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// $Id$
//
// Copyright (C) 2003 Rational Discovery LLC
//
#include <RDGeneral/export.h>
#ifndef INFOGAINFUNC_H
#define INFOGAINFUNC_H
#include <RDGeneral/types.h>
namespace RDInfoTheory {
template <class T>
double ChiSquare(T *dMat, long int dim1, long int dim2) {
// For a contingency matrix with each column corresponding to a class and each
// row to a
// the descriptor (or variable) state, the matrix looks something like for 3x3
// problem
//
// 1 2 3 Totals
// 1 | N11 N12 N13 R1
// 2 | N21 N22 N23 R2
// 3 | N31 N32 N33 R3
// Totals | C1 C2 C3 N
//
// Th chi squere formula is
// chi = sum((N/Ri)*sum(Nij^2/Cj) ) -N
T *rowSums, *colSums;
int i, j, tSum;
// find the row sum
tSum = 0;
rowSums = new T[dim1];
for (i = 0; i < dim1; i++) {
int idx1 = i * dim2;
rowSums[i] = (T)0.0;
for (j = 0; j < dim2; j++) {
rowSums[i] += dMat[idx1 + j];
}
tSum += (int)rowSums[i];
}
// find the column sums
colSums = new T[dim2];
for (i = 0; i < dim2; i++) {
colSums[i] = (T)0.0;
for (j = 0; j < dim1; j++) {
colSums[i] += dMat[j * dim2 + i];
}
}
double chi = 0.0;
for (i = 0; i < dim1; i++) {
double rchi = 0.0;
for (j = 0; j < dim2; j++) {
rchi += (pow((double)dMat[i * dim2 + j], 2) / colSums[j]);
}
chi += (((double)tSum / rowSums[i]) * rchi);
}
chi -= tSum;
delete[] rowSums;
delete[] colSums;
return chi;
}
template <class T>
double InfoEntropy(T *tPtr, long int dim) {
int i;
T nInstances = 0;
double accum = 0.0, d;
for (i = 0; i < dim; i++) {
nInstances += tPtr[i];
}
if (nInstances != 0) {
for (i = 0; i < dim; i++) {
d = (double)tPtr[i] / nInstances;
if (d != 0) {
accum += -d * log(d);
}
}
}
return accum / log(2.0);
}
template <class T>
double InfoEntropyGain(T *dMat, long int dim1, long int dim2) {
T *variableRes, *overallRes;
double gain, term2;
int tSum;
// std::cerr<<" --------\n ieg: "<<dim1<<" "<<dim2<<std::endl;
variableRes = new T[dim1];
for (long int i = 0; i < dim1; i++) {
long int idx1 = i * dim2;
variableRes[i] = (T)0.0;
for (long int j = 0; j < dim2; j++) {
variableRes[i] += dMat[idx1 + j];
// std::cerr<<" "<<i<<" "<<j<<" "<<dMat[idx1+j]<<std::endl;
}
}
overallRes = new T[dim2];
// do the col sums
for (long int i = 0; i < dim2; i++) {
overallRes[i] = (T)0.0;
for (long int j = 0; j < dim1; j++) {
overallRes[i] += dMat[j * dim2 + i];
// std::cerr<<" "<<i<<" "<<j<<" "<<dMat[j*dim2+i]<<std::endl;
}
}
term2 = 0.0;
for (long int i = 0; i < dim1; i++) {
T *tPtr;
tPtr = dMat + i * dim2;
term2 += variableRes[i] * InfoEntropy(tPtr, dim2);
}
tSum = 0;
for (long int i = 0; i < dim2; i++) {
tSum += static_cast<int>(overallRes[i]);
}
if (tSum != 0) {
term2 /= tSum;
gain = InfoEntropy(overallRes, dim2) - term2;
} else {
gain = 0.0;
}
// std::cerr<<" >gain> "<<gain<<std::endl;
delete[] overallRes;
delete[] variableRes;
return gain;
}
}
#endif
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