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Java example source code file (ZipfDistributionTest.java)

This example Java source code file (ZipfDistributionTest.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.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractrandomgenerator, ignore, integerdistribution, integerdistributionabstracttest, override, randomgenerator, test, well1024a, zipfdistribution, zipfdistributiontest

The ZipfDistributionTest.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.distribution.ZipfDistribution.ZipfRejectionInversionSampler;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.random.AbstractRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well1024a;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Ignore;
import org.junit.Test;

/**
 * Test cases for {@link ZipfDistribution}.
 * Extends IntegerDistributionAbstractTest.
 * See class javadoc for IntegerDistributionAbstractTest for details.
 */
public class ZipfDistributionTest extends IntegerDistributionAbstractTest {

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

    @Test(expected=NotStrictlyPositiveException.class)
    public void testPreconditions1() {
        new ZipfDistribution(0, 1);
    }

    @Test(expected=NotStrictlyPositiveException.class)
    public void testPreconditions2() {
        new ZipfDistribution(1, 0);
    }

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

    /** Creates the default discrete distribution instance to use in tests. */
    @Override
    public IntegerDistribution makeDistribution() {
        return new ZipfDistribution(10, 1);
    }

    /** Creates the default probability density test input values */
    @Override
    public int[] makeDensityTestPoints() {
        return new int[] {-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
    }

    /**
     * Creates the default probability density test expected values.
     * Reference values are from R, version 2.15.3 (VGAM package 0.9-0).
     */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0d, 0d, 0.341417152147, 0.170708576074, 0.113805717382, 0.0853542880369, 0.0682834304295,
            0.0569028586912, 0.0487738788782, 0.0426771440184, 0.0379352391275, 0.0341417152147, 0};
    }

    /**
     * Creates the default logarithmic probability density test expected values.
     * Reference values are from R, version 2.14.1.
     */
    @Override
    public double[] makeLogDensityTestValues() {
        return new double[] {Double.NEGATIVE_INFINITY, Double.NEGATIVE_INFINITY,
            -1.07465022926458, -1.76779740982453, -2.17326251793269, -2.46094459038447,
            -2.68408814169868, -2.86640969849264, -3.0205603783199, -3.15409177094442,
            -3.2718748066008, -3.37723532225863, 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 */
    @Override
    public double[] makeCumulativeTestValues() {
        return new double[] {0, 0, 0.341417152147, 0.512125728221, 0.625931445604, 0.71128573364,
            0.77956916407, 0.836472022761, 0.885245901639, 0.927923045658, 0.965858284785, 1d, 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, 0.3413d, 0.3415d, 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, 1, 1, 1, 1, 2, 10, 10, 10, 9, 8, 10};
    }

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

        dist = new ZipfDistribution(2, 0.5);
        Assert.assertEquals(dist.getNumericalMean(), FastMath.sqrt(2), tol);
        Assert.assertEquals(dist.getNumericalVariance(), 0.24264068711928521, tol);
    }


    /**
     * Test sampling for various number of points and exponents.
     */
    @Test
    public void testSamplingExtended() {
        int sampleSize = 1000;

        int[] numPointsValues = {
            2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100
        };
        double[] exponentValues = {
            1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 2e-1, 5e-1,
            1. - 1e-9, 1.0, 1. + 1e-9, 1.1, 1.2, 1.3, 1.5, 1.6, 1.7, 1.8, 2.0,
            2.5, 3.0, 4., 5., 6., 7., 8., 9., 10., 20., 30., 100., 150.
        };

        for (int numPoints : numPointsValues) {
            for (double exponent : exponentValues) {
                double weightSum = 0.;
                double[] weights = new double[numPoints];
                for (int i = numPoints; i>=1; i-=1) {
                    weights[i-1] = Math.pow(i, -exponent);
                    weightSum += weights[i-1];
                }

                ZipfDistribution distribution = new ZipfDistribution(numPoints, exponent);
                distribution.reseedRandomGenerator(6); // use fixed seed, the test is expected to fail for more than 50% of all seeds because each test case can fail with probability 0.001, the chance that all test cases do not fail is 0.999^(32*22) = 0.49442874426

                double[] expectedCounts = new double[numPoints];
                long[] observedCounts = new long[numPoints];
                for (int i = 0; i < numPoints; i++) {
                    expectedCounts[i] = sampleSize * (weights[i]/weightSum);
                }
                int[] sample = distribution.sample(sampleSize);
                for (int s : sample) {
                    observedCounts[s-1]++;
                }
                TestUtils.assertChiSquareAccept(expectedCounts, observedCounts, 0.001);
            }
        }
    }

    @Test
    public void testSamplerHelper1() {
        final double tol = 1e-12;
        final double[] testValues = {
            FastMath.nextUp(-1.), -1e-1, -1e-2, -1e-3, -1e-4, -1e-5, -1e-6, -1e-7, -1e-8,
            -1e-9, -1e-10, -1e-11, 0., 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6,
            1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0
        };
        for (final double testValue : testValues) {
            final double expected = FastMath.log1p(testValue);
            TestUtils.assertRelativelyEquals(expected, ZipfRejectionInversionSampler.helper1(testValue)*testValue, tol);
        }
    }


    @Test
    public void testSamplerHelper1Minus1() {
        Assert.assertEquals(Double.POSITIVE_INFINITY, ZipfRejectionInversionSampler.helper1(-1d), 0d);
    }

    @Test
    public void testSamplerHelper2() {
        final double tol = 1e-12;
        final double[] testValues = {
            -1e0, -1e-1, -1e-2, -1e-3, -1e-4, -1e-5, -1e-6, -1e-7, -1e-8,
            -1e-9, -1e-10, -1e-11, 0., 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6,
            1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0
        };
        for (double testValue : testValues) {
            final double expected = FastMath.expm1(testValue);
            TestUtils.assertRelativelyEquals(expected, ZipfRejectionInversionSampler.helper2(testValue)*testValue, tol);
        }
    }

    @Ignore
    @Test
    public void testSamplerPerformance() {
        int[] numPointsValues = {1, 2, 5, 10, 100, 1000, 10000};
        double[] exponentValues = {1e-3, 1e-2, 1e-1, 1., 2., 5., 10.};
        int  numGeneratedSamples = 1000000;

        long sum = 0;

        for (int numPoints : numPointsValues) {
            for (double exponent : exponentValues) {
                long start = System.currentTimeMillis();
                final int[] randomNumberCounter = new int[1];

                RandomGenerator randomGenerator  = new AbstractRandomGenerator() {

                    private final RandomGenerator r = new Well1024a(0L);

                    @Override
                    public void setSeed(long seed) {
                    }

                    @Override
                    public double nextDouble() {
                        randomNumberCounter[0]+=1;
                        return r.nextDouble();
                    }
                };

                final ZipfDistribution distribution = new ZipfDistribution(randomGenerator, numPoints, exponent);
                for (int i = 0; i < numGeneratedSamples; ++i) {
                    sum += distribution.sample();
                }

                long end = System.currentTimeMillis();
                System.out.println("n = " + numPoints + ", exponent = " + exponent + ", avg number consumed random values = " + (double)(randomNumberCounter[0])/numGeneratedSamples + ", measured time = " + (end-start)/1000. + "s");
            }
        }
        System.out.println(sum);
    }

}

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