| /* |
| * 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.math.distribution; |
| |
| import java.io.Serializable; |
| |
| import org.apache.commons.math.MathRuntimeException; |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.util.MathUtils; |
| import org.apache.commons.math.util.FastMath; |
| |
| /** |
| * The default implementation of {@link HypergeometricDistribution}. |
| * |
| * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $ |
| */ |
| public class HypergeometricDistributionImpl extends AbstractIntegerDistribution |
| implements HypergeometricDistribution, Serializable { |
| |
| /** Serializable version identifier */ |
| private static final long serialVersionUID = -436928820673516179L; |
| |
| /** The number of successes in the population. */ |
| private int numberOfSuccesses; |
| |
| /** The population size. */ |
| private int populationSize; |
| |
| /** The sample size. */ |
| private int sampleSize; |
| |
| /** |
| * Construct a new hypergeometric distribution with the given the population |
| * size, the number of successes in the population, and the sample size. |
| * |
| * @param populationSize the population size. |
| * @param numberOfSuccesses number of successes in the population. |
| * @param sampleSize the sample size. |
| */ |
| public HypergeometricDistributionImpl(int populationSize, |
| int numberOfSuccesses, int sampleSize) { |
| super(); |
| if (numberOfSuccesses > populationSize) { |
| throw MathRuntimeException |
| .createIllegalArgumentException( |
| LocalizedFormats.NUMBER_OF_SUCCESS_LARGER_THAN_POPULATION_SIZE, |
| numberOfSuccesses, populationSize); |
| } |
| if (sampleSize > populationSize) { |
| throw MathRuntimeException |
| .createIllegalArgumentException( |
| LocalizedFormats.SAMPLE_SIZE_LARGER_THAN_POPULATION_SIZE, |
| sampleSize, populationSize); |
| } |
| |
| setPopulationSizeInternal(populationSize); |
| setSampleSizeInternal(sampleSize); |
| setNumberOfSuccessesInternal(numberOfSuccesses); |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(X ≤ x). |
| * |
| * @param x the value at which the PDF is evaluated. |
| * @return PDF for this distribution. |
| */ |
| @Override |
| public double cumulativeProbability(int x) { |
| double ret; |
| |
| int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize); |
| if (x < domain[0]) { |
| ret = 0.0; |
| } else if (x >= domain[1]) { |
| ret = 1.0; |
| } else { |
| ret = innerCumulativeProbability(domain[0], x, 1, populationSize, |
| numberOfSuccesses, sampleSize); |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * Return the domain for the given hypergeometric distribution parameters. |
| * |
| * @param n the population size. |
| * @param m number of successes in the population. |
| * @param k the sample size. |
| * @return a two element array containing the lower and upper bounds of the |
| * hypergeometric distribution. |
| */ |
| private int[] getDomain(int n, int m, int k) { |
| return new int[] { getLowerDomain(n, m, k), getUpperDomain(m, k) }; |
| } |
| |
| /** |
| * Access the domain value lower bound, based on <code>p</code>, used to |
| * bracket a PDF root. |
| * |
| * @param p the desired probability for the critical value |
| * @return domain value lower bound, i.e. P(X < <i>lower bound</i>) < |
| * <code>p</code> |
| */ |
| @Override |
| protected int getDomainLowerBound(double p) { |
| return getLowerDomain(populationSize, numberOfSuccesses, sampleSize); |
| } |
| |
| /** |
| * Access the domain value upper bound, based on <code>p</code>, used to |
| * bracket a PDF root. |
| * |
| * @param p the desired probability for the critical value |
| * @return domain value upper bound, i.e. P(X < <i>upper bound</i>) > |
| * <code>p</code> |
| */ |
| @Override |
| protected int getDomainUpperBound(double p) { |
| return getUpperDomain(sampleSize, numberOfSuccesses); |
| } |
| |
| /** |
| * Return the lowest domain value for the given hypergeometric distribution |
| * parameters. |
| * |
| * @param n the population size. |
| * @param m number of successes in the population. |
| * @param k the sample size. |
| * @return the lowest domain value of the hypergeometric distribution. |
| */ |
| private int getLowerDomain(int n, int m, int k) { |
| return FastMath.max(0, m - (n - k)); |
| } |
| |
| /** |
| * Access the number of successes. |
| * |
| * @return the number of successes. |
| */ |
| public int getNumberOfSuccesses() { |
| return numberOfSuccesses; |
| } |
| |
| /** |
| * Access the population size. |
| * |
| * @return the population size. |
| */ |
| public int getPopulationSize() { |
| return populationSize; |
| } |
| |
| /** |
| * Access the sample size. |
| * |
| * @return the sample size. |
| */ |
| public int getSampleSize() { |
| return sampleSize; |
| } |
| |
| /** |
| * Return the highest domain value for the given hypergeometric distribution |
| * parameters. |
| * |
| * @param m number of successes in the population. |
| * @param k the sample size. |
| * @return the highest domain value of the hypergeometric distribution. |
| */ |
| private int getUpperDomain(int m, int k) { |
| return FastMath.min(k, m); |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(X = x). |
| * |
| * @param x the value at which the PMF is evaluated. |
| * @return PMF for this distribution. |
| */ |
| public double probability(int x) { |
| double ret; |
| |
| int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize); |
| if (x < domain[0] || x > domain[1]) { |
| ret = 0.0; |
| } else { |
| double p = (double) sampleSize / (double) populationSize; |
| double q = (double) (populationSize - sampleSize) / (double) populationSize; |
| double p1 = SaddlePointExpansion.logBinomialProbability(x, |
| numberOfSuccesses, p, q); |
| double p2 = |
| SaddlePointExpansion.logBinomialProbability(sampleSize - x, |
| populationSize - numberOfSuccesses, p, q); |
| double p3 = |
| SaddlePointExpansion.logBinomialProbability(sampleSize, populationSize, p, q); |
| ret = FastMath.exp(p1 + p2 - p3); |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * For the distribution, X, defined by the given hypergeometric distribution |
| * parameters, this method returns P(X = x). |
| * |
| * @param n the population size. |
| * @param m number of successes in the population. |
| * @param k the sample size. |
| * @param x the value at which the PMF is evaluated. |
| * @return PMF for the distribution. |
| */ |
| private double probability(int n, int m, int k, int x) { |
| return FastMath.exp(MathUtils.binomialCoefficientLog(m, x) + |
| MathUtils.binomialCoefficientLog(n - m, k - x) - |
| MathUtils.binomialCoefficientLog(n, k)); |
| } |
| |
| /** |
| * Modify the number of successes. |
| * |
| * @param num the new number of successes. |
| * @throws IllegalArgumentException if <code>num</code> is negative. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setNumberOfSuccesses(int num) { |
| setNumberOfSuccessesInternal(num); |
| } |
| |
| /** |
| * Modify the number of successes. |
| * |
| * @param num the new number of successes. |
| * @throws IllegalArgumentException if <code>num</code> is negative. |
| */ |
| private void setNumberOfSuccessesInternal(int num) { |
| if (num < 0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NEGATIVE_NUMBER_OF_SUCCESSES, num); |
| } |
| numberOfSuccesses = num; |
| } |
| |
| /** |
| * Modify the population size. |
| * |
| * @param size the new population size. |
| * @throws IllegalArgumentException if <code>size</code> is not positive. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setPopulationSize(int size) { |
| setPopulationSizeInternal(size); |
| } |
| |
| /** |
| * Modify the population size. |
| * |
| * @param size the new population size. |
| * @throws IllegalArgumentException if <code>size</code> is not positive. |
| */ |
| private void setPopulationSizeInternal(int size) { |
| if (size <= 0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_POPULATION_SIZE, size); |
| } |
| populationSize = size; |
| } |
| |
| /** |
| * Modify the sample size. |
| * |
| * @param size the new sample size. |
| * @throws IllegalArgumentException if <code>size</code> is negative. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setSampleSize(int size) { |
| setSampleSizeInternal(size); |
| } |
| /** |
| * Modify the sample size. |
| * |
| * @param size the new sample size. |
| * @throws IllegalArgumentException if <code>size</code> is negative. |
| */ |
| private void setSampleSizeInternal(int size) { |
| if (size < 0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_SAMPLE_SIZE, size); |
| } |
| sampleSize = size; |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(X ≥ x). |
| * |
| * @param x the value at which the CDF is evaluated. |
| * @return upper tail CDF for this distribution. |
| * @since 1.1 |
| */ |
| public double upperCumulativeProbability(int x) { |
| double ret; |
| |
| final int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize); |
| if (x < domain[0]) { |
| ret = 1.0; |
| } else if (x > domain[1]) { |
| ret = 0.0; |
| } else { |
| ret = innerCumulativeProbability(domain[1], x, -1, populationSize, numberOfSuccesses, sampleSize); |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(x0 ≤ X ≤ x1). This |
| * probability is computed by summing the point probabilities for the values |
| * x0, x0 + 1, x0 + 2, ..., x1, in the order directed by dx. |
| * |
| * @param x0 the inclusive, lower bound |
| * @param x1 the inclusive, upper bound |
| * @param dx the direction of summation. 1 indicates summing from x0 to x1. |
| * 0 indicates summing from x1 to x0. |
| * @param n the population size. |
| * @param m number of successes in the population. |
| * @param k the sample size. |
| * @return P(x0 ≤ X ≤ x1). |
| */ |
| private double innerCumulativeProbability(int x0, int x1, int dx, int n, |
| int m, int k) { |
| double ret = probability(n, m, k, x0); |
| while (x0 != x1) { |
| x0 += dx; |
| ret += probability(n, m, k, x0); |
| } |
| return ret; |
| } |
| |
| /** |
| * Returns the lower bound for the support for the distribution. |
| * |
| * For population size <code>N</code>, |
| * number of successes <code>m</code>, and |
| * sample size <code>n</code>, |
| * the lower bound of the support is |
| * <code>max(0, n + m - N)</code> |
| * |
| * @return lower bound of the support |
| * @since 2.2 |
| */ |
| public int getSupportLowerBound() { |
| return FastMath.max(0, |
| getSampleSize() + getNumberOfSuccesses() - getPopulationSize()); |
| } |
| |
| /** |
| * Returns the upper bound for the support of the distribution. |
| * |
| * For number of successes <code>m</code> and |
| * sample size <code>n</code>, |
| * the upper bound of the support is |
| * <code>min(m, n)</code> |
| * |
| * @return upper bound of the support |
| * @since 2.2 |
| */ |
| public int getSupportUpperBound() { |
| return FastMath.min(getNumberOfSuccesses(), getSampleSize()); |
| } |
| |
| /** |
| * Returns the mean. |
| * |
| * For population size <code>N</code>, |
| * number of successes <code>m</code>, and |
| * sample size <code>n</code>, the mean is |
| * <code>n * m / N</code> |
| * |
| * @return the mean |
| * @since 2.2 |
| */ |
| protected double getNumericalMean() { |
| return (double)(getSampleSize() * getNumberOfSuccesses()) / (double)getPopulationSize(); |
| } |
| |
| /** |
| * Returns the variance. |
| * |
| * For population size <code>N</code>, |
| * number of successes <code>m</code>, and |
| * sample size <code>n</code>, the variance is |
| * <code>[ n * m * (N - n) * (N - m) ] / [ N^2 * (N - 1) ]</code> |
| * |
| * @return the variance |
| * @since 2.2 |
| */ |
| public double getNumericalVariance() { |
| final double N = getPopulationSize(); |
| final double m = getNumberOfSuccesses(); |
| final double n = getSampleSize(); |
| return ( n * m * (N - n) * (N - m) ) / ( (N*N * (N - 1)) ); |
| } |
| } |