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Diffstat (limited to 'FEAST/FSToolbox/BetaGamma.c')
| -rw-r--r-- | FEAST/FSToolbox/BetaGamma.c | 188 |
1 files changed, 0 insertions, 188 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)*/ - |