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Commons Math example source code file (HypergeometricDistributionImpl.java)

This example Commons Math source code file (HypergeometricDistributionImpl.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

abstractintegerdistribution, deprecated, deprecated, hypergeometricdistribution, hypergeometricdistributionimpl, hypergeometricdistributionimpl, io, mathruntimeexception, mathruntimeexception, override, override, serializable

The Commons Math HypergeometricDistributionImpl.java source code

/*
 * 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.util.MathUtils;

/**
 * The default implementation of {@link HypergeometricDistribution}.
 *
 * @version $Revision: 920852 $ $Date: 2010-03-09 07:53:44 -0500 (Tue, 09 Mar 2010) $
 */
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(
                            "number of successes ({0}) must be less than or equal to population size ({1})",
                            numberOfSuccesses, populationSize);
        }
        if (sampleSize > populationSize) {
            throw MathRuntimeException
                    .createIllegalArgumentException(
                            "sample size ({0}) must be less than or equal to population size ({1})",
                            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, 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) <
     *         <code>p
     */
    @Override
    protected int getDomainLowerBound(double p) {
        return getLowerDomain(populationSize, numberOfSuccesses, sampleSize);
    }

    /**
     * Access the domain value upper bound, based on <code>p, 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) >
     *         <code>p
     */
    @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 Math.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 Math.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 = Math.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 Math.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 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 is negative.
     */
    private void setNumberOfSuccessesInternal(int num) {
        if (num < 0) {
            throw MathRuntimeException.createIllegalArgumentException(
                    "number of successes must be non-negative ({0})", num);
        }
        numberOfSuccesses = num;
    }

    /**
     * Modify the population size.
     *
     * @param size the new population size.
     * @throws IllegalArgumentException if <code>size 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 is not positive.
     */
    private void setPopulationSizeInternal(int size) {
        if (size <= 0) {
            throw MathRuntimeException.createIllegalArgumentException(
                    "population size must be positive ({0})", size);
        }
        populationSize = size;
    }

    /**
     * Modify the sample size.
     *
     * @param size the new sample size.
     * @throws IllegalArgumentException if <code>size 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 is negative.
     */
    private void setSampleSizeInternal(int size) {
        if (size < 0) {
            throw MathRuntimeException.createIllegalArgumentException(
                    "sample size must be positive ({0})", 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;
    }
}

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