/* Copyright (C) 2003 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
This software is provided under the terms of the Common Public License,
version 1.0, as published by http://www.opensource.org. For further
information, see the file `LICENSE' included with this distribution. */
//package cc.mallet.util;
/**
*
*
* @author Charles Sutton
* @version $Id: ArrayUtils.java,v 1.1 2007/10/22 21:37:40 mccallum Exp $
*/
public class Util {
/**
* Returns the Jensen-Shannon divergence.
*/
public static double jensenShannonDivergence(double[] p1, double[] p2) {
assert(p1.length == p2.length);
double[] average = new double[p1.length];
for (int i = 0; i < p1.length; ++i) {
average[i] += (p1[i] + p2[i])/2;
}
return (klDivergence(p1, average) + klDivergence(p2, average))/2;
}
public static final double log2 = Math.log(2);
/**
* Returns the KL divergence, K(p1 || p2).
*
* The log is w.r.t. base 2.
*
* *Note*: If any value in p2 is 0.0 then the KL-divergence
* is infinite. Limin changes it to zero instead of infinite.
*
*/
public static double klDivergence(double[] p1, double[] p2) {
double klDiv = 0.0;
for (int i = 0; i < p1.length; ++i) {
if (p1[i] == 0) { continue; }
if (p2[i] == 0.0) { continue; } // Limin
klDiv += p1[i] * Math.log( p1[i] / p2[i] );
}
return klDiv / log2; // moved this division out of the loop -DM
}
}