smile.math.distance.JensenShannonDistance Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
package smile.math.distance;
import smile.math.Math;
/**
* The Jensen-Shannon divergence is a popular method of measuring the
* similarity between two probability distributions. It is also known
* as information radius or total divergence to the average.
*
* The Jensen-Shannon divergence is a symmetrized and smoothed version of the
* Kullback-Leibler divergence . It is defined by
*
* J(P||Q) = (D(P||M) + D(Q||M)) / 2
*
* where M = (P+Q)/2 and D(·||·) is KL divergence.
* Different from the Kullback-Leibler divergence, it is always a finite value.
*
* The square root of the Jensen-Shannon divergence is a metric, which is
* calculated by this class.
*
* @author Haifeng Li
*/
public class JensenShannonDistance implements Metric {
/**
* Constructor.
*/
private JensenShannonDistance() {
}
@Override
public String toString() {
return "Jensen-Shannon distance";
}
@Override
public double d(double[] x, double[] y) {
if (x.length != y.length)
throw new IllegalArgumentException(String.format("Arrays have different length: x[%d], y[%d]", x.length, y.length));
return Math.sqrt(Math.JensenShannonDivergence(x, y));
}
}