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diff --git a/FEAST/FEAST/MIToolbox/MutualInformation.c b/FEAST/FEAST/MIToolbox/MutualInformation.c
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+/*******************************************************************************
+** MutualInformation.cpp
+** Part of the mutual information toolbox
+**
+** Contains functions to calculate the mutual information of 
+** two variables X and Y, I(X;Y), to calculate the joint mutual information
+** of two variables X & Z on the variable Y, I(XZ;Y), and the conditional
+** mutual information I(x;Y|Z)
+** 
+** Author: Adam Pocock
+** Created 19/2/2010
+**
+**  Copyright 2010 Adam Pocock, The University Of Manchester
+**  www.cs.manchester.ac.uk
+**
+**  This file is part of MIToolbox.
+**
+**  MIToolbox is free software: you can redistribute it and/or modify
+**  it under the terms of the GNU Lesser General Public License as published by
+**  the Free Software Foundation, either version 3 of the License, or
+**  (at your option) any later version.
+**
+**  MIToolbox is distributed in the hope that it will be useful,
+**  but WITHOUT ANY WARRANTY; without even the implied warranty of
+**  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+**  GNU Lesser General Public License for more details.
+**
+**  You should have received a copy of the GNU Lesser General Public License
+**  along with MIToolbox.  If not, see <http://www.gnu.org/licenses/>.
+**
+*******************************************************************************/
+
+#include "MIToolbox.h"
+#include "ArrayOperations.h"
+#include "CalculateProbability.h"
+#include "Entropy.h"
+#include "MutualInformation.h"
+
+double calculateMutualInformation(double *dataVector, double *targetVector, int vectorLength)
+{
+  double mutualInformation = 0.0;
+  int firstIndex,secondIndex;
+  int i;
+  JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength);
+    
+  /*
+  ** I(X;Y) = sum sum p(xy) * log (p(xy)/p(x)p(y))
+  */
+  for (i = 0; i < state.numJointStates; i++)
+  {
+    firstIndex = i % state.numFirstStates;
+    secondIndex = i / state.numFirstStates;
+    
+    if ((state.jointProbabilityVector[i] > 0) && (state.firstProbabilityVector[firstIndex] > 0) && (state.secondProbabilityVector[secondIndex] > 0))
+    {
+      /*double division is probably more stable than multiplying two small numbers together
+      ** mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / (state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex]));
+      */
+      mutualInformation += state.jointProbabilityVector[i] * log(state.jointProbabilityVector[i] / state.firstProbabilityVector[firstIndex] / state.secondProbabilityVector[secondIndex]);
+    }
+  }
+  
+  mutualInformation /= log(2.0);
+  
+  FREE_FUNC(state.firstProbabilityVector);
+  state.firstProbabilityVector = NULL;
+  FREE_FUNC(state.secondProbabilityVector);
+  state.secondProbabilityVector = NULL;
+  FREE_FUNC(state.jointProbabilityVector);
+  state.jointProbabilityVector = NULL;
+  
+  return mutualInformation;
+}/*calculateMutualInformation(double *,double *,int)*/
+
+double calculateConditionalMutualInformation(double *dataVector, double *targetVector, double *conditionVector, int vectorLength)
+{
+  double mutualInformation = 0.0;
+  double firstCondition, secondCondition;
+  double *mergedVector = (double *) CALLOC_FUNC(vectorLength,sizeof(double));
+  
+  mergeArrays(targetVector,conditionVector,mergedVector,vectorLength);
+  
+  /* I(X;Y|Z) = H(X|Z) - H(X|YZ) */
+  /* double calculateConditionalEntropy(double *dataVector, double *conditionVector, int vectorLength); */
+  firstCondition = calculateConditionalEntropy(dataVector,conditionVector,vectorLength);
+  secondCondition = calculateConditionalEntropy(dataVector,mergedVector,vectorLength);
+  
+  mutualInformation = firstCondition - secondCondition;
+  
+  FREE_FUNC(mergedVector);
+  mergedVector = NULL;
+  
+  return mutualInformation;
+}/*calculateConditionalMutualInformation(double *,double *,double *,int)*/
+