blob: 6f0342955df7c7e9587017614b1c46d4232e1145 (
plain)
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
|
/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
/*
QM DSP Library
Centre for Digital Music, Queen Mary, University of London.
This file copyright 2008 QMUL.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of the
License, or (at your option) any later version. See the file
COPYING included with this distribution for more information.
*/
#ifndef KLDIVERGENCE_H
#define KLDIVERGENCE_H
#include <vector>
using std::vector;
/**
* Helper methods for calculating Kullback-Leibler divergences.
*/
class KLDivergence
{
public:
KLDivergence() { }
~KLDivergence() { }
/**
* Calculate a symmetrised Kullback-Leibler divergence of Gaussian
* models based on mean and variance vectors. All input vectors
* must be of equal size.
*/
double distanceGaussian(const vector<double> &means1,
const vector<double> &variances1,
const vector<double> &means2,
const vector<double> &variances2);
/**
* Calculate a Kullback-Leibler divergence of two probability
* distributions. Input vectors must be of equal size. If
* symmetrised is true, the result will be the symmetrised
* distance (equal to KL(d1, d2) + KL(d2, d1)).
*/
double distanceDistribution(const vector<double> &d1,
const vector<double> &d2,
bool symmetrised);
};
#endif
|
