| /* |
| * 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.statistics.distribution; |
| |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.rng.sampling.distribution.AhrensDieterExponentialSampler; |
| |
| /** |
| * Implementation of the <a href="http://en.wikipedia.org/wiki/Exponential_distribution">exponential distribution</a>. |
| */ |
| public class ExponentialDistribution extends AbstractContinuousDistribution { |
| /** Support lower bound. */ |
| private static final double SUPPORT_LO = 0; |
| /** Support upper bound. */ |
| private static final double SUPPORT_HI = Double.POSITIVE_INFINITY; |
| /** The mean of this distribution. */ |
| private final double mean; |
| /** The logarithm of the mean, stored to reduce computing time. */ |
| private final double logMean; |
| |
| /** |
| * Creates a distribution. |
| * |
| * @param mean Mean of this distribution. |
| * @throws IllegalArgumentException if {@code mean <= 0}. |
| */ |
| public ExponentialDistribution(double mean) { |
| if (mean <= 0) { |
| throw new DistributionException(DistributionException.NOT_STRICTLY_POSITIVE, mean); |
| } |
| this.mean = mean; |
| logMean = Math.log(mean); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double density(double x) { |
| final double logDensity = logDensity(x); |
| return logDensity == Double.NEGATIVE_INFINITY ? 0 : Math.exp(logDensity); |
| } |
| |
| /** {@inheritDoc} **/ |
| @Override |
| public double logDensity(double x) { |
| if (x < SUPPORT_LO || |
| x >= SUPPORT_HI) { |
| return Double.NEGATIVE_INFINITY; |
| } |
| |
| return -x / mean - logMean; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The implementation of this method is based on: |
| * <ul> |
| * <li> |
| * <a href="http://mathworld.wolfram.com/ExponentialDistribution.html"> |
| * Exponential Distribution</a>, equation (1).</li> |
| * </ul> |
| */ |
| @Override |
| public double cumulativeProbability(double x) { |
| if (x <= SUPPORT_LO) { |
| return 0; |
| } |
| |
| return 1 - Math.exp(-x / mean); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * Returns {@code 0} when {@code p= = 0} and |
| * {@code Double.POSITIVE_INFINITY} when {@code p == 1}. |
| */ |
| @Override |
| public double inverseCumulativeProbability(double p) { |
| double ret; |
| |
| if (p < 0 || |
| p > 1) { |
| throw new DistributionException(DistributionException.INVALID_PROBABILITY, p); |
| } else if (p == 1) { |
| ret = Double.POSITIVE_INFINITY; |
| } else { |
| ret = -mean * Math.log(1 - p); |
| } |
| |
| return ret; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double getMean() { |
| return mean; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * For mean parameter {@code k}, the variance is {@code k^2}. |
| */ |
| @Override |
| public double getVariance() { |
| return mean * mean; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The lower bound of the support is always 0 no matter the mean parameter. |
| * |
| * @return lower bound of the support (always 0) |
| */ |
| @Override |
| public double getSupportLowerBound() { |
| return SUPPORT_LO; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The upper bound of the support is always positive infinity |
| * no matter the mean parameter. |
| * |
| * @return upper bound of the support (always Double.POSITIVE_INFINITY) |
| */ |
| @Override |
| public double getSupportUpperBound() { |
| return SUPPORT_HI; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The support of this distribution is connected. |
| * |
| * @return {@code true} |
| */ |
| @Override |
| public boolean isSupportConnected() { |
| return true; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>Sampling algorithm uses the |
| * <a href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> |
| * inversion method</a> to generate exponentially distributed |
| * random values from uniform deviates. |
| * </p> |
| */ |
| @Override |
| public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) { |
| // Exponential distribution sampler. |
| return new AhrensDieterExponentialSampler(rng, mean)::sample; |
| } |
| } |