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package de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* The square root of Jensen-Shannon divergence is metric.
*
* Reference (proof of triangle inequality, distance called D_PQ):
* <p>
* D. M. Endres, J. E. Schindelin<br />
* A new metric for probability distributions<br />
* IEEE Transactions on Information Theory, 49(7).
* </p>
*
* @author Erich Schubert
*/
@Reference(authors = "D. M. Endres, J. E. Schindelin", title = "A new metric for probability distributions", booktitle = "IEEE Transactions on Information Theory, 49(7)", url = "http://dx.doi.org/10.1109/TIT.2003.813506")
public class SqrtJensenShannonDivergenceDistanceFunction extends AbstractVectorDoubleDistanceFunction {
/**
* Static instance. Use this!
*/
public static final SqrtJensenShannonDivergenceDistanceFunction STATIC = new SqrtJensenShannonDivergenceDistanceFunction();
/**
* Constructor for sqrt Jensen Shannon divergence.
*
* @deprecated Use static instance!
*/
@Deprecated
public SqrtJensenShannonDivergenceDistanceFunction() {
super();
}
@Override
public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
final int dim = dimensionality(v1, v2);
double agg = 0.;
for(int d = 0; d < dim; d++) {
final double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
if(xd == yd) {
continue;
}
final double md = .5 * (xd + yd);
if(!(md > 0. || md < 0.)) {
continue;
}
if(xd > 0.) {
agg += xd * Math.log(xd / md);
}
if(yd > 0.) {
agg += yd * Math.log(yd / md);
}
}
return Math.sqrt(agg);
}
@Override
public boolean isMetric() {
return true;
}
@Override
public String toString() {
return "SqrtJensenShannonDivergenceDistance";
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
if(obj == this) {
return true;
}
if(this.getClass().equals(obj.getClass())) {
return true;
}
return super.equals(obj);
}
/**
* Parameterization class, using the static instance.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected SqrtJensenShannonDivergenceDistanceFunction makeInstance() {
return STATIC;
}
}
}
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