diff options
56 files changed, 215 insertions, 6769 deletions
diff --git a/FEAST/FSToolbox/BetaGamma.c b/FEAST/FSToolbox/BetaGamma.c deleted file mode 100644 index 925ef8b..0000000 --- a/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/FSToolbox/CMIM.c b/FEAST/FSToolbox/CMIM.c deleted file mode 100644 index 9ef21ad..0000000 --- a/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/FSToolbox/CompileFEAST.m b/FEAST/FSToolbox/CompileFEAST.m deleted file mode 100644 index f5dad48..0000000 --- a/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/FSToolbox/CondMI.c b/FEAST/FSToolbox/CondMI.c deleted file mode 100644 index ea6f8ee..0000000 --- a/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 <string.h> - -/* 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 deleted file mode 100644 index 3ec7676..0000000 --- a/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/FSToolbox/FCBF.m b/FEAST/FSToolbox/FCBF.m deleted file mode 100644 index dcaf3bf..0000000 --- a/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/FSToolbox/FSAlgorithms.h b/FEAST/FSToolbox/FSAlgorithms.h deleted file mode 100644 index e6ddba8..0000000 --- a/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/FSToolbox/FSToolbox.h b/FEAST/FSToolbox/FSToolbox.h deleted file mode 100644 index bf8662b..0000000 --- a/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 <math.h> -#include <string.h> - -#ifdef COMPILE_C - #define C_IMPLEMENTATION - #include <stdio.h> - #include <stdlib.h> - #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 deleted file mode 100644 index 73a9197..0000000 --- a/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/FSToolbox/ICAP.c b/FEAST/FSToolbox/ICAP.c deleted file mode 100644 index 00953a7..0000000 --- a/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/FSToolbox/JMI.c b/FEAST/FSToolbox/JMI.c deleted file mode 100644 index 30ef1bc..0000000 --- a/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/FSToolbox/MIM.m b/FEAST/FSToolbox/MIM.m deleted file mode 100644 index 31695e4..0000000 --- a/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/FSToolbox/Makefile b/FEAST/FSToolbox/Makefile deleted file mode 100644 index b8baade..0000000 --- a/FEAST/FSToolbox/Makefile +++ /dev/null @@ -1,103 +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. - -PREFIX = /usr -CXXFLAGS = -O3 -fPIC -COMPILER = gcc -LINKER = ld -MITOOLBOXPATH = ../MIToolbox/ -objects = mRMR_D.o CMIM.o JMI.o DISR.o CondMI.o ICAP.o BetaGamma.o - -libFSToolbox.so : $(objects) - $(LINKER) -lMIToolbox -lm -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 - -.PHONY : install -install: - $(MAKE) - @echo "installing libFSToolbox.so to $(PREFIX)/lib" - cp -v libFSToolbox.so $(PREFIX)/lib diff --git a/FEAST/FSToolbox/README b/FEAST/FSToolbox/README deleted file mode 100644 index 1aae2d7..0000000 --- a/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/FSToolbox/RELIEF.m b/FEAST/FSToolbox/RELIEF.m deleted file mode 100644 index 194ce7b..0000000 --- a/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/FSToolbox/feast.bib b/FEAST/FSToolbox/feast.bib deleted file mode 100644 index 2c58f0d..0000000 --- a/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/FSToolbox/feast.m b/FEAST/FSToolbox/feast.m deleted file mode 100644 index 96a685e..0000000 --- a/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/FSToolbox/license.txt b/FEAST/FSToolbox/license.txt deleted file mode 100644 index 798960e..0000000 --- a/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/FSToolbox/mRMR_D.c b/FEAST/FSToolbox/mRMR_D.c deleted file mode 100644 index 3eeb2a4..0000000 --- a/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/MIToolbox/ArrayOperations.c b/FEAST/MIToolbox/ArrayOperations.c deleted file mode 100644 index 00a8324..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 3cc9025..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 94a9ed0..0000000 --- a/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. <http://fsf.org/> - 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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index d5e9d3e..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index a8d9e92..0000000 --- a/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/MIToolbox/Entropy.c b/FEAST/MIToolbox/Entropy.c deleted file mode 100644 index 3f37cc1..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 4bdd697..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index bba6284..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#ifndef __MIToolbox_H -#define __MIToolbox_H - -#include <math.h> -#include <string.h> - -#ifdef COMPILE_C - #define C_IMPLEMENTATION - #include <stdio.h> - #include <stdlib.h> - #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 deleted file mode 100644 index 880924a..0000000 --- a/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/MIToolbox/MIToolboxMex.c b/FEAST/MIToolbox/MIToolboxMex.c deleted file mode 100644 index e27009f..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ -#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/Makefile b/FEAST/MIToolbox/Makefile deleted file mode 100644 index 992e7cd..0000000 --- a/FEAST/MIToolbox/Makefile +++ /dev/null @@ -1,91 +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 <http://www.gnu.org/licenses/>. - -PREFIX = /usr -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 -v *.o libMIToolbox.so - -.PHONY : install -install: - $(MAKE) - @echo "installing libMIToolbox.so to $(PREFIX)/lib" - @cp -v libMIToolbox.so $(PREFIX)/lib - diff --git a/FEAST/MIToolbox/MutualInformation.c b/FEAST/MIToolbox/MutualInformation.c deleted file mode 100644 index 0fb4766..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 1045912..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 7abe43d..0000000 --- a/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/MIToolbox/RenyiEntropy.c b/FEAST/MIToolbox/RenyiEntropy.c deleted file mode 100644 index 32c5ff9..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 296bc4b..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index cd235e9..0000000 --- a/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/MIToolbox/RenyiMIToolboxMex.c b/FEAST/MIToolbox/RenyiMIToolboxMex.c deleted file mode 100644 index 03ab076..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ -#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/RenyiMutualInformation.c b/FEAST/MIToolbox/RenyiMutualInformation.c deleted file mode 100644 index dc6fd51..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 07eff09..0000000 --- a/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 <http://www.gnu.org/licenses/>. -** -*******************************************************************************/ - -#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 deleted file mode 100644 index 30e4bb0..0000000 --- a/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/MIToolbox/condh.m b/FEAST/MIToolbox/condh.m deleted file mode 100644 index 9f966db..0000000 --- a/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/MIToolbox/demonstration_algorithms/CMIM.m b/FEAST/MIToolbox/demonstration_algorithms/CMIM.m deleted file mode 100644 index 8ae1f7c..0000000 --- a/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/MIToolbox/demonstration_algorithms/CMIM_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c deleted file mode 100644 index daacb34..0000000 --- a/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/MIToolbox/demonstration_algorithms/DISR.m b/FEAST/MIToolbox/demonstration_algorithms/DISR.m deleted file mode 100644 index c8f5669..0000000 --- a/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/MIToolbox/demonstration_algorithms/DISR_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c deleted file mode 100644 index 617ca81..0000000 --- a/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/MIToolbox/demonstration_algorithms/IAMB.m b/FEAST/MIToolbox/demonstration_algorithms/IAMB.m deleted file mode 100644 index 1011260..0000000 --- a/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/MIToolbox/demonstration_algorithms/compile_demos.m b/FEAST/MIToolbox/demonstration_algorithms/compile_demos.m deleted file mode 100644 index 65464b3..0000000 --- a/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/MIToolbox/demonstration_algorithms/mRMR_D.m b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D.m deleted file mode 100644 index 50b14bc..0000000 --- a/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/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c deleted file mode 100644 index 8d7b074..0000000 --- a/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/MIToolbox/h.m b/FEAST/MIToolbox/h.m deleted file mode 100644 index 8fd8999..0000000 --- a/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/MIToolbox/joint.m b/FEAST/MIToolbox/joint.m deleted file mode 100644 index f40ff25..0000000 --- a/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/MIToolbox/mi.m b/FEAST/MIToolbox/mi.m deleted file mode 100644 index 2fd8766..0000000 --- a/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/README.markdown b/README.markdown index e2222a7..86aa0f2 100644 --- a/README.markdown +++ b/README.markdown @@ -10,7 +10,8 @@ to enable researchers to utilize these feature selection algorithms in Python was only natural. At Drexel University's [EESI Lab](http://www.ece.drexel.edu/gailr/EESI/), we are using PyFeast to create a feature -selection tool for the Department of Energy's upcoming KBase platform. +selection tool for the Department of Energy's upcoming KBase platform. We are also integrating a tool that utilizes +PyFeast as a script for Qiime users: [Qiime Fizzy Branch](https://github.com/EESI/FizzyQIIME) ## Requirements In order to use the feast module, you will need the following dependencies @@ -18,28 +19,10 @@ In order to use the feast module, you will need the following dependencies * Python 2.7 * Numpy * Linux or OS X +* [FEAST](https://github.com/Craigacp/FEAST) +* [MIToolbox](https://github.com/Craigacp/MIToolbox) ## Installation -To install the FEAST interface, you'll need to build and install the FEAST -libraries first, and then install python. - -Make MIToolbox and install it: - - cd FEAST/MIToolbox - make - sudo make install - -Make FSToolbox and install it: - - cd FEAST/FSToolbox - make - sudo make install - -Run ldconfig to update your library cache: - - sudo ldconfig - -Install our PyFeast module: python ./setup.py build sudo python ./setup.py install @@ -49,8 +32,10 @@ See test/test.py for an example with uniform data and an image data set. The image data set was collected from the digits example in the Scikits-Learn toolbox. +## Documentation +We have documentation for each of the functions available [here](http://mutantturkey.github.com/PyFeast/feast-module.html) + ## References * [FEAST](http://www.cs.man.ac.uk/~gbrown/fstoolbox/) - The Feature Selection Toolbox * [Fizzy](http://www.kbase.us/developer-zone/api-documentation/fizzy-feature-selection-service/) - A KBase Service for Feature Selection -* [Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection] -(http://jmlr.csail.mit.edu/papers/v13/brown12a.html) +* [Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection](http://jmlr.csail.mit.edu/papers/v13/brown12a.html) @@ -1,4 +1,4 @@ -''' +""" The FEAST module provides an interface between the C-library for feature selection to Python. @@ -8,7 +8,7 @@ theoretic feature selection," Journal of Machine Learning Research, vol. 13, pp. 27-66, 2012. -''' +""" __author__ = "Calvin Morrison" __copyright__ = "Copyright 2013, EESI Laboratory" __credits__ = ["Calvin Morrison", "Gregory Ditzler"] @@ -19,39 +19,40 @@ __email__ = "mutantturkey@gmail.com" __status__ = "Release" import numpy as np -from ctypes import * +import ctypes as c try: - libFSToolbox = CDLL("libFSToolbox.so"); + libFSToolbox = c.CDLL("libFSToolbox.so"); except: raise Exception("Error: could not load libFSToolbox.so") def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): - ''' - BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) - - This algotihm implements conditional mutual information + """ + This algorithm implements conditional mutual information feature select, such that beta and gamma control the weight attached to the redundant mutual and conditional mutual information, respectively. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. (REQUIRED) - :n_select - number of features to select. (REQUIRED) - :beta - penalty attacted to I(X_j;X_k) - :gamma - positive weight attached to the conditional + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @param beta: penalty attacted to I(X_j;X_k) + @type beta: float between 0 and 1.0 + @param gamma: positive weight attached to the conditional redundancy term I(X_k;X_j|Y) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type gamma: float between 0 and 1.0 + @return: features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -59,83 +60,67 @@ def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - c_beta = c_double(beta) - c_gamma = c_double(gamma) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) + c_beta = c.c_double(beta) + c_gamma = c.c_double(gamma) - libFSToolbox.BetaGamma.restype = POINTER(c_double * n_select) + libFSToolbox.BetaGamma.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.BetaGamma(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)), + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)), c_beta, c_gamma ) - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - + selected_features.append(i) return selected_features - def CIFE(data, labels, n_select): - ''' - CIFE(data, labels, n_select) - + """ This function implements the Condred feature selection algorithm. beta = 1; gamma = 1; - Input - :data - data in a Numpy array such that len(data) = + @param data: A Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return selected_features: features in the order they were selected. + @rtype: list + """ return BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) - - - def CMIM(data, labels, n_select): - ''' - CMIM(data, labels, n_select) - + """ This function implements the conditional mutual information maximization feature selection algorithm. Note that this implementation does not allow for the weighting of the redundancy terms that BetaGamma will allow you to do. - Input - :data - data in a Numpy array such that len(data) = + @param data: A Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy array with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: features in the order that they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -143,52 +128,44 @@ def CMIM(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.CMIM.restype = POINTER(c_double * n_select) + libFSToolbox.CMIM.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.CMIM(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) + selected_features.append(i) return selected_features def CondMI(data, labels, n_select): - ''' - CondMI(data, labels, n_select) - + """ This function implements the conditional mutual information maximization feature selection algorithm. - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @param data: data in a Numpy array such that len(data) = n_observations, + and len(data.transpose()) = n_features + @type data: ndarray + @param labels: represented in a numpy list with + n_observations as the number of elements. That is + len(labels) = len(data) = n_observations. + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: features in the order they were selected. + @rtype list + """ data, labels = check_data(data, labels) # python values @@ -196,77 +173,65 @@ def CondMI(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.CondMI.restype = POINTER(c_double * n_select) + libFSToolbox.CondMI.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.CondMI(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) + selected_features.append(i) return selected_features def Condred(data, labels, n_select): - ''' - Condred(data, labels, n_select) - + """ This function implements the Condred feature selection algorithm. beta = 0; gamma = 1; - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) - return BetaGamma(data, labels, n_select, beta=0.0, gamma=1.0) def DISR(data, labels, n_select): - ''' - DISR(data, labels, n_select) - + """ This function implements the double input symmetrical relevance feature selection algorithm. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -274,53 +239,42 @@ def DISR(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.DISR.restype = POINTER(c_double * n_select) + libFSToolbox.DISR.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.DISR(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) + selected_features.append(i) return selected_features - - - def ICAP(data, labels, n_select): - ''' - ICAP(data, labels, n_select) - + """ This function implements the interaction capping feature selection algorithm. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -328,54 +282,42 @@ def ICAP(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.ICAP.restype = POINTER(c_double * n_select) + libFSToolbox.ICAP.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.ICAP(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) + selected_features.append(i) return selected_features - - - - def JMI(data, labels, n_select): - ''' - JMI(data, labels, n_select) - + """ This function implements the joint mutual information feature selection algorithm. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -383,102 +325,106 @@ def JMI(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.JMI.restype = POINTER(c_double * n_select) + libFSToolbox.JMI.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.JMI(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - + selected_features.append(i) return selected_features def MIFS(data, labels, n_select): - ''' - MIFS(data, labels, n_select) - + """ This function implements the MIFS algorithm. beta = 1; gamma = 0; - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ return BetaGamma(data, labels, n_select, beta=0.0, gamma=0.0) def MIM(data, labels, n_select): - ''' - MIM(data, labels, n_select) - + """ This function implements the MIM algorithm. beta = 0; gamma = 0; - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) + + # python values + n_observations, n_features = data.shape + output = np.zeros(n_select) - return BetaGamma(data, labels, n_select, beta=0.0, gamma=0.0) + # cast as C types + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) + libFSToolbox.MIM.restype = c.POINTER(c.c_double * n_select) + features = libFSToolbox.MIM(c_n_select, + c_n_observations, + c_n_features, + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) + ) + + selected_features = [] + for i in features.contents: + selected_features.append(i) + return selected_features def mRMR(data, labels, n_select): - ''' - mRMR(data, labels, n_select) - + """ This funciton implements the max-relevance min-redundancy feature selection algorithm. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list + """ data, labels = check_data(data, labels) # python values @@ -486,47 +432,37 @@ def mRMR(data, labels, n_select): output = np.zeros(n_select) # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) - libFSToolbox.mRMR_D.restype = POINTER(c_double * n_select) + libFSToolbox.mRMR_D.restype = c.POINTER(c.c_double * n_select) features = libFSToolbox.mRMR_D(c_n_select, c_n_observations, c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # turn our output into a list selected_features = [] for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - + selected_features.append(i) return selected_features def check_data(data, labels): - ''' - check_data(data, labels) - + """ Check dimensions of the data and the labels. Raise and exception if there is a problem. Data and Labels are automatically cast as doubles before calling the feature selection functions - Input - :data - :labels - Output - :data - :labels - ''' + @param data: the data + @param labels: the labels + @return (data, labels): ndarray of floats + @rtype: tuple + """ if isinstance(data, np.ndarray) is False: raise Exception("data must be an numpy ndarray.") |