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Diffstat (limited to 'FEAST/FSToolbox/ICAP.c')
| -rw-r--r-- | FEAST/FSToolbox/ICAP.c | 184 |
1 files changed, 0 insertions, 184 deletions
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[])*/ - |