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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, 199 insertions, 0 deletions
diff --git a/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c new file mode 100644 index 0000000..617ca81 --- /dev/null +++ b/FEAST/MIToolbox/demonstration_algorithms/DISR_Mex.c @@ -0,0 +1,199 @@ +/******************************************************************************* +** 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()*/ |