1 files changed, 0 insertions, 95 deletions
diff --git a/FEAST/MIToolbox/RenyiMutualInformation.c b/FEAST/MIToolbox/RenyiMutualInformation.c
deleted file mode 100644
index dc6fd51..0000000
--- a/FEAST/MIToolbox/RenyiMutualInformation.c
+++ /dev/null
@@ -1,95 +0,0 @@
-/*******************************************************************************
-** 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)*/
-