RDKit: InfoGainFuncs.h Source File

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InfoGainFuncs.h
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1 // $Id$
2 //
3 // Copyright (C) 2003 Rational Discovery LLC
4 //
5
6 #ifndef INFOGAINFUNC_H
7 #define INFOGAINFUNC_H
8
9 #include <RDGeneral/types.h>
10
11 namespace RDInfoTheory {
12
13 template <class T>
14 double ChiSquare(T *dMat, long int dim1, long int dim2) {
15 // For a contingency matrix with each column corresponding to a class and each
16 // row to a
17 // the descriptor (or variable) state, the matrix looks something like for 3x3
18 // problem
19 //
20 // 1 2 3 Totals
21 // 1 | N11 N12 N13 R1
22 // 2 | N21 N22 N23 R2
23 // 3 | N31 N32 N33 R3
24 // Totals | C1 C2 C3 N
25 //
26 // Th chi squere formula is
27 // chi = sum((N/Ri)*sum(Nij^2/Cj) ) -N
28 T *rowSums, *colSums;
29 int i, j, tSum;
30 // find the row sum
31 tSum = 0;
32 rowSums = new T[dim1];
33 for (i = 0; i < dim1; i++) {
34 int idx1 = i * dim2;
35 rowSums[i] = (T)0.0;
36 for (j = 0; j < dim2; j++) {
37 rowSums[i] += dMat[idx1 + j];
38 }
39 tSum += (int)rowSums[i];
40 }
41
42 // find the column sums
43 colSums = new T[dim2];
44 for (i = 0; i < dim2; i++) {
45 colSums[i] = (T)0.0;
46 for (j = 0; j < dim1; j++) {
47 colSums[i] += dMat[j * dim2 + i];
48 }
49 }
50
51 double chi = 0.0;
52 for (i = 0; i < dim1; i++) {
53 double rchi = 0.0;
54 for (j = 0; j < dim2; j++) {
55 rchi += (pow((double)dMat[i * dim2 + j], 2) / colSums[j]);
56 }
57 chi += (((double)tSum / rowSums[i]) * rchi);
58 }
59 chi -= tSum;
60 delete[] rowSums;
61 delete[] colSums;
62
63 return chi;
64 }
65
66 template <class T>
67 double InfoEntropy(T *tPtr, long int dim) {
68 int i;
69 T nInstances = 0;
70 double accum = 0.0, d;
71
72 for (i = 0; i < dim; i++) {
73 nInstances += tPtr[i];
74 }
75
76 if (nInstances != 0) {
77 for (i = 0; i < dim; i++) {
78 d = (double)tPtr[i] / nInstances;
79 if (d != 0) {
80 accum += -d * log(d);
81 }
82 }
83 }
84 return accum / log(2.0);
85 }
86
87 template <class T>
88 double InfoEntropyGain(T *dMat, long int dim1, long int dim2) {
89 T *variableRes, *overallRes;
90 double gain, term2;
91 int tSum;
92
93 // std::cerr<<" --------\n ieg: "<<dim1<<" "<<dim2<<std::endl;
94 variableRes = new T[dim1];
95 for (long int i = 0; i < dim1; i++) {
96 long int idx1 = i * dim2;
97 variableRes[i] = (T)0.0;
98 for (long int j = 0; j < dim2; j++) {
99 variableRes[i] += dMat[idx1 + j];
100 // std::cerr<<" "<<i<<" "<<j<<" "<<dMat[idx1+j]<<std::endl;
101 }
102 }
103
104 overallRes = new T[dim2];
105 // do the col sums
106 for (long int i = 0; i < dim2; i++) {
107 overallRes[i] = (T)0.0;
108 for (long int j = 0; j < dim1; j++) {
109 overallRes[i] += dMat[j * dim2 + i];
110 // std::cerr<<" "<<i<<" "<<j<<" "<<dMat[j*dim2+i]<<std::endl;
111 }
112 }
113
114 term2 = 0.0;
115 for (long int i = 0; i < dim1; i++) {
116 T *tPtr;
117 tPtr = dMat + i * dim2;
118 term2 += variableRes[i] * InfoEntropy(tPtr, dim2);
119 }
120 tSum = 0;
121 for (long int i = 0; i < dim2; i++) {
122 tSum += static_cast<int>(overallRes[i]);
123 }
124
125 if (tSum != 0) {
126 term2 /= tSum;
127 gain = InfoEntropy(overallRes, dim2) - term2;
128 } else {
129 gain = 0.0;
130 }
131 // std::cerr<<" >gain> "<<gain<<std::endl;
132
133 delete[] overallRes;
134 delete[] variableRes;
135 return gain;
136 }
137 }
138 #endif
types.h
RDInfoTheory
Class used to rank bits based on a specified measure of infomation.
Definition: CorrMatGenerator.h:17
RDInfoTheory::InfoEntropyGain
double InfoEntropyGain(T *dMat, long int dim1, long int dim2)
Definition: InfoGainFuncs.h:88
RDInfoTheory::ChiSquare
double ChiSquare(T *dMat, long int dim1, long int dim2)
Definition: InfoGainFuncs.h:14
RDInfoTheory::InfoEntropy
double InfoEntropy(T *tPtr, long int dim)
Definition: InfoGainFuncs.h:67