1 files changed, 0 insertions, 158 deletions
diff --git a/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c
deleted file mode 100644
index daacb34..0000000
--- a/FEAST/MIToolbox/demonstration_algorithms/CMIM_Mex.c
+++ /dev/null
@@ -1,158 +0,0 @@
-/*******************************************************************************
-** 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()*/