1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
/*******************************************************************************
** 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"
#include "util.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 = safe_calloc(uniqueInCondVector*vectorLength,sizeof(double));
int *seperateVectorCount = safe_calloc(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 = safe_calloc(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)*/
|
