlline{00021\ }\hlstd{}\\ \hlline{00022\ }\hlstd{}\hlslc{\#\# load data}\\ \hlline{00023\ }\hlstd{mData }\hlsym{= }\hlstd{}\hlkwc{load}\hlstd{}\hlsym{(}\hlstd{}\hlstr{"mData.txt"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mData"}\hlstd{}\hlsym{);}\\ \hlline{00024\ }\hlstd{mData }\hlsym{= }\hlstd{mData.mData}\hlsym{;}\\ \hlline{00025\ }\hlstd{}\hlsym{{[}}\hlstd{nRows}\hlsym{, }\hlstd{nColumns}\hlsym{{]} = }\hlstd{}\hlkwa{size}\hlstd{}\hlsym{(}\hlstd{mData}\hlsym{);}\\ \hlline{00026\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# this file contains 13 columns.}\\ \hlline{00027\ }\hlstd{\ \ \ \ }\hlslc{\# The first 12 columns are the inputs}\\ \hlline{00028\ }\hlstd{\ \ \ \ }\hlslc{\# the last column is the output,}\\ \hlline{00029\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# remove column 4, 8 and 12!}\\ \hlline{00030\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# 89 rows.}\\ \hlline{00031\ }\\ \hlline{00032\ }\hlstd{mOutput }\hlsym{= }\hlstd{mData}\hlsym{(:,}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{);}\\ \hlline{00033\ }\hlstd{mInput }\hlsym{= }\hlstd{mData}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{{-}}\hlstd{}\hlnum{1}\hlstd{}\hlsym{);}\\ \hlline{00034\ }\hlstd{mInput}\hlsym{(:,{[}}\hlstd{}\hlnum{4 8 12}\hlstd{}\hlsym{{]}) = {[}{]}; }\hlslc{\# delete column 4, 8 and 12}\\ \hlline{00035\ }\hlstd{\\ \hlline{00036\ }mData }\hlsym{= {[}}\hlstd{mInput mOutput}\hlsym{{]};}\\ \hlline{00037\ }\hlstd{}\\ \hlline{00038\ }\hlstd{}\hlslc{\# now split the data matrix in 3 pieces, train data, test data and validate}\\ \hlline{00039\ }\hlstd{}\hlslc{data}\\ \hlline{00040\ }\hlstd{}\hlslc{\# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data}\\ \hlline{00041\ }\hlstd{}\hlslc{\# in this neural network we have 12 weights, for each weight at least 3}\\ \hlline{00042\ }\hlstd{}\hlslc{train sets..}\\ \hlline{00043\ }\hlstd{}\hlslc{\# that's a rule of thumb like 1/2, 1/3 and 1/6}\\ \hlline{00044\ }\hlslc{\# 1/2 of 89 = 44.5; let's take 44 for training}\\ \hlline{00045\ }\hlsym{{[}}\hlstd{mTrain}\hlsym{,}\hlstd{mTest}\hlsym{,}\hlstd{mVali}\hlsym{{]} = }\hlkwc{subset}\hlsym{(}\hlstd{mData',}\hlnum{1}\hlstd{);}\\ \hlline{00046\ }\hlstd{}\\ \hlline{00047\ }\hlstd{[mTrainInputN,cMeanInput,cStdInput] =\hlkwc{ prestd}(mTrain(\hlnum{1}:\hlkwa{end}-\hlnum{1},:));}\\ \hlline{00048\ }\hlstd{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }\hlslc{\#standardize inputs}\\ \hlline{00049\ }\hlstd{}\\ \hlline{00050\ }\hlslc{\#\# comments: there is no reason to standardize the outputs because we have}\\ \hlline{00051\ }\hlslc{only}\\ \hlline{00052\ }\hlslc{\# one output ...}\\ \hlline{00053\ }\hlstd{}\\ \hlline{00054\ }\hlstd{}\hlslc{\# define the max and min inputs for each row}\\ \hlline{00055\ }\hlstd{mMinMaxElements = \hlkwc{min\textunderscore max}(mTrainInputN);} \hlslc{\# input matrix with (R x 2)...}\\ \hlline{00056\ }\hlstd{}\\ \hlline{00057\ }\hlstd{}\hlslc{\#\# define network}\\ \hlline{00058\ }\hlstd{nHiddenNeurons = 1;}\\ \hlline{00059\ }\hlstd{nOutputNeurons = 1;}\\ \hlline{00060\ }\hlstd{}\\ \hlline{00061\ }\hlstd{MLPnet = \hlkwc{newff}(mMinMaxElements,[nHiddenNeurons nOutputNeurons],$\backslash$}\\ \hlline{00062\ }\hlstd{}\hlstd{\ \ \ \ \ \ \ \ }\hlstd{\{\hlstr{"tansig"},\hlstr{"purelin"}\},\hlstr{"trainlm"},\hlstr{""},\hlstr{"mse"});}\\ \hlline{00063\ }\hlslc{\#\# for test purpose, define weights by hand}\\ \hlline{00064\ }\hlstd{MLPnet.IW\{1,1\}(:) = 1.5;}\\ \hlline{00065\ }\hlstd{MLPnet.LW\{2,1\}(:) = 0.5;}\\ \hlline{00066\ }\hlstd{MLPnet.b\{1,1\}(:) = 1.5;}\\ \hlline{00067\ }\hlstd{MLPnet.b\{2,1\}(:) = 0.5;}\\ \hlline{00068\ }\hlstd{}\\ \hlline{00069\ }\hlkwc{saveMLPStruct}\hlstd{(MLPnet,"MLP3test.txt");}\\ \hlline{00070\ }\hlstd{}\\ \hlline{00071\ }\hlslc{\#\# define validation data new, for matlab compatibility}\\ \hlline{00072\ }\hlstd{VV.P = mVali(1:end{-}1,:);}\\ \hlline{00073\ }\hlstd{VV.T = mVali(end,:);}\\ \hlline{00074\ }\hlstd{}\\ \hlline{00075\ }\hlslc{\#\# standardize also the validate data}\\ \hlline{00076\ }\hlstd{VV.P = trastd(VV.P,cMeanInput,cStdInput);}\\ \hlline{00077\ }\hlstd{}\\ \hlline{00078\ }\hlstd{[net] = \hlkwc{train}(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV);}\\ \hlline{00079\ }\hlstd{}\\ \hlline{00080\ }\hlslc{\# make preparations for net test and test MLPnet}\\ \hlline{00081\ }\hlslc{\#\hlstd{\ \ }standardise input \& output test data}\\ \hlline{00082\ }\hlstd{[mTestInputN] = \hlkwc{trastd}(mTest(1:end-1,:),cMeanInput,cStdInput);}\\ \hlline{00083\ }\hlstd{}\\ \hlline{00084\ }\hlstd{[simOut] = \hlkwc{sim}(net,mTestInputN);}\\ \hlline{00085\ }\hlstd{simOut}\hlstd{}\\ \mbox{} \normalfont nnet/doc/latex/users/octave/0000755000175000017500000000000011475775705015004 5ustar mikemikennet/doc/latex/users/octave/neuroPackage/0000755000175000017500000000000011475775705017410 5ustar mikemikennet/doc/latex/users/octave/neuroPackage/sim.tex0000644000175000017500000000064311133561702020702 0ustar mikemike\subsection{sim} \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent simout = sim(net,P);\\ \noindent \textbf{\textcolor{brown}{Description:}}\\ \noindent \textbf{Left-Hand-Side:}\\ \noindent simout: Output values of the simulated network.\\ \noindent \textbf{Right-Hand-Side:}\\ \noindent net: Network, created and trained with newff(...) and train(...)\\ \noindent P: Input data\\ nnet/doc/latex/users/octave/neuroPackage/neuroPackage.tex0000644000175000017500000000131511133561702022513 0ustar mikemike\chapter{Neural Network Package for Octave} This chapter describes all functions available in the neural network package of Octave. Eventhough it will be as compatible as possible to the one of MATLAB(TM). \section{Available Functions} \input{octave/neuroPackage/min_max} \input{octave/neuroPackage/newff} \input{octave/neuroPackage/prestd} \input{octave/neuroPackage/poststd} \input{octave/neuroPackage/saveMLPStruct} \input{octave/neuroPackage/sim} \input{octave/neuroPackage/subset} \input{octave/neuroPackage/train} \input{octave/neuroPackage/trastd} \section{Transfer functions} \input{octave/neuroPackage/logsig} \input{octave/neuroPackage/purelin} \input{octave/neuroPackage/tansig} nnet/doc/latex/users/octave/neuroPackage/saveMLPStruct.tex0000644000175000017500000000067411073645625022644 0ustar mikemike\subsection{saveMLPStruct} This is an additional function which doesn't exist in the neural network toolbox of MathWorks (TM). To see the network structure, you can use this command and save the complete structure to a file. Open this file and you have the same view like you would open the \textit{network type} of MATLAB(TM).\\ \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent saveMLPStruct(net,"initNetwork.txt");\\ nnet/doc/latex/users/octave/neuroPackage/subset.tex0000644000175000017500000000401711073645625021430 0ustar mikemike\subsection{subset} \textit{subset} can be used to optimize the data sets for train, test and validation of a neural network.\\ \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent [mTrain, mTest, mVali] = subset(mData,nTargets,iOpti,fTest,fVali);\\ \noindent \textbf{\textcolor{brown}{Description:}}\\ \noindent \textbf{Left-Hand-Side:}\\ \noindent mTrain: (R+T) x M matrix with R input rows, T output rows and M columns where M <= N.\\ \noindent mTest: (R+T) x S matrix with R input rows, T output rows and S columns where S <= N.\\ \noindent mVali: (R+T) x U matrix with R input rows, T output rows and U columns where U <= N. And U can only exist, if S also exist.\\ \noindent \textbf{Right-Hand-Side:}\\ \noindent mData: (R+T) x N matrix with R input rows, T output rows and N columns\\ \noindent nTargets: Number of T output rows\\ \noindent iOpti: Integer value to define level of optimization.\\ \noindent fTest: Fraction to define the percentage of data sets which should be used for testing. \\ \noindent fVali: Fraction to define the percentage of data sets which should be used for testing.\\ \noindent iOpti can have following values:\\ 0 : no optimization\\ 1 : will randomise the column order and rerange the columns containing min and max values to be in the train set\\ 2 : will NOT randomise the column order, but rerange the columns containing min and max values to be in the train set\\ \noindent fTest or fValie have following meaning:\\ Each of this arguments can be a fraction or zero. The value 1 is not allowed! The sum of both values must also be smaller than 1!\\ Example: fTest = 1/3\\ \noindent \textbf{Default values}\\ \noindent iOpti = 1\\ \noindent fTest = 1/3\\ \noindent fVali = 1/6\\ \noindent \textbf{\textcolor{brown}{Examples:}}\\ \noindent mTrain = subset(mData,2,1,0,0)\\ \noindent [mTrain,mTest] = subset(mData,2,1,1/3,0);\\ \noindent [mTrain,mTest,mVali] = subset(mData,1);\\ \noindent [mTrain,mTest,mVali] = subset(mData,1,1,1/3,1/6);\\nnet/doc/latex/users/octave/neuroPackage/newff.tex0000644000175000017500000000257111073645625021233 0ustar mikemike\subsection{newff} \textit{newff} is the short form for \textit{\textbf{new f}eed \textbf{f}orward network}. This command creates a feed-forward backpropagation network structure.\\ \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent net = newff(Rx2,[S1 S2 ... SN],\{TF1 TF2 ... TFN\},BTF,BLF,PF)\\ \noindent \textbf{\textcolor{brown}{Description:}}\\ \noindent Rx2: R x 2 matrix of min and max values for R input elements\\ \noindent Si: Size of ith layer, for N layers\\ \noindent TFi: Transfer function of ith layer, default = "tansig"\\ \noindent BTF: Backpropagation network training function, default = "trainlm" \\ \noindent BLF: Backpropagation weight/bias learning function, NOT USED, is only for MATLAB(TM) compatibility\\ \noindent PF: Performance function, default = "mse"\\ \noindent \textbf{\textcolor{brown}{Examples:}}\\ \noindent net = newff(Rx2,[2 1])\\ \noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\});\\ \noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\},"trainlm");\\ \noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\},"trainlm","notUsed","mse");\\ \noindent \textbf{\textcolor{brown}{Comments:}}\\ In this version, you can have as much output neurons as you want. The same with the number of hidden layers. This means you can have one input layer, unrestricted number of hidden layers and one output layer. That's it.\\ nnet/doc/latex/users/octave/neuroPackage/purelin.tex0000644000175000017500000000061511073645625021601 0ustar mikemike\subsection{purelin} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/purelin} \caption{Linear transfer function} \label{fig:purelinTransferFunction} \end{figure} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/purelinlogo} \caption{Linear transfer function logo} \label{fig:purelinTransferFunctionLogo} \end{figure} nnet/doc/latex/users/octave/neuroPackage/tansig.tex0000644000175000017500000000170611073645625021412 0ustar mikemike\subsection{tansig} \noindent I solved all of my real life problems with this transfer function if a non-linear function was used. In [4] page 2-6 the tansig is defined as in equation \eqref{equ:tansigTransferFunction}. A look on the MathWorks homepage with the keyword tansig will show that tansig is programed as in equation \eqref{equ:tansigTransferFunctionNnet}. \begin{equation} a = \frac{e^n - e^{-n}}{e^n + e^{-n}} \label{equ:tansigTransferFunction} \end{equation} \begin{equation} a = \frac{2}{(1 + e^{-2*n})-1} \label{equ:tansigTransferFunctionNnet} \end{equation} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/tansig} \caption{Tansig transfer function} \label{fig:tansigTransferFunction} \end{figure} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/tansiglogo} \caption{Tansig transfer function logo} \label{fig:tansigTransferFunctionLogo} \end{figure} nnet/doc/latex/users/octave/neuroPackage/poststd.tex0000644000175000017500000000002411073645625021615 0ustar mikemike\subsection{poststd}nnet/doc/latex/users/octave/neuroPackage/logsig.tex0000644000175000017500000000061611073645625021410 0ustar mikemike\subsection{logsig} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/logsig} \caption{Log-Sigmoid transfer function} \label{fig:logsigTransferFunction} \end{figure} \begin{figure}[htb] \centering \includegraphics{octave/neuroPackage/graphics/logsiglogo} \caption{Log-Sigmoid transfer function logo} \label{fig:logsigTransferFunctionLogo} \end{figure}nnet/doc/latex/users/octave/neuroPackage/trastd.tex0000644000175000017500000000002311073645625021415 0ustar mikemike\subsection{trastd}nnet/doc/latex/users/octave/neuroPackage/min_max.tex0000644000175000017500000000157711133561702021551 0ustar mikemike\subsection{min\_max} \textit{min\_max} get the minimal and maximal values of an training input matrix. So the dimension of this matrix must be an RxN matrix where R is the number of input neurons and N depends on the number of training sets.\\ \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent mMinMaxElements = min\_max(RxN);\\ \noindent \textbf{\textcolor{brown}{Description:}}\\ \noindent RxN: R x N matrix of min and max values for R input elements with N columns\\ \noindent \textbf{\textcolor{brown}{Example:}}\\ \begin{equation} \left[ \begin{array}{cc} 1 & 11 \\ 0 & 21 \end{array} \right] = min\_max\left[ \begin{array}{ccccc} 3 & 1 & 3 & 5 & 11 \\ 12& 0 & 21& 8 & 6 \\ \end{array} \right] \end{equation} nnet/doc/latex/users/octave/neuroPackage/prestd.tex0000644000175000017500000000002311073645625021415 0ustar mikemike\subsection{prestd}nnet/doc/latex/users/octave/neuroPackage/train.tex0000644000175000017500000000216611133561702021231 0ustar mikemike\subsection{train} \noindent \textbf{\textcolor{brown}{Syntax:}}\\ \noindent net = train(MLPnet,P,T,[],[],VV);\\ \noindent \textbf{\textcolor{brown}{Description:}}\\ \noindent \textbf{Left-Hand-Side:}\\ \noindent net: Trained multi-layer network.\\ \noindent \textbf{Right-Hand-Side:}\\ \noindent MLPnet: Multi-layer network, created with newff(...)\\ \noindent P: Input data for training\\ \noindent T: Target data for training\\ \noindent []: Not used right now, only for compatibility with Matlab\\ \noindent []: Not used right now, only for compatibility with Matlab\\ \noindent VV: Validation data. Contains input and target values\\ \noindent \textbf{\textcolor{brown}{Examples:}}\\ \noindent net = train(MLPnet,P,T)\\ \noindent net = train(MLPnet,P,T,[],[],VV)\\ \noindent P,T must have the same number of rows as in newff(...).\\ \noindent VV.P, VV.T must have the same number of rows as in newff(...)\\ \noindent \textbf{\textcolor{brown}{Comments:}}\\ \noindent Please be sure to put the validation values in a structure named VV.P and VV.T.\\ VV can be changed, but not .P and .T! nnet/doc/latex/users/octave/neuroPackage/graphics/0000755000175000017500000000000011475775705021210 5ustar mikemikennet/doc/latex/users/octave/neuroPackage/graphics/logsiglogo.eps0000644000175000017500000001753211073645625024065 0ustar mikemike%!PS-Adobe-3.0 EPSF-3.0 %%BoundingBox: 291 381 320 410 %%HiResBoundingBox: 291.0768 381.0768 319.9232 409.9232 %%Creator: Asymptote 1.25 %%CreationDate: 2007.07.02 21:46:43 %%Pages: 1 %%EndProlog %%Page: 1 1 gsave 291.3268 381.3268 translate 0 setgray 0 0.5 dtransform 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All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.0) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def /FullName (CMSY10) readonly def /FamilyName (Computer Modern) readonly def /Weight (Medium) readonly def /ItalicAngle -14.035 def /isFixedPitch false def end readonly def /FontName /CMSY10 def /PaintType 0 def /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0] readonly def /Encoding 256 array 0 1 255 {1 index exch /.notdef put} for dup 0 /minus put readonly def /FontBBox{-29 -960 1116 775}readonly def /UniqueID 5000820 def currentdict end currentfile eexec D9D66F633B846A97B686A97E45A3D0AA052F09F9C8ADE9D907C058B87E9B6964 7D53359E51216774A4EAA1E2B58EC3176BD1184A633B951372B4198D4E8C5EF4 A213ACB58AA0A658908035BF2ED8531779838A960DFE2B27EA49C37156989C85 E21B3ABF72E39A89232CD9F4237FC80C9E64E8425AA3BEF7DED60B122A52922A 221A37D9A807DD01161779DDE7D31FF2B87F97C73D63EECDDA4C49501773468A 27D1663E0B62F461F6E40A5D6676D1D12B51E641C1D4E8E2771864FC104F8CBF 5B78EC1D88228725F1C453A678F58A7E1B7BD7CA700717D288EB8DA1F57C4F09 0ABF1D42C5DDD0C384C7E22F8F8047BE1D4C1CC8E33368FB1AC82B4E96146730 DE3302B2E6B819CB6AE455B1AF3187FFE8071AA57EF8A6616B9CB7941D44EC7A 71A7BB3DF755178D7D2E4BB69859EFA4BBC30BD6BB1531133FD4D9438FF99F09 4ECC068A324D75B5F696B8688EEB2F17E5ED34CCD6D047A4E3806D000C199D7C 515DB70A8D4F6146FE068DC1E5DE8BC5703711DA090312BA3FC00A08C453C609 C627A8B1550654AD5E22C5F3F3CC8C1C0A6C7ADDAB55016A76EC46213FD9BAAF 03F7A5FD261BF647FCA5049118033F809370A84AC3ADA3D5BE032CBB494D7851 A6242E785CCC20D81FC5EE7871F1E588DA3E31BD321C67142C5D76BC6AC708DF C21616B4CC92F0F8B92BD37A4AB83E066D1245FAD89B480CB0AC192D4CAFA6AD 241BD8DF7AD566A2022FBC67364AB89F33608554113D210FE5D27F8FB1B2B78A F22EC999DBAAFC9C60017101D5FB2A3B6E2BF4BE47B8E5E4662B8C41AB471DFC A31EE1 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 cleartomark %%EndFont %%BeginFont: CMR12 %!PS-AdobeFont-1.1: CMR12 1.0 %%CreationDate: 1991 Aug 20 16:38:05 % Copyright (C) 1997 American Mathematical Society. All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.0) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def /FullName (CMR12) readonly def /FamilyName (Computer Modern) readonly def /Weight (Medium) readonly def /ItalicAngle 0 def /isFixedPitch false def end readonly def /FontName /CMR12 def /PaintType 0 def /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0] readonly def /Encoding 256 array 0 1 255 {1 index exch /.notdef put} for dup 40 /parenleft put dup 41 /parenright put dup 43 /plus put dup 48 /zero put dup 49 /one put dup 61 /equal put readonly def /FontBBox{-34 -251 988 750}readonly def /UniqueID 5000794 def currentdict end currentfile eexec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cleartomark %%EndFont %%BeginFont: CMMI12 %!PS-AdobeFont-1.1: CMMI12 1.100 %%CreationDate: 1996 Jul 27 08:57:55 % Copyright (C) 1997 American Mathematical Society. All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.100) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. 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All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.0) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def /FullName (CMSY10) readonly def /FamilyName (Computer Modern) readonly def /Weight (Medium) readonly def /ItalicAngle -14.035 def /isFixedPitch false def end readonly def /FontName /CMSY10 def /PaintType 0 def /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0] readonly def /Encoding 256 array 0 1 255 {1 index exch /.notdef put} for dup 0 /minus put readonly def /FontBBox{-29 -960 1116 775}readonly def /UniqueID 5000820 def currentdict end currentfile eexec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cleartomark %%EndFont %%BeginFont: CMR12 %!PS-AdobeFont-1.1: CMR12 1.0 %%CreationDate: 1991 Aug 20 16:38:05 % Copyright (C) 1997 American Mathematical Society. All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.0) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def /FullName (CMR12) readonly def /FamilyName (Computer Modern) readonly def /Weight (Medium) readonly def /ItalicAngle 0 def /isFixedPitch false def end readonly def /FontName /CMR12 def /PaintType 0 def /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0] readonly def /Encoding 256 array 0 1 255 {1 index exch /.notdef put} for dup 40 /parenleft put dup 41 /parenright put dup 43 /plus put dup 48 /zero put dup 49 /one put dup 61 /equal put readonly def /FontBBox{-34 -251 988 750}readonly def /UniqueID 5000794 def currentdict end currentfile eexec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cleartomark %%EndFont %%BeginFont: CMMI12 %!PS-AdobeFont-1.1: CMMI12 1.100 %%CreationDate: 1996 Jul 27 08:57:55 % Copyright (C) 1997 American Mathematical Society. All Rights Reserved. 11 dict begin /FontInfo 7 dict dup begin /version (1.100) readonly def /Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def /FullName (CMMI12) readonly def /FamilyName (Computer Modern) readonly def /Weight (Medium) readonly def /ItalicAngle -14.04 def /isFixedPitch false def end readonly def /FontName /CMMI12 def /PaintType 0 def /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0] readonly def /Encoding 256 array 0 1 255 {1 index exch /.notdef put} for dup 97 /a put dup 103 /g put dup 105 /i put dup 108 /l put dup 110 /n put dup 111 /o put dup 115 /s put readonly def /FontBBox{-30 -250 1026 750}readonly def /UniqueID 5087386 def currentdict end currentfile eexec D9D66F633B846A97B686A97E45A3D0AA0529731C99A784CCBE85B4993B2EEBDE 3B12D472B7CF54651EF21185116A69AB1096ED4BAD2F646635E019B6417CC77B 532F85D811C70D1429A19A5307EF63EB5C5E02C89FC6C20F6D9D89E7D91FE470 B72BEFDA23F5DF76BE05AF4CE93137A219ED8A04A9D7D6FDF37E6B7FCDE0D90B 986423E5960A5D9FBB4C956556E8DF90CBFAEC476FA36FD9A5C8175C9AF513FE D919C2DDD26BDC0D99398B9F4D03D6A8F05B47AF95EF28A9C561DBDC98C47CF5 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\input{octave/functions/isposint_netw_privOct}nnet/doc/latex/users/neuralNetworkPackageForOctaveUsersGuide.tex0000644000175000017500000000530211073645625024161 0ustar mikemike% Preambel \documentclass[a4paper,openany]{report} %\usepackage{a4wide} \usepackage[ansinew]{inputenc} \usepackage[T1]{fontenc} \RequirePackage{ifpdf} \usepackage{hyperref} \hypersetup{% colorlinks=true, % activates colored references pdfpagemode=None, % PDF-Viewer starts without content et.al. pdfstartview=FitH, % PDF-Viewer uses a defined page width %linkbordercolor=111, % citebordercolor=111, citecolor=blue, linkcolor=blue} \ifpdf \usepackage[pdftex]{graphicx} \DeclareGraphicsExtensions{.pdf} \else \usepackage[dvips]{graphicx} \DeclareGraphicsExtensions{.eps} \fi %\usepackage{asymptote} \usepackage{fancyhdr} %\usepackage{supertabular} \usepackage{booktabs} %\usepackage{longtable} %\usepackage[dvips]{rotating} \usepackage{multirow} \usepackage{multicol} \usepackage{color} \usepackage{amsmath} \usepackage{alltt} %\usepackage{array} %\usepackage{colortbl} %%%%%%%%%%%%%%%% will be used to defined a color scheme for %%%%%%%%%%%%%%%% latex pages converted with "Highlight" \newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}} \newcommand{\hlnum}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hlesc}[1]{\textcolor[rgb]{0.42,0.35,0.8}{#1}} \newcommand{\hlstr}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hldstr}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hlslc}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hlcom}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hldir}[1]{\textcolor[rgb]{0.8,0,0.8}{#1}} \newcommand{\hlsym}[1]{\textcolor[rgb]{0,0,0}{#1}} \newcommand{\hlline}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hlkwa}[1]{\textcolor[rgb]{0.65,0.16,0.16}{\bf{#1}}} \newcommand{\hlkwb}[1]{\textcolor[rgb]{0.18,0.55,0.34}{\bf{#1}}} \newcommand{\hlkwc}[1]{\textcolor[rgb]{0.15,0.37,0.93}{\bf{#1}}} \newcommand{\hlkwd}[1]{\textcolor[rgb]{0.32,0.11,0.78}{#1}} \definecolor{bgcolor}{rgb}{1,0.85,0.73} \oddsidemargin -3mm \textwidth 165,2truemm \topmargin 0truept \headheight 0truept \headsep 0truept \textheight 230truemm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \clubpenalty = 10000 \widowpenalty = 10000 \displaywidowpenalty = 10000 \definecolor{hellgrau}{gray}{0.95} \definecolor{dunkelgrau}{gray}{0.55} \definecolor{brown}{rgb}{0.75,0.004,0.3} \renewcommand{\headrulewidth}{0pt} % no head rule \renewcommand{\footrulewidth}{0pt} % no footer rule %\nointend %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start text here!! \begin{document} \pagenumbering{roman} \input{title2} \tableofcontents \pagenumbering{arabic} \include{introduction} \include{octave/neuroPackage/neuroPackage} \include{examples/examples} \include{bibliography} \end{document} nnet/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tcp0000644000175000017500000000034710566046463024247 0ustar mikemike[FormatInfo] Type=TeXnicCenterProjectInformation Version=4 [ProjectInfo] MainFile=neuralNetworkToolboxForOctaveUsersGuide.tex UseBibTeX=0 UseMakeIndex=1 ActiveProfile=LaTeX => PDF ProjectLanguage=de ProjectDialect=DE nnet/doc/latex/users/neuralNetworkPackageForOctaveUsersGuide.tcp0000644000175000017500000000034711073645625024153 0ustar mikemike[FormatInfo] Type=TeXnicCenterProjectInformation Version=4 [ProjectInfo] MainFile=neuralNetworkPackageForOctaveUsersGuide.tex UseBibTeX=0 UseMakeIndex=1 ActiveProfile=LaTeX => PDF ProjectLanguage=de ProjectDialect=DE nnet/doc/latex/users/numbering.tex0000644000175000017500000000106710651413412016212 0ustar mikemike\section{Version numbers} The first number describes the major release. Version number V1.0 will be the first toolbox release which should have the same functions like the Matlab R14 SP3 neural network Toolbox.\\ The second number defines the finished functions. So to start, only the MLPs will realised and so this will be the number V0.1.0.\\ The third number defines the status of the actual development and function. V0.1.0 means a first release with MLP. Actually it works only with Levenberg-Marquardt algorithm and Mean-Square-Error as performance function.nnet/doc/latex/users/introduction.tex0000644000175000017500000000131110566046463016752 0ustar mikemike\chapter{Introduction} \section{Compatibility to Matlab's \texttrademark Neural Network Toolbox} The compatibility is one of the strongest targets during developing this toolbox. If I have to develope an incompatibility e.g. in naming the functions, it will be descriped in this documentation. Even though it should be clear that I can't make a one to one copy. First, the m-files are copyrighted and second, octave doesn't yet support the object oriented-programming techonology.\\ If you find a bug, any not described incompatibility or have some suggestions, please write me at michaelschmid@users.sourceforge.net. This will help improving this toolbox. \input{numbering} \input{knownIncompatibilities} nnet/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tex0000644000175000017500000000513110651413412024240 0ustar mikemike% Preambel \documentclass[a4paper,openany]{report} \usepackage{a4wide} \usepackage[ansinew]{inputenc} \usepackage[T1]{fontenc} \RequirePackage{ifpdf} \usepackage{hyperref} \hypersetup{% colorlinks=true, % activates colored references pdfpagemode=None, % PDF-Viewer starts without content et.al. pdfstartview=FitH, % PDF-Viewer uses a defined page width %linkbordercolor=111, % citebordercolor=111, citecolor=blue, linkcolor=blue} \ifpdf \usepackage[pdftex]{graphicx} \DeclareGraphicsExtensions{.pdf} \else \usepackage[dvips]{graphicx} \DeclareGraphicsExtensions{.eps} \fi \usepackage{asymptote} \usepackage{fancyhdr} %\usepackage{supertabular} \usepackage{booktabs} %\usepackage{longtable} \usepackage[dvips]{rotating} \usepackage{multirow} \usepackage{multicol} \usepackage{color} \usepackage{amsmath} \usepackage{alltt} %\usepackage{array} %\usepackage{colortbl} %%%%%%%%%%%%%%%% will be used to defined a color scheme for %%%%%%%%%%%%%%%% latex pages converted with "Highlight" \newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}} \newcommand{\hlnum}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hlesc}[1]{\textcolor[rgb]{0.42,0.35,0.8}{#1}} \newcommand{\hlstr}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hldstr}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}} \newcommand{\hlslc}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hlcom}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hldir}[1]{\textcolor[rgb]{0.8,0,0.8}{#1}} \newcommand{\hlsym}[1]{\textcolor[rgb]{0,0,0}{#1}} \newcommand{\hlline}[1]{\textcolor[rgb]{0.25,0.38,0.56}{#1}} \newcommand{\hlkwa}[1]{\textcolor[rgb]{0.65,0.16,0.16}{\bf{#1}}} \newcommand{\hlkwb}[1]{\textcolor[rgb]{0.18,0.55,0.34}{\bf{#1}}} \newcommand{\hlkwc}[1]{\textcolor[rgb]{0.15,0.37,0.93}{\bf{#1}}} \newcommand{\hlkwd}[1]{\textcolor[rgb]{0.32,0.11,0.78}{#1}} \definecolor{bgcolor}{rgb}{1,0.85,0.73} \oddsidemargin -3mm \textwidth 165,2truemm \topmargin 0truept \headheight 0truept \headsep 0truept \textheight 230truemm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \clubpenalty = 10000 \widowpenalty = 10000 \displaywidowpenalty = 10000 \definecolor{hellgrau}{gray}{0.95} \definecolor{dunkelgrau}{gray}{0.55} \definecolor{brown}{rgb}{0.75,0.004,0.3} \renewcommand{\headrulewidth}{0pt} % no head rule \renewcommand{\footrulewidth}{0pt} % no footer rule %\nointend %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start text here!! \begin{document} \pagenumbering{roman} \input{title2} \tableofcontents \pagenumbering{arabic} \include{introduction} \include{octave/neuroToolbox/neuroToolbox} \include{examples/examples} \include{bibliography} \end{document} nnet/doc/latex/users/title2.tex0000644000175000017500000000023111073645625015433 0ustar mikemike% start text here!! \title{A neural network package for Octave\\ User's Guide \\ \input{../common/version}} \author{Michel D. Schmid} \maketitle nnet/doc/latex/users/knownIncompatibilities.tex0000644000175000017500000000055210566046463020764 0ustar mikemike\section{Known incompatibilities} \label{chap:intro:sec:knownIncompatibilities} \subsection{Function names} \subsubsection{minmax} \textit{minmax} is in this toolbox called \textit{min\_max}. This is because Octave already has a function whichs name is \textit{minmax}. This is a c file and the functions \textit{min} and \textit{max} are therein realized. nnet/doc/latex/common/0000755000175000017500000000000011475775705013652 5ustar mikemikennet/doc/latex/common/bibliography.tex0000644000175000017500000000133110651367144017032 0ustar mikemike% Preamble %\documentclass[a4paper]{report} %\usepackage[ngerman]{babel} %\usepackage[T1]{fontenc} %\usepackage[ansinew]{inputenc} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start text here!! %\begin{document} \begin{thebibliography}{XXXXXXX} \bibitem [1]{1} John W. Eaton GNU Octave Manual, Edition 3, PDF-Version, February 1997 \bibitem [2]{2} The MathWorks, Inc. MATLAB Online-Help \bibitem [3]{3} Steven W. Smith The Scientist and Engineer's Guide to Digital Signal Processing ISBN 0-9660176-3-3, California Technical Publishing, 1997 \bibitem [4]{4} Martin T. Hagan, Howard B. Demuth, Mark Beale Neural Network Design, ISBN 0971732108, PWS Publishing Company, USA, Boston, 1996 \end{thebibliography} %\end{document}nnet/doc/latex/common/version.tex0000644000175000017500000000002011133561702016026 0ustar mikemikeVersion: 0.1.9.1nnet/doc/latex/perl/0000755000175000017500000000000011475775705013324 5ustar mikemikennet/doc/latex/perl/createTestDocu.pl0000644000175000017500000001147711075115322016564 0ustar mikemike#!/usr/bin/perl -w use strict; use diagnostics;# Force verbose warning diagnostics. use warnings; use English; # # Modules from the Standard Perl Library. # # Own modules use analyzeOctaveSource; #--- DEFINE VARIABLES ------------------------------- my $Dir = "D:\\daten\\octave\\neuroPackage\\0.1.9\\nnet\\inst"; my $fileExt = "m"; my $testDir = "D:\\daten\\octave\\neuroPackage\\0.1.9\\nnet/doc/latex/developers/tests"; my $relTestDir = "tests/"; my $testFileExt = "tex"; my $mainLatexTestFile = "test.tex"; my $chapter = "Test"; #--- END DEFINE VARIABLES --------------------------- my @filesArray = (); my @filesName = (); my $fileName = ""; my $savePath = ""; my $nTestLines = 0; my $m = 0; # analyze directory structure my $obj = analyzeOctaveSource->new(); my @DirArray = $obj->readDirTree($Dir,$fileExt); # analyze file structure my $nFiles = @DirArray; if ($nFiles>=1){ # if $nFiles==0 readDirTree will die # if we are in this branch, we should check the files # to found those which content tests (lines beginning with # !% ) foreach (@DirArray){ # open file and search for lines beginning with # !% if (-d $_){ # if directory, do nothing }else{ print "$_\n"; sleep(0.1); open(FILE,$_) or die "File $_ not found!\n"; my @fileContent = ; chomp(@fileContent); $nTestLines = @fileContent; my @temp = grep /^%!/, @fileContent; my $nTemp = @temp; if ($nTemp>0){ # this means, # test lines are available # now create latex files without header # take the same name like the *.m file # and save in specified directory $testDir # with file extens in $testFileExt # use verbatim environment @filesName = split(/\//, $_); $fileName = $filesName[$#filesName]; # now remove file extension .m @filesName = split(/\./,$fileName); $savePath = ("$testDir" . "\\\\" . "$filesName[0]." . "$testFileExt"); open(OUTFILE, ">$savePath"); my $i = 0; print OUTFILE "\\begin{verbatim}\n"; while ($i < $nTestLines){ if ($fileContent[$i]=~/^%!/){ print OUTFILE "$fileContent[$i]\n"; } $i++; } # END while ($i <= $#fileContent) print OUTFILE "\\end{verbatim}\n"; close OUTFILE; ## now set entries in the main test latex file .. my $mainTestFile = ("$testDir" . "\\\\" . "$mainLatexTestFile"); if ($m==0){ open(TESTFILE,">$mainTestFile") or die "Could not found file $mainTestFile!\n"; print TESTFILE "\\chapter{$chapter}\n\n"; # test if back slash needed # a back slash is needed if the sign "underscore" # is used in the file name. This happens at each # "sub function". There are two underscores! my $tempString = ""; my $oldString = $filesName[0]; $_ = $filesName[0]; s/_/\\_/g; # s/ : search & replace pattern (everything between / /) # here: search underscore # if found, replace with \_ (two back slashes are needed # to get \_ as sign) # /g means: each occurence of pattern, otherwise, only one _ # will be replaced print "test file name: $_\n"; print TESTFILE "\\section{$_}\n"; $tempString = $relTestDir . $oldString; print TESTFILE "\\input{$tempString}\n"; }else{ open(TESTFILE,">>$mainTestFile") or die "Could not found file $mainTestFile!\n"; # test if back slash needed my $tempString = ""; my $oldString = $filesName[0]; # test if back slash needed # a back slash is needed if the sign "underscore" # is used in the file name. This happens at each # "sub function". There are two underscores! my $tempString = ""; my $oldString = $filesName[0]; $_ = $filesName[0]; s/_/\\_/g; # s/ : search & replace pattern (everything between / /) # here: search underscore # if found, replace with \_ (two back slashes are needed # to get \_ as sign) # /g means: each occurence of pattern, otherwise, only one _ # will be replaced print TESTFILE "\\section{$_}\n"; $tempString = $relTestDir . $oldString; print TESTFILE "\\input{$tempString}\n"; } $m++; close TESTFILE; }# END if($nTemp>0) close FILE; }# END if(-d $_) }# END foreach (@DirArray) }else{ # if $nFiles==0 print "No file found with valid file extension: .$fileExt.\n"; die; }nnet/doc/latex/perl/analyzeOctaveSource.pm0000644000175000017500000000236011054446446017637 0ustar mikemikepackage analyzeOctaveSource; use File::Find; sub new { my $Objekt = shift; my $Referenz = {}; bless($Referenz,$Objekt); return($Referenz); } sub readDirTree{ my $Objekt = shift; my $Dir = shift; my $fileExt = shift; my $File; # read directory my @dirArray = (); find sub { push @dirArray, $File::Find::name }, $Dir; # remove all files except with file ending $fileExt @DirArray = grep /.+\.$fileExt/ , @DirArray; my $nFiles = @dirArray; if ($nFiles==0){ print "No Octave files found.\n"; print "Octave files must end with *.m\n"; die " ==========================\n"; } return @dirArray; } sub searchFilesContainTestLines{ my $Objekt = shift; my @dirArray = shift; # my $fileName = ""; my @fileContent = (); my @fileArray = (); my @temp = (); my $nTemp = ""; foreach (@dirArray){ # open file and search for lines beginning with # !% if (-d $_){ # if directory, do nothing }else{ open(FILE,$_) or die "File $_ not found!\n"; @fileContent = ; close FILE; @temp = grep /^!%/, @fileContent; $nTemp = @temp; if ($nTemp>0){ @fileArray = $_ } } } return @fileArray; } 1; nnet/doc/latex/perl/createFunctionIndexDocu.pl0000644000175000017500000001164311075371503020422 0ustar mikemike#!/usr/bin/perl -w use strict; use diagnostics;# Force verbose warning diagnostics. use warnings; use English; # # Modules from the Standard Perl Library. # # Own modules use analyzeOctaveSource; #--- DEFINE VARIABLES ------------------------------- my $Dir = "D:/daten/octave/neuroPackage/0.1.9/nnet/inst"; my $fileExt = "m"; my $funcIndDir = "D:/daten/octave/neuroPackage/0.1.9/nnet/doc/latex/developers/funcindex"; my $relFuncDir = "funcindex/"; my $funcFileExt = "tex"; my $mainLatexFuncIndexFile = "funcindexCalled.tex"; my $chapter = "Function Index"; #--- END DEFINE VARIABLES --------------------------- my @filesArray = (); my @filesName = (); my $fileName = ""; my $savePath = ""; my $nTestLines = 0; my $m = 0; # analyze directory structure my $obj = analyzeOctaveSource->new(); my @DirArray = $obj->readDirTree($Dir,$fileExt); # in @DirArray should be only names, ending with *.m # but if there is a directory, we will remove it at # the next line of code my @FuncReferences = grep /.+\.$fileExt/ , @DirArray; #/ my @FuncNames = @FuncReferences; # now I have to remove the path and file extension foreach (@FuncNames) { s/\.m//g; # removes file ending s/$Dir\///g; # removes any parts of the file & directory path } my @input = (); my @calledFunction = (); my $deleteFile = 1; my $anzahl_elemente = 0; # now analyze functions to see which other functions are called foreach my $FuncRef (@FuncReferences) { open(FILE,$FuncRef); # opens e.g. 'subset.m' @input = ; # read the complete content of the file to @input # now remove all comment and test lines .. @input = grep s/^\s+//, @input; # removes white-space characters at the # beginning of a line @input = grep /^[^#|%]/ , @input; # removes lines starting with # or % # foreach (@input) # { # print "$_"; # sleep(1); # } my $actFuncName = ""; foreach my $FuncName (@FuncNames) { if ($FuncRef !~/$FuncName/) # returns true if pattern is not found { # now search for each $FuncName # inside of the @input array # if one is found, put them to a list # \todo ERRORs are still available # if the $FuncName occures in another context!! # such lines should be deleted! if (grep /$FuncName/,@input) { push (@calledFunction, "$FuncName"); } }else{ $actFuncName = $FuncName; } } # now remove double entries of @calledFunction undef my %saw; @saw{@calledFunction} = (); @calledFunction = sort keys %saw; # remove sort if undesired if (-e "$funcIndDir" . "/" . "$mainLatexFuncIndexFile") { # if the file exist, delete it if ($deleteFile){ unlink("$funcIndDir" . "/" . "$mainLatexFuncIndexFile"); $deleteFile = 0; open(DAT,">$funcIndDir" . "/" . "$mainLatexFuncIndexFile"); print DAT "\\begin{longtable}{ll}\n"; print DAT "\\textbf{main function} & \\textbf{called function} \\\\ \n"; print DAT "\\hline\n"; $_ = $actFuncName; s/_/\\_/g; # put a backslash for each underscore print DAT "$_ "; $anzahl_elemente = @calledFunction; if ($anzahl_elemente > 0) { foreach (@calledFunction){ s/_/\\_/g; # put a backslash for each underscore print DAT " & $_\\\\ \n"; } }else{ print DAT " & \\\\ \n"; } close(DAT); }else{ # file doesn't have to be deleted open(DAT,">>$funcIndDir" . "/" . "$mainLatexFuncIndexFile"); print DAT "\\hline\n"; $_ = $actFuncName; s/_/\\_/g; # put a backslash for each underscore print DAT "$_ "; $anzahl_elemente = @calledFunction; if ($anzahl_elemente > 0) { foreach (@calledFunction){ s/_/\\_/g; # put a backslash for each underscore print DAT " & $_\\\\ \n"; } }else{ print DAT " & \\\\ \n"; } close(DAT); } }else{ # File doesn't exist yet open(DAT,">$funcIndDir" . "/" . "$mainLatexFuncIndexFile"); print DAT "\\begin{longtable}{l l}\n"; print DAT "\\textbf{main function} & \\textbf{called function} \\\\ \n"; print DAT "\\hline\n"; $_ = $actFuncName; s/_/\\_/g; # put a backslash for each underscore print DAT "$_ "; $anzahl_elemente = @calledFunction; if ($anzahl_elemente > 0) { foreach (@calledFunction){ s/_/\\_/g; # put a backslash for each underscore print DAT " & $_\\\\ \n"; } }else{ print DAT " & \\\\ \n"; } close(DAT); } # print DAT "Function-File: $actFuncName\n"; # print DAT "=============================\n"; # foreach (@calledFunction){ # print DAT "$_\n"; # } @calledFunction = (); } open(DAT,">>$funcIndDir" . "/" . 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currentpoint grestore moveto 2388 2859 a Black 0.000000 TeXcolorgray eop end %%Trailer userdict /end-hook known{end-hook}if %%EOF nnet/doc/latex/asymptote/transferFunctions/0000755000175000017500000000000011475775705020124 5ustar mikemikennet/doc/latex/asymptote/transferFunctions/logsiglogo.asy0000644000175000017500000000055110651367144022775 0ustar mikemike// logsig symbol for nnet // define size of outer square = 1cm unitsize(1cm); draw(unitsquare); // in the middle one short line from left to right draw((0.1,0.3)--(0.9,0.3)); // now draw logsig import graph; real f(real x) {return tanh(x);} draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..)))); //shift(2,1); //scale(real 0.5);nnet/doc/latex/asymptote/transferFunctions/purelin.asy0000644000175000017500000000062410651367144022307 0ustar mikemike// purelin import graph; size(100,0); real f(real x) {return 1*x;} pair F(real x) {return (x,f(x));} xaxis("$n$",EndArrow); yaxis("$a$",-1.75,1.75,EndArrow); draw(graph(f,-2.5,2.5,operator ..)); draw((-2.5,-1)--(2.5,-1),currentpen+dashed); draw((-2.5,1)--(2.5,1),currentpen+dashed); label("$a = purelin(n) $",(0,-2.00)); label("$0$",(0.2,-0.3)); label("$-1$",(0.6,-1.35)); label("$+1$",(0.75,1.35));nnet/doc/latex/asymptote/transferFunctions/tansiglogo.asy0000644000175000017500000000055110651367144022776 0ustar mikemike// tansig symbol for nnet // define size of outer square = 1cm unitsize(1cm); draw(unitsquare); // in the middle one short line from left to right draw((0.1,0.5)--(0.9,0.5)); // now draw tansig import graph; real f(real x) {return tanh(x);} draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..)))); //shift(2,1); //scale(real 0.5);nnet/doc/latex/asymptote/transferFunctions/purelinlogo.asy0000644000175000017500000000054710651367144023174 0ustar mikemike// purelin symbol for nnet // define size of outer square = 1cm unitsize(1cm); draw(unitsquare); // in the middle one short line from left to right draw((0.1,0.5)--(0.9,0.5)); // now draw purelin import graph; real f(real x) {return 1*x;} draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..)))); //shift(2,1); //scale(real 0.5);nnet/doc/latex/asymptote/transferFunctions/logsig.asy0000644000175000017500000000062210651367144022113 0ustar mikemikeimport graph; size(100,0); real f(real x) {return 1/(1+exp(-x));} pair F(real x) {return (x,f(x));} xaxis("$n$",EndArrow); yaxis("$a$",-1.75,1.75,EndArrow); draw(graph(f,-2.5,2.5,operator ..)); draw((-2.5,-1)--(2.5,-1),currentpen+dashed); draw((-2.5,1)--(2.5,1),currentpen+dashed); label("$a = logsig(n) $",(0,-2.00)); label("$0$",(0.2,-0.3)); label("$-1$",(0.6,-1.35)); label("$+1$",(0.75,1.35));nnet/doc/latex/asymptote/transferFunctions/tansig.asy0000644000175000017500000000061410651367144022115 0ustar mikemikeimport graph; size(100,0); real f(real x) {return tanh(x);} pair F(real x) {return (x,f(x));} xaxis("$n$",EndArrow); yaxis("$a$",-1.75,1.75,EndArrow); draw(graph(f,-2.5,2.5,operator ..)); draw((-2.5,-1)--(2.5,-1),currentpen+dashed); draw((-2.5,1)--(2.5,1),currentpen+dashed); label("$a = tansig(n) $",(0,-2.00)); label("$0$",(0.2,-0.3)); label("$-1$",(0.6,-1.35)); label("$+1$",(0.75,1.35));nnet/doc/latex/asymptote/mlp4-2-3.asy0000644000175000017500000000556011073645625016275 0ustar mikemikeimport graph; size(100,0); // define sizes for neurons pair center1 = (10,10); pair center2 = (10,30); pair center3 = (10,50); pair center4 = (10,70); pair center1hidden = (50,30); pair center2hidden = (50,50); pair center1out = (90,20); pair center2out = (90,40); pair center3out = (90,60); real radius = (5); // define circle for neurons path in1 = circle(center1,radius); path in2 = circle(center2,radius); path in3 = circle(center3,radius); path in4 = circle(center4,radius); path hn1 = circle(center1hidden,radius); path hn2 = circle(center2hidden,radius); path on1 = circle(center1out,radius); path on2 = circle(center2out,radius); path on3 = circle(center3out,radius); // draw neurons draw(in1); draw(in2); draw(in3); draw(in4); draw(hn1); draw(hn2); draw(on1); draw(on2); draw(on3); // now draw arrows from input // to hidden neurons // (starts from bottom to top) // first input pair z11=(16,10); pair z12=(45,27); draw(z11--z12,Arrow); // first input pair z21=(16,30); pair z22=(44,29); draw(z21--z22,Arrow); // first input pair z31=(16,50); pair z32=(44,31); draw(z31--z32,Arrow); // first input pair z41=(16,70); pair z42=(45,33); draw(z41--z42,Arrow); // second input pair z11=(16,10); pair z12=(45,47); draw(z11--z12,Arrow); // second input pair z21=(16,30); pair z22=(44,49); draw(z21--z22,Arrow); // second input pair z31=(16,50); pair z32=(44,51); draw(z31--z32,Arrow); // second input pair z41=(16,70); pair z42=(45,53); draw(z41--z42,Arrow); // now draw arrows from hidden // to output neurons // (starts from bottom to top) // first hidden pair z11=(56,30); pair z12=(84,19); draw(z11--z12,Arrow); // first hidden pair z21=(56,30); pair z22=(84,39); draw(z21--z22,Arrow); // first hidden pair z21=(56,30); pair z22=(84,59); draw(z21--z22,Arrow); // second hidden pair z31=(56,50); pair z32=(84,21); draw(z31--z32,Arrow); // second hidden pair z41=(56,50); pair z42=(84,41); draw(z41--z42,Arrow); // second hidden pair z41=(56,50); pair z42=(84,61); draw(z41--z42,Arrow); // now define text for input neurons // p1 for the top neuron label(scale(0.5)*minipage("\centering \textbf{p1}",0.5cm),(0,70)); label(scale(0.5)*minipage("\centering \textbf{p2}",0.5cm),(0,50)); label(scale(0.5)*minipage("\centering \textbf{p3}",0.5cm),(0,30)); label(scale(0.5)*minipage("\centering \textbf{p4}",0.5cm),(0,10)); // now define text for hidden neurons // n1 for the top neuron label(scale(0.5)*minipage("\centering \textbf{n1}",0.5cm),(50,60)); label(scale(0.5)*minipage("\centering \textbf{n2}",0.5cm),(50,40)); // now define text for output neurons // a1 for the top neuron label(scale(0.5)*minipage("\centering 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0 R /Info 2 0 R /ID [<772768506A68BB33CFF44AC2A59E3A18><772768506A68BB33CFF44AC2A59E3A18>] >> startxref 4053 %%EOF nnet/doc/latex/developers/0000755000175000017500000000000011475775705014532 5ustar mikemikennet/doc/latex/developers/title.tex0000644000175000017500000000020410651413412016344 0ustar mikemike \title{A neural network toolbox for Octave\\ Developer's Guide\\ \input{../common/version}} \author{M. Schmid} \maketitle nnet/doc/latex/developers/codingGuideline/0000755000175000017500000000000011475775705017623 5ustar mikemikennet/doc/latex/developers/codingGuideline/codingGuideline.tex0000644000175000017500000000570310651367144023430 0ustar mikemike\chapter{Coding Guideline} Some genereal descriptions why a variable has a chosen name. This is valid for the complete toolbox... or so :-)\\ Here is only the description of variable names, which aren't visible to the user. Visible names are described in the User's Guide!\\ The variable identifiers are taken from \cite{4}. One difference is purposeful added. If a variable has only one letter, a second small letter will be added to make it searchable. Who has ever tried to search a variable called "t"? \section{Variable identifier} \begin{tabbing} \hspace*{1em} \= \textcolor{blue}{Identifier} \hspace*{3em}\= \textcolor{blue}{Description:} \\ \textbf{Aa} \> \> hold the network values after transfer function.\\ blf \> \> \textbf{b}atch \textbf{l}earning \textbf{f}unction \\ btf \> \> \textbf{b}atch \textbf{t}rainings \textbf{f}unction \\ \textbf{Jj} \> \> Jacobi matrix \\ \textbf{Nn} \> \> hold the network values before transfer function.\\ net \> \> structure which holds the neural network informations \\ pf \> \> \textbf{p}erformance \textbf{f}unction \\ \textbf{Pp} \> \>input matrix; nInputs x nDataSets \\ \textbf{Pr} \> \> input range, this is a Nx2 matrix, that's why the capitalized P \\ trf \> \> \textbf{tr}ansfer \textbf{f}unction \\ \textbf{Tt} \> \> target matrix, nTargets x nDataSets\\ \textbf{ss} \> \> row vector with numbers of neurons in the layers, for each layer, one entry \\ \textbf{vE} \> \> row vector with errors... size depends on network structure. \\ VV \> \> Validation structure \\ \textbf{xx} \> \> Weight vector in column format\\ \end{tabbing} \subsection{Nn} \textbf{Nn} is a cell array and has one entry for each layer. In reality, this will have 2 or 3 layers.\\ In \textbf{Nn\{1,1\}} are the values for the first hidden layer. The size of this matrix depends on the number of neurons used for this layer.\\ In \textbf{Nn\{2,1\}} are the values for the second hidden layer or the output layer. The size of this matrix depends on the number of neurons used for this layer and so on ...\\ \textbf{Nn\{x,1\}} where \textbf{x} can be $\infty$.\\ \subsection{Aa} \textbf{Aa} is a cell array and has one entry for each layer.\\ In \textbf{Aa\{1,1\}} are the values for the first hidden layer. The size of this matrix depends on the number of neurons used for this layer.\\ In \textbf{Aa\{2,1\}} are the values for the second hidden layer or the output layer. The size of this matrix depends on the number of neurons used for this layer.\\ See \textbf{Nn} for a more detailed description.\\ \subsection{vE} \textbf{vE} is also a cell array which holds in the last (second) element the error vector. It's not completly clear, why in the last (second) element.\\ The number of rows depends on the number of output neurons. For one output neuron, \textbf{vE} holds only one row, for 2 output neurons, this holds of course 2 rows, and so on. \subsection{Jj} This is the short term for the Jacobi matrix.nnet/doc/latex/developers/algorithm/0000755000175000017500000000000011475775705016520 5ustar mikemikennet/doc/latex/developers/algorithm/LevenbergMarquardt.tex0000644000175000017500000000465410566045112023023 0ustar mikemike\section{Levenberg Marquardt} This algorithm will be programed with \cite{4}. \subsection{Sensitivity Matrix} How does this looks like?\\ \begin{enumerate} \item for a 1-1-1 MLP \item for a 2-1-1 MLP \item for a 1-2-1 MLP \end{enumerate} \subsubsection{1-1-1 MLP} In this case, the MLP holds one input neuron, one hidden neuron and one output neuron. The number of weights needed for this MLP is 4 (2 weights, 2 biases).\\ It needs two sensitivity matrices because the two layers. Each sensitivity matrix will hold 1 element. This is taken from \cite{4}, example P12.5 page 12-44. Attention, in this example are two data sets used, this is the reason for the 4 elements...! \subsubsection{2-1-1 MLP} In this case, the MLP holds two input neurons, one hidden neuron and one output neuron. The number of weights needed for this MLP is 5 (3 weights, 2 biases).\\ It needs also two sensitivity matrices because the two layers. Actually, the input is not only a scalar, it is a vector with 2 elements. Even though, again after \cite{4}. I think the sensitivity matrices will hold only 1 element. So the number of elements will bi proportional to the number of hidden neurons and the number of output neurons. \subsubsection{1-2-1 MLP} In this case, the MLP holds one input neuron, two hidden neurons and one output neuron. The number of weights needed for this MLP is 7 (4 weights, 3 biases).\\ It needs also two sensitivity matrices because the two layers. Actually, the input is again only a scalar. Now calculating $n_1^1$ will result in a row vector with 2 elements. $n_1^2$ will hold only one element and so we have 3 elements in the sensitivity matrix.\\ We can say, the number of hidden neurons is responsible for the dimension of the sensitivity matrices. For example, a 4-3-1 MLP with 100 data sets will generate following sensitivity matrix for the first layer: $\tilde{\textbf{S}}^1 = [\tilde{\textbf{S}}^1_1 | \tilde{\textbf{S}}^1_2 | ... | \tilde{\textbf{S}}^1_{100}]$\\ \noindent $\tilde{\textbf{S}}^1_1$ will hold 3 elements $\tilde{\textbf{S}}^1_1 = [\tilde{\textbf{S}}^1_{1,1} ~ \tilde{\textbf{S}}^1_{2,1} ~ \tilde{\textbf{S}}^1_{3,1}]^T$; $\tilde{\textbf{S}}^1_2 = [\tilde{\textbf{S}}^1_{1,2} ~ \tilde{\textbf{S}}^1_{2,2} ~ \tilde{\textbf{S}}^1_{3,2}]^T$ and so on. So matrix will have a size of 3x100 for $\tilde{\textbf{S}}^1_{1}$ and a size of 1x100 for $\tilde{\textbf{S}}^1_{2}$.\\ By the way, the jacobian matrix will be a 100x20 matrix .. nnet/doc/latex/developers/algorithm/algorithmen.tex0000644000175000017500000000021110566045112021523 0ustar mikemike\chapter{Algorithm} Here are some general thoughts about calculating parts are used in algorithm. \input{algorithm/LevenbergMarquardt} nnet/doc/latex/developers/funcindex/0000755000175000017500000000000011475775705016515 5ustar mikemikennet/doc/latex/developers/funcindex/funcindex.tex0000644000175000017500000000013311075115322021172 0ustar mikemike\chapter{Function Index} \section{Who calles who} \input{funcindex/funcindexCalled} nnet/doc/latex/developers/funcindex/funcindexCalled.tex0000644000175000017500000001537211075115322022312 0ustar mikemike\begin{verbatim} Function-File: isposint ============================= \end{verbatim} \begin{verbatim} Function-File: logsig ============================= \end{verbatim} \begin{verbatim} Function-File: min_max ============================= \end{verbatim} \begin{verbatim} Function-File: newff ============================= __init __newnetwork tansig train \end{verbatim} \begin{verbatim} Function-File: poststd ============================= \end{verbatim} \begin{verbatim} Function-File: prestd ============================= \end{verbatim} \begin{verbatim} Function-File: purelin ============================= \end{verbatim} \begin{verbatim} Function-File: saveMLPStruct ============================= __checknetstruct __printAdaptFcn __printAdaptParam __printB __printBiasConnect __printBiases __printIW __printInitFcn __printInitParam __printInputConnect __printInputWeights __printInputs __printLW __printLayerConnect __printLayerWeights __printLayers __printMLPHeader __printNetworkType __printNumInputDelays __printNumInputs __printNumLayerDelays __printNumLayers __printNumOutputs __printNumTargets __printOutputConnect __printOutputs __printPerformFcn __printPerformParam __printTargetConnect __printTargets __printTrainFcn __printTrainParam \end{verbatim} \begin{verbatim} Function-File: sim ============================= __checknetstruct logsig purelin tansig \end{verbatim} \begin{verbatim} Function-File: subset ============================= __optimizedatasets \end{verbatim} \begin{verbatim} Function-File: tansig ============================= \end{verbatim} \begin{verbatim} Function-File: train ============================= __checknetstruct __trainlm \end{verbatim} \begin{verbatim} Function-File: trastd ============================= \end{verbatim} \begin{verbatim} Function-File: __analyzerows ============================= \end{verbatim} \begin{verbatim} Function-File: __calcjacobian ============================= __dlogsig __dpurelin __dtansig logsig purelin tansig \end{verbatim} \begin{verbatim} Function-File: __calcperf ============================= __mse logsig purelin tansig \end{verbatim} \begin{verbatim} Function-File: __checknetstruct ============================= \end{verbatim} \begin{verbatim} Function-File: __copycoltopos1 ============================= \end{verbatim} \begin{verbatim} Function-File: __dlogsig ============================= \end{verbatim} \begin{verbatim} Function-File: __dpurelin ============================= \end{verbatim} \begin{verbatim} Function-File: __dtansig ============================= \end{verbatim} \begin{verbatim} Function-File: __getx ============================= __checknetstruct \end{verbatim} \begin{verbatim} Function-File: __init ============================= __checknetstruct newff \end{verbatim} \begin{verbatim} Function-File: __mae ============================= \end{verbatim} \begin{verbatim} Function-File: __mse ============================= \end{verbatim} \begin{verbatim} Function-File: __newnetwork ============================= __checknetstruct isposint newff train \end{verbatim} \begin{verbatim} Function-File: __optimizedatasets ============================= __analyzerows __randomisecols __rerangecolumns \end{verbatim} \begin{verbatim} Function-File: __printAdaptFcn ============================= \end{verbatim} \begin{verbatim} Function-File: __printAdaptParam ============================= \end{verbatim} \begin{verbatim} Function-File: __printB ============================= \end{verbatim} \begin{verbatim} Function-File: __printBiasConnect ============================= \end{verbatim} \begin{verbatim} Function-File: __printBiases ============================= \end{verbatim} \begin{verbatim} Function-File: __printInitFcn ============================= \end{verbatim} \begin{verbatim} Function-File: __printInitParam ============================= \end{verbatim} \begin{verbatim} Function-File: __printInputConnect ============================= \end{verbatim} \begin{verbatim} Function-File: __printInputs ============================= \end{verbatim} \begin{verbatim} Function-File: __printInputWeights ============================= \end{verbatim} \begin{verbatim} Function-File: __printIW ============================= \end{verbatim} \begin{verbatim} Function-File: __printLayerConnect ============================= \end{verbatim} \begin{verbatim} Function-File: __printLayers ============================= \end{verbatim} \begin{verbatim} Function-File: __printLayerWeights ============================= \end{verbatim} \begin{verbatim} Function-File: __printLW ============================= \end{verbatim} \begin{verbatim} Function-File: __printMLPHeader ============================= \end{verbatim} \begin{verbatim} Function-File: __printNetworkType ============================= newff \end{verbatim} \begin{verbatim} Function-File: __printNumInputDelays ============================= \end{verbatim} \begin{verbatim} Function-File: __printNumInputs ============================= \end{verbatim} \begin{verbatim} Function-File: __printNumLayerDelays ============================= \end{verbatim} \begin{verbatim} Function-File: __printNumLayers ============================= \end{verbatim} \begin{verbatim} Function-File: __printNumOutputs ============================= \end{verbatim} \begin{verbatim} Function-File: __printNumTargets ============================= \end{verbatim} \begin{verbatim} Function-File: __printOutputConnect ============================= \end{verbatim} \begin{verbatim} Function-File: __printOutputs ============================= \end{verbatim} \begin{verbatim} Function-File: __printPerformFcn ============================= \end{verbatim} \begin{verbatim} Function-File: __printPerformParam ============================= \end{verbatim} \begin{verbatim} Function-File: __printTargetConnect ============================= \end{verbatim} \begin{verbatim} Function-File: __printTargets ============================= \end{verbatim} \begin{verbatim} Function-File: __printTrainFcn ============================= train \end{verbatim} \begin{verbatim} Function-File: 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begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.2) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.1) cvn /DEST pdfmark endŸUïsrc:1introduction.texó#Ù?VE½q½qecbx2074ÎChapter‘Vº1ŽŸ2ÉIn‘ÿF¾tro‘¹AductionŽŸ4ïsrc:2introduction.tex¹This–`›doGcumenš¸ètation“isn't“a“w˜ell“dened“and“structured“doGcu“for“the“neural“net˜w˜ork“to•Golb“o˜x.‘“ñIt'sޤ more–U a“ó$½HЃ ecti1000Ïc–ÿ}/ol‘‚Ðle“ction–“°of“my“ide‘ÿ}/as“and“minds¹.ŽŸ~ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.1.1) cvn /DEST pdfmark endŸXó&&Lt$ffffecbx1440Ñ1.1Ž‘$FInstalled‘G\systemŽŸæ~ïsrc:7introduction.tex¹I'm–'Wdevš¸èeloping“and“testing‘'Vthe“actual“v˜ersion“of“the“neural“net˜w˜ork‘'Vto•Golb“o˜x–'Won“follo˜wing“programŽ¡v¸èersions:Ž©ïcolor push Black‘ó !",š cmsy10¸Ž‘ï color popŽŽ‘ïsrc:11introduction.tex¹Octa•¸èv“e‘U 2.9.5ޤïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:12introduction.tex¹oGcta•¸èv“e-forge-2006.01.28Ž¡ïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:13introduction.tex¹OctPlot–U svn“v¸èersionަ‘ïsrc:16introduction.tex..‘q€and–U on“another“systemަïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:19introduction.tex¹Octa•¸èv“e‘U 2.9.12Ž¡ïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:20introduction.tex¹oGcta•¸èv“e-forge›U pac“k‘ÿqÐages˜...Ž¡ïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:21introduction.tex¹OctPlot–U svn“v¸èersionŽŸÖïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.1.2) cvn /DEST pdfmark endŸÑ1.2Ž‘$FV‘þ®(ersion›G\n•cum“bpœers˜of˜the˜neural˜net“w“ork˜to•pœolb“ocxŽŸæ~ïsrc:3numbering¹The–¯arst“n•¸èum“b•Ger›¯adescrib“es˜the˜ma‘Ž0jor‘¯brelease.‘:@V‘ÿ*¸ersion˜n•¸èum“b•Ger˜V1.0‘¯bwill˜b“e˜the˜rst˜to“olb“o¸èx˜releaseޤ whicš¸èh–U should“ha˜v˜e“the“same“functions“lik˜e“the“Matlab“R14“SP3“neural“net˜w˜ork“T‘ÿ*¸o•Golb“o˜x.Ž¡¡‘ïsrc:5numberingThe›çQsecond‘çRn•¸èum“bGer˜denes–çRthe˜nished˜functions.‘LçSo˜to˜start,‘ýHonly˜the“MLPs˜will“realised˜andŽ¡so–U this“will“bšGe“the“n•¸èum“b˜er‘U V0.1.0.Ž¡¡‘ïsrc:7numberingThe›third‘n•¸èum“bGer˜denes˜the˜status–of˜the˜actual“dev•¸èelopmen“t˜and˜function.‘Z*V0.0.1‘means˜noŽ¡release–U with“MLP›ÿ*¸,“actually˜,“ev¸èerything“under“construction...‘q€;-D.Ž¡¡‘ïsrc:9numberingRigh•¸èt›_Üno“w˜it's˜v“ersion˜V0.1.3˜whic“h˜means˜MLP‘_˜w“orks˜and˜curren“tly˜the˜transfer˜functionŽ¡logsig–U is“added.ŽŽŸ‘Gïcolor push Black’ÑË22Ž’¨–ï color popŽŽŽŒ‹j9ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch 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will›ôYha•¸èv“e˜the˜same˜names˜lik“e‘ôZin˜the˜b•Go“ok˜with˜one˜exception:‘AV‘ÿ*¸ariables,–´only˜with˜one˜letter,“willŽ¡ha•¸èv“e›U t“w“o˜letters,˜e.g.ޤïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:6codeConventionó  b> cmmi10µX‘ú¸!‘ǵX‘ÈâxŽ¡ïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:7codeConventionµQ–Ǹ!“µQqŽ¡ïcolor push Black‘¸Ž‘ï color popŽŽ‘ïsrc:8codeConventionµa–Ǹ!“µaaŽ¡ïsrc:10codeConvention¹and–U so“on“...ޤ ¡‘ïsrc:12codeConventionThis–U is“only“to“makš¸èe“it“pGossible“to“searc˜h“for“v‘ÿqÐariable“names.ŽŽŸ‘Gïcolor push Black’ÑË23Ž’¨–ï color popŽŽŽŒ‹v¹ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.4) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.2) cvn /DEST pdfmark endŸUïsrc:1octave/octave.texÎChapter‘Vº2ŽŸ2ÉOcta–ÿF¾v“e's›¯a“v‘þ|ailable˜functionsŽŸ(ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.2.1) cvn /DEST pdfmark endŸÑ2.1Ž‘$FA‘þ=‹v‘ÿÆailable‘G\functionsŽŸ æ~ïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.2.1.1) cvn /DEST pdfmark endŸó'¥!¢N ecbx1200Ò2.1.1Ž‘)=lmin_maxŽŸtîïsrc:2octave/functions/min_max¹Chec¸èks–U for“minimal“and“maximal“v‘ÿqÐalues“of“an“input“matrix“for“Ênew¹.ޤ ©ýYÊSyn®>tax:ŽŸf–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.2) cvn /DEST pdfmark endŸ Xïsrc:6octave/functions/min_maxµpr‘5óKñ`y cmr10²=‘ǵmin¹_ŽŽ‘Æ µmax²(µmI‘Èânputs²)Ž¡¦ÊDescription:ŽŸf–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.3) cvn /DEST pdfmark endŸ Xïsrc:9octave/functions/min_maxÏmInputs‘:<¹m¸èust–h'bGe“a“matrix“with“input“training“data“sets.‘ª•This“means‘h&in“the“case,‘¬éfor“a“9-2-1Ž¡MLP‘&û(this–'means›'9“input-,‘0?2“hidden-˜and“1˜output-neuron)“with“100˜input“training˜data“sets,‘0?theŽ¡matrix–ÄØm¸èust“bGe“an›ÄÙ9x100“matrix.‘À¨Ïpr‘ØU¹will˜then“bGe“a“9x2“matrix“with“minimal˜v‘ÿqÐalues“in“the“rstŽ¡column–’¾and›’½maximal“v‘ÿqÐalues“in“the˜second“column.‘*YIf“a“ro¸èw˜holds“2“zeros,‘¢%a“w¸èarning˜will“appGearŽ¡(no–U information“in“this“ro¸èw!).ަÊImpQÂortan®>t:ŽŸf–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.4) cvn /DEST pdfmark endŸ Xïsrc:14octave/functions/min_max¹The–;¾equiv‘ÿqÐal›;½function“in“MA‘ÿ*¸TLAB(TM)‘;vis“called“Ïminmax¹.‘µThis“is˜not“pšGossible“b˜ecause‘;½the“functionsŽ¡Ïmin‘u¹and–U Ïmax‘‰P¹in“Octa•¸èv“e–U are“programed“in“minmax.cc!ŽŽŸ‘Gïcolor push Black’ÑË24Ž’¨–ï color popŽŽŽŒ‹}Ìïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.5) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.3) cvn /DEST pdfmark endŸUï(src:1codingGuideline/codingGuideline.texÎChapter‘Vº3ŽŸ2ÉCo‘¹Ading‘¯GuidelineŽŸ4ï(src:2codingGuideline/codingGuideline.tex¹Some–$whic¸èh“aren't“visible˜to“the“user.‘gìVisible˜names“areŽ¡describGed–U in“the“User's“Guide!Ž¡The–°]v‘ÿqÐariable“idenš¸ètiers“are“tak˜en“from“[ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 14ï color popŽ‘ÿ°Ÿù•¼ïps:SDict begin H.R 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whicš¸èh“holds“the“neural“net˜w˜ork“informationsŽŽ¡pf‘M‚DÊp¹erformance‘U Êf‘ ¹unctionŽŽ¡ÊPp‘Gß¹input–U matrix;“nInputs“x“nDataSetsŽŽ¡ÊPr‘I†¹input–U range,“this“is“a“Nx2“matrix,“that's“wh¸èy“the“capitalized“PŽŽ¡trf‘KBhÊtr¹ansfer‘U Êf‘ ¹unctionŽŽ¡ÊTt‘I¦¹target–U matrix,“nT‘ÿ*¸argets“x“nDataSetsŽŽ¡Êss‘M @¹ro•¸èw›U v“ector˜with˜n“um“bGers˜of˜neurons˜in˜the˜la“y“ers,˜for˜eac“h˜la“y“er,˜one˜en“tryŽŽ¡ÊvE‘H¹ro•¸èw›U v“ector˜with˜errors...‘q€size˜depGends˜on˜net“w“ork˜structure.ŽŽ¡VV‘GV‘ÿ*¸alidation‘U structureŽŽ¡Êxx‘Iû`¹W‘ÿ*¸eigh•¸èt›U v“ector˜in˜column˜formatŽŽ¡Ÿ–õïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.3.1.1) cvn /DEST pdfmark endŸ fdÒ3.1.1Ž‘)=lNnŽŸtîï)src:31codingGuideline/codingGuideline.texÊNn–U ¹is“a“cell“arraš¸èy“and“has“one“en˜try“for“eac˜h“la˜y˜er.‘q€In“realit˜y‘ÿ*¸,“this“will“ha˜v˜e“2“or“3“la˜y˜ers.Ž¡In–eÊNn{1,1}›e¹are“the“v‘ÿqÐalues˜for“the˜rst“hidden“la•¸èy“er.‘¡fThe˜size–eof˜this“matrix“depGends˜on“theŽ¡n•¸èum“bGer–U of“neurons“used“for“this“la•¸èy“er.Ž¡In–ÝKÊNn{2,1}“¹are“the“v‘ÿqÐalues“for‘ÝJthe“second“hidden“la•¸èy“er–ÝKor“the“output“la•¸èy“er.‘IŽThe–ÝKsize“of“this“matrixŽ¡depšGends–U on“the“n•¸èum“b˜er–U of“neurons“used“for“this“la•¸èy“er–U and“so“on“...Ž¡ÊNn{x,1}–U ¹where“Êx“¹can“bGe“¸1¹.Ž¡ŽŸ‘Gïcolor push Black’ÑË25Ž’¨–ï color popŽŽŽŒ‹ˆ,ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.6) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.3.1.2) cvn /DEST pdfmark endŸ Ò3.1.2Ž‘)=lAaŽ©tîï)src:39codingGuideline/codingGuideline.texÊAa–U ¹is“a“cell“arraš¸èy“and“has“one“en˜try“for“eac˜h“la˜y˜er.ޤ In–s÷ÊAa{1,1}“¹are“the“v‘ÿqÐalues“for“the“rst“hidden“la•¸èy“er.‘ÎThe–s÷size“of“this“matrix“depGends“on“theŽ¡n•¸èum“bGer–U of“neurons“used“for“this“la•¸èy“er.Ž¡In–í™ÊAa{2,1}›혹are“the“v‘ÿqÐalues˜for“the“second˜hidden“la•¸èy“er–í™or˜the“output“la•¸èy“er.‘NýThe–í™size“of˜this“matrixŽ¡depšGends–U on“the“n•¸èum“b˜er–U of“neurons“used“for“this“la•¸èy“er.Ž¡See–U ÊNn“¹for“a“more“detailed“description.Ž¡Ÿ ýYïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.3.1.3) cvn /DEST pdfmark endŸÒ3.1.3Ž‘)=lvEަï)src:47codingGuideline/codingGuideline.texÊvE‘âP¹is–âmalso“a“cell“arraš¸èy“whic˜h‘ânholds“in“the“last“(second)“elemen˜t“the“error“v˜ector.‘KEIt's“not“completlyŽ¡clear,–U whš¸èy“in“the“last“(second)“elemen˜t.Ž¡The›rn•¸èum“bGer‘qof˜ro“ws–qdepGends˜on“the˜n•¸èum“bGer–qof˜output“neurons.‘^œF‘ÿ*¸or“one˜output“neuron,‘'ÈÊvE‘c¹holdsŽ¡only–U one“roš¸èw,“for“2“output“neurons,“this“holds“of“course“2“ro˜ws,“and“so“on.ŽŸïïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.3.1.4) cvn /DEST pdfmark endŸ XÒ3.1.4Ž‘)=lJjަï)src:51codingGuideline/codingGuideline.tex¹This–U is“the“short“term“for“the“Jacobi“matrix.ŽŽŸ‘Gïcolor push Black’ÑË26Ž’¨–ï color popŽŽŽŒ‹–Ñïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.7) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.4) cvn /DEST pdfmark endŸUïsrc:1algorithm/algorithmen.texÎChapter‘Vº4ŽŸ2ÉAlgorithmŽŸ4ïsrc:2algorithm/algorithmen.tex¹Here–U are“some“general“though¸èts“abGout“calculating“parts“are“used“in“algorithm.ŽŸ¿Kïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.4.1) cvn /DEST pdfmark endŸXÑ4.1Ž‘$FLev•cen“bpœerg‘G\MarquardtŽŸæ~ï!src:2algorithm/LevenbergMarquardt¹This–U algorithm“will“bGe“programed“with“[ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 14ï color popŽ‘ÿ°Ÿù•¼ïps:SDict begin H.R endŽ‘ÿ°ï|ps:SDict begin [ /Color [0 1 0] /H /I /Border [0 0 0]BorderArrayPatch /Subtype /Link /Dest (cite.4) cvn H.B /ANN pdfmark endï color popŽ‘ÿ°].ŽŸ9ÿïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.4.1.1) cvn /DEST pdfmark endŸ €(Ò4.1.1Ž‘)=lSensitivit y‘¸MatrixŽŸtîï!src:5algorithm/LevenbergMarquardt¹Ho¸èw–U došGes“this“lo˜oks“lik¸èe?ŽŸ ŸÕïps:SDict begin H.S endïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (Item.1) cvn /DEST pdfmark end¤±ñïcolor push Black‘ 9b1.Ž‘ï color popŽŽ‘ï!src:7algorithm/LevenbergMarquardtfor–U a“1-1-1“MLPŽ©±òïps:SDict begin H.S endïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (Item.2) cvn /DEST pdfmark end¡ïcolor push Black‘ 9b2.Ž‘ï color popŽŽ‘ï!src:8algorithm/LevenbergMarquardtfor–U a“2-1-1“MLPަïps:SDict begin H.S endïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (Item.3) cvn /DEST pdfmark endŸ±òïcolor push Black‘ 9b3.Ž‘ï color popŽŽ‘ï!src:9algorithm/LevenbergMarquardtfor–U a“1-2-1“MLPŽŸº&Ê1-1-1‘ÕMLPŽ©tîïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.5) cvn /DEST pdfmark end¤ ï"src:13algorithm/LevenbergMarquardt¹In›Óãthis–Óâcase,‘ó”the“MLP‘ÓÂholds˜one“input˜neuron,‘ó“one˜hidden˜neuron“and˜one“output˜neuron.‘íÈTheŽ¡n•¸èum“bGer–U of“w•¸èeigh“ts–U needed“for“this“MLP“is“4“(2“w•¸èeigh“ts,–U 2“biases).Ž¡¡‘ï"src:15algorithm/LevenbergMarquardtIt–àöneeds“t•¸èw“o›àõsensitivit“y–àömatrices“bGecause“the˜t•¸èw“o›àöla“y“ers.‘Eac“h˜sensitivit“y˜matrix˜will‘àõhold˜1Ž¡elemenš¸èt.‘CBThis–ðais“tak˜en“from“[ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 14ï color popŽ‘ÿ°Ÿù•¼ïps:SDict begin H.R endŽ‘ÿ°ï|ps:SDict begin [ /Color [0 1 0] /H /I /Border [0 0 0]BorderArrayPatch /Subtype /Link /Dest (cite.4) cvn H.B /ANN pdfmark endï color popŽ‘ÿ°],‘1example“P12.5‘ð`page“12-44.‘CCA˜tten˜tion,‘1in“this‘ð`example“are“t˜w˜oŽ¡data–U sets“used,“this“is“the“reason“for“the“4“elemen¸èts...!ŽŸº'Ê2-1-1‘ÕMLPަïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.6) cvn /DEST pdfmark end¡ï"src:19algorithm/LevenbergMarquardt¹In–• this›•¡case,‘¥Áthe“MLP‘•holds“t•¸èw“o˜input–• neurons,‘¥Áone“hidden˜neuron“and˜one“output˜neuron.‘3TheŽ¡n•¸èum“bGer–U of“w•¸èeigh“ts–U needed“for“this“MLP“is“5“(3“w•¸èeigh“ts,–U 2“biases).Ž¡¡‘ï"src:21algorithm/LevenbergMarquardtIt–Bªneeds‘B«also“t•¸èw“o›Bªsensitivit“y˜matrices‘B«bGecause˜the˜t“w“o˜la“y“ers.‘kYA“ctually‘ÿ*¸,‘F\the˜input˜is˜not‘B«only˜aŽ¡scalar,‘ Ñit–öýis“a‘öüvš¸èector“with“2“elemen˜ts.‘REv˜en“though,‘ Ñagain“after“[ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 14ï color popŽ‘ÿ°Ÿù•¼ïps:SDict begin H.R endŽ‘ÿ°ï|ps:SDict begin [ /Color [0 1 0] /H /I /Border [0 0 0]BorderArrayPatch /Subtype /Link /Dest (cite.4) cvn H.B /ANN pdfmark endï color popŽ‘ÿ°].‘RI‘öåthink“the“sensitivit˜y“matricesŽ¡will–3Šhold“only“1–3‰elemenš¸èt.‘fOSo“the–3Šn˜um˜bGer“of“elemen˜ts“will“bi“propGortional“to‘3‰the“n˜um˜bGer“of“hiddenŽ¡neurons–U and“the“n•¸èum“bGer–U of“output“neurons.ŽŸº&Ê1-2-1‘ÕMLPަïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.7) cvn /DEST pdfmark end¡ï"src:24algorithm/LevenbergMarquardt¹In–• this›•¡case,‘¥Áthe“MLP‘•holds“one˜input“neuron,‘¥Át•¸èw“o–• hidden˜neurons“and˜one“output˜neuron.‘3TheŽ¡n•¸èum“bGer–U of“w•¸èeigh“ts–U needed“for“this“MLP“is“7“(4“w•¸èeigh“ts,–U 3“biases).Ž¡ŽŸ‘Gïcolor push Black’ÑË27Ž’¨–ï color popŽŽŽŒ‹ŸEïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.8) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘!Gï"src:26algorithm/LevenbergMarquardt¹It–7Lneeds›7Malso“t•¸èw“o˜sensitivit“y–7Lmatrices˜bGecause“the“t•¸èw“o˜la“y“ers.‘gA“ctually‘ÿ*¸,‘=Cthe˜input–7Lis˜again“onlyޤ ‘Ga–î"scalar.‘<†Noš¸èw“calculating‘î!µnŸü^ÿóÙ“ Rcmr7±1ŽŸl1ŽŽ‘j•¹will“result“in“a“ro˜w“v˜ector“with“2“elemen˜ts.‘<…µnŸü^ÿ±2ŽŸl1ŽŽ‘j•¹will“hold“only“oneŽ¡‘Gelemenš¸èt–U and“so“w˜e“ha˜v˜e“3“elemen˜ts“in“the“sensitivit˜y“matrix.Ž¡¡‘!Gï"src:29algorithm/LevenbergMarquardtW–ÿ*¸e›±Âcan‘±Ãsa¸èy“,‘Èëthe˜n•¸èum“bGer˜of–±Ãhidden˜neurons˜is“respGonsible˜for“the˜dimension˜of“the˜sensitivit¸èyŽ¡‘Gmatrices.‘‹ŠF‘ÿ*¸or–]Îexample,‘_ùa“4-3-1“MLP‘]Ìwith“100‘]Ídata“sets“will“generate“folloš¸èwing“sensitivit˜y“matrixŽŸAÌ‘Gfor–U the“rst“la•¸èy“er:ŸýgŠ‘#²~ŽŸ˜v‘q€ÊSŽŽ‘ Ô¨Ÿù2´±1Ž‘3²=‘Ç[ŸýgŠ‘±”~ŽŸ˜vÊSŽŽŸù2´‘c(±1ŽŸ F,‘c(1ŽŽ– ß›¸jŸýgŠ‘±”²~ŽŸ˜vÊSŽŽŸù2´‘c(±1ŽŸ F,‘c(2ŽŽ“¸jµ:::¸jŸýgŠ‘±”²~ŽŸ˜vÊSŽŽŸù2´‘c(±1ŽŸ F,‘c(100ŽŽ‘ز]ŽŸÐ$ŸýgŠ‘ÂÛ~ŽŸ˜v‘GÊSŽŽŸù2´‘to±1ŽŸ F,‘to1ŽŽ‘ ®ü¹will›¾hold–¾3“elemen¸ètsŸýgŠ‘o­²~ŽŸ˜v˜ÊSŽŽŸù2´‘ !A±1ŽŸ F,‘ !A1ŽŽ‘˲=‘v[ŸýgŠ‘±”~ŽŸ˜vÊSŽŽŸù2´‘c(±1ŽŸ F,‘c(1ó 0e—rcmmi7´;±1ŽŽŸýgŠ‘«»²~ŽŸ˜v‘ú'ÊSŽŽŸù2´‘]O±1ŽŸ F,‘]O2´;±1ŽŽŸýgŠ‘*¥ã²~ŽŸ˜v‘)ôOÊSŽŽŸù2´‘0Ww±1ŽŸ F,‘0Ww3´;±1ŽŽ–;0]²]Ÿü^ÿ´TŽ‘L¶¹;ŸýgŠ‘¤*²~ŽŸ˜v‘ò–ÊSŽŽŸù2´‘ U¾±1ŽŸ F,‘ U¾2ŽŽ‘HI²=‘v[ŸýgŠ‘±”~ŽŸ˜vÊSŽŽŸù2´‘c(±1ŽŸ F,‘c(1´;±2ŽŽŸýgŠ‘«»²~ŽŸ˜v‘ú'ÊSŽŽŸù2´‘]O±1ŽŸ F,‘]O2´;±2ŽŽŸýgŠ‘*¥ã²~ŽŸ˜v‘)ôOÊSŽŽŸù2´‘0Ww±1ŽŸ F,‘0Ww3´;±2ŽŽ“²]Ÿü^ÿ´TŽ‘ йand˜so–¾on.‘¬mSo˜matrix“willŽŸ%u‘Gha•¸èv“e–U a“size“of“3x100“forŸýgŠ‘´²~ŽŸ˜v“ÊSŽŽŸù2´‘ ¸H±1ŽŸ F,‘ ¸H1ŽŽ‘‰Û¹and“a“size“of“1x100“forŸýgŠ‘´²~ŽŸ˜v“ÊSŽŽŸù2´‘ ¸H±1ŽŸ F,‘ ¸H2ŽŽ‘4»¹.Ž¡¡‘!Gï"src:36algorithm/LevenbergMarquardtBy–U the“w•¸èa“y‘ÿ*¸,–U the“jacobian“matrix“will“bGe“a“100x20“matrix“..ŽŽŸ‘Gïcolor push Black’ÑË28Ž’¨–ï color popŽŽŽŒ‹ ²Ïïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïFps:SDict begin [ /View [/XYZ H.V] /Dest (page.9) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.5) cvn /DEST pdfmark endŸUïsrc:1tests/test.texÎChapter‘Vº5ŽŸ2ÉT‘ýÔ:estŽŸ(ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.1) cvn /DEST pdfmark endŸÑ5.1Ž‘$FisppœosinctŽ©æ~‘ûïcolor push Blackï color popŽŽïsrc:11tests/isposintó,qLË ectt1000×%!sharedޤ %!–?¬disp("testing“isposint")Ž¡%!assert(isposint(1))–?¬#“this“should“passŽ¡%!assert(isposint(0.5),0)–?¬#“should“return“zeroŽ¡%!assert(isposint(-1),0)–?¬#“should“return“zeroŽ¡%!assert(isposint(-1.5),0)–?¬#“should“return“zeroŽ¡%!assert(isposint(0),0)–?¬#“should“return“zeroŽ¡%!fail("isposint([0–?¬0])","Input“argument“must“not“be“a“vector,“only“scalars“are“allowed!")Ž¡%!fail("isposint('testString')","Input–?¬argument“must“not“be“a“vector,“only“scalars“are“allowed!")ŽŸI–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.2) cvn /DEST pdfmark endŸÇ@Ñ5.2Ž‘$Fmin_maxަ‘ûïcolor push Blackï color popŽŽïsrc:9tests/min_max×%!sharedŽ¡%!–?¬disp("testing“min_max")Ž¡%!test–?¬fail("min_max(1)","Argument“must“be“a“matrix.")Ž¡%!test–?¬fail("min_max('testString')","Argument“must“be“a“matrix.")Ž¡%!test–?¬fail("min_max(cellA{1}=1)","Argument“must“be“a“matrix.")Ž¡%!test–?¬fail("min_max([1+1i,“2+2i])","Argument“must“be“a“matrix.")Ž¡%!test–?¬fail("min_max([1+1i,“2+2i;“3+1i,“4+2i])","Argument“has“illegal“type.")ŽŸI–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.3) cvn /DEST pdfmark endŸÇ@Ñ5.3Ž‘$Fnewަ‘ûïcolor push Blackï color popŽŽïsrc:32tests/newff×%!sharedŽ¡%!–?¬disp("testing“newff")Ž¡%!testŽ¡%!–?¬Pr“=“[1;2];Ž¡%!–?¬fail("newff(Pr,[1“1],{'tansig','purelin'},'trainlm','unused','mse')","Input“ranges“must“be“a“two“column“matrix!")Ž¡%!testŽ¡%!–?¬Pr“=“[1“2“;“4‘ X6];Ž¡%!–?¬assert(__checknetstruct(newff(Pr,[1“1],{'tansig','purelin'},'trainlm','unused','mse')))Ž¡%!testŽ¡%!–?¬Pr“=“[1“2“3;“4“5“6];Ž¡%!–?¬fail("newff(Pr,[1“1],{'tansig','purelin'},'trainlm','unused','mse')","Input“ranges“must“be“a“two“column“matrix!")Ž¡%!testŽ¡%!–?¬Pr“=“[5“3;“4“5];ŽŽŸ‘Gïcolor push Black’ÑË2¹9Ž’¨–ï color popŽŽŽŒ‹ ¼œïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.10) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘G×%!–?¬fail("newff(Pr,[1“1],{'tansig','purelin'},'trainlm','unused','mse')",\ޤ ‘G%!‘ X"Input–?¬ranges“has“values“in“the“second“column“larger“as“in“the“same“row“of“the“first“column.")Ž¡‘G%!testŽ¡‘G%!–?¬Pr“=“[1“2“;“4“6];Ž¡‘G%!–?¬fail("newff(Pr,[1“1;“2“3],{'tansig','purelin'},'trainlm','unused','mse')",\Ž¡‘G%!‘ X"Layer–?¬sizes“is“not“a“row“vector.")Ž¡‘G%!testŽ¡‘G%!–?¬Pr“=“[1“2“;“4“6];Ž¡‘G%!–?¬assert(__checknetstruct(newff(Pr,[“2“3],{'tansig','purelin'},'trainlm','unused','mse')))Ž¡‘G%!testŽ¡‘G%!–?¬Pr“=“[1“2“;“4“6];Ž¡‘G%!‘?¬fail("newff(Pr,[1],{'tansig','purelin'},'trainlm','unused','mse')",\Ž¡‘G%!‘ X"There–?¬must“be“at“least“one“hidden“layer“and“one“output“layer!")Ž¡‘G%!testŽ¡‘G%!–?¬Pr“=“[1“2“;“4“6];Ž¡‘G%!–?¬fail("newff(Pr,[-1“1],{'tansig','purelin'},'trainlm','unused','mse')",\Ž¡‘G%!‘ X"Layer–?¬sizes“is“not“a“row“vector“of“positive“integers.")Ž‘G©I–ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.4) cvn /DEST pdfmark endŸÇ@Ñ5.4Ž‘$FprestdŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:7tests/prestd×%!shared–?¬Pp,“Tt,“pnŽ¡%!‘ XPp–?¬=“[1“2“3“4;“-1“3“2“-1];Ž¡%!‘ XTt–?¬=“[3“4“5“6];Ž¡%!‘ X[pn,meanp,stdp]–?¬=“prestd(Pp);Ž¡%!assert(pn,[-1.16190–?¬-0.38730“0.38730“1.16190;“-0.84887“1.09141“0.60634“-0.84887],0.00001);ަïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.5) cvn /DEST pdfmark endŸÇ@Ñ5.5Ž‘$FpurelinŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:7tests/purelin×%!assert(purelin(2),2);Ž¡%!assert(purelin(-2),-2);Ž¡%!assert(purelin(0),0);Ž¡%!error‘ X#–?¬this“test“must“throw“an“error!Ž¡%!‘?¬assert(purelin(2),1);ަïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.6) cvn /DEST pdfmark endŸÇ@Ñ5.6Ž‘$FsubsetŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:67tests/subset×%!shared–?¬matrix,“nTargets,“mTrain,“mTest,“mValiŽ¡%!–?¬disp("testing“subset")Ž¡%!–?¬matrix“=“[1“2“3“4“5“6“7“8“9“10“11“12“13“14“15“16“17“18“18“20;“\Ž¡%!‘ X0–?¬2“4“1“3“5“3“4“1“-1“-2“-9“-1“10“12“20“11“11“11“11;“\Ž¡%!–?¬-2“2“2“2“2“0“0“0“0‘ X0“10“12“13“12“13“44“33“32“98“11;“\Ž¡%!› X0–?¬0“0“0“1“1“1“1“0˜0˜1˜1˜1˜0˜0˜1˜1˜1˜0˜0;“\Ž¡%!‘9¼d4–?¬4“4“4“4“4“4“4“4– X4“4“4“4“4“4“4“4“4“4“4;‘?¬\Ž¡%!‘9¼d1–?¬2“3“4“5“6“7“8“9“10“11“12“13“33“44“55“66“77“88“99];Ž¡%!–?¬nTargets“=“1;“#“the“last“row“is“equivalent“to“the“target“values.Ž¡%!–?¬[mTrain,“mTest,“mVali]“=“subset(matrix,nTargets);‘ X############################Ž¡%!assert(size(mTrain,2)==10);#–?¬50%“of“20Ž¡%!assert(size(mTest,2)==6);#–?¬1/3“of“20“=“6“(floor)Ž¡%!assert(size(mVali,2)==4);#–?¬1/6“of“20“=“4“(floor)Ž¡%!–?¬#“It's“not“possible“to“test“the“column“order“with“this“call!Ž¡%!–?¬#“randomizing“is“used!“But“all“max“and“min“values“should“beŽ¡%!–?¬#“in“the“training“setŽŽŸ‘Gïcolor push Black’ÏKZ¹10Ž’¨–ï color popŽŽŽŒ‹ Çéïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.11) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘G×%!assert(max(mTrain(1,:))==max(matrix(1,:)));ޤ ‘G%!assert(min(mTrain(1,:))==min(matrix(1,:)));Ž¡‘G%!assert(max(mTrain(2,:))==max(matrix(2,:)));Ž¡‘G%!assert(min(mTrain(2,:))==min(matrix(2,:)));Ž¡‘G%!assert(max(mTrain(3,:))==max(matrix(3,:)));Ž¡‘G%!assert(min(mTrain(3,:))==min(matrix(3,:)));Ž¡‘G%!assert(max(mTrain(4,:))==max(matrix(4,:)));Ž¡‘G%!assert(min(mTrain(4,:))==min(matrix(4,:)));Ž¡‘G%!Ž¡‘G%!Ž¡‘G%!–?¬[mTrain,“mTest,“mVali]“=“subset(matrix,nTargets,0);‘ X############################Ž¡‘G%!assert(size(mTrain,2)==10);#–?¬50%“of“20Ž¡‘G%!assert(size(mTest,2)==6);#–?¬1/3“of“20“=“6“(floor)Ž¡‘G%!assert(size(mVali,2)==4);#–?¬1/6“of“20“=“4“(floor)Ž¡‘G%!assert(mTrain==matrix(:,1:10));Ž¡‘G%!assert(mTest==matrix(:,11:16));Ž¡‘G%!assert(mVali==matrix(:,17:20));Ž¡‘G%!Ž¡‘G%!Ž¡‘G%!–?¬[mTrain,“mTest,“mVali]“=“subset(matrix,nTargets,2);‘ X############################Ž¡‘G%!assert(size(mTrain,2)==10);#–?¬50%“of“20Ž¡‘G%!assert(size(mTest,2)==6);#–?¬1/3“of“20“=“6“(floor)Ž¡‘G%!assert(size(mVali,2)==4);#–?¬1/6“of“20“=“4“(floor)Ž¡‘G%!assert(max(mTrain(1,:))==max(matrix(1,:)));Ž¡‘G%!assert(min(mTrain(1,:))==min(matrix(1,:)));Ž¡‘G%!assert(max(mTrain(2,:))==max(matrix(2,:)));Ž¡‘G%!assert(min(mTrain(2,:))==min(matrix(2,:)));Ž¡‘G%!assert(max(mTrain(3,:))==max(matrix(3,:)));Ž¡‘G%!assert(min(mTrain(3,:))==min(matrix(3,:)));Ž¡‘G%!assert(max(mTrain(4,:))==max(matrix(4,:)));Ž¡‘G%!assert(min(mTrain(4,:))==min(matrix(4,:)));Ž¡‘G%!Ž¡‘G%!Ž¡‘G%!–?¬##“next“test“...“optimize“twiceŽ¡‘G%!–?¬matrix“=“[1“2“3“4“5“6“7“20“8“10“11“12“13“14“15“16“17“18“18“9;“\Ž¡‘G%!‘ X0–?¬2“4“1“3“5“3“4“1“-1“-2“-9“-1“10“12“20“11“11“11“11;“\Ž¡‘G%!–?¬-2“2“2“2“2“0“0“0“0‘ X0“10“12“13“12“13“44“33“32“98“11;“\Ž¡‘G%!› X0–?¬0“0“0“1“1“1“1“0˜0˜1˜1˜1˜0˜0˜1˜1˜1˜0˜0;“\Ž¡‘G%!‘9¼d4–?¬4“4“4“4“4“4“4“4– X4“4“4“4“4“4“4“4“4“4“4;‘?¬\Ž¡‘G%!‘9¼d1–?¬2“3“4“5“6“7“8“9“10“11“12“13“33“44“55“66“77“88“99];Ž¡‘G%!–?¬[mTrain,“mTest,“mVali]“=“subset(matrix,nTargets,2);‘ X############################Ž¡‘G%!assert(max(mTrain(1,:))==max(matrix(1,:)));Ž¡‘G%!assert(min(mTrain(1,:))==min(matrix(1,:)));Ž¡‘G%!assert(max(mTrain(2,:))==max(matrix(2,:)));Ž¡‘G%!assert(min(mTrain(2,:))==min(matrix(2,:)));Ž¡‘G%!assert(max(mTrain(3,:))==max(matrix(3,:)));Ž¡‘G%!assert(min(mTrain(3,:))==min(matrix(3,:)));Ž¡‘G%!assert(max(mTrain(4,:))==max(matrix(4,:)));Ž¡‘G%!assert(min(mTrain(4,:))==min(matrix(4,:)));Ž‘GŸtNïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.7) cvn /DEST pdfmark endŸœˆÑ5.7Ž‘$F__analyzerocwsŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:23tests/__analyzerows×%!shared–?¬b,“retmatŽŽŸ‘Gïcolor push Black’ÏKZ¹11Ž’¨–ï color popŽŽŽŒ‹ Õµïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.12) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘G×%!–?¬disp("testing“__analyzerows")ޤ ‘G%!–?¬b“=“[1“0“0“1;“1“0“0“0;“1“2“0“1];Ž¡‘G%!–?¬retmat“=“__analyzerows(b);Ž¡‘G%!assert(retmat(1,1)==1);#%!assert(retmat(1,1)==1);Ž¡‘G%!assert(retmat(2,1)==1);Ž¡‘G%!assert(retmat(3,1)==0);Ž¡‘G%!–?¬b“=“[1“0“0“2;“1“0“0“0;“1“1“1“1];Ž¡‘G%!–?¬retmat“=“__analyzerows(b);Ž¡‘G%!assert(retmat(1,2)==0);Ž¡‘G%!assert(retmat(2,2)==0);Ž¡‘G%!assert(retmat(3,2)==1);Ž¡‘G%!–?¬b“=“[1“0“0“2;“1“0“0“0;“1“1“1“1];Ž¡‘G%!–?¬retmat“=“__analyzerows(b);Ž¡‘G%!assert(retmat(1,3)==2);Ž¡‘G%!assert(retmat(2,3)==0);Ž¡‘G%!assert(retmat(3,3)==0);Ž¡‘G%!–?¬retmat“=“__analyzerows(b);Ž¡‘G%!assert(retmat(1,4)==1);Ž¡‘G%!assert(retmat(2,4)==0);Ž¡‘G%!assert(retmat(3,4)==0);Ž‘G©tNïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.8) cvn /DEST pdfmark endŸœˆÑ5.8Ž‘$F__copcycoltoppœos1ŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:11tests/__copycoltopos1×%!shared–?¬a,“retmatŽ¡%!–?¬disp("testing“__copycoltopos1")Ž¡%!–?¬a“=“[0“1“2“3“4;“5“6“7“8“9];Ž¡%!–?¬retmat“=“__copycoltopos1(a,3);Ž¡%!assert(retmat(1,1)==2);Ž¡%!assert(retmat(2,1)==7);Ž¡%!–?¬retmat“=“__copycoltopos1(a,5);Ž¡%!assert(retmat(1,1)==4);Ž¡%!assert(retmat(2,1)==9);ަïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.9) cvn /DEST pdfmark endŸœˆÑ5.9Ž‘$F__optimizedatasetsŽŸæ~‘ûïcolor push Blackï color popŽŽïsrc:23tests/__optimizedatasets×%!shared–?¬retmatrix,“matrixŽ¡%!–?¬disp("testing“__optimizedatasets")Ž¡%!–?¬matrix“=“[1“2“3“2“1“2“3“0“5“4“3“2“2“2“2“2“2;“\Ž¡%!‘ X0–?¬1“1“0“0“0“0“0“0“0“0“0“0“0“1“1“0;“\Ž¡%!–?¬-1“3“2“4“9“1“1“1“1“1“9“1“1“1“9“9“0;“\Ž¡%!‘ X2–?¬3“2“3“2“2“2“2“3“3“3“3“1“1“1“1“1];Ž¡%!–?¬##“The“last“row“is“equal“to“the“neural“network“targetsŽ¡%!–?¬retmatrix“=“__optimizedatasets(matrix,9,1);Ž¡%!–?¬##“the“above“statement“can't“be“tested“with“assert!Ž¡%!–?¬##“it“contains“random“values!“So“pass“a“"success"“messageŽ¡%!assert(1==1);Ž¡%!–?¬matrix“=“[1“2“3“2“1“2“3“0“5“4“3“2“2“2“2“2“2;“\Ž¡%!‘ X0–?¬1“1“0“0“0“0“0“0“0“0“0“0“0“1“1“0;“\Ž¡%!–?¬-1“3“2“4“9“1“1“1“1“1“9“1“1“1“9“9“0;“\Ž¡%!‘ X2–?¬3“2“3“2“2“2“2“3“3“3“3“1“1“1“1“1];Ž¡%!–?¬##“The“last“row“is“equal“to“the“neural“network“targetsŽ¡%!–?¬retmatrix“=“__optimizedatasets(matrix,9,1,0);ŽŽŸ‘Gïcolor push Black’ÏKZ¹12Ž’¨–ï color popŽŽŽŒ‹ âìïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.13) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘G×%!assert(retmatrix(1,1)==5);ޤ ‘G%!assert(retmatrix(2,1)==0);Ž¡‘G%!assert(retmatrix(3,1)==1);Ž¡‘G%!assert(retmatrix(4,1)==3);Ž‘GŸtNïps:SDict begin H.S endïps:SDict begin 12 H.A endïLps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.10) cvn /DEST pdfmark endŸœˆÑ5.10Ž‘+ýO__randomisecolsŽ©æ~‘ûïcolor push Blackï color popŽŽïsrc:4tests/__randomisecols×%!#–?¬no“test“possible,“contains“randperm“which“is“usingŽ¡%!#–?¬some“randome“functionsŽŸæïps:SDict begin H.S endïps:SDict begin 12 H.A endïLps:SDict begin [ /View [/XYZ H.V] /Dest (section.5.11) cvn /DEST pdfmark endŸ*¸Ñ5.11Ž‘+ýO__rerangecolumnsަ‘ûïcolor push Blackï color popŽŽïsrc:32tests/__rerangecolumns×%!shared–?¬matrix,analyzeMatrix,nTrainSets,“returnmatrixŽ¡%!–?¬disp("testing“__rerangecolumns")Ž¡%!–?¬matrix“=“[0“1“0“0“0“0“1“0“1“1;‘ X\Ž¡%!› X4–?¬4“4“4“4“4“4“4“4“4;˜\Ž¡%!‘)ý`-1.1–?¬-1.1“2“3“4“3.2“1“8“9“10;“\Ž¡%!‘9¼d0–?¬1.1“3“4“5“2“10“10“2“3;“\Ž¡%!‘4|¸-1–?¬1“1“1“1“2“3“4“1“5];Ž¡%!–?¬analyzeMatrix“=“[1“0“0“0;“0“1“0“0;“0“0“2“1;“0“0“1“2;“0“0“1“1];Ž¡%!–?¬nTrainSets“=“8;Ž¡%!–?¬returnmatrix“=“__rerangecolumns(matrix,analyzeMatrix,nTrainSets);Ž¡%!assert(returnmatrix(1,1)==1);Ž¡%!assert(returnmatrix(2,1)==4);Ž¡%!assert(returnmatrix(3,1)==1);Ž¡%!assert(returnmatrix(4,1)==10);Ž¡%!assert(returnmatrix(5,1)==3);Ž¡%!–?¬matrix“=“[0“1“0“0“0“0“1“0“1“1;‘ X\Ž¡%!› X4–?¬4“4“4“4“4“4“4“4“4;˜\Ž¡%!‘4|¸-1.1–?¬-1.1“2“3“4“3.2“1“8“9“10;‘ X\Ž¡%!‘9¼d0–?¬1.1“3“4“5“2“10“10“2“3;‘ X\Ž¡%!‘4|¸-1–?¬1“1“1“1“2“3“4“1“5;‘~\Ž¡%!› X0–?¬1“2“1“2“1“2“3“4“5;];˜#“the“last“row“is“euqal“to“the“nnet“targetsŽ¡%!–?¬analyzeMatrix“=“[1“0“0“0;“0“1“0“0;“0“0“2“1;“0“0“1“2;“0“0“1“1];Ž¡%!–?¬nTrainSets“=“8;Ž¡%!–?¬returnmatrix“=“__rerangecolumns(matrix,analyzeMatrix,nTrainSets);Ž¡%!assert(returnmatrix(1,1)==1);Ž¡%!assert(returnmatrix(2,1)==4);Ž¡%!assert(returnmatrix(3,1)==1);Ž¡%!assert(returnmatrix(4,1)==10);Ž¡%!assert(returnmatrix(5,1)==3);Ž¡%!assert(returnmatrix(6,1)==2);ŽŽŸ‘Gïcolor push Black’ÏKZ¹13Ž’¨–ï color popŽŽŽŒ‹î‘ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.14) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïIps:SDict begin [ /View [/XYZ H.V] /Dest (chapter.6) cvn /DEST pdfmark endŸUïsrc:1analyzing/matlab.texÎChapter‘Vº6ŽŸ2Éanalyzing–¯matlab“functionsŽŸ,Ö+ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.6.1) cvn /DEST pdfmark endŸ )ÕÑ6.1Ž‘$Fanalyzing‘G\newŽŸæ~ïsrc:2analyzing/analyzingNewff¹First,‘qÉÏnew‘WM¹will–8ÚbGe›8Ûanalyzed“for˜a“X-X-X‘8 mlp.‘°This“means,‘qÉmaxim¸èum˜3“la•¸èy“ers,‘qÊincluding‘8Útheޤ input›¿Ìla•¸èy“er.‘±…Or˜in‘¿Íw“ords,‘Úwone˜input-˜one‘¿Íhidden-˜and˜one˜output-la“y“er.‘±…The˜n“um“bGer‘¿Íof˜neuronsŽ¡are‘U c¸èhoGosable.Ž¡‘ïsrc:4analyzing/analyzingNewffF‘ÿ*¸olloš¸èwing–U command“will“bGe“used,“to“create“a“new“feed-forw˜ard“neural“net˜w˜ork:Ž¡MLPnet–U =“new(mMinMaxElemen¸èts,[nHiddenNeurons“nOutputNeurons],...Ž¡{'tansig','purelin'},'trainlm','learngdm','mse');Ž¡¡‘ïsrc:8analyzing/analyzingNewffnew–‘ßis›‘Þthe“matlab“command,‘¡mMinMaxElemen¸èts“is“a˜µRÇx²2¹-Matrix“with“minim¸èum˜and“max-Ž¡im¸èum–§Pv‘ÿqÐalues›§Oof“the“inputs.‘hµR‘»¹is“equal˜to“the“n•¸èum“bGer˜of–§Pinput˜neurons.‘h[nHiddenNeurons“nOut-Ž¡putNeurons]–1are›2the“scalar“v‘ÿqÐalues,‘Þöto“dene˜the“n•¸èum“bGer–1of“neurons˜in“the“hidden˜and“outputŽ¡la•¸èy“er.‘KOne›áÍv‘ÿqÐalue,‘øßfor‘áÎeac“h˜la“y“er.‘K{'tansig','purelin'}–áÎare“the“transfer˜functions,‘øßfor“eac•¸èh˜la“y“er.‘KThisŽ¡means,‘+Z'tansig'–‚for“the“hidden“la•¸èy“er–‚and‘'purelin'“for“the“output“la•¸èy“er.‘s¥'trainlm'–‚is“the“trainingŽ¡algorithm,‘†éin‘|õthis›|ôcase,‘†êLev•¸èen“bGerg-Marquardt.‘èý'learngdm'˜is–|õthe˜learn˜algorithm“and˜'mse'“is˜theŽ¡pGerformance–U function,“Êm¹ean-Ês¹quare-Êe¹rror.Ž¡MLPnet–U will“bGe“a“structure“with“folloš¸èwing“con˜ten˜t:ŽŸ‘ûïcolor push Blackï color popŽŽïsrc:64analyzing/analyzingNewff×Neural–?¬Network“object:Ž¡¡‘þ°architecture:Ž¡¡‘/= numInputs:‘?¬1Ž¡‘/= numLayers:‘?¬2Ž¡‘$½´biasConnect:–?¬[1;“1]Ž¡‘~inputConnect:–?¬[1;“0]Ž¡‘~layerConnect:–?¬[0“0;“1“0]Ž¡‘>\outputConnect:–?¬[0“1]Ž¡‘>\targetConnect:–?¬[0“1]Ž¡¡‘)ý`numOutputs:‘?¬1‘ X(read-only)Ž¡‘)ý`numTargets:‘?¬1‘ X(read-only)Ž¡‘þ°numInputDelays:‘?¬0‘ X(read-only)Ž¡‘þ°numLayerDelays:‘?¬0‘ X(read-only)Ž¡¡‘þ°subobject‘?¬structures:Ž¡¡‘>üinputs:–?¬{1x1“cell}“of“inputsŽ¡‘>ülayers:–?¬{2x1“cell}“of“layersŽŽŸ‘Gïcolor push Black’ÏKZ¹14Ž’¨–ï color popŽŽŽŒ‹øÌïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.15) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘KÍ«×outputs:–?¬{1x2“cell}“containing“1“outputޤ ‘KÍ«targets:–?¬{1x2“cell}“containing“1“targetŽ¡‘Q Wbiases:–?¬{2x1“cell}“containing“2“biasesŽ¡‘1OinputWeights:–?¬{2x1“cell}“containing“1“input“weightŽ¡‘1OlayerWeights:–?¬{2x2“cell}“containing“1“layer“weightŽ¡¡‘'÷functions:Ž¡¡‘FÿadaptFcn:‘?¬'trains'Ž¡‘KÍ«initFcn:‘?¬'initlay'Ž¡‘<§performFcn:‘?¬'mse'Ž¡‘FÿtrainFcn:‘?¬'trainlm'Ž¡¡‘'÷parameters:Ž¡¡‘<§adaptParam:‘?¬.passesŽ¡‘ANSinitParam:‘?¬(none)Ž¡‘1OperformParam:‘?¬(none)Ž¡‘<§trainParam:–?¬.epochs,“.goal,“.max_fail,“.mem_reduc,Ž¡‘{ ·.min_grad,–?¬.mu,“.mu_dec,“.mu_inc,Ž¡‘{ ·.mu_max,–?¬.show,“.timeŽ¡¡‘'÷weight–?¬and“bias“values:Ž¡¡‘f IW:–?¬{2x1“cell}“containing“1“input“weight“matrixŽ¡‘f LW:–?¬{2x2“cell}“containing“1“layer“weight“matrixŽ¡‘kK³b:–?¬{2x1“cell}“containing“2“bias“vectorsŽ¡¡‘'÷other:Ž¡¡‘Fÿuserdata:–?¬(user“stuff)ŽŸª«‘Gïsrc:65analyzing/analyzingNewffÏnumInputs:‘™P1‘Zð¹:‘q€one–U input“la•¸èy“erŽ¡‘GÏnumL‘ÿ}/ayers:‘™P2‘Zð¹:‘q€one–U hidden“and“one“output“la•¸èy“erŽ¡‘GÏbiasConne‘ÿ}/ct:‘™P[1;‘“°1]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏinputConne‘ÿ}/ct:‘™P[1;‘“°0]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏlayerConne‘ÿ}/ct:‘™P[0–“°0;“1“0]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏoutputConne‘ÿ}/ct:‘™P[0‘“°1]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏtar–ÿ}/getConne“ct:‘™P[0‘“°1]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumOutputs:‘™P1‘“°(r–ÿ}/e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumT‘ÿ;Èar–ÿ}/gets:‘™P1‘“°(r“e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumInputDelays:‘™P0‘“°(r–ÿ}/e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumL–ÿ}/ayerDelays:‘™P0‘“°(r“e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏinputs:‘™P1x1–“°c‘ÿ}/el‘‚Ðl“of“inputs‘Ò¹:‘q€input›U la•¸èy“er˜denitionŽ¡‘GBecause›¶|w•¸èe‘¶{ha“v“e˜dened–¶{only˜one“input˜la•¸èy“er,‘ÎÒy“ou˜can–¶{see˜the“detailed˜denition“with˜follo¸èwingŽ¡‘Gcommand–U in“the“matlab“prompt:Ž¡ŸUU‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:87analyzing/analyzingNewff×MLPnet.inputs{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘6Îûrange:–?¬[26x2“double]Ž¡‘<§size:‘?¬26Ž¡‘'÷userdata:–?¬[1x1“struct]ŽŽŸ‘Gïcolor push Black’ÏKZ¹15Ž’¨–ï color popŽŽŽŒ‹Éïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.16) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘Gïsrc:88analyzing/analyzingNewff¹range–…‹are›…Šthe“min.–Àand˜max.“v‘ÿqÐalues–…‹of˜the“inputs.‘Àsize˜is“the“n•¸èum“bGer˜of–…‹input˜neurons“andޤ ‘Guserdata–U are“user“spGecied“inputs...!Ž¡‘GÏlayers:‘™P2x1–“°c‘ÿ}/el‘‚Ðl“of“layers‘Ò¹:‘q€hidden–U and“output“la•¸èy“er‘U denitionŽ¡‘GThe–´dimension“of“²2µx²1µcel2`l‘R¹is‘³bGecause“wš¸èe“ha˜v˜e“one“hidden“and“one“output“la˜y˜er.‘Ñ;So“toGo“see“theŽ¡‘Gdetails–U of“the“hidden“la•¸èy“er–U denitions,“wš¸èe“ha˜v˜e“to“en˜ter:Ž©‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:106analyzing/analyzingNewff×K>>‘?¬MLPnet.layers{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘,O£dimensions:‘?¬2Ž¡‘'÷distanceFcn:‘?¬''Ž¡‘1Odistances:‘?¬[]Ž¡‘<§initFcn:‘?¬'initnw'Ž¡‘'÷netInputFcn:‘?¬'netsum'Ž¡‘1Opositions:–?¬[0“1]Ž¡‘KÍ«size:‘?¬2Ž¡‘'÷topologyFcn:‘?¬'hextop'Ž¡‘'÷transferFcn:‘?¬'tansig'Ž¡‘6Îûuserdata:–?¬[1x1“struct]ަ‘Gïsrc:107analyzing/analyzingNewff¹and–U for“the“output“la•¸èy“er:ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:123analyzing/analyzingNewff×K>>‘?¬MLPnet.layers{2}Ž¡¡‘Gans‘?¬=Ž¡¡‘,O£dimensions:‘?¬1Ž¡‘'÷distanceFcn:‘?¬''Ž¡‘1Odistances:‘?¬[]Ž¡‘<§initFcn:‘?¬'initnw'Ž¡‘'÷netInputFcn:‘?¬'netsum'Ž¡‘1Opositions:‘?¬0Ž¡‘KÍ«size:‘?¬1Ž¡‘'÷topologyFcn:‘?¬'hextop'Ž¡‘'÷transferFcn:‘?¬'purelin'Ž¡‘6Îûuserdata:–?¬[1x1“struct]ަ‘Gïsrc:125analyzing/analyzingNewffÏoutputs:‘™P1x2–“°c›ÿ}/el‘‚Ðl“c˜ontaining“1“output‘òɹ:‘q€output›U la•¸èy“er˜denitionsŽ¡¦‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:132analyzing/analyzingNewff×K>>‘?¬MLPnet.outputsŽ¡¡‘Gans‘?¬=Ž¡¡‘,O£[]‘þ°[1x1‘?¬struct]ަ‘Gïsrc:133analyzing/analyzingNewff¹Ho•¸èw›ekno“ws,‘‰õwh“y˜this˜is‘da˜²1µx²2µcel•2`l“¹?‘ðNThe˜next–dcommand˜will˜also“sho¸èw˜the“detailed˜denition!‘ðNOfŽ¡‘Gcourse,–U realy“simple.ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:141analyzing/analyzingNewff×K>>‘?¬MLPnet.outputs{2}Ž¡¡‘Gans‘?¬=Ž¡¡‘<§size:‘?¬1Ž¡‘'÷userdata:–?¬[1x1“struct]ŽŽŸ‘Gïcolor push Black’ÏKZ¹16Ž’¨–ï color popŽŽŽŒ‹Èïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.17) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘Gïsrc:143analyzing/analyzingNewffÏtar–ÿ}/gets:‘™P1x2›“°c“el‘‚Ðl˜c“ontaining˜1˜tar“get‘òɹ:‘q€unknoš¸èw–U till“no˜wޤ ¡‘Gïsrc:145analyzing/analyzingNewffÏbiases:‘™P2x1–“°c›ÿ}/el‘‚Ðl“c˜ontaining“2“biases‘Ò¹:‘q€detailed–U denitions,“for“the“biasesŽ¡Ÿe˜‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:175analyzing/analyzingNewff×K>>‘?¬MLPnet.biasesŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷[1x1‘?¬struct]Ž¡‘'÷[1x1‘?¬struct]Ž¡¡‘GK>>‘?¬MLPnet.biases{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘6ÎûinitFcn:‘?¬''Ž¡‘ANSlearn:‘?¬1Ž¡‘1OlearnFcn:‘?¬'learngdm'Ž¡‘'÷learnParam:–?¬[1x1“struct]Ž¡‘Fÿsize:‘?¬2Ž¡‘1Ouserdata:–?¬[1x1“struct]Ž¡¡‘GK>>‘?¬MLPnet.biases{2}Ž¡¡‘Gans‘?¬=Ž¡¡‘6ÎûinitFcn:‘?¬''Ž¡‘ANSlearn:‘?¬1Ž¡‘1OlearnFcn:‘?¬'learngdm'Ž¡‘'÷learnParam:–?¬[1x1“struct]Ž¡‘Fÿsize:‘?¬1Ž¡‘1Ouserdata:–?¬[1x1“struct]ŽŸe—‘Gïsrc:176analyzing/analyzingNewff¹inputW‘ÿ*¸eigh•¸èts:‘f~2x1‘?cell›?con“taining˜1‘?input˜w“eigh“t˜la“y“erW‘ÿ*¸eigh“ts:‘f~2x2‘?cell˜con“taining˜1‘?la“y“er˜w“eigh“tŽ‘G¤ÍÆïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.8) cvn /DEST pdfmark end© XÊw•®>eigh“t–Õand“bias“v‘ÿ\|alues:‘ ÿ`ïsrc:182analyzing/analyzingNewffŽ¡ïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (section*.9) cvn /DEST pdfmark end¦‘IW:‘ ÿ`ïsrc:183analyzing/analyzingNewffŽ©e˜‘ûïcolor push Blackï color popŽŽïsrc:190analyzing/analyzingNewff×K>>‘?¬MLPnet.IWޤ ¡ans‘?¬=Ž¡¡‘þ°[2x26‘?¬double]Ž¡‘N»[]ŽŸ±eïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section*.10) cvn /DEST pdfmark endŸ *¸‘ÊL‘þ¸øW:‘ ÿ`ïsrc:193analyzing/analyzingNewffަ‘ûïcolor push Blackï color popŽŽïsrc:200analyzing/analyzingNewff×K>>‘?¬MLPnet.LWŽ¡¡ans‘?¬=Ž¡¡‘I{h[]‘>\[]Ž¡‘þ°[1x2‘?¬double]‘>\[]ŽŽŸ‘Gïcolor push Black’ÏKZ¹17Ž’¨–ï color popŽŽŽŒ‹ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.18) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section*.11) cvn /DEST pdfmark endŸ ‘Êb:‘ ÿ`ïsrc:203analyzing/analyzingNewffŽ©‘ûïcolor push Blackï color popŽŽïsrc:210analyzing/analyzingNewff×K>>‘?¬MLPnet.bޤ ¡ans‘?¬=Ž¡¡‘þ°[2x1‘?¬double]Ž¡‘þ°[‘¿-0.3908]ŽŸÒ¡ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section*.12) cvn /DEST pdfmark endŸ *¸Ênet.trainP®>aram:‘ ÿ`ïsrc:214analyzing/analyzingNewff¹Output–U for“the“Lev•¸èen“bGerg-Marquardt–U train“algorithm.ަ‘ûïcolor push Blackï color popŽŽïsrc:231analyzing/analyzingNewff×K>>‘?¬MLPnet.trainParamŽ¡¡ans‘?¬=Ž¡¡‘$½´epochs:‘?¬100Ž¡‘/= goal:‘?¬0Ž¡‘>\max_fail:‘?¬5Ž¡‘þ°mem_reduc:‘?¬1Ž¡‘>\min_grad:‘?¬1.0000e-010Ž¡‘9¼dmu:‘?¬0.0010Ž¡‘$½´mu_dec:‘?¬0.1000Ž¡‘$½´mu_inc:‘?¬10Ž¡‘$½´mu_max:‘?¬1.0000e+010Ž¡‘/= show:‘?¬25Ž¡‘/= time:‘?¬InfŽŸÖïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.6.2) cvn /DEST pdfmark endŸÑ6.2Ž‘$Fanalyzing‘G\newpŽŸæ~ïsrc:4analyzing/analyzingNewp¹F‘ÿ*¸ollo¸èwing–U command“will“bšGe“used,“to“create“a“new“neural“p˜erceptron:Ž¡net–U =“newfp(mMinMaxElemen¸èts,nNeurons);Ž¡¡‘ïsrc:7analyzing/analyzingNewpnewp–]£is›]¤the“matlab“command,‘_ÄmMinMaxElemen¸èts“is˜a“µRÇx²2¹-Matrix“with“minim¸èum˜and“maxi-Ž¡mš¸èum–U v‘ÿqÐalues“of“the“inputs.‘q€µR‘hç¹is“equal“to“the“n˜um˜bGer“of“input“neurons.Ž¡¡‘ïsrc:9analyzing/analyzingNewpnet–U =“newp([0“1;“-2“2],1)ŽŸ‘ûïcolor push Blackï color popŽŽïsrc:64analyzing/analyzingNewp×net‘?¬=Ž¡¡‘þ°Neural–?¬Network“object:Ž¡¡‘þ°architecture:Ž¡¡‘/= numInputs:‘?¬1Ž¡‘/= numLayers:‘?¬1Ž¡‘$½´biasConnect:‘?¬[1]Ž¡‘~inputConnect:‘?¬[1]Ž¡‘~layerConnect:‘?¬[0]Ž¡‘>\outputConnect:‘?¬[1]Ž¡‘>\targetConnect:‘?¬[1]Ž¡¡‘)ý`numOutputs:‘?¬1‘ X(read-only)Ž¡‘)ý`numTargets:‘?¬1‘ X(read-only)Ž¡‘þ°numInputDelays:‘?¬0‘ X(read-only)ŽŽŸ‘Gïcolor push Black’ÏKZ¹18Ž’¨–ï color popŽŽŽŒ‹#Øïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.19) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘'÷×numLayerDelays:‘?¬0‘ X(read-only)ޤ ¡‘'÷subobject‘?¬structures:Ž¡¡‘Q Winputs:–?¬{1x1“cell}“of“inputsŽ¡‘Q Wlayers:–?¬{1x1“cell}“of“layersŽ¡‘KÍ«outputs:–?¬{1x1“cell}“containing“1“outputŽ¡‘KÍ«targets:–?¬{1x1“cell}“containing“1“targetŽ¡‘Q 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u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏtar–ÿ}/getConne“ct:‘™P[1]‘ u¹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumOutputs:‘™P1‘“°(r–ÿ}/e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumT‘ÿ;Èar–ÿ}/gets:‘™P1‘“°(r“e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumInputDelays:‘™P0‘“°(r–ÿ}/e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏnumL–ÿ}/ayerDelays:‘™P0‘“°(r“e“ad-only)^޹:‘q€unknoš¸èwn–U till“no˜w!!Ž¡‘GÏinputs:‘™P1x1–“°c‘ÿ}/el‘‚Ðl“of“inputs‘Ò¹:‘q€input›U la•¸èy“er˜denitionŽ¡‘GBecause›¶|w•¸èe‘¶{ha“v“e˜dened–¶{only˜one“input˜la•¸èy“er,‘ÎÒy“ou˜can–¶{see˜the“detailed˜denition“with˜follo¸èwingŽ¡‘Gcommand–U in“the“matlab“prompt:Ž¡ŸUU‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:106analyzing/analyzingNewp×>>‘?¬net.inputs{1}Ž¡¡‘Gans‘?¬=ŽŽŸ‘Gïcolor push Black’ÏKZ¹19Ž’¨–ï color popŽŽŽŒ‹.#ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.20) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„¤ ‘6Îû×range:–?¬[2x2“double]Ž¡‘<§size:‘?¬2Ž¡‘'÷userdata:–?¬[1x1“struct]Ž¡¡‘Gnet.inputs{1}.rangeŽ¡¡‘Gans‘?¬=Ž¡¡‘,O£0‘>\1Ž¡‘'÷-2‘>\2Ž¡¡‘Gnet.inputs{1}.sizeŽ¡¡‘Gans‘?¬=Ž¡¡‘,O£2Ž¡¡‘Gnet.inputs{1}.userdataŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷note:–?¬'Put“your“custom“input“information“here.'Ž©Ûn‘Gïsrc:107analyzing/analyzingNewp¹range–…‹are›…Šthe“min.–Àand˜max.“v‘ÿqÐalues–…‹of˜the“inputs.‘Àsize˜is“the“n•¸èum“bGer˜of–…‹input˜neurons“andŽ¡‘Guserdata–U are“user“spGecied“inputs...!Ž¡‘GÏlayers:‘™P1x1–“°c‘ÿ}/el‘‚Ðl“of“layers‘Ò¹:‘q€actually–U no“idea“what's“inside“this“cell!!!Ž¡‘GT‘ÿ*¸o–U see“the“details“of“this“la•¸èy“er–U denition,“wš¸èe“ha˜v˜e“to“en˜ter:ŽŸÛm‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:125analyzing/analyzingNewp×net.layers{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘,O£dimensions:‘?¬1Ž¡‘'÷distanceFcn:‘?¬''Ž¡‘1Odistances:‘?¬[]Ž¡‘<§initFcn:‘?¬'initwb'Ž¡‘'÷netInputFcn:‘?¬'netsum'Ž¡‘1Opositions:‘?¬0Ž¡‘KÍ«size:‘?¬1Ž¡‘'÷topologyFcn:‘?¬'hextop'Ž¡‘'÷transferFcn:‘?¬'hardlim'Ž¡‘6Îûuserdata:–?¬[1x1“struct]ަ‘Gïsrc:126analyzing/analyzingNewp¹and–U for“the“output“la•¸èy“er:ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:134analyzing/analyzingNewp×net.outputs{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘<§size:‘?¬1Ž¡‘'÷userdata:–?¬[1x1“struct]ŽŸ’I‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:142analyzing/analyzingNewp‘?¬net.outputs{1}.userdataŽ¡¡‘Gans‘?¬=ŽŽŸ‘Gïcolor push Black’ÏKZ¹20Ž’¨–ï color popŽŽŽŒ‹8èïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.21) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„¤ ‘'÷×note:–?¬'Put“your“custom“output“information“here.'Ž©Ûn‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:150analyzing/analyzingNewpnet.targetsŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷[1x1‘?¬struct]ŽŸÛm‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:159analyzing/analyzingNewpnet.targets{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘<§size:‘?¬1Ž¡‘'÷userdata:–?¬[1x1“struct]ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:167analyzing/analyzingNewpnet.targets{1}.userdataŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷note:–?¬'Put“your“custom“output“information“here.'ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:180analyzing/analyzingNewpnet.biases{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘6ÎûinitFcn:‘?¬'initzero'Ž¡‘ANSlearn:‘?¬1Ž¡‘1OlearnFcn:‘?¬'learnp'Ž¡‘'÷learnParam:‘?¬[]Ž¡‘Fÿsize:‘?¬1Ž¡‘1Ouserdata:–?¬[1x1“struct]ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:189analyzing/analyzingNewpnet.biases{1}.userdataŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷note:–?¬'Put“your“custom“bias“information“here.'ŽŸÛm‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:198analyzing/analyzingNewpnet.inputWeightsŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷[1x1‘?¬struct]ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:214analyzing/analyzingNewpnet.inputWeights{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘<§delays:‘?¬0Ž¡‘6ÎûinitFcn:‘?¬'initzero'Ž¡‘ANSlearn:‘?¬1Ž¡‘1OlearnFcn:‘?¬'learnp'Ž¡‘'÷learnParam:‘?¬[]Ž¡‘Fÿsize:–?¬[1“2]Ž¡‘1Ouserdata:–?¬[1x1“struct]Ž¡‘,O£weightFcn:‘?¬'dotprod'ŽŽŸ‘Gïcolor push Black’ÏKZ¹21Ž’¨–ï color popŽŽŽŒ‹@óïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.22) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ý„‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:222analyzing/analyzingNewp×net.layerWeightsޤ ¡‘Gans‘?¬=Ž¡¡‘'÷{[]}Ž©‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:230analyzing/analyzingNewpnet.LWŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷{[]}ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:238analyzing/analyzingNewpnet.IWŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷[1x2‘?¬double]ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:246analyzing/analyzingNewpnet.IW{1}Ž¡¡‘Gans‘?¬=Ž¡¡‘,O£0‘>\0ަ‘ Gïcolor push Blackï color popŽŽ‘Gïsrc:254analyzing/analyzingNewpnet.bŽ¡¡‘Gans‘?¬=Ž¡¡‘'÷[0]ŽŽŸ‘Gïcolor push Black’ÏKZ¹22Ž’¨–ï color popŽŽŽŒ‹HÛïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.23) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ‘G ýzïps:SDict begin H.S endïps:SDict begin 12 H.A endïJps:SDict begin [ /View [/XYZ H.V] /Dest (appendix.A) cvn /DEST pdfmark endŸUïsrc:1examples/examples.texÎApp‘œendix‘VºAŽŸ2ÉExamplesŽŸ,Ö+ïps:SDict begin H.S endïps:SDict begin 12 H.A endïKps:SDict begin [ /View [/XYZ H.V] /Dest (section.A.1) cvn /DEST pdfmark endŸ )ÕÑA.1Ž‘(µExample‘G\1ŽŸæ~ïsrc:3examples/example1¹This–„7MLP‘„*is›„6designed“with“2-2-1.‘þÃThis“is˜not“a˜complete“example˜but“it˜will“help˜to“understandޤ the–U dimensions“of“all“matrices“and“vš¸èectores“are“used“inside“the“Lev˜en˜bGerg-Marquardt“algorithm.ŽŸïïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.A.1.1) cvn /DEST pdfmark endŸ XÒA.1.1Ž‘,®üData‘¸matricesŽŸtîïsrc:7examples/example1¹The– æinput“matrix› åwill“bGe“dened“lik¸èe˜in“equation“(ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 1A.1ï color popŽ‘F(Ÿùàïps:SDict begin H.R endŽ‘F(ï„ps:SDict begin [ /Color [1 0 0] /H /I /Border [0 0 0]BorderArrayPatch /Subtype /Link /Dest (equation.A.1.1) cvn H.B /ANN pdfmark endï color pop)Ž‘ ”and˜the“output“matrix“lik¸èe˜in“equationŽ¡(ïcolor push rgb 0 0 1ïps:SDict begin H.S endïcolor push rgb 0 0 1A.2ï color popŽ‘F(Ÿùàïps:SDict begin H.R endŽ‘F(ï„ps:SDict begin [ /Color [1 0 0] /H /I /Border [0 0 0]BorderArrayPatch /Subtype /Link /Dest (equation.A.1.2) cvn H.B /ANN pdfmark endï color pop)Ž‘ È.ŽŸØïps:SDict begin H.S endïps:SDict begin 12 H.A endïNps:SDict begin [ /View [/XYZ H.V] /Dest (equation.A.1.1) cvn /DEST pdfmark endŸ €(‘ïsrc:10examples/example1Ž©€ ’—ËyµmI‘Èânput–Dz=ŸëæZ“óú±u cmex10«2ŽŸ “4ŽŽŸóæd‘qIJ1Ž‘qÅ2Ž‘,qÆ3Ž‘;qÇ1ŽŽ¡‘qÄ1Ž‘qÅ1Ž‘,qÆ1Ž‘;qÇ2ŽŽ¡‘qÄ1Ž‘qÅ2Ž‘,qÆ1Ž‘;qÇ2ŽŽŽŸëæZ‘EqÈ«3ŽŸ ‘EqÈ5ŽŽŽŽ’‘‰L¹(A.1)ŽŽŽ¦ïps:SDict begin H.S endïps:SDict begin 12 H.A endïNps:SDict begin [ /View [/XYZ H.V] /Dest (equation.A.1.2) cvn /DEST pdfmark endŸ‘ïsrc:20examples/example1Ž¡’“`rµmOGutput–Dz=“Ÿ÷æb«Žžæd‘ ñIJ1Ž‘ñÅ1µ:²5Ž‘1¸ä2Ž‘@¸å3ŽŽŽ‘J¸æŸ÷æb«ŽŽŽ’‘‰L¹(A.2)ŽŽŽŸ}]ïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.A.1.2) cvn /DEST pdfmark endŸ üÒA.1.2Ž‘,®üW‘þàeigh t‘¸matricesŽ©tîïsrc:32examples/example1¹The–hùrst“la•¸èy“er›húmatrix–hùwill“hold“2x3“w•¸èeigh“ts.‘­ The–hùsecond“la•¸èy“er˜matrix–hùwill“hold“1x2“w•¸èeigh“ts.‘­ TheŽ¡rst–U bias“holds“3x1“w•¸èeigh“ts–U and“the“second“holds“only“a“scalar“elemen¸èt.ŽŸïïps:SDict begin H.S endïps:SDict begin 12 H.A endïPps:SDict begin [ /View [/XYZ H.V] /Dest (subsection.A.1.3) cvn /DEST pdfmark endŸ XÒA.1.3Ž‘,®üSensitivit y‘¸Matricesަïsrc:36examples/example1¹This– part“is‘ righš¸èt“no˜w“not“so“clear‘ in“m˜y“mind.‘rEWhat“is“the“dimension“of‘ these“t˜w˜o“matrices?Ž¡The›ÛLrst‘ÛMla•¸èy“er˜sensitivit“y–ÛMmatrix˜should“b•Ge˜ab“out›ÛM2x71.‘Num¸èb“er˜of–ÛLhidden˜neurons“in˜the“ro¸èwsŽ¡and›U n•¸èum“bGer˜of˜train˜data˜sets˜in˜the˜columns.Ž¡¡‘ïsrc:39examples/example1In–8Éthe›8Êactual“v¸èersion,‘>tthe˜dimension“is“abGout˜71x71“..‘hso“it˜seems“to˜ha•¸èv“e–8Éa“mistak¸èe˜inside“theŽ¡algorithm‘U :-(ŽŽŸ‘Gïcolor push Black’ÏKZ23Ž’¨–ï color popŽŽŽŒ‹M‘ïþps:SDict begin /product where{pop product(Distiller)search{pop pop pop version(.)search{exch pop exch pop(3011)eq{gsave newpath 0 0 moveto closepath clip/Courier findfont 10 scalefont setfont 72 72 moveto(.)show grestore}if}{pop}ifelse}{pop}ifelse}if endŸþÿ ‘Rïcolor push gray 0ïps:SDict begin H.S endïcolor push gray 0ï color popŽïps:SDict begin H.R endïGps:SDict begin [ /View [/XYZ H.V] /Dest (page.24) cvn /DEST pdfmark endï color popŽŽ É ýC‘Gïcolor push Black’¨–ï color popŽŽ Ÿ ýÔ‘Gï src:15../common/bibliography.texÉBibliograph‘ÿF¾yŽ‘GŸ,Ö+ïps:SDict begin H.S endïps:SDict begin 12 H.A endïLps:SDict begin [ /View [/XYZ H.V] /Dest (appendix*.13) cvn /DEST pdfmark endŸˆïcolor push Blackïps:SDict begin H.S end¹[1]ïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (cite.1) cvn /DEST pdfmark end‘/}ï color popŽŽ‘? ˜ï src:17../common/bibliography.texJohn–U W.“Eatonޤ‘? ˜ï src:19../common/bibliography.texGNU›U Octa•¸èv“e˜Man“ual,˜Edition˜3,˜PDF-V–ÿ*¸ersion,˜F“ebruary˜1997Ž©ïcolor push Blackïps:SDict begin H.S end[2]ïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (cite.2) cvn /DEST pdfmark end‘/}ï color popŽŽ‘? ˜ï src:21../common/bibliography.texThe›U Math–ÿ*¸W“orks,˜Inc.Ž¡‘? ˜ï src:23../common/bibliography.texMA‘ÿ*¸TLAB‘U Online-Helpަïcolor push Blackïps:SDict begin H.S end[3]ïps:SDict begin 12 H.A endïFps:SDict begin [ /View [/XYZ H.V] /Dest (cite.3) cvn /DEST pdfmark end‘/}ï color popŽŽ‘? ˜ï src:25../common/bibliography.texStev¸èen–U W.“SmithŽ¡‘? ˜ï src:27../common/bibliography.texThe–Ë\Scien¸ètist›Ë]and“Engineer's˜Guide“to˜Digital“Signal˜ProGcessing“ISBN‘Ë>0-9660176-ŽŸ ‘? ˜3-3,–U California“T‘ÿ*¸ec¸èhnical“Publishing,“1997ަïcolor push Blackïps:SDict begin H.S end[4]ïps:SDict begin 12 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00000 n 0000218889 00000 n 0000219059 00000 n 0000219249 00000 n 0000219419 00000 n 0000219588 00000 n 0000219758 00000 n 0000219920 00000 n 0000220105 00000 n 0000220299 00000 n 0000220494 00000 n 0000220694 00000 n 0000220893 00000 n 0000221108 00000 n 0000221346 00000 n 0000221453 00000 n 0000221564 00000 n 0000221677 00000 n 0000221783 00000 n 0000221879 00000 n 0000221917 00000 n 0000222087 00000 n trailer << /Size 867 /Root 865 0 R /Info 866 0 R /ID [<03CB07D7115B689E95975E6DAECB173D> <03CB07D7115B689E95975E6DAECB173D>] >> startxref 222360 %%EOF nnet/doc/latex/developers/examples/0000755000175000017500000000000011475775705016350 5ustar mikemikennet/doc/latex/developers/examples/example1.tex0000644000175000017500000000255010566045112020566 0ustar mikemike\section{Example 1} This MLP is designed with 2-2-1. This is not a complete example but it will help to understand the dimensions of all matrices and vectores are used inside the Levenberg-Marquardt algorithm. \subsection{Data matrices} The input matrix will be defined like in equation \eqref{equ:mInput} and the output matrix like in equation \eqref{equ:mOutput}. \begin{equation} mInput = \left[ \begin{array}{c c c c} 1 & 2 & 3 & 1 \\ 1 & 1 & 1 & 2 \\ 1 & 2 & 1 & 2 \\ \end{array} \right] \label{equ:mInput} \end{equation} \begin{equation} mOutput = \left[ \begin{array}{c c c c} 1 & 1.5 & 2 & 3 \\ \end{array} \right] \label{equ:mOutput} \end{equation} \subsection{Weight matrices} The first layer matrix will hold 2x3 weights. The second layer matrix will hold 1x2 weights. The first bias holds 3x1 weights and the second holds only a scalar element. \subsection{Sensitivity Matrices} This part is right now not so clear in my mind. What is the dimension of these two matrices? The first layer sensitivity matrix should be about 2x71. Number of hidden neurons in the rows and number of train data sets in the columns.\\ In the actual version, the dimension is about 71x71 .. so it seems to have a mistake inside the algorithm :-( nnet/doc/latex/developers/examples/example2.tex0000644000175000017500000000150310566045112020564 0ustar mikemike\section{Example 2} This MLP is designed with 26-2-1. Why starting easy if it's possible to start difficult? The input matrix which will be given to the training algorithm has the dimension of 26x71. The jacobian matrix should reach a size of 71x57. \subsection{Weight matrices} The first layer matrix will hold 2x26 weights. The second layer matrix will hold 1x2 weights. The first bias holds 26x1 weights and the second holds only a scalar element. \subsection{Sensitivity Matrices} This part is right now not so clear in my mind. What is the dimension of these two matrices? The first layer sensitivity matrix should be about 2x71. Number of hidden neurons in the rows and number of train data sets in the columns.\\ In the actual version, the dimension is about 71x71 .. so it seems to have a mistake inside the algorithm :-( nnet/doc/latex/developers/examples/examples.tex0000644000175000017500000000005610566045112020667 0ustar mikemike\chapter{Examples} \input{examples/example1}nnet/doc/latex/developers/octave/0000755000175000017500000000000011475775705016013 5ustar mikemikennet/doc/latex/developers/octave/functions/0000755000175000017500000000000011475775705020023 5ustar mikemikennet/doc/latex/developers/octave/functions/sim.tex0000644000175000017500000000145110651573247021326 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/newff.tex0000644000175000017500000000145110651573247021643 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/isposintOct.tex0000644000175000017500000000070710651573247023057 0ustar mikemike\section{isposint} Checks if value is a positive integer.\\ \subsubsection{Syntax:} $f = isposint(val)$\\ $f = 0$ if val is negative integer or float and is 0 if val is a positive float.\\ \subsubsection{Example 1:} val = 5.3;\\ isposint(val)\\ ans = 0\\ \subsubsection{Example 2:} val = -5;\\ isposint(val)\\ ans = 0\\ \subsubsection{Example 3:} val = 0;\\ isposint(val)\\ ans = 1\\ \subsubsection{Example 4:} val = 5;\\ isposint(val)\\ ans = 1\\ nnet/doc/latex/developers/octave/functions/poststd.tex0000644000175000017500000000145110651573247022236 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/trastd.tex0000644000175000017500000000145110651573247022037 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/min_max.tex0000644000175000017500000000145110651573247022166 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/prestd.tex0000644000175000017500000000145110651573247022037 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/functions/train.tex0000644000175000017500000000145110651573247021653 0ustar mikemike\subsection{min\_max} Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\ \subsubsection{Syntax:} $pr = min\_max(mInputs)$\\ \subsubsection{Description:} \textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP (this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!). \subsubsection{Important:} The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!nnet/doc/latex/developers/octave/octave.tex0000644000175000017500000000014610651413412017772 0ustar mikemike\chapter{Octave's available functions} \section{Available functions} \input{octave/functions/min_max}nnet/doc/latex/developers/octave/directorys.tex0000644000175000017500000000270510566046463020720 0ustar mikemike\section{Functions location} \subsection{Directory \textit{nnet/nnet}} \noindent Available functions: \begin{enumerate} \item min\_max \item network \item newff \end{enumerate} \subsection{Directory \textit{nnet/nnutils}} This directory holds a further subdirectory: \textit{saveMLPStructure}.\\ \noindent Available functions: \begin{enumerate} \item saveMLPStruct \end{enumerate} Function \textit{saveMLPStruct} doesn't exist in MATLAB(TM). This is a helper function to print a neural network architecture. \subsubsection{saveMLPStructure} Inside this directory are a lot of different functions which will help to print a text file to display the neural network architecture. The look will be the same like you would open the type \textit{network} in MATLAB(TM). \begin{itemize} \item printAdaptFcn \item printAdaptParam \item printB \item printBiasConnect \item printBiases \item printInitFcn \item printInputConnect \item printInputs \item printInputWeights \item printIW \item printLayerConnect \item printLayers \item printLayerWeights \item printLW \item printMLPHeader \item printNumInputDelays \item printNumInputs \item printNumLayerDelays \item printNumLayers \item printNumOutputs \item printNumTargets \item printOutputConnect \item printOutputs \item printPerformFcn \item printPerformParam \item printTargetConnect \item printTargets \item printTrainFcn \item printTrainParam \end{itemize}nnet/doc/latex/developers/analyzing/0000755000175000017500000000000011475775705016526 5ustar mikemikennet/doc/latex/developers/analyzing/matlab.tex0000644000175000017500000000014711073350143020465 0ustar mikemike\chapter{analyzing matlab functions} \input{analyzing/analyzingNewff} \input{analyzing/analyzingNewp} nnet/doc/latex/developers/analyzing/analyzingNewff.tex0000644000175000017500000001447711073645625022236 0ustar mikemike\section{analyzing newff} First, \textit{newff} will be analyzed for a X-X-X mlp. This means, maximum 3 layers, including the input layer. Or in words, one input- one hidden- and one output-layer. The number of neurons are choosable. Following command will be used, to create a new feed-forward neural network:\\ \noindent MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],...\newline \{'tansig','purelin'\},'trainlm','learngdm','mse');\\ newff is the matlab command, mMinMaxElements is a $Rx2$-Matrix with minimum and maximum values of the inputs. $R$ is equal to the number of input neurons. [nHiddenNeurons nOutputNeurons] are the scalar values, to define the number of neurons in the hidden and output layer. One value, for each layer. \{'tansig','purelin'\} are the transfer functions, for each layer. This means, 'tansig' for the hidden layer and 'purelin' for the output layer. 'trainlm' is the training algorithm, in this case, Levenberg-Marquardt. 'learngdm' is the learn algorithm and 'mse' is the performance function, \textbf{m}ean-\textbf{s}quare-\textbf{e}rror.\\ MLPnet will be a structure with following content: \begin{verbatim} Neural Network object: architecture: numInputs: 1 numLayers: 2 biasConnect: [1; 1] inputConnect: [1; 0] layerConnect: [0 0; 1 0] outputConnect: [0 1] targetConnect: [0 1] numOutputs: 1 (read-only) numTargets: 1 (read-only) numInputDelays: 0 (read-only) numLayerDelays: 0 (read-only) subobject structures: inputs: {1x1 cell} of inputs layers: {2x1 cell} of layers outputs: {1x2 cell} containing 1 output targets: {1x2 cell} containing 1 target biases: {2x1 cell} containing 2 biases inputWeights: {2x1 cell} containing 1 input weight layerWeights: {2x2 cell} containing 1 layer weight functions: adaptFcn: 'trains' initFcn: 'initlay' performFcn: 'mse' trainFcn: 'trainlm' parameters: adaptParam: .passes initParam: (none) performParam: (none) trainParam: .epochs, .goal, .max_fail, .mem_reduc, .min_grad, .mu, .mu_dec, .mu_inc, .mu_max, .show, .time weight and bias values: IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors other: userdata: (user stuff) \end{verbatim} \textit{numInputs: 1}: one input layer\\ \noindent \textit{numLayers: 2}: one hidden and one output layer\\ \noindent \textit{biasConnect: [1; 1]}: unknown till now!!\\ \noindent \textit{inputConnect: [1; 0]}: unknown till now!!\\ \noindent \textit{layerConnect: [0 0; 1 0]}: unknown till now!!\\ \noindent \textit{outputConnect: [0 1]}: unknown till now!!\\ \noindent \textit{targetConnect: [0 1]}: unknown till now!!\\ \noindent \textit{numOutputs: 1 (read-only)}: unknown till now!!\\ \noindent \textit{numTargets: 1 (read-only)}: unknown till now!!\\ \noindent \textit{numInputDelays: 0 (read-only)}: unknown till now!!\\ \noindent \textit{numLayerDelays: 0 (read-only)}: unknown till now!!\\ \noindent \textit{inputs: {1x1 cell} of inputs}: input layer definition\\ Because we have defined only one input layer, you can see the detailed definition with following command in the matlab prompt:\\ \begin{verbatim} MLPnet.inputs{1} ans = range: [26x2 double] size: 26 userdata: [1x1 struct] \end{verbatim} range are the min. and max. values of the inputs. size is the number of input neurons and userdata are user specified inputs...!\\ \noindent \textit{layers: {2x1 cell} of layers}: hidden and output layer definition\\ The dimension of $2x1 cell$ is because we have one hidden and one output layer. So too see the details of the hidden layer definitions, we have to enter: \begin{verbatim} K>> MLPnet.layers{1} ans = dimensions: 2 distanceFcn: '' distances: [] initFcn: 'initnw' netInputFcn: 'netsum' positions: [0 1] size: 2 topologyFcn: 'hextop' transferFcn: 'tansig' userdata: [1x1 struct] \end{verbatim} and for the output layer: \begin{verbatim} K>> MLPnet.layers{2} ans = dimensions: 1 distanceFcn: '' distances: [] initFcn: 'initnw' netInputFcn: 'netsum' positions: 0 size: 1 topologyFcn: 'hextop' transferFcn: 'purelin' userdata: [1x1 struct] \end{verbatim} \noindent \textit{outputs: {1x2 cell} containing 1 output}: output layer definitions\\ \begin{verbatim} K>> MLPnet.outputs ans = [] [1x1 struct] \end{verbatim} How knows, why this is a $1x2 cell$? The next command will also show the detailed definition! Of course, realy simple. \begin{verbatim} K>> MLPnet.outputs{2} ans = size: 1 userdata: [1x1 struct] \end{verbatim} \noindent \textit{targets: {1x2 cell} containing 1 target}: unknow till now\\ \noindent \textit{biases: {2x1 cell} containing 2 biases}: detailed definitions, for the biases\\ \begin{verbatim} K>> MLPnet.biases ans = [1x1 struct] [1x1 struct] K>> MLPnet.biases{1} ans = initFcn: '' learn: 1 learnFcn: 'learngdm' learnParam: [1x1 struct] size: 2 userdata: [1x1 struct] K>> MLPnet.biases{2} ans = initFcn: '' learn: 1 learnFcn: 'learngdm' learnParam: [1x1 struct] size: 1 userdata: [1x1 struct] \end{verbatim} inputWeights: {2x1 cell} containing 1 input weight layerWeights: {2x2 cell} containing 1 layer weight \paragraph{weight and bias values:} \subparagraph{IW:} \begin{verbatim} K>> MLPnet.IW ans = [2x26 double] [] \end{verbatim} \subparagraph{LW:} \begin{verbatim} K>> MLPnet.LW ans = [] [] [1x2 double] [] \end{verbatim} \subparagraph{b:} \begin{verbatim} K>> MLPnet.b ans = [2x1 double] [ -0.3908] \end{verbatim} \paragraph{net.trainParam:} Output for the Levenberg-Marquardt train algorithm. \begin{verbatim} K>> MLPnet.trainParam ans = epochs: 100 goal: 0 max_fail: 5 mem_reduc: 1 min_grad: 1.0000e-010 mu: 0.0010 mu_dec: 0.1000 mu_inc: 10 mu_max: 1.0000e+010 show: 25 time: Inf \end{verbatim}nnet/doc/latex/developers/varietes.tex0000644000175000017500000000714610566045112017064 0ustar mikemike\section{Varietes} \label{chap:intro:sec:varietes} \subsection{Object-oriented programming} Some of the functions for Octave will have not the same name, like the matlab ones has. This difference is because the object-oriented programming technology. The object-oriented functions will have a name postfix in Octave.\\ As example: \textit{isposint\_netw\_priv} means, \textit{isposint} is a private function from the matlab function \textit{network}.\\ This is a kind of "`simulation"' for the object-oriented programming technology.\\ \subsubsection{Data-Typ \textit{network} and m-file \textit{subsasgn}} Matlab has a further data type \textit{network}. A basic neural network will be initialized in a structure and than changed to the \textit{network} type with the \textit{class} command. The class command from Octave doesn't support the creation of this network type! In the Matlab m-file \textit{network.m} on row 256, the network type will be created! From this moment, structure subscription assignment will call the file subsasgn from the \textit{@network} directory. Back in the \textit{newff} m-file on row 134, the @network subsasgn will be called the first time. \paragraph{newff row 134; net.biasConnect} net=subsasgn(net,subscripts,v) will hold the following values: \begin{itemize} \item net: the whole net structure \item subscripts: 1x1 structure;\\ subscripts.type = '.'\\ subscripts.subs = 'biasConnect' \item v: 2x1 double [1; 1] \end{itemize} \paragraph{newff row 135; net.inputConnect} net=subsasgn(net,subscripts,v) will hold the following values: \begin{itemize} \item net: the whole net structure \item subscripts: 1x2 structure;\\ subscripts(1).type = '.'\\ subscripts(1).subs = 'inputConnect'\\ subscripts(2).type = '()'\\ subscripts(2).subs = [1] [1] \item v: 1x1 double 1 \end{itemize} \textcolor{red}{bis hier sollte es erledigt sein...!} \paragraph{newff row 137; net.layerConnect} net=subsasgn(net,subscripts,v) will hold the following values: \begin{itemize} \item net: the whole net structure \item subscripts: 1x1 structure;\\ subscripts(1).type = '.'\\ subscripts(1).subs = 'layerConnect' \item v: 2x2 logical [0 0; 1 0] \end{itemize} \paragraph{newff row 138; net.outputConnect} net=subsasgn(net,subscripts,v) will hold the following values: \begin{itemize} \item net: the whole net structure \item subscripts: 1x2 structure;\\ subscripts(1).type = '.'\\ subscripts(1).subs = 'outputConnect'\\ subscripts(2).type = '()'\\ subscripts(2).subs = '[2]' \item v: 1x1 double = 1 \end{itemize} \paragraph{newff row 139; net.targetConnect} net=subsasgn(net,subscripts,v) will hold the following values: \begin{itemize} \item net: the whole net structure \item subscripts: 1x2 structure;\\ subscripts(1).type = '.'\\ subscripts(1).subs = 'targetConnect'\\ subscripts(2).type = '()'\\ subscripts(2).subs = '[2]' \item v: 1x1 double = 1 \end{itemize} \subsection{Data types} \begin{table} \centering \begin{tabular}{c c c} \toprule & Matlab & Octave\\ \midrule & double & double \\ & int8 & \\ & int16 & \\ & int32 & \\ & int64 & \\ Numeric & single & \\ & uint8 & \\ & uint16 & \\ & uint32 & \\ & uint64 & \\ & complex & complex\\ \midrule characters & x & \\ \midrule string & & \\ \midrule cell & x & x \\ \midrule structure & x & x \\ \bottomrule \end{tabular} \end{table}nnet/doc/latex/developers/tests/0000755000175000017500000000000011475775705015674 5ustar mikemikennet/doc/latex/developers/tests/__copycoltopos1.tex0000644000175000017500000000045411073645625021524 0ustar mikemike\begin{verbatim} %!shared a, retmat %! disp("testing __copycoltopos1") %! a = [0 1 2 3 4; 5 6 7 8 9]; %! retmat = __copycoltopos1(a,3); %!assert(retmat(1,1)==2); %!assert(retmat(2,1)==7); %! retmat = __copycoltopos1(a,5); %!assert(retmat(1,1)==4); %!assert(retmat(2,1)==9); \end{verbatim} nnet/doc/latex/developers/tests/test.tex0000644000175000017500000000107111073645625017363 0ustar mikemike\chapter{Test} \section{isposint} \input{tests/isposint} \section{min\_max} \input{tests/min_max} \section{newff} \input{tests/newff} \section{prestd} \input{tests/prestd} \section{purelin} \input{tests/purelin} \section{subset} \input{tests/subset} \section{\_\_analyzerows} \input{tests/__analyzerows} \section{\_\_copycoltopos1} \input{tests/__copycoltopos1} \section{\_\_optimizedatasets} \input{tests/__optimizedatasets} \section{\_\_randomisecols} \input{tests/__randomisecols} \section{\_\_rerangecolumns} \input{tests/__rerangecolumns} nnet/doc/latex/developers/tests/subset.tex0000644000175000017500000000615311073645625017717 0ustar mikemike\begin{verbatim} %!shared matrix, nTargets, mTrain, mTest, mVali %! disp("testing subset") %! matrix = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20; \ %! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \ %! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \ %! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \ %! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \ %! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99]; %! nTargets = 1; # the last row is equivalent to the target values. %! [mTrain, mTest, mVali] = subset(matrix,nTargets); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %! # It's not possible to test the column order with this call! %! # randomizing is used! But all max and min values should be %! # in the training set %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); %! %! %! [mTrain, mTest, mVali] = subset(matrix,nTargets,0); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %!assert(mTrain==matrix(:,1:10)); %!assert(mTest==matrix(:,11:16)); %!assert(mVali==matrix(:,17:20)); %! %! %! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); %! %! %! ## next test ... optimize twice %! matrix = [1 2 3 4 5 6 7 20 8 10 11 12 13 14 15 16 17 18 18 9; \ %! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \ %! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \ %! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \ %! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \ %! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99]; %! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################ %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); \end{verbatim} nnet/doc/latex/developers/tests/__randomisecols.tex0000644000175000017500000000016611073645625021550 0ustar mikemike\begin{verbatim} %!# no test possible, contains randperm which is using %!# some randome functions \end{verbatim} nnet/doc/latex/developers/tests/newff.tex0000644000175000017500000000243111073645625017512 0ustar mikemike\begin{verbatim} %!shared %! disp("testing newff") %!test %! Pr = [1;2]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!") %!test %! Pr = [1 2 ; 4 6]; %! assert(__checknetstruct(newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse'))) %!test %! Pr = [1 2 3; 4 5 6]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!") %!test %! Pr = [5 3; 4 5]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Input ranges has values in the second column larger as in the same row of the first column.") %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[1 1; 2 3],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Layer sizes is not a row vector.") %!test %! Pr = [1 2 ; 4 6]; %! assert(__checknetstruct(newff(Pr,[ 2 3],{'tansig','purelin'},'trainlm','unused','mse'))) %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "There must be at least one hidden layer and one output layer!") %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[-1 1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Layer sizes is not a row vector of positive integers.") \end{verbatim} nnet/doc/latex/developers/tests/__optimizedatasets.tex0000644000175000017500000000165011073645625022276 0ustar mikemike\begin{verbatim} %!shared retmatrix, matrix %! disp("testing __optimizedatasets") %! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \ %! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \ %! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \ %! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1]; %! ## The last row is equal to the neural network targets %! retmatrix = __optimizedatasets(matrix,9,1); %! ## the above statement can't be tested with assert! %! ## it contains random values! So pass a "success" message %!assert(1==1); %! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \ %! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \ %! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \ %! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1]; %! ## The last row is equal to the neural network targets %! retmatrix = __optimizedatasets(matrix,9,1,0); %!assert(retmatrix(1,1)==5); %!assert(retmatrix(2,1)==0); %!assert(retmatrix(3,1)==1); %!assert(retmatrix(4,1)==3); \end{verbatim} nnet/doc/latex/developers/tests/__analyzerows.tex0000644000175000017500000000124111073645625021257 0ustar mikemike\begin{verbatim} %!shared b, retmat %! disp("testing __analyzerows") %! b = [1 0 0 1; 1 0 0 0; 1 2 0 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,1)==1);#%!assert(retmat(1,1)==1); %!assert(retmat(2,1)==1); %!assert(retmat(3,1)==0); %! b = [1 0 0 2; 1 0 0 0; 1 1 1 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,2)==0); %!assert(retmat(2,2)==0); %!assert(retmat(3,2)==1); %! b = [1 0 0 2; 1 0 0 0; 1 1 1 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,3)==2); %!assert(retmat(2,3)==0); %!assert(retmat(3,3)==0); %! retmat = __analyzerows(b); %!assert(retmat(1,4)==1); %!assert(retmat(2,4)==0); %!assert(retmat(3,4)==0); \end{verbatim} nnet/doc/latex/developers/tests/__rerangecolumns.tex0000644000175000017500000000237311073645625021734 0ustar mikemike\begin{verbatim} %!shared matrix,analyzeMatrix,nTrainSets, returnmatrix %! disp("testing __rerangecolumns") %! matrix = [0 1 0 0 0 0 1 0 1 1; \ %! 4 4 4 4 4 4 4 4 4 4; \ %! -1.1 -1.1 2 3 4 3.2 1 8 9 10; \ %! 0 1.1 3 4 5 2 10 10 2 3; \ %! -1 1 1 1 1 2 3 4 1 5]; %! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1]; %! nTrainSets = 8; %! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets); %!assert(returnmatrix(1,1)==1); %!assert(returnmatrix(2,1)==4); %!assert(returnmatrix(3,1)==1); %!assert(returnmatrix(4,1)==10); %!assert(returnmatrix(5,1)==3); %! matrix = [0 1 0 0 0 0 1 0 1 1; \ %! 4 4 4 4 4 4 4 4 4 4; \ %! -1.1 -1.1 2 3 4 3.2 1 8 9 10; \ %! 0 1.1 3 4 5 2 10 10 2 3; \ %! -1 1 1 1 1 2 3 4 1 5; \ %! 0 1 2 1 2 1 2 3 4 5;]; # the last row is euqal to the nnet targets %! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1]; %! nTrainSets = 8; %! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets); %!assert(returnmatrix(1,1)==1); %!assert(returnmatrix(2,1)==4); %!assert(returnmatrix(3,1)==1); %!assert(returnmatrix(4,1)==10); %!assert(returnmatrix(5,1)==3); %!assert(returnmatrix(6,1)==2); \end{verbatim} nnet/doc/latex/developers/tests/purelin.tex0000644000175000017500000000026411073645625020065 0ustar mikemike\begin{verbatim} %!assert(purelin(2),2); %!assert(purelin(-2),-2); %!assert(purelin(0),0); %!error # this test must throw an error! %! assert(purelin(2),1); \end{verbatim} nnet/doc/latex/developers/tests/isposint.tex0000644000175000017500000000076011073645625020260 0ustar mikemike\begin{verbatim} %!shared %! disp("testing isposint") %!assert(isposint(1)) # this should pass %!assert(isposint(0.5),0) # should return zero %!assert(isposint(-1),0) # should return zero %!assert(isposint(-1.5),0) # should return zero %!assert(isposint(0),0) # should return zero %!fail("isposint([0 0])","Input argument must not be a vector, only scalars are allowed!") %!fail("isposint('testString')","Input argument must not be a vector, only scalars are allowed!") \end{verbatim} nnet/doc/latex/developers/tests/min_max.tex0000644000175000017500000000062611073645625020041 0ustar mikemike\begin{verbatim} %!shared %! disp("testing min_max") %!test fail("min_max(1)","Argument must be a matrix.") %!test fail("min_max('testString')","Argument must be a matrix.") %!test fail("min_max(cellA{1}=1)","Argument must be a matrix.") %!test fail("min_max([1+1i, 2+2i])","Argument must be a matrix.") %!test fail("min_max([1+1i, 2+2i; 3+1i, 4+2i])","Argument has illegal type.") \end{verbatim} nnet/doc/latex/developers/tests/prestd.tex0000644000175000017500000000035511073645625017711 0ustar mikemike\begin{verbatim} %!shared Pp, Tt, pn %! Pp = [1 2 3 4; -1 3 2 -1]; %! Tt = [3 4 5 6]; %! [pn,meanp,stdp] = prestd(Pp); %!assert(pn,[-1.16190 -0.38730 0.38730 1.16190; -0.84887 1.09141 0.60634 -0.84887],0.00001); \end{verbatim} nnet/doc/latex/developers/numbering.tex0000644000175000017500000000120310651413412017211 0ustar mikemike\section{Version numbers of the neural network toolbox} The first number describes the major release. Version number V1.0 will be the first toolbox release which should have the same functions like the Matlab R14 SP3 neural network Toolbox.\\ The second number defines the finished functions. So to start, only the MLPs will realised and so this will be the number V0.1.0.\\ The third number defines the status of the actual development and function. V0.0.1 means no release with MLP, actually, everything under construction... ;-D.\\ Right now it's version V0.1.3 which means MLP works and currently the transfer function logsig is added.nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.tex0000644000175000017500000000311611075115322026164 0ustar mikemike% Preambel \documentclass[a4paper,openany]{report} \usepackage{a4wide} \usepackage[ansinew]{inputenc} \usepackage[T1]{fontenc} \RequirePackage{ifpdf} \usepackage{hyperref} \hypersetup{% colorlinks=true, % activates colored references pdfpagemode=None, % PDF-Viewer starts without content et.al. pdfstartview=FitH, % PDF-Viewer uses a defined page width %linkbordercolor=111, % citebordercolor=111, citecolor=blue, linkcolor=blue} \ifpdf \usepackage[pdftex]{graphicx} \DeclareGraphicsExtensions{.pdf} \else \usepackage[dvips]{graphicx} \DeclareGraphicsExtensions{.eps} \fi \usepackage{fancyhdr} \usepackage{booktabs} %\usepackage[dvips]{rotating} \usepackage{multirow} \usepackage{multicol} \usepackage{color} \usepackage{amsmath} \usepackage{alltt} %\usepackage{array} %\usepackage{colortbl} \clubpenalty = 10000 \widowpenalty = 10000 \displaywidowpenalty = 10000 \definecolor{hellgrau}{gray}{0.95} \definecolor{dunkelgrau}{gray}{0.55} \renewcommand{\headrulewidth}{0pt} % no head rule \renewcommand{\footrulewidth}{0pt} % no foot rule %\nointend %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start text here!! \begin{document} \pagenumbering{roman} \input{title} \tableofcontents \pagenumbering{arabic} \include{introduction} \include{octave/octave} \include{codingGuideline/codingGuideline} \include{algorithm/algorithmen} \include{funcindex/funcindex} \include{tests/test} \include{analyzing/matlab} \appendix \include{examples/examples} \include{../common/bibliography} \end{document} nnet/doc/latex/developers/codeConvention.tex0000644000175000017500000000067210566045112020214 0ustar mikemike\section{Code convention} The main function of this toolbox will be programed with help of the book in \cite{4}. So the variables will have the same names like in the book with one exception: Variables, only with one letter, will have two letters, e.g. \begin{itemize} \item $X \rightarrow Xx$ \item $Q \rightarrow Qq$ \item $a \rightarrow aa$ \end{itemize} and so on ...\\ This is only to make it possible to search for variable names.nnet/doc/latex/developers/introduction.tex0000644000175000017500000000113511073645625017764 0ustar mikemike\chapter{Introduction} This documentation isn't a well defined and structured docu for the neural network toolbox. It's more a \textit{collection of my ideas and minds}. \section{Installed system} I'm developing and testing the actual version of the neural network toolbox on following program versions: \begin{itemize} \item Octave 2.9.5 \item octave-forge-2006.01.28 \item OctPlot svn version \end{itemize} .. and on another system \begin{itemize} \item Octave 2.9.12 \item octave-forge packages ... \item OctPlot svn version \end{itemize} \input{numbering} \input{codeConvention} nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.tcp0000644000175000017500000000035411073350143026153 0ustar mikemike[FormatInfo] Type=TeXnicCenterProjectInformation Version=4 [ProjectInfo] MainFile=neuralNetworkPackageForOctaveDevelopersGuide.tex UseBibTeX=0 UseMakeIndex=1 ActiveProfile=LaTeX => PDF ProjectLanguage=de ProjectDialect=DE nnet/doc/pdf/0000755000175000017500000000000011475775705012016 5ustar 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00000 n 0000218889 00000 n 0000219059 00000 n 0000219249 00000 n 0000219419 00000 n 0000219588 00000 n 0000219758 00000 n 0000219920 00000 n 0000220105 00000 n 0000220299 00000 n 0000220494 00000 n 0000220694 00000 n 0000220893 00000 n 0000221108 00000 n 0000221346 00000 n 0000221453 00000 n 0000221564 00000 n 0000221677 00000 n 0000221783 00000 n 0000221879 00000 n 0000221917 00000 n 0000222087 00000 n trailer << /Size 867 /Root 865 0 R /Info 866 0 R /ID [<03CB07D7115B689E95975E6DAECB173D> <03CB07D7115B689E95975E6DAECB173D>] >> startxref 222360 %%EOF nnet/doc/docReadme0000644000175000017500000000305511073645625013045 0ustar mikemikeThe directory nnet/doc contains two subdirectories. 1. latex: This includes five (5) subdirectories. All of them are used to create the newest version of the documentation. asymptote: Includes asy-scripts to create graphics, used in subdirectory users/octave/neuroToolbox/graphics. For pdf output the graphic files must be in pdf format. For dvi output, the graphics must be in eps format. That's why I create each time both types. Used asymptote is version: 1.42 win version common: Contains some .tex files which are used in both documentations. developer as users guide. developers: Contains .tex files with a lot of informations about how I did the most parts of this package. Not all of them are completly written ... :-( perl: Contains two files. The first one is a "modul" to put somewhere in a lib directory in the perl installation. This file ends with .pm. The second file (createTestDocu.pl) is a short perl script to generate some .tex files for the developer guide which contains all the test cases programmed in the function files. In the file createTestDocu.pl must be changed some variables to the local system likewise it must be copied to the "developers" directory! users: The user's guide. The official documentation to the neural network package! 2. pdf: Contains documentation generated of the latex directory. nnet/inst/0000755000175000017500000000000011475775705011455 5ustar mikemikennet/inst/__printInitFcn.m0000644000175000017500000000240010751613601014512 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printInitFcn (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printInitFcn(fid,net) if isfield(net,"initFcn") if isempty(net.initFcn) fprintf(fid," initFcn: '%s'\n","empty"); else fprintf(fid," initFcn: '%s'\n",net.initFcn); endif endif endfunctionnnet/inst/__analyzerows.m0000644000175000017500000000645211073170664014501 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{retmatrix} = __analyzerows(@var{matrix}) ## @code{__analyzerows} takes a matrix as input argument and checks what kind of ## data are contained in the rows. ## a.) binary values? Means the row contains only 0 and 1 ## b.) unique values? ## c.) Min values are several times contained in the row ## d.) Max values are several times contained in the row ## @end deftypefn ## Author: mds function retmatrix = __analyzerows(matrix) ## check number of inputs error(nargchk(1,1,nargin)); nRows = size(matrix,1); # get number or rows retmatrix = zeros(nRows,4); doneVec = zeros(nRows,1); ## now let's check which rows are binary i = 1; while (i <= nRows) vec = matrix(i,:); n1 = find(vec==1); n0 = find(vec==0); if (length(n1)==0 || length(n0)==0) #do nothing else if (length(vec)==(length(n1)+length(n0))) # in this case, the vector contains only ones and zeros retmatrix(i,1) = 1; doneVec(i) = 1; endif endif i += 1; endwhile ## now let's check which rows are unique i = 1; while (i <= nRows) if (doneVec(i)==0) vec = matrix(i,:); n1 = find(vec==vec(1)); if (length(vec)==(length(n1))) # in this case, the vector contains only unique data retmatrix(i,2) = 1; doneVec(i) = 1; endif endif i += 1; endwhile ## now let's check how often we can find the min value i = 1; while (i <= nRows) if (doneVec(i)==0) vec = matrix(i,:); n1 = min(vec); retmatrix(i,3) = length(find(n1==vec)); endif i += 1; endwhile ## now let's check how often we can find the max value i = 1; while (i <= nRows) if (doneVec(i)==0) vec = matrix(i,:); n1 = max(vec); retmatrix(i,4) = length(find(n1==vec)); endif i += 1; endwhile endfunction %!shared b, retmat %! disp("testing __analyzerows") %! b = [1 0 0 1; 1 0 0 0; 1 2 0 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,1)==1);#%!assert(retmat(1,1)==1); %!assert(retmat(2,1)==1); %!assert(retmat(3,1)==0); %! b = [1 0 0 2; 1 0 0 0; 1 1 1 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,2)==0); %!assert(retmat(2,2)==0); %!assert(retmat(3,2)==1); %! b = [1 0 0 2; 1 0 0 0; 1 1 1 1]; %! retmat = __analyzerows(b); %!assert(retmat(1,3)==2); %!assert(retmat(2,3)==0); %!assert(retmat(3,3)==0); %! retmat = __analyzerows(b); %!assert(retmat(1,4)==1); %!assert(retmat(2,4)==0); %!assert(retmat(3,4)==0); nnet/inst/__printPerformFcn.m0000644000175000017500000000241610751613601015230 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printPerformFcn (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printPerformFcn(fid,net) if isfield(net,"performFcn") if isempty(net.performFcn) fprintf(fid," performFcn: '%s'\n","empty"); else fprintf(fid," performFcn: '%s'\n",net.performFcn); endif endif endfunctionnnet/inst/poststd.m0000644000175000017500000000521111054451466013316 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{Pp},@var{Tt}] = poststd(@var{Pn},@var{meanp},,@var{stdP},@var{Tn},@var{meanT},@var{stdT}) ## @code{poststd} postprocesses the data which has been preprocessed by @code{prestd}. ## @end deftypefn ## @seealso{prestd,trastd} ## Author: Michel D. Schmid function [Pp,Tt] = poststd(Pn,meanp,stdp,Tn,meant,stdt) ## check range of input arguments error(nargchk(3,6,nargin)) if (nargin==4) error("4 input arguments are not allowed!"); endif if (nargin==5) error("5 input arguments are not allowed!"); endif ## do first inputs ## set all standard deviations which are zero to 1 [nRowsII, nColumnsII] = size(Pn); rowZeros = zeros(nRowsII,1); findZeros = find(stdp==0); rowZeros(findZeros)=1; nequal = !rowZeros; if (sum(rowZeros) != 0) warning("Some standard deviations are zero. Those inputs won't be transformed."); meanpZero = meanp.*nequal; stdpZero = stdp.*nequal + 1*rowZeros; else meanpZero = meanp; stdpZero = stdp; endif ## calculate the postprocessed inputs nColumnsIIone = ones(1,nColumnsII); Pp = (stdpZero*nColumnsIIone).*Pn + meanpZero*nColumnsIIone; ## do also targets if ( nargin==6 ) # now set all standard deviations which are zero to 1 [nRowsIII, nColumnsIII] = size(stdt); rowZeros = zeros(nRowsIII,1); findZeros = find(stdt==0); rowZeros(findZeros)=1; nequal = !rowZeros; if (sum(rowZeros) != 0) warning("Some standard deviations are zero. Those targets won't be transformed."); meantZero = meant.*nequal; stdtZero = stdt.*nequal + 1*rowZeros; else meantZero = meant; stdtZero = stdt; endif ## calculate the postprocessed targets nColumnsIIIone = ones(1,nColumnsIII); Tt = (stdtZero*nColumnsIIIone).*Tn + meantZero*nColumnsIIIone; endif endfunctionnnet/inst/train.m0000644000175000017500000001441311475772132012742 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{net}] = train (@var{MLPnet},@var{mInputN},@var{mOutput},@var{[]},@var{[]},@var{VV}) ## A neural feed-forward network will be trained with @code{train} ## ## @example ## [net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); ## @end example ## @noindent ## ## @example ## left side arguments: ## net: the trained network of the net structure @code{MLPnet} ## @end example ## @noindent ## ## @example ## right side arguments: ## MLPnet : the untrained network, created with @code{newff} ## mInputN: normalized input matrix ## mOutput: output matrix (normalized or not) ## [] : unused parameter ## [] : unused parameter ## VV : validize structure ## @end example ## @end deftypefn ## @seealso{newff,prestd,trastd} ## Author: Michel D. Schmid ## Comments: see in "A neural network toolbox for Octave User's Guide" [4] ## for variable naming... there have inputs or targets only one letter, ## e.g. for inputs is P written. To write a program, this is stupid, you can't ## search for 1 letter variable... that's why it is written here like Pp, or Tt ## instead only P or T. function [net] = train(net,Pp,Tt,notUsed1,notUsed2,VV) ## check range of input arguments error(nargchk(3,6,nargin)) ## set defaults doValidation = 0; if nargin==6 # doValidation=1; ## check if VV is in MATLAB(TM) notation [VV, doValidation] = checkVV(VV); endif ## check input args checkInputArgs(net,Pp,Tt) ## nargin ... switch(nargin) case 3 [Pp,Tt] = trainArgs(net,Pp,Tt); VV = []; case 6 [Pp,Tt] = trainArgs(net,Pp,Tt); if isempty(VV) VV = []; else if !isfield(VV,"Pp") error("VV.Pp must be defined or VV must be [].") endif if !isfield(VV,"Tt") error("VV.Tt must be defined or VV must be [].") endif [VV.Pp,VV.Tt] = trainArgs(net,VV.Pp,VV.Tt); endif otherwise error("train: impossible code execution in switch(nargin)") endswitch ## so now, let's start training the network ##=========================================== ## first let's check if a train function is defined ... if isempty(net.trainFcn) error("train:net.trainFcn not defined") endif ## calculate input matrix Im [nRowsInputs, nColumnsInputs] = size(Pp); Im = ones(nRowsInputs,nColumnsInputs).*Pp{1,1}; if (doValidation) [nRowsVal, nColumnsVal] = size(VV.Pp); VV.Im = ones(nRowsVal,nColumnsVal).*VV.Pp{1,1}; endif ## make it MATLAB(TM) compatible nLayers = net.numLayers; Tt{nLayers,1} = Tt{1,1}; Tt{1,1} = []; if (!isempty(VV)) VV.Tt{nLayers,1} = VV.Tt{1,1}; VV.Tt{1,1} = []; endif ## which training algorithm should be used switch(net.trainFcn) case "trainlm" if !strcmp(net.performFcn,"mse") error("Levenberg-Marquardt algorithm is defined with the MSE performance function, so please set MSE in NEWFF!") endif net = __trainlm(net,Im,Pp,Tt,VV); otherwise error("train algorithm argument is not valid!") endswitch # ======================================================= # # additional check functions... # # ======================================================= function checkInputArgs(net,Pp,Tt) ## check "net", must be a net structure if !__checknetstruct(net) error("Structure doesn't seem to be a neural network!") endif ## check Pp (inputs) nInputSize = net.inputs{1}.size; #only one exists [nRowsPp, nColumnsPp] = size(Pp); if ( (nColumnsPp>0) ) if ( nInputSize==nRowsPp ) # seems to be everything i.o. else error("Number of rows must be the same, like in net.inputs.size defined!") endif else error("At least one column must exist") endif ## check Tt (targets) [nRowsTt, nColumnsTt] = size(Tt); if ( (nRowsTt | nColumnsTt)==0 ) error("No targets defined!") elseif ( nColumnsTt!=nColumnsPp ) error("Inputs and targets must have the same number of data sets (columns).") elseif ( net.layers{net.numLayers}.size!=nRowsTt ) error("Defined number of output neurons are not identically to targets data sets!") endif endfunction # ------------------------------------------------------- function [Pp,Tt] = trainArgs(net,Pp,Tt); ## check number of arguments error(nargchk(3,3,nargin)); [PpRows, PpColumns] = size(Pp); Pp = mat2cell(Pp,PpRows,PpColumns); # mat2cell is the reason # why octave-2.9.5 doesn't work # octave-2.9.x with x>=6 should be # ok [TtRows, TtColumns] = size(Tt); Tt = mat2cell(Tt,TtRows,TtColumns); endfunction # ------------------------------------------------------- function [VV, doValidation] = checkVV(VV) ## check number of arguments error(nargchk(1,1,nargin)); if (isempty(VV)) doValidation = 0; else doValidation = 1; ## check if MATLAB(TM) naming convention is used if isfield(VV,"P") VV.Pp = VV.P; VV.P = []; elseif !isfield(VV,"Pp") error("VV is defined but inside exist no VV.P or VV.Pp") endif if isfield(VV,"T") VV.Tt = VV.T; VV.T = []; elseif !isfield(VV,"Tt") error("VV is defined but inside exist no VV.TP or VV.Tt") endif endif endfunction # ============================================================ endfunctionnnet/inst/__printLayerConnect.m0000644000175000017500000000555410751613601015563 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printLayerConnect (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printLayerConnect(fid,net) if isfield(net,"layerConnect") # net.layerConnect can be a matrix..! # check if it's a matrix if isscalar(net.layerConnect) error("unsure if this is possible..") elseif isnumeric(net.layerConnect) if ismatrix(net.layerConnect) if issquare(net.layerConnect) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," layerConnect: ["); [nRows nColumns] = size(net.layerConnect); for k = 1:1:nRows for i = 1:1:nColumns if i ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} [@var{YY},@var{PS}] = mapstd (@var{XX},@var{ymean},@var{ystd}) ## Map values to mean 0 and standard derivation to 1. ## ## @example ## [YY,PS] = mapstd(XX,ymean,ystd) ## ## Apply the conversion and returns YY as (YY-ymean)/ystd. ## ## [YY,PS] = mapstd(XX,FP) ## ## Apply the conversion but using an struct to inform target mean/stddev. ## This is the same of [YY,PS]=mapstd(XX,FP.ymean, FP.ystd). ## ## YY = mapstd('apply',XX,PS) ## ## Reapply the conversion based on a previous operation data. ## PS stores the mean and stddev of the first XX used. ## ## XX = mapstd('reverse',YY,PS) ## ## Reverse a conversion of a previous applied operation. ## ## dx_dy = mapstd('dx',XX,YY,PS) ## ## Returns the derivative of Y with respect to X. ## ## dx_dy = mapstd('dx',XX,[],PS) ## ## Returns the derivative (less efficient). ## ## name = mapstd('name'); ## ## Returns the name of this convesion process. ## ## FP = mapstd('pdefaults'); ## ## Returns the default process parameters. ## ## names = mapstd('pnames'); ## ## Returns the description of the process parameters. ## ## mapstd('pcheck',FP); ## ## Raises an error if FP has some inconsistent. ## @end example ## ## @end deftypefn function [out1,out2]=mapstd(in1,in2,in3,in4) # # Map values to mean 0 and standard derivation to 1. # # [YY,PS] = mapstd(XX,ymean,ystd) # # Apply the conversion and returns YY as (YY-ymean)/ystd. # # [YY,PS] = mapstd(XX,FP) # # Apply the conversion but using an struct to inform target mean/stddev. # This is the same of [YY,PS]=mapstd(XX,FP.ymean, FP.ystd). # # YY = mapstd('apply',XX,PS) # # Reapply the conversion based on a previous operation data. # PS stores the mean and stddev of the first XX used. # # XX = mapstd('reverse',YY,PS) # # Reverse a conversion of a previous applied operation. # # dx_dy = mapstd('dx',XX,YY,PS) # # Returns the derivative of Y with respect to X. # # dx_dy = mapstd('dx',XX,[],PS) # # Returns the derivative (less efficient). # # name = mapstd('name'); # # Returns the name of this convesion process. # # FP = mapstd('pdefaults'); # # Returns the default process parameters. # # names = mapstd('pnames'); # # Returns the description of the process parameters. # # mapstd('pcheck',FP); # # Raises an error if FP has some inconsistent. # if nargin==0 error("Not enough arguments.") endif # Defaults ps.name="mapstd"; ps.ymean=0; ps.ystd=1; if ischar(in1) switch in1 case "name" if nargout>1 error("Too many output arguments"); endif if nargin>1 error("Too many input arguments"); endif out1="Map Mean and Standard Deviation"; return; case "pdefaults" if nargout>1 error("Too many output arguments"); endif if nargin>1 error("Too many input arguments"); endif out1=ps; case "pcheck" if nargout>1 error("Too many output arguments"); endif if nargin<2 error("Not enough input arguments"); endif if nargin>2 error("Too many input arguments"); endif fp=in2; if ~isstruct(fp) error("FP must be a struct") elseif ~isfield(fp,"ymean") error("FP must include ymean field") elseif ~isfield(fp,"ystd") error("FP must include ystd field") elseif isdouble(fp.ymean) error("FP.ymean must be a real scalar value") elseif isdouble(fp.ystd) error("FP.ystd must be a real scalar value") else out1=''; endif return; # MATLAB uses pnames but documents as pdesc (that does not work) case "pnames" if nargout>1 error("Too many output arguments"); endif if nargin>1 error("Too many input arguments"); endif # MATLAB seems to be buggy in the second element #out1={'Mean value for each row of Y.','Maximum value for each #row of Y.'}; out1={"Mean value for each row of Y.","Standart deviation value for each row of Y."}; case "apply" if nargin<3 error("Not enough input arguments"); endif if nargin>3 error("Too many input arguments"); endif if nargout>1 error("Too many output arguments"); endif xx=in2; ps=in3; yy=apply(xx,ps); out1=yy; out2=ps; return; case "reverse" if nargin<3 error("Not enough input arguments"); endif if nargin>3 error("Too many input arguments"); endif if nargout>1 error("Too many output arguments"); endif yy=in2; ps=in3; xx=reverse(yy,ps); out1=xx; out2=ps; return; case "dx" if nargin<3 error("Not enough input arguments"); endif if nargin>3 error("Too many input arguments"); endif if nargout>1 error("Too many output arguments"); endif xx=in2; yy=in3; ps=in4; xx_yy=derivate(xx,yy,ps); out1=xx_yy; return; endswitch else xx=in1; ps.xrows=size(xx,1); ps.yrows=size(xx,1); ps.xmean=mean(xx,2); ps.xstd=std(xx,0,2); if nargin==1 # All correct elseif nargin==2 if isstruct(in2) ps.ymean=in2.ymean; ps.ystd=in2.ystd; else ps.ymean=in2; endif elseif nargin == 3 ps.ymean=in2; ps.ystd=in3; else error("Too many input arguments"); endif out1=apply(xx,ps); out2=ps; endif # Verify args function checkargs(values,ps) # check xx is matrix if ~isnumeric(values) error("Just numeric values are accepted") endif # check ps is struct if ~isstruct(ps) error("PS should be a struct") endif # check ymean,ystd if ~isa(ps.ymean,"double") error("PS.ymean should be a double") endif if ~isa(ps.ystd,"double") error("PS.ystd should be a double") endif if ~all(size(ps.ymean)==[1 1]) error("PS.ymean should be a scalar") endif if ~all(size(ps.ystd)==[1 1]) error("PS.ystd should be a scalar") endif # check xmean,ystd if ~isnumeric(ps.xmean) error("PS.xmean should be a numeric") endif if ~isnumeric(ps.xstd) error("PS.xstd should be a numeric") endif if ~all(size(ps.xmean)==size(ps.xstd)) error("Size of PS.xmean and PS.xstd must match") endif endfunction # Apply the mapping operation function [yy]=apply(xx,ps) checkargs(xx,ps) if ~all(size(xx,1)==size(ps.xmean,1)) error("Size of XX rows should match PS.xmean and PS.xstd") endif # Avoid multiply/division by zero ps.xstd(ps.xstd == 0) = 1; yy=(xx - (ps.xmean*ones(1,size(xx,2)))) ./ (ps.xstd*ones(1,size(xx,2))); yy=(yy + ps.ymean) .* ps.ystd; endfunction # Reverse the mapping operation function [xx]=reverse(yy,ps) checkargs(yy,ps) if ~all(size(yy,1)==size(ps.xmean,1)) error("Size of YY rows should match PS.xmean and PS.xstd") endif # Avoid multiply/division by zero ps.xstd(ps.xstd == 0) = 1; yy=(yy ./ ps.ystd) - ps.ymean; xx=(yy .* (ps.xstd*ones(1,size(yy,2)))) + (ps.xmean*ones(1,size(yy,2))); endfunction # I don't know why this exists but matlab implements it function [dy_dx]=derivate(xx,yy,ps) checkargs(yy,ps) checkargs(xx,ps) cols = size(xx,2); diagonal = diag(ps.ystd ./ ps.xstd); dy_dx = diagonal(:,:,ones(1,cols)); endfunction #end endfunctionnnet/inst/dsatlin.m0000644000175000017500000000236111112254025013242 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = dsatlin (@var{n}) ## ## @end deftypefn ## @seealso{dpurelin,dtansig,dlogsig} ## Author: Michel D. Schmid function a = dsatlin(n) # the derivative of satlin is easy: # where satlin is constant, the derivative is 0 # else, because without variable n, the derivative is 1 if (n>=0 && n<=1) a = 1; else a = 0; endif endfunctionnnet/inst/__printLayers.m0000644000175000017500000000270310751613601014425 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printLayers (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printLayers(fid,net) if isfield(net,"layers") # check if it's cell array if iscell(net.layers) [nRows, nColumns] = size(net.layers); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," layers: {%dx%d cell} of layers\n",nRows,nColumns); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/__newnetwork.m0000644000175000017500000001421711112254025014310 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}{@var{net}} = __newnetwork(@var{numInputs},@var{numLayers},@var{numOutputs},@var{networkType}) ## @code{__newnetwork} create a custom 'zero'-network ## ## ## @example ## net = __newnetwork(numInputs,numLayers,numOutputs,networkType) ## ## numInputs : number of input vectors, actually only 1 allowed ## numLayers : number of layers ## numOutputs: number of output vectors, actually only 1 allowed ## networkType: e.g. feed-forward-network "newff" ## @end example ## ## @example ## net = __newnetwork(1,2,1,"newff") ## 1 input layer, two hidden layers, one output layer ## and the network type ## @end example ## ## @noindent ## @end deftypefn ## Author: Michel D. Schmid function net = __newnetwork(numInputs,numLayers,numOutputs,networkType) ## check range of input arguments error(nargchk(4,4,nargin)) ## check input args if ( !isposint(numInputs) ) error("network: at least 1 input must be defined! ") # this can't happen actually, only one is allowed and this # one is hard coded elseif ( !isposint(numLayers) ) error("network: at least 1 hidden- and one output layer must be defined! ") endif ## second check for numLayers... must be at least "2" for the ## newff, this means at least 1 hidden and 1 output layer if (strcmp(networkType,"newff") && (numLayers<2)) error("network: not enough layers are defined! ") endif ## define network type net.networkType = networkType; ## ZERO NETWORK net.numInputs = 0; net.numLayers = 0; net.numInputDelays = 0; net.numLayerDelays = 0; # the next five parameters aren't used till now, they are used # only for matlab nnet type compatibility ==> saveMLPStruct net.biasConnect = []; # not used parameter till now net.inputConnect = []; # not used parameter till now net.layerConnect = []; # not used parameter till now net.outputConnect = []; # not used parameter till now net.targetConnect = []; # not used parameter till now net.numOutputs = 0; net.numTargets = 0; net.inputs = cell(0,1); net.layers = cell(0,1); net.biases = cell(0,1); net.inputWeights = cell(0,0); net.layerWeights = cell(0,0); net.outputs = cell(1,0); net.targets = cell(1,0); net.performFcn = ""; net.performParam = []; net.trainFcn = ""; net.trainParam = []; net.IW = {}; net.LW = {}; net.b = cell(0,1); net.userdata.note = "Put your custom network information here."; ## ARCHITECTURE ## define everything with "inputs" net.numInputs = numInputs; ## actually, it's only possible to have "one" input vector net.inputs{1,1}.range = [0 0]; net.inputs{1,1}.size = 0; net.inputs{1,1}.userdata = "Put your custom informations here!"; ## define everything with "layers" net.numLayers = numLayers; net = newLayers(net,numLayers); ## define unused variables, must be defined for saveMLPStruct net.biasConnect = [0; 0]; net.inputConnect = [0; 0]; net.layerConnect = [0 0; 0 0]; net.outputConnect = [0 0]; net.targetConnect = [0 0]; net.numInputDelays = 0; net.numLayerDelays = 0; ## define everything with "outputs" net.numOutputs = numOutputs; net.outputs = cell(1,numLayers); for i=1:numLayers if (i==numLayers) net.outputs{i}.size = 1; # nothing else allowed till now net.outputs{i}.userdata = "Put your custom informations here!"; else net.outputs{i} = []; endif endfor ## define everything with "biases" net = newBiases(net,numLayers); #===================================================== # # Additional ARCHITECTURE Functions # #===================================================== function net = newLayers(net,numLayers) ## check range of input arguments error(nargchk(2,2,nargin)) ## check type of arguments if ( !isscalar(numLayers) | !isposint(numLayers) ) error("second argument must be a positive integer scalar value!") endif if ( !__checknetstruct(net) ) error("first argument must be a network structure!") endif for iRuns=1:numLayers net.layers{iRuns,1}.dimension = 0; net.layers{iRuns,1}.netInputFcn = ""; net.layers{iRuns,1}.size = 0; ### TODO: test with newff net.layers{iRuns,1}.transferFcn = "tansig"; net.layers{iRuns,1}.transferFcn = ""; net.layers{iRuns,1}.userdata = "Put your custom informations here!"; endfor endfunction #----------------------------------------------------- function net = newBiases(net,numLayers) ## check range of input arguments error(nargchk(2,2,nargin)) ## check type of arguments if ( !isscalar(numLayers) | !isposint(numLayers) ) error("second argument must be a positive integer scalar value!") endif if ( !isstruct(net) ) error("first argument must be a network structure!") endif for iRuns=1:numLayers net.biases{iRuns,1}.learn = 1; net.biases{iRuns,1}.learnFcn = ""; net.biases{iRuns,1}.learnParam = "undefined..."; net.biases{iRuns,1}.size = 0; net.biases{iRuns,1}.userdata = "Put your custom informations here!"; endfor endfunction # ================================================================ # # END Additional ARCHITECTURE Functions # # ================================================================ endfunctionnnet/inst/__printOutputConnect.m0000644000175000017500000000560610751613601016005 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printOutputConnect (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printOutputConnect(fid,net) if isfield(net,"outputConnect") # net.outputConnect can be a matrix..! # check if it's a matrix if isscalar(net.outputConnect) error("unsure if this is possible..") elseif isnumeric(net.outputConnect) if ismatrix(net.outputConnect) if issquare(net.outputConnect) fprintf(fid," outputConnect: ["); [nRows nColumns] = size(net.outputConnect); for k = 1:1:nRows for i = 1:1:nColumns if i ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = satlins (@var{n}) ## A neural feed-forward network will be trained with @code{trainlm} ## ## @end deftypefn ## @seealso{purelin,tansig,logsig,satlin,hardlim,hardlims} ## Author: Michel D. Schmid function a = satlins(n) if (n<-1) a = -1; elseif (n>=-1 && n<=1) a = n; else a = 1; # if n>1 endif endfunctionnnet/inst/vec2ind.m0000644000175000017500000000226311153230335013142 0ustar mikemike## Copyright (C) 2009 Luiz Angelo Daros de Luca ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {@var{ind}} = vec2ind (@var{vector}) ## @code{vec2ind} convert vectors to indices ## ## ## @example ## EXAMPLE 1 ## vec = [1 2 3; 4 5 6; 7 8 9]; ## ## ind = vec2ind(vec) ## The prompt output will be: ## ans = ## 1 2 3 1 2 3 1 2 3 ## @end example ## ## @end deftypefn ## @seealso{ind2vec} function [ind]=vec2ind(vector) # Convert vectors to indices # # [ind,col,vals] = find(vector); ind=ind'; endfunction nnet/inst/hardlim.m0000644000175000017500000000214711112254025013226 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = hardlim (@var{n}) ## ## @end deftypefn ## @seealso{purelin,tansig} ## Author: Michel D. Schmid function a = hardlim(n) # a=1 if n>=0, a=0 otherwise a = (n>=0); endfunctionnnet/inst/__printNumLayers.m0000644000175000017500000000273310751613601015110 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumLayers (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumLayers(fid,net) if isfield(net,"numLayers") if isscalar(net.numLayers) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," numLayers: %d\n",net.numLayers); # net.numLayers must be an integer... till now, 11-01-2006 else error("numLayers must be a scalar value!"); endif endif endfunctionnnet/inst/__setx.m0000644000175000017500000000370210751613601013074 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{net} = __setx (@var{net},@var{X2}) ## @code{__setx} sets the new weights to the neural network structure ## @end deftypefn ## @seealso{getx} ## Author: Michel D. Schmid function net = __setx(net,xx) ## check number of inputs error(nargchk(2,2,nargin)); ## check input args ## check "net", must be a net structure if !__checknetstruct(net) error("Structure doesn't seem to be a neural network") endif ## inputs [nRows, nColumns] = size(net.IW{1,1}); nElementsIW = nRows*nColumns; net.IW{1,1}(:) = xx(1:nElementsIW); [nRows, nColumns] = size(net.b{1,1}); nElementsB1 = nRows*nColumns; net.b{1,1}(:) = xx(1+nElementsIW:nElementsIW+nElementsB1); start = nElementsIW + nElementsB1; ## layers nLayers = net.numLayers; for i = 2:nLayers [nRows, nColumns] = size(net.LW{i,i-1}); nElementsLW = nRows*nColumns; net.LW{i,i-1}(:) = xx(1+start:start+nElementsLW); [nRows, nColumns] = size(net.b{i,1}); nElementsBx = nRows*nColumns; net.b{i,1}(:) = xx(1+start+nElementsLW:start+nElementsLW+nElementsBx); start = start + nElementsLW + nElementsBx; endfor endfunctionnnet/inst/__printBiasConnect.m0000644000175000017500000000423410751613601015357 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printBiasConnect (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printBiasConnect(fid,net) if isfield(net,"biasConnect") # net.biasConnect can be a matrix..! # check if it's a matrix if isscalar(net.biasConnect) error("unsure if this is possible..") elseif isnumeric(net.biasConnect) if ismatrix(net.biasConnect) if issquare(net.biasConnect) # nothing prgrammed till now elseif isvector(net.biasConnect) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" # print bracket for open fprintf(fid," biasConnect: ["); [nRows nColumns] = size(net.biasConnect); for k = 1:1:nRows for i = 1:1:nColumns fprintf(fid,"%d",net.biasConnect(i*k)); endfor if k!=nRows #print ; for newline in matrix fprintf(fid,";"); endif endfor # print last bracket fprintf(fid,"] not yet used item\n"); endif # if issquare.. endif #if ismatrix endif # isscalar(net.biasConnect) endif # if isfield(...) endfunctionnnet/inst/__printTargets.m0000644000175000017500000000274510751613601014605 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printTargets (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printTargets(fid,net) if isfield(net,"targets") # check if it's cell array if iscell(net.targets) [nRows, nColumns] = size(net.targets); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," targets: {%dx%d cell} containing %d targets\n",nRows,nColumns,net.numTargets); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/__printIW.m0000644000175000017500000000320510751613601013503 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printIW (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printIW(fid,net) if isfield(net,"IW") nInputs = 0; # check if it's cell array if iscell(net.IW) [nRows, nColumns] = size(net.IW); for i=1:nRows for k=1:nColumns if !isempty(net.IW{i,k}) nInputs = nInputs+1; endif endfor endfor # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," IW: {%dx%d cell} containing %d input weight matrix\n",nRows,nColumns,nInputs); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/isposint.m0000644000175000017500000000365411073170664013476 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{f} = isposint(@var{n}) ## @code{isposint} returns true for positive integer values. ## ## @example ## isposint(1) # this returns TRUE ## isposint(0.5) # this returns FALSE ## isposint(0) # this also return FALSE ## isposint(-1) # this also returns FALSE ## @end example ## ## ## @end deftypefn ## Author: Michel D. Schmid function f = isposint(n) ## check range of input arguments error(nargchk(1,1,nargin)) ## check input arg if (length(n)>1) error("Input argument must not be a vector, only scalars are allowed!") endif f = 1; if ( (!isreal(n)) | (n<=0) | (round(n) != n) ) f = 0; endif endfunction %!shared %! disp("testing isposint") %!assert(isposint(1)) # this should pass %!assert(isposint(0.5),0) # should return zero %!assert(isposint(-1),0) # should return zero %!assert(isposint(-1.5),0) # should return zero %!assert(isposint(0),0) # should return zero %!fail("isposint([0 0])","Input argument must not be a vector, only scalars are allowed!") %!fail("isposint('testString')","Input argument must not be a vector, only scalars are allowed!") nnet/inst/dsatlins.m0000644000175000017500000000240011112254025013417 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = satlins (@var{n}) ## A neural feed-forward network will be trained with @code{trainlm} ## ## @end deftypefn ## @seealso{purelin,tansig,logsig,satlin,hardlim,hardlims} ## Author: Michel D. Schmid function a = dsatlins(n) # comment see dsatlin # a = 1 if (n>=-1 && n<=1), # 0 otherwise if (n>=-1 && n<=1) a = 1; else a = 0; endif endfunctionnnet/inst/__getx.m0000644000175000017500000000275610751613601013070 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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 ; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{x} = __getx (@var{net}) ## @code{__getx} will rerange the weights in one columns vector. ## ## ## @noindent ## @end deftypefn ## Author: Michel D. Schmid function x = __getx(net) ## check number of inputs error(nargchk(1,1,nargin)); ## check input args ## check "net", must be a net structure if !__checknetstruct(net) error("Structure doesn't seem to be a neural network") endif ## inputs x = net.IW{1,1}(:); x = [x; net.b{1}(:)]; nNumLayers = net.numLayers; for iLayers = 2:nNumLayers # 1 would be the input layer ## layers x = [x; net.LW{iLayers,iLayers-1}(:)]; x = [x; net.b{iLayers}(:)]; endfor endfunctionnnet/inst/__printTrainFcn.m0000644000175000017500000000240610751613601014672 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printTrainFcn (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printTrainFcn(fid,net) if isfield(net,"trainFcn") if isempty(net.trainFcn) fprintf(fid," trainFcn: '%s'\n","empty"); else fprintf(fid," trainFcn: '%s'\n",net.trainFcn); endif endif endfunctionnnet/inst/subset.m0000644000175000017500000002041711073170664013127 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{mTrain}, @var{mTest}, @var{mVali}] = subset (@var{mData},@var{nTargets},@var{iOpti},@var{fTest},@var{fVali}) ## @code{subset} splits the main data matrix which contains inputs and targets into 2 or 3 subsets ## depending on the parameters. ## ## The first parameter @var{mData} must be in row order. This means if the network ## contains three inputs, the matrix must be have 3 rows and x columns to define the ## data for the inputs. And some more rows for the outputs (targets), e.g. a neural network ## with three inputs and two outputs must have 5 rows with x columns! ## The second parameter @var{nTargets} defines the number or rows which contains the target values! ## The third argument @code{iOpti} is optional and can have three status: ## 0: no optimization ## 1: will randomise the column order and order the columns containing min and max values to be in the train set ## 2: will NOT randomise the column order, but order the columns containing min and max values to be in the train set ## default value is @code{1} ## The fourth argument @code{fTest} is also optional and defines how ## much data sets will be in the test set. Default value is @code{1/3} ## The fifth parameter @code{fTrain} is also optional and defines how ## much data sets will be in the train set. Default value is @code{1/6} ## So we have 50% of all data sets which are for training with the default values. ## ## @example ## [mTrain, mTest] = subset(mData,1) ## returns three subsets of the complete matrix ## with randomized and optimized columns! ## @end example ## @example ## [mTrain, mTest] = subset(mData,1,) ## returns two subsets ## @end example ## ## @end deftypefn ## Author: Michel D. Schmid function [mTrain, mTest, mVali] = subset(mData,nTargets,iOpti,fTest,fVali) ## check range of input arguments error(nargchk(2,5,nargin)) ## check the input arguments ...! if (nTargets==0) error("No TARGETS defined! This doesn't make any sense for feed-forward neural networks! Please define at least one row of targets") endif ## set default values if (nargin==2) iOpti = 1; fTest = 1/3; fVali = 1/6; elseif (nargin==3) fTest = 1/3; fVali = 1/6; elseif (nargin==4) ## if fTest is set and nothing is set ## for fVali I assume that fVali is not used! fVali = 0; endif ## calculate the number of train, test and validation sets fTrain = 1-fTest-fVali; nTrainSets = floor(size(mData,2)*fTrain); diffRestSets = size(mData,2)-nTrainSets; nTestSets = floor(size(mData,2)*fTest); nValiSets = size(mData,2)-nTrainSets-nTestSets; ## now let's see if matrix must be optimized! bOptiAgain = 1; while (bOptiAgain) if (iOpti == 1) # check that only one optimizing run is enough!! # maybe it's necessary to do it twice ..! # check that all min and max values are in the train set ...! mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti); mTrain = mData(:,1:nTrainSets); iRuns = size(mTrain,1); i = 1; j = 1; while (i < iRuns) if ( max(mTrain(i,:)) == max(mData(i,:)) ) j += 1; endif i +=1; endwhile if (i==j) bOptiAgain = 0; endif elseif (iOpti == 2) # check that only one optimizing run is enough!! # maybe it's necessary to do it twice ..! # check that all min and max values are in the train set ...! mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti); mTrain = mData(:,1:nTrainSets); iRuns = size(mTrain,1); j = 1; i = 1; while (i < iRuns) if (max(mTrain(i,:))==max(mData(i,:))) j += 1; endif i += 1; endwhile if (i==j) bOptiAgain = 0; endif else ## in this case, iOpti must be 0 ==> nothing todo bOptiAgain = 0; endif endwhile #END OF while(bOptiAgain) ## now split up if (nargout==1) mTrain = mData; elseif (nargout==2); mTrain = mData(:,1:nTrainSets); mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets); elseif (nargout==3) mTrain = mData(:,1:nTrainSets); mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets); mVali = mData(:,nTrainSets+nTestSets+1:end); endif endfunction %!shared matrix, nTargets, mTrain, mTest, mVali %! disp("testing subset") %! matrix = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20; \ %! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \ %! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \ %! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \ %! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \ %! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99]; %! nTargets = 1; # the last row is equivalent to the target values. %! [mTrain, mTest, mVali] = subset(matrix,nTargets); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %! # It's not possible to test the column order with this call! %! # randomizing is used! But all max and min values should be %! # in the training set %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); %! %! %! [mTrain, mTest, mVali] = subset(matrix,nTargets,0); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %!assert(mTrain==matrix(:,1:10)); %!assert(mTest==matrix(:,11:16)); %!assert(mVali==matrix(:,17:20)); %! %! %! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################ %!assert(size(mTrain,2)==10);# 50% of 20 %!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor) %!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor) %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); %! %! %! ## next test ... optimize twice %! matrix = [1 2 3 4 5 6 7 20 8 10 11 12 13 14 15 16 17 18 18 9; \ %! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \ %! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \ %! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \ %! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \ %! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99]; %! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################ %!assert(max(mTrain(1,:))==max(matrix(1,:))); %!assert(min(mTrain(1,:))==min(matrix(1,:))); %!assert(max(mTrain(2,:))==max(matrix(2,:))); %!assert(min(mTrain(2,:))==min(matrix(2,:))); %!assert(max(mTrain(3,:))==max(matrix(3,:))); %!assert(min(mTrain(3,:))==min(matrix(3,:))); %!assert(max(mTrain(4,:))==max(matrix(4,:))); %!assert(min(mTrain(4,:))==min(matrix(4,:))); ## \todo, a lot of tests to be sure, everything is working OK!! ## all combinations of arguments must be testet! nnet/inst/__printBiases.m0000644000175000017500000000274110751613601014376 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printBiases (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printBiases(fid,net) if isfield(net,"biases") # check if it's cell array if iscell(net.biases) [nRows, nColumns] = size(net.biases); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," biases: {%dx%d cell} containing %d biases\n",nRows,nColumns,length(net.biases)); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/tansig.m0000644000175000017500000000251011054451466013102 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{a} = tansig (@var{n}) ## @code{tansig} is a non-linear transfer function used to train ## neural networks. ## This function can be used in newff(...) to create a new feed forward ## multi-layer neural network. ## ## @end deftypefn ## @seealso{purelin, logsig} ## Author: Michel D. Schmid function a = tansig(n) ## see MATLAB(TM) online help a = 2 ./ (1 + exp(-2*n)) - 1; ## attention with critical values ==> infinite values ## must be set to 1 i = find(!finite(a)); a(i) = sign(n(i)); endfunctionnnet/inst/__mse.m0000644000175000017500000000273511112254025012673 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{perf} = __mse (@var{E}) ## @code{__mse} returns the Mean-Square-Error of a vector E ## ## @example ## ## This function is used to calculate the network performance ## @end example ## ## @end deftypefn ## @seealso{__mae} ## Author: Michel D. Schmid function perf = __mse(E) ## check number of inputs error(nargchk(1,1,nargin)); if iscell(E) perf = 0; elements = 0; for i=1:size(E,1) for j=1:size(E,2) perf = perf + sum(sum(E{i,j}.^2)); elements = elements + prod(size(E{i,j})); endfor endfor perf = perf / elements; else error("Error vector should be a cell array!") endif endfunctionnnet/inst/newff.m0000644000175000017500000002161411360415253012722 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {@var{net}} = newff (@var{Pr},@var{ss},@var{trf},@var{btf},@var{blf},@var{pf}) ## @code{newff} create a feed-forward backpropagation network ## ## @example ## Pr - R x 2 matrix of min and max values for R input elements ## Ss - 1 x Ni row vector with size of ith layer, for N layers ## trf - 1 x Ni list with transfer function of ith layer, ## default = "tansig" ## btf - Batch network training function, ## default = "trainlm" ## blf - Batch weight/bias learning function, ## default = "learngdm" ## pf - Performance function, ## default = "mse". ## @end example ## ## @example ## EXAMPLE 1 ## Pr = [0.1 0.8; 0.1 0.75; 0.01 0.8]; ## it's a 3 x 2 matrix, this means 3 input neurons ## ## net = newff(Pr, [4 1], @{"tansig","purelin"@}, "trainlm", "learngdm", "mse"); ## @end example ## ## @end deftypefn ## @seealso{sim, init, train} ## Author: Michel D. Schmid function net = newff(Pr,ss,transFunc,trainFunc,notUsed,performFunc) ## initial descriptipn ## newff(Pr,ss,transfunc,trainFunc,notUsed,performFunc) ## * Pr is a nx2 matrix with min and max values of standardized inputs ## Pr means: p-range ## * ss is a row vector, the first element describes the number ## of hidden neurons, the second element describes the number ## of output neurons ## * transFunc is a cell array of transfer function, standard is "tansig" ## * trainFunc is the training algorithm ## * notUsed exist only because we have only one train algorithm which doesn't ## need a weight learning function ## * performFunc is written for the performance function, standard is "mse" ## check range of input arguments error(nargchk(2,6,nargin)) ## get number of layers (without input layer) nLayers = length(ss); ## set defaults if (nargin <3) # the number of transfer functions depends on the number of # hidden layers, so we have to create a loop here 30.09.09 (dd.mm.yy) for i=1:nLayers if (i==nLayers) transFunc{i,1} = "purelin"; else transFunc{i,1}= "tansig"; endif endfor endif if (nargin <4) trainFunc = "trainlm"; endif if (nargin <5) notUsed = "noSense"; endif if (nargin==5) ## it doesn't matter what nargin 5 is ...! ## it won't be used ... it's only for matlab compatibility notUsed = "noSense" endif if (nargin <6) performFunc = "mse"; endif ## check input args checkInputArgs(Pr,ss); ## Standard architecture of neural network net = __newnetwork(1,nLayers,1,"newff"); ## description: ## first argument: number of inputs, nothing else allowed till now ## it's not the same like the number of neurons in this input ## second argument: number of layers, including output layer ## third argument: number of outputs, nothing else allowed till now ## it's not the same like the number of neurons in this output ## set inputs with limit of only ONE input net.inputs{1}.range = Pr; [nRows, nColumns] = size(Pr); net.inputs{1}.size = nRows; ## set size of IW net.IW{1,1} = zeros(1,nRows); ## set more needed empty cells for iLayers = 2:nLayers net.IW{iLayers,1} = []; # net.IW{2:nLayers,1} = []; # old code endfor ## set number of bias, one per layer for iBiases = 1:nLayers net.b{iBiases,1} = 0; endfor ## set rest of layers ## set size of LayerWeights LW ## the numbers of rows and columns depends on the ## number of hidden neurons and output neurons... ## 2 hidden neurons match 2 columns ... ## 2 output neurons match 2 rows ... for i=2:nLayers net.LW{i,i-1} = zeros(ss(i),ss(i-1)); endfor for iLayers = 1:nLayers net.layers{iLayers}.size = ss(iLayers); net.layers{iLayers}.transferFcn = transFunc{iLayers}; endfor ## define everything with "targets" net.numTargets = ss(end); net.targets = cell(1,nLayers); for i=1:nLayers if (i==nLayers) net.targets{i}.size = ss(end); ## next row of code is only for MATLAB(TM) compatibility ## I never used this the last 4 years ... net.targets{i}.userdata = "Put your custom informations here!"; else net.targets{i} = []; endif endfor ## Performance net.performFcn = performFunc; ## Adaption for i=1:nLayers # net.biases{i}.learnFcn = blf; # net.layerWeights{i,:}.learnFcn = blf; net.biases{i}.size = ss(i); endfor ## Training net.trainFcn = trainFunc; # actually, only trainlm will exist net = setTrainParam(net); ## Initialization net = __init(net); # ====================================================== # # additional check functions... # # ====================================================== function checkInputArgs(Pr,ss) ## check if Pr has correct format if !isreal(Pr) | (size(Pr,2)!=2) error("Input ranges must be a two column matrix!") endif if any(Pr(:,1) > Pr(:,2)) # check if numbers in the second column are larger as in the first one error("Input ranges has values in the second column larger as in the same row of the first column.") endif ## check if ss has correct format, must be 1xR row vector if (size(ss,1)!=1) error("Layer sizes is not a row vector.") endif if (size(ss,2)<2) error("There must be at least one hidden layer and one output layer!") endif for k=1:length(ss) sk = ss(k); if !isreal(sk) | any(sk<1) | any(round(sk)!=sk) error("Layer sizes is not a row vector of positive integers.") endif endfor endfunction # ====================================================== # # additional set functions... # # ====================================================== function net = setTrainParam(net) trainFunc = net.trainFcn; switch(trainFunc) case "trainlm" net.trainParam.epochs = 100; net.trainParam.goal = 0; net.trainParam.max_fail = 5; net.trainParam.mem_reduc = 1; net.trainParam.min_grad = 1.0000e-010; net.trainParam.mu = 0.0010; net.trainParam.mu_dec = 0.1; net.trainParam.mu_inc = 10; net.trainParam.mu_max = 1.0000e+010; net.trainParam.show = 50; net.trainParam.time = Inf; otherwise error("newff:setTrainParam: this train algorithm isn't available till now!") endswitch endfunction # ======================================================== endfunction %!shared %! disp("testing newff") # if input range Pr has only one column %!test %! Pr = [1;2]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!") # if input range Pr has two columns %!test %! Pr = [1 2 ; 4 6]; %! assert(__checknetstruct(newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse'))) ## __checknetstruct returns TRUE is input arg is a network structure ... # if input range Pr has three columns %!test %! Pr = [1 2 3; 4 5 6]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!") # if input range has in the second col greater values as in the first col ... %!test %! Pr = [5 3; 4 5]; %! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Input ranges has values in the second column larger as in the same row of the first column.") # check if ss has correct format %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[1 1; 2 3],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Layer sizes is not a row vector.") # check if ss has correct format %!test %! Pr = [1 2 ; 4 6]; %! assert(__checknetstruct(newff(Pr,[ 2 3],{'tansig','purelin'},'trainlm','unused','mse'))) # check if ss has correct format %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "There must be at least one hidden layer and one output layer!") # check if ss has correct format %!test %! Pr = [1 2 ; 4 6]; %! fail("newff(Pr,[-1 1],{'tansig','purelin'},'trainlm','unused','mse')",\ %! "Layer sizes is not a row vector of positive integers.") nnet/inst/__dpurelin.m0000644000175000017500000000220110751613601013724 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{a} = __dpurelin (@var{n}) ## @code{dpurelin}, first derivative of purelin ## @example ## ## purelin is a linear transfer function used by neural networks ## @end example ## ## @end deftypefn ## Author: Michel D. Schmid function a = __dpurelin(n) [nRows, nColumns] = size(n); a = ones(nRows,nColumns); endfunctionnnet/inst/purelin.m0000644000175000017500000000222011047755046013274 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{a}= purelin (@var{n}) ## @code{purelin} is a linear transfer function used ## by neural networks ## @end deftypefn ## Author: Michel D. Schmid function a = purelin(n) a = n; endfunction %!assert(purelin(2),2); %!assert(purelin(-2),-2); %!assert(purelin(0),0); %!error # this test must throw an error! %! assert(purelin(2),1);nnet/inst/__printTrainParam.m0000644000175000017500000000414010751613601015221 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printTrainParam (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printTrainParam(fid,net) if isfield(net,"trainParam") str2 = ""; str3 = ""; if isempty(net.trainParam) fprintf(fid," trainParam: '%s'\n","not yet used item"); else cellFieldNames = fieldnames(net.trainParam); [nRows, nColumns] = size(cellFieldNames); if (nRows<4) else for iRuns = 1:nRows if (iRuns==1) str1 = ["." char(cellFieldNames(iRuns,1)) ", "]; endif if (iRuns<=4 & iRuns>1) str1 = [str1 "." char(cellFieldNames(iRuns,1)) ", "]; endif if (iRuns>4 & iRuns<=8) str2 = [str2 "." char(cellFieldNames(iRuns,1)) ", "]; endif if (iRuns>8) str3 = [str3 "." char(cellFieldNames(iRuns,1)) ", "]; endif endfor fprintf(fid," trainParam: %s\n",str1); fprintf(fid," %s\n",str2); fprintf(fid," %s\n",str3); endif endif else fprintf(fid,"field trainparam not found\n"); endif endfunctionnnet/inst/poslin.m0000644000175000017500000000217111112254025013107 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{a}= poslin (@var{n}) ## @code{poslin} is a positive linear transfer function used ## by neural networks ## @end deftypefn ## Author: Michel D. Schmid function a = poslin(n) if (n<0) a = 0; else a = n; endif endfunctionnnet/inst/__checknetstruct.m0000644000175000017500000000253610751613601015146 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{isTrue}] = __checknetstruct (@var{net}) ## This function will check if a valid structure seems to be a neural network ## structure ## ## @noindent ## ## left side arguments: ## @noindent ## ## right side arguments: ## @noindent ## ## ## @noindent ## are equivalent. ## @end deftypefn ## @seealso{newff,prestd,trastd} ## Author: Michel D. Schmid function isTrue = __checknetstruct(net) isTrue = 0; ## first check, if it's a structure if (isstruct(net) && isfield(net,"networkType")) isTrue = 1; endif endfunctionnnet/inst/__printInputs.m0000644000175000017500000000273310751613601014453 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printInputs (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printInputs(fid,net) if isfield(net,"inputs") # check if it's cell array if iscell(net.inputs) [nRows, nColumns] = size(net.inputs); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," inputs: {%dx%d cell} of inputs\n",nRows,nColumns); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunction nnet/inst/__printLW.m0000644000175000017500000000320610751613601013507 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printLW (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printLW(fid,net) if isfield(net,"LW") nLayers = 0; # check if it's cell array if iscell(net.LW) [nRows, nColumns] = size(net.LW); for i=1:nRows for k=1:nColumns if !isempty(net.LW{i,k}) nLayers = nLayers+1; endif endfor endfor # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," LW: {%dx%d cell} containing %d layer weight matrix\n",nRows,nColumns,nLayers); else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/__mae.m0000644000175000017500000000305211112254025012642 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{perf} = __mae (@var{E}) ## @code{__mse} returns the Mean-Square-Error of a vector E ## ## @example ## ## This function is used to calculate the perceptron performance ## @end example ## ## @end deftypefn ## @seealso{__mse} ## Author: Michel D. Schmid function perf = __mae(E) ## check number of inputs error(nargchk(1,1,nargin)); if iscell(E) perf = 0; elements = 0; for i=1:size(E,1) for j=1:size(E,2) perf = perf + sum(sum(E{i,j}.^2)); elements = elements + prod(size(E{i,j})); endfor endfor perf = perf / elements; else error("Error vector should be a cell array!") endif endfunctionnnet/inst/radbas.m0000644000175000017500000000212211276121667013053 0ustar mikemike## Copyright (C) 2009 Luca Favatella ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} radbas (@var{n}) ## Radial basis transfer function. ## ## @code{radbas(n) = exp(-n^2)} ## ## @end deftypefn ## Author: Luca Favatella ## Version: 0.1 function retval = radbas (n) if (nargin != 1) print_usage (); else retval = exp (-n^2); endif endfunction %!assert (radbas (3), exp (-3^2)); nnet/inst/saveMLPStruct.m0000644000175000017500000001040510751613601014325 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} saveMLPStruct (@var{net},@var{strFileName}) ## @code{saveStruct} saves a neural network structure to *.txt files ## @end deftypefn ## Author: Michel D. Schmid function saveMLPStruct(net,strFileName) ## the variable net holds the neural network structure.. # check if "net" is a structure type if !__checknetstruct(net) error("Structure doesn't seem to be a neural network") endif # open the first level file fid1 = fopen(strFileName,"w+t","ieee-le"); if (fid1 < 0) error ("Can not open %s", strFileName); endif ## print header # try ## wird nicht mehr benötigt.. __printMLPHeader(fid1); # catch # ## Add saveMLPStructure directory to the path and try again # addpath ([fileparts(mfilename()),"/saveMLPStructure"]); # __printMLPHeader(fid1); # end_try_catch ## check for field "networkType" __printNetworkType(fid1,net); ## check for field "numInputs" __printNumInputs(fid1,net); ## check for field "numLayers" __printNumLayers(fid1,net) ## check for field "biasConnect" __printBiasConnect(fid1,net) ## check for field "inputConnect" __printInputConnect(fid1,net) ## check for field "layerConnect" __printLayerConnect(fid1,net) ## check for field "outputConnect" __printOutputConnect(fid1,net) ## check for field "targetConnect" __printTargetConnect(fid1,net) ## print one empty line fprintf(fid1,"\n"); ## check for numOutputs __printNumOutputs(fid1,net); ## check for numTargets __printNumTargets(fid1,net); ## check for numInputDelays __printNumInputDelays(fid1,net); ## check for numLayerDelays __printNumLayerDelays(fid1,net); ## print one empty line fprintf(fid1,"\n"); ## print subobject structures: fprintf(fid1," subobject structures:\n"); ## print one empty line fprintf(fid1,"\n"); ## print inputs __printInputs(fid1,net); ## print layers __printLayers(fid1,net); ## print outputs __printOutputs(fid1,net); ## print targets __printTargets(fid1,net); ## print biases __printBiases(fid1,net); ## print inputweights __printInputWeights(fid1,net); ## print layerweights __printLayerWeights(fid1,net); ## print one empty line fprintf(fid1,"\n"); ## print subobject structures: fprintf(fid1," functions:\n"); ## print one empty line fprintf(fid1,"\n"); ## print adaptFcn __printAdaptFcn(fid1,net); ## print initFcn __printInitFcn(fid1,net); ## print performFcn __printPerformFcn(fid1,net); ## print performFcn __printTrainFcn(fid1,net); ## print one empty line fprintf(fid1,"\n"); ## print subobject structures: fprintf(fid1," parameters:\n"); ## print one empty line fprintf(fid1,"\n"); ## print adaptParam __printAdaptParam(fid1,net); ## print initParam __printInitParam(fid1,net); ## print performParam __printPerformParam(fid1,net); ## print trainParam __printTrainParam(fid1,net); ## print one empty line fprintf(fid1,"\n"); ## print subobject structures: fprintf(fid1," weight & bias values:\n"); ## print one empty line fprintf(fid1,"\n"); ## print IW __printIW(fid1,net); ## print LW __printLW(fid1,net); ## print b __printB(fid1,net); ## print one empty line fprintf(fid1,"\n"); ## print subobject structures: fprintf(fid1," other:\n"); fclose(fid1); endfunction nnet/inst/__printAdaptParam.m0000644000175000017500000000243110751613601015176 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printAdaptParam (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printAdaptParam(fid,net) if isfield(net,"adaptParam") if isempty(net.adaptParam) fprintf(fid," adaptParam: '%s'\n","not yet used item"); else fprintf(fid," adaptParam: '%s'\n",net.adaptParam); endif endif endfunctionnnet/inst/__init.m0000644000175000017500000000470311112254025013047 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{net} = __init (@var{net}) ## @code{__init} initializes a neural network. This will be done ## with the function @code{rand} from octave. ## ## @example ## net = __init(net); ## @end example ## ## This function takes the octave function "rand" to init the ## neural network weights. ## ## @noindent ## @end deftypefn ## Author: Michel D. Schmid function net=__init(net) ## check number of inputs error(nargchk(1,1,nargin)); ## check input if ( !__checknetstruct(net) ) error("__init: wrong argument type, must be a structure!"); endif if (strcmp(net.networkType,"newff")) ## init with random numbers between +-1 ## input weight layer mRand = rand(net.layers{1}.size,net.inputs{1}.size); net.IW{1} = mRand*2-1; ## hidden layers nLayers = net.numLayers; for i=2:nLayers mRand = rand(net.layers{i}.size,net.layers{i-1}.size); net.LW{i,i-1} = mRand*2-1; endfor for i=1:nLayers mRand = rand(net.biases{i}.size,1); net.b{i} = mRand*2-1; endfor elseif (strcmp(net.networkType,"newp")) ## init with zeros inputRows = size(net.inputs{1,1}.range,1); net.IW{1} = zeros(inputRows,1); net.b{1} = zeros(1,1); endif ## warn user of constant inputs for i=1:net.numInputs prange = net.inputs{i}.range; if (any(prange(:,1) == prange(:,2))) fprintf("\n") fprintf("** Warning in INIT\n") fprintf("** Network net.inputs{%g}.range has a row with equal min and max values.\n",i) fprintf("** Constant inputs do not provide useful information.\n") fprintf("\n") end end endfunction nnet/inst/min_max.m0000644000175000017500000000373011073170664013251 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{Pr} = min_max (@var{Pp}) ## @code{min_max} returns variable Pr with range of matrix rows ## ## @example ## PR - R x 2 matrix of min and max values for R input elements ## @end example ## ## @example ## Pp = [1 2 3; -1 -0.5 -3] ## pr = min_max(Pp); ## pr = [1 3; -0.5 -3]; ## @end example ## @end deftypefn ## Author: Michel D. Schmid function Pr = min_max(Pp) ## check number of input args error(nargchk(1,1,nargin)) Pr = []; # returns an empty matrix #if ismatrix(Pp) if (!(size(Pp,1)==1) && !(size(Pp,2)==1)) # ismatrix(1) will return 1!!! if isreal(Pp) # be sure, this is no complex matrix Pr = [min(Pp,[],2) max(Pp,[],2)]; else error("Argument has illegal type.") endif else error("Argument must be a matrix.") endif endfunction %!shared %! disp("testing min_max") %!test fail("min_max(1)","Argument must be a matrix.") %!test fail("min_max('testString')","Argument must be a matrix.") %!test fail("min_max(cellA{1}=1)","Argument must be a matrix.") %!test fail("min_max([1+1i, 2+2i])","Argument must be a matrix.") %!test fail("min_max([1+1i, 2+2i; 3+1i, 4+2i])","Argument has illegal type.") nnet/inst/__trainlm.m0000644000175000017500000002642311360415253013564 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{netOut}] = __trainlm (@var{net},@var{mInputN},@var{mOutput},@var{[]},@var{[]},@var{VV}) ## A neural feed-forward network will be trained with @code{__trainlm} ## ## @example ## [netOut,tr,out,E] = __trainlm(net,mInputN,mOutput,[],[],VV); ## @end example ## @noindent ## ## left side arguments: ## @example ## netOut: the trained network of the net structure @code{MLPnet} ## tr : ## out: ## E : Error ## @end example ## @noindent ## ## right side arguments: ## @example ## net : the untrained network, created with @code{newff} ## mInputN: normalized input matrix ## mOutput: output matrix ## [] : unused parameter ## [] : unused parameter ## VV : validize structure ## out: ## E : Error ## @end example ## @noindent ## ## ## @noindent ## are equivalent. ## @end deftypefn ## @seealso{newff,prestd,trastd} ## Author: Michel D. Schmid ## Comments: see in "A neural network toolbox for Octave User's Guide" [4] ## for variable naming... there have inputs or targets only one letter, ## e.g. for inputs is P written. To write a program, this is stupid, you can't ## search for 1 letter variable... that's why it is written here like Pp, or Tt ## instead only P or T. function [net] = __trainlm(net,Im,Pp,Tt,VV) ## check range of input arguments error(nargchk(5,5,nargin)) ## Initialize ##------------ ## get parameters for training epochs = net.trainParam.epochs; goal = net.trainParam.goal; maxFail = net.trainParam.max_fail; minGrad = net.trainParam.min_grad; mu = net.trainParam.mu; muInc = net.trainParam.mu_inc; muDec = net.trainParam.mu_dec; muMax = net.trainParam.mu_max; show = net.trainParam.show; time = net.trainParam.time; ## parameter checking checkParameter(epochs,goal,maxFail,minGrad,mu,\ muInc,muDec,muMax,show,time); ## Constants shortStr = "TRAINLM"; # TODO: shortStr is longer as TRAINLM !!!!!!!!!!! doValidation = !isempty(VV); stop = ""; #startTime = clock(); # TODO: maybe this row can be placed # some rows later ## the weights are used in column vector format xx = __getx(net); # x is the variable with respect to, but no # variables with only one letter!! ## define identity matrix muI = eye(length(xx)); startTime = clock(); # if the next some tests are OK, I can delete # startTime = clock(); 9 rows above.. ## calc performance of the actual net [perf,vE,Aa,Nn] = __calcperf(net,xx,Im,Tt); if (doValidation) ## calc performance if validation is used VV.net = net; # save the actual net in the validate # structure... if no train loop will show better validate # performance, this will be the returned net vperf = __calcperf(net,xx,VV.Im,VV.Tt); VV.perf = vperf; VV.numFail = 0; # one of the stop criterias endif nLayers = net.numLayers; for iEpochs = 0:epochs # longest loop & one of the stop criterias ve = vE{nLayers,1}; ## calc jacobian ## Jj is jacobian matrix [Jj] = __calcjacobian(net,Im,Nn,Aa,vE); ## rerange error vector for jacobi matrix ve = ve(:); Jjve = (Jj' * ve); # will be used to calculate the gradient normGradX = sqrt(Jjve'*Jjve); ## record training progress for later plotting ## if requested trainRec.perf(iEpochs+1) = perf; trainRec.mu(iEpochs+1) = mu; if (doValidation) trainRec.vperf(iEpochs+1) = VV.perf; endif ## stoping criteria [stop,currentTime] = stopifnecessary(stop,startTime,perf,goal,\ iEpochs,epochs,time,normGradX,minGrad,mu,muMax,\ doValidation,VV,maxFail); ## show train progress showtrainprogress(show,stop,iEpochs,epochs,time,currentTime, \ goal,perf,minGrad,normGradX,shortStr,net); ## show performance plot, if needed if !isnan(show) # if no performance plot is needed ## now make it possible to define after how much loops the ## performance plot should be updated if (mod(iEpochs,show)==0) plot(1:length(trainRec.perf),trainRec.perf); if (doValidation) hold on; plot(1:length(trainRec.vperf),trainRec.vperf,"--g"); endif endif endif # if !(strcmp(show,"NaN")) # legend("Training","Validation"); ## stop if one of the criterias is reached. if length(stop) if (doValidation) net = VV.net; endif break endif ## calculate DeltaX while (mu <= muMax) ## calculate change in x ## see [4], page 12-21 dx = -((Jj' * Jj) + (muI*mu)) \ Jjve; ## add changes in x to actual x values (xx) x1 = xx + dx; ## now add x1 to a new network to see if performance will be better net1 = __setx(net,x1); ## calc now new performance with the new net [perf1,vE1,Aa1,N1] = __calcperf(net1,x1,Im,Tt); if (perf1 < perf) ## this means, net performance with new weight values is better... ## so save the new values xx = x1; net = net1; Nn = N1; Aa = Aa1; vE = vE1; perf = perf1; mu = mu * muDec; if (mu < 1e-20) # 1e-20 is properly the hard coded parameter in MATLAB(TM) mu = 1e-20; endif break endif mu = mu * muInc; endwhile ## validate with DeltaX if (doValidation) vperf = __calcperf(net,xx,VV.Im,VV.Tt); if (vperf < VV.perf) VV.perf = vperf; VV.net = net; ## if actual validation performance is better, ## set numFail to zero again VV.numFail = 0; elseif (vperf > VV.perf) VV.numFail = VV.numFail + 1; endif endif endfor #for iEpochs = 0:epochs #======================================================= # # additional functions # #======================================================= function checkParameter(epochs,goal,maxFail,minGrad,mu,\ muInc, muDec, muMax, show, time) ## Parameter Checking ## epochs must be a positive integer if ( !isposint(epochs) ) error("Epochs is not a positive integer.") endif ## goal can be zero or a positive double if ( (goal<0) | !(isa(goal,"double")) ) error("Goal is not zero or a positive real value.") endif ## maxFail must be also a positive integer if ( !isposint(maxFail) ) # this will be used, to see if validation can # break the training error("maxFail is not a positive integer.") endif if (!isa(minGrad,"double")) | (!isreal(minGrad)) | (!isscalar(minGrad)) | \ (minGrad < 0) error("minGrad is not zero or a positive real value.") end ## mu must be a positive real value. this parameter is responsible ## for moving from stepest descent to quasi newton if ((!isa(mu,"double")) | (!isreal(mu)) | (any(size(mu)) != 1) | (mu <= 0)) error("mu is not a positive real value.") endif ## muDec defines the decrement factor if ((!isa(muDec,"double")) | (!isreal(muDec)) | (any(size(muDec)) != 1) | \ (muDec < 0) | (muDec > 1)) error("muDec is not a real value between 0 and 1.") endif ## muInc defines the increment factor if (~isa(muInc,"double")) | (!isreal(muInc)) | (any(size(muInc)) != 1) | \ (muInc < 1) error("muInc is not a real value greater than 1.") endif ## muMax is the upper boundary for the mu value if (!isa(muMax,"double")) | (!isreal(muMax)) | (any(size(muMax)) != 1) | \ (muMax <= 0) error("muMax is not a positive real value.") endif ## check for actual mu value if (mu > muMax) error("mu is greater than muMax.") end ## check if show is activated if (!isnan(show)) if (!isposint(show)) error(["Show is not " "NaN" " or a positive integer."]) endif endif ## check at last the time argument, must be zero or a positive real value if (!isa(time,"double")) | (!isreal(time)) | (any(size(time)) != 1) | \ (time < 0) error("Time is not zero or a positive real value.") end endfunction # parameter checking # # ----------------------------------------------------------------------------- # function showtrainprogress(show,stop,iEpochs,epochs,time,currentTime, \ goal,perf,minGrad,normGradX,shortStr,net) ## check number of inputs error(nargchk(12,12,nargin)); ## show progress if isfinite(show) & (!rem(iEpochs,show) | length(stop)) fprintf(shortStr); # outputs the training algorithm if isfinite(epochs) fprintf(", Epoch %g/%g",iEpochs, epochs); endif if isfinite(time) fprintf(", Time %4.1f%%",currentTime/time*100); # \todo: Time wird nicht ausgegeben endif if isfinite(goal) fprintf(", %s %g/%g",upper(net.performFcn),perf,goal); # outputs the performance function endif if isfinite(minGrad) fprintf(", Gradient %g/%g",normGradX,minGrad); endif fprintf("\n") if length(stop) fprintf("%s, %s\n\n",shortStr,stop); endif fflush(stdout); # writes output to stdout as soon as output messages are available endif endfunction # # ----------------------------------------------------------------------------- # function [stop,currentTime] = stopifnecessary(stop,startTime,perf,goal,\ iEpochs,epochs,time,normGradX,minGrad,mu,muMax,\ doValidation,VV,maxFail) ## check number of inputs error(nargchk(14,14,nargin)); currentTime = etime(clock(),startTime); if (perf <= goal) stop = "Performance goal met."; elseif (iEpochs == epochs) stop = "Maximum epoch reached, performance goal was not met."; elseif (currentTime > time) stop = "Maximum time elapsed, performance goal was not met."; elseif (normGradX < minGrad) stop = "Minimum gradient reached, performance goal was not met."; elseif (mu > muMax) stop = "Maximum MU reached, performance goal was not met."; elseif (doValidation) if (VV.numFail > maxFail) stop = "Validation stop."; endif endif endfunction # ===================================================================== # # END additional functions # # ===================================================================== endfunctionnnet/inst/__printB.m0000644000175000017500000000316710751613601013354 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printB (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printB(fid,net) if isfield(net,"b") nBiases = 0; # check if it's cell array if iscell(net.b) [nRows, nColumns] = size(net.b); for i=1:nRows for k=1:nColumns if !isempty(net.b{i,k}) nBiases = nBiases+1; endif endfor endfor # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," b: {%dx%d cell} containing %d bias vectors\n",nRows,nColumns,nBiases); else fprintf(fid,"unsure if this is possible\n") endif endif endfunctionnnet/inst/__printPerformParam.m0000644000175000017500000000243510751613601015563 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} printInputs (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printPerformParam(fid,net) if isfield(net,"performParam") if isempty(net.performParam) fprintf(fid," performParam: '%s'\n","not yet used item"); else fprintf(fid," performParam: '%s'\n",net.performParam); endif endif endfunctionnnet/inst/prestd.m0000644000175000017500000000652011051037770013116 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{pn},@var{meanp},@var{stdp},@var{tn},@var{meant},@var{stdt}] =prestd(@var{p},@var{t}) ## @code{prestd} preprocesses the data so that the mean is 0 and the standard deviation is 1. ## @end deftypefn ## @seealso{trastd} ## Author: Michel D. Schmid function [pn,meanp,stdp,tn,meant,stdt] = prestd(Pp,Tt) ## inital description ## prestd(p,t) ## * p are the general descriptions for the inputs of ## neural networks ## * t is written for "targets" and these are the outputs ## of a neural network ## some more detailed description: ## for more informations about this ## formula programmed in this file, see: ## 1. http://en.wikipedia.org/wiki/Standard_score ## 2. http://www.statsoft.com/textbook/stathome.html ## choose "statistical glossary", choose "standardization" ## check range of input arguments error(nargchk(1,2,nargin)) ## do first inputs meanp = mean(Pp')'; stdp = std(Pp')'; [nRows,nColumns]=size(Pp); rowOnes = ones(1,nColumns); ## now set all standard deviations which are zero to 1 [nRowsII, nColumnsII] = size(stdp); rowZeros = zeros(nRowsII,1); # returning a row containing only zeros findZeros = find(stdp==0); # returning a vector containing index where zeros are rowZeros(findZeros)=1; # nequal = !rowZeros; if (sum(rowZeros) != 0) warning("Some standard deviations are zero. Those inputs won't be transformed."); meanpZero = meanp.*nequal; stdpZero = stdp.*nequal + 1*rowZeros; else meanpZero = meanp; stdpZero = stdp; endif ## calculate the standardized inputs pn = (Pp-meanpZero*rowOnes)./(stdpZero*rowOnes); ## do also targets if ( nargin==2 ) meant = mean(Tt')'; stdt = std(Tt')'; ## now set all standard deviations which are zero to 1 [nRowsIII, nColumnsIII] = size(stdt); rowZeros = zeros(nRowsIII,1); findZeros = find(stdt==0); rowZeros(findZeros)=1; nequal = !rowZeros; if (sum(rowZeros) != 0) warning("Some standard deviations are zero. Those targets won't be transformed."); meantZero = meant.*nequal; stdtZero = stdt.*nequal + 1*rowZeros; else meantZero = meant; stdtZero = stdt; endif ## calculate the standardized targets tn = (Tt-meantZero*rowOnes)./(stdtZero*rowOnes); endif endfunction %!shared Pp, Tt, pn %! Pp = [1 2 3 4; -1 3 2 -1]; %! Tt = [3 4 5 6]; %! [pn,meanp,stdp] = prestd(Pp); %!assert(pn,[-1.16190 -0.38730 0.38730 1.16190; -0.84887 1.09141 0.60634 -0.84887],0.00001);nnet/inst/__dtansig.m0000644000175000017500000000222310751613601013537 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{n} = __dtansig (@var{n}) ## first derivative of @code{tansig} ## ## @example ## ## tansig is a symmetric non linear transfer function ## used by neural networks. ## Input n must be calculated with "n = tansig(n)". ## @end example ## ## @end deftypefn ## Author: Michel D. Schmid function a = __dtansig(n) a = 1-(n.*n); endfunctionnnet/inst/__printTargetConnect.m0000644000175000017500000000600210751613601015722 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printTargetConnect (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printTargetConnect(fid,net) if isfield(net,"targetConnect") # net.targetConnect can be a matrix..! # check if it's a matrix if isscalar(net.targetConnect) error("unsure if this is possible..") elseif isnumeric(net.targetConnect) if ismatrix(net.targetConnect) if issquare(net.targetConnect) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," targetConnect: ["); [nRows nColumns] = size(net.targetConnect); for k = 1:1:nRows for i = 1:1:nColumns if i ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printOutputs (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printOutputs(fid,net) if isfield(net,"outputs") # check if it's cell array if iscell(net.outputs) [nRows, nColumns] = size(net.outputs); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" if (net.numOutputs>1) fprintf(fid," outputs: {%dx%d cell} containing %d output\n",nRows,nColumns,net.numOutputs); else fprintf(fid," outputs: {%dx%d cell} containing %d output\n",nRows,nColumns,net.numOutputs); endif else fprintf(fid,"unsure if this is possible\n"); endif endif endfunctionnnet/inst/trastd.m0000644000175000017500000000453111054451466013123 0ustar mikemike## Copyright (C) 2005 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{pn} = trastd (@var{p},@var{meanp},@var{stdp}) ## @code{trastd} preprocess additional data for neural network simulation. ## ## @example ## @code{p} : test input data ## @code{meanp}: vector with standardization parameters of prestd(...) ## @code{stdp} : vector with standardization parameters of prestd(...) ## ## meanp = [2.5; 6.5]; ## stdp = [1.2910; 1.2910]; ## p = [1 4; 2 5]; ## ## pn = trastd(p,meanp,stdp); ## @end example ## @noindent ## @end deftypefn ## @seealso{prestd, poststd} ## Author: Michel D. Schmid function [Pn] = trastd(Pp,meanp,stdp) ## check number of inputs error(nargchk(3,3,nargin)); [nRows,nColumns]=size(Pp); rowOnes = ones(1,nColumns); ## now set all standard deviations which are zero to 1 [nRowsII, nColumnsII] = size(stdp); rowZeros = zeros(nRowsII,1); findZeros = find(stdp==0); rowZeros(findZeros)=1; equal = rowZeros; nequal = !equal; if ( sum(equal) != 0 ) warning("Some standard deviations are zero. Those inputs won't be transformed."); meanp = meanp.*nequal; stdp = stdp.*nequal + 1*equal; end Pn = (Pp-meanp*rowOnes)./(stdp*rowOnes); endfunction ## ## >> mInput = [1 2 3 4; 5 6 7 8] ## ## mInput = ## ## 1 2 3 4 ## 5 6 7 8 ## ## >> [pn,meanp,stdp] = prestd(mInput) ## ## pn = ## ## -1.1619 -0.3873 0.3873 1.1619 ## -1.1619 -0.3873 0.3873 1.1619 ## ## ## meanp = ## ## 2.5000 ## 6.5000 ## ## ## stdp = ## ## 1.2910 ## 1.2910nnet/inst/logsig.m0000644000175000017500000000251011054451466013101 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{a} = logsig (@var{n}) ## @code{logsig} is a non-linear transfer function used to train ## neural networks. ## This function can be used in newff(...) to create a new feed forward ## multi-layer neural network. ## ## @end deftypefn ## @seealso{purelin,tansig} ## Author: Michel D. Schmid function a = logsig(n) a = 1 ./ (1 + exp(-n)); ## attention with critical values ==> infinite values ## must be set to 1! Still the same problem as in "tansig" i = find(!finite(a)); a(i) = sign(n(i)); endfunctionnnet/inst/__printNumTargets.m0000644000175000017500000000317210751613601015260 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumTargets (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumTargets(fid,net) ## now check the structure fields.. cellNetFields = fieldnames(net); # search for numTargets if isfield(net,"numTargets") # test on scalar if isscalar(net.numTargets) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," numTargets: %d (read-only)\n",net.numTargets); # net.numTargets must be an integer... till now, 11-01-2006 else error("numTargets must be a scalar value!"); endif endif endfunctionnnet/inst/__copycoltopos1.m0000644000175000017500000000303711073170664014735 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{retmatrix} = __copycoltopos1(@var{matrix},@var{colIndex}) ## @code{__copycoltopos1} copies the column of position colIndex to the first position. ## Moves the rest of the matrix one position to the right. ## @end deftypefn ## Author: mds function retmatrix = __copycoltopos1(matrix,colIndex) ## check number of inputs error(nargchk(2,2,nargin)); temp = matrix(:,colIndex); matrix(:,colIndex) = []; # delete col retmatrix = [temp matrix ]; endfunction %!shared a, retmat %! disp("testing __copycoltopos1") %! a = [0 1 2 3 4; 5 6 7 8 9]; %! retmat = __copycoltopos1(a,3); %!assert(retmat(1,1)==2); %!assert(retmat(2,1)==7); %! retmat = __copycoltopos1(a,5); %!assert(retmat(1,1)==4); %!assert(retmat(2,1)==9); nnet/inst/hardlims.m0000644000175000017500000000222311112254025013404 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = hardlims (@var{n}) ## ## @end deftypefn ## @seealso{purelin,tansig,hardlim} ## Author: Michel D. Schmid function a = hardlims(n) # a=1 if n>0, a=-1 otherwise if n>=0 a=1; else a=-1; endif endfunctionnnet/inst/__printInputConnect.m0000644000175000017500000000416310751613601015601 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printInputConnect (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printInputConnect(fid,net) if isfield(net,"inputConnect") # net.inputConnect can be a matrix..! # check if it's a matrix if isscalar(net.inputConnect) error("unsure if this is possible..") elseif isnumeric(net.inputConnect) if ismatrix(net.inputConnect) if issquare(net.inputConnect) # nothing prgrammed till now elseif isvector(net.inputConnect) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" # print bracket for open fprintf(fid," inputConnect: ["); [nRows nColumns] = size(net.inputConnect); for k = 1:1:nRows for i = 1:1:nColumns fprintf(fid,"%d",net.inputConnect(i*k)); endfor if k!=nRows #print ; for newline in matrix fprintf(fid,";"); endif endfor # print last bracket fprintf(fid,"] not yet used item\n"); endif # if issquare.. endif #if ismatrix endif endif endfunctionnnet/inst/__printMLPHeader.m0000644000175000017500000000276610751613601014740 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printMLPHeader (@var{fid}) ## @code{__printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printMLPHeader(fid) # one empty row fprintf(fid,"\n"); # write "net=" fprintf(fid,"net=\n"); # next empty row fprintf(fid,"\n"); # write "Neural Network object:", insert two spaces.. fprintf(fid," Neural Network object:\n"); # next empty row fprintf(fid,"\n"); # write "architecture:", insert two spaces.. fprintf(fid," architecture:\n"); # one more time an empty row fprintf(fid,"\n"); endfunctionnnet/inst/__optimizedatasets.m0000644000175000017500000000715311073170664015513 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{retmatrix} = __optimizedatasets (@var{matrix},@var{nTrainSets},@var{nTargets},@var{bRand}) ## @code{__optimizedatasets} reranges the data sets depending on the input arguments. ## @code{matrix} is the data set matrix containing inputs and outputs (targets) in row order. ## This means for example: the first three rows are inputs and the fourth row is an output row. ## The second argument is used in the optimizing algorithm. All cols with min and max values must ## be in the range of the train data sets. The third argument defines how much rows are equal to the ## neural network targets. These rows must be at the end of the data set! ## The fourth arguemnt is optional and defines if the data sets have to be randomised before ## optimizing. ## Default value for bRand is 1, means randomise the columns. ## @end deftypefn ## Author: mds function retmatrix = __optimizedatasets(matrix,nTrainSets,nTargets,bRand) ## check number of inputs error(nargchk(3,4,nargin)); # set default values bRandomise = 1; if (nargin==4) bRandomise = bRand; endif # if needed, randomise the cols if (bRandomise) matrix = __randomisecols(matrix); endif # analyze matrix, which row contains what kind of data? # a.) binary values? Means the row contains only 0 and 1 # b.) unique values? # c.) Min values are several times contained in the row # d.) Max values are several times contained in the row matrix1 = matrix(1:end-nTargets,:); analyzeMatrix = __analyzerows(matrix1); # now sort "matrix" with help of analyzeMatrix # following conditions must be kept: # a.) rows containing unique values aren't sorted! # b.) sort first rows which contains min AND max values only once # c.) sort secondly rows which contains min OR max values only once # d.) at last, sort binary data if still needed! retmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets); endfunction %!shared retmatrix, matrix %! disp("testing __optimizedatasets") %! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \ %! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \ %! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \ %! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1]; %! ## The last row is equal to the neural network targets %! retmatrix = __optimizedatasets(matrix,9,1); %! ## the above statement can't be tested with assert! %! ## it contains random values! So pass a "success" message %!assert(1==1); %! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \ %! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \ %! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \ %! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1]; %! ## The last row is equal to the neural network targets %! retmatrix = __optimizedatasets(matrix,9,1,0); %!assert(retmatrix(1,1)==5); %!assert(retmatrix(2,1)==0); %!assert(retmatrix(3,1)==1); %!assert(retmatrix(4,1)==3);nnet/inst/__printLayerWeights.m0000644000175000017500000000312110751613601015570 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printLayerWeights (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printLayerWeights(fid,net) if isfield(net,"layerweights") # check if it's cell array if iscell(net.layerweights) [nRows, nColumns] = size(net.layerweights); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," layerweights: {%dx%d cell} containing xx layer weight\n",nRows,nColumns); else fprintf(fid,"layerweights:unsure if this is possible\n"); endif else fprintf(fid,"field layerweights not found & not yet used item\n"); endif endfunctionnnet/inst/dhardlim.m0000644000175000017500000000177711112254025013402 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = dhardlim (@var{n}) ## ## @end deftypefn ## Author: Michel D. Schmid function a = dhardlim(n) a = 0; endfunctionnnet/inst/satlin.m0000644000175000017500000000230011112254025013067 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = satlin (@var{n}) ## A neural feed-forward network will be trained with @code{trainlm} ## ## @end deftypefn ## @seealso{purelin,tansig,logsig} ## Author: Michel D. Schmid function a = satlin(n) if (n<0) a = 0; elseif (n>=0 && n<=1) a = n; else a = 1; # if n>1 endif endfunctionnnet/inst/__printInitParam.m0000644000175000017500000000242410751613601015052 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printInitParam (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printInitParam(fid,net) if isfield(net,"initParam") if isempty(net.initParam) fprintf(fid," initParam: '%s'\n","not yet used item"); else fprintf(fid," initParam: '%s'\n",net.initParam); endif endif endfunctionnnet/inst/__printNumLayerDelays.m0000644000175000017500000000323210751613601016062 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumLayerDelays (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumLayerDelays(fid,net) ## now check the structure fields.. cellNetFields = fieldnames(net); # search for numLayerDelays if isfield(net,"numLayerDelays") # test on scalar if isscalar(net.numLayerDelays) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," numLayerDelays: %d (read-only)\n",net.numLayerDelays); # net.numLayerDelays must be an integer... till now, 11-01-2006 else error("numLayerDelays must be a scalar value!"); endif endif endfunctionnnet/inst/__calcjacobian.m0000644000175000017500000002370511406227755014520 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{Jj} = __calcjacobian (@var{net},@var{Im},@var{Nn},@var{Aa},@var{vE}) ## This function calculates the jacobian matrix. It's used inside the ## Levenberg-Marquardt algorithm of the neural network toolbox. ## PLEASE DO NOT USE IT ELSEWEHRE, it proparly will not work! ## @end deftypefn ## Author: Michel D. Schmid function [Jj] = __calcjacobian(net,Im,Nn,Aa,vE) ## comment: ## - return value Jj is jacobi matrix ## for this calculation, see "Neural Network Design; Hagan, Demuth & Beale page 12-45" ## check range of input arguments error(nargchk(5,5,nargin)) ## get signals from inside the network bias = net.b; ## calculate some help matrices mInputWeight = net.IW{1} * Im; nLayers = net.numLayers; for i=2:nLayers mLayerWeight{i,1} = net.LW{i,i-1} * Aa{i-1,1}; endfor ## calculate number of columns and rows in jacobi matrix ## firstly, number of columns a = ones(nLayers+1,1); # +1 is for the input a(1) = net.inputs{1}.size; for iLayers = 1:nLayers a(iLayers+1) = net.layers{iLayers}.size; endfor nColumnsJacobiMatrix = 0; for iLayers = 1:nLayers nColumnsJacobiMatrix = (a(iLayers)+1)*a(iLayers+1) + nColumnsJacobiMatrix; endfor ## secondly, number of rows ve = vE{nLayers,1}; nRowsJacobiMatrix = length(ve(:)); ## FIRST STEP ----------------------------------------------------- ## calculate the neuron outputs without the transfer function ## - n1_1 = W^1*a_1^0+b^1: the ^x factor defined the xth train data set ## the _x factor defines the layer ## ********** this datas should be hold in Nn ## ********** should be calculated in "__calcperf" ## ********** so Nn{1} means hidden layer ## ********** so Nn{2} means second hidden layer or output layer ## ********** and so on ... ## END FIRST STEP ------------------------------------------------- ## now we can rerange the signals ... this will be done only for ## matrix calculation ... [nRowsError nColumnsError] = size(ve); errorSize = size(ve(:),1); # this will calculate, if only one row # of errors exist... in other words... two rows will be reranged to # one row with the same number of elements. rerangeIndex = floor([0:(errorSize-1)]/nRowsError)+1; nLayers = net.numLayers; for i = 1:nLayers Nn{i,1} = Nn{i,1}(:,rerangeIndex); Aa{i,1} = Aa{i,1}(:,rerangeIndex); [nRows nColumns] = size(Nn{i,1}); bTemp = bias{i,1}; bias{i,1} = repmat(bTemp,1,nColumns); bias{i,1} = bias{i,1}(:,rerangeIndex); endfor mInputWeight = mInputWeight(:,rerangeIndex); for i=2:nLayers mLayerWeight{i,1} = mLayerWeight{i,1}(:,rerangeIndex); endfor Im = Im(:,rerangeIndex); ## define how the errors are connected ## ATTENTION! this happens in row order... numTargets = net.numTargets; mIdentity = -eye(numTargets); cols = size(mIdentity,2); mIdentity = mIdentity(:,rem(0:(cols*nColumnsError-1),cols)+1); errorConnect = cell(net.numLayers,1); startPos = 0; for i=net.numLayers targSize = net.layers{i}.size; errorConnect{i} = mIdentity(startPos+[1:targSize],:); startPos = startPos + targSize; endfor ## SECOND STEP ---------------------------------------------- ## define and calculate the derivative matrix dF ## - this is "done" by the two first derivative functions ## of the transfer functions ## e.g. __dpureline, __dtansig, __dlogsig and so on ... ## calculate the sensitivity matrix tildeS ## start at the end layer, this means of course the output layer, ## the transfer function is selectable ## for calculating the last layer ## this should happen like following: ## tildeSx = -dFx(n_x^x) ## use mIdentity to calculate the number of targets correctly ## for all other layers, use instead: ## tildeSx(-1) = dF1(n_x^(x-1))(W^x)' * tildeSx; for iLayers = nLayers:-1:1 # this will count from the last # layer to the first layer ... n = Nn{iLayers}; # nLayers holds the value of the last layer... ## which transfer function should be used? if (iLayers==nLayers) switch(net.layers{iLayers}.transferFcn) case "radbas" tildeSxTemp = __dradbas(n); case "purelin" tildeSxTemp = __dpurelin(n); case "tansig" n = tansig(n); tildeSxTemp = __dtansig(n); case "logsig" n = logsig(n); tildeSxTemp = __dlogsig(n); otherwise error(["transfer function argument: " net.layers{iLayers}.transferFcn " is not valid!"]) endswitch tildeSx{iLayers,1} = tildeSxTemp .* mIdentity; n = bias{nLayers,1}; switch(net.layers{iLayers}.transferFcn) case "radbas" tildeSbxTemp = __dradbas(n); case "purelin" tildeSbxTemp = __dpurelin(n); case "tansig" n = tansig(n); tildeSbxTemp = __dtansig(n); case "logsig" n = logsig(n); tildeSbxTemp = __dlogsig(n); otherwise error(["transfer function argument: " net.layers{iLayers}.transferFcn " is not valid!"]) endswitch tildeSbx{iLayers,1} = tildeSbxTemp .* mIdentity; endif if (iLayers double of rows in the ## jacobi matrix ... 3 targets --> three times the number of rows like ## with one target and so on. ## now calculate jacobi matrix ## to do this, define first the transposed of it ## this makes it easier to calculate on the "batch" way, means all inputs ## at the same time... ## and it makes it easier to use the matrix calculation way.. JjTrans = zeros(nRowsJacobiMatrix,nColumnsJacobiMatrix)'; # transposed jacobi matrix ## Weight Gradients for i=1:net.numLayers if i==1 newInputs = Im; newTemps = tildeSx{i,1}; gIW{i,1} = copyRows(newTemps,net.inputs{i}.size) .* copyRowsInt(newInputs,net.layers{i}.size); endif if i>1 Ad = cell2mat(Aa(i-1,1)'); newInputs = Ad; newTemps = tildeSx{i,1}; gLW{i,1} = copyRows(newTemps,net.layers{i-1}.size) .* copyRowsInt(newInputs,net.layers{i}.size); endif endfor for i=1:net.numLayers [nRows, nColumns] = size(Im); if (i==1) nWeightElements = a(i)*a(i+1); # n inputs * n hidden neurons JjTrans(1:nWeightElements,:) = gIW{i}(1:nWeightElements,:); nWeightBias = a(i+1); start = nWeightElements; JjTrans(start+1:start+nWeightBias,:) = tildeSbx{i,1}; start = start+nWeightBias; endif if (i>1) nLayerElements = a(i)*a(i+1); # n hidden neurons * n output neurons JjTrans(start+1:start+nLayerElements,:)=gLW{i}(1:nLayerElements,:); start = start + nLayerElements; nLayerBias = a(i+1); JjTrans(start+1:start+nLayerBias,:) = tildeSbx{i,1}; start = start + nLayerBias; endif endfor Jj = JjTrans'; ## END THIRD STEP ------------------------------------------------- #======================================================= # # additional functions # #======================================================= function k = copyRows(k,m) # make copies of the ROWS of Aa matrix mRows = size(k,1); k = k(rem(0:(mRows*m-1),mRows)+1,:); endfunction # ------------------------------------------------------- function k = copyRowsInt(k,m) # make copies of the ROWS of matrix with elements INTERLEAVED mRows = size(k,1); k = k(floor([0:(mRows*m-1)]/m)+1,:); endfunction # ===================================================================== # # END additional functions # # ===================================================================== endfunction nnet/inst/__calcperf.m0000644000175000017500000000741111111062606013663 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{perf}, @var{Ee}, @var{Aa}, @var{Nn}] = __calcperf (@var{net},@var{xx},@var{Im},@var{Tt}) ## @code{__calcperf} calculates the performance of a multi-layer neural network. ## PLEASE DON'T USE IT ELSEWHERE, it proparly won't work. ## @end deftypefn ## Author: Michel D. Schmid function [perf,Ee,Aa,Nn] = __calcperf(net,xx,Im,Tt) ## comment: ## perf, net performance.. from input to output through the hidden layers ## Aa, output values of the hidden and last layer (output layer) ## is used for NEWFF network types ## calculate bias terms ## must have the same number of columns like the input matrix Im [nRows, nColumns] = size(Im); Btemp = cell(net.numLayers,1); # Btemp: bias matrix ones1xQ = ones(1,nColumns); for i= 1:net.numLayers Btemp{i} = net.b{i}(:,ones1xQ); endfor ## shortcuts IWtemp = cell(net.numLayers,net.numInputs,1);# IW: input weights ... LWtemp = cell(net.numLayers,net.numLayers,1);# LW: layer weights ... Aa = cell(net.numLayers,1);# Outputs hidden and output layer Nn = cell(net.numLayers,1);# outputs before the transfer function IW = net.IW; # input weights LW = net.LW; # layer weights ## calculate the whole network till outputs are reached... for iLayers = 1:net.numLayers ## calculate first input weights to weighted inputs.. ## this can be done with matrix calculation... ## called "dotprod" ## to do this, there must be a special matrix ... ## e.g. IW = [1 2 3 4 5; 6 7 8 9 10] * [ 1 2 3; 4 5 6; 7 8 9; 10 11 12; 1 2 3]; if (iLayers==1) IWtemp{iLayers,1} = IW{iLayers,1} * Im; onlyTempVar = [IWtemp(iLayers,1) Btemp(iLayers)]; else IWtemp{iLayers,1} = []; endif ## now calculate layer weights to weighted layer outputs if (iLayers>1) Ad = Aa{iLayers-1,1}; LWtemp{iLayers,1} = LW{iLayers,iLayers-1} * Ad; onlyTempVar = [LWtemp(iLayers,1) Btemp(iLayers)]; else LWtemp{iLayers,1} = []; endif Nn{iLayers,1} = onlyTempVar{1}; for k=2:length(onlyTempVar) Nn{iLayers,1} = Nn{iLayers,1} + onlyTempVar{k}; endfor ## now calculate with the transfer functions the layer output switch net.layers{iLayers}.transferFcn case "purelin" Aa{iLayers,1} = purelin(Nn{iLayers,1}); case "tansig" Aa{iLayers,1} = tansig(Nn{iLayers,1}); case "logsig" Aa{iLayers,1} = logsig(Nn{iLayers,1}); otherwise error(["Transfer function: " net.layers{iLayers}.transferFcn " doesn't exist!"]) endswitch endfor # iLayers = 1:net.numLayers ## now calc network error Ee = cell(net.numLayers,1); for i=net.numLayers Ee{i,1} = Tt{i,1} - Aa{i,1};# Tt: target # Ee will be the error vector cell array endfor ## now calc network performance switch(net.performFcn) case "mse" perf = __mse(Ee); otherwise error("for performance functions, only mse is currently valid!") endswitch endfunction nnet/inst/__dradbas.m0000644000175000017500000000244011341254410013502 0ustar mikemike## Copyright (C) 2010 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __dradbas (@var{n}) ## First derivative of the radial basis transfer function. ## ## @code{__dradbas(n) = exp(-n^2)*-2*x} ## ## @end deftypefn ## Author: Michel D. Schmid function retval = __dradbas (n) if (nargin != 1) print_usage (); else retval = exp (-n^2)*(-2)*x; # the derivative of exp(-n^2) must be calculated # with help of the chain-rule! # d/dx of e^x = e^x # d/dx of -x^2 = -2x # now calculate the product of both endif endfunction #%!assert (radbas (3), exp (-3^2)); nnet/inst/__printAdaptFcn.m0000644000175000017500000000240310751613601014643 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## 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 software 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 software; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printAdaptFcn (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printAdaptFcn(fid,net) if isfield(net,"adaptFcn") if isempty(net.adaptFcn) fprintf(fid," adaptFcn: '%s'\n","empty"); else fprintf(fid," adaptFcn: '%s'\n",net.adaptFcn); endif endif endfunction nnet/inst/newp.m0000644000175000017500000001073711475772132012603 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {@var{net}} = newp (@var{Pr},@var{ss},@var{transFunc},@var{learnFunc}) ## @code{newp} create a perceptron ## ## @example ## PLEASE DON'T USE THIS FUNCTIONS, IT'S STILL NOT FINISHED! ## ========================================================= ## @end example ## @example ## Pr - R x 2 matrix of min and max values for R input elements ## ss - a scalar value with the number of neurons ## transFunc - a string with the transfer function ## default = "hardlim" ## learnFunc - a string with the learning function ## default = "learnp" ## @end example ## ## ## @end deftypefn ## @seealso{} ## Author: Michel D. Schmid function net = newp(Pr,ss,transFunc,learnFunc) ## initial descriptipn ## newp(Pr,ss,transFunc,learnFunc) ## * Pr is a nx2 matrix with min and max values of standardized inputs ## Pr means: p-range ## * ss is a scalar value which describes the number of neurons ## of output neurons ## * transFunc is the transfer function, standard is "hardlim" ## * learnFunc is the learning function, standard is "learnp" ## check range of input arguments error(nargchk(1,4,nargin)) ## set defaults if (nargin <2) ss = 1; # means one neuron endif if (nargin <3) transFunc = "hardlim"; endif if (nargin <4) learnFunc = "learnp"; endif ## check input args checkInputArgs(Pr,ss); # ## get number of layers (without input layer) # nLayers = length(ss); ## Standard architecture of neural network net = __newnetwork(1,1,1,"newp"); ## description: ## first argument: number of inputs, nothing else allowed till now ## it's not the same like the number of neurons in this input ## second argument: number of layers, including output layer ## third argument: number of outputs, nothing else allowed till now ## it's not the same like the number of neurons in this output ## fourth argument: network type ## set inputs with limit of only ONE input net.inputs{1}.range = Pr; [nRows, nColumns] = size(Pr); net.inputs{1}.size = nRows; ## set size of IW net.IW{1,1} = zeros(1,nRows); ## set number of bias, one per layer net.b{iBiases,1} = 0; ## define everything with "layers" net.numLayers = ss(end); net.layers = cell(1,1); net.layers{1}.size = ss(end); net.layers{1}.transFcn = transFunc; ## next row of code is only for MATLAB(TM) compatibility ## I never used this the last 4 years ... net.targets{i}.userdata = "Put your custom informations here!"; ## performance function net.performFnc = "mae"; ## learning net.biases{1}.learnFcn = learnFunc; net.inputWeights{1,1}.learnFcn = learnFunc; ## adaption net.adaptFcn = "trains"; ## Training net.trainFcn = "trainc"; ## Initialization net = __init(net); # ====================================================== # # additional check functions... # # ====================================================== function checkInputArgs(Pr,ss) ## check if Pr has correct format if !isreal(Pr) | (size(Pr,2)!=2) error("Input ranges must be a two column matrix!") endif if any(Pr(:,1) > Pr(:,2)) # check if numbers in the second column are larger as in the first one error("Input ranges has values in the second column larger as in the same row of the first column.") endif ## check if ss has correct format, must be a scalar value if ( (size(ss,1)!=1) || (size(ss,2)!=1)) error("Layer sizes is not a scalar value.") endif endfunction # ======================================================== endfunctionnnet/inst/__printNumInputs.m0000644000175000017500000000315210751613601015127 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumInputs (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumInputs(fid,net) ## now check the structure fields.. cellNetFields = fieldnames(net); # search for numInputs if isfield(net,"numInputs") # test on scalar if isscalar(net.numInputs) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," numInputs: %d\n",net.numInputs); # net.numInputs must be an integer... till now, 11-01-2006 else error("numInputs must be a scalar value!"); endif endif endfunctionnnet/inst/dposlin.m0000644000175000017500000000217211112254025013254 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {}@var{a}= poslin (@var{n}) ## @code{poslin} is a positive linear transfer function used ## by neural networks ## @end deftypefn ## Author: Michel D. Schmid function a = dposlin(n) if (n<0) a = 0; else a = 1; endif endfunctionnnet/inst/__rerangecolumns.m0000644000175000017500000001401311073170664015137 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{retmatrix} = __rerangecolumns (@var{matrix},@var{analyzeMatrix},@var{nTrainSets}) ## @code{__rerangecolumns} reranges the data sets depending on the input arguments. ## @code{matrix} is the data set matrix containing inputs and outputs (targets) in row order. ## This means for example: the first three rows are inputs and the fourth row is an output row. ## The second argument is used in the optimizing algorithm. This matrix contains informations about ## the description of the rows data of matrix. ## The third argument is used to be sure, rerange all the columns to the correct position. ## @end deftypefn ## Author: mds function retmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets) ## check number of inputs error(nargchk(3,3,nargin)); # set default values # now sort "matrix" with help of analyzeMatrix # following conditions must be kept: # a.) rows containing unique values aren't sorted! # b.) sort first rows which contains min AND max values only once # c.) sort secondly rows which contains min OR max values only once # d.) at last, sort binary data if still needed! nRows = size(analyzeMatrix,1); # get number of rows ## create i-vector i = 1; iVec = []; while (i <= nRows) if ( (analyzeMatrix(i,3)==1) && (analyzeMatrix(i,4)==1) ) iVec = [iVec i]; endif i += 1; endwhile i = 1; while (i <= nRows) if ( (analyzeMatrix(i,3)>1) || (analyzeMatrix(i,4)>1) ) iVec = [iVec i]; endif i += 1; endwhile i = 1; while (i <= nRows) if (analyzeMatrix(i,1)==1) iVec = [iVec i]; endif i += 1; endwhile ## now do main loop j = 1; i = iVec(j); nRows = length(iVec); while (j < nRows) if (analyzeMatrix(i,2)==1) # easiest case, nothing to do else # now let's see if min AND max values are only once in the row if ( (analyzeMatrix(i,3)==1) && (analyzeMatrix(i,4)==1) ) # search at which index the min value is minVal = min(matrix(i,:)); [rowInd, colInd] = find(matrix(i,:)==minVal);# colInd is searched if (colInd >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd); endif # search at which index the max value is maxVal = max(matrix(i,:)); [rowInd, colInd] = find(matrix(i,:)==maxVal);# colInd is searched if (colInd >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd); endif else # now here, we have to copy the rows, if min OR max values are more than once in a row if ( (analyzeMatrix(i,3)>=1) || (analyzeMatrix(i,4)>=1) ) # search at which index the min value is minVal = min(matrix(i,:)); [rowInd, colInd] = find(matrix(i,:)==minVal);# colInd is searched if (colInd(1) >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd(1)); endif # search at which index the max value is maxVal = max(matrix(i,:)); [rowInd, colInd] = find(matrix(i,:) == maxVal);# colInd is searched if (colInd(1) >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd(1)); endif else # now sort binary data, if needed # search at which index the 0-value is [rowInd, colInd] = find(matrix(i,:)==0);# colInd is searched if (colInd(1) >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd(1)); endif # search at which index the 1-value is [rowInd, colInd] = find(matrix(i,:)==1);# colInd is searched if (colInd(1) >= nTrainSets ) # move column matrix = __copycoltopos1(matrix,colInd(1)); endif endif# END OF if ( (analyzeMatrix(i,3)>=1) || (analyzeMatrix(i,4)>=1) ) endif # END OF if ( (analyzeMatrix(i,3)==1) AND (analyzeMatrix(i,4)==1) ) endif # END OF if (analyzeMatrix(i,2)==1) j += 1; i = iVec(j); endwhile retmatrix = matrix; endfunction %!shared matrix,analyzeMatrix,nTrainSets, returnmatrix %! disp("testing __rerangecolumns") %! matrix = [0 1 0 0 0 0 1 0 1 1; \ %! 4 4 4 4 4 4 4 4 4 4; \ %! -1.1 -1.1 2 3 4 3.2 1 8 9 10; \ %! 0 1.1 3 4 5 2 10 10 2 3; \ %! -1 1 1 1 1 2 3 4 1 5]; %! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1]; %! nTrainSets = 8; %! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets); %!assert(returnmatrix(1,1)==1); %!assert(returnmatrix(2,1)==4); %!assert(returnmatrix(3,1)==1); %!assert(returnmatrix(4,1)==10); %!assert(returnmatrix(5,1)==3); %! matrix = [0 1 0 0 0 0 1 0 1 1; \ %! 4 4 4 4 4 4 4 4 4 4; \ %! -1.1 -1.1 2 3 4 3.2 1 8 9 10; \ %! 0 1.1 3 4 5 2 10 10 2 3; \ %! -1 1 1 1 1 2 3 4 1 5; \ %! 0 1 2 1 2 1 2 3 4 5;]; # the last row is euqal to the nnet targets %! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1]; %! nTrainSets = 8; %! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets); %!assert(returnmatrix(1,1)==1); %!assert(returnmatrix(2,1)==4); %!assert(returnmatrix(3,1)==1); %!assert(returnmatrix(4,1)==10); %!assert(returnmatrix(5,1)==3); %!assert(returnmatrix(6,1)==2);nnet/inst/dividerand.m0000644000175000017500000000534711153230335013727 0ustar mikemike## Copyright (C) 2009 Luiz Angelo Daros de Luca ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} [@var{trainVectors},@var{validationVectors},@var{testVectors},@var{indexOfTrain},@var{indexOfValidation},@var{indexOfTest}] = dividerand (@var{allCases},@var{trainRatio},@var{valRatio},@var{testRatio}) ## Divide the vectors in training, validation and test group according to ## the informed ratios ## ## ## @example ## ## [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio ## n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio) ## ## The ratios are normalized. This way: ## ## dividerand(xx,1,2,3) == dividerand(xx,10,20,30) ## ## @end example ## ## @end deftypefn function [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidation,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio) # # Divide the vectors in training, validation and test group according to # the informed ratios # # [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio # n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio) # # The ratios are normalized. This way: # # dividerand(xx,1,2,3) == dividerand(xx,10,20,30) # ## Normalize ratios total = trainRatio + valRatio + testRatio; #trainRatio = trainRatio / total; not used validationRatio = valRatio / total; testRatio = testRatio / total; ## Calculate the number of cases for each type numerOfCases = size(allCases,2); numberOfValidation = floor(validationRatio*numerOfCases); numberOfTest = floor(testRatio*numerOfCases); numberOfTrain = numerOfCases - numberOfValidation - numberOfTest; ## Find their indexes indexOfAll=randperm(numerOfCases); indexOfValidation=sort(indexOfAll(1:numberOfValidation)); indexOfTest=sort(indexOfAll((1:numberOfTest)+numberOfValidation)); indexOfTrain=sort(indexOfAll((1:numberOfTrain)+numberOfTest+numberOfValidation)); ## Return vectors trainVectors = allCases(:,indexOfTrain); testVectors = allCases(:,indexOfTest); validationVectors = allCases(:,indexOfValidation); endfunction nnet/inst/__printInputWeights.m0000644000175000017500000000310310751613601015613 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printInputsWeights (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printInputWeights(fid,net) if isfield(net,"inputweights") # check if it's cell array if iscell(net.inputweights) [nRows, nColumns] = size(net.inputweights); # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," inputweights: {%dx%d cell} containing xx input weight\n",nRows,nColumns); else fprintf(fid,"unsure if this is possible\n"); endif else fprintf(fid,"field inputweights not found & not yet used item\n"); endif endfunctionnnet/inst/sim.m0000644000175000017500000000523311112254025012375 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {@var{netoutput} =} sim (@var{net}, @var{mInput}) ## @code{sim} is usuable to simulate a before defined neural network. ## @code{net} is created with newff(@dots{}) and @var{mInput} should be the ## corresponding input data set! ## @end deftypefn ## Author: Michel D. Schmid ## Comments: see in "A neural network toolbox for Octave User's Guide" [4] ## for variable naming... there have inputs or targets only one letter, ## e.g. for inputs is written P. To write a program, this is stupid, you can't ## search for 1 letter variable... that's why it is written here like Pp, or Tt ## instead only P or T. function [netoutput] = sim(net,mInput) ## check range of input arguments error(nargchk(2,2,nargin)) ## check input args ## check "net", must be a net structure if !__checknetstruct(net) error("Structure doesn't seem to be a neural network") endif ## check "mInput", must have defined size [nRows, nColumns] = size(mInput); if (nRows != net.inputs{1}.size) error(["Simulation input data must have: " num2str(net.inputs{1}.size) " rows."]) endif ## first get weights... IW = net.IW{1}; b1 = net.b{1}; b1 = repmat(b1,1,size(mInput,2)); nLoops = net.numLayers; for i=1:nLoops trf = net.layers{i}.transferFcn; ## calculate the outputs for each layer from input to output if i==1 Nn{i,1} = IW*mInput + b1; else LWx = net.LW{i,i-1}; bx = net.b{i}; bx = repmat(bx,1,size(Aa{i-1,1},2)); Nn{i,1} = LWx*Aa{i-1,1} + bx; endif switch(trf) case "tansig" Aa{i,1} = tansig(Nn{i,1}); case "purelin" Aa{i,1} = purelin(Nn{i,1}); case "logsig" Aa{i,1} = logsig(Nn{i,1}); otherwise error(["sim:Unknown transfer fucntion: " trf "!"]); endswitch endfor netoutput = Aa{i,1}; endfunction nnet/inst/__printNumOutputs.m0000644000175000017500000000317210751613601015332 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumOutputs (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumOutputs(fid,net) ## now check the structure fields.. cellNetFields = fieldnames(net); # search for numOutputs if isfield(net,"numOutputs") # test on scalar if isscalar(net.numOutputs) # insert enough spaces to put ":" to position 20 # insert 2 spaces for distance between ":" and "%" fprintf(fid," numOutputs: %d (read-only)\n",net.numOutputs); # net.numOutputs must be an integer... till now, 11-01-2006 else error("numOutputs must be a scalar value!"); endif endif endfunctionnnet/inst/ind2vec.m0000644000175000017500000000231511153230335013140 0ustar mikemike## Copyright (C) 2009 Luiz Angelo Daros de Luca ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {@var{vec}} = ind2vec (@var{ind}) ## @code{vec2ind} convert indices to vector ## ## ## @example ## EXAMPLE 1 ## vec = [1 2 3; 4 5 6; 7 8 9]; ## ## ind = vec2ind(vec) ## The prompt output will be: ## ans = ## 1 2 3 1 2 3 1 2 3 ## @end example ## ## @end deftypefn ## @seealso{vec2ind} function [vector]=ind2vec(ind) # Converts indices to vectors # # vectors = length(ind); vector = sparse(ind,1:vectors,ones(1,vectors)); endfunction nnet/inst/__printNumInputDelays.m0000644000175000017500000000304310751613601016105 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printNumInputDelays (@var{fid}) ## @code{printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNumInputDelays(fid,net) ## now check the structure fields.. cellNetFields = fieldnames(net); # search for numInputDelays if isfield(net,"numInputDelays") # test on scalar if isscalar(net.numInputDelays) fprintf(fid," numInputDelays: %d (read-only)\n",net.numInputDelays); # net.numInputDelays must be an integer... till now, 11-01-2006 else error("numInputDelays must be a scalar value!"); endif endif endfunctionnnet/inst/__printNetworkType.m0000644000175000017500000000251710751613601015464 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} __printMLPHeader (@var{fid}) ## @code{__printMLPHeader} saves the header of a neural network structure ## to a *.txt file with identification @code{fid}. ## @end deftypefn ## Author: Michel D. Schmid function __printNetworkType(fid,net) if isfield(net,"networkType") if strcmp(net.networkType,"newff") fprintf(fid," Network type: '%s'\n","Feed forward multi-layer network"); else fprintf(fid," Network type: '%s'\n","error: undefined network type"); endif endif endfunctionnnet/inst/__randomisecols.m0000644000175000017500000000263311073170664014762 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {} @var{retmatrix} = __randomisecols (@var{matrix}) ## @code{__randomisecols} takes a matrix as input argument and changes the order ## of the columns. The rows aren't affected. ## @end deftypefn ## Author: mds function [retmatrix] = __randomisecols(matrix) ## check number of inputs error(nargchk(1,1,nargin)); # get number of cols nCols = size(matrix,2); # now create random column order colOrder = randperm(nCols); # now sort the matrix new retmatrix = matrix(:,[colOrder]); endfunction %!# no test possible, contains randperm which is using %!# some randome functions nnet/inst/__dlogsig.m0000644000175000017500000000174410751613601013545 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## -*- texinfo -*- ## @deftypefn {Function File} {}[@var{a} = __dlogsig (@var{n}) ## ## @end deftypefn ## @seealso{__dpurelin,__dtansig} ## Author: Michel D. Schmid function a = __dlogsig(n) a = n.*(1-n); endfunctionnnet/COPYING0000644000175000017500000004307710751613601011524 0ustar mikemike GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 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. 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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. 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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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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. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. 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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. 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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, see . 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. nnet/Makefile0000644000175000017500000000104011073645625012122 0ustar mikemikesinclude ../../Makeconf PKG_FILES = COPYING DESCRIPTION INDEX $(wildcard inst/*) \ doc/pdf/neuralNetworkPackageForOctaveUsersGuide.pdf \ $(wildcard doc/latex/user/*.tex) \ $(wildcard doc/latex/user/examples/*.tex) \ $(wildcard doc/latex/user/examples/1/*.tex) \ $(wildcard doc/latex/user/examples/1/*.txt) \ $(wildcard doc/latex/user/examples/1/*.m) \ $(wildcard doc/latex/user/octave/neuroPackage/*.tex) \ $(wildcard doc/latex/user/octave/neuroPackage/graphics/*.eps) \ $(wildcard doc/latex/user/octave/neuroPackage/graphics/*.pdf) nnet/tests/0000755000175000017500000000000011475775705011642 5ustar mikemikennet/tests/test_nnet_win32.pl0000644000175000017500000000473411073173005015207 0ustar mikemike#!C:/perl/bin/perl.exe ## ## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . use strict; use diagnostics; # Force verbose warning diagnostics. use warnings; use English; #use vars qw( $VERSION ); #use Getopt::Long; #use Win32; use Win32::Process; use Win32::Console::ANSI; use Term::ANSIScreen qw/:color /; #my $command = "D:\\programs\\programming\\octave\\bin\\octave.exe"; my $args = "D:\\programs\\programming\\octave\\bin\\octave.exe D:\\daten\\octave\\neuroPackage\\0.1.8.1\\nnet\\tests\\nnetTest.m"; my $numberOfFailedTests = 0; my $numberOfSuccessfullTests = 0; my $testingFile; my @tokens=[]; #my $process; # Prozess-Objekt print "Starting with tests for the neural network package\n"; # Win32::Process::Create($process, # $command, # $args, # 1, # CREATE_NEW_CONSOLE, # '.'); #$process->Wait(INFINITE); # script will wait until process is finished # my $pid = $process->GetProcessID(); # print "new process pid: $pid\n"; open(COUNTER, "$args |") or die("...: $!\n"); while () { if (/^testing/) { chomp ($testingFile = $_); } if (/^!!!!!/) { $numberOfFailedTests += 1; print colored("$testingFile $_",'red'); }elsif(/^PASSES/) { @tokens = split(/ /, $_); $numberOfSuccessfullTests += $tokens[4]; print colored("$testingFile $_",'yellow'); } } print "\n\n"; print colored("Summary:\n",'green'); print colored("Number of files containing failed tests: $numberOfFailedTests!\n",'red'); print colored("Number of successfull tests: $numberOfSuccessfullTests!\n",'yellow'); my $allTests = $numberOfFailedTests + $numberOfSuccessfullTests; print colored("\nRunning complete $allTests tests!\n",'green'); close COUNTER; nnet/tests/nnetTest.m0000644000175000017500000000215111073173005013576 0ustar mikemike## Copyright (C) 2008 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## Running this file will test all function files and tests which are ## available in the nnet package # change to directory where the function files are cd ../inst # now run the function files in functional order if needed test __analyzerows test __copycoltopos1 test isposint test min_max test newff # go back to the test directory cd ../tests nnet/tests/readme0000644000175000017500000000134711073173005013002 0ustar mikemiketests directory: ================ nnetTest.m: ----------- Runs all the m-files containing test cases test_nnet_win32.pl: ------------------- Is a perl script. To run this, you must have installed a perl interpreter. I tested it with the ActiveState Perl for Win32 systems. Currently, it does nothing else as nnetTest.m does. subdirectory MLP: ================= MLPScripts.m: ------------- to run some very basic tests please run MLPScripts. This will run all scripts inside the subdirectories "example1" and "example2". If no error occur, the first test is passed :-) nnetTestMLP.m ---------- these tests don't run on the windows version of octave-forge. Please use a linux/unix version. I tested it on octave-3.0.0 compiled on ubuntu. nnet/tests/MLP/0000755000175000017500000000000011475775705012272 5ustar mikemikennet/tests/MLP/example1/0000755000175000017500000000000011475775705014006 5ustar mikemikennet/tests/MLP/example1/mlp9_2_1_tansig.m0000644000175000017500000000714411054757072017047 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 0.5; MLPnet.IW{1,1}(2,:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(:) = 0.5; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_2_3_tansig.dat0000644000175000017500000000146011050311226017337 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 134.203/0, Gradient 1028.61/1e-010 TRAINLM, Epoch 14/100, MSE 14.8413/0, Gradient 0.138881/1e-010 TRAINLM, Validation stop. 2.1874 2.0916 1.1052 2.1874 2.1874 1.1052 1.1052 2.1874 2.1874 2.1874 1.1052 1.1931 2.1874 2.1874 1.1052 2.3418 1.1052 1.1052 2.1874 2.1874 2.1874 2.3418 1.1052 1.1052 2.1874 1.1052 2.3412 2.1874 1.1052 1.1052 8.7496 8.3662 4.4206 8.7496 8.7496 4.4208 4.4206 8.7496 8.7496 8.7496 4.4206 4.7724 8.7496 8.7496 4.4206 9.3672 4.4206 4.4206 8.7496 8.7496 8.7496 9.3672 4.4206 4.4206 8.7496 4.4206 9.3648 8.7496 4.4206 4.4206 21.8740 20.9156 11.0515 21.8740 21.8740 11.0520 11.0515 21.8740 21.8740 21.8740 11.0515 11.9311 21.8740 21.8740 11.0515 23.4180 11.0515 11.0515 21.8740 21.8740 21.8740 23.4180 11.0515 11.0515 21.8740 11.0515 23.4119 21.8740 11.0515 11.0515nnet/tests/MLP/example1/orig/0000755000175000017500000000000011475775705014746 5ustar mikemikennet/tests/MLP/example1/orig/mData.txt0000644000175000017500000000564211050311226016511 0ustar mikemike390 47 0 0 17 45 1 0 3 33 2 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 412 50 0 0 25 47 0 0 11 29 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 406 77 1 0 15 24 2 0 6 37 1 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 445 74 0 0 19 30 0 0 4 43 0 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 331 169 0 0 19 66 1 0 0 34 1 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 337 177 0 0 11 47 1 0 4 39 3 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 286 207 1 0 5 74 2 0 1 38 4 0 1 429 44 0 0 37 19 0 0 23 19 0 0 1 459 22 0 0 46 14 0 0 27 17 0 0 1 340 163 1 0 16 72 3 0 3 31 1 0 1 311 236 0 0 6 66 4 0 2 24 9 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 258 236 2 0 2 72 7 0 0 26 8 0 1 457 15 0 0 44 1 0 0 30 13 0 0 1 471 8 0 0 58 2 0 0 25 13 0 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 489 44 0 0 21 12 0 0 29 21 0 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 368 188 1 0 6 49 0 0 3 37 0 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 492 14 0 0 44 4 0 0 40 7 0 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 349 224 1 0 3 66 3 0 2 41 1 0 1 295 271 3 0 6 71 5 0 3 29 4 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 264 298 4 0 7 68 10 0 1 30 7 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 344 240 2 0 4 43 1 0 4 39 6 0 2 303 269 3 0 10 66 2 0 1 32 7 0 2 306 286 2 0 12 61 2 0 3 28 4 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 472 139 0 0 16 25 0 0 11 38 0 0 2 454 173 1 0 14 21 0 0 2 42 0 0 2 458 36 0 0 12 7 0 0 24 19 0 0 2 405 199 0 0 13 37 0 0 6 45 0 0 2 475 186 1 0 15 28 0 0 5 35 0 0 2 447 189 0 0 11 34 0 0 5 45 0 0 2 387 294 1 0 10 37 0 0 4 30 5 0 2 396 217 0 0 16 43 2 0 6 30 0 0 2 492 98 0 0 7 23 0 0 13 36 0 0 2 521 137 0 0 16 17 0 0 7 24 0 0 2 511 48 0 0 35 7 0 0 27 15 0 0 2 532 57 0 0 19 6 0 0 28 15 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 481 43 0 0 38 11 0 0 21 24 0 0 2 484 222 0 0 16 12 0 0 9 24 0 0 2 484 37 0 0 50 13 0 0 29 18 0 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 541 269 1 0 12 5 0 0 6 20 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 496 319 0 0 2 4 0 0 2 14 0 0 3 470 273 1 0 9 17 0 0 2 32 0 0 3 429 433 3 0 8 11 0 0 5 15 0 0 3 506 64 0 0 32 23 0 0 13 29 0 0 3 487 36 0 0 34 10 0 0 19 27 0 0 3 475 34 0 0 38 13 0 0 28 18 0 0 3 423 25 0 0 26 16 0 0 16 29 0 0 3 488 83 0 0 34 18 0 0 15 34 0 0 3 487 35 0 0 34 12 0 0 29 24 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 496 34 0 0 49 6 0 0 31 11 0 0 3 493 21 0 0 33 16 0 0 22 19 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 506 24 0 0 34 12 0 0 26 17 0 0 3 502 34 0 0 25 9 0 0 23 11 0 0 3 485 29 0 0 33 7 0 0 14 20 0 0 3 495 34 0 0 39 4 0 0 21 21 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3nnet/tests/MLP/example1/mlp9_2_1_tansig.dat0000644000175000017500000000055411050311226017340 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 2.5516/0, Gradient 69.8821/1e-010 TRAINLM, Epoch 16/100, MSE 0.45224/0, Gradient 0.0212947/1e-010 TRAINLM, Validation stop. 2.0386 2.0386 1.3267 2.2917 2.0386 2.0386 2.2917 2.0386 2.0386 2.0386 2.0386 1.3592 2.2917 2.0386 1.3267 1.3267 2.0386 1.3267 1.3405 2.0386 2.0386 1.3267 1.5778 2.0386 2.0386 1.3267 1.3292 2.0386 2.2917 1.3267nnet/tests/MLP/example1/mlp9_1_1_tansig.m0000644000175000017500000000715711054757072017052 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## ## This is a test to train a 9-1-1 MLP (was a real project). ## ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 1; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(:) = 1.5; MLPnet.b{2,1}(:) = 0.5; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_2_2_1_tansig.m0000644000175000017500000000711111054757072017262 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... %% define network nHiddenNeurons = [2 2]; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{'tansig','tansig','tansig','purelin'},'trainlm','learngdm','mse'); %% for test purpose, define weights by hand # MLPnet.IW{1,1}(:) = 1.5; # MLPnet.LW{2,1}(:) = 0.5; # MLPnet.b{1,1}(:) = 1.5; # MLPnet.b{2,1}(:) = 0.5; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_5_3_tansig.m0000644000175000017500000000747611054757072017064 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4; mOutput*10]; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 5; nOutputNeurons = 3; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.IW{1,1}(3:5,:) = 1; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.LW{2,1}(3,:) = 0.1; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{1,1}(3:5,:) = 1; MLPnet.b{2,1}(1,:) = 0.5; MLPnet.b{2,1}(2,:) = 0.5; MLPnet.b{2,1}(3,:) = 0.1; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_1_1_tansig.dat0000644000175000017500000000055311050311226017336 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 2.27367/0, Gradient 54.9248/1e-10 TRAINLM, Epoch 8/100, MSE 0.570748/0, Gradient 0.0154084/1e-10 TRAINLM, Validation stop. 1.8392 1.8391 1.5589 1.5589 1.5589 1.5589 1.5589 1.8392 1.8392 1.8392 1.8392 1.8392 1.5589 1.8392 1.8392 1.5589 1.8392 1.8392 1.5589 1.8384 1.8392 1.5589 1.5589 1.8392 1.5589 1.5589 1.5589 1.8392 1.5589 1.5589nnet/tests/MLP/example1/mlp9_5_3_tansig.dat0000644000175000017500000000145411050311226017345 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 141.889/0, Gradient 1253.56/1e-010 TRAINLM, Epoch 8/100, MSE 13.6739/0, Gradient 137.397/1e-010 TRAINLM, Validation stop. 0.8081 2.5413 2.5667 1.1749 1.3791 1.1761 1.2381 0.6851 0.6435 1.4331 2.2694 2.5965 1.0768 0.7442 0.9235 2.5681 1.8238 0.4295 1.0288 0.9430 0.4290 1.1847 1.1398 1.8846 1.7315 0.9500 2.4366 0.4475 1.2249 2.5670 4.7533 8.7954 8.3791 6.1910 5.8888 6.1933 6.3116 4.5980 4.4590 6.6755 8.2791 8.5445 5.8721 4.4152 5.3732 8.3835 7.4296 3.7666 5.4819 5.3490 3.7647 6.2098 6.0769 7.5455 7.0122 5.3092 8.5951 3.8224 6.2864 8.3796 12.1298 21.4848 20.4710 15.8321 14.6769 15.8372 16.0940 11.8536 11.5048 16.8779 20.3651 20.8561 15.0359 11.2229 13.7898 20.4812 18.5210 9.7772 14.1764 13.6925 9.7724 15.8731 15.5474 18.7726 17.4322 13.8107 21.0503 9.9154 16.0392 20.4721nnet/tests/MLP/example1/mlp9_1_1_tansig.dat_orig0000644000175000017500000000103311050311226020350 0ustar mikemike>> TRAINLM, Epoch 0/100, MSE 2.27367/0, Gradient 54.9248/1e-010 TRAINLM, Epoch 8/100, MSE 0.570748/0, Gradient 0.0154084/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 17 1.8392 1.8391 1.5589 1.5589 1.5589 1.5589 1.5589 1.8392 1.8392 1.8392 1.8392 1.8392 1.5589 1.8392 1.8392 1.5589 1.8392 Columns 18 through 30 1.8392 1.5589 1.8384 1.8392 1.5589 1.5589 1.8392 1.5589 1.5589 1.5589 1.8392 1.5589 1.5589 >> nnet/tests/MLP/example1/mlp9_2_2_tansig.dat0000644000175000017500000000107611050311226017341 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 26.3503/0, Gradient 416.299/1e-010 TRAINLM, Epoch 9/100, MSE 3.26824/0, Gradient 0.969955/1e-010 TRAINLM, Validation stop. 2.0431 2.0431 1.0154 2.0035 2.0431 1.0154 2.0035 2.0431 1.0154 2.0431 1.0154 1.0154 0.9758 2.0431 1.0154 1.0154 1.0154 0.9758 2.0431 2.0431 2.0431 1.0154 1.0154 1.0154 2.0431 1.0154 1.0154 2.0431 2.0037 1.0154 8.1802 8.1802 4.0490 8.0207 8.1802 4.0490 8.0207 8.1802 4.0490 8.1802 4.0490 4.0490 3.8895 8.1802 4.0490 4.0490 4.0490 3.8895 8.1802 8.1802 8.1802 4.0490 4.0490 4.0490 8.1802 4.0490 4.0490 8.1802 8.0213 4.0490nnet/tests/MLP/example1/mData.txt0000644000175000017500000000603711050311226015550 0ustar mikemike# Created by Octave 2.9.5, Wed May 24 10:33:36 2006 # name: mData # type: matrix # rows: 89 # columns: 13 306 286 2 0 12 61 2 0 3 28 4 0 2 368 188 1 0 6 49 0 0 3 37 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 492 98 0 0 7 23 0 0 13 36 0 0 2 506 64 0 0 32 23 0 0 13 29 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3 429 44 0 0 37 19 0 0 23 19 0 0 1 521 137 0 0 16 17 0 0 7 24 0 0 2 340 163 1 0 16 72 3 0 3 31 1 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 344 240 2 0 4 43 1 0 4 39 6 0 2 459 22 0 0 46 14 0 0 27 17 0 0 1 487 36 0 0 34 10 0 0 19 27 0 0 3 331 169 0 0 19 66 1 0 0 34 1 0 1 541 269 1 0 12 5 0 0 6 20 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 475 186 1 0 15 28 0 0 5 35 0 0 2 496 319 0 0 2 4 0 0 2 14 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 484 37 0 0 50 13 0 0 29 18 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 429 433 3 0 8 11 0 0 5 15 0 0 3 471 8 0 0 58 2 0 0 25 13 0 0 1 303 269 3 0 10 66 2 0 1 32 7 0 2 445 74 0 0 19 30 0 0 4 43 0 0 1 488 83 0 0 34 18 0 0 15 34 0 0 3 264 298 4 0 7 68 10 0 1 30 7 0 2 489 44 0 0 21 12 0 0 29 21 0 0 1 475 34 0 0 38 13 0 0 28 18 0 0 3 492 14 0 0 44 4 0 0 40 7 0 0 1 454 173 1 0 14 21 0 0 2 42 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 349 224 1 0 3 66 3 0 2 41 1 0 1 447 189 0 0 11 34 0 0 5 45 0 0 2 496 34 0 0 49 6 0 0 31 11 0 0 3 484 222 0 0 16 12 0 0 9 24 0 0 2 412 50 0 0 25 47 0 0 11 29 0 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 495 34 0 0 39 4 0 0 21 21 0 0 3 387 294 1 0 10 37 0 0 4 30 5 0 2 258 236 2 0 2 72 7 0 0 26 8 0 1 423 25 0 0 26 16 0 0 16 29 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 511 48 0 0 35 7 0 0 27 15 0 0 2 295 271 3 0 6 71 5 0 3 29 4 0 1 502 34 0 0 25 9 0 0 23 11 0 0 3 458 36 0 0 12 7 0 0 24 19 0 0 2 470 273 1 0 9 17 0 0 2 32 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 406 77 1 0 15 24 2 0 6 37 1 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 390 47 0 0 17 45 1 0 3 33 2 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 487 35 0 0 34 12 0 0 29 24 0 0 3 532 57 0 0 19 6 0 0 28 15 0 0 2 286 207 1 0 5 74 2 0 1 38 4 0 1 405 199 0 0 13 37 0 0 6 45 0 0 2 337 177 0 0 11 47 1 0 4 39 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 506 24 0 0 34 12 0 0 26 17 0 0 3 396 217 0 0 16 43 2 0 6 30 0 0 2 457 15 0 0 44 1 0 0 30 13 0 0 1 472 139 0 0 16 25 0 0 11 38 0 0 2 493 21 0 0 33 16 0 0 22 19 0 0 3 311 236 0 0 6 66 4 0 2 24 9 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 485 29 0 0 33 7 0 0 14 20 0 0 3 481 43 0 0 38 11 0 0 21 24 0 0 2 nnet/tests/MLP/example1/mlp9_2_2_tansig.m0000644000175000017500000000724311054757072017050 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4]; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 2; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(:) = 0.5; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_2_3_tansig.m0000644000175000017500000000740711054757072017053 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4; mOutput*10]; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 3; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.LW{2,1}(3,:) = 0.1; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(1,:) = 0.5; MLPnet.b{2,1}(2,:) = 0.5; MLPnet.b{2,1}(3,:) = 0.1; ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); ## now train the network structure MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); ## make preparations for net test and test MLPnet # standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # will output the network results [simOut] = sim(net,mTestInputN); nnet/tests/MLP/example1/mlp9_2_2_1_tansig.dat0000644000175000017500000000055311050311226017560 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 2.78009/0, Gradient 79.2812/1e-010 TRAINLM, Epoch 6/100, MSE 0.202812/0, Gradient 2.34665/1e-010 TRAINLM, Validation stop. 1.4051 0.5235 1.3323 1.4051 1.4051 1.4051 1.4051 1.4051 0.1939 1.0805 1.4051 0.9002 1.4051 1.3975 1.1845 1.3363 0.5385 1.4051 1.3051 1.4051 1.4051 1.4051 1.3527 1.4021 1.4051 0.6403 1.4051 1.4029 1.4051 1.4051nnet/tests/MLP/MLPScripts.m0000644000175000017500000000260711054757072014443 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## This file is used to call all MLP tests, but without ## assert and so on, means if no error occure, everything ## seems to be ok. ## This is only a very basic test which should be run ## from this directory tic cd example1; mlp9_1_1_tansig mlp9_2_1_tansig mlp9_2_2_1_tansig mlp9_2_2_tansig mlp9_2_3_tansig mlp9_5_3_tansig cd .. cd example2 mlp9_1_1_logsig mlp9_2_1_logsig mlp9_2_2_1_logsig mlp9_2_2_logsig mlp9_2_3_logsig mlp9_5_3_logsig cd .. elapsed_time = toc; disp("Running 12 very basic tests successfully!") disp("Secondes needed to running all the tests: "); disp(elapsed_time); nnet/tests/MLP/nnetTestMLP.m0000644000175000017500000000025111073173005014576 0ustar mikemike test testExample1_1 # runs test for complete mlp scripts #test testExample1_2 # runs test for complete mlp scripts #delete('log_test1_1'); #delete('log_test1_2');nnet/tests/MLP/preparedata9_x_2.m0000644000175000017500000000467511054775373015607 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("example1/mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4]; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets);nnet/tests/MLP/testExample1_2.m0000644000175000017500000000532211064216326015226 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## This file is used to test all the m-files inside ## the example1 directory. ## it exist for each m-file a corresponding dat-file with ## the numerical results of matlab ## actually, following m-files will be tested: ## A. One hidden layer ## ================== ## 1. mlp9_1_1_tansig ## 2. mlp9_2_1_tansig ## 3. mlp9_2_2_tansig ## 4. mlp9_2_3_tansig ## 5. mlp9_5_3_tansig ## ## B. Two hidden layer ## ================== ###### mlp9_2_1_tansig ###### %!shared cAr, mTestResults, simOut, line, fid %! diary log_test1_2 %! dir = "example1"; %! [cAr, mTestResults] = loadtestresults([dir "/mlp9_2_1_tansig.dat"]); %! preparedata9_x_1 %! [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs %! mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... %! nHiddenNeurons = 2; %! nOutputNeurons = 1; %! MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); %! MLPnet.IW{1,1}(1,:) = 0.5; %! MLPnet.IW{1,1}(2,:) = 1.5; %! MLPnet.LW{2,1}(:) = 0.5; %! MLPnet.b{1,1}(1,:) = 0.5; %! MLPnet.b{1,1}(2,:) = 1.5; %! MLPnet.b{2,1}(:) = 0.5; %! VV.P = mValiInput; %! VV.T = mValliOutput; %! VV.P = trastd(VV.P,cMeanInput,cStdInput); %! [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); %! [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); %! [simOut] = sim(net,mTestInputN); %! diary off %!assert(simOut,mTestResults,0.0001) %! fid = fopen("log_test1_2","r"); %! line = fgetl(fid); %!assert(substr(line,16,1),substr(cAr{1,1},16,1)) %!assert(substr(line,27,7),substr(cAr{1,1},27,7)) %!assert(substr(line,48,7),substr(cAr{1,1},48,7)) %! line = fgetl(fid); %!assert(substr(line,16,2),substr(cAr{1,2},16,2)) %!assert(substr(line,27,7),substr(cAr{1,2},27,7)) %!assert(substr(line,48,6),substr(cAr{1,2},48,6)) %!assert(strcmp("TRAINLM, Validation stop.",substr(cAr{1,3},1,25))) %! fclose(fid); nnet/tests/MLP/example2/0000755000175000017500000000000011475775705014007 5ustar mikemikennet/tests/MLP/example2/mlp9_1_1_logsig.m0000644000175000017500000000720711054757072017046 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; %mOutput = [mOutput; mOutput*4]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... ## define network nHiddenNeurons = 1; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(:) = 1.5; MLPnet.b{2,1}(:) = 0.5; #saveMLPStruct(MLPnet,"MLP3test.txt"); #disp("network structure saved, press any key to continue...") #pause ## define validation data new, for matlab compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # saveMLPStruct(net,"MLP3testNachTraining.txt"); # disp("network structure saved, press any key to continue...") # pause # % make preparations for net test and test MLPnet # % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); [simOut] = sim(net,mTestInputN);#%,Pi,Ai,mTestOutput); nnet/tests/MLP/example2/mlp9_1_1_logsig.dat0000644000175000017500000000107211050311226017333 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 3.19597/0, Gradient 87.4172/1e-010 TRAINLM, Epoch 6/100, MSE 0.219207/0, Gradient 0.455596/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 1.0000 0.6866 0.8512 1.0000 1.0000 0.9998 1.0000 1.0000 0.6207 0.7705 1.0000 0.7405 Columns 13 through 24 0.9997 0.9371 0.7935 0.8539 0.6889 1.0000 0.8356 1.0000 1.0000 1.0000 0.8666 0.9578 Columns 25 through 30 0.9999 0.7036 1.0000 0.9635 1.0000 1.0000 >> >>nnet/tests/MLP/example2/mlp9_2_1_logsig.m0000644000175000017500000000774711054757072017060 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## load data mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; %mOutput = [mOutput; mOutput*4]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 0.5; MLPnet.IW{1,1}(2,:) = 1.5; MLPnet.LW{2,1}(:) = 0.5; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(:) = 0.5; saveMLPStruct(MLPnet,"MLP3test.txt"); #disp("network structure saved, press any key to continue...") #pause ## define validation data new, for matlab compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # saveMLPStruct(net,"MLP3testNachTraining.txt"); # disp("network structure saved, press any key to continue...") # pause # % the names in matlab help, see "train" in help for more informations # tr.perf(max(tr.epoch)+1); # # % make preparations for net test and test MLPnet # % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # % [mTestOutputN] = trastd(mTestOutput,cMeanOutput,cStdOutput); # % define unused parameters to get E-variable of simulation # Pi = zeros(6,0); # Ai = zeros(6,0); # % simulate net [simOut] = sim(net,mTestInputN);#%,Pi,Ai,mTestOutput);nnet/tests/MLP/example2/orig/0000755000175000017500000000000011475775705014747 5ustar mikemikennet/tests/MLP/example2/orig/mData.txt0000644000175000017500000000564211050311226016512 0ustar mikemike390 47 0 0 17 45 1 0 3 33 2 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 412 50 0 0 25 47 0 0 11 29 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 406 77 1 0 15 24 2 0 6 37 1 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 445 74 0 0 19 30 0 0 4 43 0 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 331 169 0 0 19 66 1 0 0 34 1 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 337 177 0 0 11 47 1 0 4 39 3 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 286 207 1 0 5 74 2 0 1 38 4 0 1 429 44 0 0 37 19 0 0 23 19 0 0 1 459 22 0 0 46 14 0 0 27 17 0 0 1 340 163 1 0 16 72 3 0 3 31 1 0 1 311 236 0 0 6 66 4 0 2 24 9 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 258 236 2 0 2 72 7 0 0 26 8 0 1 457 15 0 0 44 1 0 0 30 13 0 0 1 471 8 0 0 58 2 0 0 25 13 0 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 489 44 0 0 21 12 0 0 29 21 0 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 368 188 1 0 6 49 0 0 3 37 0 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 492 14 0 0 44 4 0 0 40 7 0 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 349 224 1 0 3 66 3 0 2 41 1 0 1 295 271 3 0 6 71 5 0 3 29 4 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 264 298 4 0 7 68 10 0 1 30 7 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 344 240 2 0 4 43 1 0 4 39 6 0 2 303 269 3 0 10 66 2 0 1 32 7 0 2 306 286 2 0 12 61 2 0 3 28 4 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 472 139 0 0 16 25 0 0 11 38 0 0 2 454 173 1 0 14 21 0 0 2 42 0 0 2 458 36 0 0 12 7 0 0 24 19 0 0 2 405 199 0 0 13 37 0 0 6 45 0 0 2 475 186 1 0 15 28 0 0 5 35 0 0 2 447 189 0 0 11 34 0 0 5 45 0 0 2 387 294 1 0 10 37 0 0 4 30 5 0 2 396 217 0 0 16 43 2 0 6 30 0 0 2 492 98 0 0 7 23 0 0 13 36 0 0 2 521 137 0 0 16 17 0 0 7 24 0 0 2 511 48 0 0 35 7 0 0 27 15 0 0 2 532 57 0 0 19 6 0 0 28 15 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 481 43 0 0 38 11 0 0 21 24 0 0 2 484 222 0 0 16 12 0 0 9 24 0 0 2 484 37 0 0 50 13 0 0 29 18 0 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 541 269 1 0 12 5 0 0 6 20 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 496 319 0 0 2 4 0 0 2 14 0 0 3 470 273 1 0 9 17 0 0 2 32 0 0 3 429 433 3 0 8 11 0 0 5 15 0 0 3 506 64 0 0 32 23 0 0 13 29 0 0 3 487 36 0 0 34 10 0 0 19 27 0 0 3 475 34 0 0 38 13 0 0 28 18 0 0 3 423 25 0 0 26 16 0 0 16 29 0 0 3 488 83 0 0 34 18 0 0 15 34 0 0 3 487 35 0 0 34 12 0 0 29 24 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 496 34 0 0 49 6 0 0 31 11 0 0 3 493 21 0 0 33 16 0 0 22 19 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 506 24 0 0 34 12 0 0 26 17 0 0 3 502 34 0 0 25 9 0 0 23 11 0 0 3 485 29 0 0 33 7 0 0 14 20 0 0 3 495 34 0 0 39 4 0 0 21 21 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3nnet/tests/MLP/example2/mlp9_2_2_3_logsig.dat0000644000175000017500000000223511050311226017561 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 227.919/0, Gradient 1662.64/1e-010 TRAINLM, Epoch 7/100, MSE 9.72986/0, Gradient 2.23561e-009/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 Columns 13 through 24 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 Columns 25 through 30 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 -3.5612 2.4161 2.4161 2.4161 2.4161 2.4161 2.4161 14.3706 14.3706 14.3706 14.3706 14.3706 14.3706 >>nnet/tests/MLP/example2/mlp9_5_3_logsig.dat0000644000175000017500000000223011050311226017336 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 240.504/0, Gradient 1971.95/1e-010 TRAINLM, Epoch 6/100, MSE 7.95197/0, Gradient 203.206/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 7.9886 3.6782 5.2496 7.9843 7.9995 7.9278 7.9880 8.0000 3.0623 4.4398 8.0000 4.1632 2.9962 1.5594 2.0832 2.9948 2.9998 2.9759 2.9960 3.0000 1.3541 1.8133 3.0000 1.7211 0.5992 0.3119 0.4166 0.5990 0.6000 0.5952 0.5992 0.6000 0.2708 0.3627 0.6000 0.3442 Columns 13 through 24 7.9206 6.3668 4.6573 5.2794 3.6986 7.9768 5.0815 8.0000 7.9871 7.9997 5.4228 6.7236 2.9735 2.4556 1.8858 2.0931 1.5662 2.9923 2.0272 3.0000 2.9957 2.9999 2.1409 2.5745 0.5947 0.4911 0.3772 0.4186 0.3132 0.5985 0.4054 0.6000 0.5991 0.6000 0.4282 0.5149 Columns 25 through 30 7.9450 3.8310 8.0000 6.8332 7.9936 7.9993 2.9817 1.6103 3.0000 2.6111 2.9979 2.9998 0.5963 0.3221 0.6000 0.5222 0.5996 0.6000 >>nnet/tests/MLP/example2/mlp9_2_2_logsig.m0000644000175000017500000000745611054757072017056 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## load data mData = load("mData.txt","mData"); # mData = mData.mData(1:10,:); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 2; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(:) = 0.5; # saveMLPStruct(MLPnet,"MLP3test.txt"); #disp("network structure saved, press any key to continue...") #pause ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # saveMLPStruct(net,"MLP3testNachTraining.txt"); # disp("network structure saved, press any key to continue...") # disp("nach Training") # pause # % make preparations for net test and test MLPnet # % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # % simulate net [simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);nnet/tests/MLP/example2/mlp9_2_1_logsig.dat0000644000175000017500000000106611050311226017337 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 2.35298/0, Gradient 79.3291/1e-010 TRAINLM, Epoch 6/100, MSE 0.154324/0, Gradient 0.341107/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 1.4912 0.9151 1.1368 1.4883 1.4996 1.4632 1.4907 1.5000 0.8235 1.0274 1.5000 0.9873 Columns 13 through 24 1.4608 1.2652 1.0581 1.1405 0.9181 1.4839 1.1152 1.5000 1.4901 1.4997 1.1583 1.3023 Columns 25 through 30 1.4694 0.9380 1.5000 1.3135 1.4947 1.4993 >>nnet/tests/MLP/example2/mlp9_2_3_logsig.dat0000644000175000017500000000222711050311226017341 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 245.153/0, Gradient 1576.3/1e-010 TRAINLM, Epoch 6/100, MSE 8.10503/0, Gradient 15.6612/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 3.4901 1.8132 2.3724 3.4869 3.4995 3.4557 3.4896 3.5000 1.6319 2.0664 3.5000 1.9704 1.4967 0.9377 1.1241 1.4956 1.4998 1.4852 1.4965 1.5000 0.8773 1.0221 1.5000 0.9901 0.2993 0.1875 0.2248 0.2991 0.3000 0.2970 0.2993 0.3000 0.1755 0.2044 0.3000 0.1980 Columns 13 through 24 3.4524 2.8360 2.1451 2.3844 1.8195 3.4818 2.3061 3.5000 3.4889 3.4997 2.4421 2.9859 1.4841 1.2787 1.0484 1.1281 0.9398 1.4939 1.1020 1.5000 1.4963 1.4999 1.1474 1.3286 0.2968 0.2557 0.2097 0.2256 0.1880 0.2988 0.2204 0.3000 0.2993 0.3000 0.2295 0.2657 Columns 25 through 30 3.4639 1.8612 3.5000 3.0314 3.4941 3.4993 1.4880 0.9537 1.5000 1.3438 1.4980 1.4998 0.2976 0.1907 0.3000 0.2688 0.2996 0.3000 >>nnet/tests/MLP/example2/mlp9_2_2_logsig.dat0000644000175000017500000000154611050311226017343 0ustar mikemikeTRAINLM, Epoch 0/100, MSE 42.6863/0, Gradient 532.547/1e-010 TRAINLM, Epoch 6/100, MSE 1.62318/0, Gradient 3.12953/1e-010 TRAINLM, Validation stop. simOut = Columns 1 through 12 3.4901 1.8132 2.3724 3.4869 3.4995 3.4557 3.4896 3.5000 1.6319 2.0664 3.5000 1.9704 1.4967 0.9377 1.1241 1.4956 1.4998 1.4852 1.4965 1.5000 0.8773 1.0221 1.5000 0.9901 Columns 13 through 24 3.4524 2.8360 2.1451 2.3844 1.8195 3.4818 2.3061 3.5000 3.4889 3.4997 2.4421 2.9859 1.4841 1.2787 1.0484 1.1281 0.9398 1.4939 1.1020 1.5000 1.4963 1.4999 1.1474 1.3286 Columns 25 through 30 3.4639 1.8612 3.5000 3.0314 3.4941 3.4993 1.4880 0.9537 1.5000 1.3438 1.4980 1.4998 >>nnet/tests/MLP/example2/mlp9_5_3_logsig.m0000644000175000017500000000770411054757072017056 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## load data mData = load("mData.txt","mData"); # mData = mData.mData(1:10,:); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4; mOutput*10]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... ## define network nHiddenNeurons = 5; nOutputNeurons = 3; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.IW{1,1}(3:5,:) = 1; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.LW{2,1}(3,:) = 0.1; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{1,1}(3:5,:) = 1; MLPnet.b{2,1}(1,:) = 0.5; MLPnet.b{2,1}(2,:) = 0.5; MLPnet.b{2,1}(3,:) = 0.1; # saveMLPStruct(MLPnet,"MLP3test.txt"); #disp("network structure saved, press any key to continue...") #pause ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # saveMLPStruct(net,"MLP3testNachTraining.txt"); # disp("network structure saved, press any key to continue...") # disp("nach Training") # pause # % make preparations for net test and test MLPnet # % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # % simulate net [simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);nnet/tests/MLP/example2/mlp9_2_2_1_logsig.m0000644000175000017500000000703011054757072017262 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd mData = load("mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; % now split the data matrix in 3 pieces, train data, test data and validate data % the proportion should be about 1/2 train, 1/3 test and 1/6 validate data % in this neural network we have 12 weights, for each weight at least 3 train sets.. % (that's a rule of thumb like 1/2, 1/3 and 1/6) % 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); % now the rest of the sets are again 100% % ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);% standardize inputs %% comments: there is no reason to standardize the outputs because we have only % one output ... % define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... %% define network nHiddenNeurons = [2 2]; nOutputNeurons = 1; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{'tansig','tansig','purelin'},'trainlm','learngdm','mse'); %% for test purpose, define weights by hand # MLPnet.IW{1,1}(:) = 1.5; # MLPnet.LW{2,1}(:) = 0.5; # MLPnet.b{1,1}(:) = 1.5; # MLPnet.b{2,1}(:) = 0.5; saveMLPStruct(MLPnet,'MLP9-2-2-1.txt'); # disp('network structure saved, press any key to continue...') ## define validation data new, for matlab compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); %[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # # % % make preparations for net test and test MLPnet # % % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # #[simOut,Pf,Af,simE,simPerf] = sim(net,mTestInputN); # [simOut] = sim(net,mTestInputN);nnet/tests/MLP/example2/mlp9_2_3_logsig.m0000644000175000017500000000762011054757072017050 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## load data mData = load("mData.txt","mData"); # mData = mData.mData(1:10,:); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## first permute the whole matrix in row wise ## this won't be used right now for debug and test purpose # order = randperm(nRows); # mData(order,:) = mData; mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4; mOutput*10]; # now split the data matrix in 3 pieces, train data, test data and validate data # the proportion should be about 1/2 train, 1/3 test and 1/6 validate data # in this neural network we have 12 weights, for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; mOutput(:,1:nValiSets) = []; mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; mOutput(:,1:nTestSets) = []; mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets); [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs ## comments: there is no reason to standardize the outputs because we have only # one output ... # define the max and min inputs for each row mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)... ## define network nHiddenNeurons = 2; nOutputNeurons = 3; MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse"); ## for test purpose, define weights by hand MLPnet.IW{1,1}(1,:) = 1.5; MLPnet.IW{1,1}(2,:) = 0.5; MLPnet.LW{2,1}(1,:) = 1.5; MLPnet.LW{2,1}(2,:) = 0.5; MLPnet.LW{2,1}(3,:) = 0.1; MLPnet.b{1,1}(1,:) = 0.5; MLPnet.b{1,1}(2,:) = 1.5; MLPnet.b{2,1}(1,:) = 0.5; MLPnet.b{2,1}(2,:) = 0.5; MLPnet.b{2,1}(3,:) = 0.1; # saveMLPStruct(MLPnet,"MLP3test.txt"); #disp("network structure saved, press any key to continue...") #pause ## define validation data new, for MATLAB(TM) compatibility VV.P = mValiInput; VV.T = mValliOutput; ## standardize also the validate data VV.P = trastd(VV.P,cMeanInput,cStdInput); #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); MLPnet.trainParam.show = NaN; [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); # saveMLPStruct(net,"MLP3testNachTraining.txt"); # disp("network structure saved, press any key to continue...") # disp("nach Training") # pause # % make preparations for net test and test MLPnet # % standardise input & output test data [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); # % simulate net [simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);nnet/tests/MLP/example2/mData.txt0000644000175000017500000000603711050311226015551 0ustar mikemike# Created by Octave 2.9.5, Wed May 24 10:33:36 2006 # name: mData # type: matrix # rows: 89 # columns: 13 306 286 2 0 12 61 2 0 3 28 4 0 2 368 188 1 0 6 49 0 0 3 37 0 0 1 511 73 0 0 40 21 0 0 16 22 0 0 1 418 43 0 0 34 30 1 0 9 27 5 0 1 299 173 1 0 8 63 1 0 1 37 3 0 1 312 253 0 0 2 63 2 0 0 35 3 0 1 492 98 0 0 7 23 0 0 13 36 0 0 2 506 64 0 0 32 23 0 0 13 29 0 0 3 476 41 0 0 32 5 0 0 19 26 0 0 3 483 66 0 0 17 16 0 0 10 28 2 0 3 429 44 0 0 37 19 0 0 23 19 0 0 1 521 137 0 0 16 17 0 0 7 24 0 0 2 340 163 1 0 16 72 3 0 3 31 1 0 1 323 177 0 0 8 68 4 0 0 37 2 0 1 344 240 2 0 4 43 1 0 4 39 6 0 2 459 22 0 0 46 14 0 0 27 17 0 0 1 487 36 0 0 34 10 0 0 19 27 0 0 3 331 169 0 0 19 66 1 0 0 34 1 0 1 541 269 1 0 12 5 0 0 6 20 0 0 3 475 23 0 0 37 5 0 0 26 14 0 0 3 475 186 1 0 15 28 0 0 5 35 0 0 2 496 319 0 0 2 4 0 0 2 14 0 0 3 525 41 0 0 38 9 0 0 37 18 0 0 3 484 37 0 0 50 13 0 0 29 18 0 0 2 511 55 0 0 32 15 0 0 23 21 0 0 2 515 44 0 0 29 16 0 0 19 31 0 0 2 478 101 0 0 15 34 0 0 11 40 0 0 2 429 433 3 0 8 11 0 0 5 15 0 0 3 471 8 0 0 58 2 0 0 25 13 0 0 1 303 269 3 0 10 66 2 0 1 32 7 0 2 445 74 0 0 19 30 0 0 4 43 0 0 1 488 83 0 0 34 18 0 0 15 34 0 0 3 264 298 4 0 7 68 10 0 1 30 7 0 2 489 44 0 0 21 12 0 0 29 21 0 0 1 475 34 0 0 38 13 0 0 28 18 0 0 3 492 14 0 0 44 4 0 0 40 7 0 0 1 454 173 1 0 14 21 0 0 2 42 0 0 2 306 285 5 0 5 68 3 0 1 33 1 0 2 508 64 0 0 31 14 0 0 17 33 0 0 2 477 61 0 0 36 13 0 0 22 15 0 0 2 349 224 1 0 3 66 3 0 2 41 1 0 1 447 189 0 0 11 34 0 0 5 45 0 0 2 496 34 0 0 49 6 0 0 31 11 0 0 3 484 222 0 0 16 12 0 0 9 24 0 0 2 412 50 0 0 25 47 0 0 11 29 0 0 1 316 184 0 0 13 65 3 0 5 34 3 0 1 345 163 1 0 17 57 2 0 2 32 1 0 1 285 273 2 0 7 50 13 0 1 28 10 0 1 317 179 0 0 11 64 0 0 4 35 0 0 1 500 68 0 0 23 15 0 0 18 30 0 0 1 495 34 0 0 39 4 0 0 21 21 0 0 3 387 294 1 0 10 37 0 0 4 30 5 0 2 258 236 2 0 2 72 7 0 0 26 8 0 1 423 25 0 0 26 16 0 0 16 29 0 0 3 501 32 0 0 40 11 0 0 24 20 0 0 3 459 37 0 0 46 4 0 0 32 16 0 0 3 511 48 0 0 35 7 0 0 27 15 0 0 2 295 271 3 0 6 71 5 0 3 29 4 0 1 502 34 0 0 25 9 0 0 23 11 0 0 3 458 36 0 0 12 7 0 0 24 19 0 0 2 470 273 1 0 9 17 0 0 2 32 0 0 3 477 30 0 0 24 14 0 0 18 26 0 0 3 406 77 1 0 15 24 2 0 6 37 1 0 1 291 251 2 0 8 53 2 0 0 43 2 0 1 407 51 0 0 28 47 0 0 5 35 0 0 1 390 47 0 0 17 45 1 0 3 33 2 0 1 347 123 1 0 7 52 7 0 4 38 3 0 1 300 175 0 0 8 71 2 0 0 35 0 0 1 473 59 0 0 44 15 0 0 22 23 0 0 1 487 35 0 0 34 12 0 0 29 24 0 0 3 532 57 0 0 19 6 0 0 28 15 0 0 2 286 207 1 0 5 74 2 0 1 38 4 0 1 405 199 0 0 13 37 0 0 6 45 0 0 2 337 177 0 0 11 47 1 0 4 39 3 0 1 527 20 0 0 32 6 0 0 23 16 0 0 1 326 218 1 0 4 71 2 0 1 33 1 0 1 486 14 0 0 41 4 0 0 40 8 0 0 1 420 43 0 0 26 30 9 0 9 24 4 0 1 286 293 3 0 7 83 9 0 1 22 5 0 1 395 59 0 0 30 33 0 0 4 36 0 0 1 506 24 0 0 34 12 0 0 26 17 0 0 3 396 217 0 0 16 43 2 0 6 30 0 0 2 457 15 0 0 44 1 0 0 30 13 0 0 1 472 139 0 0 16 25 0 0 11 38 0 0 2 493 21 0 0 33 16 0 0 22 19 0 0 3 311 236 0 0 6 66 4 0 2 24 9 0 1 490 23 0 0 34 6 0 0 30 14 0 0 1 485 29 0 0 33 7 0 0 14 20 0 0 3 481 43 0 0 38 11 0 0 21 24 0 0 2 nnet/tests/MLP/loadtestresults.m0000644000175000017500000000310411475772132015676 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## Author: Michel D. Schmid function [cAr mData] = loadtestresults(strFileName) ## check range of input arguments error(nargchk(1,1,nargin)) i = 1; mData = []; cAr = {}; fid = fopen(strFileName,"rt"); # open read only strLine = fgetl(fid); while (!feof(fid)) # this means, while not eof [val, count] = sscanf (strLine, "%f"); if (count) mData = [mData; val']; else cAr{i} = strLine; endif strLine = fgetl(fid); i += 1; endwhile # here, the strLine contains the last row of a file # so do the complete coding once more [val, count] = sscanf (strLine, "%f"); if (count) mData = [mData; val']; else cAr{i} = strLine; endif fclose(fid); endfunctionnnet/tests/MLP/testExample1_1.m0000644000175000017500000000530611064216326015227 0ustar mikemike## Copyright (C) 2007 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, see ## . ## author: msd ## This file is used to test all the m-files inside ## the example1 directory. ## it exist for each m-file a corresponding dat-file with ## the numerical results of matlab ## actually, following m-files will be tested: ## A. One hidden layer ## ================== ## 1. mlp9_1_1_tansig ## 2. mlp9_2_1_tansig ## 3. mlp9_2_2_tansig ## 4. mlp9_2_3_tansig ## 5. mlp9_5_3_tansig ## ## B. Two hidden layer ## ================== ####### mlp9_1_1_tansig ###### %!shared cAr, mTestResults, simOut, line, fid %! diary log_test1_1 %! dir = "example1"; %! [cAr, mTestResults] = loadtestresults([dir "/mlp9_1_1_tansig.dat"]); %! preparedata9_x_1; %! [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs %! mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)... %! nHiddenNeurons = 1; %! nOutputNeurons = 1; %! MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse"); %! MLPnet.IW{1,1}(:) = 1.5; %! MLPnet.LW{2,1}(:) = 0.5; %! MLPnet.b{1,1}(:) = 1.5; %! MLPnet.b{2,1}(:) = 0.5; %! VV.P = mValiInput; %! VV.T = mValliOutput; %! VV.P = trastd(VV.P,cMeanInput,cStdInput); %! [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV); %! [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput); %! [simOut] = sim(net,mTestInputN); %!assert(simOut,mTestResults,0.0001) %! diary off ; %! fid = fopen("log_test1_1","r"); %! line = fgetl(fid); %! if (line==-1) %! error("no String in Line: Row 67"); %! endif %!assert(substr(line,16,1),substr(cAr{1,1},16,1)) %!assert(substr(line,27,7),substr(cAr{1,1},27,7)) %!assert(substr(line,47,7),substr(cAr{1,1},47,7)) %! line = fgetl(fid); %!assert(substr(line,16,1),substr(cAr{1,2},16,1)) %!assert(substr(line,27,7),substr(cAr{1,2},27,7)) %!assert(substr(line,48,6),substr(cAr{1,2},48,6)) %!assert(strcmp("TRAINLM, Validation stop.",substr(cAr{1,3},1,25))) %! fclose(fid); nnet/tests/MLP/preparedata9_x_1.m0000644000175000017500000000463611054775373015603 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("example1/mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets);nnet/tests/MLP/preparedata9_x_3.m0000644000175000017500000000471111054775373015577 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("example1/mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; mOutput = [mOutput; mOutput*4; mOutput*10]; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets);nnet/tests/MLP/preparedata9_x_x_1.m0000644000175000017500000000463611054775373016132 0ustar mikemike## Copyright (C) 2006 Michel D. Schmid ## ## ## 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; see the file COPYING. If not, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## author: msd ## load data mData = load("example1/mData.txt","mData"); mData = mData.mData; [nRows, nColumns] = size(mData); # this file contains 13 columns. The first 12 columns are the inputs # the last column is the output # remove column 4, 8 and 12 # 89 rows ## this neural network example isn't a mirror of "the real life work" ## it will be used to compare the results with MATLAB(TM) results. mOutput = mData(:,end); mInput = mData(:,1:end-1); mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12 ## now prepare data mInput = mInput'; mOutput = mOutput'; # now split the data matrix in 3 pieces, # train data, test data and validate data # the proportion should be about 1/2 train, # 1/3 test and 1/6 validate data # in this neural network we have 12 weights, # for each weight at least 3 train sets.. # (that's a rule of thumb like 1/2, 1/3 and 1/6) # 1/2 of 89 = 44.5; let's take 44 for training nTrainSets = floor(nRows/2); # now the rest of the sets are again 100% # ==> 2/3 for test sets and 1/3 for validate sets nTestSets = (nRows-nTrainSets)/3*2; nValiSets = nRows-nTrainSets-nTestSets; # now divide mInput & mOutput in the three sets mValiInput = mInput(:,1:nValiSets); mValliOutput = mOutput(:,1:nValiSets); mInput(:,1:nValiSets) = []; # delete validation set mOutput(:,1:nValiSets) = []; # delete validation set mTestInput = mInput(:,1:nTestSets); mTestOutput = mOutput(:,1:nTestSets); mInput(:,1:nTestSets) = []; # delete test set mOutput(:,1:nTestSets) = []; # delete test set mTrainInput = mInput(:,1:nTrainSets); mTrainOutput = mOutput(:,1:nTrainSets);