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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 java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathArithmeticException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.util.Pair;
/**
* Class for representing
* mixture model distributions.
*
* @param Type of the mixture components.
*
* @since 3.1
*/
public class MixtureMultivariateRealDistribution
extends AbstractMultivariateRealDistribution {
/** Normalized weight of each mixture component. */
private final double[] weight;
/** Mixture components. */
private final List distribution;
/**
* Creates a mixture model from a list of distributions and their
* associated weights.
*
* Note: this constructor will implicitly create an instance of
* {@link 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 MixtureMultivariateRealDistribution(List> components) {
this(new Well19937c(), 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 MixtureMultivariateRealDistribution(RandomGenerator rng,
List> components) {
super(rng, components.get(0).getSecond().getDimension());
final int numComp = components.size();
final int dim = getDimension();
double weightSum = 0;
for (int i = 0; i < numComp; i++) {
final Pair comp = components.get(i);
if (comp.getSecond().getDimension() != dim) {
throw new DimensionMismatchException(comp.getSecond().getDimension(), dim);
}
if (comp.getFirst() < 0) {
throw new NotPositiveException(comp.getFirst());
}
weightSum += comp.getFirst();
}
// Check for overflow.
if (Double.isInfinite(weightSum)) {
throw new MathArithmeticException(LocalizedFormats.OVERFLOW);
}
// Store each distribution and its normalized weight.
distribution = new ArrayList();
weight = new double[numComp];
for (int i = 0; i < numComp; i++) {
final Pair comp = components.get(i);
weight[i] = comp.getFirst() / weightSum;
distribution.add(comp.getSecond());
}
}
/** {@inheritDoc} */
public double density(final double[] values) {
double p = 0;
for (int i = 0; i < weight.length; i++) {
p += weight[i] * distribution.get(i).density(values);
}
return p;
}
/** {@inheritDoc} */
@Override
public double[] sample() {
// Sampled values.
double[] vals = null;
// Determine which component to sample from.
final double randomValue = random.nextDouble();
double sum = 0;
for (int i = 0; i < weight.length; i++) {
sum += weight[i];
if (randomValue <= sum) {
// pick model i
vals = distribution.get(i).sample();
break;
}
}
if (vals == null) {
// This should never happen, but it ensures we won't return a null in
// case the loop above has some floating point inequality problem on
// the final iteration.
vals = distribution.get(weight.length - 1).sample();
}
return vals;
}
/** {@inheritDoc} */
@Override
public void reseedRandomGenerator(long seed) {
// Seed needs to be propagated to underlying components
// in order to maintain consistency between runs.
super.reseedRandomGenerator(seed);
for (int i = 0; i < distribution.size(); i++) {
// Make each component's seed different in order to avoid
// using the same sequence of random numbers.
distribution.get(i).reseedRandomGenerator(i + 1 + seed);
}
}
/**
* Gets the distributions that make up the mixture model.
*
* @return the component distributions and associated weights.
*/
public List> getComponents() {
final List> list = new ArrayList>(weight.length);
for (int i = 0; i < weight.length; i++) {
list.add(new Pair(weight[i], distribution.get(i)));
}
return list;
}
}