remove FEAST from our source, as our changes went upstream

1 files changed, 0 insertions, 184 deletions

diff --git a/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c b/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c
deleted file mode 100644
index 8d7b074..0000000
--- a/FEAST/MIToolbox/demonstration_algorithms/mRMR_D_Mex.c
+++ /dev/null
@@ -1,184 +0,0 @@
-/*******************************************************************************
-** Demonstration feature selection algorithm - MATLAB r2009a
-**
-** Initial Version - 13/06/2008
-** Updated - 07/07/2010
-** based on mRMR_D.m
-**
-** Minimum Relevance Maximum Redundancy
-** in
-** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy"
-** H. Peng et al. (2005)
-**
-** Author - Adam Pocock
-** Demonstration code for MIToolbox
-*******************************************************************************/
-
-#include "mex.h"
-#include "MutualInformation.h"
-
-void mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)
-{
-  double **feature2D = (double**) mxCalloc(noOfFeatures,sizeof(double*));
-  /*holds the class MI values*/
-  double *classMI = (double *)mxCalloc(noOfFeatures,sizeof(double));
-  int *selectedFeatures = (int *)mxCalloc(noOfFeatures,sizeof(int));
-  /*holds the intra feature MI values*/
-  int sizeOfMatrix = k*noOfFeatures;
-  double *featureMIMatrix = (double *)mxCalloc(sizeOfMatrix,sizeof(double));
-  
-  double maxMI = 0.0;
-  int maxMICounter = -1;
-  
-  /*init variables*/
-  
-  double score, currentScore, totalFeatureMI;
-  int currentHighestFeature;
-  
-  int arrayPosition, 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++)
-  {
-    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
-  ** Now we move into the mRMR-D algorithm
-  *************/
-  
-  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 = -1000.0;
-    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 = classMI[j];
-        totalFeatureMI = 0.0;
-        
-        for (x = 0; x < i; x++)
-        {
-          arrayPosition = x*noOfFeatures + j;
-          if (featureMIMatrix[arrayPosition] == -1)
-          {
-            /*work out intra MI*/
-            
-            /*double calculateMutualInformation(double *firstVector, double *secondVector, int vectorLength);*/
-            featureMIMatrix[arrayPosition] = calculateMutualInformation(feature2D[(int) outputFeatures[x]], feature2D[j], noOfSamples);
-          }
-          
-          totalFeatureMI += featureMIMatrix[arrayPosition];
-        }/*for the number of already selected features*/
-        
-        currentScore -= (totalFeatureMI/i);
-			  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*/
-  
-  for (i = 0; i < k; i++)
-  {
-    outputFeatures[i] += 1; /*C indexes from 0 not 1*/
-  }/*for number of selected features*/
-  
-}/*mRMRCalculation(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 mRMRCalculation(int k, int noOfSamples, int noOfFeatures,double *featureMatrix, double *classColumn, double *outputFeatures)*/
-    mRMRCalculation(k,numberOfSamples,numberOfFeatures,featureMatrix,targets,output);
-  }
-  
-  return;
-}/*mexFunction()*/