| /* |
| * 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.numbers.gamma.RegularizedBeta; |
| |
| /** |
| * Implementation of the <a href="http://en.wikipedia.org/wiki/Binomial_distribution">binomial distribution</a>. |
| */ |
| public class BinomialDistribution extends AbstractDiscreteDistribution { |
| /** The number of trials. */ |
| private final int numberOfTrials; |
| /** The probability of success. */ |
| private final double probabilityOfSuccess; |
| |
| /** |
| * Creates a binomial distribution. |
| * |
| * @param trials Number of trials. |
| * @param p Probability of success. |
| * @throws IllegalArgumentException if {@code trials < 0}, or if |
| * {@code p < 0} or {@code p > 1}. |
| */ |
| public BinomialDistribution(int trials, |
| double p) { |
| if (trials < 0) { |
| throw new DistributionException(DistributionException.NEGATIVE, |
| trials); |
| } |
| if (p < 0 || |
| p > 1) { |
| throw new DistributionException(DistributionException.INVALID_PROBABILITY, p); |
| } |
| |
| probabilityOfSuccess = p; |
| numberOfTrials = trials; |
| } |
| |
| /** |
| * Access the number of trials for this distribution. |
| * |
| * @return the number of trials. |
| */ |
| public int getNumberOfTrials() { |
| return numberOfTrials; |
| } |
| |
| /** |
| * Access the probability of success for this distribution. |
| * |
| * @return the probability of success. |
| */ |
| public double getProbabilityOfSuccess() { |
| return probabilityOfSuccess; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double probability(int x) { |
| return Math.exp(logProbability(x)); |
| } |
| |
| /** {@inheritDoc} **/ |
| @Override |
| public double logProbability(int x) { |
| if (numberOfTrials == 0) { |
| return (x == 0) ? 0. : Double.NEGATIVE_INFINITY; |
| } else if (x < 0 || x > numberOfTrials) { |
| return Double.NEGATIVE_INFINITY; |
| } |
| return SaddlePointExpansionUtils.logBinomialProbability(x, |
| numberOfTrials, probabilityOfSuccess, |
| 1.0 - probabilityOfSuccess); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double cumulativeProbability(int x) { |
| if (x < 0) { |
| return 0.0; |
| } else if (x >= numberOfTrials) { |
| return 1.0; |
| } |
| // Use a helper function to compute the complement of the survival probability |
| return RegularizedBetaUtils.complement(probabilityOfSuccess, |
| x + 1.0, (double) numberOfTrials - x); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double survivalProbability(int x) { |
| if (x < 0) { |
| return 1.0; |
| } else if (x >= numberOfTrials) { |
| return 0.0; |
| } |
| return RegularizedBeta.value(probabilityOfSuccess, |
| x + 1.0, (double) numberOfTrials - x); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * For {@code n} trials and probability parameter {@code p}, the mean is |
| * {@code n * p}. |
| */ |
| @Override |
| public double getMean() { |
| return numberOfTrials * probabilityOfSuccess; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * For {@code n} trials and probability parameter {@code p}, the variance is |
| * {@code n * p * (1 - p)}. |
| */ |
| @Override |
| public double getVariance() { |
| final double p = probabilityOfSuccess; |
| return numberOfTrials * p * (1 - p); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The lower bound of the support is always 0 except for the probability |
| * parameter {@code p = 1}. |
| * |
| * @return lower bound of the support (0 or the number of trials) |
| */ |
| @Override |
| public int getSupportLowerBound() { |
| return probabilityOfSuccess < 1.0 ? 0 : numberOfTrials; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The upper bound of the support is the number of trials except for the |
| * probability parameter {@code p = 0}. |
| * |
| * @return upper bound of the support (number of trials or 0) |
| */ |
| @Override |
| public int getSupportUpperBound() { |
| return probabilityOfSuccess > 0.0 ? numberOfTrials : 0; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * The support of this distribution is connected. |
| * |
| * @return {@code true} |
| */ |
| @Override |
| public boolean isSupportConnected() { |
| return true; |
| } |
| } |