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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 org.apache.commons.math3.distribution;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.util.Pair;
import java.util.ArrayList;
import java.util.List;
/**
* Multivariate normal mixture distribution. This class is mainly syntactic sugar.
*
* @see MixtureMultivariateRealDistribution
* @since 3.2
*/
public class MixtureMultivariateNormalDistribution
extends MixtureMultivariateRealDistribution<MultivariateNormalDistribution> {
/**
* Creates a multivariate normal mixture distribution.
*
* <p><b>Note:</b> this constructor will implicitly create an instance of {@link
* org.apache.commons.math3.random.Well19937c Well19937c} as random generator to be used for
* sampling only (see {@link #sample()} and {@link #sample(int)}). In case no sampling is needed
* for the created distribution, it is advised to pass {@code null} as random generator via the
* appropriate constructors to avoid the additional initialisation overhead.
*
* @param weights Weights of each component.
* @param means Mean vector for each component.
* @param covariances Covariance matrix for each component.
*/
public MixtureMultivariateNormalDistribution(
double[] weights, double[][] means, double[][][] covariances) {
super(createComponents(weights, means, covariances));
}
/**
* Creates a mixture model from a list of distributions and their associated weights.
*
* <p><b>Note:</b> this constructor will implicitly create an instance of {@link
* org.apache.commons.math3.random.Well19937c Well19937c} as random generator to be used for
* sampling only (see {@link #sample()} and {@link #sample(int)}). In case no sampling is needed
* for the created distribution, it is advised to pass {@code null} as random generator via the
* appropriate constructors to avoid the additional initialisation overhead.
*
* @param components List of (weight, distribution) pairs from which to sample.
*/
public MixtureMultivariateNormalDistribution(
List<Pair<Double, MultivariateNormalDistribution>> components) {
super(components);
}
/**
* Creates a mixture model from a list of distributions and their associated weights.
*
* @param rng Random number generator.
* @param components Distributions from which to sample.
* @throws NotPositiveException if any of the weights is negative.
* @throws DimensionMismatchException if not all components have the same number of variables.
*/
public MixtureMultivariateNormalDistribution(
RandomGenerator rng, List<Pair<Double, MultivariateNormalDistribution>> components)
throws NotPositiveException, DimensionMismatchException {
super(rng, components);
}
/**
* @param weights Weights of each component.
* @param means Mean vector for each component.
* @param covariances Covariance matrix for each component.
* @return the list of components.
*/
private static List<Pair<Double, MultivariateNormalDistribution>> createComponents(
double[] weights, double[][] means, double[][][] covariances) {
final List<Pair<Double, MultivariateNormalDistribution>> mvns =
new ArrayList<Pair<Double, MultivariateNormalDistribution>>(weights.length);
for (int i = 0; i < weights.length; i++) {
final MultivariateNormalDistribution dist =
new MultivariateNormalDistribution(means[i], covariances[i]);
mvns.add(new Pair<Double, MultivariateNormalDistribution>(weights[i], dist));
}
return mvns;
}
}
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