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Diffstat (limited to 'FEAST/MIToolbox/RenyiEntropy.c')
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1 files changed, 191 insertions, 0 deletions
diff --git a/FEAST/MIToolbox/RenyiEntropy.c b/FEAST/MIToolbox/RenyiEntropy.c new file mode 100644 index 0000000..32c5ff9 --- /dev/null +++ b/FEAST/MIToolbox/RenyiEntropy.c @@ -0,0 +1,191 @@ +/******************************************************************************* +** RenyiEntropy.cpp +** Part of the mutual information toolbox +** +** Contains functions to calculate the Renyi alpha entropy of a single variable +** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the +** conditional Renyi entropy H_\alpha(X|Y) +** +** 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 "ArrayOperations.h" +#include "CalculateProbability.h" +#include "Entropy.h" + +double calculateRenyiEntropy(double alpha, double *dataVector, int vectorLength) +{ + double entropy = 0.0; + double tempValue = 0.0; + int i; + ProbabilityState state = calculateProbability(dataVector,vectorLength); + + /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ + for (i = 0; i < state.numStates; i++) + { + tempValue = state.probabilityVector[i]; + + if (tempValue > 0) + { + entropy += pow(tempValue,alpha); + /*printf("Entropy = %f, i = %d\n", entropy,i);*/ + } + } + + /*printf("Entropy = %f\n", entropy);*/ + + entropy = log(entropy); + + entropy /= log(2.0); + + entropy /= (1.0-alpha); + + /*printf("Entropy = %f\n", entropy);*/ + FREE_FUNC(state.probabilityVector); + state.probabilityVector = NULL; + + return entropy; +}/*calculateRenyiEntropy(double,double*,int)*/ + +double calculateJointRenyiEntropy(double alpha, double *firstVector, double *secondVector, int vectorLength) +{ + double jointEntropy = 0.0; + double tempValue = 0.0; + int i; + JointProbabilityState state = calculateJointProbability(firstVector,secondVector,vectorLength); + + /*H_\alpha(XY) = 1/(1-alpha) * log(2)(sum p(xy)^alpha)*/ + for (i = 0; i < state.numJointStates; i++) + { + tempValue = state.jointProbabilityVector[i]; + if (tempValue > 0) + { + jointEntropy += pow(tempValue,alpha); + } + } + + jointEntropy = log(jointEntropy); + + jointEntropy /= log(2.0); + + jointEntropy /= (1.0-alpha); + + FREE_FUNC(state.firstProbabilityVector); + state.firstProbabilityVector = NULL; + FREE_FUNC(state.secondProbabilityVector); + state.secondProbabilityVector = NULL; + FREE_FUNC(state.jointProbabilityVector); + state.jointProbabilityVector = NULL; + + return jointEntropy; +}/*calculateJointRenyiEntropy(double,double*,double*,int)*/ + +double calcCondRenyiEnt(double alpha, double *dataVector, double *conditionVector, int uniqueInCondVector, int vectorLength) +{ + /*uniqueInCondVector = is the number of unique values in the cond vector.*/ + + /*condEntropy = sum p(y) * sum p(x|y)^alpha(*/ + + /* + ** first generate the seperate variables + */ + + double *seperateVectors = (double *) CALLOC_FUNC(uniqueInCondVector*vectorLength,sizeof(double)); + int *seperateVectorCount = (int *) CALLOC_FUNC(uniqueInCondVector,sizeof(int)); + double seperateVectorProb = 0.0; + int i,j; + double entropy = 0.0; + double tempValue = 0.0; + int currentValue; + double tempEntropy; + ProbabilityState state; + + double **seperateVectors2D = (double **) CALLOC_FUNC(uniqueInCondVector,sizeof(double*)); + for(j=0; j < uniqueInCondVector; j++) + seperateVectors2D[j] = seperateVectors + (int)j*vectorLength; + + for (i = 0; i < vectorLength; i++) + { + currentValue = (int) (conditionVector[i] - 1.0); + /*printf("CurrentValue = %d\n",currentValue);*/ + seperateVectors2D[currentValue][seperateVectorCount[currentValue]] = dataVector[i]; + seperateVectorCount[currentValue]++; + } + + + + for (j = 0; j < uniqueInCondVector; j++) + { + tempEntropy = 0.0; + seperateVectorProb = ((double)seperateVectorCount[j]) / vectorLength; + state = calculateProbability(seperateVectors2D[j],seperateVectorCount[j]); + + /*H_\alpha(X) = 1/(1-alpha) * log(2)(sum p(x)^alpha)*/ + for (i = 0; i < state.numStates; i++) + { + tempValue = state.probabilityVector[i]; + + if (tempValue > 0) + { + tempEntropy += pow(tempValue,alpha); + /*printf("Entropy = %f, i = %d\n", entropy,i);*/ + } + } + + /*printf("Entropy = %f\n", entropy);*/ + + tempEntropy = log(tempEntropy); + + tempEntropy /= log(2.0); + + tempEntropy /= (1.0-alpha); + + entropy += tempEntropy; + + FREE_FUNC(state.probabilityVector); + } + + FREE_FUNC(seperateVectors2D); + seperateVectors2D = NULL; + + FREE_FUNC(seperateVectors); + FREE_FUNC(seperateVectorCount); + + seperateVectors = NULL; + seperateVectorCount = NULL; + + return entropy; +}/*calcCondRenyiEnt(double *,double *,int)*/ + +double calculateConditionalRenyiEntropy(double alpha, double *dataVector, double *conditionVector, int vectorLength) +{ + /*calls this: + **double calculateConditionalRenyiEntropy(double alpha, double *firstVector, double *condVector, int uniqueInCondVector, int vectorLength) + **after determining uniqueInCondVector + */ + int numUnique = numberOfUniqueValues(conditionVector, vectorLength); + + return calcCondRenyiEnt(alpha, dataVector, conditionVector, numUnique, vectorLength); +}/*calculateConditionalRenyiEntropy(double,double*,double*,int)*/ + |