moved FEAST Libraries

1 files changed, 142 insertions, 0 deletions

diff --git a/FEAST/FSToolbox/CMIM.c b/FEAST/FSToolbox/CMIM.c
new file mode 100644
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+++ b/FEAST/FSToolbox/CMIM.c
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+/*******************************************************************************
+** CMIM.c, implements a discrete version of the 
+** Conditional Mutual Information Maximisation criterion, using the fast
+** exact implementation from
+**
+** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation"
+** F. Fleuret, JMLR (2004)
+**
+** Initial Version - 13/06/2008
+** Updated - 23/06/2011
+**
+** Author - Adam Pocock
+** 
+** Part of the Feature Selection Toolbox, please reference
+** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual
+** Information Feature Selection"
+** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan
+** Journal of Machine Learning Research (JMLR), 2011
+**
+** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates.
+** 
+** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester
+** All rights reserved.
+** 
+** Redistribution and use in source and binary forms, with or without modification,
+** are permitted provided that the following conditions are met:
+** 
+**   - Redistributions of source code must retain the above copyright notice, this 
+**     list of conditions and the following disclaimer.
+**   - Redistributions in binary form must reproduce the above copyright notice, 
+**     this list of conditions and the following disclaimer in the documentation 
+**     and/or other materials provided with the distribution.
+**   - Neither the name of The University of Manchester nor the names of its 
+**     contributors may be used to endorse or promote products derived from this 
+**     software without specific prior written permission.
+** 
+** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 
+** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 
+** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 
+** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 
+** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON 
+** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 
+** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
+** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+**
+*******************************************************************************/
+
+#include "FSAlgorithms.h"
+#include "FSToolbox.h"
+
+/* MIToolbox includes */
+#include "MutualInformation.h"
+  
+double* CMIM(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 *)CALLOC_FUNC(noOfFeatures,sizeof(double));
+  /*in the CMIM paper, m = lastUsedFeature*/
+  int *lastUsedFeature = (int *)CALLOC_FUNC(noOfFeatures,sizeof(int));
+  
+  double score, conditionalInfo;
+  int iMinus, currentFeature;
+  
+  double maxMI = 0.0;
+  int maxMICounter = -1;
+  
+  int j,i;
+
+  double **feature2D = (double**) CALLOC_FUNC(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*/
+  
+  FREE_FUNC(classMI);
+  FREE_FUNC(lastUsedFeature);
+  FREE_FUNC(feature2D);
+
+  classMI = NULL;
+  lastUsedFeature = NULL;
+  feature2D = NULL;
+
+  return outputFeatures;
+}/*CMIM(int,int,int,double[][],double[],double[])*/
+