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Commons Math example source code file (HypergeometricDistributionTest.java)

This example Commons Math source code file (HypergeometricDistributionTest.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

exception, exception, expected, expected, hypergeometricdistribution, hypergeometricdistributionimpl, hypergeometricdistributionimpl, hypergeometricdistributiontest, illegalargumentexception, integerdistribution, integerdistributionabstracttest, override, override

The Commons Math HypergeometricDistributionTest.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 org.apache.commons.math.TestUtils;

/**
 * Test cases for HyperGeometriclDistribution.
 * Extends IntegerDistributionAbstractTest.  See class javadoc for
 * IntegerDistributionAbstractTest for details.
 *
 * @version $Revision: 812147 $ $Date: 2009-09-07 10:08:16 -0400 (Mon, 07 Sep 2009) $
 */
public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {

    /**
     * Constructor for ChiSquareDistributionTest.
     * @param name
     */
    public HypergeometricDistributionTest(String name) {
        super(name);
    }

//-------------- Implementations for abstract methods -----------------------

    /** Creates the default discrete distribution instance to use in tests. */
    @Override
    public IntegerDistribution makeDistribution() {
        return new HypergeometricDistributionImpl(10,5, 5);
    }

    /** Creates the default probability density test input values */
    @Override
    public int[] makeDensityTestPoints() {
        return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
    }

    /** Creates the default probability density test expected values */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0d, 0.003968d, 0.099206d, 0.396825d, 0.396825d,
                0.099206d, 0.003968d, 0d};
    }

    /** Creates the default cumulative probability density test input values */
    @Override
    public int[] makeCumulativeTestPoints() {
        return makeDensityTestPoints();
    }

    /** Creates the default cumulative probability density test expected values */
    @Override
    public double[] makeCumulativeTestValues() {
        return new double[] {0d, .003968d, .103175d, .50000d, .896825d, .996032d,
                1.00000d, 1d};
    }

    /** Creates the default inverse cumulative probability test input values */
    @Override
    public double[] makeInverseCumulativeTestPoints() {
        return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
                0.990d, 0.975d, 0.950d, 0.900d, 1d};
    }

    /** Creates the default inverse cumulative probability density test expected values */
    @Override
    public int[] makeInverseCumulativeTestValues() {
        return new int[] {-1, -1, 0, 0, 0, 0, 4, 3, 3, 3, 3, 5};
    }

    //-------------------- Additional test cases ------------------------------

    /** Verify that if there are no failures, mass is concentrated on sampleSize */
    public void testDegenerateNoFailures() throws Exception {
        setDistribution(new HypergeometricDistributionImpl(5,5,3));
        setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
        setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
        setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
        setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
        setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
        setInverseCumulativeTestValues(new int[] {2, 2});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
    }

    /** Verify that if there are no successes, mass is concentrated on 0 */
    public void testDegenerateNoSuccesses() throws Exception {
        setDistribution(new HypergeometricDistributionImpl(5,0,3));
        setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
        setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
        setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
        setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
        setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
        setInverseCumulativeTestValues(new int[] {-1, -1});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
    }

    /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
    public void testDegenerateFullSample() throws Exception {
        setDistribution(new HypergeometricDistributionImpl(5,3,5));
        setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
        setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
        setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
        setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
        setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
        setInverseCumulativeTestValues(new int[] {2, 2});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
    }

    public void testPopulationSize() {
        HypergeometricDistribution dist = new HypergeometricDistributionImpl(5,3,5);
        try {
            dist.setPopulationSize(-1);
            fail("negative population size.  IllegalArgumentException expected");
        } catch(IllegalArgumentException ex) {
        }

        dist.setPopulationSize(10);
        assertEquals(10, dist.getPopulationSize());
    }

    public void testLargeValues() {
        int populationSize = 3456;
        int sampleSize = 789;
        int numberOfSucceses = 101;
        double[][] data = {
            {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0},
            {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244},
            {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673},
            {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792},
            {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375},
            {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203},
            {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781},
            {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701},
            {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381},
            {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199},
            {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718},
            {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418},
            {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57},
            {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59},
            {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61},
            {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63},
            {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65},
            {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68},
        };

        testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
    }

    private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) {
        HypergeometricDistributionImpl dist = new HypergeometricDistributionImpl(populationSize, numberOfSucceses, sampleSize);
        for (int i = 0; i < data.length; ++i) {
            int x = (int)data[i][0];
            double pdf = data[i][1];
            double actualPdf = dist.probability(x);
            TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> pdf",pdf, actualPdf, 1.0e-9);

            double cdf = data[i][2];
            double actualCdf = dist.cumulativeProbability(x);
            TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf",cdf, actualCdf, 1.0e-9);

            double cdf1 = data[i][3];
            double actualCdf1 = dist.upperCumulativeProbability(x);
            TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf1",cdf1, actualCdf1, 1.0e-9);
        }
    }

    public void testMoreLargeValues() {
        int populationSize = 26896;
        int sampleSize = 895;
        int numberOfSucceses = 55;
        double[][] data = {
            {0.0, 0.155168304750504, 0.155168304750504, 1.0},
            {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496},
            {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036},
            {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033},
            {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247},
            {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237},
            {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16},
            {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17},
            {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18},
            {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20},
            {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21},
            {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23},
            {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69},
            {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71},
            {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74},
            {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76},
            {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79},
            {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82},
        };
        testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
    }
}

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