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diff --git a/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c
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+++ b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c
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+/*******************************************************************************
+** Demonstration feature selection algorithm - MATLAB r2009a
+**
+** Initial Version - 13/06/2008
+** Updated - 07/07/2010
+** based on CMIM.m
+**
+** Conditional Mutual Information Maximisation
+** in
+** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation
+** F. Fleuret (2004)
+**
+** Author - Adam Pocock
+** Demonstration code for MIToolbox
+*******************************************************************************/
+
+#include "mex.h"
+#include "MutualInformation.h"
+  
+void CMIMCalculation(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 *)mxCalloc(noOfFeatures,sizeof(double));
+  /*in the CMIM paper, m = lastUsedFeature*/
+  int *lastUsedFeature = (int *)mxCalloc(noOfFeatures,sizeof(int));
+  
+  double score, conditionalInfo;
+  int iMinus, currentFeature;
+  
+  double maxMI = 0.0;
+  int maxMICounter = -1;
+  
+  int j,i;
+
+  double **feature2D = (double**) mxCalloc(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*/
+  
+}/*CMIMCalculation*/
+
+/*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 CMIMCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/
+    CMIMCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output);
+  }  
+  
+  return;
+}/*mexFunction()*/