| /* |
| * 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.math4.legacy.distribution; |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| |
| import org.apache.commons.math4.legacy.exception.DimensionMismatchException; |
| import org.apache.commons.math4.legacy.exception.MathArithmeticException; |
| import org.apache.commons.math4.legacy.exception.NotPositiveException; |
| import org.apache.commons.math4.legacy.exception.util.LocalizedFormats; |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.math4.legacy.core.Pair; |
| |
| /** |
| * Class for representing <a href="http://en.wikipedia.org/wiki/Mixture_model"> |
| * mixture model</a> distributions. |
| * |
| * @param <T> Type of the mixture components. |
| * |
| * @since 3.1 |
| */ |
| public class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution> |
| extends AbstractMultivariateRealDistribution { |
| /** Normalized weight of each mixture component. */ |
| private final double[] weight; |
| /** Mixture components. */ |
| private final List<T> distribution; |
| |
| /** |
| * Creates a mixture model from a list of distributions and their |
| * associated weights. |
| * |
| * @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(List<Pair<Double, T>> components) { |
| super(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<Double, T> 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<Double, T> comp = components.get(i); |
| weight[i] = comp.getFirst() / weightSum; |
| distribution.add(comp.getSecond()); |
| } |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| 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; |
| } |
| |
| /** |
| * Gets the distributions that make up the mixture model. |
| * |
| * @return the component distributions and associated weights. |
| */ |
| public List<Pair<Double, T>> getComponents() { |
| final List<Pair<Double, T>> list = new ArrayList<>(weight.length); |
| |
| for (int i = 0; i < weight.length; i++) { |
| list.add(new Pair<>(weight[i], distribution.get(i))); |
| } |
| |
| return list; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public MultivariateRealDistribution.Sampler createSampler(UniformRandomProvider rng) { |
| return new MixtureSampler(rng); |
| } |
| |
| /** |
| * Sampler. |
| */ |
| private class MixtureSampler implements MultivariateRealDistribution.Sampler { |
| /** RNG. */ |
| private final UniformRandomProvider rng; |
| /** Sampler for each of the distribution in the mixture. */ |
| private final MultivariateRealDistribution.Sampler[] samplers; |
| |
| /** |
| * @param generator RNG. |
| */ |
| MixtureSampler(UniformRandomProvider generator) { |
| rng = generator; |
| |
| samplers = new MultivariateRealDistribution.Sampler[weight.length]; |
| for (int i = 0; i < weight.length; i++) { |
| samplers[i] = distribution.get(i).createSampler(rng); |
| } |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double[] sample() { |
| // Sampled values. |
| double[] vals = null; |
| |
| // Determine which component to sample from. |
| final double randomValue = rng.nextDouble(); |
| double sum = 0; |
| |
| for (int i = 0; i < weight.length; i++) { |
| sum += weight[i]; |
| if (randomValue <= sum) { |
| // pick model i |
| vals = samplers[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 = samplers[weight.length - 1].sample(); |
| } |
| |
| return vals; |
| } |
| } |
| } |