| /* |
| * 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.combinatorics.BinomialCoefficientDouble; |
| import org.apache.commons.numbers.combinatorics.LogBinomialCoefficient; |
| import org.apache.commons.numbers.gamma.RegularizedBeta; |
| |
| /** |
| * Implementation of the <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Pascal distribution.</a> |
| * |
| * The Pascal distribution is a special case of the Negative Binomial distribution |
| * where the number of successes parameter is an integer. |
| * |
| * There are various ways to express the probability mass and distribution |
| * functions for the Pascal distribution. The present implementation represents |
| * the distribution of the number of failures before {@code r} successes occur. |
| * This is the convention adopted in e.g. |
| * <a href="http://mathworld.wolfram.com/NegativeBinomialDistribution.html">MathWorld</a>, |
| * but <em>not</em> in |
| * <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Wikipedia</a>. |
| * |
| * For a random variable {@code X} whose values are distributed according to this |
| * distribution, the probability mass function is given by<br> |
| * {@code P(X = k) = C(k + r - 1, r - 1) * p^r * (1 - p)^k,}<br> |
| * where {@code r} is the number of successes, {@code p} is the probability of |
| * success, and {@code X} is the total number of failures. {@code C(n, k)} is |
| * the binomial coefficient ({@code n} choose {@code k}). The mean and variance |
| * of {@code X} are<br> |
| * {@code E(X) = (1 - p) * r / p, var(X) = (1 - p) * r / p^2.}<br> |
| * Finally, the cumulative distribution function is given by<br> |
| * {@code P(X <= k) = I(p, r, k + 1)}, |
| * where I is the regularized incomplete Beta function. |
| */ |
| public class PascalDistribution extends AbstractDiscreteDistribution { |
| /** The number of successes. */ |
| private final int numberOfSuccesses; |
| /** The probability of success. */ |
| private final double probabilityOfSuccess; |
| /** The value of {@code log(p) * n}, where {@code p} is the probability of success |
| * and {@code n} is the number of successes, stored for faster computation. */ |
| private final double logProbabilityOfSuccessByNumOfSuccesses; |
| /** The value of {@code log(1-p)}, where {@code p} is the probability of success, |
| * stored for faster computation. */ |
| private final double log1mProbabilityOfSuccess; |
| /** The value of {@code p^n}, where {@code p} is the probability of success |
| * and {@code n} is the number of successes, stored for faster computation. */ |
| private final double probabilityOfSuccessPowNumOfSuccesses; |
| |
| /** |
| * Create a Pascal distribution with the given number of successes and |
| * probability of success. |
| * |
| * @param r Number of successes. |
| * @param p Probability of success. |
| * @throws IllegalArgumentException if {@code r <= 0} or {@code p < 0} |
| * or {@code p > 1}. |
| */ |
| public PascalDistribution(int r, |
| double p) { |
| if (r <= 0) { |
| throw new DistributionException(DistributionException.NOT_STRICTLY_POSITIVE, r); |
| } |
| if (p < 0 || |
| p > 1) { |
| throw new DistributionException(DistributionException.INVALID_PROBABILITY, p); |
| } |
| |
| numberOfSuccesses = r; |
| probabilityOfSuccess = p; |
| logProbabilityOfSuccessByNumOfSuccesses = Math.log(p) * numberOfSuccesses; |
| log1mProbabilityOfSuccess = Math.log1p(-p); |
| probabilityOfSuccessPowNumOfSuccesses = Math.pow(probabilityOfSuccess, numberOfSuccesses); |
| } |
| |
| /** |
| * Access the number of successes for this distribution. |
| * |
| * @return the number of successes. |
| */ |
| public int getNumberOfSuccesses() { |
| return numberOfSuccesses; |
| } |
| |
| /** |
| * 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) { |
| if (x < 0) { |
| return 0.0; |
| } else if (x == 0) { |
| // Special case exploiting cancellation. |
| return probabilityOfSuccessPowNumOfSuccesses; |
| } |
| final int n = x + numberOfSuccesses - 1; |
| if (n < 0) { |
| // overflow |
| return 0.0; |
| } |
| return BinomialCoefficientDouble.value(n, numberOfSuccesses - 1) * |
| probabilityOfSuccessPowNumOfSuccesses * |
| Math.pow(1.0 - probabilityOfSuccess, x); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double logProbability(int x) { |
| if (x < 0) { |
| return Double.NEGATIVE_INFINITY; |
| } else if (x == 0) { |
| // Special case exploiting cancellation. |
| return logProbabilityOfSuccessByNumOfSuccesses; |
| } |
| final int n = x + numberOfSuccesses - 1; |
| if (n < 0) { |
| // overflow |
| return Double.NEGATIVE_INFINITY; |
| } |
| return LogBinomialCoefficient.value(x + |
| numberOfSuccesses - 1, numberOfSuccesses - 1) + |
| logProbabilityOfSuccessByNumOfSuccesses + |
| log1mProbabilityOfSuccess * x; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double cumulativeProbability(int x) { |
| if (x < 0) { |
| return 0.0; |
| } |
| return RegularizedBeta.value(probabilityOfSuccess, |
| numberOfSuccesses, x + 1.0); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double survivalProbability(int x) { |
| if (x < 0) { |
| return 1.0; |
| } |
| // Use a helper function to compute the complement of the cumulative probability |
| return RegularizedBetaUtils.complement(probabilityOfSuccess, |
| numberOfSuccesses, x + 1.0); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>For number of successes {@code r} and probability of success {@code p}, |
| * the mean is {@code r * (1 - p) / p}. |
| */ |
| @Override |
| public double getMean() { |
| final double p = getProbabilityOfSuccess(); |
| final double r = getNumberOfSuccesses(); |
| return (r * (1 - p)) / p; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>For number of successes {@code r} and probability of success {@code p}, |
| * the variance is {@code r * (1 - p) / p^2}. |
| */ |
| @Override |
| public double getVariance() { |
| final double p = getProbabilityOfSuccess(); |
| final double r = getNumberOfSuccesses(); |
| return r * (1 - p) / (p * p); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>The lower bound of the support is always 0 no matter the parameters. |
| * |
| * @return lower bound of the support (always 0) |
| */ |
| @Override |
| public int getSupportLowerBound() { |
| return 0; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>The upper bound of the support is always positive infinity no matter the |
| * parameters. Positive infinity is symbolized by {@code Integer.MAX_VALUE}. |
| * |
| * @return upper bound of the support (always {@code Integer.MAX_VALUE} |
| * for positive infinity) |
| */ |
| @Override |
| public int getSupportUpperBound() { |
| return Integer.MAX_VALUE; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * <p>The support of this distribution is connected. |
| * |
| * @return {@code true} |
| */ |
| @Override |
| public boolean isSupportConnected() { |
| return true; |
| } |
| } |