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Java example source code file (HypergeometricDistributionTest.java)

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

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Java - Java tags/keywords

expected, hypergeometricdistribution, hypergeometricdistributiontest, integerdistributionabstracttest, notpositiveexception, notstrictlypositiveexception, numberistoolargeexception, override, test

The HypergeometricDistributionTest.java Java example 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.math3.distribution;

import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test cases for HyperGeometriclDistribution.
 * Extends IntegerDistributionAbstractTest.  See class javadoc for
 * IntegerDistributionAbstractTest for details.
 *
 */
public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {

    /**
     * Constructor to override default tolerance.
     */
    public HypergeometricDistributionTest() {
        setTolerance(1e-12);
    }

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

    /** Creates the default discrete distribution instance to use in tests. */
    @Override
    public IntegerDistribution makeDistribution() {
        return new HypergeometricDistribution(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
     * Reference values are from R, version 2.15.3.
     */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0d, 0.00396825396825, 0.0992063492063, 0.396825396825, 0.396825396825,
            0.0992063492063, 0.00396825396825, 0d};
    }

    /**
     * Creates the default probability log density test expected values
     * Reference values are from R, version 2.14.1.
     */
    @Override
    public double[] makeLogDensityTestValues() {
        //-Inf  -Inf
        return new double[] {Double.NEGATIVE_INFINITY, -5.52942908751142, -2.31055326264322, -0.924258901523332,
                -0.924258901523332, -2.31055326264322, -5.52942908751142, Double.NEGATIVE_INFINITY};
    }

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

    /**
     * Creates the default cumulative probability density test expected values
     * Reference values are from R, version 2.15.3.
     */
    @Override
    public double[] makeCumulativeTestValues() {
        return new double[] {0d, 0.00396825396825, 0.103174603175, .5, 0.896825396825, 0.996031746032,
                1, 1};
    }

    /** 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[] {0, 0, 1, 1, 1, 1, 5, 4, 4, 4, 4, 5};
    }

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

    /** Verify that if there are no failures, mass is concentrated on sampleSize */
    @Test
    public void testDegenerateNoFailures() {
        HypergeometricDistribution dist = new HypergeometricDistribution(5,5,3);
        setDistribution(dist);
        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[] {3, 3});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
        Assert.assertEquals(dist.getSupportLowerBound(), 3);
        Assert.assertEquals(dist.getSupportUpperBound(), 3);
    }

    /** Verify that if there are no successes, mass is concentrated on 0 */
    @Test
    public void testDegenerateNoSuccesses() {
        HypergeometricDistribution dist = new HypergeometricDistribution(5,0,3);
        setDistribution(dist);
        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[] {0, 0});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
        Assert.assertEquals(dist.getSupportLowerBound(), 0);
        Assert.assertEquals(dist.getSupportUpperBound(), 0);
    }

    /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
    @Test
    public void testDegenerateFullSample() {
        HypergeometricDistribution dist = new HypergeometricDistribution(5,3,5);
        setDistribution(dist);
        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[] {3, 3});
        verifyDensities();
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
        Assert.assertEquals(dist.getSupportLowerBound(), 3);
        Assert.assertEquals(dist.getSupportUpperBound(), 3);
    }

    @Test
    public void testPreconditions() {
        try {
            new HypergeometricDistribution(0, 3, 5);
            Assert.fail("negative population size. NotStrictlyPositiveException expected");
        } catch(NotStrictlyPositiveException ex) {
            // Expected.
        }
        try {
            new HypergeometricDistribution(5, -1, 5);
            Assert.fail("negative number of successes. NotPositiveException expected");
        } catch(NotPositiveException ex) {
            // Expected.
        }
        try {
            new HypergeometricDistribution(5, 3, -1);
            Assert.fail("negative sample size. NotPositiveException expected");
        } catch(NotPositiveException ex) {
            // Expected.
        }
        try {
            new HypergeometricDistribution(5, 6, 5);
            Assert.fail("numberOfSuccesses > populationSize. NumberIsTooLargeException expected");
        } catch(NumberIsTooLargeException ex) {
            // Expected.
        }
        try {
            new HypergeometricDistribution(5, 3, 6);
            Assert.fail("sampleSize > populationSize. NumberIsTooLargeException expected");
        } catch(NumberIsTooLargeException ex) {
            // Expected.
        }
    }

    @Test
    public void testAccessors() {
        HypergeometricDistribution dist = new HypergeometricDistribution(5, 3, 4);
        Assert.assertEquals(5, dist.getPopulationSize());
        Assert.assertEquals(3, dist.getNumberOfSuccesses());
        Assert.assertEquals(4, dist.getSampleSize());
    }

    @Test
    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) {
        HypergeometricDistribution dist = new HypergeometricDistribution(populationSize, numberOfSucceses, sampleSize);
        for (int i = 0; i < data.length; ++i) {
            int x = (int)data[i][0];
            double pmf = data[i][1];
            double actualPmf = dist.probability(x);
            TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> pmf",pmf, actualPmf, 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);
        }
    }

    @Test
    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);
    }

    @Test
    public void testMoments() {
        final double tol = 1e-9;
        HypergeometricDistribution dist;

        dist = new HypergeometricDistribution(1500, 40, 100);
        Assert.assertEquals(dist.getNumericalMean(), 40d * 100d / 1500d, tol);
        Assert.assertEquals(dist.getNumericalVariance(), ( 100d * 40d * (1500d - 100d) * (1500d - 40d) ) / ( (1500d * 1500d * 1499d) ), tol);

        dist = new HypergeometricDistribution(3000, 55, 200);
        Assert.assertEquals(dist.getNumericalMean(), 55d * 200d / 3000d, tol);
        Assert.assertEquals(dist.getNumericalVariance(), ( 200d * 55d * (3000d - 200d) * (3000d - 55d) ) / ( (3000d * 3000d * 2999d) ), tol);
    }

    @Test
    public void testMath644() {
        int N = 14761461;  // population
        int m = 1035;      // successes in population
        int n = 1841;      // number of trials

        int k = 0;
        final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n);

        Assert.assertTrue(Precision.compareTo(1.0, dist.upperCumulativeProbability(k), 1) == 0);
        Assert.assertTrue(Precision.compareTo(dist.cumulativeProbability(k), 0.0, 1) > 0);

        // another way to calculate the upper cumulative probability
        double upper = 1.0 - dist.cumulativeProbability(k) + dist.probability(k);
        Assert.assertTrue(Precision.compareTo(1.0, upper, 1) == 0);
    }

    @Test
    public void testMath1021() {
        final int N = 43130568;
        final int m = 42976365;
        final int n = 50;
        final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n);

        for (int i = 0; i < 100; i++) {
            final int sample = dist.sample();
            Assert.assertTrue("sample=" + sample, 0 <= sample);
            Assert.assertTrue("sample=" + sample, sample <= n);
        }
    }
}

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