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Diffstat (limited to 'FEAST/FSToolbox/CMIM.c')
| -rw-r--r-- | FEAST/FSToolbox/CMIM.c | 142 |
1 files changed, 0 insertions, 142 deletions
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[])*/ - |