| /* |
| * 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.rng.sampling.distribution; |
| |
| import org.apache.commons.rng.UniformRandomProvider; |
| |
| /** |
| * Sampler for the <a href="http://mathworld.wolfram.com/PoissonDistribution.html">Poisson distribution</a>. |
| * |
| * <ul> |
| * <li> |
| * For small means, a Poisson process is simulated using uniform deviates, as described in |
| * <blockquote> |
| * Knuth (1969). <i>Seminumerical Algorithms</i>. The Art of Computer Programming, |
| * Volume 2. Chapter 3.4.1.F.3 Important integer-valued distributions: The Poisson distribution. |
| * Addison Wesley. |
| * </blockquote> |
| * The Poisson process (and hence, the returned value) is bounded by {@code 1000 * mean}. |
| * </li> |
| * </ul> |
| * |
| * <p>This sampler is suitable for {@code mean < 40}. |
| * For large means, {@link LargeMeanPoissonSampler} should be used instead.</p> |
| * |
| * <p>Sampling uses {@link UniformRandomProvider#nextDouble()} and requires on average |
| * {@code mean + 1} deviates per sample.</p> |
| * |
| * @since 1.1 |
| */ |
| public class SmallMeanPoissonSampler |
| implements SharedStateDiscreteSampler { |
| /** |
| * Pre-compute {@code Math.exp(-mean)}. |
| * Note: This is the probability of the Poisson sample {@code P(n=0)}. |
| */ |
| private final double p0; |
| /** Pre-compute {@code 1000 * mean} as the upper limit of the sample. */ |
| private final int limit; |
| /** Underlying source of randomness. */ |
| private final UniformRandomProvider rng; |
| |
| /** |
| * @param rng Generator of uniformly distributed random numbers. |
| * @param mean Mean. |
| * @throws IllegalArgumentException if {@code mean <= 0} or {@code Math.exp(-mean) == 0} |
| */ |
| public SmallMeanPoissonSampler(UniformRandomProvider rng, |
| double mean) { |
| this.rng = rng; |
| if (mean <= 0) { |
| throw new IllegalArgumentException("mean is not strictly positive: " + mean); |
| } |
| p0 = Math.exp(-mean); |
| if (p0 > 0) { |
| // The returned sample is bounded by 1000 * mean |
| limit = (int) Math.ceil(1000 * mean); |
| } else { |
| // This excludes NaN values for the mean |
| throw new IllegalArgumentException("No p(x=0) probability for mean: " + mean); |
| } |
| } |
| |
| /** |
| * @param rng Generator of uniformly distributed random numbers. |
| * @param source Source to copy. |
| */ |
| private SmallMeanPoissonSampler(UniformRandomProvider rng, |
| SmallMeanPoissonSampler source) { |
| this.rng = rng; |
| p0 = source.p0; |
| limit = source.limit; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public int sample() { |
| int n = 0; |
| double r = 1; |
| |
| while (n < limit) { |
| r *= rng.nextDouble(); |
| if (r >= p0) { |
| n++; |
| } else { |
| break; |
| } |
| } |
| return n; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public String toString() { |
| return "Small Mean Poisson deviate [" + rng.toString() + "]"; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * @since 1.3 |
| */ |
| @Override |
| public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng) { |
| return new SmallMeanPoissonSampler(rng, this); |
| } |
| |
| /** |
| * Creates a new sampler for the Poisson distribution. |
| * |
| * @param rng Generator of uniformly distributed random numbers. |
| * @param mean Mean of the distribution. |
| * @return the sampler |
| * @throws IllegalArgumentException if {@code mean <= 0} or {@code Math.exp(-mean) == 0}. |
| * @since 1.3 |
| */ |
| public static SharedStateDiscreteSampler of(UniformRandomProvider rng, |
| double mean) { |
| return new SmallMeanPoissonSampler(rng, mean); |
| } |
| } |