1 files changed, 95 insertions, 0 deletions
diff --git a/FEAST/MIToolbox/RenyiMutualInformation.c b/FEAST/MIToolbox/RenyiMutualInformation.c
new file mode 100644
index 0000000..dc6fd51
--- /dev/null
+++ b/FEAST/MIToolbox/RenyiMutualInformation.c
@@ -0,0 +1,95 @@
+/*******************************************************************************
+** RenyiMutualInformation.cpp
+** Part of the mutual information toolbox
+**
+** Contains functions to calculate the Renyi mutual information of 
+** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and 
+** the joint entropy difference
+** 
+** Author: Adam Pocock
+** Created 26/3/2010
+**
+**  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 "CalculateProbability.h"
+#include "RenyiEntropy.h"
+#include "RenyiMutualInformation.h"
+
+double calculateRenyiMIDivergence(double alpha, double *dataVector, double *targetVector, int vectorLength)
+{
+  double mutualInformation = 0.0;
+  int firstIndex,secondIndex;
+  int i;
+  double jointTemp = 0.0;
+  double seperateTemp = 0.0;
+  double invAlpha = 1.0 - alpha;
+  JointProbabilityState state = calculateJointProbability(dataVector,targetVector,vectorLength);
+    
+  /* standard MI is D_KL(p(x,y)||p(x)p(y))
+  ** which expands to
+  ** D_KL(p(x,y)||p(x)p(y)) = sum(p(x,y) * log(p(x,y)/(p(x)p(y))))
+  **
+  ** Renyi alpha divergence D_alpha(p(x,y)||p(x)p(y))
+  ** expands to
+  ** D_alpha(p(x,y)||p(x)p(y)) = 1/(alpha-1) * log(sum((p(x,y)^alpha)*((p(x)p(y))^(1-alpha))))
+  */
+  
+  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))
+    {      
+      jointTemp = pow(state.jointProbabilityVector[i],alpha);
+      seperateTemp = state.firstProbabilityVector[firstIndex] * state.secondProbabilityVector[secondIndex];
+      seperateTemp = pow(seperateTemp,invAlpha);
+      mutualInformation += (jointTemp * seperateTemp);
+    }
+  }
+
+  mutualInformation = log(mutualInformation);
+  mutualInformation /= log(2.0);  
+  mutualInformation /= (alpha-1.0);  
+  
+  FREE_FUNC(state.firstProbabilityVector);
+  state.firstProbabilityVector = NULL;
+  FREE_FUNC(state.secondProbabilityVector);
+  state.secondProbabilityVector = NULL;
+  FREE_FUNC(state.jointProbabilityVector);
+  state.jointProbabilityVector = NULL;
+  
+  return mutualInformation;
+}/*calculateRenyiMIDivergence(double, double *, double *, int)*/
+
+double calculateRenyiMIJoint(double alpha, double *dataVector, double *targetVector, int vectorLength)
+{
+  double hY = calculateRenyiEntropy(alpha, targetVector, vectorLength);
+  double hX = calculateRenyiEntropy(alpha, dataVector, vectorLength);
+  
+  double hXY = calculateJointRenyiEntropy(alpha, dataVector, targetVector, vectorLength);
+  
+  double answer = hX + hY - hXY;
+  
+  return answer;
+}/*calculateRenyiMIJoint(double, double*, double*, int)*/
+