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Diffstat (limited to 'FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c')
| -rw-r--r-- | FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c | 199 |
1 files changed, 0 insertions, 199 deletions
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()*/ |