From 94da049a53423a9aca04d8376b75347ca7eadcbe Mon Sep 17 00:00:00 2001 From: Calvin Date: Tue, 26 Mar 2013 13:21:36 -0400 Subject: moved FEAST Libraries --- FEAST/FEAST/FSToolbox/BetaGamma.c | 188 ------ FEAST/FEAST/FSToolbox/CMIM.c | 142 ----- FEAST/FEAST/FSToolbox/CompileFEAST.m | 4 - FEAST/FEAST/FSToolbox/CondMI.c | 166 ----- FEAST/FEAST/FSToolbox/DISR.c | 182 ------ FEAST/FEAST/FSToolbox/FCBF.m | 58 -- FEAST/FEAST/FSToolbox/FSAlgorithms.h | 138 ----- FEAST/FEAST/FSToolbox/FSToolbox.h | 70 --- FEAST/FEAST/FSToolbox/FSToolboxMex.c | 290 --------- FEAST/FEAST/FSToolbox/FSToolboxMex.mexa64 | Bin 22290 -> 0 bytes FEAST/FEAST/FSToolbox/ICAP.c | 184 ------ FEAST/FEAST/FSToolbox/JMI.c | 177 ------ FEAST/FEAST/FSToolbox/MIM.m | 17 - FEAST/FEAST/FSToolbox/Makefile | 96 --- FEAST/FEAST/FSToolbox/README | 80 --- FEAST/FEAST/FSToolbox/RELIEF.m | 61 -- FEAST/FEAST/FSToolbox/feast.bib | 122 ---- FEAST/FEAST/FSToolbox/feast.m | 100 --- FEAST/FEAST/FSToolbox/license.txt | 32 - FEAST/FEAST/FSToolbox/mRMR_D.c | 170 ------ FEAST/FEAST/MIToolbox/ArrayOperations.c | 288 --------- FEAST/FEAST/MIToolbox/ArrayOperations.h | 88 --- FEAST/FEAST/MIToolbox/COPYING | 674 --------------------- FEAST/FEAST/MIToolbox/COPYING.LESSER | 165 ----- FEAST/FEAST/MIToolbox/CalculateProbability.c | 184 ------ FEAST/FEAST/MIToolbox/CalculateProbability.h | 80 --- FEAST/FEAST/MIToolbox/CompileMIToolbox.m | 4 - FEAST/FEAST/MIToolbox/Entropy.c | 130 ---- FEAST/FEAST/MIToolbox/Entropy.h | 71 --- FEAST/FEAST/MIToolbox/MIToolbox.h | 53 -- FEAST/FEAST/MIToolbox/MIToolbox.m | 83 --- FEAST/FEAST/MIToolbox/MIToolboxMex.c | 494 --------------- FEAST/FEAST/MIToolbox/MIToolboxMex.mexa64 | Bin 17663 -> 0 bytes FEAST/FEAST/MIToolbox/Makefile | 84 --- FEAST/FEAST/MIToolbox/MutualInformation.c | 95 --- FEAST/FEAST/MIToolbox/MutualInformation.h | 64 -- FEAST/FEAST/MIToolbox/README | 71 --- FEAST/FEAST/MIToolbox/RenyiEntropy.c | 191 ------ FEAST/FEAST/MIToolbox/RenyiEntropy.h | 68 --- FEAST/FEAST/MIToolbox/RenyiMIToolbox.m | 48 -- FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.c | 197 ------ FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 | Bin 17811 -> 0 bytes FEAST/FEAST/MIToolbox/RenyiMutualInformation.c | 95 --- FEAST/FEAST/MIToolbox/RenyiMutualInformation.h | 60 -- FEAST/FEAST/MIToolbox/cmi.m | 31 - FEAST/FEAST/MIToolbox/condh.m | 26 - .../MIToolbox/demonstration_algorithms/CMIM.m | 49 -- .../MIToolbox/demonstration_algorithms/CMIM_Mex.c | 158 ----- .../MIToolbox/demonstration_algorithms/DISR.m | 73 --- .../MIToolbox/demonstration_algorithms/DISR_Mex.c | 199 ------ .../MIToolbox/demonstration_algorithms/IAMB.m | 56 -- .../demonstration_algorithms/compile_demos.m | 3 - .../MIToolbox/demonstration_algorithms/mRMR_D.m | 69 --- .../demonstration_algorithms/mRMR_D_Mex.c | 184 ------ FEAST/FEAST/MIToolbox/h.m | 13 - FEAST/FEAST/MIToolbox/joint.m | 16 - FEAST/FEAST/MIToolbox/mi.m | 20 - FEAST/FSToolbox/BetaGamma.c | 188 ++++++ FEAST/FSToolbox/CMIM.c | 142 +++++ FEAST/FSToolbox/CompileFEAST.m | 4 + FEAST/FSToolbox/CondMI.c | 166 +++++ FEAST/FSToolbox/DISR.c | 182 ++++++ FEAST/FSToolbox/FCBF.m | 58 ++ FEAST/FSToolbox/FSAlgorithms.h | 138 +++++ FEAST/FSToolbox/FSToolbox.h | 70 +++ FEAST/FSToolbox/FSToolboxMex.c | 290 +++++++++ FEAST/FSToolbox/FSToolboxMex.mexa64 | Bin 0 -> 22290 bytes FEAST/FSToolbox/ICAP.c | 184 ++++++ FEAST/FSToolbox/JMI.c | 177 ++++++ FEAST/FSToolbox/MIM.m | 17 + FEAST/FSToolbox/Makefile | 96 +++ FEAST/FSToolbox/README | 80 +++ FEAST/FSToolbox/RELIEF.m | 61 ++ FEAST/FSToolbox/feast.bib | 122 ++++ FEAST/FSToolbox/feast.m | 100 +++ FEAST/FSToolbox/license.txt | 32 + FEAST/FSToolbox/mRMR_D.c | 170 ++++++ FEAST/MIToolbox/ArrayOperations.c | 288 +++++++++ FEAST/MIToolbox/ArrayOperations.h | 88 +++ FEAST/MIToolbox/COPYING | 674 +++++++++++++++++++++ FEAST/MIToolbox/COPYING.LESSER | 165 +++++ FEAST/MIToolbox/CalculateProbability.c | 184 ++++++ FEAST/MIToolbox/CalculateProbability.h | 80 +++ FEAST/MIToolbox/CompileMIToolbox.m | 4 + FEAST/MIToolbox/Entropy.c | 130 ++++ FEAST/MIToolbox/Entropy.h | 71 +++ FEAST/MIToolbox/MIToolbox.h | 53 ++ FEAST/MIToolbox/MIToolbox.m | 83 +++ FEAST/MIToolbox/MIToolboxMex.c | 494 +++++++++++++++ FEAST/MIToolbox/MIToolboxMex.mexa64 | Bin 0 -> 17663 bytes FEAST/MIToolbox/Makefile | 84 +++ FEAST/MIToolbox/MutualInformation.c | 95 +++ FEAST/MIToolbox/MutualInformation.h | 64 ++ FEAST/MIToolbox/README | 71 +++ FEAST/MIToolbox/RenyiEntropy.c | 191 ++++++ FEAST/MIToolbox/RenyiEntropy.h | 68 +++ FEAST/MIToolbox/RenyiMIToolbox.m | 48 ++ FEAST/MIToolbox/RenyiMIToolboxMex.c | 197 ++++++ FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 | Bin 0 -> 17811 bytes FEAST/MIToolbox/RenyiMutualInformation.c | 95 +++ FEAST/MIToolbox/RenyiMutualInformation.h | 60 ++ FEAST/MIToolbox/cmi.m | 31 + FEAST/MIToolbox/condh.m | 26 + FEAST/MIToolbox/demonstration_algorithms/CMIM.m | 49 ++ .../MIToolbox/demonstration_algorithms/CMIM_Mex.c | 158 +++++ FEAST/MIToolbox/demonstration_algorithms/DISR.m | 73 +++ .../MIToolbox/demonstration_algorithms/DISR_Mex.c | 199 ++++++ FEAST/MIToolbox/demonstration_algorithms/IAMB.m | 56 ++ .../demonstration_algorithms/compile_demos.m | 3 + FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m | 69 +++ .../demonstration_algorithms/mRMR_D_Mex.c | 184 ++++++ FEAST/MIToolbox/h.m | 13 + FEAST/MIToolbox/joint.m | 16 + FEAST/MIToolbox/mi.m | 20 + 114 files changed, 6461 insertions(+), 6461 deletions(-) delete mode 100644 FEAST/FEAST/FSToolbox/BetaGamma.c delete mode 100644 FEAST/FEAST/FSToolbox/CMIM.c delete mode 100644 FEAST/FEAST/FSToolbox/CompileFEAST.m delete mode 100644 FEAST/FEAST/FSToolbox/CondMI.c delete mode 100644 FEAST/FEAST/FSToolbox/DISR.c delete mode 100644 FEAST/FEAST/FSToolbox/FCBF.m delete mode 100644 FEAST/FEAST/FSToolbox/FSAlgorithms.h delete mode 100644 FEAST/FEAST/FSToolbox/FSToolbox.h delete mode 100644 FEAST/FEAST/FSToolbox/FSToolboxMex.c delete mode 100755 FEAST/FEAST/FSToolbox/FSToolboxMex.mexa64 delete mode 100644 FEAST/FEAST/FSToolbox/ICAP.c delete mode 100644 FEAST/FEAST/FSToolbox/JMI.c delete mode 100644 FEAST/FEAST/FSToolbox/MIM.m delete mode 100644 FEAST/FEAST/FSToolbox/Makefile delete mode 100644 FEAST/FEAST/FSToolbox/README delete mode 100644 FEAST/FEAST/FSToolbox/RELIEF.m delete mode 100644 FEAST/FEAST/FSToolbox/feast.bib delete mode 100644 FEAST/FEAST/FSToolbox/feast.m delete mode 100644 FEAST/FEAST/FSToolbox/license.txt delete mode 100644 FEAST/FEAST/FSToolbox/mRMR_D.c delete mode 100644 FEAST/FEAST/MIToolbox/ArrayOperations.c delete mode 100644 FEAST/FEAST/MIToolbox/ArrayOperations.h delete mode 100644 FEAST/FEAST/MIToolbox/COPYING delete mode 100644 FEAST/FEAST/MIToolbox/COPYING.LESSER delete mode 100644 FEAST/FEAST/MIToolbox/CalculateProbability.c delete mode 100644 FEAST/FEAST/MIToolbox/CalculateProbability.h delete mode 100644 FEAST/FEAST/MIToolbox/CompileMIToolbox.m delete mode 100644 FEAST/FEAST/MIToolbox/Entropy.c delete mode 100644 FEAST/FEAST/MIToolbox/Entropy.h delete mode 100644 FEAST/FEAST/MIToolbox/MIToolbox.h delete mode 100644 FEAST/FEAST/MIToolbox/MIToolbox.m delete mode 100644 FEAST/FEAST/MIToolbox/MIToolboxMex.c delete mode 100755 FEAST/FEAST/MIToolbox/MIToolboxMex.mexa64 delete mode 100644 FEAST/FEAST/MIToolbox/Makefile delete mode 100644 FEAST/FEAST/MIToolbox/MutualInformation.c delete mode 100644 FEAST/FEAST/MIToolbox/MutualInformation.h delete mode 100644 FEAST/FEAST/MIToolbox/README delete mode 100644 FEAST/FEAST/MIToolbox/RenyiEntropy.c delete mode 100644 FEAST/FEAST/MIToolbox/RenyiEntropy.h delete mode 100644 FEAST/FEAST/MIToolbox/RenyiMIToolbox.m delete mode 100644 FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.c delete mode 100755 FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 delete mode 100644 FEAST/FEAST/MIToolbox/RenyiMutualInformation.c delete mode 100644 FEAST/FEAST/MIToolbox/RenyiMutualInformation.h delete mode 100644 FEAST/FEAST/MIToolbox/cmi.m delete mode 100644 FEAST/FEAST/MIToolbox/condh.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/IAMB.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m delete mode 100644 FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c delete mode 100644 FEAST/FEAST/MIToolbox/h.m delete mode 100644 FEAST/FEAST/MIToolbox/joint.m delete mode 100644 FEAST/FEAST/MIToolbox/mi.m create mode 100644 FEAST/FSToolbox/BetaGamma.c create mode 100644 FEAST/FSToolbox/CMIM.c create mode 100644 FEAST/FSToolbox/CompileFEAST.m create mode 100644 FEAST/FSToolbox/CondMI.c create mode 100644 FEAST/FSToolbox/DISR.c create mode 100644 FEAST/FSToolbox/FCBF.m create mode 100644 FEAST/FSToolbox/FSAlgorithms.h create mode 100644 FEAST/FSToolbox/FSToolbox.h create mode 100644 FEAST/FSToolbox/FSToolboxMex.c create mode 100755 FEAST/FSToolbox/FSToolboxMex.mexa64 create mode 100644 FEAST/FSToolbox/ICAP.c create mode 100644 FEAST/FSToolbox/JMI.c create mode 100644 FEAST/FSToolbox/MIM.m create mode 100644 FEAST/FSToolbox/Makefile create mode 100644 FEAST/FSToolbox/README create mode 100644 FEAST/FSToolbox/RELIEF.m create mode 100644 FEAST/FSToolbox/feast.bib create mode 100644 FEAST/FSToolbox/feast.m create mode 100644 FEAST/FSToolbox/license.txt create mode 100644 FEAST/FSToolbox/mRMR_D.c create mode 100644 FEAST/MIToolbox/ArrayOperations.c create mode 100644 FEAST/MIToolbox/ArrayOperations.h create mode 100644 FEAST/MIToolbox/COPYING create mode 100644 FEAST/MIToolbox/COPYING.LESSER create mode 100644 FEAST/MIToolbox/CalculateProbability.c create mode 100644 FEAST/MIToolbox/CalculateProbability.h create mode 100644 FEAST/MIToolbox/CompileMIToolbox.m create mode 100644 FEAST/MIToolbox/Entropy.c create mode 100644 FEAST/MIToolbox/Entropy.h create mode 100644 FEAST/MIToolbox/MIToolbox.h create mode 100644 FEAST/MIToolbox/MIToolbox.m create mode 100644 FEAST/MIToolbox/MIToolboxMex.c create mode 100755 FEAST/MIToolbox/MIToolboxMex.mexa64 create mode 100644 FEAST/MIToolbox/Makefile create mode 100644 FEAST/MIToolbox/MutualInformation.c create mode 100644 FEAST/MIToolbox/MutualInformation.h create mode 100644 FEAST/MIToolbox/README create mode 100644 FEAST/MIToolbox/RenyiEntropy.c create mode 100644 FEAST/MIToolbox/RenyiEntropy.h create mode 100644 FEAST/MIToolbox/RenyiMIToolbox.m create mode 100644 FEAST/MIToolbox/RenyiMIToolboxMex.c create mode 100755 FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 create mode 100644 FEAST/MIToolbox/RenyiMutualInformation.c create mode 100644 FEAST/MIToolbox/RenyiMutualInformation.h create mode 100644 FEAST/MIToolbox/cmi.m create mode 100644 FEAST/MIToolbox/condh.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/CMIM.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c create mode 100644 FEAST/MIToolbox/demonstration_algorithms/DISR.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c create mode 100644 FEAST/MIToolbox/demonstration_algorithms/IAMB.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/compile_demos.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m create mode 100644 FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c create mode 100644 FEAST/MIToolbox/h.m create mode 100644 FEAST/MIToolbox/joint.m create mode 100644 FEAST/MIToolbox/mi.m (limited to 'FEAST') diff --git a/FEAST/FEAST/FSToolbox/BetaGamma.c b/FEAST/FEAST/FSToolbox/BetaGamma.c deleted file mode 100644 index 925ef8b..0000000 --- a/FEAST/FEAST/FSToolbox/BetaGamma.c +++ /dev/null @@ -1,188 +0,0 @@ -/******************************************************************************* -** betaGamma() implements the Beta-Gamma space from Brown (2009). -** This incoporates MIFS, CIFE, and CondRed. -** -** MIFS - "Using mutual information for selecting features in supervised neural net learning" -** R. Battiti, IEEE Transactions on Neural Networks, 1994 -** -** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" -** D. Lin and X. Tang, European Conference on Computer Vision (2006) -** -** The Beta Gamma space is explained in Brown (2009) and Brown et al. (2011) -** -** Initial Version - 13/06/2008 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" - -double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double betaParam, double gammaParam) -{ - double **feature2D = (double **) CALLOC_FUNC(noOfFeatures,sizeof(double *)); - - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double score, currentScore, totalFeatureMI; - int currentHighestFeature, arrayPosition; - - int i,j,m; - - /*********************************************************** - ** because the array is passed as - ** s a m p l e s - ** f - ** e - ** a - ** t - ** u - ** r - ** e - ** s - ** - ** this pulls out a pointer to the first sample of - ** each feature and stores it as a multidimensional array - ** so it can be indexed nicely - ***********************************************************/ - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix; i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - /*********************************************************** - ** SETUP COMPLETE - ** Algorithm starts here - ***********************************************************/ - - for (i = 0; i < noOfFeatures; i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /************* - ** Now we have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the JMI algorithm - *************/ - - for (i = 1; i < k; i++) - { - /************************************************************ - ** to ensure it selects some features - ** if this is zero then it will not pick features where the - ** redundancy is greater than the relevance - ************************************************************/ - score = -HUGE_VAL; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (!selectedFeatures[j]) - { - currentScore = classMI[j]; - totalFeatureMI = 0.0; - - for (m = 0; m < i; m++) - { - arrayPosition = m*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - featureMIMatrix[arrayPosition] = betaParam*calculateMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], noOfSamples); - - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double* conditionVector, int vectorLength);*/ - featureMIMatrix[arrayPosition] -= gammaParam*calculateConditionalMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], classColumn, noOfSamples); - }/*if not already known*/ - - totalFeatureMI += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - currentScore -= (totalFeatureMI); - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C++ indexes from 0 not 1*/ - }/*for number of selected features*/ - - return outputFeatures; -}/*BetaGamma(int,int,int,double[][],double[],double[],double,double)*/ - diff --git a/FEAST/FEAST/FSToolbox/CMIM.c b/FEAST/FEAST/FSToolbox/CMIM.c deleted file mode 100644 index 9ef21ad..0000000 --- a/FEAST/FEAST/FSToolbox/CMIM.c +++ /dev/null @@ -1,142 +0,0 @@ -/******************************************************************************* -** CMIM.c, implements a discrete version of the -** Conditional Mutual Information Maximisation criterion, using the fast -** exact implementation from -** -** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" -** F. Fleuret, JMLR (2004) -** -** Initial Version - 13/06/2008 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" - -double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values - **the class MI doubles as the partial score from the CMIM paper - */ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - /*in the CMIM paper, m = lastUsedFeature*/ - int *lastUsedFeature = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int)); - - double score, conditionalInfo; - int iMinus, currentFeature; - - double maxMI = 0.0; - int maxMICounter = -1; - - int j,i; - - double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < noOfFeatures;i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - outputFeatures[0] = maxMICounter; - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the CMIM algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - iMinus = i-1; - - for (j = 0; j < noOfFeatures; j++) - { - while ((classMI[j] > score) && (lastUsedFeature[j] < i)) - { - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ - currentFeature = (int) outputFeatures[lastUsedFeature[j]]; - conditionalInfo = calculateConditionalMutualInformation(feature2D[j],classColumn,feature2D[currentFeature],noOfSamples); - if (classMI[j] > conditionalInfo) - { - classMI[j] = conditionalInfo; - }/*reset classMI*/ - /*moved due to C indexing from 0 rather than 1*/ - lastUsedFeature[j] += 1; - }/*while partial score greater than score & not reached last feature*/ - if (classMI[j] > score) - { - score = classMI[j]; - outputFeatures[i] = j; - }/*if partial score still greater than score*/ - }/*for number of features*/ - }/*for the number of features to select*/ - - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - - FREE_FUNC(classMI); - FREE_FUNC(lastUsedFeature); - FREE_FUNC(feature2D); - - classMI = NULL; - lastUsedFeature = NULL; - feature2D = NULL; - - return outputFeatures; -}/*CMIM(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/FSToolbox/CompileFEAST.m b/FEAST/FEAST/FSToolbox/CompileFEAST.m deleted file mode 100644 index f5dad48..0000000 --- a/FEAST/FEAST/FSToolbox/CompileFEAST.m +++ /dev/null @@ -1,4 +0,0 @@ -%Compiles the FEAST Toolbox into a mex executable for use with MATLAB - -mex -I../MIToolbox FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c ../MIToolbox/MutualInformation.c ../MIToolbox/Entropy.c ../MIToolbox/CalculateProbability.c ../MIToolbox/ArrayOperations.c - diff --git a/FEAST/FEAST/FSToolbox/CondMI.c b/FEAST/FEAST/FSToolbox/CondMI.c deleted file mode 100644 index ea6f8ee..0000000 --- a/FEAST/FEAST/FSToolbox/CondMI.c +++ /dev/null @@ -1,166 +0,0 @@ -/******************************************************************************* -** CondMI.c, implements the CMI criterion using a greedy forward search -** -** Initial Version - 19/08/2010 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* for memcpy */ -#include - -/* MIToolbox includes */ -#include "MutualInformation.h" -#include "ArrayOperations.h" - -double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - - char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - double *conditionVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); - - int arrayPosition; - double mi, tripEntropy; - - int i,j,x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - for (i = 0; i < noOfFeatures;i++) - { - /*calculate mutual info - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - */ - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - memcpy(conditionVector,feature2D[maxMICounter],sizeof(double)*noOfSamples); - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the CondMI algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - currentHighestFeature = -1; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = 0.0; - totalFeatureMI = 0.0; - - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ - currentScore = calculateConditionalMutualInformation(feature2D[j],classColumn,conditionVector,noOfSamples); - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - outputFeatures[i] = currentHighestFeature; - - if (currentHighestFeature != -1) - { - selectedFeatures[currentHighestFeature] = 1; - mergeArrays(feature2D[currentHighestFeature],conditionVector,conditionVector,noOfSamples); - } - - }/*for the number of features to select*/ - - FREE_FUNC(classMI); - FREE_FUNC(conditionVector); - FREE_FUNC(feature2D); - FREE_FUNC(featureMIMatrix); - FREE_FUNC(selectedFeatures); - - classMI = NULL; - conditionVector = NULL; - feature2D = NULL; - featureMIMatrix = NULL; - selectedFeatures = NULL; - - return outputFeatures; -}/*CondMI(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/FSToolbox/DISR.c b/FEAST/FEAST/FSToolbox/DISR.c deleted file mode 100644 index 3ec7676..0000000 --- a/FEAST/FEAST/FSToolbox/DISR.c +++ /dev/null @@ -1,182 +0,0 @@ -/******************************************************************************* -** DISR.c, implements the Double Input Symmetrical Relevance criterion -** from -** -** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" -** P. Meyer and G. Bontempi, (2006) -** -** Initial Version - 13/06/2008 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" -#include "Entropy.h" -#include "ArrayOperations.h" - -double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - - char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - double *mergedVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); - - int arrayPosition; - double mi, tripEntropy; - - int i,j,x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - - for (i = 0; i < noOfFeatures;i++) - { - /*calculate mutual info - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - */ - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the DISR algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (x = 0; x < i; x++) - { - arrayPosition = x*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - /* - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - **double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); - */ - - mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); - mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); - tripEntropy = calculateJointEntropy(mergedVector, classColumn, noOfSamples); - - featureMIMatrix[arrayPosition] = mi / tripEntropy; - }/*if not already known*/ - currentScore += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - - FREE_FUNC(classMI); - FREE_FUNC(mergedVector); - FREE_FUNC(feature2D); - FREE_FUNC(featureMIMatrix); - FREE_FUNC(selectedFeatures); - - classMI = NULL; - mergedVector = NULL; - feature2D = NULL; - featureMIMatrix = NULL; - selectedFeatures = NULL; - - return outputFeatures; -}/*DISR(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/FSToolbox/FCBF.m b/FEAST/FEAST/FSToolbox/FCBF.m deleted file mode 100644 index dcaf3bf..0000000 --- a/FEAST/FEAST/FSToolbox/FCBF.m +++ /dev/null @@ -1,58 +0,0 @@ -function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold) -%function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold) -% -%Performs feature selection using the FCBF measure by Yu and Liu 2004. -% -%Instead of selecting a fixed number of features it provides a relevancy threshold and selects all -%features which score above that and are not redundant -% -% The license is in the license.txt provided. - -numFeatures = size(featureMatrix,2); -classScore = zeros(numFeatures,1); - -for i = 1:numFeatures - classScore(i) = SU(featureMatrix(:,i),classColumn); -end - -[classScore indexScore] = sort(classScore,1,'descend'); - -indexScore = indexScore(classScore > threshold); -classScore = classScore(classScore > threshold); - -if ~isempty(indexScore) - curPosition = 1; -else - curPosition = 0; -end - -while curPosition <= length(indexScore) - j = curPosition + 1; - curFeature = indexScore(curPosition); - while j <= length(indexScore) - scoreij = SU(featureMatrix(:,curFeature),featureMatrix(:,indexScore(j))); - if scoreij > classScore(j) - indexScore(j) = []; - classScore(j) = []; - else - j = j + 1; - end - end - curPosition = curPosition + 1; -end - -selectedFeatures = indexScore; - -end - -function [score] = SU(firstVector,secondVector) -%function [score] = SU(firstVector,secondVector) -% -%calculates SU = 2 * (I(X;Y)/(H(X) + H(Y))) - -hX = h(firstVector); -hY = h(secondVector); -iXY = mi(firstVector,secondVector); - -score = (2 * iXY) / (hX + hY); -end diff --git a/FEAST/FEAST/FSToolbox/FSAlgorithms.h b/FEAST/FEAST/FSToolbox/FSAlgorithms.h deleted file mode 100644 index e6ddba8..0000000 --- a/FEAST/FEAST/FSToolbox/FSAlgorithms.h +++ /dev/null @@ -1,138 +0,0 @@ -/******************************************************************************* -** -** FSAlgorithms.h -** Provides the function definitions for the list of algorithms implemented -** in the FSToolbox. -** -** Author: Adam Pocock -** Created: 27/06/2011 -** -** Copyright 2010/2011 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** Part of the FEAture Selection Toolbox (FEAST), please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -/******************************************************************************* - * All algorithms take an integer k which determines how many features to - * select, the number of samples and the number of features. Additionally each - * algorithm takes pointers to the data matrix, and the label vector, and - * a pointer to the output vector. The output vector should be pre-allocated - * with sizeof(double)*k bytes. - * - * Some algorithms take additional parameters, which given at the end of the - * standard parameter list. - * - * Each algorithm uses a forward search, and selects the feature which has - * the maxmimum MI with the labels first. - * - * All the algorithms except CMIM use an optimised variant which caches the - * previously calculated MI values. This trades space for time, but can - * allocate large amounts of memory. CMIM uses the optimised implementation - * given in Fleuret (2004). - *****************************************************************************/ - -#ifndef __FSAlgorithms_H -#define __FSAlgorithms_H - -/******************************************************************************* -** mRMR_D() implements the minimum Relevance Maximum Redundancy criterion -** using the difference variant, from -** -** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" -** H. Peng et al. IEEE Pattern Analysis and Machine Intelligence (PAMI) (2005) -*******************************************************************************/ -double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** CMIM() implements a discrete version of the -** Conditional Mutual Information Maximisation criterion, using the fast -** exact implementation from -** -** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" -** F. Fleuret, JMLR (2004) -*******************************************************************************/ -double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** JMI() implements the JMI criterion from -** -** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" -** H. Yang and J. Moody, NIPS (1999) -*******************************************************************************/ -double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** DISR() implements the Double Input Symmetrical Relevance criterion -** from -** -** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" -** P. Meyer and G. Bontempi, (2006) -*******************************************************************************/ -double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** ICAP() implements the Interaction Capping criterion from -** -** "Machine Learning Based on Attribute Interactions" -** A. Jakulin, PhD Thesis (2005) -*******************************************************************************/ -double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** CondMI() implements the CMI criterion using a greedy forward search -*******************************************************************************/ -double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** betaGamma() implements the Beta-Gamma space from Brown (2009). -** This incoporates MIFS, CIFE, and CondRed. -** -** MIFS - "Using mutual information for selecting features in supervised neural net learning" -** R. Battiti, IEEE Transactions on Neural Networks, 1994 -** -** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" -** D. Lin and X. Tang, European Conference on Computer Vision (2006) -** -** The Beta Gamma space is explained in our paper -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -*******************************************************************************/ -double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma); - -#endif diff --git a/FEAST/FEAST/FSToolbox/FSToolbox.h b/FEAST/FEAST/FSToolbox/FSToolbox.h deleted file mode 100644 index bf8662b..0000000 --- a/FEAST/FEAST/FSToolbox/FSToolbox.h +++ /dev/null @@ -1,70 +0,0 @@ -/******************************************************************************* ** -** FSToolbox.h -** Provides the header files and #defines to ensure compatibility with MATLAB -** and C/C++. By default it compiles to MATLAB, if COMPILE_C is defined it -** links to the C memory allocation functions. -** -** Author: Adam Pocock -** Created: 27/06/2011 -** -** Copyright 2010/2011 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** Part of the FEAture Selection Toolbox (FEAST), please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#ifndef __FSToolbox_H -#define __FSToolbox_H - -#include -#include - -#ifdef COMPILE_C - #define C_IMPLEMENTATION - #include - #include - #define CALLOC_FUNC calloc - #define FREE_FUNC free -#else - #define MEX_IMPLEMENTATION - #include "mex.h" - #define CALLOC_FUNC mxCalloc - #define FREE_FUNC mxFree - #define printf mexPrintf /*for Octave-3.2*/ -#endif - -#endif - diff --git a/FEAST/FEAST/FSToolbox/FSToolboxMex.c b/FEAST/FEAST/FSToolbox/FSToolboxMex.c deleted file mode 100644 index 73a9197..0000000 --- a/FEAST/FEAST/FSToolbox/FSToolboxMex.c +++ /dev/null @@ -1,290 +0,0 @@ -/******************************************************************************* -** FSToolboxMex.c is the entry point for the feature selection toolbox. -** It provides a MATLAB interface to the various selection algorithms. -** -** Initial Version - 27/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSToolbox.h" -#include "FSAlgorithms.h" -#include "Entropy.h" - -/****************************************************************************** -** entry point for the mex call -** nlhs - number of outputs -** plhs - pointer to array of outputs -** nrhs - number of inputs -** prhs - pointer to array of inputs -******************************************************************************/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /************************************************************* - ** this function takes 4-6 arguments: - ** flag = which algorithm to use, - ** k = number of features to select, - ** featureMatrix[][] = matrix of features, - ** classColumn[] = targets, - ** optionalParam1 = (path angle or beta value), - ** optionalParam2 = (gamma value), - ** the arguments should all be discrete integers. - ** and has one output: - ** selectedFeatures[] of size k - *************************************************************/ - - int flag, k; - double optionalParam1, optionalParam2; - int numberOfFeatures, numberOfSamples, numberOfTargets; - double *featureMatrix, *targets, *output, *outputFeatures; - - double entropyTest; - int i,j; - - /************************************************************ - ** number to function map - ** 1 = MIFS - ** 2 = mRMR - ** 3 = CMIM - ** 4 = JMI - ** 5 = DISR - ** 6 = CIFE - ** 7 = ICAP - ** 8 = CondRed - ** 9 = BetaGamma - ** 10 = CMI - *************************************************************/ - if (nlhs > 1) - { - printf("Incorrect number of output arguments"); - }/*if not 1 output*/ - if ((nrhs < 4) || (nrhs > 6)) - { - printf("Incorrect number of input arguments"); - return; - }/*if not 4-6 inputs*/ - - /*get the flag for which algorithm*/ - flag = (int) mxGetScalar(prhs[0]); - - /*get the number of features to select, cast out as it is a double*/ - k = (int) mxGetScalar(prhs[1]); - - numberOfFeatures = mxGetN(prhs[2]); - numberOfSamples = mxGetM(prhs[2]); - - numberOfTargets = mxGetM(prhs[3]); - - if (nrhs == 6) - { - optionalParam1 = (double) mxGetScalar(prhs[4]); - optionalParam2 = (double) mxGetScalar(prhs[5]); - } - else if (nrhs == 5) - { - optionalParam1 = (double) mxGetScalar(prhs[4]); - optionalParam2 = 0.0; - } - - if (numberOfTargets != numberOfSamples) - { - printf("Number of targets must match number of samples\n"); - printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); - - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - return; - }/*if size mismatch*/ - else if ((k < 1) || (k > numberOfFeatures)) - { - printf("You have requested k = %d features, which is not possible\n",k); - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - return; - } - else - { - featureMatrix = mxGetPr(prhs[2]); - targets = mxGetPr(prhs[3]); - - /*double calculateEntropy(double *dataVector, int vectorLength)*/ - entropyTest = calculateEntropy(targets,numberOfSamples); - if (entropyTest < 0.0000001) - { - printf("The class label Y has entropy of 0, therefore all mutual informations containing Y will be 0. No feature selection is performed\n"); - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - return; - } - else - { - /*printf("Flag = %d, k = %d, numFeatures = %d, numSamples = %d\n",flag,k,numberOfFeatures,numberOfSamples);*/ - switch (flag) - { - case 1: /* MIFS */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - if (nrhs == 4) - { - /* MIFS is Beta = 1, Gamma = 0 */ - optionalParam1 = 1.0; - optionalParam2 = 0.0; - } - - /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ - BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); - break; - } - case 2: /* mRMR */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void mRMR_D(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - mRMR_D(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - break; - } - case 3: /* CMIM */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void CMIM(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - CMIM(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - break; - } - case 4: /* JMI */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void JMI(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - JMI(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - break; - } - case 5: /* DISR */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void DISR(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - DISR(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - break; - } - case 6: /* CIFE */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /* CIFE is Beta = 1, Gamma = 1 */ - optionalParam1 = 1.0; - optionalParam2 = 1.0; - - /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ - BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); - break; - } - case 7: /* ICAP */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void ICAP(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output);*/ - ICAP(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - break; - } - case 8: /* CondRed */ - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /* CondRed is Beta = 0, Gamma = 1 */ - optionalParam1 = 0.0; - optionalParam2 = 1.0; - - /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ - BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); - break; - } - case 9: /* BetaGamma */ - { - if (nrhs != 6) - { - printf("Insufficient arguments specified for Beta Gamma FS\n"); - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - return; - } - else - { - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ - BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); - } - break; - } - case 10: /* CMI */ - { - output = (double *)mxCalloc(k,sizeof(double)); - - /*void CondMI(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - CondMI(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - - i = 0; - - while((output[i] != -1) && (i < k)) - { - i++; - } - - plhs[0] = mxCreateDoubleMatrix(i,1,mxREAL); - outputFeatures = (double *)mxGetPr(plhs[0]); - - for (j = 0; j < i; j++) - { - outputFeatures[j] = output[j] + 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - - mxFree(output); - output = NULL; - break; - } - }/*switch on flag*/ - return; - } - } -}/*mex function entry*/ diff --git a/FEAST/FEAST/FSToolbox/FSToolboxMex.mexa64 b/FEAST/FEAST/FSToolbox/FSToolboxMex.mexa64 deleted file mode 100755 index 0015254..0000000 Binary files a/FEAST/FEAST/FSToolbox/FSToolboxMex.mexa64 and /dev/null differ diff --git a/FEAST/FEAST/FSToolbox/ICAP.c b/FEAST/FEAST/FSToolbox/ICAP.c deleted file mode 100644 index 00953a7..0000000 --- a/FEAST/FEAST/FSToolbox/ICAP.c +++ /dev/null @@ -1,184 +0,0 @@ -/******************************************************************************* -** ICAP.c implements the Interaction Capping criterion from -** -** "Machine Learning Based on Attribute Interactions" -** A. Jakulin, PhD Thesis (2005) -** -** Initial Version - 19/08/2010 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" - -double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); - - /*separates out the features*/ - double **feature2D = (double **) CALLOC_FUNC(noOfFeatures,sizeof(double *)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - double *featureCMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double score, currentScore, totalFeatureInteraction, interactionInfo; - int currentHighestFeature, arrayPosition; - - int i, j, m; - - for (j = 0; j < noOfFeatures; j++) - feature2D[j] = featureMatrix + (int) j * noOfSamples; - - for (i = 0; i < sizeOfMatrix; i++) - { - featureMIMatrix[i] = -1; - featureCMIMatrix[i] = -1; - }/*for featureMIMatrix and featureCMIMatrix - blank to -1*/ - - /*SETUP COMPLETE*/ - /*Algorithm starts here*/ - - for (i = 0; i < noOfFeatures;i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /************* - ** Now we have populated the classMI array, and selected the highest - ** MI feature as the first output feature - *************/ - - for (i = 1; i < k; i++) - { - /********************************************************************** - ** to ensure it selects some features - **if this is zero then it will not pick features where the redundancy is greater than the - **relevance - **********************************************************************/ - score = -HUGE_VAL; - currentHighestFeature = 0; - currentScore = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (!selectedFeatures[j]) - { - currentScore = classMI[j]; - totalFeatureInteraction = 0.0; - - for (m = 0; m < i; m++) - { - arrayPosition = m*noOfFeatures + j; - - if (featureMIMatrix[arrayPosition] == -1) - { - /*work out interaction*/ - - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], noOfSamples); - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double* conditionVector, int vectorLength);*/ - featureCMIMatrix[arrayPosition] = calculateConditionalMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], classColumn, noOfSamples); - }/*if not already known*/ - - interactionInfo = featureCMIMatrix[arrayPosition] - featureMIMatrix[arrayPosition]; - - if (interactionInfo < 0) - { - totalFeatureInteraction += interactionInfo; - } - }/*for the number of already selected features*/ - - currentScore += totalFeatureInteraction; - - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - /*C++ indexes from 0 not 1, so we need to increment all the feature indices*/ - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; - }/*for number of selected features*/ - - FREE_FUNC(classMI); - FREE_FUNC(feature2D); - FREE_FUNC(featureMIMatrix); - FREE_FUNC(featureCMIMatrix); - FREE_FUNC(selectedFeatures); - - classMI = NULL; - feature2D = NULL; - featureMIMatrix = NULL; - featureCMIMatrix = NULL; - selectedFeatures = NULL; - - return outputFeatures; -}/*ICAP(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/FSToolbox/JMI.c b/FEAST/FEAST/FSToolbox/JMI.c deleted file mode 100644 index 30ef1bc..0000000 --- a/FEAST/FEAST/FSToolbox/JMI.c +++ /dev/null @@ -1,177 +0,0 @@ -/******************************************************************************* -** JMI.c implements the JMI criterion from -** -** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" -** H. Yang and J. Moody, NIPS (1999) -** -** Initial Version - 19/08/2010 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" -#include "ArrayOperations.h" - -double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - - char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - double *mergedVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); - - int arrayPosition; - double mi, tripEntropy; - - int i,j,x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - - for (i = 0; i < noOfFeatures;i++) - { - /*calculate mutual info - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - */ - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the JMI algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (x = 0; x < i; x++) - { - arrayPosition = x*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); - - featureMIMatrix[arrayPosition] = mi; - }/*if not already known*/ - currentScore += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - - FREE_FUNC(classMI); - FREE_FUNC(feature2D); - FREE_FUNC(featureMIMatrix); - FREE_FUNC(mergedVector); - FREE_FUNC(selectedFeatures); - - classMI = NULL; - feature2D = NULL; - featureMIMatrix = NULL; - mergedVector = NULL; - selectedFeatures = NULL; - - return outputFeatures; - -}/*JMI(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/FSToolbox/MIM.m b/FEAST/FEAST/FSToolbox/MIM.m deleted file mode 100644 index 31695e4..0000000 --- a/FEAST/FEAST/FSToolbox/MIM.m +++ /dev/null @@ -1,17 +0,0 @@ -function [selectedFeatures scoreVector] = MIM(k, data, labels) -%function [selectedFeatures scoreVector] = MIM(k, data, labels) -% -%Mutual information Maximisation -% -% The license is in the license.txt provided. - -numf = size(data,2); -classMI = zeros(numf,1); - -for n = 1 : numf - classMI(n) = mi(data(:,n),labels); -end - -[scoreVector index] = sort(classMI,'descend'); - -selectedFeatures = index(1:k); diff --git a/FEAST/FEAST/FSToolbox/Makefile b/FEAST/FEAST/FSToolbox/Makefile deleted file mode 100644 index c0806a9..0000000 --- a/FEAST/FEAST/FSToolbox/Makefile +++ /dev/null @@ -1,96 +0,0 @@ -# makefile for FEAST -# Author: Adam Pocock, apocock@cs.man.ac.uk -# Created: 29/06/2011 -# -# Part of the Feature Selection Toolbox, please reference -# "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -# Information Feature Selection" -# G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -# Journal of Machine Learning Research (JMLR), 2012 -# -# Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -# -# Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without modification, -# are permitted provided that the following conditions are met: -# -# - Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# - Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# - Neither the name of The University of Manchester nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -CXXFLAGS = -O3 -fPIC -COMPILER = gcc -MITOOLBOXPATH = ../MIToolbox/ -objects = mRMR_D.o CMIM.o JMI.o DISR.o CondMI.o ICAP.o BetaGamma.o - -libFSToolbox.so : $(objects) - $(COMPILER) $(CXXFLAGS) -lMIToolbox -L$(MITOOLBOXPATH) -shared -o libFSToolbox.so $(objects) - -mRMR_D.o: mRMR_D.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c mRMR_D.c -I$(MITOOLBOXPATH) - -CMIM.o: CMIM.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CMIM.c -I$(MITOOLBOXPATH) - -JMI.o: JMI.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c JMI.c -I$(MITOOLBOXPATH) - -DISR.o: DISR.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c DISR.c -I$(MITOOLBOXPATH) - -CondMI.o: CondMI.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CondMI.c -I$(MITOOLBOXPATH) - -ICAP.o: ICAP.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c ICAP.c -I$(MITOOLBOXPATH) - -BetaGamma.o: BetaGamma.c - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c BetaGamma.c -I$(MITOOLBOXPATH) - -.PHONY : debug -debug: - $(MAKE) libFSToolbox.so "CXXFLAGS = -g -DDEBUG -fPIC" - -.PHONY : x86 -x86: - $(MAKE) libFSToolbox.so "CXXFLAGS = -O3 -fPIC -m32" - -.PHONY : x64 -x64: - $(MAKE) libFSToolbox.so "CXXFLAGS = -O3 -fPIC -m64" - -.PHONY : matlab -matlab: - mex -I$(MITOOLBOXPATH) FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c $(MITOOLBOXPATH)MutualInformation.c $(MITOOLBOXPATH)Entropy.c $(MITOOLBOXPATH)CalculateProbability.c $(MITOOLBOXPATH)ArrayOperations.c - -.PHONY : matlab-debug -matlab-debug: - mex -g -I$(MITOOLBOXPATH) FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c $(MITOOLBOXPATH)MutualInformation.c $(MITOOLBOXPATH)Entropy.c $(MITOOLBOXPATH)CalculateProbability.c $(MITOOLBOXPATH)ArrayOperations.c - -.PHONY : intel -intel: - $(MAKE) libFSToolbox.so "COMPILER = icc" "CXXFLAGS = -O2 -fPIC -xHost" - -.PHONY : clean -clean: - rm *.o - rm libFSToolbox.so - diff --git a/FEAST/FEAST/FSToolbox/README b/FEAST/FEAST/FSToolbox/README deleted file mode 100644 index 1aae2d7..0000000 --- a/FEAST/FEAST/FSToolbox/README +++ /dev/null @@ -1,80 +0,0 @@ -FEAST v1.0 -A feature selection toolbox for C/C++ and MATLAB/OCTAVE - -FEAST provides implementations of common mutual information based filter -feature selection algorithms, and an implementation of RELIEF. All -functions expect discrete inputs (except RELIEF, which does not depend -on the MIToolbox), and they return the selected feature indices. These -implementations were developed to help our research into the similarities -between these algorithms, and our results are presented in the following paper: - - Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection - G.Brown, A.Pocock, M.Lujan, M.-J.Zhao - Journal of Machine Learning Research (in press, to appear 2012) - -All FEAST code is licensed under the BSD 3-Clause License. -If you use these implementations for academic research please cite the paper above. - -Contains implementations of: - mim, mrmr, mifs, cmim, jmi, disr, cife, icap, condred, cmi, relief, fcbf, betagamma - -References for these algorithms are provided in the accompanying feast.bib file (in BibTeX format). - -MATLAB Example (using "data" as our feature matrix, and "labels" as the class label vector): - ->> size(data) -ans = - (569,30) %% denoting 569 examples, and 30 features - ->> selectedIndices = feast('jmi',5,data,labels) %% selecting the top 5 features using the jmi algorithm -selectedIndices = - - 28 - 21 - 8 - 27 - 23 - ->> selectedIndices = feast('mrmr',10,data,labels) %% selecting the top 10 features using the mrmr algorithm -selectedIndices = - - 28 - 24 - 22 - 8 - 27 - 21 - 29 - 4 - 7 - 25 - ->> selectedIndices = feast('mifs',5,data,labels,0.7) %% selecting the top 5 features using the mifs algorithm with beta = 0.7 -selectedIndices = - - 28 - 24 - 22 - 20 - 29 - -The library is written in ANSI C for compatibility with the MATLAB mex compiler, -except for MIM, FCBF and RELIEF, which are written in MATLAB/OCTAVE script. - -If you wish to use MIM in a C program you can use the BetaGamma function with -Beta = 0, Gamma = 0, as this is equivalent to MIM (but slower than the other implementation). -MIToolbox is required to compile these algorithms, and these implementations -supercede the example implementations given in that package (they have more robust behaviour -when used with unexpected inputs). - -MIToolbox can be found at: - http://www.cs.man.ac.uk/~gbrown/mitoolbox/ -and v1.03 is included in the ZIP for the FEAST package. - -Compilation instructions: - MATLAB/OCTAVE - run CompileFEAST.m, - Linux C shared library - use the included makefile - -Update History -08/11/2011 - v1.0 - Public Release to complement the JMLR publication. - diff --git a/FEAST/FEAST/FSToolbox/RELIEF.m b/FEAST/FEAST/FSToolbox/RELIEF.m deleted file mode 100644 index 194ce7b..0000000 --- a/FEAST/FEAST/FSToolbox/RELIEF.m +++ /dev/null @@ -1,61 +0,0 @@ -% RELIEF - Kira & Rendell 1992 -% T is number of patterns to use -% Defaults to all patterns if not specified. -% -% The license is in the license.txt provided. -% -% function w = RELIEF( data, labels, T ) -% -function [w bestidx] = RELIEF ( data, labels, T ) - -if ~exist('T','var') - T=size(data,1); -end - -idx = randperm(length(labels)); -idx = idx(1:T); - -w = zeros(size(data,2),1); -for t = 1:T - - x = data(idx(t),:); - y = labels(idx(t)); - - %copy the x - protos = repmat(x, length(labels), 1); - %measure the distance from x to every other example - distances = [sqrt(sum((data-protos).^2,2)) labels]; - %sort them according to distances (find nearest neighbours) - [distances originalidx] = sortrows(distances,1); - - foundhit = false; hitidx=0; - foundmiss = false; missidx=0; - i=2; %start from the second one - while (~foundhit || ~foundmiss) - - if distances(i,2) == y - hitidx = originalidx(i); - foundhit = true; - end - if distances(i,2) ~= y - missidx = originalidx(i); - foundmiss = true; - end - - i=i+1; - - end - - alpha = 1/T; - for f = 1:size(data,2)%each feature - hitpenalty = (x(f)-data(hitidx,f)) / (max(data(:,f))-min(data(:,f))); - misspenalty = (x(f)-data(missidx,f)) / (max(data(:,f))-min(data(:,f))); - - w(f) = w(f) - alpha*hitpenalty^2 + alpha*misspenalty^2; - end - -end - -[~,bestidx] = sort(w,'descend'); - - diff --git a/FEAST/FEAST/FSToolbox/feast.bib b/FEAST/FEAST/FSToolbox/feast.bib deleted file mode 100644 index 2c58f0d..0000000 --- a/FEAST/FEAST/FSToolbox/feast.bib +++ /dev/null @@ -1,122 +0,0 @@ -% MIM (Mutual Information Maximisation) -@inproceedings{MIM, - author = {David D. Lewis}, - title = {Feature Selection and Feature Extraction for Text Categorization}, - booktitle = {In Proceedings of Speech and Natural Language Workshop}, - year = {1992}, - pages = {212--217}, - publisher = {Morgan Kaufmann} -} - -% MIFS (Mutual Information Feature Selection ) -@article{MIFS, - author={Battiti, R.}, - journal={Neural Networks, IEEE Transactions on}, - title={Using mutual information for selecting features in supervised neural net learning}, - year={1994}, - month={jul}, - volume={5}, - number={4}, - pages={537 -550}, - ISSN={1045-9227} -} - -% mRMR (minimum Redundancy Maximum Relevance) -@article{mRMR, - title={Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy}, - author={Peng, H. and Long, F. and Ding, C.}, - journal={IEEE Transactions on pattern analysis and machine intelligence}, - pages={1226--1238}, - year={2005}, - publisher={Published by the IEEE Computer Society} -} - -% CMIM (Conditional Mutual Information Maximisation) -@article{CMIM, - author = {Fleuret, Fran\c{c}ois}, - title = {Fast Binary Feature Selection with Conditional Mutual Information}, - journal = {Journal of Machine Learning Research}, - volume = {5}, - month = {December}, - year = {2004}, - issn = {1532-4435}, - pages = {1531--1555}, - publisher = {JMLR.org} -} - -% JMI (Joint Mutual Information) -@inproceedings{JMI, - title={Feature selection based on joint mutual information}, - author={Yang, H. and Moody, J.}, - booktitle={Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis}, - pages={22--25}, - year={1999} -} - -% DISR (Double Input Symmetrical Relevance) -@incollection{DISR, - author = {Meyer, Patrick and Bontempi, Gianluca}, - title = {On the Use of Variable Complementarity for Feature Selection in Cancer Classification}, - booktitle = {Applications of Evolutionary Computing}, - publisher = {Springer Berlin / Heidelberg}, - pages = {91-102}, - volume = {3907}, - url = {http://dx.doi.org/10.1007/11732242_9}, - year = {2006} -} - -% ICAP (Interaction Capping) -@article{ICAP, - title={Machine learning based on attribute interactions}, - author={Jakulin, A.}, - journal={Fakulteta za racunalni{\v{s}}tvo in informatiko, Univerza v Ljubljani}, - year={2005} -} - -% CIFE (Conditional Informative Feature Extraction) -@incollection{CIFE - author = {Lin, Dahua and Tang, Xiaoou}, - title = {Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion}, - booktitle = {Computer Vision – ECCV 2006}, - series = {Lecture Notes in Computer Science}, - publisher = {Springer Berlin / Heidelberg}, - pages = {68-82}, - volume = {3951}, - url = {http://dx.doi.org/10.1007/11744023_6}, - year = {2006} -} - -% Beta Gamma Space -@inproceedings{BetaGamma, - title={A new perspective for information theoretic feature selection}, - author={Brown, G.}, - booktitle={12th International Conference on Artificial Intelligence and Statistics}, - volume={5}, - pages={49--56}, - year={2009} -} - -% FCBF (Fast Correlation-Based Filter) -@article{FCBF, - author = {Yu, Lei and Liu, Huan}, - title = {Efficient Feature Selection via Analysis of Relevance and Redundancy}, - journal = {Journal of Machine Learning Research}, - volume = {5}, - year = {2004}, - issn = {1532-4435}, - pages = {1205--1224}, - publisher = {JMLR.org} -} - -% RELIEF -@inproceedings{RELIEF, - author = {Kira, Kenji and Rendell, Larry A.}, - title = {The feature selection problem: traditional methods and a new algorithm}, - booktitle = {Proceedings of the tenth national conference on Artificial intelligence}, - series = {AAAI'92}, - year = {1992}, - pages = {129--134}, - publisher = {AAAI Press} -} - - diff --git a/FEAST/FEAST/FSToolbox/feast.m b/FEAST/FEAST/FSToolbox/feast.m deleted file mode 100644 index 96a685e..0000000 --- a/FEAST/FEAST/FSToolbox/feast.m +++ /dev/null @@ -1,100 +0,0 @@ -function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin) -%function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin) -% -%Provides access to the feature selection algorithms in FSToolboxMex -% -%Expects the features to be columns of the data matrix, and -%requires that both the features and labels are integers. -% -%Algorithms are called as follows -% -%[selectedFeatures] = feast('algName',numToSelect,data,labels) -% where algName is: -% mim, mrmr, cmim, jmi, disr, cife, icap, condred, cmi, relief -% -%[selectedFeatures] = feast('algName',numToSelect,data,labels,beta) -% where algName is: -% mifs (defaults to beta = 1.0 if unspecified) -% -%[selectedFeatures] = feast('algName',numToSelect,data,labels,beta,gamma) -% where algName is: -% betagamma -%[selectedFeatures] = feast('algName',numToSelect,data,labels,threshold) -% where algName is: -% fcbf (note this ignores the numToSelect) -% -% The license is in the license.txt provided. - - -%Internal FSToolbox Criteria to number mapping -%MIFS = 1 -%mRMR = 2 -%CMIM = 3 -%JMI = 4 -%DISR = 5 -%CIFE = 6 -%ICAP = 7 -%CondRed = 8 -%BetaGamma = 9 -%CMI = 10 -% - -if ((numToSelect < 1) || (numToSelect > size(data,2))) - error(['You have requested ' num2str(numToSelect) ' features, which is not possible']); -end - -finiteDataCount = sum(sum(isfinite(data))); -finiteLabelsCount = sum(sum(isfinite(labels))); - -totalData = numel(data); -totalLabels = numel(labels); - -if ((finiteDataCount ~= totalData) || (finiteLabelsCount ~= totalLabels)) - error(['Some elements are NaNs or infinite. Please check your data']); -end - -if (strcmpi(criteria,'mim')) - selectedFeatures = MIM(numToSelect,data,labels); -elseif (strcmpi(criteria,'mifs')) - if (nargin == 4) - beta = 1; - else - beta = varargin{1}; - end - selectedFeatures = FSToolboxMex(1,numToSelect,data,labels,beta); -elseif (strcmpi(criteria,'mrmr')) - selectedFeatures = FSToolboxMex(2,numToSelect,data,labels); -elseif (strcmpi(criteria,'cmim')) - selectedFeatures = FSToolboxMex(3,numToSelect,data,labels); -elseif (strcmpi(criteria,'jmi')) - selectedFeatures = FSToolboxMex(4,numToSelect,data,labels); -elseif (strcmpi(criteria,'disr')) - selectedFeatures = FSToolboxMex(5,numToSelect,data,labels); -elseif ((strcmpi(criteria,'cife')) || (strcmpi(criteria,'fou'))) - selectedFeatures = FSToolboxMex(6,numToSelect,data,labels); -elseif (strcmpi(criteria,'icap')) - selectedFeatures = FSToolboxMex(7,numToSelect,data,labels); -elseif (strcmpi(criteria,'condred')) - selectedFeatures = FSToolboxMex(8,numToSelect,data,labels); -elseif (strcmpi(criteria,'betagamma')) - if (nargin ~= 6) - error('BetaGamma criteria expects a beta and a gamma'); - else - beta = varargin{1}; - gamma = varargin{2}; - end - selectedFeatures = FSToolboxMex(9,numToSelect,data,labels,beta,gamma); -elseif (strcmpi(criteria,'cmi')) - selectedFeatures = FSToolboxMex(10,numToSelect,data,labels); -elseif (strcmpi(criteria,'fcbf')) - if (nargin == 4) - error('Threshold for FCBF not supplied'); - else - selectedFeatures = FCBF(data,labels,varargin{1}); - end -elseif (strcmpi(criteria,'relief')) - [tmp selectedFeatures] = RELIEF(data,labels); -else - selectedFeatures = []; - disp(['Unrecognised criteria ' criteria]); -end diff --git a/FEAST/FEAST/FSToolbox/license.txt b/FEAST/FEAST/FSToolbox/license.txt deleted file mode 100644 index 798960e..0000000 --- a/FEAST/FEAST/FSToolbox/license.txt +++ /dev/null @@ -1,32 +0,0 @@ -This is FEAST (a FEAture Selection Toolbox for C and MATLAB) -if you use this code in academic work could you please reference: -"Conditional Likelihood Maximisation: A Unifying Framework for Mutual -Information Feature Selection" -G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -Journal of Machine Learning Research (JMLR), 2011 - -Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - - Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - - Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - Neither the name of The University of Manchester nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/FEAST/FEAST/FSToolbox/mRMR_D.c b/FEAST/FEAST/FSToolbox/mRMR_D.c deleted file mode 100644 index 3eeb2a4..0000000 --- a/FEAST/FEAST/FSToolbox/mRMR_D.c +++ /dev/null @@ -1,170 +0,0 @@ -/******************************************************************************* -** mRMR_D.c implements the minimum Relevance Maximum Redundancy criterion -** using the difference variant, from -** -** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" -** H. Peng et al. IEEE PAMI (2005) -** -** Initial Version - 13/06/2008 -** Updated - 23/06/2011 -** -** Author - Adam Pocock -** -** Part of the Feature Selection Toolbox, please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -#include "FSAlgorithms.h" -#include "FSToolbox.h" - -/* MIToolbox includes */ -#include "MutualInformation.h" - -double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) -{ - double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); - /*holds the class MI values*/ - double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); - int *selectedFeatures = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int)); - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - /*init variables*/ - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - int arrayPosition, i, j, x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - - for (i = 0; i < noOfFeatures;i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /************* - ** Now we have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the mRMR-D algorithm - *************/ - - for (i = 1; i < k; i++) - { - /**************************************************** - ** to ensure it selects some features - **if this is zero then it will not pick features where the redundancy is greater than the - **relevance - ****************************************************/ - score = -HUGE_VAL; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = classMI[j]; - totalFeatureMI = 0.0; - - for (x = 0; x < i; x++) - { - arrayPosition = x*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - /*work out intra MI*/ - - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[x]], feature2D[j], noOfSamples); - } - - totalFeatureMI += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - currentScore -= (totalFeatureMI/i); - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - - FREE_FUNC(classMI); - FREE_FUNC(feature2D); - FREE_FUNC(featureMIMatrix); - FREE_FUNC(selectedFeatures); - - classMI = NULL; - feature2D = NULL; - featureMIMatrix = NULL; - selectedFeatures = NULL; - - return outputFeatures; -}/*mRMR(int,int,int,double[][],double[],double[])*/ - diff --git a/FEAST/FEAST/MIToolbox/ArrayOperations.c b/FEAST/FEAST/MIToolbox/ArrayOperations.c deleted file mode 100644 index 00a8324..0000000 --- a/FEAST/FEAST/MIToolbox/ArrayOperations.c +++ /dev/null @@ -1,288 +0,0 @@ -/******************************************************************************* -** ArrayOperations.cpp -** Part of the mutual information toolbox -** -** Contains functions to floor arrays, and to merge arrays into a joint -** state. -** -** Author: Adam Pocock -** Created 17/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "ArrayOperations.h" - -void printDoubleVector(double *vector, int vectorLength) -{ - int i; - for (i = 0; i < vectorLength; i++) - { - if (vector[i] > 0) - printf("Val at i=%d, is %f\n",i,vector[i]); - }/*for number of items in vector*/ -}/*printDoubleVector(double*,int)*/ - -void printIntVector(int *vector, int vectorLength) -{ - int i; - for (i = 0; i < vectorLength; i++) - { - printf("Val at i=%d, is %d\n",i,vector[i]); - }/*for number of items in vector*/ -}/*printIntVector(int*,int)*/ - -int numberOfUniqueValues(double *featureVector, int vectorLength) -{ - int uniqueValues = 0; - double *valuesArray = (double *) CALLOC_FUNC(vectorLength,sizeof(double)); - - int found = 0; - int j = 0; - int i; - - for (i = 0; i < vectorLength; i++) - { - found = 0; - j = 0; - while ((j < uniqueValues) && (found == 0)) - { - if (valuesArray[j] == featureVector[i]) - { - found = 1; - featureVector[i] = (double) (j+1); - } - j++; - } - if (!found) - { - valuesArray[uniqueValues] = featureVector[i]; - uniqueValues++; - featureVector[i] = (double) uniqueValues; - } - }/*for vectorlength*/ - - FREE_FUNC(valuesArray); - valuesArray = NULL; - - return uniqueValues; -}/*numberOfUniqueValues(double*,int)*/ - -/******************************************************************************* -** normaliseArray takes an input vector and writes an output vector -** which is a normalised version of the input, and returns the number of states -** A normalised array has min value = 0, max value = number of states -** and all values are integers -** -** length(inputVector) == length(outputVector) == vectorLength otherwise there -** is a memory leak -*******************************************************************************/ -int normaliseArray(double *inputVector, int *outputVector, int vectorLength) -{ - int minVal = 0; - int maxVal = 0; - int currentValue; - int i; - - if (vectorLength > 0) - { - minVal = (int) floor(inputVector[0]); - maxVal = (int) floor(inputVector[0]); - - for (i = 0; i < vectorLength; i++) - { - currentValue = (int) floor(inputVector[i]); - outputVector[i] = currentValue; - - if (currentValue < minVal) - { - minVal = currentValue; - } - - if (currentValue > maxVal) - { - maxVal = currentValue; - } - }/*for loop over vector*/ - - for (i = 0; i < vectorLength; i++) - { - outputVector[i] = outputVector[i] - minVal; - } - - maxVal = (maxVal - minVal) + 1; - } - - return maxVal; -}/*normaliseArray(double*,double*,int)*/ - - -/******************************************************************************* -** mergeArrays takes in two arrays and writes the joint state of those arrays -** to the output vector, returning the number of joint states -** -** the length of the vectors must be the same and equal to vectorLength -** outputVector must be malloc'd before calling this function -*******************************************************************************/ -int mergeArrays(double *firstVector, double *secondVector, double *outputVector, int vectorLength) -{ - int *firstNormalisedVector; - int *secondNormalisedVector; - int firstNumStates; - int secondNumStates; - int i; - int *stateMap; - int stateCount; - int curIndex; - - firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - - firstNumStates = normaliseArray(firstVector,firstNormalisedVector,vectorLength); - secondNumStates = normaliseArray(secondVector,secondNormalisedVector,vectorLength); - - /* - ** printVector(firstNormalisedVector,vectorLength); - ** printVector(secondNormalisedVector,vectorLength); - */ - stateMap = (int *) CALLOC_FUNC(firstNumStates*secondNumStates,sizeof(int)); - stateCount = 1; - for (i = 0; i < vectorLength; i++) - { - curIndex = firstNormalisedVector[i] + (secondNormalisedVector[i] * firstNumStates); - if (stateMap[curIndex] == 0) - { - stateMap[curIndex] = stateCount; - stateCount++; - } - outputVector[i] = stateMap[curIndex]; - } - - FREE_FUNC(firstNormalisedVector); - FREE_FUNC(secondNormalisedVector); - FREE_FUNC(stateMap); - - firstNormalisedVector = NULL; - secondNormalisedVector = NULL; - stateMap = NULL; - - /*printVector(outputVector,vectorLength);*/ - return stateCount; -}/*mergeArrays(double *,double *,double *, int, bool)*/ - -int mergeArraysArities(double *firstVector, int numFirstStates, double *secondVector, int numSecondStates, double *outputVector, int vectorLength) -{ - int *firstNormalisedVector; - int *secondNormalisedVector; - int i; - int totalStates; - int firstStateCheck, secondStateCheck; - - firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - - firstStateCheck = normaliseArray(firstVector,firstNormalisedVector,vectorLength); - secondStateCheck = normaliseArray(secondVector,secondNormalisedVector,vectorLength); - - if ((firstStateCheck <= numFirstStates) && (secondStateCheck <= numSecondStates)) - { - for (i = 0; i < vectorLength; i++) - { - outputVector[i] = firstNormalisedVector[i] + (secondNormalisedVector[i] * numFirstStates) + 1; - } - totalStates = numFirstStates * numSecondStates; - } - else - { - totalStates = -1; - } - - FREE_FUNC(firstNormalisedVector); - FREE_FUNC(secondNormalisedVector); - - firstNormalisedVector = NULL; - secondNormalisedVector = NULL; - - return totalStates; -}/*mergeArraysArities(double *,int,double *,int,double *,int)*/ - -int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength) -{ - int i = 0; - int currentIndex; - int currentNumStates; - int *normalisedVector; - - if (matrixWidth > 1) - { - currentNumStates = mergeArrays(inputMatrix, (inputMatrix + vectorLength), outputVector,vectorLength); - for (i = 2; i < matrixWidth; i++) - { - currentIndex = i * vectorLength; - currentNumStates = mergeArrays(outputVector,(inputMatrix + currentIndex),outputVector,vectorLength); - } - } - else - { - normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - currentNumStates = normaliseArray(inputMatrix,normalisedVector,vectorLength); - for (i = 0; i < vectorLength; i++) - { - outputVector[i] = inputMatrix[i]; - } - } - - return currentNumStates; -}/*mergeMultipleArrays(double *, double *, int, int, bool)*/ - - -int mergeMultipleArraysArities(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength) -{ - int i = 0; - int currentIndex; - int currentNumStates; - int *normalisedVector; - - if (matrixWidth > 1) - { - currentNumStates = mergeArraysArities(inputMatrix, arities[0], (inputMatrix + vectorLength), arities[1], outputVector,vectorLength); - for (i = 2; i < matrixWidth; i++) - { - currentIndex = i * vectorLength; - currentNumStates = mergeArraysArities(outputVector,currentNumStates,(inputMatrix + currentIndex),arities[i],outputVector,vectorLength); - if (currentNumStates == -1) - break; - } - } - else - { - normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - currentNumStates = normaliseArray(inputMatrix,normalisedVector,vectorLength); - for (i = 0; i < vectorLength; i++) - { - outputVector[i] = inputMatrix[i]; - } - } - - return currentNumStates; -}/*mergeMultipleArraysArities(double *, double *, int, int, bool)*/ - - diff --git a/FEAST/FEAST/MIToolbox/ArrayOperations.h b/FEAST/FEAST/MIToolbox/ArrayOperations.h deleted file mode 100644 index 3cc9025..0000000 --- a/FEAST/FEAST/MIToolbox/ArrayOperations.h +++ /dev/null @@ -1,88 +0,0 @@ -/******************************************************************************* -** ArrayOperations.h -** Part of the mutual information toolbox -** -** Contains functions to floor arrays, and to merge arrays into a joint -** state. -** -** Author: Adam Pocock -** Created 17/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __ArrayOperations_H -#define __ArrayOperations_H - -#ifdef __cplusplus -extern "C" { -#endif - -/******************************************************************************* -** Simple print function for debugging -*******************************************************************************/ -void printDoubleVector(double *vector, int vectorLength); - -void printIntVector(int *vector, int vectorLength); - -/******************************************************************************* -** numberOfUniqueValues finds the number of unique values in an array by -** repeatedly iterating through the array and checking if a value has been -** seen previously -*******************************************************************************/ -int numberOfUniqueValues(double *featureVector, int vectorLength); - -/******************************************************************************* -** normaliseArray takes an input vector and writes an output vector -** which is a normalised version of the input, and returns the number of states -** A normalised array has min value = 0, max value = number of states -** and all values are integers -** -** length(inputVector) == length(outputVector) == vectorLength otherwise there -** is a memory leak -*******************************************************************************/ -int normaliseArray(double *inputVector, int *outputVector, int vectorLength); - -/******************************************************************************* -** mergeArrays takes in two arrays and writes the joint state of those arrays -** to the output vector -** -** the length of the vectors must be the same and equal to vectorLength -*******************************************************************************/ -int mergeArrays(double *firstVector, double *secondVector, double *outputVector, int vectorLength); -int mergeArraysArities(double *firstVector, int numFirstStates, double *secondVector, int numSecondStates, double *outputVector, int vectorLength); - -/******************************************************************************* -** mergeMultipleArrays takes in a matrix and repeatedly merges the matrix using -** merge arrays and writes the joint state of that matrix -** to the output vector -** -** the length of the vectors must be the same and equal to vectorLength -** matrixWidth = the number of columns in the matrix -*******************************************************************************/ -int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength); -int mergeMultipleArraysArities(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength); - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/COPYING b/FEAST/FEAST/MIToolbox/COPYING deleted file mode 100644 index 94a9ed0..0000000 --- a/FEAST/FEAST/MIToolbox/COPYING +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 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 GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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See the -** GNU Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "ArrayOperations.h" -#include "CalculateProbability.h" - -JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector, int vectorLength) -{ - int *firstNormalisedVector; - int *secondNormalisedVector; - int *firstStateCounts; - int *secondStateCounts; - int *jointStateCounts; - double *firstStateProbs; - double *secondStateProbs; - double *jointStateProbs; - int firstNumStates; - int secondNumStates; - int jointNumStates; - int i; - double length = vectorLength; - JointProbabilityState state; - - firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - - firstNumStates = normaliseArray(firstVector,firstNormalisedVector,vectorLength); - secondNumStates = normaliseArray(secondVector,secondNormalisedVector,vectorLength); - jointNumStates = firstNumStates * secondNumStates; - - firstStateCounts = (int *) CALLOC_FUNC(firstNumStates,sizeof(int)); - secondStateCounts = (int *) CALLOC_FUNC(secondNumStates,sizeof(int)); - jointStateCounts = (int *) CALLOC_FUNC(jointNumStates,sizeof(int)); - - firstStateProbs = (double *) CALLOC_FUNC(firstNumStates,sizeof(double)); - secondStateProbs = (double *) CALLOC_FUNC(secondNumStates,sizeof(double)); - jointStateProbs = (double *) CALLOC_FUNC(jointNumStates,sizeof(double)); - - /* optimised version, less numerically stable - double fractionalState = 1.0 / vectorLength; - - for (i = 0; i < vectorLength; i++) - { - firstStateProbs[firstNormalisedVector[i]] += fractionalState; - secondStateProbs[secondNormalisedVector[i]] += fractionalState; - jointStateProbs[secondNormalisedVector[i] * firstNumStates + firstNormalisedVector[i]] += fractionalState; - } - */ - - /* Optimised for number of FP operations now O(states) instead of O(vectorLength) */ - for (i = 0; i < vectorLength; i++) - { - firstStateCounts[firstNormalisedVector[i]] += 1; - secondStateCounts[secondNormalisedVector[i]] += 1; - jointStateCounts[secondNormalisedVector[i] * firstNumStates + firstNormalisedVector[i]] += 1; - } - - for (i = 0; i < firstNumStates; i++) - { - firstStateProbs[i] = firstStateCounts[i] / length; - } - - for (i = 0; i < secondNumStates; i++) - { - secondStateProbs[i] = secondStateCounts[i] / length; - } - - for (i = 0; i < jointNumStates; i++) - { - jointStateProbs[i] = jointStateCounts[i] / length; - } - - FREE_FUNC(firstNormalisedVector); - FREE_FUNC(secondNormalisedVector); - FREE_FUNC(firstStateCounts); - FREE_FUNC(secondStateCounts); - FREE_FUNC(jointStateCounts); - - firstNormalisedVector = NULL; - secondNormalisedVector = NULL; - firstStateCounts = NULL; - secondStateCounts = NULL; - jointStateCounts = NULL; - - /* - **typedef struct - **{ - ** double *jointProbabilityVector; - ** int numJointStates; - ** double *firstProbabilityVector; - ** int numFirstStates; - ** double *secondProbabilityVector; - ** int numSecondStates; - **} JointProbabilityState; - */ - - state.jointProbabilityVector = jointStateProbs; - state.numJointStates = jointNumStates; - state.firstProbabilityVector = firstStateProbs; - state.numFirstStates = firstNumStates; - state.secondProbabilityVector = secondStateProbs; - state.numSecondStates = secondNumStates; - - return state; -}/*calculateJointProbability(double *,double *, int)*/ - -ProbabilityState calculateProbability(double *dataVector, int vectorLength) -{ - int *normalisedVector; - int *stateCounts; - double *stateProbs; - int numStates; - /*double fractionalState;*/ - ProbabilityState state; - int i; - double length = vectorLength; - - normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); - - numStates = normaliseArray(dataVector,normalisedVector,vectorLength); - - stateCounts = (int *) CALLOC_FUNC(numStates,sizeof(int)); - stateProbs = (double *) CALLOC_FUNC(numStates,sizeof(double)); - - /* optimised version, may have floating point problems - fractionalState = 1.0 / vectorLength; - - for (i = 0; i < vectorLength; i++) - { - stateProbs[normalisedVector[i]] += fractionalState; - } - */ - - /* Optimised for number of FP operations now O(states) instead of O(vectorLength) */ - for (i = 0; i < vectorLength; i++) - { - stateCounts[normalisedVector[i]] += 1; - } - - for (i = 0; i < numStates; i++) - { - stateProbs[i] = stateCounts[i] / length; - } - - FREE_FUNC(stateCounts); - FREE_FUNC(normalisedVector); - - stateCounts = NULL; - normalisedVector = NULL; - - state.probabilityVector = stateProbs; - state.numStates = numStates; - - return state; -}/*calculateProbability(double *,int)*/ - diff --git a/FEAST/FEAST/MIToolbox/CalculateProbability.h b/FEAST/FEAST/MIToolbox/CalculateProbability.h deleted file mode 100644 index d5e9d3e..0000000 --- a/FEAST/FEAST/MIToolbox/CalculateProbability.h +++ /dev/null @@ -1,80 +0,0 @@ -/******************************************************************************* -** CalculateProbability.h -** Part of the mutual information toolbox -** -** Contains functions to calculate the probability of each state in the array -** and to calculate the probability of the joint state of two arrays -** -** Author: Adam Pocock -** Created 17/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __CalculateProbability_H -#define __CalculateProbability_H - -#ifdef __cplusplus -extern "C" { -#endif - -typedef struct jpState -{ - double *jointProbabilityVector; - int numJointStates; - double *firstProbabilityVector; - int numFirstStates; - double *secondProbabilityVector; - int numSecondStates; -} JointProbabilityState; - -typedef struct pState -{ - double *probabilityVector; - int numStates; -} ProbabilityState; - -/******************************************************************************* -** calculateJointProbability returns the joint probability vector of two vectors -** and the marginal probability vectors in a struct. -** It is the base operation for all information theory calculations involving -** two or more variables. -** -** length(firstVector) == length(secondVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector, int vectorLength); - -/******************************************************************************* -** calculateProbability returns the probability vector from one vector. -** It is the base operation for all information theory calculations involving -** one variable -** -** length(dataVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -ProbabilityState calculateProbability(double *dataVector, int vectorLength); - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/CompileMIToolbox.m b/FEAST/FEAST/MIToolbox/CompileMIToolbox.m deleted file mode 100644 index a8d9e92..0000000 --- a/FEAST/FEAST/MIToolbox/CompileMIToolbox.m +++ /dev/null @@ -1,4 +0,0 @@ -% Compiles the MIToolbox functions - -mex MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c -mex RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c diff --git a/FEAST/FEAST/MIToolbox/Entropy.c b/FEAST/FEAST/MIToolbox/Entropy.c deleted file mode 100644 index 3f37cc1..0000000 --- a/FEAST/FEAST/MIToolbox/Entropy.c +++ /dev/null @@ -1,130 +0,0 @@ -/******************************************************************************* -** Entropy.cpp -** Part of the mutual information toolbox -** -** Contains functions to calculate the entropy of a single variable H(X), -** the joint entropy of two variables H(X,Y), and the conditional entropy -** H(X|Y) -** -** Author: Adam Pocock -** Created 19/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "CalculateProbability.h" -#include "Entropy.h" - -double calculateEntropy(double *dataVector, int vectorLength) -{ - double entropy = 0.0; - double tempValue = 0.0; - int i; - ProbabilityState state = calculateProbability(dataVector,vectorLength); - - /*H(X) = - sum p(x) log p(x)*/ - for (i = 0; i < state.numStates; i++) - { - tempValue = state.probabilityVector[i]; - - if (tempValue > 0) - { - entropy -= tempValue * log(tempValue); - } - } - - entropy /= log(2.0); - - FREE_FUNC(state.probabilityVector); - state.probabilityVector = NULL; - - return entropy; -}/*calculateEntropy(double *,int)*/ - -double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength) -{ - double jointEntropy = 0.0; - double tempValue = 0.0; - int i; - JointProbabilityState state = calculateJointProbability(firstVector,secondVector,vectorLength); - - /*H(XY) = - sumx sumy p(xy) log p(xy)*/ - for (i = 0; i < state.numJointStates; i++) - { - tempValue = state.jointProbabilityVector[i]; - if (tempValue > 0) - { - jointEntropy -= tempValue * log(tempValue); - } - } - - jointEntropy /= log(2.0); - - FREE_FUNC(state.firstProbabilityVector); - state.firstProbabilityVector = NULL; - FREE_FUNC(state.secondProbabilityVector); - state.secondProbabilityVector = NULL; - FREE_FUNC(state.jointProbabilityVector); - state.jointProbabilityVector = NULL; - - return jointEntropy; -}/*calculateJointEntropy(double *, double *, int)*/ - -double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength) -{ - /* - ** Conditional entropy - ** H(X|Y) = - sumx sumy p(xy) log p(xy)/p(y) - */ - - double condEntropy = 0.0; - double jointValue = 0.0; - double condValue = 0.0; - int i; - JointProbabilityState state = calculateJointProbability(dataVector,conditionVector,vectorLength); - - /*H(X|Y) = - sumx sumy p(xy) log p(xy)/p(y)*/ - /* to index by numFirstStates use modulus of i - ** to index by numSecondStates use integer division of i by numFirstStates - */ - for (i = 0; i < state.numJointStates; i++) - { - jointValue = state.jointProbabilityVector[i]; - condValue = state.secondProbabilityVector[i / state.numFirstStates]; - if ((jointValue > 0) && (condValue > 0)) - { - condEntropy -= jointValue * log(jointValue / condValue); - } - } - - condEntropy /= log(2.0); - - FREE_FUNC(state.firstProbabilityVector); - state.firstProbabilityVector = NULL; - FREE_FUNC(state.secondProbabilityVector); - state.secondProbabilityVector = NULL; - FREE_FUNC(state.jointProbabilityVector); - state.jointProbabilityVector = NULL; - - return condEntropy; - -}/*calculateConditionalEntropy(double *, double *, int)*/ - diff --git a/FEAST/FEAST/MIToolbox/Entropy.h b/FEAST/FEAST/MIToolbox/Entropy.h deleted file mode 100644 index 4bdd697..0000000 --- a/FEAST/FEAST/MIToolbox/Entropy.h +++ /dev/null @@ -1,71 +0,0 @@ -/******************************************************************************* -** Entropy.h -** Part of the mutual information toolbox -** -** Contains functions to calculate the entropy of a single variable H(X), -** the joint entropy of two variables H(X,Y), and the conditional entropy -** H(X|Y) -** -** Author: Adam Pocock -** Created 19/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __Entropy_H -#define __Entropy_H - -#ifdef __cplusplus -extern "C" { -#endif - -/******************************************************************************* -** calculateEntropy returns the entropy in log base 2 of dataVector -** H(X) -** -** length(dataVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateEntropy(double *dataVector, int vectorLength); - -/******************************************************************************* -** calculateJointEntropy returns the entropy in log base 2 of the joint -** variable of firstVector and secondVector H(XY) -** -** length(firstVector) == length(secondVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); - -/******************************************************************************* -** calculateConditionalEntropy returns the entropy in log base 2 of dataVector -** conditioned on conditionVector, H(X|Y) -** -** length(dataVector) == length(conditionVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength); - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/MIToolbox.h b/FEAST/FEAST/MIToolbox/MIToolbox.h deleted file mode 100644 index bba6284..0000000 --- a/FEAST/FEAST/MIToolbox/MIToolbox.h +++ /dev/null @@ -1,53 +0,0 @@ -/******************************************************************************* -** -** MIToolbox.h -** Provides the header files and #defines to ensure compatibility with MATLAB -** and C/C++. Uncomment the correct lines to setup the correct memory -** allocation and freeing operations. -** -** Author: Adam Pocock -** Created 17/2/2010 -** -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __MIToolbox_H -#define __MIToolbox_H - -#include -#include - -#ifdef COMPILE_C - #define C_IMPLEMENTATION - #include - #include - #define CALLOC_FUNC calloc - #define FREE_FUNC free -#else - #define MEX_IMPLEMENTATION - #include "mex.h" - #define CALLOC_FUNC mxCalloc - #define FREE_FUNC mxFree - #define printf mexPrintf /*for Octave-3.2*/ -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/MIToolbox.m b/FEAST/FEAST/MIToolbox/MIToolbox.m deleted file mode 100644 index 880924a..0000000 --- a/FEAST/FEAST/MIToolbox/MIToolbox.m +++ /dev/null @@ -1,83 +0,0 @@ -function [varargout] = MIToolbox(functionName, varargin) -%function [varargout] = MIToolbox(functionName, varargin) -% -%Provides access to the functions in MIToolboxMex -% -%Expects column vectors, will not work with row vectors -% -%Function list -%"joint" = joint variable of the matrix -%"entropy" or "h" = H(X) -%"ConditionalEntropy" or "condh" = H(X|Y) -%"mi" = I(X;Y) -%"ConditionalMI" or "cmi" = I(X;Y|Z) -% -%Arguments and returned values -%[jointVariable] = joint(matrix) -%[entropy] = H(X) = H(vector) -%[entropy] = H(X|Y) = H(vector,condition) -%[mi] = I(X;Y) = I(vector,target) -%[mi] = I(X;Y|Z) = I(vector,target,condition) -% -%Internal MIToolbox function number -%Joint = 3 -%Entropy = 4 -%Conditional Entropy = 6 -%Mutual Information = 7 -%Conditional MI = 8 - -if (strcmpi(functionName,'Joint') || strcmpi(functionName,'Merge')) - [varargout{1}] = MIToolboxMex(3,varargin{1}); -elseif (strcmpi(functionName,'Entropy') || strcmpi(functionName,'h')) - %disp('Calculating Entropy'); - if (size(varargin{1},2)>1) - mergedVector = MIToolboxMex(3,varargin{1}); - else - mergedVector = varargin{1}; - end - [varargout{1}] = MIToolboxMex(4,mergedVector); -elseif ((strcmpi(functionName,'ConditionalEntropy')) || strcmpi(functionName,'condh')) - if (size(varargin{1},2)>1) - mergedFirst = MIToolboxMex(3,varargin{1}); - else - mergedFirst = varargin{1}; - end - if (size(varargin{2},2)>1) - mergedSecond = MIToolboxMex(3,varargin{2}); - else - mergedSecond = varargin{2}; - end - [varargout{1}] = MIToolboxMex(6,mergedFirst,mergedSecond); -elseif (strcmpi(functionName,'mi')) - if (size(varargin{1},2)>1) - mergedFirst = MIToolboxMex(3,varargin{1}); - else - mergedFirst = varargin{1}; - end - if (size(varargin{2},2)>1) - mergedSecond = MIToolboxMex(3,varargin{2}); - else - mergedSecond = varargin{2}; - end - [varargout{1}] = MIToolboxMex(7,mergedFirst,mergedSecond); -elseif (strcmpi(functionName,'ConditionalMI') || strcmpi(functionName,'cmi')) - if (size(varargin{1},2)>1) - mergedFirst = MIToolboxMex(3,varargin{1}); - else - mergedFirst = varargin{1}; - end - if (size(varargin{2},2)>1) - mergedSecond = MIToolboxMex(3,varargin{2}); - else - mergedSecond = varargin{2}; - end - if (size(varargin{3},2)>1) - mergedThird = MIToolboxMex(3,varargin{3}); - else - mergedThird = varargin{3}; - end - [varargout{1}] = MIToolboxMex(8,mergedFirst,mergedSecond,mergedThird); -else - varargout{1} = 0; - disp(['Unrecognised functionName ' functionName]); -end diff --git a/FEAST/FEAST/MIToolbox/MIToolboxMex.c b/FEAST/FEAST/MIToolbox/MIToolboxMex.c deleted file mode 100644 index e27009f..0000000 --- a/FEAST/FEAST/MIToolbox/MIToolboxMex.c +++ /dev/null @@ -1,494 +0,0 @@ -/******************************************************************************* -** -** MIToolboxMex.cpp -** is the MATLAB entry point for the MIToolbox functions when called from -** a MATLAB/OCTAVE script. -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ -#include "MIToolbox.h" -#include "ArrayOperations.h" -#include "CalculateProbability.h" -#include "Entropy.h" -#include "MutualInformation.h" - -/******************************************************************************* -**entry point for the mex call -**nlhs - number of outputs -**plhs - pointer to array of outputs -**nrhs - number of inputs -**prhs - pointer to array of inputs -*******************************************************************************/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /***************************************************************************** - ** this function takes a flag and a variable number of arguments - ** depending on the value of the flag and returns either a construct - ** containing probability estimates, a merged vector or a double value - ** representing an entropy or mutual information - *****************************************************************************/ - - int flag, i, numberOfSamples, checkSamples, thirdCheckSamples, numberOfFeatures, checkFeatures, thirdCheckFeatures; - int numArities, errorTest; - double *dataVector, *condVector, *targetVector, *firstVector, *secondVector, *output, *numStates; - double *matrix, *mergedVector, *arities; - int *outputIntVector, *intArities; - - double *jointOutput, *numJointStates, *firstOutput, *numFirstStates, *secondOutput, *numSecondStates; - - ProbabilityState state; - JointProbabilityState jointState; - - /*if (nlhs != 1) - { - printf("Incorrect number of output arguments\n"); - }//if not 1 output - */ - if (nrhs == 2) - { - /*printf("Must be H(X), calculateProbability(X), merge(X), normaliseArray(X)\n");*/ - } - else if (nrhs == 3) - { - /*printf("Must be H(XY), H(X|Y), calculateJointProbability(XY), I(X;Y)\n");*/ - } - else if (nrhs == 4) - { - /*printf("Must be I(X;Y|Z)\n");*/ - } - else - { - printf("Incorrect number of arguments, format is MIToolbox(\"FLAG\",varargin)\n"); - } - - /* number to function map - ** 1 = calculateProbability - ** 2 = calculateJointProbability - ** 3 = mergeArrays - ** 4 = H(X) - ** 5 = H(XY) - ** 6 = H(X|Y) - ** 7 = I(X;Y) - ** 8 = I(X;Y|Z) - ** 9 = normaliseArray - */ - - flag = *mxGetPr(prhs[0]); - - switch (flag) - { - case 1: - { - /* - **calculateProbability - */ - numberOfSamples = mxGetM(prhs[1]); - dataVector = (double *) mxGetPr(prhs[1]); - - /*ProbabilityState calculateProbability(double *dataVector, int vectorLength);*/ - state = calculateProbability(dataVector,numberOfSamples); - - plhs[0] = mxCreateDoubleMatrix(state.numStates,1,mxREAL); - plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - numStates = (double *) mxGetPr(plhs[1]); - - *numStates = state.numStates; - - for (i = 0; i < state.numStates; i++) - { - output[i] = state.probabilityVector[i]; - } - - break; - }/*case 1 - calculateProbability*/ - case 2: - { - /* - **calculateJointProbability - */ - numberOfSamples = mxGetM(prhs[1]); - firstVector = (double *) mxGetPr(prhs[1]); - secondVector = (double *) mxGetPr(prhs[2]); - - /*JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector int vectorLength);*/ - jointState = calculateJointProbability(firstVector,secondVector,numberOfSamples); - - plhs[0] = mxCreateDoubleMatrix(jointState.numJointStates,1,mxREAL); - plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); - plhs[2] = mxCreateDoubleMatrix(jointState.numFirstStates,1,mxREAL); - plhs[3] = mxCreateDoubleMatrix(1,1,mxREAL); - plhs[4] = mxCreateDoubleMatrix(jointState.numSecondStates,1,mxREAL); - plhs[5] = mxCreateDoubleMatrix(1,1,mxREAL); - jointOutput = (double *)mxGetPr(plhs[0]); - numJointStates = (double *) mxGetPr(plhs[1]); - firstOutput = (double *)mxGetPr(plhs[2]); - numFirstStates = (double *) mxGetPr(plhs[3]); - secondOutput = (double *)mxGetPr(plhs[4]); - numSecondStates = (double *) mxGetPr(plhs[5]); - - *numJointStates = jointState.numJointStates; - *numFirstStates = jointState.numFirstStates; - *numSecondStates = jointState.numSecondStates; - - for (i = 0; i < jointState.numJointStates; i++) - { - jointOutput[i] = jointState.jointProbabilityVector[i]; - } - for (i = 0; i < jointState.numFirstStates; i++) - { - firstOutput[i] = jointState.firstProbabilityVector[i]; - } - for (i = 0; i < jointState.numSecondStates; i++) - { - secondOutput[i] = jointState.secondProbabilityVector[i]; - } - - break; - }/*case 2 - calculateJointProbability */ - case 3: - { - /* - **mergeArrays - */ - numberOfSamples = mxGetM(prhs[1]); - numberOfFeatures = mxGetN(prhs[1]); - - numArities = 0; - if (nrhs > 2) - { - numArities = mxGetN(prhs[2]); - /*printf("arities = %d, features = %d, samples = %d\n",numArities,numberOfFeatures,numberOfSamples);*/ - } - - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - - if (numArities == 0) - { - /* - **no arities therefore compress output - */ - if ((numberOfFeatures > 0) && (numberOfSamples > 0)) - { - matrix = (double *) mxGetPr(prhs[1]); - mergedVector = (double *) mxCalloc(numberOfSamples,sizeof(double)); - - plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength)*/ - mergeMultipleArrays(matrix, mergedVector, numberOfFeatures, numberOfSamples); - for (i = 0; i < numberOfSamples; i++) - { - output[i] = mergedVector[i]; - } - - mxFree(mergedVector); - mergedVector = NULL; - } - } - else if (numArities == numberOfFeatures) - { - if ((numberOfFeatures > 0) && (numberOfSamples > 0)) - { - - matrix = (double *) mxGetPr(prhs[1]); - mergedVector = (double *) mxCalloc(numberOfSamples,sizeof(double)); - - arities = (double *) mxGetPr(prhs[2]); - intArities = (int *) mxCalloc(numberOfFeatures,sizeof(int)); - for (i = 0; i < numArities; i++) - { - intArities[i] = (int) floor(arities[i]); - } - - /*int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength);*/ - errorTest = mergeMultipleArraysArities(matrix, mergedVector, numberOfFeatures, intArities, numberOfSamples); - - if (errorTest != -1) - { - plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - for (i = 0; i < numberOfSamples; i++) - { - output[i] = mergedVector[i]; - } - } - else - { - printf("Incorrect arities supplied. More states in data than specified\n"); - } - - mxFree(mergedVector); - mergedVector = NULL; - } - } - else - { - printf("Number of arities does not match number of features, arities should be a row vector\n"); - } - - break; - }/*case 3 - mergeArrays*/ - case 4: - { - /* - **H(X) - */ - numberOfSamples = mxGetM(prhs[1]); - numberOfFeatures = mxGetN(prhs[1]); - - dataVector = (double *) mxGetPr(prhs[1]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if (numberOfFeatures == 1) - { - /*double calculateEntropy(double *dataVector, int vectorLength);*/ - *output = calculateEntropy(dataVector,numberOfSamples); - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - - break; - }/*case 4 - H(X)*/ - case 5: - { - /* - **H(XY) - */ - numberOfSamples = mxGetM(prhs[1]); - checkSamples = mxGetM(prhs[2]); - - numberOfFeatures = mxGetN(prhs[1]); - checkFeatures = mxGetN(prhs[2]); - - firstVector = mxGetPr(prhs[1]); - secondVector = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if ((numberOfSamples == 0) && (checkSamples == 0)) - { - *output = 0.0; - } - else if (numberOfSamples == 0) - { - *output = calculateEntropy(secondVector,numberOfSamples); - } - else if (checkSamples == 0) - { - *output = calculateEntropy(firstVector,numberOfSamples); - } - else if (numberOfSamples == checkSamples) - { - /*double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength);*/ - *output = calculateJointEntropy(firstVector,secondVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length\n"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - - break; - }/*case 5 - H(XY)*/ - case 6: - { - /* - **H(X|Y) - */ - numberOfSamples = mxGetM(prhs[1]); - checkSamples = mxGetM(prhs[2]); - - numberOfFeatures = mxGetN(prhs[1]); - checkFeatures = mxGetN(prhs[2]); - - dataVector = mxGetPr(prhs[1]); - condVector = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if (numberOfSamples == 0) - { - *output = 0.0; - } - else if (checkSamples == 0) - { - *output = calculateEntropy(dataVector,numberOfSamples); - } - else if (numberOfSamples == checkSamples) - { - /*double calculateConditionalEntropy(double *dataVector, double *condVector, int vectorLength);*/ - *output = calculateConditionalEntropy(dataVector,condVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length\n"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 6 - H(X|Y)*/ - case 7: - { - /* - **I(X;Y) - */ - numberOfSamples = mxGetM(prhs[1]); - checkSamples = mxGetM(prhs[2]); - - numberOfFeatures = mxGetN(prhs[1]); - checkFeatures = mxGetN(prhs[2]); - - firstVector = mxGetPr(prhs[1]); - secondVector = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if ((numberOfSamples == 0) || (checkSamples == 0)) - { - *output = 0.0; - } - else if (numberOfSamples == checkSamples) - { - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - *output = calculateMutualInformation(firstVector,secondVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length\n"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 7 - I(X;Y)*/ - case 8: - { - /* - **I(X;Y|Z) - */ - numberOfSamples = mxGetM(prhs[1]); - checkSamples = mxGetM(prhs[2]); - thirdCheckSamples = mxGetM(prhs[3]); - - numberOfFeatures = mxGetN(prhs[1]); - checkFeatures = mxGetN(prhs[2]); - thirdCheckFeatures = mxGetN(prhs[3]); - - firstVector = mxGetPr(prhs[1]); - targetVector = mxGetPr(prhs[2]); - condVector = mxGetPr(prhs[3]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if ((numberOfSamples == 0) || (checkSamples == 0)) - { - *output = 0.0; - } - else if ((thirdCheckSamples == 0) || (thirdCheckFeatures != 1)) - { - *output = calculateMutualInformation(firstVector,targetVector,numberOfSamples); - } - else if ((numberOfSamples == checkSamples) && (numberOfSamples == thirdCheckSamples)) - { - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *condVector, int vectorLength);*/ - *output = calculateConditionalMutualInformation(firstVector,targetVector,condVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length\n"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 8 - I(X;Y|Z)*/ - case 9: - { - /* - **normaliseArray - */ - numberOfSamples = mxGetM(prhs[1]); - dataVector = (double *) mxGetPr(prhs[1]); - - outputIntVector = (int *) mxCalloc(numberOfSamples,sizeof(int)); - - plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); - plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - numStates = (double *) mxGetPr(plhs[1]); - - /*int normaliseArray(double *inputVector, int *outputVector, int vectorLength);*/ - *numStates = normaliseArray(dataVector, outputIntVector, numberOfSamples); - - for (i = 0; i < numberOfSamples; i++) - { - output[i] = outputIntVector[i]; - } - - break; - }/*case 9 - normaliseArray*/ - default: - { - printf("Unrecognised flag\n"); - break; - }/*default*/ - }/*switch(flag)*/ - - return; -}/*mexFunction()*/ diff --git a/FEAST/FEAST/MIToolbox/MIToolboxMex.mexa64 b/FEAST/FEAST/MIToolbox/MIToolboxMex.mexa64 deleted file mode 100755 index 440611d..0000000 Binary files a/FEAST/FEAST/MIToolbox/MIToolboxMex.mexa64 and /dev/null differ diff --git a/FEAST/FEAST/MIToolbox/Makefile b/FEAST/FEAST/MIToolbox/Makefile deleted file mode 100644 index 0135410..0000000 --- a/FEAST/FEAST/MIToolbox/Makefile +++ /dev/null @@ -1,84 +0,0 @@ -#makefile for MIToolbox -#Author: Adam Pocock, apocock@cs.man.ac.uk -#Created 11/3/2010 -# -# -#Copyright 2010 Adam Pocock, The University Of Manchester -#www.cs.manchester.ac.uk -# -#This file is part of MIToolbox. -# -#MIToolbox is free software: you can redistribute it and/or modify -#it under the terms of the GNU Lesser General Public License as published by -#the Free Software Foundation, either version 3 of the License, or -#(at your option) any later version. -# -#MIToolbox 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 Lesser General Public License for more details. -# -#You should have received a copy of the GNU Lesser General Public License -#along with MIToolbox. If not, see . - -CXXFLAGS = -O3 -fPIC -COMPILER = gcc -objects = ArrayOperations.o CalculateProbability.o Entropy.o \ - MutualInformation.o RenyiEntropy.o RenyiMutualInformation.o - -libMIToolbox.so : $(objects) - $(COMPILER) $(CXXFLAGS) -shared -o libMIToolbox.so $(objects) - -RenyiMutualInformation.o: RenyiMutualInformation.c MIToolbox.h ArrayOperations.h \ - CalculateProbability.h RenyiEntropy.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c RenyiMutualInformation.c - -RenyiEntropy.o: RenyiEntropy.c MIToolbox.h ArrayOperations.h \ - CalculateProbability.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c RenyiEntropy.c - -MutualInformation.o: MutualInformation.c MIToolbox.h ArrayOperations.h \ - CalculateProbability.h Entropy.h MutualInformation.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c MutualInformation.c - -Entropy.o: Entropy.c MIToolbox.h ArrayOperations.h CalculateProbability.h \ - Entropy.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c Entropy.c - -CalculateProbability.o: CalculateProbability.c MIToolbox.h ArrayOperations.h \ - CalculateProbability.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CalculateProbability.c - -ArrayOperations.o: ArrayOperations.c MIToolbox.h ArrayOperations.h - $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c ArrayOperations.c - -.PHONY : debug -debug: - $(MAKE) libMIToolbox.so "CXXFLAGS = -g -DDEBUG -fPIC" - -.PHONY : x86 -x86: - $(MAKE) libMIToolbox.so "CXXFLAGS = -O3 -fPIC -m32" - -.PHONY : x64 -x64: - $(MAKE) libMIToolbox.so "CXXFLAGS = -O3 -fPIC -m64" - -.PHONY : matlab -matlab: - mex MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c - mex RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c - -.PHONY : matlab-debug -matlab-debug: - mex -g MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c - mex -g RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c - -.PHONY : intel -intel: - $(MAKE) libMIToolbox.so "COMPILER = icc" "CXXFLAGS = -O2 -fPIC -xHost" - -.PHONY : clean -clean: - rm *.o libMIToolbox.so - diff --git a/FEAST/FEAST/MIToolbox/MutualInformation.c b/FEAST/FEAST/MIToolbox/MutualInformation.c deleted file mode 100644 index 0fb4766..0000000 --- a/FEAST/FEAST/MIToolbox/MutualInformation.c +++ /dev/null @@ -1,95 +0,0 @@ -/******************************************************************************* -** MutualInformation.cpp -** Part of the mutual information toolbox -** -** Contains functions to calculate the mutual information of -** two variables X and Y, I(X;Y), to calculate the joint mutual information -** of two variables X & Z on the variable Y, I(XZ;Y), and the conditional -** mutual information I(x;Y|Z) -** -** Author: Adam Pocock -** Created 19/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "ArrayOperations.h" -#include "CalculateProbability.h" -#include "Entropy.h" -#include "MutualInformation.h" - -double calculateMutualInformation(double *dataVector, double *targetVector, int vectorLength) -{ - double mutualInformation = 0.0; - int firstIndex,secondIndex; - int i; - JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength); - - /* - ** I(X;Y) = sum sum p(xy) * log (p(xy)/p(x)p(y)) - */ - for (i = 0; i < state.numJointStates; i++) - { - firstIndex = i % state.numFirstStates; - secondIndex = i / state.numFirstStates; - - if ((state.jointProbabilityVector[i] > 0) && (state.firstProbabilityVector[firstIndex] > 0) && (state.secondProbabilityVector[secondIndex] > 0)) - { - /*double division is probably more stable than multiplying two small numbers together - ** mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / (state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex])); - */ - mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / state.firstProbabilityVector[firstIndex] / state.secondProbabilityVector[secondIndex]); - } - } - - mutualInformation /= log(2.0); - - FREE_FUNC(state.firstProbabilityVector); - state.firstProbabilityVector = NULL; - FREE_FUNC(state.secondProbabilityVector); - state.secondProbabilityVector = NULL; - FREE_FUNC(state.jointProbabilityVector); - state.jointProbabilityVector = NULL; - - return mutualInformation; -}/*calculateMutualInformation(double *,double *,int)*/ - -double calculateConditionalMutualInformation(double *dataVector, double *targetVector, double *conditionVector, int vectorLength) -{ - double mutualInformation = 0.0; - double firstCondition, secondCondition; - double *mergedVector = (double *) CALLOC_FUNC(vectorLength,sizeof(double)); - - mergeArrays(targetVector,conditionVector,mergedVector,vectorLength); - - /* I(X;Y|Z) = H(X|Z) - H(X|YZ) */ - /* double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength); */ - firstCondition = calculateConditionalEntropy(dataVector,conditionVector,vectorLength); - secondCondition = calculateConditionalEntropy(dataVector,mergedVector,vectorLength); - - mutualInformation = firstCondition - secondCondition; - - FREE_FUNC(mergedVector); - mergedVector = NULL; - - return mutualInformation; -}/*calculateConditionalMutualInformation(double *,double *,double *,int)*/ - diff --git a/FEAST/FEAST/MIToolbox/MutualInformation.h b/FEAST/FEAST/MIToolbox/MutualInformation.h deleted file mode 100644 index 1045912..0000000 --- a/FEAST/FEAST/MIToolbox/MutualInformation.h +++ /dev/null @@ -1,64 +0,0 @@ -/******************************************************************************* -** MutualInformation.h -** Part of the mutual information toolbox -** -** Contains functions to calculate the mutual information of -** two variables X and Y, I(X;Y), to calculate the joint mutual information -** of two variables X & Z on the variable Y, I(XZ;Y), and the conditional -** mutual information I(x;Y|Z) -** -** Author: Adam Pocock -** Created 19/2/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __MutualInformation_H -#define __MutualInformation_H - -#ifdef __cplusplus -extern "C" { -#endif - -/******************************************************************************* -** calculateMutualInformation returns the log base 2 mutual information between -** dataVector and targetVector, I(X;Y) -** -** length(dataVector) == length(targetVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateMutualInformation(double *dataVector, double *targetVector, int vectorLength); - -/******************************************************************************* -** calculateConditionalMutualInformation returns the log base 2 -** mutual information between dataVector and targetVector, conditioned on -** conditionVector, I(X;Y|Z) -** -** length(dataVector) == length(targetVector) == length(condtionVector) == vectorLength -** otherwise it will error with a segmentation fault -*******************************************************************************/ -double calculateConditionalMutualInformation(double *dataVector, double *targetVector, double *conditionVector, int vectorLength); - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/README b/FEAST/FEAST/MIToolbox/README deleted file mode 100644 index 7abe43d..0000000 --- a/FEAST/FEAST/MIToolbox/README +++ /dev/null @@ -1,71 +0,0 @@ -MIToolbox v1.03 for C/C++ and MATLAB/OCTAVE - -The MIToolbox contains a set of functions to calculate information theoretic -quantities from data, such as the entropy and mutual information. The toolbox -contains implementations of the most popular Shannon entropies, and also the -lesser known Renyi entropy. The toolbox only supports discrete distributions, -as opposed to continuous. All real-valued numbers will be processed by x = floor(x) - -These functions are targeted for use with feature selection algorithms rather -than communication channels and so expect all the data to be available before -execution and sample their own probability distributions from the data. - -Things you can do: - - Entropy - - Conditional Entropy - - Mutual Information - - Conditional Mutual Information - - generating a joint variable - - generating a probability distribution from a discrete random variable - - Renyi's Entropy - - Renyi's Mutual Information - -Note: all functions are calculated in log base 2, so return units of "bits". - -====== - -Examples: - ->> y = [1 1 1 0 0]'; ->> x = [1 0 1 1 0]'; - ->> mi(x,y) %% mutual information I(X;Y) -ans = - 0.0200 - ->> h(x) %% entropy H(X) -ans = - 0.9710 - ->> condh(x,y) %% conditional entropy H(X|Y) -ans = - 0.9510 - ->> h( [x,y] ) %% joint entropy H(X,Y) -ans = - 1.9219 - ->> joint([x,y]) %% joint random variable XY -ans = - 1 - 2 - 1 - 3 - 4 - -====== - -To compile the library for use in MATLAB/OCTAVE, execute CompileMIToolbox.m -from within MATLAB, or run 'make matlab' from a terminal. - -To compile the library for C/C++, run 'make' at a terminal. - -The C source files are licensed under the LGPL v3. The MATLAB wrappers and -demonstration feature selection algorithms are provided as is with no warranty -as examples of how to use the library in MATLAB. - -Update History -08/11/2011 - v1.03 - Minor documentation changes to accompany the JMLR publication. -15/10/2010 - v1.02 - Fixed bug where MIToolbox would cause a segmentation fault if a x by 0 empty matrix was passed in. Now prints an error message and returns gracefully -02/09/2010 - v1.01 - Updated CMIM.m in demonstration_algorithms, due to a bug where the last feature would not be selected first if it had the highest MI -07/07/2010 - v1.00 - Initial Release diff --git a/FEAST/FEAST/MIToolbox/RenyiEntropy.c b/FEAST/FEAST/MIToolbox/RenyiEntropy.c deleted file mode 100644 index 32c5ff9..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiEntropy.c +++ /dev/null @@ -1,191 +0,0 @@ -/******************************************************************************* -** RenyiEntropy.cpp -** Part of the mutual information toolbox -** -** Contains functions to calculate the Renyi alpha entropy of a single variable -** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the -** conditional Renyi entropy H_\alpha(X|Y) -** -** Author: Adam Pocock -** Created 26/3/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "ArrayOperations.h" -#include "CalculateProbability.h" -#include "Entropy.h" - -double calculateRenyiEntropy(double alpha, double *dataVector, int vectorLength) -{ - double entropy = 0.0; - double tempValue = 0.0; - int i; - ProbabilityState state = calculateProbability(dataVector,vectorLength); - - /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ - for (i = 0; i < state.numStates; i++) - { - tempValue = state.probabilityVector[i]; - - if (tempValue > 0) - { - entropy += pow(tempValue,alpha); - /*printf("Entropy = %f, i = %d\n", entropy,i);*/ - } - } - - /*printf("Entropy = %f\n", entropy);*/ - - entropy = log(entropy); - - entropy /= log(2.0); - - entropy /= (1.0-alpha); - - /*printf("Entropy = %f\n", entropy);*/ - FREE_FUNC(state.probabilityVector); - state.probabilityVector = NULL; - - return entropy; -}/*calculateRenyiEntropy(double,double*,int)*/ - -double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, int vectorLength) -{ - double jointEntropy = 0.0; - double tempValue = 0.0; - int i; - JointProbabilityState state = calculateJointProbability(firstVector,secondVector,vectorLength); - - /*H_\alpha(XY) = 1/(1-alpha) * log(2)(sum p(xy)^alpha)*/ - for (i = 0; i < state.numJointStates; i++) - { - tempValue = state.jointProbabilityVector[i]; - if (tempValue > 0) - { - jointEntropy += pow(tempValue,alpha); - } - } - - jointEntropy = log(jointEntropy); - - jointEntropy /= log(2.0); - - jointEntropy /= (1.0-alpha); - - FREE_FUNC(state.firstProbabilityVector); - state.firstProbabilityVector = NULL; - FREE_FUNC(state.secondProbabilityVector); - state.secondProbabilityVector = NULL; - FREE_FUNC(state.jointProbabilityVector); - state.jointProbabilityVector = NULL; - - return jointEntropy; -}/*calculateJointRenyiEntropy(double,double*,double*,int)*/ - -double calcCondRenyiEnt(double alpha, double *dataVector, double *conditionVector, int uniqueInCondVector, int vectorLength) -{ - /*uniqueInCondVector = is the number of unique values in the cond vector.*/ - - /*condEntropy = sum p(y) * sum p(x|y)^alpha(*/ - - /* - ** first generate the seperate variables - */ - - double *seperateVectors = (double *) CALLOC_FUNC(uniqueInCondVector*vectorLength,sizeof(double)); - int *seperateVectorCount = (int *) CALLOC_FUNC(uniqueInCondVector,sizeof(int)); - double seperateVectorProb = 0.0; - int i,j; - double entropy = 0.0; - double tempValue = 0.0; - int currentValue; - double tempEntropy; - ProbabilityState state; - - double **seperateVectors2D = (double **) CALLOC_FUNC(uniqueInCondVector,sizeof(double*)); - for(j=0; j < uniqueInCondVector; j++) - seperateVectors2D[j] = seperateVectors + (int)j*vectorLength; - - for (i = 0; i < vectorLength; i++) - { - currentValue = (int) (conditionVector[i] - 1.0); - /*printf("CurrentValue = %d\n",currentValue);*/ - seperateVectors2D[currentValue][seperateVectorCount[currentValue]] = dataVector[i]; - seperateVectorCount[currentValue]++; - } - - - - for (j = 0; j < uniqueInCondVector; j++) - { - tempEntropy = 0.0; - seperateVectorProb = ((double)seperateVectorCount[j]) / vectorLength; - state = calculateProbability(seperateVectors2D[j],seperateVectorCount[j]); - - /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ - for (i = 0; i < state.numStates; i++) - { - tempValue = state.probabilityVector[i]; - - if (tempValue > 0) - { - tempEntropy += pow(tempValue,alpha); - /*printf("Entropy = %f, i = %d\n", entropy,i);*/ - } - } - - /*printf("Entropy = %f\n", entropy);*/ - - tempEntropy = log(tempEntropy); - - tempEntropy /= log(2.0); - - tempEntropy /= (1.0-alpha); - - entropy += tempEntropy; - - FREE_FUNC(state.probabilityVector); - } - - FREE_FUNC(seperateVectors2D); - seperateVectors2D = NULL; - - FREE_FUNC(seperateVectors); - FREE_FUNC(seperateVectorCount); - - seperateVectors = NULL; - seperateVectorCount = NULL; - - return entropy; -}/*calcCondRenyiEnt(double *,double *,int)*/ - -double calculateConditionalRenyiEntropy(double alpha, double *dataVector, double *conditionVector, int vectorLength) -{ - /*calls this: - **double calculateConditionalRenyiEntropy(double alpha, double *firstVector, double *condVector, int uniqueInCondVector, int vectorLength) - **after determining uniqueInCondVector - */ - int numUnique = numberOfUniqueValues(conditionVector, vectorLength); - - return calcCondRenyiEnt(alpha, dataVector, conditionVector, numUnique, vectorLength); -}/*calculateConditionalRenyiEntropy(double,double*,double*,int)*/ - diff --git a/FEAST/FEAST/MIToolbox/RenyiEntropy.h b/FEAST/FEAST/MIToolbox/RenyiEntropy.h deleted file mode 100644 index 296bc4b..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiEntropy.h +++ /dev/null @@ -1,68 +0,0 @@ -/******************************************************************************* -** RenyiEntropy.h -** Part of the mutual information toolbox -** -** Contains functions to calculate the Renyi alpha entropy of a single variable -** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the -** conditional Renyi entropy H_\alpha(X|Y) -** -** Author: Adam Pocock -** Created 26/3/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __Renyi_Entropy_H -#define __Renyi_Entropy_H - -#ifdef __cplusplus -extern "C" { -#endif - -/******************************************************************************* -** calculateRenyiEntropy returns the Renyi entropy in log base 2 of dataVector -** H_{\alpha}(X), for \alpha != 1 -** -** length(dataVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateRenyiEntropy(double alpha, double *dataVector, int vectorLength); - -/******************************************************************************* -** calculateJointRenyiEntropy returns the Renyi entropy in log base 2 of the -** joint variable of firstVector and secondVector H_{\alpha}(XY), -** for \alpha != 1 -** -** length(firstVector) == length(secondVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, int vectorLength); - -/* This function does not return a valid conditonal entropy as it has no -** meaning in Renyi's extension of entropy -double calculateConditionalRenyiEntropy(double alpha, double *dataVector, double *conditionVector, int vectorLength); -*/ - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/RenyiMIToolbox.m b/FEAST/FEAST/MIToolbox/RenyiMIToolbox.m deleted file mode 100644 index cd235e9..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiMIToolbox.m +++ /dev/null @@ -1,48 +0,0 @@ -function [varargout] = RenyiMIToolbox(functionName, alpha, varargin) -%function [varargout] = RenyiMIToolbox(functionName, alpha, varargin) -% -%Provides access to the functions in RenyiMIToolboxMex -% -%Expects column vectors, will not work with row vectors -% -%Function list -%"Entropy" = H_{\alpha}(X) = 1 -%"MI" = I_{\alpha}(X;Y) = 3 -% -%Arguments and returned values -%[entropy] = H_\alpha(X) = H(alpha,vector) -%[mi] = I_\alpha(X;Y) = I(alpha,vector,target) -% -%Internal RenyiMIToolbox function number -%Renyi Entropy = 1; -%Renyi MI = 3; - -if (alpha ~= 1) - if (strcmpi(functionName,'Entropy') || strcmpi(functionName,'h')) - %disp('Calculating Entropy'); - if (size(varargin{1},2)>1) - mergedVector = MIToolboxMex(3,varargin{1}); - else - mergedVector = varargin{1}; - end - [varargout{1}] = RenyiMIToolboxMex(1,alpha,mergedVector); - elseif (strcmpi(functionName,'MI')) - if (size(varargin{1},2)>1) - mergedFirst = MIToolboxMex(3,varargin{1}); - else - mergedFirst = varargin{1}; - end - if (size(varargin{2},2)>1) - mergedSecond = MIToolboxMex(3,varargin{2}); - else - mergedSecond = varargin{2}; - end - [varargout{1}] = RenyiMIToolboxMex(3,alpha,mergedFirst,mergedSecond); - else - varargout{1} = 0; - disp(['Unrecognised functionName ' functionName]); - end -else - disp('For alpha = 1 use functions in MIToolbox.m'); - disp('as those functions are the implementation of Shannon''s Information Theory'); -end diff --git a/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.c b/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.c deleted file mode 100644 index 03ab076..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.c +++ /dev/null @@ -1,197 +0,0 @@ -/******************************************************************************* -** -** RenyiMIToolboxMex.cpp -** is the MATLAB entry point for the Renyi Entropy and MI MIToolbox functions -** when called from a MATLAB/OCTAVE script. -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ -#include "MIToolbox.h" -#include "RenyiEntropy.h" -#include "RenyiMutualInformation.h" - -/******************************************************************************* -**entry point for the mex call -**nlhs - number of outputs -**plhs - pointer to array of outputs -**nrhs - number of inputs -**prhs - pointer to array of inputs -*******************************************************************************/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /***************************************************************************** - ** this function takes a flag and 2 or 3 other arguments - ** the first is a scalar alpha value, and the remainder are - ** arrays. It returns a Renyi entropy or mutual information using the - ** alpha divergence. - *****************************************************************************/ - - int flag, numberOfSamples, checkSamples, numberOfFeatures, checkFeatures; - double alpha; - double *dataVector, *firstVector, *secondVector, *output; - - /*if (nlhs != 1) - { - printf("Incorrect number of output arguments\n"); - }//if not 1 output - */ - if (nrhs == 3) - { - /*printf("Must be H(X)\n");*/ - } - else if (nrhs == 4) - { - /*printf("Must be H(XY), I(X;Y)\n");*/ - } - else - { - printf("Incorrect number of arguments, format is RenyiMIToolbox(\"FLAG\",varargin)\n"); - } - - /* number to function map - ** 1 = H(X) - ** 2 = H(XY) - ** 3 = I(X;Y) - */ - - flag = *mxGetPr(prhs[0]); - - switch (flag) - { - case 1: - { - /* - **H_{\alpha}(X) - */ - alpha = mxGetScalar(prhs[1]); - numberOfSamples = mxGetM(prhs[2]); - numberOfFeatures = mxGetN(prhs[2]); - dataVector = (double *) mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *) mxGetPr(plhs[0]); - - if (numberOfFeatures == 1) - { - /*double calculateRenyiEntropy(double alpha, double *dataVector, long vectorLength);*/ - *output = calculateRenyiEntropy(alpha,dataVector,numberOfSamples); - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 1 - H_{\alpha}(X)*/ - case 2: - { - /* - **H_{\alpha}(XY) - */ - alpha = mxGetScalar(prhs[1]); - - numberOfSamples = mxGetM(prhs[2]); - checkSamples = mxGetM(prhs[3]); - - numberOfFeatures = mxGetN(prhs[2]); - checkFeatures = mxGetN(prhs[3]); - - firstVector = mxGetPr(prhs[2]); - secondVector = mxGetPr(prhs[3]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if ((numberOfSamples == 0) || (checkSamples == 0)) - { - *output = 0.0; - } - else if (numberOfSamples == checkSamples) - { - /*double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, long vectorLength);*/ - *output = calculateJointRenyiEntropy(alpha,firstVector,secondVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 2 - H_{\alpha}(XY)*/ - case 3: - { - /* - **I_{\alpha}(X;Y) - */ - alpha = mxGetScalar(prhs[1]); - - numberOfSamples = mxGetM(prhs[2]); - checkSamples = mxGetM(prhs[3]); - - numberOfFeatures = mxGetN(prhs[2]); - checkFeatures = mxGetN(prhs[3]); - - firstVector = mxGetPr(prhs[2]); - secondVector = mxGetPr(prhs[3]); - - plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - if ((numberOfFeatures == 1) && (checkFeatures == 1)) - { - if ((numberOfSamples == 0) || (checkSamples == 0)) - { - *output = 0.0; - } - else if (numberOfSamples == checkSamples) - { - /*double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, long vectorLength);*/ - *output = calculateRenyiMIDivergence(alpha,firstVector,secondVector,numberOfSamples); - } - else - { - printf("Vector lengths do not match, they must be the same length"); - *output = -1.0; - } - } - else - { - printf("No columns in input\n"); - *output = -1.0; - } - break; - }/*case 3 - I_{\alpha}(X;Y)*/ - default: - { - printf("Unrecognised flag\n"); - break; - }/*default*/ - }/*switch(flag)*/ - - return; -}/*mexFunction()*/ diff --git a/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 b/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 deleted file mode 100755 index 6bff1db..0000000 Binary files a/FEAST/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 and /dev/null differ diff --git a/FEAST/FEAST/MIToolbox/RenyiMutualInformation.c b/FEAST/FEAST/MIToolbox/RenyiMutualInformation.c deleted file mode 100644 index dc6fd51..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiMutualInformation.c +++ /dev/null @@ -1,95 +0,0 @@ -/******************************************************************************* -** RenyiMutualInformation.cpp -** Part of the mutual information toolbox -** -** Contains functions to calculate the Renyi mutual information of -** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and -** the joint entropy difference -** -** Author: Adam Pocock -** Created 26/3/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#include "MIToolbox.h" -#include "CalculateProbability.h" -#include "RenyiEntropy.h" -#include "RenyiMutualInformation.h" - -double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, int vectorLength) -{ - double mutualInformation = 0.0; - int firstIndex,secondIndex; - int i; - double jointTemp = 0.0; - double seperateTemp = 0.0; - double invAlpha = 1.0 - alpha; - JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength); - - /* standard MI is D_KL(p(x,y)||p(x)p(y)) - ** which expands to - ** D_KL(p(x,y)||p(x)p(y)) = sum(p(x,y) * log(p(x,y)/(p(x)p(y)))) - ** - ** Renyi alpha divergence D_alpha(p(x,y)||p(x)p(y)) - ** expands to - ** D_alpha(p(x,y)||p(x)p(y)) = 1/(alpha-1) * log(sum((p(x,y)^alpha)*((p(x)p(y))^(1-alpha)))) - */ - - for (i = 0; i < state.numJointStates; i++) - { - firstIndex = i % state.numFirstStates; - secondIndex = i / state.numFirstStates; - - if ((state.jointProbabilityVector[i] > 0) && (state.firstProbabilityVector[firstIndex] > 0) && (state.secondProbabilityVector[secondIndex] > 0)) - { - jointTemp = pow(state.jointProbabilityVector[i],alpha); - seperateTemp = state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex]; - seperateTemp = pow(seperateTemp,invAlpha); - mutualInformation += (jointTemp * seperateTemp); - } - } - - mutualInformation = log(mutualInformation); - mutualInformation /= log(2.0); - mutualInformation /= (alpha-1.0); - - FREE_FUNC(state.firstProbabilityVector); - state.firstProbabilityVector = NULL; - FREE_FUNC(state.secondProbabilityVector); - state.secondProbabilityVector = NULL; - FREE_FUNC(state.jointProbabilityVector); - state.jointProbabilityVector = NULL; - - return mutualInformation; -}/*calculateRenyiMIDivergence(double, double *, double *, int)*/ - -double calculateRenyiMIJoint(double alpha, double *dataVector, double *targetVector, int vectorLength) -{ - double hY = calculateRenyiEntropy(alpha, targetVector, vectorLength); - double hX = calculateRenyiEntropy(alpha, dataVector, vectorLength); - - double hXY = calculateJointRenyiEntropy(alpha, dataVector, targetVector, vectorLength); - - double answer = hX + hY - hXY; - - return answer; -}/*calculateRenyiMIJoint(double, double*, double*, int)*/ - diff --git a/FEAST/FEAST/MIToolbox/RenyiMutualInformation.h b/FEAST/FEAST/MIToolbox/RenyiMutualInformation.h deleted file mode 100644 index 07eff09..0000000 --- a/FEAST/FEAST/MIToolbox/RenyiMutualInformation.h +++ /dev/null @@ -1,60 +0,0 @@ -/******************************************************************************* -** RenyiMutualInformation.h -** Part of the mutual information toolbox -** -** Contains functions to calculate the Renyi mutual information of -** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and -** the joint entropy difference -** -** Author: Adam Pocock -** Created 26/3/2010 -** -** Copyright 2010 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** This file is part of MIToolbox. -** -** MIToolbox is free software: you can redistribute it and/or modify -** it under the terms of the GNU Lesser General Public License as published by -** the Free Software Foundation, either version 3 of the License, or -** (at your option) any later version. -** -** MIToolbox 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 Lesser General Public License for more details. -** -** You should have received a copy of the GNU Lesser General Public License -** along with MIToolbox. If not, see . -** -*******************************************************************************/ - -#ifndef __Renyi_MutualInformation_H -#define __Renyi_MutualInformation_H - -#ifdef __cplusplus -extern "C" { -#endif - -/******************************************************************************* -** calculateRenyiMIDivergence returns the log base 2 Renyi mutual information -** between dataVector and targetVector, I_{\alpha}(X;Y), for \alpha != 1 -** This uses Renyi's generalised alpha divergence as the difference measure -** instead of the KL-divergence as in Shannon's Mutual Information -** -** length(dataVector) == length(targetVector) == vectorLength otherwise there -** will be a segmentation fault -*******************************************************************************/ -double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, int vectorLength); - -/* This function returns a different value to the alpha divergence mutual -** information, and thus is not a correct mutual information -double calculateRenyiMIJoint(double alpha, double *dataVector, double *targetVector, int vectorLength); -*/ - -#ifdef __cplusplus -} -#endif - -#endif - diff --git a/FEAST/FEAST/MIToolbox/cmi.m b/FEAST/FEAST/MIToolbox/cmi.m deleted file mode 100644 index 30e4bb0..0000000 --- a/FEAST/FEAST/MIToolbox/cmi.m +++ /dev/null @@ -1,31 +0,0 @@ -function output = cmi(X,Y,Z) -%function output = cmi(X,Y,Z) -%X, Y & Z can be matrices which are converted into a joint variable -%before computation -% -%expects variables to be column-wise -% -%returns the mutual information between X and Y conditioned on Z, I(X;Y|Z) - -if nargin == 3 - if (size(X,2)>1) - mergedFirst = MIToolboxMex(3,X); - else - mergedFirst = X; - end - if (size(Y,2)>1) - mergedSecond = MIToolboxMex(3,Y); - else - mergedSecond = Y; - end - if (size(Z,2)>1) - mergedThird = MIToolboxMex(3,Z); - else - mergedThird = Z; - end - [output] = MIToolboxMex(8,mergedFirst,mergedSecond,mergedThird); -elseif nargin == 2 - output = mi(X,Y); -else - output = 0; -end diff --git a/FEAST/FEAST/MIToolbox/condh.m b/FEAST/FEAST/MIToolbox/condh.m deleted file mode 100644 index 9f966db..0000000 --- a/FEAST/FEAST/MIToolbox/condh.m +++ /dev/null @@ -1,26 +0,0 @@ -function output = condh(X,Y) -%function output = condh(X,Y) -%X & Y can be matrices which are converted into a joint variable -%before computation -% -%expects variables to be column-wise -% -%returns the conditional entropy of X given Y, H(X|Y) - -if nargin == 2 - if (size(X,2)>1) - mergedFirst = MIToolboxMex(3,X); - else - mergedFirst = X; - end - if (size(Y,2)>1) - mergedSecond = MIToolboxMex(3,Y); - else - mergedSecond = Y; - end - [output] = MIToolboxMex(6,mergedFirst,mergedSecond); -elseif nargin == 1 - output = h(X); -else - output = 0; -end diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM.m b/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM.m deleted file mode 100644 index 8ae1f7c..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM.m +++ /dev/null @@ -1,49 +0,0 @@ -function selectedFeatures = CMIM(k, featureMatrix, classColumn) -%function selectedFeatures = CMIM(k, featureMatrix, classColumn) -%Computes conditional mutual information maximisation algorithm from -%"Fast Binary Feature Selection with Conditional Mutual Information" -%by F. Fleuret (2004) - -%Computes the top k features from -%a dataset featureMatrix with n training examples and m features -%with the classes held in classColumn. - -noOfTraining = size(classColumn,1); -noOfFeatures = size(featureMatrix,2); - -partialScore = zeros(noOfFeatures,1); -m = zeros(noOfFeatures,1); -score = 0; -answerFeatures = zeros(k,1); -highestMI = 0; -highestMICounter = 0; - -for n = 1 : noOfFeatures - partialScore(n) = mi(featureMatrix(:,n),classColumn); - if partialScore(n) > highestMI - highestMI = partialScore(n); - highestMICounter = n; - end -end - -answerFeatures(1) = highestMICounter; - -for i = 2 : k - score = 0; - limitI = i - 1; - for n = 1 : noOfFeatures - while ((partialScore(n) >= score) && (m(n) < limitI)) - m(n) = m(n) + 1; - conditionalInfo = cmi(featureMatrix(:,n),classColumn,featureMatrix(:,answerFeatures(m(n)))); - if partialScore(n) > conditionalInfo - partialScore(n) = conditionalInfo; - end - end - if partialScore(n) >= score - score = partialScore(n); - answerFeatures(i) = n; - end - end -end - -selectedFeatures = answerFeatures; diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c b/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c deleted file mode 100644 index daacb34..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c +++ /dev/null @@ -1,158 +0,0 @@ -/******************************************************************************* -** Demonstration feature selection algorithm - MATLAB r2009a -** -** Initial Version - 13/06/2008 -** Updated - 07/07/2010 -** based on CMIM.m -** -** Conditional Mutual Information Maximisation -** in -** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation -** F. Fleuret (2004) -** -** Author - Adam Pocock -** Demonstration code for MIToolbox -*******************************************************************************/ - -#include "mex.h" -#include "MutualInformation.h" - -void CMIMCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values - **the class MI doubles as the partial score from the CMIM paper - */ - double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); - /*in the CMIM paper, m = lastUsedFeature*/ - int *lastUsedFeature = (int *)mxCalloc(noOfFeatures,sizeof(int)); - - double score, conditionalInfo; - int iMinus, currentFeature; - - double maxMI = 0.0; - int maxMICounter = -1; - - int j,i; - - double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < noOfFeatures;i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - outputFeatures[0] = maxMICounter; - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the CMIM algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - iMinus = i-1; - - for (j = 0; j < noOfFeatures; j++) - { - while ((classMI[j] > score) && (lastUsedFeature[j] < i)) - { - /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ - currentFeature = (int) outputFeatures[lastUsedFeature[j]]; - conditionalInfo = calculateConditionalMutualInformation(feature2D[j],classColumn,feature2D[currentFeature],noOfSamples); - if (classMI[j] > conditionalInfo) - { - classMI[j] = conditionalInfo; - }/*reset classMI*/ - /*moved due to C indexing from 0 rather than 1*/ - lastUsedFeature[j] += 1; - }/*while partial score greater than score & not reached last feature*/ - if (classMI[j] > score) - { - score = classMI[j]; - outputFeatures[i] = j; - }/*if partial score still greater than score*/ - }/*for number of features*/ - }/*for the number of features to select*/ - - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - -}/*CMIMCalculation*/ - -/*entry point for the mex call -**nlhs - number of outputs -**plhs - pointer to array of outputs -**nrhs - number of inputs -**prhs - pointer to array of inputs -*/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /************************************************************* - ** this function takes 3 arguments: - ** k = number of features to select, - ** featureMatrix[][] = matrix of features - ** classColumn[] = targets - ** the arguments should all be discrete integers. - ** and has one output: - ** selectedFeatures[] of size k - *************************************************************/ - - int k, numberOfFeatures, numberOfSamples, numberOfTargets; - double *featureMatrix, *targets, *output; - - - if (nlhs != 1) - { - printf("Incorrect number of output arguments"); - }/*if not 1 output*/ - if (nrhs != 3) - { - printf("Incorrect number of input arguments"); - }/*if not 3 inputs*/ - - /*get the number of features to select, cast out as it is a double*/ - k = (int) mxGetScalar(prhs[0]); - - numberOfFeatures = mxGetN(prhs[1]); - numberOfSamples = mxGetM(prhs[1]); - - numberOfTargets = mxGetM(prhs[2]); - - if (numberOfTargets != numberOfSamples) - { - printf("Number of targets must match number of samples\n"); - printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); - - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - }/*if size mismatch*/ - else - { - - featureMatrix = mxGetPr(prhs[1]); - targets = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void CMIMCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - CMIMCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - } - - return; -}/*mexFunction()*/ diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR.m b/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR.m deleted file mode 100644 index c8f5669..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR.m +++ /dev/null @@ -1,73 +0,0 @@ -function selectedFeatures = DISR(k, featureMatrix, classColumn) -%function selectedFeatures = DISR(k, featureMatrix, classColumn) -% -%Computers optimal features according to DISR algorithm from -%On the Use of variable "complementarity for feature selection" -%by P Meyer, G Bontempi (2006) -% -%Computes the top k features from -%a dataset featureMatrix with n training examples and m features -%with the classes held in classColumn. -% -%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y))) -%where SimRel = MI(Xij,Y) / H(Xij,Y) - -totalFeatures = size(featureMatrix,2); -classMI = zeros(totalFeatures,1); -unselectedFeatures = ones(totalFeatures,1); -score = 0; -currentScore = 0; -innerScore = 0; -iMinus = 0; -answerFeatures = zeros(k,1); -highestMI = 0; -highestMICounter = 0; -currentHighestFeature = 0; - -%create a matrix to hold the SRs of a feature pair. -%initialised to -1 as you can't get a negative SR. -featureSRMatrix = -(ones(k,totalFeatures)); - -for n = 1 : totalFeatures - classMI(n) = mi(featureMatrix(:,n),classColumn); - if classMI(n) > highestMI - highestMI = classMI(n); - highestMICounter = n; - end -end - -answerFeatures(1) = highestMICounter; -unselectedFeatures(highestMICounter) = 0; - -for i = 2 : k - score = 0; - currentHighestFeature = 0; - iMinus = i-1; - for j = 1 : totalFeatures - if unselectedFeatures(j) == 1 - %DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y))) - %where SimRel = MI(Xij,Y) / H(Xij,Y) - currentScore = 0; - for m = 1 : iMinus - if featureSRMatrix(m,j) == -1 - unionedFeatures = joint([featureMatrix(:,answerFeatures(m)),featureMatrix(:,j)]); - tempUnionMI = mi(unionedFeatures,classColumn); - tempTripEntropy = h([unionedFeatures,classColumn]); - featureSRMatrix(m,j) = tempUnionMI/tempTripEntropy; - end - - currentScore = currentScore + featureSRMatrix(m,j); - end - if (currentScore > score) - score = currentScore; - currentHighestFeature = j; - end - end - end - %now highest feature is selected in currentHighestFeature - %store it - unselectedFeatures(currentHighestFeature) = 0; - answerFeatures(i) = currentHighestFeature; -end - -selectedFeatures = answerFeatures; diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c b/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c deleted file mode 100644 index 617ca81..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c +++ /dev/null @@ -1,199 +0,0 @@ -/******************************************************************************* -** Demonstration feature selection algorithm - MATLAB r2009a -** -** Initial Version - 13/06/2008 -** Updated - 07/07/2010 -** based on DISR.m -** -** Double Input Symmetrical Relevance -** in -** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" -** P. Meyer and G. Bontempi (2006) -** -** Author - Adam Pocock -** Demonstration code for MIToolbox -*******************************************************************************/ - -#include "mex.h" -#include "MutualInformation.h" -#include "Entropy.h" -#include "ArrayOperations.h" - -void DISRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) -{ - /*holds the class MI values*/ - double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); - - char *selectedFeatures = (char *)mxCalloc(noOfFeatures,sizeof(char)); - - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)mxCalloc(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - double *mergedVector = (double *) mxCalloc(noOfSamples,sizeof(double)); - - int arrayPosition; - double mi, tripEntropy; - - int i,j,x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - - for (i = 0; i < noOfFeatures;i++) - { - /*calculate mutual info - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - */ - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /***************************************************************************** - ** We have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the DISR algorithm - *****************************************************************************/ - - for (i = 1; i < k; i++) - { - score = 0.0; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (x = 0; x < i; x++) - { - arrayPosition = x*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - /* - **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); - **double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); - */ - - mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); - mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); - tripEntropy = calculateJointEntropy(mergedVector, classColumn, noOfSamples); - - featureMIMatrix[arrayPosition] = mi / tripEntropy; - }/*if not already known*/ - currentScore += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - mxFree(mergedVector); - mergedVector = NULL; - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - -}/*DISRCalculation(double[][],double[])*/ - -/*entry point for the mex call -**nlhs - number of outputs -**plhs - pointer to array of outputs -**nrhs - number of inputs -**prhs - pointer to array of inputs -*/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /************************************************************* - ** this function takes 3 arguments: - ** k = number of features to select, - ** featureMatrix[][] = matrix of features - ** classColumn[] = targets - ** the arguments should all be discrete integers. - ** and has one output: - ** selectedFeatures[] of size k - *************************************************************/ - - int k, numberOfFeatures, numberOfSamples, numberOfTargets; - double *featureMatrix, *targets, *output; - - - if (nlhs != 1) - { - printf("Incorrect number of output arguments"); - }/*if not 1 output*/ - if (nrhs != 3) - { - printf("Incorrect number of input arguments"); - }/*if not 3 inputs*/ - - /*get the number of features to select, cast out as it is a double*/ - k = (int) mxGetScalar(prhs[0]); - - numberOfFeatures = mxGetN(prhs[1]); - numberOfSamples = mxGetM(prhs[1]); - - numberOfTargets = mxGetM(prhs[2]); - - if (numberOfTargets != numberOfSamples) - { - printf("Number of targets must match number of samples\n"); - printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); - - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - }/*if size mismatch*/ - else - { - - featureMatrix = mxGetPr(prhs[1]); - targets = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void DISRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - DISRCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - } - - return; -}/*mexFunction()*/ diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/IAMB.m b/FEAST/FEAST/MIToolbox/demonstration_algorithms/IAMB.m deleted file mode 100644 index 1011260..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/IAMB.m +++ /dev/null @@ -1,56 +0,0 @@ -function [cmb association] = IAMB( data, targetindex, THRESHOLD) -%function [cmb association] = IAMB( data, targetindex, THRESHOLD) -% -%Performs the IAMB algorithm of Tsmardinos et al. (2003) -%from "Towards principled feature selection: Relevancy, filters and wrappers" - -if (nargin == 2) - THRESHOLD = 0.02; -end - -numf = size(data,2); -targets = data(:,targetindex); -data(:,targetindex) = -10; - -cmb = []; - -finished = false; -while ~finished - for n = 1:numf - cmbVector = joint(data(:,cmb)); - if isempty(cmb) - association(n) = mi( data(:,n), targets ); - end - - if ismember(n,cmb) - association(n) = -10; %arbtirary large negative constant - else - association(n) = cmi( data(:,n), targets, cmbVector); - end - end - - [maxval maxidx] = max(association); - if maxval < THRESHOLD - finished = true; - else - cmb = [ cmb maxidx ]; - end -end - -finished = false; -while ~finished && ~isempty(cmb) - association = []; - for n = 1:length(cmb) - cmbwithoutn = cmb; - cmbwithoutn(n)=[]; - association(n) = cmi( data(:,cmb(n)), targets, data(:,cmbwithoutn) ); - end - - [minval minidx] = min(association); - if minval > THRESHOLD - finished = true; - else - cmb(minidx) = []; - end -end - diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m b/FEAST/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m deleted file mode 100644 index 65464b3..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m +++ /dev/null @@ -1,3 +0,0 @@ -mex -I.. CMIM_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c -mex -I.. DISR_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c -mex -I.. mRMR_D_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c \ No newline at end of file diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m b/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m deleted file mode 100644 index 50b14bc..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m +++ /dev/null @@ -1,69 +0,0 @@ -function selectedFeatures = mRMR_D(k, featureMatrix, classColumn) -%function selectedFeatures = mRMR_D(k, featureMatrix, classColumn) -% -%Selects optimal features according to the mRMR-D algorithm from -%"Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" -%by H. Peng et al. (2005) -% -%Calculates the top k features -%a dataset featureMatrix with n training examples and m features -%with the classes held in classColumn (an n x 1 vector) - -noOfTraining = size(classColumn,1); -noOfFeatures = size(featureMatrix,2); -unselectedFeatures = ones(noOfFeatures,1); - -classMI = zeros(noOfFeatures,1); -answerFeatures = zeros(k,1); -highestMI = 0; -highestMICounter = 0; -currentHighestFeature = 0; - -featureMIMatrix = -(ones(k,noOfFeatures)); - -%setup the mi against the class -for n = 1 : noOfFeatures - classMI(n) = mi(featureMatrix(:,n),classColumn); - if classMI(n) > highestMI - highestMI = classMI(n); - highestMICounter = n; - end -end - -answerFeatures(1) = highestMICounter; -unselectedFeatures(highestMICounter) = 0; - -%iterate over the number of features to select -for i = 2:k - score = -100; - currentHighestFeature = 0; - iMinus = i-1; - for j = 1 : noOfFeatures - if unselectedFeatures(j) == 1 - currentMIScore = 0; - for m = 1 : iMinus - if featureMIMatrix(m,j) == -1 - featureMIMatrix(m,j) = mi(featureMatrix(:,j),featureMatrix(:,answerFeatures(m))); - end - currentMIScore = currentMIScore + featureMIMatrix(m,j); - end - currentScore = classMI(j) - (currentMIScore/iMinus); - - if (currentScore > score) - score = currentScore; - currentHighestFeature = j; - end - end - end - - if score < 0 - disp(['at selection ' int2str(j) ' mRMRD is negative with value ' num2str(score)]); - end - - %now highest feature is selected in currentHighestFeature - %store it - unselectedFeatures(currentHighestFeature) = 0; - answerFeatures(i) = currentHighestFeature; -end - -selectedFeatures = answerFeatures; diff --git a/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c b/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c deleted file mode 100644 index 8d7b074..0000000 --- a/FEAST/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c +++ /dev/null @@ -1,184 +0,0 @@ -/******************************************************************************* -** Demonstration feature selection algorithm - MATLAB r2009a -** -** Initial Version - 13/06/2008 -** Updated - 07/07/2010 -** based on mRMR_D.m -** -** Minimum Relevance Maximum Redundancy -** in -** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" -** H. Peng et al. (2005) -** -** Author - Adam Pocock -** Demonstration code for MIToolbox -*******************************************************************************/ - -#include "mex.h" -#include "MutualInformation.h" - -void mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) -{ - double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); - /*holds the class MI values*/ - double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); - int *selectedFeatures = (int *)mxCalloc(noOfFeatures,sizeof(int)); - /*holds the intra feature MI values*/ - int sizeOfMatrix = k*noOfFeatures; - double *featureMIMatrix = (double *)mxCalloc(sizeOfMatrix,sizeof(double)); - - double maxMI = 0.0; - int maxMICounter = -1; - - /*init variables*/ - - double score, currentScore, totalFeatureMI; - int currentHighestFeature; - - int arrayPosition, i, j, x; - - for(j = 0; j < noOfFeatures; j++) - { - feature2D[j] = featureMatrix + (int)j*noOfSamples; - } - - for (i = 0; i < sizeOfMatrix;i++) - { - featureMIMatrix[i] = -1; - }/*for featureMIMatrix - blank to -1*/ - - - for (i = 0; i < noOfFeatures;i++) - { - classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); - if (classMI[i] > maxMI) - { - maxMI = classMI[i]; - maxMICounter = i; - }/*if bigger than current maximum*/ - }/*for noOfFeatures - filling classMI*/ - - selectedFeatures[maxMICounter] = 1; - outputFeatures[0] = maxMICounter; - - /************* - ** Now we have populated the classMI array, and selected the highest - ** MI feature as the first output feature - ** Now we move into the mRMR-D algorithm - *************/ - - for (i = 1; i < k; i++) - { - /*to ensure it selects some features - **if this is zero then it will not pick features where the redundancy is greater than the - **relevance - */ - score = -1000.0; - currentHighestFeature = 0; - currentScore = 0.0; - totalFeatureMI = 0.0; - - for (j = 0; j < noOfFeatures; j++) - { - /*if we haven't selected j*/ - if (selectedFeatures[j] == 0) - { - currentScore = classMI[j]; - totalFeatureMI = 0.0; - - for (x = 0; x < i; x++) - { - arrayPosition = x*noOfFeatures + j; - if (featureMIMatrix[arrayPosition] == -1) - { - /*work out intra MI*/ - - /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ - featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[x]], feature2D[j], noOfSamples); - } - - totalFeatureMI += featureMIMatrix[arrayPosition]; - }/*for the number of already selected features*/ - - currentScore -= (totalFeatureMI/i); - if (currentScore > score) - { - score = currentScore; - currentHighestFeature = j; - } - }/*if j is unselected*/ - }/*for number of features*/ - - selectedFeatures[currentHighestFeature] = 1; - outputFeatures[i] = currentHighestFeature; - - }/*for the number of features to select*/ - - for (i = 0; i < k; i++) - { - outputFeatures[i] += 1; /*C indexes from 0 not 1*/ - }/*for number of selected features*/ - -}/*mRMRCalculation(double[][],double[])*/ - -/*entry point for the mex call -**nlhs - number of outputs -**plhs - pointer to array of outputs -**nrhs - number of inputs -**prhs - pointer to array of inputs -*/ -void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) -{ - /************************************************************* - ** this function takes 3 arguments: - ** k = number of features to select, - ** featureMatrix[][] = matrix of features - ** classColumn[] = targets - ** the arguments should all be discrete integers. - ** and has one output: - ** selectedFeatures[] of size k - *************************************************************/ - - int k, numberOfFeatures, numberOfSamples, numberOfTargets; - double *featureMatrix, *targets, *output; - - - if (nlhs != 1) - { - printf("Incorrect number of output arguments"); - }/*if not 1 output*/ - if (nrhs != 3) - { - printf("Incorrect number of input arguments"); - }/*if not 3 inputs*/ - - /*get the number of features to select, cast out as it is a double*/ - k = (int) mxGetScalar(prhs[0]); - - numberOfFeatures = mxGetN(prhs[1]); - numberOfSamples = mxGetM(prhs[1]); - - numberOfTargets = mxGetM(prhs[2]); - - if (numberOfTargets != numberOfSamples) - { - printf("Number of targets must match number of samples\n"); - printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); - - plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); - }/*if size mismatch*/ - else - { - - featureMatrix = mxGetPr(prhs[1]); - targets = mxGetPr(prhs[2]); - - plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); - output = (double *)mxGetPr(plhs[0]); - - /*void mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ - mRMRCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); - } - - return; -}/*mexFunction()*/ diff --git a/FEAST/FEAST/MIToolbox/h.m b/FEAST/FEAST/MIToolbox/h.m deleted file mode 100644 index 8fd8999..0000000 --- a/FEAST/FEAST/MIToolbox/h.m +++ /dev/null @@ -1,13 +0,0 @@ -function output = h(X) -%function output = h(X) -%X can be a matrix which is converted into a joint variable before calculation -%expects variables to be column-wise -% -%returns the entropy of X, H(X) - -if (size(X,2)>1) - mergedVector = MIToolboxMex(3,X); -else - mergedVector = X; -end -[output] = MIToolboxMex(4,mergedVector); diff --git a/FEAST/FEAST/MIToolbox/joint.m b/FEAST/FEAST/MIToolbox/joint.m deleted file mode 100644 index f40ff25..0000000 --- a/FEAST/FEAST/MIToolbox/joint.m +++ /dev/null @@ -1,16 +0,0 @@ -function output = joint(X,arities) -%function output = joint(X,arities) -%returns the joint random variable of the matrix X -%assuming the variables are in columns -% -%if passed a vector of the arities then it produces a correct -%joint variable, otherwise it may not include all states -% -%if the joint variable is only compared with variables using the same samples, -%then arity information is not required - -if (nargin == 2) - [output] = MIToolboxMex(3,X,arities); -else - [output] = MIToolboxMex(3,X); -end diff --git a/FEAST/FEAST/MIToolbox/mi.m b/FEAST/FEAST/MIToolbox/mi.m deleted file mode 100644 index 2fd8766..0000000 --- a/FEAST/FEAST/MIToolbox/mi.m +++ /dev/null @@ -1,20 +0,0 @@ -function output = mi(X,Y) -%function output = mi(X,Y) -%X & Y can be matrices which are converted into a joint variable -%before computation -% -%expects variables to be column-wise -% -%returns the mutual information between X and Y, I(X;Y) - -if (size(X,2)>1) - mergedFirst = MIToolboxMex(3,X); -else - mergedFirst = X; -end -if (size(Y,2)>1) - mergedSecond = MIToolboxMex(3,Y); -else - mergedSecond = Y; -end -[output] = MIToolboxMex(7,mergedFirst,mergedSecond); diff --git a/FEAST/FSToolbox/BetaGamma.c b/FEAST/FSToolbox/BetaGamma.c new file mode 100644 index 0000000..925ef8b --- /dev/null +++ b/FEAST/FSToolbox/BetaGamma.c @@ -0,0 +1,188 @@ +/******************************************************************************* +** betaGamma() implements the Beta-Gamma space from Brown (2009). +** This incoporates MIFS, CIFE, and CondRed. +** +** MIFS - "Using mutual information for selecting features in supervised neural net learning" +** R. Battiti, IEEE Transactions on Neural Networks, 1994 +** +** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" +** D. Lin and X. Tang, European Conference on Computer Vision (2006) +** +** The Beta Gamma space is explained in Brown (2009) and Brown et al. (2011) +** +** Initial Version - 13/06/2008 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" + +double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double betaParam, double gammaParam) +{ + double **feature2D = (double **) CALLOC_FUNC(noOfFeatures,sizeof(double *)); + + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double score, currentScore, totalFeatureMI; + int currentHighestFeature, arrayPosition; + + int i,j,m; + + /*********************************************************** + ** because the array is passed as + ** s a m p l e s + ** f + ** e + ** a + ** t + ** u + ** r + ** e + ** s + ** + ** this pulls out a pointer to the first sample of + ** each feature and stores it as a multidimensional array + ** so it can be indexed nicely + ***********************************************************/ + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix; i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + /*********************************************************** + ** SETUP COMPLETE + ** Algorithm starts here + ***********************************************************/ + + for (i = 0; i < noOfFeatures; i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /************* + ** Now we have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the JMI algorithm + *************/ + + for (i = 1; i < k; i++) + { + /************************************************************ + ** to ensure it selects some features + ** if this is zero then it will not pick features where the + ** redundancy is greater than the relevance + ************************************************************/ + score = -HUGE_VAL; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (!selectedFeatures[j]) + { + currentScore = classMI[j]; + totalFeatureMI = 0.0; + + for (m = 0; m < i; m++) + { + arrayPosition = m*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + featureMIMatrix[arrayPosition] = betaParam*calculateMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], noOfSamples); + + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double* conditionVector, int vectorLength);*/ + featureMIMatrix[arrayPosition] -= gammaParam*calculateConditionalMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], classColumn, noOfSamples); + }/*if not already known*/ + + totalFeatureMI += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + currentScore -= (totalFeatureMI); + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C++ indexes from 0 not 1*/ + }/*for number of selected features*/ + + return outputFeatures; +}/*BetaGamma(int,int,int,double[][],double[],double[],double,double)*/ + diff --git a/FEAST/FSToolbox/CMIM.c b/FEAST/FSToolbox/CMIM.c new file mode 100644 index 0000000..9ef21ad --- /dev/null +++ b/FEAST/FSToolbox/CMIM.c @@ -0,0 +1,142 @@ +/******************************************************************************* +** CMIM.c, implements a discrete version of the +** Conditional Mutual Information Maximisation criterion, using the fast +** exact implementation from +** +** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" +** F. Fleuret, JMLR (2004) +** +** Initial Version - 13/06/2008 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" + +double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values + **the class MI doubles as the partial score from the CMIM paper + */ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + /*in the CMIM paper, m = lastUsedFeature*/ + int *lastUsedFeature = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int)); + + double score, conditionalInfo; + int iMinus, currentFeature; + + double maxMI = 0.0; + int maxMICounter = -1; + + int j,i; + + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the CMIM algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + iMinus = i-1; + + for (j = 0; j < noOfFeatures; j++) + { + while ((classMI[j] > score) && (lastUsedFeature[j] < i)) + { + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ + currentFeature = (int) outputFeatures[lastUsedFeature[j]]; + conditionalInfo = calculateConditionalMutualInformation(feature2D[j],classColumn,feature2D[currentFeature],noOfSamples); + if (classMI[j] > conditionalInfo) + { + classMI[j] = conditionalInfo; + }/*reset classMI*/ + /*moved due to C indexing from 0 rather than 1*/ + lastUsedFeature[j] += 1; + }/*while partial score greater than score & not reached last feature*/ + if (classMI[j] > score) + { + score = classMI[j]; + outputFeatures[i] = j; + }/*if partial score still greater than score*/ + }/*for number of features*/ + }/*for the number of features to select*/ + + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(lastUsedFeature); + FREE_FUNC(feature2D); + + classMI = NULL; + lastUsedFeature = NULL; + feature2D = NULL; + + return outputFeatures; +}/*CMIM(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/FSToolbox/CompileFEAST.m b/FEAST/FSToolbox/CompileFEAST.m new file mode 100644 index 0000000..f5dad48 --- /dev/null +++ b/FEAST/FSToolbox/CompileFEAST.m @@ -0,0 +1,4 @@ +%Compiles the FEAST Toolbox into a mex executable for use with MATLAB + +mex -I../MIToolbox FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c ../MIToolbox/MutualInformation.c ../MIToolbox/Entropy.c ../MIToolbox/CalculateProbability.c ../MIToolbox/ArrayOperations.c + diff --git a/FEAST/FSToolbox/CondMI.c b/FEAST/FSToolbox/CondMI.c new file mode 100644 index 0000000..ea6f8ee --- /dev/null +++ b/FEAST/FSToolbox/CondMI.c @@ -0,0 +1,166 @@ +/******************************************************************************* +** CondMI.c, implements the CMI criterion using a greedy forward search +** +** Initial Version - 19/08/2010 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* for memcpy */ +#include + +/* MIToolbox includes */ +#include "MutualInformation.h" +#include "ArrayOperations.h" + +double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + + char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + double *conditionVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); + + int arrayPosition; + double mi, tripEntropy; + + int i,j,x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + for (i = 0; i < noOfFeatures;i++) + { + /*calculate mutual info + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + */ + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + memcpy(conditionVector,feature2D[maxMICounter],sizeof(double)*noOfSamples); + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the CondMI algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + currentHighestFeature = -1; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = 0.0; + totalFeatureMI = 0.0; + + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ + currentScore = calculateConditionalMutualInformation(feature2D[j],classColumn,conditionVector,noOfSamples); + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + outputFeatures[i] = currentHighestFeature; + + if (currentHighestFeature != -1) + { + selectedFeatures[currentHighestFeature] = 1; + mergeArrays(feature2D[currentHighestFeature],conditionVector,conditionVector,noOfSamples); + } + + }/*for the number of features to select*/ + + FREE_FUNC(classMI); + FREE_FUNC(conditionVector); + FREE_FUNC(feature2D); + FREE_FUNC(featureMIMatrix); + FREE_FUNC(selectedFeatures); + + classMI = NULL; + conditionVector = NULL; + feature2D = NULL; + featureMIMatrix = NULL; + selectedFeatures = NULL; + + return outputFeatures; +}/*CondMI(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/FSToolbox/DISR.c b/FEAST/FSToolbox/DISR.c new file mode 100644 index 0000000..3ec7676 --- /dev/null +++ b/FEAST/FSToolbox/DISR.c @@ -0,0 +1,182 @@ +/******************************************************************************* +** DISR.c, implements the Double Input Symmetrical Relevance criterion +** from +** +** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" +** P. Meyer and G. Bontempi, (2006) +** +** Initial Version - 13/06/2008 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" +#include "Entropy.h" +#include "ArrayOperations.h" + +double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + + char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + double *mergedVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); + + int arrayPosition; + double mi, tripEntropy; + + int i,j,x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + + for (i = 0; i < noOfFeatures;i++) + { + /*calculate mutual info + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + */ + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the DISR algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (x = 0; x < i; x++) + { + arrayPosition = x*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + /* + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + **double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); + */ + + mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); + mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); + tripEntropy = calculateJointEntropy(mergedVector, classColumn, noOfSamples); + + featureMIMatrix[arrayPosition] = mi / tripEntropy; + }/*if not already known*/ + currentScore += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(mergedVector); + FREE_FUNC(feature2D); + FREE_FUNC(featureMIMatrix); + FREE_FUNC(selectedFeatures); + + classMI = NULL; + mergedVector = NULL; + feature2D = NULL; + featureMIMatrix = NULL; + selectedFeatures = NULL; + + return outputFeatures; +}/*DISR(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/FSToolbox/FCBF.m b/FEAST/FSToolbox/FCBF.m new file mode 100644 index 0000000..dcaf3bf --- /dev/null +++ b/FEAST/FSToolbox/FCBF.m @@ -0,0 +1,58 @@ +function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold) +%function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold) +% +%Performs feature selection using the FCBF measure by Yu and Liu 2004. +% +%Instead of selecting a fixed number of features it provides a relevancy threshold and selects all +%features which score above that and are not redundant +% +% The license is in the license.txt provided. + +numFeatures = size(featureMatrix,2); +classScore = zeros(numFeatures,1); + +for i = 1:numFeatures + classScore(i) = SU(featureMatrix(:,i),classColumn); +end + +[classScore indexScore] = sort(classScore,1,'descend'); + +indexScore = indexScore(classScore > threshold); +classScore = classScore(classScore > threshold); + +if ~isempty(indexScore) + curPosition = 1; +else + curPosition = 0; +end + +while curPosition <= length(indexScore) + j = curPosition + 1; + curFeature = indexScore(curPosition); + while j <= length(indexScore) + scoreij = SU(featureMatrix(:,curFeature),featureMatrix(:,indexScore(j))); + if scoreij > classScore(j) + indexScore(j) = []; + classScore(j) = []; + else + j = j + 1; + end + end + curPosition = curPosition + 1; +end + +selectedFeatures = indexScore; + +end + +function [score] = SU(firstVector,secondVector) +%function [score] = SU(firstVector,secondVector) +% +%calculates SU = 2 * (I(X;Y)/(H(X) + H(Y))) + +hX = h(firstVector); +hY = h(secondVector); +iXY = mi(firstVector,secondVector); + +score = (2 * iXY) / (hX + hY); +end diff --git a/FEAST/FSToolbox/FSAlgorithms.h b/FEAST/FSToolbox/FSAlgorithms.h new file mode 100644 index 0000000..e6ddba8 --- /dev/null +++ b/FEAST/FSToolbox/FSAlgorithms.h @@ -0,0 +1,138 @@ +/******************************************************************************* +** +** FSAlgorithms.h +** Provides the function definitions for the list of algorithms implemented +** in the FSToolbox. +** +** Author: Adam Pocock +** Created: 27/06/2011 +** +** Copyright 2010/2011 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** Part of the FEAture Selection Toolbox (FEAST), please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +/******************************************************************************* + * All algorithms take an integer k which determines how many features to + * select, the number of samples and the number of features. Additionally each + * algorithm takes pointers to the data matrix, and the label vector, and + * a pointer to the output vector. The output vector should be pre-allocated + * with sizeof(double)*k bytes. + * + * Some algorithms take additional parameters, which given at the end of the + * standard parameter list. + * + * Each algorithm uses a forward search, and selects the feature which has + * the maxmimum MI with the labels first. + * + * All the algorithms except CMIM use an optimised variant which caches the + * previously calculated MI values. This trades space for time, but can + * allocate large amounts of memory. CMIM uses the optimised implementation + * given in Fleuret (2004). + *****************************************************************************/ + +#ifndef __FSAlgorithms_H +#define __FSAlgorithms_H + +/******************************************************************************* +** mRMR_D() implements the minimum Relevance Maximum Redundancy criterion +** using the difference variant, from +** +** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" +** H. Peng et al. IEEE Pattern Analysis and Machine Intelligence (PAMI) (2005) +*******************************************************************************/ +double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** CMIM() implements a discrete version of the +** Conditional Mutual Information Maximisation criterion, using the fast +** exact implementation from +** +** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" +** F. Fleuret, JMLR (2004) +*******************************************************************************/ +double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** JMI() implements the JMI criterion from +** +** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" +** H. Yang and J. Moody, NIPS (1999) +*******************************************************************************/ +double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** DISR() implements the Double Input Symmetrical Relevance criterion +** from +** +** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" +** P. Meyer and G. Bontempi, (2006) +*******************************************************************************/ +double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** ICAP() implements the Interaction Capping criterion from +** +** "Machine Learning Based on Attribute Interactions" +** A. Jakulin, PhD Thesis (2005) +*******************************************************************************/ +double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** CondMI() implements the CMI criterion using a greedy forward search +*******************************************************************************/ +double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** betaGamma() implements the Beta-Gamma space from Brown (2009). +** This incoporates MIFS, CIFE, and CondRed. +** +** MIFS - "Using mutual information for selecting features in supervised neural net learning" +** R. Battiti, IEEE Transactions on Neural Networks, 1994 +** +** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" +** D. Lin and X. Tang, European Conference on Computer Vision (2006) +** +** The Beta Gamma space is explained in our paper +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +*******************************************************************************/ +double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma); + +#endif diff --git a/FEAST/FSToolbox/FSToolbox.h b/FEAST/FSToolbox/FSToolbox.h new file mode 100644 index 0000000..bf8662b --- /dev/null +++ b/FEAST/FSToolbox/FSToolbox.h @@ -0,0 +1,70 @@ +/******************************************************************************* ** +** FSToolbox.h +** Provides the header files and #defines to ensure compatibility with MATLAB +** and C/C++. By default it compiles to MATLAB, if COMPILE_C is defined it +** links to the C memory allocation functions. +** +** Author: Adam Pocock +** Created: 27/06/2011 +** +** Copyright 2010/2011 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** Part of the FEAture Selection Toolbox (FEAST), please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#ifndef __FSToolbox_H +#define __FSToolbox_H + +#include +#include + +#ifdef COMPILE_C + #define C_IMPLEMENTATION + #include + #include + #define CALLOC_FUNC calloc + #define FREE_FUNC free +#else + #define MEX_IMPLEMENTATION + #include "mex.h" + #define CALLOC_FUNC mxCalloc + #define FREE_FUNC mxFree + #define printf mexPrintf /*for Octave-3.2*/ +#endif + +#endif + diff --git a/FEAST/FSToolbox/FSToolboxMex.c b/FEAST/FSToolbox/FSToolboxMex.c new file mode 100644 index 0000000..73a9197 --- /dev/null +++ b/FEAST/FSToolbox/FSToolboxMex.c @@ -0,0 +1,290 @@ +/******************************************************************************* +** FSToolboxMex.c is the entry point for the feature selection toolbox. +** It provides a MATLAB interface to the various selection algorithms. +** +** Initial Version - 27/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSToolbox.h" +#include "FSAlgorithms.h" +#include "Entropy.h" + +/****************************************************************************** +** entry point for the mex call +** nlhs - number of outputs +** plhs - pointer to array of outputs +** nrhs - number of inputs +** prhs - pointer to array of inputs +******************************************************************************/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /************************************************************* + ** this function takes 4-6 arguments: + ** flag = which algorithm to use, + ** k = number of features to select, + ** featureMatrix[][] = matrix of features, + ** classColumn[] = targets, + ** optionalParam1 = (path angle or beta value), + ** optionalParam2 = (gamma value), + ** the arguments should all be discrete integers. + ** and has one output: + ** selectedFeatures[] of size k + *************************************************************/ + + int flag, k; + double optionalParam1, optionalParam2; + int numberOfFeatures, numberOfSamples, numberOfTargets; + double *featureMatrix, *targets, *output, *outputFeatures; + + double entropyTest; + int i,j; + + /************************************************************ + ** number to function map + ** 1 = MIFS + ** 2 = mRMR + ** 3 = CMIM + ** 4 = JMI + ** 5 = DISR + ** 6 = CIFE + ** 7 = ICAP + ** 8 = CondRed + ** 9 = BetaGamma + ** 10 = CMI + *************************************************************/ + if (nlhs > 1) + { + printf("Incorrect number of output arguments"); + }/*if not 1 output*/ + if ((nrhs < 4) || (nrhs > 6)) + { + printf("Incorrect number of input arguments"); + return; + }/*if not 4-6 inputs*/ + + /*get the flag for which algorithm*/ + flag = (int) mxGetScalar(prhs[0]); + + /*get the number of features to select, cast out as it is a double*/ + k = (int) mxGetScalar(prhs[1]); + + numberOfFeatures = mxGetN(prhs[2]); + numberOfSamples = mxGetM(prhs[2]); + + numberOfTargets = mxGetM(prhs[3]); + + if (nrhs == 6) + { + optionalParam1 = (double) mxGetScalar(prhs[4]); + optionalParam2 = (double) mxGetScalar(prhs[5]); + } + else if (nrhs == 5) + { + optionalParam1 = (double) mxGetScalar(prhs[4]); + optionalParam2 = 0.0; + } + + if (numberOfTargets != numberOfSamples) + { + printf("Number of targets must match number of samples\n"); + printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); + + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + return; + }/*if size mismatch*/ + else if ((k < 1) || (k > numberOfFeatures)) + { + printf("You have requested k = %d features, which is not possible\n",k); + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + return; + } + else + { + featureMatrix = mxGetPr(prhs[2]); + targets = mxGetPr(prhs[3]); + + /*double calculateEntropy(double *dataVector, int vectorLength)*/ + entropyTest = calculateEntropy(targets,numberOfSamples); + if (entropyTest < 0.0000001) + { + printf("The class label Y has entropy of 0, therefore all mutual informations containing Y will be 0. No feature selection is performed\n"); + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + return; + } + else + { + /*printf("Flag = %d, k = %d, numFeatures = %d, numSamples = %d\n",flag,k,numberOfFeatures,numberOfSamples);*/ + switch (flag) + { + case 1: /* MIFS */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + if (nrhs == 4) + { + /* MIFS is Beta = 1, Gamma = 0 */ + optionalParam1 = 1.0; + optionalParam2 = 0.0; + } + + /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ + BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); + break; + } + case 2: /* mRMR */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void mRMR_D(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + mRMR_D(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + break; + } + case 3: /* CMIM */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void CMIM(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + CMIM(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + break; + } + case 4: /* JMI */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void JMI(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + JMI(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + break; + } + case 5: /* DISR */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void DISR(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + DISR(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + break; + } + case 6: /* CIFE */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /* CIFE is Beta = 1, Gamma = 1 */ + optionalParam1 = 1.0; + optionalParam2 = 1.0; + + /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ + BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); + break; + } + case 7: /* ICAP */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void ICAP(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output);*/ + ICAP(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + break; + } + case 8: /* CondRed */ + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /* CondRed is Beta = 0, Gamma = 1 */ + optionalParam1 = 0.0; + optionalParam2 = 1.0; + + /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ + BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); + break; + } + case 9: /* BetaGamma */ + { + if (nrhs != 6) + { + printf("Insufficient arguments specified for Beta Gamma FS\n"); + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + return; + } + else + { + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void BetaGamma(int k, long noOfSamples, long noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma)*/ + BetaGamma(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output,optionalParam1,optionalParam2); + } + break; + } + case 10: /* CMI */ + { + output = (double *)mxCalloc(k,sizeof(double)); + + /*void CondMI(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + CondMI(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + + i = 0; + + while((output[i] != -1) && (i < k)) + { + i++; + } + + plhs[0] = mxCreateDoubleMatrix(i,1,mxREAL); + outputFeatures = (double *)mxGetPr(plhs[0]); + + for (j = 0; j < i; j++) + { + outputFeatures[j] = output[j] + 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + mxFree(output); + output = NULL; + break; + } + }/*switch on flag*/ + return; + } + } +}/*mex function entry*/ diff --git a/FEAST/FSToolbox/FSToolboxMex.mexa64 b/FEAST/FSToolbox/FSToolboxMex.mexa64 new file mode 100755 index 0000000..0015254 Binary files /dev/null and b/FEAST/FSToolbox/FSToolboxMex.mexa64 differ diff --git a/FEAST/FSToolbox/ICAP.c b/FEAST/FSToolbox/ICAP.c new file mode 100644 index 0000000..00953a7 --- /dev/null +++ b/FEAST/FSToolbox/ICAP.c @@ -0,0 +1,184 @@ +/******************************************************************************* +** ICAP.c implements the Interaction Capping criterion from +** +** "Machine Learning Based on Attribute Interactions" +** A. Jakulin, PhD Thesis (2005) +** +** Initial Version - 19/08/2010 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" + +double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); + + /*separates out the features*/ + double **feature2D = (double **) CALLOC_FUNC(noOfFeatures,sizeof(double *)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + double *featureCMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double score, currentScore, totalFeatureInteraction, interactionInfo; + int currentHighestFeature, arrayPosition; + + int i, j, m; + + for (j = 0; j < noOfFeatures; j++) + feature2D[j] = featureMatrix + (int) j * noOfSamples; + + for (i = 0; i < sizeOfMatrix; i++) + { + featureMIMatrix[i] = -1; + featureCMIMatrix[i] = -1; + }/*for featureMIMatrix and featureCMIMatrix - blank to -1*/ + + /*SETUP COMPLETE*/ + /*Algorithm starts here*/ + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /************* + ** Now we have populated the classMI array, and selected the highest + ** MI feature as the first output feature + *************/ + + for (i = 1; i < k; i++) + { + /********************************************************************** + ** to ensure it selects some features + **if this is zero then it will not pick features where the redundancy is greater than the + **relevance + **********************************************************************/ + score = -HUGE_VAL; + currentHighestFeature = 0; + currentScore = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (!selectedFeatures[j]) + { + currentScore = classMI[j]; + totalFeatureInteraction = 0.0; + + for (m = 0; m < i; m++) + { + arrayPosition = m*noOfFeatures + j; + + if (featureMIMatrix[arrayPosition] == -1) + { + /*work out interaction*/ + + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], noOfSamples); + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double* conditionVector, int vectorLength);*/ + featureCMIMatrix[arrayPosition] = calculateConditionalMutualInformation(feature2D[(int) outputFeatures[m]], feature2D[j], classColumn, noOfSamples); + }/*if not already known*/ + + interactionInfo = featureCMIMatrix[arrayPosition] - featureMIMatrix[arrayPosition]; + + if (interactionInfo < 0) + { + totalFeatureInteraction += interactionInfo; + } + }/*for the number of already selected features*/ + + currentScore += totalFeatureInteraction; + + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + /*C++ indexes from 0 not 1, so we need to increment all the feature indices*/ + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(feature2D); + FREE_FUNC(featureMIMatrix); + FREE_FUNC(featureCMIMatrix); + FREE_FUNC(selectedFeatures); + + classMI = NULL; + feature2D = NULL; + featureMIMatrix = NULL; + featureCMIMatrix = NULL; + selectedFeatures = NULL; + + return outputFeatures; +}/*ICAP(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/FSToolbox/JMI.c b/FEAST/FSToolbox/JMI.c new file mode 100644 index 0000000..30ef1bc --- /dev/null +++ b/FEAST/FSToolbox/JMI.c @@ -0,0 +1,177 @@ +/******************************************************************************* +** JMI.c implements the JMI criterion from +** +** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" +** H. Yang and J. Moody, NIPS (1999) +** +** Initial Version - 19/08/2010 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" +#include "ArrayOperations.h" + +double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + + char *selectedFeatures = (char *)CALLOC_FUNC(noOfFeatures,sizeof(char)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + double *mergedVector = (double *) CALLOC_FUNC(noOfSamples,sizeof(double)); + + int arrayPosition; + double mi, tripEntropy; + + int i,j,x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + + for (i = 0; i < noOfFeatures;i++) + { + /*calculate mutual info + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + */ + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the JMI algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (x = 0; x < i; x++) + { + arrayPosition = x*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); + + featureMIMatrix[arrayPosition] = mi; + }/*if not already known*/ + currentScore += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(feature2D); + FREE_FUNC(featureMIMatrix); + FREE_FUNC(mergedVector); + FREE_FUNC(selectedFeatures); + + classMI = NULL; + feature2D = NULL; + featureMIMatrix = NULL; + mergedVector = NULL; + selectedFeatures = NULL; + + return outputFeatures; + +}/*JMI(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/FSToolbox/MIM.m b/FEAST/FSToolbox/MIM.m new file mode 100644 index 0000000..31695e4 --- /dev/null +++ b/FEAST/FSToolbox/MIM.m @@ -0,0 +1,17 @@ +function [selectedFeatures scoreVector] = MIM(k, data, labels) +%function [selectedFeatures scoreVector] = MIM(k, data, labels) +% +%Mutual information Maximisation +% +% The license is in the license.txt provided. + +numf = size(data,2); +classMI = zeros(numf,1); + +for n = 1 : numf + classMI(n) = mi(data(:,n),labels); +end + +[scoreVector index] = sort(classMI,'descend'); + +selectedFeatures = index(1:k); diff --git a/FEAST/FSToolbox/Makefile b/FEAST/FSToolbox/Makefile new file mode 100644 index 0000000..c0806a9 --- /dev/null +++ b/FEAST/FSToolbox/Makefile @@ -0,0 +1,96 @@ +# makefile for FEAST +# Author: Adam Pocock, apocock@cs.man.ac.uk +# Created: 29/06/2011 +# +# Part of the Feature Selection Toolbox, please reference +# "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +# Information Feature Selection" +# G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +# Journal of Machine Learning Research (JMLR), 2012 +# +# Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +# +# Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without modification, +# are permitted provided that the following conditions are met: +# +# - Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# - Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# - Neither the name of The University of Manchester nor the names of its +# contributors may be used to endorse or promote products derived from this +# software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +CXXFLAGS = -O3 -fPIC +COMPILER = gcc +MITOOLBOXPATH = ../MIToolbox/ +objects = mRMR_D.o CMIM.o JMI.o DISR.o CondMI.o ICAP.o BetaGamma.o + +libFSToolbox.so : $(objects) + $(COMPILER) $(CXXFLAGS) -lMIToolbox -L$(MITOOLBOXPATH) -shared -o libFSToolbox.so $(objects) + +mRMR_D.o: mRMR_D.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c mRMR_D.c -I$(MITOOLBOXPATH) + +CMIM.o: CMIM.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CMIM.c -I$(MITOOLBOXPATH) + +JMI.o: JMI.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c JMI.c -I$(MITOOLBOXPATH) + +DISR.o: DISR.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c DISR.c -I$(MITOOLBOXPATH) + +CondMI.o: CondMI.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CondMI.c -I$(MITOOLBOXPATH) + +ICAP.o: ICAP.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c ICAP.c -I$(MITOOLBOXPATH) + +BetaGamma.o: BetaGamma.c + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c BetaGamma.c -I$(MITOOLBOXPATH) + +.PHONY : debug +debug: + $(MAKE) libFSToolbox.so "CXXFLAGS = -g -DDEBUG -fPIC" + +.PHONY : x86 +x86: + $(MAKE) libFSToolbox.so "CXXFLAGS = -O3 -fPIC -m32" + +.PHONY : x64 +x64: + $(MAKE) libFSToolbox.so "CXXFLAGS = -O3 -fPIC -m64" + +.PHONY : matlab +matlab: + mex -I$(MITOOLBOXPATH) FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c $(MITOOLBOXPATH)MutualInformation.c $(MITOOLBOXPATH)Entropy.c $(MITOOLBOXPATH)CalculateProbability.c $(MITOOLBOXPATH)ArrayOperations.c + +.PHONY : matlab-debug +matlab-debug: + mex -g -I$(MITOOLBOXPATH) FSToolboxMex.c BetaGamma.c CMIM.c CondMI.c DISR.c ICAP.c JMI.c mRMR_D.c $(MITOOLBOXPATH)MutualInformation.c $(MITOOLBOXPATH)Entropy.c $(MITOOLBOXPATH)CalculateProbability.c $(MITOOLBOXPATH)ArrayOperations.c + +.PHONY : intel +intel: + $(MAKE) libFSToolbox.so "COMPILER = icc" "CXXFLAGS = -O2 -fPIC -xHost" + +.PHONY : clean +clean: + rm *.o + rm libFSToolbox.so + diff --git a/FEAST/FSToolbox/README b/FEAST/FSToolbox/README new file mode 100644 index 0000000..1aae2d7 --- /dev/null +++ b/FEAST/FSToolbox/README @@ -0,0 +1,80 @@ +FEAST v1.0 +A feature selection toolbox for C/C++ and MATLAB/OCTAVE + +FEAST provides implementations of common mutual information based filter +feature selection algorithms, and an implementation of RELIEF. All +functions expect discrete inputs (except RELIEF, which does not depend +on the MIToolbox), and they return the selected feature indices. These +implementations were developed to help our research into the similarities +between these algorithms, and our results are presented in the following paper: + + Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection + G.Brown, A.Pocock, M.Lujan, M.-J.Zhao + Journal of Machine Learning Research (in press, to appear 2012) + +All FEAST code is licensed under the BSD 3-Clause License. +If you use these implementations for academic research please cite the paper above. + +Contains implementations of: + mim, mrmr, mifs, cmim, jmi, disr, cife, icap, condred, cmi, relief, fcbf, betagamma + +References for these algorithms are provided in the accompanying feast.bib file (in BibTeX format). + +MATLAB Example (using "data" as our feature matrix, and "labels" as the class label vector): + +>> size(data) +ans = + (569,30) %% denoting 569 examples, and 30 features + +>> selectedIndices = feast('jmi',5,data,labels) %% selecting the top 5 features using the jmi algorithm +selectedIndices = + + 28 + 21 + 8 + 27 + 23 + +>> selectedIndices = feast('mrmr',10,data,labels) %% selecting the top 10 features using the mrmr algorithm +selectedIndices = + + 28 + 24 + 22 + 8 + 27 + 21 + 29 + 4 + 7 + 25 + +>> selectedIndices = feast('mifs',5,data,labels,0.7) %% selecting the top 5 features using the mifs algorithm with beta = 0.7 +selectedIndices = + + 28 + 24 + 22 + 20 + 29 + +The library is written in ANSI C for compatibility with the MATLAB mex compiler, +except for MIM, FCBF and RELIEF, which are written in MATLAB/OCTAVE script. + +If you wish to use MIM in a C program you can use the BetaGamma function with +Beta = 0, Gamma = 0, as this is equivalent to MIM (but slower than the other implementation). +MIToolbox is required to compile these algorithms, and these implementations +supercede the example implementations given in that package (they have more robust behaviour +when used with unexpected inputs). + +MIToolbox can be found at: + http://www.cs.man.ac.uk/~gbrown/mitoolbox/ +and v1.03 is included in the ZIP for the FEAST package. + +Compilation instructions: + MATLAB/OCTAVE - run CompileFEAST.m, + Linux C shared library - use the included makefile + +Update History +08/11/2011 - v1.0 - Public Release to complement the JMLR publication. + diff --git a/FEAST/FSToolbox/RELIEF.m b/FEAST/FSToolbox/RELIEF.m new file mode 100644 index 0000000..194ce7b --- /dev/null +++ b/FEAST/FSToolbox/RELIEF.m @@ -0,0 +1,61 @@ +% RELIEF - Kira & Rendell 1992 +% T is number of patterns to use +% Defaults to all patterns if not specified. +% +% The license is in the license.txt provided. +% +% function w = RELIEF( data, labels, T ) +% +function [w bestidx] = RELIEF ( data, labels, T ) + +if ~exist('T','var') + T=size(data,1); +end + +idx = randperm(length(labels)); +idx = idx(1:T); + +w = zeros(size(data,2),1); +for t = 1:T + + x = data(idx(t),:); + y = labels(idx(t)); + + %copy the x + protos = repmat(x, length(labels), 1); + %measure the distance from x to every other example + distances = [sqrt(sum((data-protos).^2,2)) labels]; + %sort them according to distances (find nearest neighbours) + [distances originalidx] = sortrows(distances,1); + + foundhit = false; hitidx=0; + foundmiss = false; missidx=0; + i=2; %start from the second one + while (~foundhit || ~foundmiss) + + if distances(i,2) == y + hitidx = originalidx(i); + foundhit = true; + end + if distances(i,2) ~= y + missidx = originalidx(i); + foundmiss = true; + end + + i=i+1; + + end + + alpha = 1/T; + for f = 1:size(data,2)%each feature + hitpenalty = (x(f)-data(hitidx,f)) / (max(data(:,f))-min(data(:,f))); + misspenalty = (x(f)-data(missidx,f)) / (max(data(:,f))-min(data(:,f))); + + w(f) = w(f) - alpha*hitpenalty^2 + alpha*misspenalty^2; + end + +end + +[~,bestidx] = sort(w,'descend'); + + diff --git a/FEAST/FSToolbox/feast.bib b/FEAST/FSToolbox/feast.bib new file mode 100644 index 0000000..2c58f0d --- /dev/null +++ b/FEAST/FSToolbox/feast.bib @@ -0,0 +1,122 @@ +% MIM (Mutual Information Maximisation) +@inproceedings{MIM, + author = {David D. Lewis}, + title = {Feature Selection and Feature Extraction for Text Categorization}, + booktitle = {In Proceedings of Speech and Natural Language Workshop}, + year = {1992}, + pages = {212--217}, + publisher = {Morgan Kaufmann} +} + +% MIFS (Mutual Information Feature Selection ) +@article{MIFS, + author={Battiti, R.}, + journal={Neural Networks, IEEE Transactions on}, + title={Using mutual information for selecting features in supervised neural net learning}, + year={1994}, + month={jul}, + volume={5}, + number={4}, + pages={537 -550}, + ISSN={1045-9227} +} + +% mRMR (minimum Redundancy Maximum Relevance) +@article{mRMR, + title={Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy}, + author={Peng, H. and Long, F. and Ding, C.}, + journal={IEEE Transactions on pattern analysis and machine intelligence}, + pages={1226--1238}, + year={2005}, + publisher={Published by the IEEE Computer Society} +} + +% CMIM (Conditional Mutual Information Maximisation) +@article{CMIM, + author = {Fleuret, Fran\c{c}ois}, + title = {Fast Binary Feature Selection with Conditional Mutual Information}, + journal = {Journal of Machine Learning Research}, + volume = {5}, + month = {December}, + year = {2004}, + issn = {1532-4435}, + pages = {1531--1555}, + publisher = {JMLR.org} +} + +% JMI (Joint Mutual Information) +@inproceedings{JMI, + title={Feature selection based on joint mutual information}, + author={Yang, H. and Moody, J.}, + booktitle={Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis}, + pages={22--25}, + year={1999} +} + +% DISR (Double Input Symmetrical Relevance) +@incollection{DISR, + author = {Meyer, Patrick and Bontempi, Gianluca}, + title = {On the Use of Variable Complementarity for Feature Selection in Cancer Classification}, + booktitle = {Applications of Evolutionary Computing}, + publisher = {Springer Berlin / Heidelberg}, + pages = {91-102}, + volume = {3907}, + url = {http://dx.doi.org/10.1007/11732242_9}, + year = {2006} +} + +% ICAP (Interaction Capping) +@article{ICAP, + title={Machine learning based on attribute interactions}, + author={Jakulin, A.}, + journal={Fakulteta za racunalni{\v{s}}tvo in informatiko, Univerza v Ljubljani}, + year={2005} +} + +% CIFE (Conditional Informative Feature Extraction) +@incollection{CIFE + author = {Lin, Dahua and Tang, Xiaoou}, + title = {Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion}, + booktitle = {Computer Vision – ECCV 2006}, + series = {Lecture Notes in Computer Science}, + publisher = {Springer Berlin / Heidelberg}, + pages = {68-82}, + volume = {3951}, + url = {http://dx.doi.org/10.1007/11744023_6}, + year = {2006} +} + +% Beta Gamma Space +@inproceedings{BetaGamma, + title={A new perspective for information theoretic feature selection}, + author={Brown, G.}, + booktitle={12th International Conference on Artificial Intelligence and Statistics}, + volume={5}, + pages={49--56}, + year={2009} +} + +% FCBF (Fast Correlation-Based Filter) +@article{FCBF, + author = {Yu, Lei and Liu, Huan}, + title = {Efficient Feature Selection via Analysis of Relevance and Redundancy}, + journal = {Journal of Machine Learning Research}, + volume = {5}, + year = {2004}, + issn = {1532-4435}, + pages = {1205--1224}, + publisher = {JMLR.org} +} + +% RELIEF +@inproceedings{RELIEF, + author = {Kira, Kenji and Rendell, Larry A.}, + title = {The feature selection problem: traditional methods and a new algorithm}, + booktitle = {Proceedings of the tenth national conference on Artificial intelligence}, + series = {AAAI'92}, + year = {1992}, + pages = {129--134}, + publisher = {AAAI Press} +} + + diff --git a/FEAST/FSToolbox/feast.m b/FEAST/FSToolbox/feast.m new file mode 100644 index 0000000..96a685e --- /dev/null +++ b/FEAST/FSToolbox/feast.m @@ -0,0 +1,100 @@ +function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin) +%function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin) +% +%Provides access to the feature selection algorithms in FSToolboxMex +% +%Expects the features to be columns of the data matrix, and +%requires that both the features and labels are integers. +% +%Algorithms are called as follows +% +%[selectedFeatures] = feast('algName',numToSelect,data,labels) +% where algName is: +% mim, mrmr, cmim, jmi, disr, cife, icap, condred, cmi, relief +% +%[selectedFeatures] = feast('algName',numToSelect,data,labels,beta) +% where algName is: +% mifs (defaults to beta = 1.0 if unspecified) +% +%[selectedFeatures] = feast('algName',numToSelect,data,labels,beta,gamma) +% where algName is: +% betagamma +%[selectedFeatures] = feast('algName',numToSelect,data,labels,threshold) +% where algName is: +% fcbf (note this ignores the numToSelect) +% +% The license is in the license.txt provided. + + +%Internal FSToolbox Criteria to number mapping +%MIFS = 1 +%mRMR = 2 +%CMIM = 3 +%JMI = 4 +%DISR = 5 +%CIFE = 6 +%ICAP = 7 +%CondRed = 8 +%BetaGamma = 9 +%CMI = 10 +% + +if ((numToSelect < 1) || (numToSelect > size(data,2))) + error(['You have requested ' num2str(numToSelect) ' features, which is not possible']); +end + +finiteDataCount = sum(sum(isfinite(data))); +finiteLabelsCount = sum(sum(isfinite(labels))); + +totalData = numel(data); +totalLabels = numel(labels); + +if ((finiteDataCount ~= totalData) || (finiteLabelsCount ~= totalLabels)) + error(['Some elements are NaNs or infinite. Please check your data']); +end + +if (strcmpi(criteria,'mim')) + selectedFeatures = MIM(numToSelect,data,labels); +elseif (strcmpi(criteria,'mifs')) + if (nargin == 4) + beta = 1; + else + beta = varargin{1}; + end + selectedFeatures = FSToolboxMex(1,numToSelect,data,labels,beta); +elseif (strcmpi(criteria,'mrmr')) + selectedFeatures = FSToolboxMex(2,numToSelect,data,labels); +elseif (strcmpi(criteria,'cmim')) + selectedFeatures = FSToolboxMex(3,numToSelect,data,labels); +elseif (strcmpi(criteria,'jmi')) + selectedFeatures = FSToolboxMex(4,numToSelect,data,labels); +elseif (strcmpi(criteria,'disr')) + selectedFeatures = FSToolboxMex(5,numToSelect,data,labels); +elseif ((strcmpi(criteria,'cife')) || (strcmpi(criteria,'fou'))) + selectedFeatures = FSToolboxMex(6,numToSelect,data,labels); +elseif (strcmpi(criteria,'icap')) + selectedFeatures = FSToolboxMex(7,numToSelect,data,labels); +elseif (strcmpi(criteria,'condred')) + selectedFeatures = FSToolboxMex(8,numToSelect,data,labels); +elseif (strcmpi(criteria,'betagamma')) + if (nargin ~= 6) + error('BetaGamma criteria expects a beta and a gamma'); + else + beta = varargin{1}; + gamma = varargin{2}; + end + selectedFeatures = FSToolboxMex(9,numToSelect,data,labels,beta,gamma); +elseif (strcmpi(criteria,'cmi')) + selectedFeatures = FSToolboxMex(10,numToSelect,data,labels); +elseif (strcmpi(criteria,'fcbf')) + if (nargin == 4) + error('Threshold for FCBF not supplied'); + else + selectedFeatures = FCBF(data,labels,varargin{1}); + end +elseif (strcmpi(criteria,'relief')) + [tmp selectedFeatures] = RELIEF(data,labels); +else + selectedFeatures = []; + disp(['Unrecognised criteria ' criteria]); +end diff --git a/FEAST/FSToolbox/license.txt b/FEAST/FSToolbox/license.txt new file mode 100644 index 0000000..798960e --- /dev/null +++ b/FEAST/FSToolbox/license.txt @@ -0,0 +1,32 @@ +This is FEAST (a FEAture Selection Toolbox for C and MATLAB) +if you use this code in academic work could you please reference: +"Conditional Likelihood Maximisation: A Unifying Framework for Mutual +Information Feature Selection" +G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +Journal of Machine Learning Research (JMLR), 2011 + +Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + - Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + - Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + - Neither the name of The University of Manchester nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/FEAST/FSToolbox/mRMR_D.c b/FEAST/FSToolbox/mRMR_D.c new file mode 100644 index 0000000..3eeb2a4 --- /dev/null +++ b/FEAST/FSToolbox/mRMR_D.c @@ -0,0 +1,170 @@ +/******************************************************************************* +** mRMR_D.c implements the minimum Relevance Maximum Redundancy criterion +** using the difference variant, from +** +** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" +** H. Peng et al. IEEE PAMI (2005) +** +** Initial Version - 13/06/2008 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" + +double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + /*holds the class MI values*/ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + int *selectedFeatures = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int)); + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)CALLOC_FUNC(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + /*init variables*/ + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + int arrayPosition, i, j, x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /************* + ** Now we have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the mRMR-D algorithm + *************/ + + for (i = 1; i < k; i++) + { + /**************************************************** + ** to ensure it selects some features + **if this is zero then it will not pick features where the redundancy is greater than the + **relevance + ****************************************************/ + score = -HUGE_VAL; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = classMI[j]; + totalFeatureMI = 0.0; + + for (x = 0; x < i; x++) + { + arrayPosition = x*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + /*work out intra MI*/ + + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[x]], feature2D[j], noOfSamples); + } + + totalFeatureMI += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + currentScore -= (totalFeatureMI/i); + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(feature2D); + FREE_FUNC(featureMIMatrix); + FREE_FUNC(selectedFeatures); + + classMI = NULL; + feature2D = NULL; + featureMIMatrix = NULL; + selectedFeatures = NULL; + + return outputFeatures; +}/*mRMR(int,int,int,double[][],double[],double[])*/ + diff --git a/FEAST/MIToolbox/ArrayOperations.c b/FEAST/MIToolbox/ArrayOperations.c new file mode 100644 index 0000000..00a8324 --- /dev/null +++ b/FEAST/MIToolbox/ArrayOperations.c @@ -0,0 +1,288 @@ +/******************************************************************************* +** ArrayOperations.cpp +** Part of the mutual information toolbox +** +** Contains functions to floor arrays, and to merge arrays into a joint +** state. +** +** Author: Adam Pocock +** Created 17/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "ArrayOperations.h" + +void printDoubleVector(double *vector, int vectorLength) +{ + int i; + for (i = 0; i < vectorLength; i++) + { + if (vector[i] > 0) + printf("Val at i=%d, is %f\n",i,vector[i]); + }/*for number of items in vector*/ +}/*printDoubleVector(double*,int)*/ + +void printIntVector(int *vector, int vectorLength) +{ + int i; + for (i = 0; i < vectorLength; i++) + { + printf("Val at i=%d, is %d\n",i,vector[i]); + }/*for number of items in vector*/ +}/*printIntVector(int*,int)*/ + +int numberOfUniqueValues(double *featureVector, int vectorLength) +{ + int uniqueValues = 0; + double *valuesArray = (double *) CALLOC_FUNC(vectorLength,sizeof(double)); + + int found = 0; + int j = 0; + int i; + + for (i = 0; i < vectorLength; i++) + { + found = 0; + j = 0; + while ((j < uniqueValues) && (found == 0)) + { + if (valuesArray[j] == featureVector[i]) + { + found = 1; + featureVector[i] = (double) (j+1); + } + j++; + } + if (!found) + { + valuesArray[uniqueValues] = featureVector[i]; + uniqueValues++; + featureVector[i] = (double) uniqueValues; + } + }/*for vectorlength*/ + + FREE_FUNC(valuesArray); + valuesArray = NULL; + + return uniqueValues; +}/*numberOfUniqueValues(double*,int)*/ + +/******************************************************************************* +** normaliseArray takes an input vector and writes an output vector +** which is a normalised version of the input, and returns the number of states +** A normalised array has min value = 0, max value = number of states +** and all values are integers +** +** length(inputVector) == length(outputVector) == vectorLength otherwise there +** is a memory leak +*******************************************************************************/ +int normaliseArray(double *inputVector, int *outputVector, int vectorLength) +{ + int minVal = 0; + int maxVal = 0; + int currentValue; + int i; + + if (vectorLength > 0) + { + minVal = (int) floor(inputVector[0]); + maxVal = (int) floor(inputVector[0]); + + for (i = 0; i < vectorLength; i++) + { + currentValue = (int) floor(inputVector[i]); + outputVector[i] = currentValue; + + if (currentValue < minVal) + { + minVal = currentValue; + } + + if (currentValue > maxVal) + { + maxVal = currentValue; + } + }/*for loop over vector*/ + + for (i = 0; i < vectorLength; i++) + { + outputVector[i] = outputVector[i] - minVal; + } + + maxVal = (maxVal - minVal) + 1; + } + + return maxVal; +}/*normaliseArray(double*,double*,int)*/ + + +/******************************************************************************* +** mergeArrays takes in two arrays and writes the joint state of those arrays +** to the output vector, returning the number of joint states +** +** the length of the vectors must be the same and equal to vectorLength +** outputVector must be malloc'd before calling this function +*******************************************************************************/ +int mergeArrays(double *firstVector, double *secondVector, double *outputVector, int vectorLength) +{ + int *firstNormalisedVector; + int *secondNormalisedVector; + int firstNumStates; + int secondNumStates; + int i; + int *stateMap; + int stateCount; + int curIndex; + + firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + + firstNumStates = normaliseArray(firstVector,firstNormalisedVector,vectorLength); + secondNumStates = normaliseArray(secondVector,secondNormalisedVector,vectorLength); + + /* + ** printVector(firstNormalisedVector,vectorLength); + ** printVector(secondNormalisedVector,vectorLength); + */ + stateMap = (int *) CALLOC_FUNC(firstNumStates*secondNumStates,sizeof(int)); + stateCount = 1; + for (i = 0; i < vectorLength; i++) + { + curIndex = firstNormalisedVector[i] + (secondNormalisedVector[i] * firstNumStates); + if (stateMap[curIndex] == 0) + { + stateMap[curIndex] = stateCount; + stateCount++; + } + outputVector[i] = stateMap[curIndex]; + } + + FREE_FUNC(firstNormalisedVector); + FREE_FUNC(secondNormalisedVector); + FREE_FUNC(stateMap); + + firstNormalisedVector = NULL; + secondNormalisedVector = NULL; + stateMap = NULL; + + /*printVector(outputVector,vectorLength);*/ + return stateCount; +}/*mergeArrays(double *,double *,double *, int, bool)*/ + +int mergeArraysArities(double *firstVector, int numFirstStates, double *secondVector, int numSecondStates, double *outputVector, int vectorLength) +{ + int *firstNormalisedVector; + int *secondNormalisedVector; + int i; + int totalStates; + int firstStateCheck, secondStateCheck; + + firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + + firstStateCheck = normaliseArray(firstVector,firstNormalisedVector,vectorLength); + secondStateCheck = normaliseArray(secondVector,secondNormalisedVector,vectorLength); + + if ((firstStateCheck <= numFirstStates) && (secondStateCheck <= numSecondStates)) + { + for (i = 0; i < vectorLength; i++) + { + outputVector[i] = firstNormalisedVector[i] + (secondNormalisedVector[i] * numFirstStates) + 1; + } + totalStates = numFirstStates * numSecondStates; + } + else + { + totalStates = -1; + } + + FREE_FUNC(firstNormalisedVector); + FREE_FUNC(secondNormalisedVector); + + firstNormalisedVector = NULL; + secondNormalisedVector = NULL; + + return totalStates; +}/*mergeArraysArities(double *,int,double *,int,double *,int)*/ + +int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength) +{ + int i = 0; + int currentIndex; + int currentNumStates; + int *normalisedVector; + + if (matrixWidth > 1) + { + currentNumStates = mergeArrays(inputMatrix, (inputMatrix + vectorLength), outputVector,vectorLength); + for (i = 2; i < matrixWidth; i++) + { + currentIndex = i * vectorLength; + currentNumStates = mergeArrays(outputVector,(inputMatrix + currentIndex),outputVector,vectorLength); + } + } + else + { + normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + currentNumStates = normaliseArray(inputMatrix,normalisedVector,vectorLength); + for (i = 0; i < vectorLength; i++) + { + outputVector[i] = inputMatrix[i]; + } + } + + return currentNumStates; +}/*mergeMultipleArrays(double *, double *, int, int, bool)*/ + + +int mergeMultipleArraysArities(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength) +{ + int i = 0; + int currentIndex; + int currentNumStates; + int *normalisedVector; + + if (matrixWidth > 1) + { + currentNumStates = mergeArraysArities(inputMatrix, arities[0], (inputMatrix + vectorLength), arities[1], outputVector,vectorLength); + for (i = 2; i < matrixWidth; i++) + { + currentIndex = i * vectorLength; + currentNumStates = mergeArraysArities(outputVector,currentNumStates,(inputMatrix + currentIndex),arities[i],outputVector,vectorLength); + if (currentNumStates == -1) + break; + } + } + else + { + normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + currentNumStates = normaliseArray(inputMatrix,normalisedVector,vectorLength); + for (i = 0; i < vectorLength; i++) + { + outputVector[i] = inputMatrix[i]; + } + } + + return currentNumStates; +}/*mergeMultipleArraysArities(double *, double *, int, int, bool)*/ + + diff --git a/FEAST/MIToolbox/ArrayOperations.h b/FEAST/MIToolbox/ArrayOperations.h new file mode 100644 index 0000000..3cc9025 --- /dev/null +++ b/FEAST/MIToolbox/ArrayOperations.h @@ -0,0 +1,88 @@ +/******************************************************************************* +** ArrayOperations.h +** Part of the mutual information toolbox +** +** Contains functions to floor arrays, and to merge arrays into a joint +** state. +** +** Author: Adam Pocock +** Created 17/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __ArrayOperations_H +#define __ArrayOperations_H + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************* +** Simple print function for debugging +*******************************************************************************/ +void printDoubleVector(double *vector, int vectorLength); + +void printIntVector(int *vector, int vectorLength); + +/******************************************************************************* +** numberOfUniqueValues finds the number of unique values in an array by +** repeatedly iterating through the array and checking if a value has been +** seen previously +*******************************************************************************/ +int numberOfUniqueValues(double *featureVector, int vectorLength); + +/******************************************************************************* +** normaliseArray takes an input vector and writes an output vector +** which is a normalised version of the input, and returns the number of states +** A normalised array has min value = 0, max value = number of states +** and all values are integers +** +** length(inputVector) == length(outputVector) == vectorLength otherwise there +** is a memory leak +*******************************************************************************/ +int normaliseArray(double *inputVector, int *outputVector, int vectorLength); + +/******************************************************************************* +** mergeArrays takes in two arrays and writes the joint state of those arrays +** to the output vector +** +** the length of the vectors must be the same and equal to vectorLength +*******************************************************************************/ +int mergeArrays(double *firstVector, double *secondVector, double *outputVector, int vectorLength); +int mergeArraysArities(double *firstVector, int numFirstStates, double *secondVector, int numSecondStates, double *outputVector, int vectorLength); + +/******************************************************************************* +** mergeMultipleArrays takes in a matrix and repeatedly merges the matrix using +** merge arrays and writes the joint state of that matrix +** to the output vector +** +** the length of the vectors must be the same and equal to vectorLength +** matrixWidth = the number of columns in the matrix +*******************************************************************************/ +int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength); +int mergeMultipleArraysArities(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength); + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/COPYING b/FEAST/MIToolbox/COPYING new file mode 100644 index 0000000..94a9ed0 --- /dev/null +++ b/FEAST/MIToolbox/COPYING @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 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 GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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See the +** GNU Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "ArrayOperations.h" +#include "CalculateProbability.h" + +JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector, int vectorLength) +{ + int *firstNormalisedVector; + int *secondNormalisedVector; + int *firstStateCounts; + int *secondStateCounts; + int *jointStateCounts; + double *firstStateProbs; + double *secondStateProbs; + double *jointStateProbs; + int firstNumStates; + int secondNumStates; + int jointNumStates; + int i; + double length = vectorLength; + JointProbabilityState state; + + firstNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + secondNormalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + + firstNumStates = normaliseArray(firstVector,firstNormalisedVector,vectorLength); + secondNumStates = normaliseArray(secondVector,secondNormalisedVector,vectorLength); + jointNumStates = firstNumStates * secondNumStates; + + firstStateCounts = (int *) CALLOC_FUNC(firstNumStates,sizeof(int)); + secondStateCounts = (int *) CALLOC_FUNC(secondNumStates,sizeof(int)); + jointStateCounts = (int *) CALLOC_FUNC(jointNumStates,sizeof(int)); + + firstStateProbs = (double *) CALLOC_FUNC(firstNumStates,sizeof(double)); + secondStateProbs = (double *) CALLOC_FUNC(secondNumStates,sizeof(double)); + jointStateProbs = (double *) CALLOC_FUNC(jointNumStates,sizeof(double)); + + /* optimised version, less numerically stable + double fractionalState = 1.0 / vectorLength; + + for (i = 0; i < vectorLength; i++) + { + firstStateProbs[firstNormalisedVector[i]] += fractionalState; + secondStateProbs[secondNormalisedVector[i]] += fractionalState; + jointStateProbs[secondNormalisedVector[i] * firstNumStates + firstNormalisedVector[i]] += fractionalState; + } + */ + + /* Optimised for number of FP operations now O(states) instead of O(vectorLength) */ + for (i = 0; i < vectorLength; i++) + { + firstStateCounts[firstNormalisedVector[i]] += 1; + secondStateCounts[secondNormalisedVector[i]] += 1; + jointStateCounts[secondNormalisedVector[i] * firstNumStates + firstNormalisedVector[i]] += 1; + } + + for (i = 0; i < firstNumStates; i++) + { + firstStateProbs[i] = firstStateCounts[i] / length; + } + + for (i = 0; i < secondNumStates; i++) + { + secondStateProbs[i] = secondStateCounts[i] / length; + } + + for (i = 0; i < jointNumStates; i++) + { + jointStateProbs[i] = jointStateCounts[i] / length; + } + + FREE_FUNC(firstNormalisedVector); + FREE_FUNC(secondNormalisedVector); + FREE_FUNC(firstStateCounts); + FREE_FUNC(secondStateCounts); + FREE_FUNC(jointStateCounts); + + firstNormalisedVector = NULL; + secondNormalisedVector = NULL; + firstStateCounts = NULL; + secondStateCounts = NULL; + jointStateCounts = NULL; + + /* + **typedef struct + **{ + ** double *jointProbabilityVector; + ** int numJointStates; + ** double *firstProbabilityVector; + ** int numFirstStates; + ** double *secondProbabilityVector; + ** int numSecondStates; + **} JointProbabilityState; + */ + + state.jointProbabilityVector = jointStateProbs; + state.numJointStates = jointNumStates; + state.firstProbabilityVector = firstStateProbs; + state.numFirstStates = firstNumStates; + state.secondProbabilityVector = secondStateProbs; + state.numSecondStates = secondNumStates; + + return state; +}/*calculateJointProbability(double *,double *, int)*/ + +ProbabilityState calculateProbability(double *dataVector, int vectorLength) +{ + int *normalisedVector; + int *stateCounts; + double *stateProbs; + int numStates; + /*double fractionalState;*/ + ProbabilityState state; + int i; + double length = vectorLength; + + normalisedVector = (int *) CALLOC_FUNC(vectorLength,sizeof(int)); + + numStates = normaliseArray(dataVector,normalisedVector,vectorLength); + + stateCounts = (int *) CALLOC_FUNC(numStates,sizeof(int)); + stateProbs = (double *) CALLOC_FUNC(numStates,sizeof(double)); + + /* optimised version, may have floating point problems + fractionalState = 1.0 / vectorLength; + + for (i = 0; i < vectorLength; i++) + { + stateProbs[normalisedVector[i]] += fractionalState; + } + */ + + /* Optimised for number of FP operations now O(states) instead of O(vectorLength) */ + for (i = 0; i < vectorLength; i++) + { + stateCounts[normalisedVector[i]] += 1; + } + + for (i = 0; i < numStates; i++) + { + stateProbs[i] = stateCounts[i] / length; + } + + FREE_FUNC(stateCounts); + FREE_FUNC(normalisedVector); + + stateCounts = NULL; + normalisedVector = NULL; + + state.probabilityVector = stateProbs; + state.numStates = numStates; + + return state; +}/*calculateProbability(double *,int)*/ + diff --git a/FEAST/MIToolbox/CalculateProbability.h b/FEAST/MIToolbox/CalculateProbability.h new file mode 100644 index 0000000..d5e9d3e --- /dev/null +++ b/FEAST/MIToolbox/CalculateProbability.h @@ -0,0 +1,80 @@ +/******************************************************************************* +** CalculateProbability.h +** Part of the mutual information toolbox +** +** Contains functions to calculate the probability of each state in the array +** and to calculate the probability of the joint state of two arrays +** +** Author: Adam Pocock +** Created 17/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __CalculateProbability_H +#define __CalculateProbability_H + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct jpState +{ + double *jointProbabilityVector; + int numJointStates; + double *firstProbabilityVector; + int numFirstStates; + double *secondProbabilityVector; + int numSecondStates; +} JointProbabilityState; + +typedef struct pState +{ + double *probabilityVector; + int numStates; +} ProbabilityState; + +/******************************************************************************* +** calculateJointProbability returns the joint probability vector of two vectors +** and the marginal probability vectors in a struct. +** It is the base operation for all information theory calculations involving +** two or more variables. +** +** length(firstVector) == length(secondVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector, int vectorLength); + +/******************************************************************************* +** calculateProbability returns the probability vector from one vector. +** It is the base operation for all information theory calculations involving +** one variable +** +** length(dataVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +ProbabilityState calculateProbability(double *dataVector, int vectorLength); + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/CompileMIToolbox.m b/FEAST/MIToolbox/CompileMIToolbox.m new file mode 100644 index 0000000..a8d9e92 --- /dev/null +++ b/FEAST/MIToolbox/CompileMIToolbox.m @@ -0,0 +1,4 @@ +% Compiles the MIToolbox functions + +mex MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c +mex RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c diff --git a/FEAST/MIToolbox/Entropy.c b/FEAST/MIToolbox/Entropy.c new file mode 100644 index 0000000..3f37cc1 --- /dev/null +++ b/FEAST/MIToolbox/Entropy.c @@ -0,0 +1,130 @@ +/******************************************************************************* +** Entropy.cpp +** Part of the mutual information toolbox +** +** Contains functions to calculate the entropy of a single variable H(X), +** the joint entropy of two variables H(X,Y), and the conditional entropy +** H(X|Y) +** +** Author: Adam Pocock +** Created 19/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "CalculateProbability.h" +#include "Entropy.h" + +double calculateEntropy(double *dataVector, int vectorLength) +{ + double entropy = 0.0; + double tempValue = 0.0; + int i; + ProbabilityState state = calculateProbability(dataVector,vectorLength); + + /*H(X) = - sum p(x) log p(x)*/ + for (i = 0; i < state.numStates; i++) + { + tempValue = state.probabilityVector[i]; + + if (tempValue > 0) + { + entropy -= tempValue * log(tempValue); + } + } + + entropy /= log(2.0); + + FREE_FUNC(state.probabilityVector); + state.probabilityVector = NULL; + + return entropy; +}/*calculateEntropy(double *,int)*/ + +double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength) +{ + double jointEntropy = 0.0; + double tempValue = 0.0; + int i; + JointProbabilityState state = calculateJointProbability(firstVector,secondVector,vectorLength); + + /*H(XY) = - sumx sumy p(xy) log p(xy)*/ + for (i = 0; i < state.numJointStates; i++) + { + tempValue = state.jointProbabilityVector[i]; + if (tempValue > 0) + { + jointEntropy -= tempValue * log(tempValue); + } + } + + jointEntropy /= log(2.0); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return jointEntropy; +}/*calculateJointEntropy(double *, double *, int)*/ + +double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength) +{ + /* + ** Conditional entropy + ** H(X|Y) = - sumx sumy p(xy) log p(xy)/p(y) + */ + + double condEntropy = 0.0; + double jointValue = 0.0; + double condValue = 0.0; + int i; + JointProbabilityState state = calculateJointProbability(dataVector,conditionVector,vectorLength); + + /*H(X|Y) = - sumx sumy p(xy) log p(xy)/p(y)*/ + /* to index by numFirstStates use modulus of i + ** to index by numSecondStates use integer division of i by numFirstStates + */ + for (i = 0; i < state.numJointStates; i++) + { + jointValue = state.jointProbabilityVector[i]; + condValue = state.secondProbabilityVector[i / state.numFirstStates]; + if ((jointValue > 0) && (condValue > 0)) + { + condEntropy -= jointValue * log(jointValue / condValue); + } + } + + condEntropy /= log(2.0); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return condEntropy; + +}/*calculateConditionalEntropy(double *, double *, int)*/ + diff --git a/FEAST/MIToolbox/Entropy.h b/FEAST/MIToolbox/Entropy.h new file mode 100644 index 0000000..4bdd697 --- /dev/null +++ b/FEAST/MIToolbox/Entropy.h @@ -0,0 +1,71 @@ +/******************************************************************************* +** Entropy.h +** Part of the mutual information toolbox +** +** Contains functions to calculate the entropy of a single variable H(X), +** the joint entropy of two variables H(X,Y), and the conditional entropy +** H(X|Y) +** +** Author: Adam Pocock +** Created 19/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __Entropy_H +#define __Entropy_H + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************* +** calculateEntropy returns the entropy in log base 2 of dataVector +** H(X) +** +** length(dataVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateEntropy(double *dataVector, int vectorLength); + +/******************************************************************************* +** calculateJointEntropy returns the entropy in log base 2 of the joint +** variable of firstVector and secondVector H(XY) +** +** length(firstVector) == length(secondVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); + +/******************************************************************************* +** calculateConditionalEntropy returns the entropy in log base 2 of dataVector +** conditioned on conditionVector, H(X|Y) +** +** length(dataVector) == length(conditionVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength); + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/MIToolbox.h b/FEAST/MIToolbox/MIToolbox.h new file mode 100644 index 0000000..bba6284 --- /dev/null +++ b/FEAST/MIToolbox/MIToolbox.h @@ -0,0 +1,53 @@ +/******************************************************************************* +** +** MIToolbox.h +** Provides the header files and #defines to ensure compatibility with MATLAB +** and C/C++. Uncomment the correct lines to setup the correct memory +** allocation and freeing operations. +** +** Author: Adam Pocock +** Created 17/2/2010 +** +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __MIToolbox_H +#define __MIToolbox_H + +#include +#include + +#ifdef COMPILE_C + #define C_IMPLEMENTATION + #include + #include + #define CALLOC_FUNC calloc + #define FREE_FUNC free +#else + #define MEX_IMPLEMENTATION + #include "mex.h" + #define CALLOC_FUNC mxCalloc + #define FREE_FUNC mxFree + #define printf mexPrintf /*for Octave-3.2*/ +#endif + +#endif + diff --git a/FEAST/MIToolbox/MIToolbox.m b/FEAST/MIToolbox/MIToolbox.m new file mode 100644 index 0000000..880924a --- /dev/null +++ b/FEAST/MIToolbox/MIToolbox.m @@ -0,0 +1,83 @@ +function [varargout] = MIToolbox(functionName, varargin) +%function [varargout] = MIToolbox(functionName, varargin) +% +%Provides access to the functions in MIToolboxMex +% +%Expects column vectors, will not work with row vectors +% +%Function list +%"joint" = joint variable of the matrix +%"entropy" or "h" = H(X) +%"ConditionalEntropy" or "condh" = H(X|Y) +%"mi" = I(X;Y) +%"ConditionalMI" or "cmi" = I(X;Y|Z) +% +%Arguments and returned values +%[jointVariable] = joint(matrix) +%[entropy] = H(X) = H(vector) +%[entropy] = H(X|Y) = H(vector,condition) +%[mi] = I(X;Y) = I(vector,target) +%[mi] = I(X;Y|Z) = I(vector,target,condition) +% +%Internal MIToolbox function number +%Joint = 3 +%Entropy = 4 +%Conditional Entropy = 6 +%Mutual Information = 7 +%Conditional MI = 8 + +if (strcmpi(functionName,'Joint') || strcmpi(functionName,'Merge')) + [varargout{1}] = MIToolboxMex(3,varargin{1}); +elseif (strcmpi(functionName,'Entropy') || strcmpi(functionName,'h')) + %disp('Calculating Entropy'); + if (size(varargin{1},2)>1) + mergedVector = MIToolboxMex(3,varargin{1}); + else + mergedVector = varargin{1}; + end + [varargout{1}] = MIToolboxMex(4,mergedVector); +elseif ((strcmpi(functionName,'ConditionalEntropy')) || strcmpi(functionName,'condh')) + if (size(varargin{1},2)>1) + mergedFirst = MIToolboxMex(3,varargin{1}); + else + mergedFirst = varargin{1}; + end + if (size(varargin{2},2)>1) + mergedSecond = MIToolboxMex(3,varargin{2}); + else + mergedSecond = varargin{2}; + end + [varargout{1}] = MIToolboxMex(6,mergedFirst,mergedSecond); +elseif (strcmpi(functionName,'mi')) + if (size(varargin{1},2)>1) + mergedFirst = MIToolboxMex(3,varargin{1}); + else + mergedFirst = varargin{1}; + end + if (size(varargin{2},2)>1) + mergedSecond = MIToolboxMex(3,varargin{2}); + else + mergedSecond = varargin{2}; + end + [varargout{1}] = MIToolboxMex(7,mergedFirst,mergedSecond); +elseif (strcmpi(functionName,'ConditionalMI') || strcmpi(functionName,'cmi')) + if (size(varargin{1},2)>1) + mergedFirst = MIToolboxMex(3,varargin{1}); + else + mergedFirst = varargin{1}; + end + if (size(varargin{2},2)>1) + mergedSecond = MIToolboxMex(3,varargin{2}); + else + mergedSecond = varargin{2}; + end + if (size(varargin{3},2)>1) + mergedThird = MIToolboxMex(3,varargin{3}); + else + mergedThird = varargin{3}; + end + [varargout{1}] = MIToolboxMex(8,mergedFirst,mergedSecond,mergedThird); +else + varargout{1} = 0; + disp(['Unrecognised functionName ' functionName]); +end diff --git a/FEAST/MIToolbox/MIToolboxMex.c b/FEAST/MIToolbox/MIToolboxMex.c new file mode 100644 index 0000000..e27009f --- /dev/null +++ b/FEAST/MIToolbox/MIToolboxMex.c @@ -0,0 +1,494 @@ +/******************************************************************************* +** +** MIToolboxMex.cpp +** is the MATLAB entry point for the MIToolbox functions when called from +** a MATLAB/OCTAVE script. +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ +#include "MIToolbox.h" +#include "ArrayOperations.h" +#include "CalculateProbability.h" +#include "Entropy.h" +#include "MutualInformation.h" + +/******************************************************************************* +**entry point for the mex call +**nlhs - number of outputs +**plhs - pointer to array of outputs +**nrhs - number of inputs +**prhs - pointer to array of inputs +*******************************************************************************/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /***************************************************************************** + ** this function takes a flag and a variable number of arguments + ** depending on the value of the flag and returns either a construct + ** containing probability estimates, a merged vector or a double value + ** representing an entropy or mutual information + *****************************************************************************/ + + int flag, i, numberOfSamples, checkSamples, thirdCheckSamples, numberOfFeatures, checkFeatures, thirdCheckFeatures; + int numArities, errorTest; + double *dataVector, *condVector, *targetVector, *firstVector, *secondVector, *output, *numStates; + double *matrix, *mergedVector, *arities; + int *outputIntVector, *intArities; + + double *jointOutput, *numJointStates, *firstOutput, *numFirstStates, *secondOutput, *numSecondStates; + + ProbabilityState state; + JointProbabilityState jointState; + + /*if (nlhs != 1) + { + printf("Incorrect number of output arguments\n"); + }//if not 1 output + */ + if (nrhs == 2) + { + /*printf("Must be H(X), calculateProbability(X), merge(X), normaliseArray(X)\n");*/ + } + else if (nrhs == 3) + { + /*printf("Must be H(XY), H(X|Y), calculateJointProbability(XY), I(X;Y)\n");*/ + } + else if (nrhs == 4) + { + /*printf("Must be I(X;Y|Z)\n");*/ + } + else + { + printf("Incorrect number of arguments, format is MIToolbox(\"FLAG\",varargin)\n"); + } + + /* number to function map + ** 1 = calculateProbability + ** 2 = calculateJointProbability + ** 3 = mergeArrays + ** 4 = H(X) + ** 5 = H(XY) + ** 6 = H(X|Y) + ** 7 = I(X;Y) + ** 8 = I(X;Y|Z) + ** 9 = normaliseArray + */ + + flag = *mxGetPr(prhs[0]); + + switch (flag) + { + case 1: + { + /* + **calculateProbability + */ + numberOfSamples = mxGetM(prhs[1]); + dataVector = (double *) mxGetPr(prhs[1]); + + /*ProbabilityState calculateProbability(double *dataVector, int vectorLength);*/ + state = calculateProbability(dataVector,numberOfSamples); + + plhs[0] = mxCreateDoubleMatrix(state.numStates,1,mxREAL); + plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + numStates = (double *) mxGetPr(plhs[1]); + + *numStates = state.numStates; + + for (i = 0; i < state.numStates; i++) + { + output[i] = state.probabilityVector[i]; + } + + break; + }/*case 1 - calculateProbability*/ + case 2: + { + /* + **calculateJointProbability + */ + numberOfSamples = mxGetM(prhs[1]); + firstVector = (double *) mxGetPr(prhs[1]); + secondVector = (double *) mxGetPr(prhs[2]); + + /*JointProbabilityState calculateJointProbability(double *firstVector, double *secondVector int vectorLength);*/ + jointState = calculateJointProbability(firstVector,secondVector,numberOfSamples); + + plhs[0] = mxCreateDoubleMatrix(jointState.numJointStates,1,mxREAL); + plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); + plhs[2] = mxCreateDoubleMatrix(jointState.numFirstStates,1,mxREAL); + plhs[3] = mxCreateDoubleMatrix(1,1,mxREAL); + plhs[4] = mxCreateDoubleMatrix(jointState.numSecondStates,1,mxREAL); + plhs[5] = mxCreateDoubleMatrix(1,1,mxREAL); + jointOutput = (double *)mxGetPr(plhs[0]); + numJointStates = (double *) mxGetPr(plhs[1]); + firstOutput = (double *)mxGetPr(plhs[2]); + numFirstStates = (double *) mxGetPr(plhs[3]); + secondOutput = (double *)mxGetPr(plhs[4]); + numSecondStates = (double *) mxGetPr(plhs[5]); + + *numJointStates = jointState.numJointStates; + *numFirstStates = jointState.numFirstStates; + *numSecondStates = jointState.numSecondStates; + + for (i = 0; i < jointState.numJointStates; i++) + { + jointOutput[i] = jointState.jointProbabilityVector[i]; + } + for (i = 0; i < jointState.numFirstStates; i++) + { + firstOutput[i] = jointState.firstProbabilityVector[i]; + } + for (i = 0; i < jointState.numSecondStates; i++) + { + secondOutput[i] = jointState.secondProbabilityVector[i]; + } + + break; + }/*case 2 - calculateJointProbability */ + case 3: + { + /* + **mergeArrays + */ + numberOfSamples = mxGetM(prhs[1]); + numberOfFeatures = mxGetN(prhs[1]); + + numArities = 0; + if (nrhs > 2) + { + numArities = mxGetN(prhs[2]); + /*printf("arities = %d, features = %d, samples = %d\n",numArities,numberOfFeatures,numberOfSamples);*/ + } + + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + + if (numArities == 0) + { + /* + **no arities therefore compress output + */ + if ((numberOfFeatures > 0) && (numberOfSamples > 0)) + { + matrix = (double *) mxGetPr(prhs[1]); + mergedVector = (double *) mxCalloc(numberOfSamples,sizeof(double)); + + plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int vectorLength)*/ + mergeMultipleArrays(matrix, mergedVector, numberOfFeatures, numberOfSamples); + for (i = 0; i < numberOfSamples; i++) + { + output[i] = mergedVector[i]; + } + + mxFree(mergedVector); + mergedVector = NULL; + } + } + else if (numArities == numberOfFeatures) + { + if ((numberOfFeatures > 0) && (numberOfSamples > 0)) + { + + matrix = (double *) mxGetPr(prhs[1]); + mergedVector = (double *) mxCalloc(numberOfSamples,sizeof(double)); + + arities = (double *) mxGetPr(prhs[2]); + intArities = (int *) mxCalloc(numberOfFeatures,sizeof(int)); + for (i = 0; i < numArities; i++) + { + intArities[i] = (int) floor(arities[i]); + } + + /*int mergeMultipleArrays(double *inputMatrix, double *outputVector, int matrixWidth, int *arities, int vectorLength);*/ + errorTest = mergeMultipleArraysArities(matrix, mergedVector, numberOfFeatures, intArities, numberOfSamples); + + if (errorTest != -1) + { + plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + for (i = 0; i < numberOfSamples; i++) + { + output[i] = mergedVector[i]; + } + } + else + { + printf("Incorrect arities supplied. More states in data than specified\n"); + } + + mxFree(mergedVector); + mergedVector = NULL; + } + } + else + { + printf("Number of arities does not match number of features, arities should be a row vector\n"); + } + + break; + }/*case 3 - mergeArrays*/ + case 4: + { + /* + **H(X) + */ + numberOfSamples = mxGetM(prhs[1]); + numberOfFeatures = mxGetN(prhs[1]); + + dataVector = (double *) mxGetPr(prhs[1]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if (numberOfFeatures == 1) + { + /*double calculateEntropy(double *dataVector, int vectorLength);*/ + *output = calculateEntropy(dataVector,numberOfSamples); + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + + break; + }/*case 4 - H(X)*/ + case 5: + { + /* + **H(XY) + */ + numberOfSamples = mxGetM(prhs[1]); + checkSamples = mxGetM(prhs[2]); + + numberOfFeatures = mxGetN(prhs[1]); + checkFeatures = mxGetN(prhs[2]); + + firstVector = mxGetPr(prhs[1]); + secondVector = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if ((numberOfSamples == 0) && (checkSamples == 0)) + { + *output = 0.0; + } + else if (numberOfSamples == 0) + { + *output = calculateEntropy(secondVector,numberOfSamples); + } + else if (checkSamples == 0) + { + *output = calculateEntropy(firstVector,numberOfSamples); + } + else if (numberOfSamples == checkSamples) + { + /*double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength);*/ + *output = calculateJointEntropy(firstVector,secondVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length\n"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + + break; + }/*case 5 - H(XY)*/ + case 6: + { + /* + **H(X|Y) + */ + numberOfSamples = mxGetM(prhs[1]); + checkSamples = mxGetM(prhs[2]); + + numberOfFeatures = mxGetN(prhs[1]); + checkFeatures = mxGetN(prhs[2]); + + dataVector = mxGetPr(prhs[1]); + condVector = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if (numberOfSamples == 0) + { + *output = 0.0; + } + else if (checkSamples == 0) + { + *output = calculateEntropy(dataVector,numberOfSamples); + } + else if (numberOfSamples == checkSamples) + { + /*double calculateConditionalEntropy(double *dataVector, double *condVector, int vectorLength);*/ + *output = calculateConditionalEntropy(dataVector,condVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length\n"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 6 - H(X|Y)*/ + case 7: + { + /* + **I(X;Y) + */ + numberOfSamples = mxGetM(prhs[1]); + checkSamples = mxGetM(prhs[2]); + + numberOfFeatures = mxGetN(prhs[1]); + checkFeatures = mxGetN(prhs[2]); + + firstVector = mxGetPr(prhs[1]); + secondVector = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if ((numberOfSamples == 0) || (checkSamples == 0)) + { + *output = 0.0; + } + else if (numberOfSamples == checkSamples) + { + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + *output = calculateMutualInformation(firstVector,secondVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length\n"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 7 - I(X;Y)*/ + case 8: + { + /* + **I(X;Y|Z) + */ + numberOfSamples = mxGetM(prhs[1]); + checkSamples = mxGetM(prhs[2]); + thirdCheckSamples = mxGetM(prhs[3]); + + numberOfFeatures = mxGetN(prhs[1]); + checkFeatures = mxGetN(prhs[2]); + thirdCheckFeatures = mxGetN(prhs[3]); + + firstVector = mxGetPr(prhs[1]); + targetVector = mxGetPr(prhs[2]); + condVector = mxGetPr(prhs[3]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if ((numberOfSamples == 0) || (checkSamples == 0)) + { + *output = 0.0; + } + else if ((thirdCheckSamples == 0) || (thirdCheckFeatures != 1)) + { + *output = calculateMutualInformation(firstVector,targetVector,numberOfSamples); + } + else if ((numberOfSamples == checkSamples) && (numberOfSamples == thirdCheckSamples)) + { + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *condVector, int vectorLength);*/ + *output = calculateConditionalMutualInformation(firstVector,targetVector,condVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length\n"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 8 - I(X;Y|Z)*/ + case 9: + { + /* + **normaliseArray + */ + numberOfSamples = mxGetM(prhs[1]); + dataVector = (double *) mxGetPr(prhs[1]); + + outputIntVector = (int *) mxCalloc(numberOfSamples,sizeof(int)); + + plhs[0] = mxCreateDoubleMatrix(numberOfSamples,1,mxREAL); + plhs[1] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + numStates = (double *) mxGetPr(plhs[1]); + + /*int normaliseArray(double *inputVector, int *outputVector, int vectorLength);*/ + *numStates = normaliseArray(dataVector, outputIntVector, numberOfSamples); + + for (i = 0; i < numberOfSamples; i++) + { + output[i] = outputIntVector[i]; + } + + break; + }/*case 9 - normaliseArray*/ + default: + { + printf("Unrecognised flag\n"); + break; + }/*default*/ + }/*switch(flag)*/ + + return; +}/*mexFunction()*/ diff --git a/FEAST/MIToolbox/MIToolboxMex.mexa64 b/FEAST/MIToolbox/MIToolboxMex.mexa64 new file mode 100755 index 0000000..440611d Binary files /dev/null and b/FEAST/MIToolbox/MIToolboxMex.mexa64 differ diff --git a/FEAST/MIToolbox/Makefile b/FEAST/MIToolbox/Makefile new file mode 100644 index 0000000..0135410 --- /dev/null +++ b/FEAST/MIToolbox/Makefile @@ -0,0 +1,84 @@ +#makefile for MIToolbox +#Author: Adam Pocock, apocock@cs.man.ac.uk +#Created 11/3/2010 +# +# +#Copyright 2010 Adam Pocock, The University Of Manchester +#www.cs.manchester.ac.uk +# +#This file is part of MIToolbox. +# +#MIToolbox is free software: you can redistribute it and/or modify +#it under the terms of the GNU Lesser General Public License as published by +#the Free Software Foundation, either version 3 of the License, or +#(at your option) any later version. +# +#MIToolbox 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 Lesser General Public License for more details. +# +#You should have received a copy of the GNU Lesser General Public License +#along with MIToolbox. If not, see . + +CXXFLAGS = -O3 -fPIC +COMPILER = gcc +objects = ArrayOperations.o CalculateProbability.o Entropy.o \ + MutualInformation.o RenyiEntropy.o RenyiMutualInformation.o + +libMIToolbox.so : $(objects) + $(COMPILER) $(CXXFLAGS) -shared -o libMIToolbox.so $(objects) + +RenyiMutualInformation.o: RenyiMutualInformation.c MIToolbox.h ArrayOperations.h \ + CalculateProbability.h RenyiEntropy.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c RenyiMutualInformation.c + +RenyiEntropy.o: RenyiEntropy.c MIToolbox.h ArrayOperations.h \ + CalculateProbability.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c RenyiEntropy.c + +MutualInformation.o: MutualInformation.c MIToolbox.h ArrayOperations.h \ + CalculateProbability.h Entropy.h MutualInformation.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c MutualInformation.c + +Entropy.o: Entropy.c MIToolbox.h ArrayOperations.h CalculateProbability.h \ + Entropy.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c Entropy.c + +CalculateProbability.o: CalculateProbability.c MIToolbox.h ArrayOperations.h \ + CalculateProbability.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c CalculateProbability.c + +ArrayOperations.o: ArrayOperations.c MIToolbox.h ArrayOperations.h + $(COMPILER) $(CXXFLAGS) -DCOMPILE_C -c ArrayOperations.c + +.PHONY : debug +debug: + $(MAKE) libMIToolbox.so "CXXFLAGS = -g -DDEBUG -fPIC" + +.PHONY : x86 +x86: + $(MAKE) libMIToolbox.so "CXXFLAGS = -O3 -fPIC -m32" + +.PHONY : x64 +x64: + $(MAKE) libMIToolbox.so "CXXFLAGS = -O3 -fPIC -m64" + +.PHONY : matlab +matlab: + mex MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c + mex RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c + +.PHONY : matlab-debug +matlab-debug: + mex -g MIToolboxMex.c MutualInformation.c Entropy.c CalculateProbability.c ArrayOperations.c + mex -g RenyiMIToolboxMex.c RenyiMutualInformation.c RenyiEntropy.c CalculateProbability.c ArrayOperations.c + +.PHONY : intel +intel: + $(MAKE) libMIToolbox.so "COMPILER = icc" "CXXFLAGS = -O2 -fPIC -xHost" + +.PHONY : clean +clean: + rm *.o libMIToolbox.so + diff --git a/FEAST/MIToolbox/MutualInformation.c b/FEAST/MIToolbox/MutualInformation.c new file mode 100644 index 0000000..0fb4766 --- /dev/null +++ b/FEAST/MIToolbox/MutualInformation.c @@ -0,0 +1,95 @@ +/******************************************************************************* +** MutualInformation.cpp +** Part of the mutual information toolbox +** +** Contains functions to calculate the mutual information of +** two variables X and Y, I(X;Y), to calculate the joint mutual information +** of two variables X & Z on the variable Y, I(XZ;Y), and the conditional +** mutual information I(x;Y|Z) +** +** Author: Adam Pocock +** Created 19/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "ArrayOperations.h" +#include "CalculateProbability.h" +#include "Entropy.h" +#include "MutualInformation.h" + +double calculateMutualInformation(double *dataVector, double *targetVector, int vectorLength) +{ + double mutualInformation = 0.0; + int firstIndex,secondIndex; + int i; + JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength); + + /* + ** I(X;Y) = sum sum p(xy) * log (p(xy)/p(x)p(y)) + */ + for (i = 0; i < state.numJointStates; i++) + { + firstIndex = i % state.numFirstStates; + secondIndex = i / state.numFirstStates; + + if ((state.jointProbabilityVector[i] > 0) && (state.firstProbabilityVector[firstIndex] > 0) && (state.secondProbabilityVector[secondIndex] > 0)) + { + /*double division is probably more stable than multiplying two small numbers together + ** mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / (state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex])); + */ + mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / state.firstProbabilityVector[firstIndex] / state.secondProbabilityVector[secondIndex]); + } + } + + mutualInformation /= log(2.0); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return mutualInformation; +}/*calculateMutualInformation(double *,double *,int)*/ + +double calculateConditionalMutualInformation(double *dataVector, double *targetVector, double *conditionVector, int vectorLength) +{ + double mutualInformation = 0.0; + double firstCondition, secondCondition; + double *mergedVector = (double *) CALLOC_FUNC(vectorLength,sizeof(double)); + + mergeArrays(targetVector,conditionVector,mergedVector,vectorLength); + + /* I(X;Y|Z) = H(X|Z) - H(X|YZ) */ + /* double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength); */ + firstCondition = calculateConditionalEntropy(dataVector,conditionVector,vectorLength); + secondCondition = calculateConditionalEntropy(dataVector,mergedVector,vectorLength); + + mutualInformation = firstCondition - secondCondition; + + FREE_FUNC(mergedVector); + mergedVector = NULL; + + return mutualInformation; +}/*calculateConditionalMutualInformation(double *,double *,double *,int)*/ + diff --git a/FEAST/MIToolbox/MutualInformation.h b/FEAST/MIToolbox/MutualInformation.h new file mode 100644 index 0000000..1045912 --- /dev/null +++ b/FEAST/MIToolbox/MutualInformation.h @@ -0,0 +1,64 @@ +/******************************************************************************* +** MutualInformation.h +** Part of the mutual information toolbox +** +** Contains functions to calculate the mutual information of +** two variables X and Y, I(X;Y), to calculate the joint mutual information +** of two variables X & Z on the variable Y, I(XZ;Y), and the conditional +** mutual information I(x;Y|Z) +** +** Author: Adam Pocock +** Created 19/2/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __MutualInformation_H +#define __MutualInformation_H + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************* +** calculateMutualInformation returns the log base 2 mutual information between +** dataVector and targetVector, I(X;Y) +** +** length(dataVector) == length(targetVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateMutualInformation(double *dataVector, double *targetVector, int vectorLength); + +/******************************************************************************* +** calculateConditionalMutualInformation returns the log base 2 +** mutual information between dataVector and targetVector, conditioned on +** conditionVector, I(X;Y|Z) +** +** length(dataVector) == length(targetVector) == length(condtionVector) == vectorLength +** otherwise it will error with a segmentation fault +*******************************************************************************/ +double calculateConditionalMutualInformation(double *dataVector, double *targetVector, double *conditionVector, int vectorLength); + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/README b/FEAST/MIToolbox/README new file mode 100644 index 0000000..7abe43d --- /dev/null +++ b/FEAST/MIToolbox/README @@ -0,0 +1,71 @@ +MIToolbox v1.03 for C/C++ and MATLAB/OCTAVE + +The MIToolbox contains a set of functions to calculate information theoretic +quantities from data, such as the entropy and mutual information. The toolbox +contains implementations of the most popular Shannon entropies, and also the +lesser known Renyi entropy. The toolbox only supports discrete distributions, +as opposed to continuous. All real-valued numbers will be processed by x = floor(x) + +These functions are targeted for use with feature selection algorithms rather +than communication channels and so expect all the data to be available before +execution and sample their own probability distributions from the data. + +Things you can do: + - Entropy + - Conditional Entropy + - Mutual Information + - Conditional Mutual Information + - generating a joint variable + - generating a probability distribution from a discrete random variable + - Renyi's Entropy + - Renyi's Mutual Information + +Note: all functions are calculated in log base 2, so return units of "bits". + +====== + +Examples: + +>> y = [1 1 1 0 0]'; +>> x = [1 0 1 1 0]'; + +>> mi(x,y) %% mutual information I(X;Y) +ans = + 0.0200 + +>> h(x) %% entropy H(X) +ans = + 0.9710 + +>> condh(x,y) %% conditional entropy H(X|Y) +ans = + 0.9510 + +>> h( [x,y] ) %% joint entropy H(X,Y) +ans = + 1.9219 + +>> joint([x,y]) %% joint random variable XY +ans = + 1 + 2 + 1 + 3 + 4 + +====== + +To compile the library for use in MATLAB/OCTAVE, execute CompileMIToolbox.m +from within MATLAB, or run 'make matlab' from a terminal. + +To compile the library for C/C++, run 'make' at a terminal. + +The C source files are licensed under the LGPL v3. The MATLAB wrappers and +demonstration feature selection algorithms are provided as is with no warranty +as examples of how to use the library in MATLAB. + +Update History +08/11/2011 - v1.03 - Minor documentation changes to accompany the JMLR publication. +15/10/2010 - v1.02 - Fixed bug where MIToolbox would cause a segmentation fault if a x by 0 empty matrix was passed in. Now prints an error message and returns gracefully +02/09/2010 - v1.01 - Updated CMIM.m in demonstration_algorithms, due to a bug where the last feature would not be selected first if it had the highest MI +07/07/2010 - v1.00 - Initial Release diff --git a/FEAST/MIToolbox/RenyiEntropy.c b/FEAST/MIToolbox/RenyiEntropy.c new file mode 100644 index 0000000..32c5ff9 --- /dev/null +++ b/FEAST/MIToolbox/RenyiEntropy.c @@ -0,0 +1,191 @@ +/******************************************************************************* +** RenyiEntropy.cpp +** Part of the mutual information toolbox +** +** Contains functions to calculate the Renyi alpha entropy of a single variable +** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the +** conditional Renyi entropy H_\alpha(X|Y) +** +** Author: Adam Pocock +** Created 26/3/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "ArrayOperations.h" +#include "CalculateProbability.h" +#include "Entropy.h" + +double calculateRenyiEntropy(double alpha, double *dataVector, int vectorLength) +{ + double entropy = 0.0; + double tempValue = 0.0; + int i; + ProbabilityState state = calculateProbability(dataVector,vectorLength); + + /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ + for (i = 0; i < state.numStates; i++) + { + tempValue = state.probabilityVector[i]; + + if (tempValue > 0) + { + entropy += pow(tempValue,alpha); + /*printf("Entropy = %f, i = %d\n", entropy,i);*/ + } + } + + /*printf("Entropy = %f\n", entropy);*/ + + entropy = log(entropy); + + entropy /= log(2.0); + + entropy /= (1.0-alpha); + + /*printf("Entropy = %f\n", entropy);*/ + FREE_FUNC(state.probabilityVector); + state.probabilityVector = NULL; + + return entropy; +}/*calculateRenyiEntropy(double,double*,int)*/ + +double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, int vectorLength) +{ + double jointEntropy = 0.0; + double tempValue = 0.0; + int i; + JointProbabilityState state = calculateJointProbability(firstVector,secondVector,vectorLength); + + /*H_\alpha(XY) = 1/(1-alpha) * log(2)(sum p(xy)^alpha)*/ + for (i = 0; i < state.numJointStates; i++) + { + tempValue = state.jointProbabilityVector[i]; + if (tempValue > 0) + { + jointEntropy += pow(tempValue,alpha); + } + } + + jointEntropy = log(jointEntropy); + + jointEntropy /= log(2.0); + + jointEntropy /= (1.0-alpha); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return jointEntropy; +}/*calculateJointRenyiEntropy(double,double*,double*,int)*/ + +double calcCondRenyiEnt(double alpha, double *dataVector, double *conditionVector, int uniqueInCondVector, int vectorLength) +{ + /*uniqueInCondVector = is the number of unique values in the cond vector.*/ + + /*condEntropy = sum p(y) * sum p(x|y)^alpha(*/ + + /* + ** first generate the seperate variables + */ + + double *seperateVectors = (double *) CALLOC_FUNC(uniqueInCondVector*vectorLength,sizeof(double)); + int *seperateVectorCount = (int *) CALLOC_FUNC(uniqueInCondVector,sizeof(int)); + double seperateVectorProb = 0.0; + int i,j; + double entropy = 0.0; + double tempValue = 0.0; + int currentValue; + double tempEntropy; + ProbabilityState state; + + double **seperateVectors2D = (double **) CALLOC_FUNC(uniqueInCondVector,sizeof(double*)); + for(j=0; j < uniqueInCondVector; j++) + seperateVectors2D[j] = seperateVectors + (int)j*vectorLength; + + for (i = 0; i < vectorLength; i++) + { + currentValue = (int) (conditionVector[i] - 1.0); + /*printf("CurrentValue = %d\n",currentValue);*/ + seperateVectors2D[currentValue][seperateVectorCount[currentValue]] = dataVector[i]; + seperateVectorCount[currentValue]++; + } + + + + for (j = 0; j < uniqueInCondVector; j++) + { + tempEntropy = 0.0; + seperateVectorProb = ((double)seperateVectorCount[j]) / vectorLength; + state = calculateProbability(seperateVectors2D[j],seperateVectorCount[j]); + + /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ + for (i = 0; i < state.numStates; i++) + { + tempValue = state.probabilityVector[i]; + + if (tempValue > 0) + { + tempEntropy += pow(tempValue,alpha); + /*printf("Entropy = %f, i = %d\n", entropy,i);*/ + } + } + + /*printf("Entropy = %f\n", entropy);*/ + + tempEntropy = log(tempEntropy); + + tempEntropy /= log(2.0); + + tempEntropy /= (1.0-alpha); + + entropy += tempEntropy; + + FREE_FUNC(state.probabilityVector); + } + + FREE_FUNC(seperateVectors2D); + seperateVectors2D = NULL; + + FREE_FUNC(seperateVectors); + FREE_FUNC(seperateVectorCount); + + seperateVectors = NULL; + seperateVectorCount = NULL; + + return entropy; +}/*calcCondRenyiEnt(double *,double *,int)*/ + +double calculateConditionalRenyiEntropy(double alpha, double *dataVector, double *conditionVector, int vectorLength) +{ + /*calls this: + **double calculateConditionalRenyiEntropy(double alpha, double *firstVector, double *condVector, int uniqueInCondVector, int vectorLength) + **after determining uniqueInCondVector + */ + int numUnique = numberOfUniqueValues(conditionVector, vectorLength); + + return calcCondRenyiEnt(alpha, dataVector, conditionVector, numUnique, vectorLength); +}/*calculateConditionalRenyiEntropy(double,double*,double*,int)*/ + diff --git a/FEAST/MIToolbox/RenyiEntropy.h b/FEAST/MIToolbox/RenyiEntropy.h new file mode 100644 index 0000000..296bc4b --- /dev/null +++ b/FEAST/MIToolbox/RenyiEntropy.h @@ -0,0 +1,68 @@ +/******************************************************************************* +** RenyiEntropy.h +** Part of the mutual information toolbox +** +** Contains functions to calculate the Renyi alpha entropy of a single variable +** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the +** conditional Renyi entropy H_\alpha(X|Y) +** +** Author: Adam Pocock +** Created 26/3/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __Renyi_Entropy_H +#define __Renyi_Entropy_H + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************* +** calculateRenyiEntropy returns the Renyi entropy in log base 2 of dataVector +** H_{\alpha}(X), for \alpha != 1 +** +** length(dataVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateRenyiEntropy(double alpha, double *dataVector, int vectorLength); + +/******************************************************************************* +** calculateJointRenyiEntropy returns the Renyi entropy in log base 2 of the +** joint variable of firstVector and secondVector H_{\alpha}(XY), +** for \alpha != 1 +** +** length(firstVector) == length(secondVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, int vectorLength); + +/* This function does not return a valid conditonal entropy as it has no +** meaning in Renyi's extension of entropy +double calculateConditionalRenyiEntropy(double alpha, double *dataVector, double *conditionVector, int vectorLength); +*/ + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/RenyiMIToolbox.m b/FEAST/MIToolbox/RenyiMIToolbox.m new file mode 100644 index 0000000..cd235e9 --- /dev/null +++ b/FEAST/MIToolbox/RenyiMIToolbox.m @@ -0,0 +1,48 @@ +function [varargout] = RenyiMIToolbox(functionName, alpha, varargin) +%function [varargout] = RenyiMIToolbox(functionName, alpha, varargin) +% +%Provides access to the functions in RenyiMIToolboxMex +% +%Expects column vectors, will not work with row vectors +% +%Function list +%"Entropy" = H_{\alpha}(X) = 1 +%"MI" = I_{\alpha}(X;Y) = 3 +% +%Arguments and returned values +%[entropy] = H_\alpha(X) = H(alpha,vector) +%[mi] = I_\alpha(X;Y) = I(alpha,vector,target) +% +%Internal RenyiMIToolbox function number +%Renyi Entropy = 1; +%Renyi MI = 3; + +if (alpha ~= 1) + if (strcmpi(functionName,'Entropy') || strcmpi(functionName,'h')) + %disp('Calculating Entropy'); + if (size(varargin{1},2)>1) + mergedVector = MIToolboxMex(3,varargin{1}); + else + mergedVector = varargin{1}; + end + [varargout{1}] = RenyiMIToolboxMex(1,alpha,mergedVector); + elseif (strcmpi(functionName,'MI')) + if (size(varargin{1},2)>1) + mergedFirst = MIToolboxMex(3,varargin{1}); + else + mergedFirst = varargin{1}; + end + if (size(varargin{2},2)>1) + mergedSecond = MIToolboxMex(3,varargin{2}); + else + mergedSecond = varargin{2}; + end + [varargout{1}] = RenyiMIToolboxMex(3,alpha,mergedFirst,mergedSecond); + else + varargout{1} = 0; + disp(['Unrecognised functionName ' functionName]); + end +else + disp('For alpha = 1 use functions in MIToolbox.m'); + disp('as those functions are the implementation of Shannon''s Information Theory'); +end diff --git a/FEAST/MIToolbox/RenyiMIToolboxMex.c b/FEAST/MIToolbox/RenyiMIToolboxMex.c new file mode 100644 index 0000000..03ab076 --- /dev/null +++ b/FEAST/MIToolbox/RenyiMIToolboxMex.c @@ -0,0 +1,197 @@ +/******************************************************************************* +** +** RenyiMIToolboxMex.cpp +** is the MATLAB entry point for the Renyi Entropy and MI MIToolbox functions +** when called from a MATLAB/OCTAVE script. +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ +#include "MIToolbox.h" +#include "RenyiEntropy.h" +#include "RenyiMutualInformation.h" + +/******************************************************************************* +**entry point for the mex call +**nlhs - number of outputs +**plhs - pointer to array of outputs +**nrhs - number of inputs +**prhs - pointer to array of inputs +*******************************************************************************/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /***************************************************************************** + ** this function takes a flag and 2 or 3 other arguments + ** the first is a scalar alpha value, and the remainder are + ** arrays. It returns a Renyi entropy or mutual information using the + ** alpha divergence. + *****************************************************************************/ + + int flag, numberOfSamples, checkSamples, numberOfFeatures, checkFeatures; + double alpha; + double *dataVector, *firstVector, *secondVector, *output; + + /*if (nlhs != 1) + { + printf("Incorrect number of output arguments\n"); + }//if not 1 output + */ + if (nrhs == 3) + { + /*printf("Must be H(X)\n");*/ + } + else if (nrhs == 4) + { + /*printf("Must be H(XY), I(X;Y)\n");*/ + } + else + { + printf("Incorrect number of arguments, format is RenyiMIToolbox(\"FLAG\",varargin)\n"); + } + + /* number to function map + ** 1 = H(X) + ** 2 = H(XY) + ** 3 = I(X;Y) + */ + + flag = *mxGetPr(prhs[0]); + + switch (flag) + { + case 1: + { + /* + **H_{\alpha}(X) + */ + alpha = mxGetScalar(prhs[1]); + numberOfSamples = mxGetM(prhs[2]); + numberOfFeatures = mxGetN(prhs[2]); + dataVector = (double *) mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *) mxGetPr(plhs[0]); + + if (numberOfFeatures == 1) + { + /*double calculateRenyiEntropy(double alpha, double *dataVector, long vectorLength);*/ + *output = calculateRenyiEntropy(alpha,dataVector,numberOfSamples); + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 1 - H_{\alpha}(X)*/ + case 2: + { + /* + **H_{\alpha}(XY) + */ + alpha = mxGetScalar(prhs[1]); + + numberOfSamples = mxGetM(prhs[2]); + checkSamples = mxGetM(prhs[3]); + + numberOfFeatures = mxGetN(prhs[2]); + checkFeatures = mxGetN(prhs[3]); + + firstVector = mxGetPr(prhs[2]); + secondVector = mxGetPr(prhs[3]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if ((numberOfSamples == 0) || (checkSamples == 0)) + { + *output = 0.0; + } + else if (numberOfSamples == checkSamples) + { + /*double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, long vectorLength);*/ + *output = calculateJointRenyiEntropy(alpha,firstVector,secondVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 2 - H_{\alpha}(XY)*/ + case 3: + { + /* + **I_{\alpha}(X;Y) + */ + alpha = mxGetScalar(prhs[1]); + + numberOfSamples = mxGetM(prhs[2]); + checkSamples = mxGetM(prhs[3]); + + numberOfFeatures = mxGetN(prhs[2]); + checkFeatures = mxGetN(prhs[3]); + + firstVector = mxGetPr(prhs[2]); + secondVector = mxGetPr(prhs[3]); + + plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + if ((numberOfFeatures == 1) && (checkFeatures == 1)) + { + if ((numberOfSamples == 0) || (checkSamples == 0)) + { + *output = 0.0; + } + else if (numberOfSamples == checkSamples) + { + /*double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, long vectorLength);*/ + *output = calculateRenyiMIDivergence(alpha,firstVector,secondVector,numberOfSamples); + } + else + { + printf("Vector lengths do not match, they must be the same length"); + *output = -1.0; + } + } + else + { + printf("No columns in input\n"); + *output = -1.0; + } + break; + }/*case 3 - I_{\alpha}(X;Y)*/ + default: + { + printf("Unrecognised flag\n"); + break; + }/*default*/ + }/*switch(flag)*/ + + return; +}/*mexFunction()*/ diff --git a/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 b/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 new file mode 100755 index 0000000..6bff1db Binary files /dev/null and b/FEAST/MIToolbox/RenyiMIToolboxMex.mexa64 differ diff --git a/FEAST/MIToolbox/RenyiMutualInformation.c b/FEAST/MIToolbox/RenyiMutualInformation.c new file mode 100644 index 0000000..dc6fd51 --- /dev/null +++ b/FEAST/MIToolbox/RenyiMutualInformation.c @@ -0,0 +1,95 @@ +/******************************************************************************* +** RenyiMutualInformation.cpp +** Part of the mutual information toolbox +** +** Contains functions to calculate the Renyi mutual information of +** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and +** the joint entropy difference +** +** Author: Adam Pocock +** Created 26/3/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#include "MIToolbox.h" +#include "CalculateProbability.h" +#include "RenyiEntropy.h" +#include "RenyiMutualInformation.h" + +double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, int vectorLength) +{ + double mutualInformation = 0.0; + int firstIndex,secondIndex; + int i; + double jointTemp = 0.0; + double seperateTemp = 0.0; + double invAlpha = 1.0 - alpha; + JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength); + + /* standard MI is D_KL(p(x,y)||p(x)p(y)) + ** which expands to + ** D_KL(p(x,y)||p(x)p(y)) = sum(p(x,y) * log(p(x,y)/(p(x)p(y)))) + ** + ** Renyi alpha divergence D_alpha(p(x,y)||p(x)p(y)) + ** expands to + ** D_alpha(p(x,y)||p(x)p(y)) = 1/(alpha-1) * log(sum((p(x,y)^alpha)*((p(x)p(y))^(1-alpha)))) + */ + + for (i = 0; i < state.numJointStates; i++) + { + firstIndex = i % state.numFirstStates; + secondIndex = i / state.numFirstStates; + + if ((state.jointProbabilityVector[i] > 0) && (state.firstProbabilityVector[firstIndex] > 0) && (state.secondProbabilityVector[secondIndex] > 0)) + { + jointTemp = pow(state.jointProbabilityVector[i],alpha); + seperateTemp = state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex]; + seperateTemp = pow(seperateTemp,invAlpha); + mutualInformation += (jointTemp * seperateTemp); + } + } + + mutualInformation = log(mutualInformation); + mutualInformation /= log(2.0); + mutualInformation /= (alpha-1.0); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return mutualInformation; +}/*calculateRenyiMIDivergence(double, double *, double *, int)*/ + +double calculateRenyiMIJoint(double alpha, double *dataVector, double *targetVector, int vectorLength) +{ + double hY = calculateRenyiEntropy(alpha, targetVector, vectorLength); + double hX = calculateRenyiEntropy(alpha, dataVector, vectorLength); + + double hXY = calculateJointRenyiEntropy(alpha, dataVector, targetVector, vectorLength); + + double answer = hX + hY - hXY; + + return answer; +}/*calculateRenyiMIJoint(double, double*, double*, int)*/ + diff --git a/FEAST/MIToolbox/RenyiMutualInformation.h b/FEAST/MIToolbox/RenyiMutualInformation.h new file mode 100644 index 0000000..07eff09 --- /dev/null +++ b/FEAST/MIToolbox/RenyiMutualInformation.h @@ -0,0 +1,60 @@ +/******************************************************************************* +** RenyiMutualInformation.h +** Part of the mutual information toolbox +** +** Contains functions to calculate the Renyi mutual information of +** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and +** the joint entropy difference +** +** Author: Adam Pocock +** Created 26/3/2010 +** +** Copyright 2010 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** This file is part of MIToolbox. +** +** MIToolbox is free software: you can redistribute it and/or modify +** it under the terms of the GNU Lesser General Public License as published by +** the Free Software Foundation, either version 3 of the License, or +** (at your option) any later version. +** +** MIToolbox 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 Lesser General Public License for more details. +** +** You should have received a copy of the GNU Lesser General Public License +** along with MIToolbox. If not, see . +** +*******************************************************************************/ + +#ifndef __Renyi_MutualInformation_H +#define __Renyi_MutualInformation_H + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************* +** calculateRenyiMIDivergence returns the log base 2 Renyi mutual information +** between dataVector and targetVector, I_{\alpha}(X;Y), for \alpha != 1 +** This uses Renyi's generalised alpha divergence as the difference measure +** instead of the KL-divergence as in Shannon's Mutual Information +** +** length(dataVector) == length(targetVector) == vectorLength otherwise there +** will be a segmentation fault +*******************************************************************************/ +double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, int vectorLength); + +/* This function returns a different value to the alpha divergence mutual +** information, and thus is not a correct mutual information +double calculateRenyiMIJoint(double alpha, double *dataVector, double *targetVector, int vectorLength); +*/ + +#ifdef __cplusplus +} +#endif + +#endif + diff --git a/FEAST/MIToolbox/cmi.m b/FEAST/MIToolbox/cmi.m new file mode 100644 index 0000000..30e4bb0 --- /dev/null +++ b/FEAST/MIToolbox/cmi.m @@ -0,0 +1,31 @@ +function output = cmi(X,Y,Z) +%function output = cmi(X,Y,Z) +%X, Y & Z can be matrices which are converted into a joint variable +%before computation +% +%expects variables to be column-wise +% +%returns the mutual information between X and Y conditioned on Z, I(X;Y|Z) + +if nargin == 3 + if (size(X,2)>1) + mergedFirst = MIToolboxMex(3,X); + else + mergedFirst = X; + end + if (size(Y,2)>1) + mergedSecond = MIToolboxMex(3,Y); + else + mergedSecond = Y; + end + if (size(Z,2)>1) + mergedThird = MIToolboxMex(3,Z); + else + mergedThird = Z; + end + [output] = MIToolboxMex(8,mergedFirst,mergedSecond,mergedThird); +elseif nargin == 2 + output = mi(X,Y); +else + output = 0; +end diff --git a/FEAST/MIToolbox/condh.m b/FEAST/MIToolbox/condh.m new file mode 100644 index 0000000..9f966db --- /dev/null +++ b/FEAST/MIToolbox/condh.m @@ -0,0 +1,26 @@ +function output = condh(X,Y) +%function output = condh(X,Y) +%X & Y can be matrices which are converted into a joint variable +%before computation +% +%expects variables to be column-wise +% +%returns the conditional entropy of X given Y, H(X|Y) + +if nargin == 2 + if (size(X,2)>1) + mergedFirst = MIToolboxMex(3,X); + else + mergedFirst = X; + end + if (size(Y,2)>1) + mergedSecond = MIToolboxMex(3,Y); + else + mergedSecond = Y; + end + [output] = MIToolboxMex(6,mergedFirst,mergedSecond); +elseif nargin == 1 + output = h(X); +else + output = 0; +end diff --git a/FEAST/MIToolbox/demonstration_algorithms/CMIM.m b/FEAST/MIToolbox/demonstration_algorithms/CMIM.m new file mode 100644 index 0000000..8ae1f7c --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/CMIM.m @@ -0,0 +1,49 @@ +function selectedFeatures = CMIM(k, featureMatrix, classColumn) +%function selectedFeatures = CMIM(k, featureMatrix, classColumn) +%Computes conditional mutual information maximisation algorithm from +%"Fast Binary Feature Selection with Conditional Mutual Information" +%by F. Fleuret (2004) + +%Computes the top k features from +%a dataset featureMatrix with n training examples and m features +%with the classes held in classColumn. + +noOfTraining = size(classColumn,1); +noOfFeatures = size(featureMatrix,2); + +partialScore = zeros(noOfFeatures,1); +m = zeros(noOfFeatures,1); +score = 0; +answerFeatures = zeros(k,1); +highestMI = 0; +highestMICounter = 0; + +for n = 1 : noOfFeatures + partialScore(n) = mi(featureMatrix(:,n),classColumn); + if partialScore(n) > highestMI + highestMI = partialScore(n); + highestMICounter = n; + end +end + +answerFeatures(1) = highestMICounter; + +for i = 2 : k + score = 0; + limitI = i - 1; + for n = 1 : noOfFeatures + while ((partialScore(n) >= score) && (m(n) < limitI)) + m(n) = m(n) + 1; + conditionalInfo = cmi(featureMatrix(:,n),classColumn,featureMatrix(:,answerFeatures(m(n)))); + if partialScore(n) > conditionalInfo + partialScore(n) = conditionalInfo; + end + end + if partialScore(n) >= score + score = partialScore(n); + answerFeatures(i) = n; + end + end +end + +selectedFeatures = answerFeatures; diff --git a/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c new file mode 100644 index 0000000..daacb34 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c @@ -0,0 +1,158 @@ +/******************************************************************************* +** Demonstration feature selection algorithm - MATLAB r2009a +** +** Initial Version - 13/06/2008 +** Updated - 07/07/2010 +** based on CMIM.m +** +** Conditional Mutual Information Maximisation +** in +** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation +** F. Fleuret (2004) +** +** Author - Adam Pocock +** Demonstration code for MIToolbox +*******************************************************************************/ + +#include "mex.h" +#include "MutualInformation.h" + +void CMIMCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values + **the class MI doubles as the partial score from the CMIM paper + */ + double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); + /*in the CMIM paper, m = lastUsedFeature*/ + int *lastUsedFeature = (int *)mxCalloc(noOfFeatures,sizeof(int)); + + double score, conditionalInfo; + int iMinus, currentFeature; + + double maxMI = 0.0; + int maxMICounter = -1; + + int j,i; + + double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the CMIM algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + iMinus = i-1; + + for (j = 0; j < noOfFeatures; j++) + { + while ((classMI[j] > score) && (lastUsedFeature[j] < i)) + { + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ + currentFeature = (int) outputFeatures[lastUsedFeature[j]]; + conditionalInfo = calculateConditionalMutualInformation(feature2D[j],classColumn,feature2D[currentFeature],noOfSamples); + if (classMI[j] > conditionalInfo) + { + classMI[j] = conditionalInfo; + }/*reset classMI*/ + /*moved due to C indexing from 0 rather than 1*/ + lastUsedFeature[j] += 1; + }/*while partial score greater than score & not reached last feature*/ + if (classMI[j] > score) + { + score = classMI[j]; + outputFeatures[i] = j; + }/*if partial score still greater than score*/ + }/*for number of features*/ + }/*for the number of features to select*/ + + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + +}/*CMIMCalculation*/ + +/*entry point for the mex call +**nlhs - number of outputs +**plhs - pointer to array of outputs +**nrhs - number of inputs +**prhs - pointer to array of inputs +*/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /************************************************************* + ** this function takes 3 arguments: + ** k = number of features to select, + ** featureMatrix[][] = matrix of features + ** classColumn[] = targets + ** the arguments should all be discrete integers. + ** and has one output: + ** selectedFeatures[] of size k + *************************************************************/ + + int k, numberOfFeatures, numberOfSamples, numberOfTargets; + double *featureMatrix, *targets, *output; + + + if (nlhs != 1) + { + printf("Incorrect number of output arguments"); + }/*if not 1 output*/ + if (nrhs != 3) + { + printf("Incorrect number of input arguments"); + }/*if not 3 inputs*/ + + /*get the number of features to select, cast out as it is a double*/ + k = (int) mxGetScalar(prhs[0]); + + numberOfFeatures = mxGetN(prhs[1]); + numberOfSamples = mxGetM(prhs[1]); + + numberOfTargets = mxGetM(prhs[2]); + + if (numberOfTargets != numberOfSamples) + { + printf("Number of targets must match number of samples\n"); + printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); + + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + }/*if size mismatch*/ + else + { + + featureMatrix = mxGetPr(prhs[1]); + targets = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void CMIMCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + CMIMCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + } + + return; +}/*mexFunction()*/ diff --git a/FEAST/MIToolbox/demonstration_algorithms/DISR.m b/FEAST/MIToolbox/demonstration_algorithms/DISR.m new file mode 100644 index 0000000..c8f5669 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/DISR.m @@ -0,0 +1,73 @@ +function selectedFeatures = DISR(k, featureMatrix, classColumn) +%function selectedFeatures = DISR(k, featureMatrix, classColumn) +% +%Computers optimal features according to DISR algorithm from +%On the Use of variable "complementarity for feature selection" +%by P Meyer, G Bontempi (2006) +% +%Computes the top k features from +%a dataset featureMatrix with n training examples and m features +%with the classes held in classColumn. +% +%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y))) +%where SimRel = MI(Xij,Y) / H(Xij,Y) + +totalFeatures = size(featureMatrix,2); +classMI = zeros(totalFeatures,1); +unselectedFeatures = ones(totalFeatures,1); +score = 0; +currentScore = 0; +innerScore = 0; +iMinus = 0; +answerFeatures = zeros(k,1); +highestMI = 0; +highestMICounter = 0; +currentHighestFeature = 0; + +%create a matrix to hold the SRs of a feature pair. +%initialised to -1 as you can't get a negative SR. +featureSRMatrix = -(ones(k,totalFeatures)); + +for n = 1 : totalFeatures + classMI(n) = mi(featureMatrix(:,n),classColumn); + if classMI(n) > highestMI + highestMI = classMI(n); + highestMICounter = n; + end +end + +answerFeatures(1) = highestMICounter; +unselectedFeatures(highestMICounter) = 0; + +for i = 2 : k + score = 0; + currentHighestFeature = 0; + iMinus = i-1; + for j = 1 : totalFeatures + if unselectedFeatures(j) == 1 + %DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y))) + %where SimRel = MI(Xij,Y) / H(Xij,Y) + currentScore = 0; + for m = 1 : iMinus + if featureSRMatrix(m,j) == -1 + unionedFeatures = joint([featureMatrix(:,answerFeatures(m)),featureMatrix(:,j)]); + tempUnionMI = mi(unionedFeatures,classColumn); + tempTripEntropy = h([unionedFeatures,classColumn]); + featureSRMatrix(m,j) = tempUnionMI/tempTripEntropy; + end + + currentScore = currentScore + featureSRMatrix(m,j); + end + if (currentScore > score) + score = currentScore; + currentHighestFeature = j; + end + end + end + %now highest feature is selected in currentHighestFeature + %store it + unselectedFeatures(currentHighestFeature) = 0; + answerFeatures(i) = currentHighestFeature; +end + +selectedFeatures = answerFeatures; diff --git a/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c new file mode 100644 index 0000000..617ca81 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c @@ -0,0 +1,199 @@ +/******************************************************************************* +** Demonstration feature selection algorithm - MATLAB r2009a +** +** Initial Version - 13/06/2008 +** Updated - 07/07/2010 +** based on DISR.m +** +** Double Input Symmetrical Relevance +** in +** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" +** P. Meyer and G. Bontempi (2006) +** +** Author - Adam Pocock +** Demonstration code for MIToolbox +*******************************************************************************/ + +#include "mex.h" +#include "MutualInformation.h" +#include "Entropy.h" +#include "ArrayOperations.h" + +void DISRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values*/ + double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); + + char *selectedFeatures = (char *)mxCalloc(noOfFeatures,sizeof(char)); + + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)mxCalloc(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + double *mergedVector = (double *) mxCalloc(noOfSamples,sizeof(double)); + + int arrayPosition; + double mi, tripEntropy; + + int i,j,x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + + for (i = 0; i < noOfFeatures;i++) + { + /*calculate mutual info + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + */ + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the DISR algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (x = 0; x < i; x++) + { + arrayPosition = x*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + /* + **double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength); + **double calculateJointEntropy(double *firstVector, double *secondVector, int vectorLength); + */ + + mergeArrays(feature2D[(int) outputFeatures[x]], feature2D[j],mergedVector,noOfSamples); + mi = calculateMutualInformation(mergedVector, classColumn, noOfSamples); + tripEntropy = calculateJointEntropy(mergedVector, classColumn, noOfSamples); + + featureMIMatrix[arrayPosition] = mi / tripEntropy; + }/*if not already known*/ + currentScore += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + mxFree(mergedVector); + mergedVector = NULL; + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + +}/*DISRCalculation(double[][],double[])*/ + +/*entry point for the mex call +**nlhs - number of outputs +**plhs - pointer to array of outputs +**nrhs - number of inputs +**prhs - pointer to array of inputs +*/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /************************************************************* + ** this function takes 3 arguments: + ** k = number of features to select, + ** featureMatrix[][] = matrix of features + ** classColumn[] = targets + ** the arguments should all be discrete integers. + ** and has one output: + ** selectedFeatures[] of size k + *************************************************************/ + + int k, numberOfFeatures, numberOfSamples, numberOfTargets; + double *featureMatrix, *targets, *output; + + + if (nlhs != 1) + { + printf("Incorrect number of output arguments"); + }/*if not 1 output*/ + if (nrhs != 3) + { + printf("Incorrect number of input arguments"); + }/*if not 3 inputs*/ + + /*get the number of features to select, cast out as it is a double*/ + k = (int) mxGetScalar(prhs[0]); + + numberOfFeatures = mxGetN(prhs[1]); + numberOfSamples = mxGetM(prhs[1]); + + numberOfTargets = mxGetM(prhs[2]); + + if (numberOfTargets != numberOfSamples) + { + printf("Number of targets must match number of samples\n"); + printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); + + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + }/*if size mismatch*/ + else + { + + featureMatrix = mxGetPr(prhs[1]); + targets = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void DISRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + DISRCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + } + + return; +}/*mexFunction()*/ diff --git a/FEAST/MIToolbox/demonstration_algorithms/IAMB.m b/FEAST/MIToolbox/demonstration_algorithms/IAMB.m new file mode 100644 index 0000000..1011260 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/IAMB.m @@ -0,0 +1,56 @@ +function [cmb association] = IAMB( data, targetindex, THRESHOLD) +%function [cmb association] = IAMB( data, targetindex, THRESHOLD) +% +%Performs the IAMB algorithm of Tsmardinos et al. (2003) +%from "Towards principled feature selection: Relevancy, filters and wrappers" + +if (nargin == 2) + THRESHOLD = 0.02; +end + +numf = size(data,2); +targets = data(:,targetindex); +data(:,targetindex) = -10; + +cmb = []; + +finished = false; +while ~finished + for n = 1:numf + cmbVector = joint(data(:,cmb)); + if isempty(cmb) + association(n) = mi( data(:,n), targets ); + end + + if ismember(n,cmb) + association(n) = -10; %arbtirary large negative constant + else + association(n) = cmi( data(:,n), targets, cmbVector); + end + end + + [maxval maxidx] = max(association); + if maxval < THRESHOLD + finished = true; + else + cmb = [ cmb maxidx ]; + end +end + +finished = false; +while ~finished && ~isempty(cmb) + association = []; + for n = 1:length(cmb) + cmbwithoutn = cmb; + cmbwithoutn(n)=[]; + association(n) = cmi( data(:,cmb(n)), targets, data(:,cmbwithoutn) ); + end + + [minval minidx] = min(association); + if minval > THRESHOLD + finished = true; + else + cmb(minidx) = []; + end +end + diff --git a/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m b/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m new file mode 100644 index 0000000..65464b3 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m @@ -0,0 +1,3 @@ +mex -I.. CMIM_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c +mex -I.. DISR_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c +mex -I.. mRMR_D_Mex.c ../MutualInformation.c ../Entropy.c ../CalculateProbability.c ../ArrayOperations.c \ No newline at end of file diff --git a/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m new file mode 100644 index 0000000..50b14bc --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m @@ -0,0 +1,69 @@ +function selectedFeatures = mRMR_D(k, featureMatrix, classColumn) +%function selectedFeatures = mRMR_D(k, featureMatrix, classColumn) +% +%Selects optimal features according to the mRMR-D algorithm from +%"Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" +%by H. Peng et al. (2005) +% +%Calculates the top k features +%a dataset featureMatrix with n training examples and m features +%with the classes held in classColumn (an n x 1 vector) + +noOfTraining = size(classColumn,1); +noOfFeatures = size(featureMatrix,2); +unselectedFeatures = ones(noOfFeatures,1); + +classMI = zeros(noOfFeatures,1); +answerFeatures = zeros(k,1); +highestMI = 0; +highestMICounter = 0; +currentHighestFeature = 0; + +featureMIMatrix = -(ones(k,noOfFeatures)); + +%setup the mi against the class +for n = 1 : noOfFeatures + classMI(n) = mi(featureMatrix(:,n),classColumn); + if classMI(n) > highestMI + highestMI = classMI(n); + highestMICounter = n; + end +end + +answerFeatures(1) = highestMICounter; +unselectedFeatures(highestMICounter) = 0; + +%iterate over the number of features to select +for i = 2:k + score = -100; + currentHighestFeature = 0; + iMinus = i-1; + for j = 1 : noOfFeatures + if unselectedFeatures(j) == 1 + currentMIScore = 0; + for m = 1 : iMinus + if featureMIMatrix(m,j) == -1 + featureMIMatrix(m,j) = mi(featureMatrix(:,j),featureMatrix(:,answerFeatures(m))); + end + currentMIScore = currentMIScore + featureMIMatrix(m,j); + end + currentScore = classMI(j) - (currentMIScore/iMinus); + + if (currentScore > score) + score = currentScore; + currentHighestFeature = j; + end + end + end + + if score < 0 + disp(['at selection ' int2str(j) ' mRMRD is negative with value ' num2str(score)]); + end + + %now highest feature is selected in currentHighestFeature + %store it + unselectedFeatures(currentHighestFeature) = 0; + answerFeatures(i) = currentHighestFeature; +end + +selectedFeatures = answerFeatures; diff --git a/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c new file mode 100644 index 0000000..8d7b074 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c @@ -0,0 +1,184 @@ +/******************************************************************************* +** Demonstration feature selection algorithm - MATLAB r2009a +** +** Initial Version - 13/06/2008 +** Updated - 07/07/2010 +** based on mRMR_D.m +** +** Minimum Relevance Maximum Redundancy +** in +** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" +** H. Peng et al. (2005) +** +** Author - Adam Pocock +** Demonstration code for MIToolbox +*******************************************************************************/ + +#include "mex.h" +#include "MutualInformation.h" + +void mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures) +{ + double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*)); + /*holds the class MI values*/ + double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double)); + int *selectedFeatures = (int *)mxCalloc(noOfFeatures,sizeof(int)); + /*holds the intra feature MI values*/ + int sizeOfMatrix = k*noOfFeatures; + double *featureMIMatrix = (double *)mxCalloc(sizeOfMatrix,sizeof(double)); + + double maxMI = 0.0; + int maxMICounter = -1; + + /*init variables*/ + + double score, currentScore, totalFeatureMI; + int currentHighestFeature; + + int arrayPosition, i, j, x; + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < sizeOfMatrix;i++) + { + featureMIMatrix[i] = -1; + }/*for featureMIMatrix - blank to -1*/ + + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + selectedFeatures[maxMICounter] = 1; + outputFeatures[0] = maxMICounter; + + /************* + ** Now we have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the mRMR-D algorithm + *************/ + + for (i = 1; i < k; i++) + { + /*to ensure it selects some features + **if this is zero then it will not pick features where the redundancy is greater than the + **relevance + */ + score = -1000.0; + currentHighestFeature = 0; + currentScore = 0.0; + totalFeatureMI = 0.0; + + for (j = 0; j < noOfFeatures; j++) + { + /*if we haven't selected j*/ + if (selectedFeatures[j] == 0) + { + currentScore = classMI[j]; + totalFeatureMI = 0.0; + + for (x = 0; x < i; x++) + { + arrayPosition = x*noOfFeatures + j; + if (featureMIMatrix[arrayPosition] == -1) + { + /*work out intra MI*/ + + /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/ + featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[x]], feature2D[j], noOfSamples); + } + + totalFeatureMI += featureMIMatrix[arrayPosition]; + }/*for the number of already selected features*/ + + currentScore -= (totalFeatureMI/i); + if (currentScore > score) + { + score = currentScore; + currentHighestFeature = j; + } + }/*if j is unselected*/ + }/*for number of features*/ + + selectedFeatures[currentHighestFeature] = 1; + outputFeatures[i] = currentHighestFeature; + + }/*for the number of features to select*/ + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + +}/*mRMRCalculation(double[][],double[])*/ + +/*entry point for the mex call +**nlhs - number of outputs +**plhs - pointer to array of outputs +**nrhs - number of inputs +**prhs - pointer to array of inputs +*/ +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /************************************************************* + ** this function takes 3 arguments: + ** k = number of features to select, + ** featureMatrix[][] = matrix of features + ** classColumn[] = targets + ** the arguments should all be discrete integers. + ** and has one output: + ** selectedFeatures[] of size k + *************************************************************/ + + int k, numberOfFeatures, numberOfSamples, numberOfTargets; + double *featureMatrix, *targets, *output; + + + if (nlhs != 1) + { + printf("Incorrect number of output arguments"); + }/*if not 1 output*/ + if (nrhs != 3) + { + printf("Incorrect number of input arguments"); + }/*if not 3 inputs*/ + + /*get the number of features to select, cast out as it is a double*/ + k = (int) mxGetScalar(prhs[0]); + + numberOfFeatures = mxGetN(prhs[1]); + numberOfSamples = mxGetM(prhs[1]); + + numberOfTargets = mxGetM(prhs[2]); + + if (numberOfTargets != numberOfSamples) + { + printf("Number of targets must match number of samples\n"); + printf("Number of targets = %d, Number of Samples = %d, Number of Features = %d\n",numberOfTargets,numberOfSamples,numberOfFeatures); + + plhs[0] = mxCreateDoubleMatrix(0,0,mxREAL); + }/*if size mismatch*/ + else + { + + featureMatrix = mxGetPr(prhs[1]); + targets = mxGetPr(prhs[2]); + + plhs[0] = mxCreateDoubleMatrix(k,1,mxREAL); + output = (double *)mxGetPr(plhs[0]); + + /*void mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/ + mRMRCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output); + } + + return; +}/*mexFunction()*/ diff --git a/FEAST/MIToolbox/h.m b/FEAST/MIToolbox/h.m new file mode 100644 index 0000000..8fd8999 --- /dev/null +++ b/FEAST/MIToolbox/h.m @@ -0,0 +1,13 @@ +function output = h(X) +%function output = h(X) +%X can be a matrix which is converted into a joint variable before calculation +%expects variables to be column-wise +% +%returns the entropy of X, H(X) + +if (size(X,2)>1) + mergedVector = MIToolboxMex(3,X); +else + mergedVector = X; +end +[output] = MIToolboxMex(4,mergedVector); diff --git a/FEAST/MIToolbox/joint.m b/FEAST/MIToolbox/joint.m new file mode 100644 index 0000000..f40ff25 --- /dev/null +++ b/FEAST/MIToolbox/joint.m @@ -0,0 +1,16 @@ +function output = joint(X,arities) +%function output = joint(X,arities) +%returns the joint random variable of the matrix X +%assuming the variables are in columns +% +%if passed a vector of the arities then it produces a correct +%joint variable, otherwise it may not include all states +% +%if the joint variable is only compared with variables using the same samples, +%then arity information is not required + +if (nargin == 2) + [output] = MIToolboxMex(3,X,arities); +else + [output] = MIToolboxMex(3,X); +end diff --git a/FEAST/MIToolbox/mi.m b/FEAST/MIToolbox/mi.m new file mode 100644 index 0000000..2fd8766 --- /dev/null +++ b/FEAST/MIToolbox/mi.m @@ -0,0 +1,20 @@ +function output = mi(X,Y) +%function output = mi(X,Y) +%X & Y can be matrices which are converted into a joint variable +%before computation +% +%expects variables to be column-wise +% +%returns the mutual information between X and Y, I(X;Y) + +if (size(X,2)>1) + mergedFirst = MIToolboxMex(3,X); +else + mergedFirst = X; +end +if (size(Y,2)>1) + mergedSecond = MIToolboxMex(3,Y); +else + mergedSecond = Y; +end +[output] = MIToolboxMex(7,mergedFirst,mergedSecond); -- cgit v1.2.3