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Diffstat (limited to 'FEAST/FSToolbox/CMIM.c')
| -rw-r--r-- | FEAST/FSToolbox/CMIM.c | 142 |
1 files changed, 142 insertions, 0 deletions
diff --git a/FEAST/FSToolbox/CMIM.c b/FEAST/FSToolbox/CMIM.c new file mode 100644 index 0000000..9ef21ad --- /dev/null +++ b/FEAST/FSToolbox/CMIM.c @@ -0,0 +1,142 @@ +/******************************************************************************* +** CMIM.c, implements a discrete version of the +** Conditional Mutual Information Maximisation criterion, using the fast +** exact implementation from +** +** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" +** F. Fleuret, JMLR (2004) +** +** Initial Version - 13/06/2008 +** Updated - 23/06/2011 +** +** Author - Adam Pocock +** +** Part of the Feature Selection Toolbox, please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +#include "FSAlgorithms.h" +#include "FSToolbox.h" + +/* MIToolbox includes */ +#include "MutualInformation.h" + +double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures) +{ + /*holds the class MI values + **the class MI doubles as the partial score from the CMIM paper + */ + double *classMI = (double *)CALLOC_FUNC(noOfFeatures,sizeof(double)); + /*in the CMIM paper, m = lastUsedFeature*/ + int *lastUsedFeature = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int)); + + double score, conditionalInfo; + int iMinus, currentFeature; + + double maxMI = 0.0; + int maxMICounter = -1; + + int j,i; + + double **feature2D = (double**) CALLOC_FUNC(noOfFeatures,sizeof(double*)); + + for(j = 0; j < noOfFeatures; j++) + { + feature2D[j] = featureMatrix + (int)j*noOfSamples; + } + + for (i = 0; i < noOfFeatures;i++) + { + classMI[i] = calculateMutualInformation(feature2D[i], classColumn, noOfSamples); + + if (classMI[i] > maxMI) + { + maxMI = classMI[i]; + maxMICounter = i; + }/*if bigger than current maximum*/ + }/*for noOfFeatures - filling classMI*/ + + outputFeatures[0] = maxMICounter; + + /***************************************************************************** + ** We have populated the classMI array, and selected the highest + ** MI feature as the first output feature + ** Now we move into the CMIM algorithm + *****************************************************************************/ + + for (i = 1; i < k; i++) + { + score = 0.0; + iMinus = i-1; + + for (j = 0; j < noOfFeatures; j++) + { + while ((classMI[j] > score) && (lastUsedFeature[j] < i)) + { + /*double calculateConditionalMutualInformation(double *firstVector, double *targetVector, double *conditionVector, int vectorLength);*/ + currentFeature = (int) outputFeatures[lastUsedFeature[j]]; + conditionalInfo = calculateConditionalMutualInformation(feature2D[j],classColumn,feature2D[currentFeature],noOfSamples); + if (classMI[j] > conditionalInfo) + { + classMI[j] = conditionalInfo; + }/*reset classMI*/ + /*moved due to C indexing from 0 rather than 1*/ + lastUsedFeature[j] += 1; + }/*while partial score greater than score & not reached last feature*/ + if (classMI[j] > score) + { + score = classMI[j]; + outputFeatures[i] = j; + }/*if partial score still greater than score*/ + }/*for number of features*/ + }/*for the number of features to select*/ + + + for (i = 0; i < k; i++) + { + outputFeatures[i] += 1; /*C indexes from 0 not 1*/ + }/*for number of selected features*/ + + FREE_FUNC(classMI); + FREE_FUNC(lastUsedFeature); + FREE_FUNC(feature2D); + + classMI = NULL; + lastUsedFeature = NULL; + feature2D = NULL; + + return outputFeatures; +}/*CMIM(int,int,int,double[][],double[],double[])*/ + |