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Java example source code file (RandomDataGeneratorTest.java)

This example Java source code file (RandomDataGeneratorTest.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

chisquaretest, decimalformat, frequency, hashset, mathillegalargumentexception, object, pascaldistribution, poissondistribution, randomdatagenerator, randomdatageneratortest, retry, string, stringbuilder, test, text, util

The RandomDataGeneratorTest.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.random;

import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;

import org.apache.commons.math3.Retry;
import org.apache.commons.math3.RetryRunner;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.distribution.BetaDistribution;
import org.apache.commons.math3.distribution.BinomialDistribution;
import org.apache.commons.math3.distribution.BinomialDistributionTest;
import org.apache.commons.math3.distribution.CauchyDistribution;
import org.apache.commons.math3.distribution.ChiSquaredDistribution;
import org.apache.commons.math3.distribution.ExponentialDistribution;
import org.apache.commons.math3.distribution.FDistribution;
import org.apache.commons.math3.distribution.GammaDistribution;
import org.apache.commons.math3.distribution.HypergeometricDistribution;
import org.apache.commons.math3.distribution.HypergeometricDistributionTest;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.distribution.PascalDistribution;
import org.apache.commons.math3.distribution.PascalDistributionTest;
import org.apache.commons.math3.distribution.PoissonDistribution;
import org.apache.commons.math3.distribution.TDistribution;
import org.apache.commons.math3.distribution.WeibullDistribution;
import org.apache.commons.math3.distribution.ZipfDistribution;
import org.apache.commons.math3.distribution.ZipfDistributionTest;
import org.apache.commons.math3.stat.Frequency;
import org.apache.commons.math3.stat.inference.ChiSquareTest;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.junit.Assert;
import org.junit.Test;
import org.junit.runner.RunWith;

/**
 * Test cases for the RandomDataGenerator class.
 *
 */
@RunWith(RetryRunner.class)
public class RandomDataGeneratorTest {

    public RandomDataGeneratorTest() {
        randomData = new RandomDataGenerator();
        randomData.reSeed(1000);
    }

    protected final long smallSampleSize = 1000;
    protected final double[] expected = { 250, 250, 250, 250 };
    protected final int largeSampleSize = 10000;
    private final String[] hex = { "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
            "a", "b", "c", "d", "e", "f" };
    protected RandomDataGenerator randomData = null;
    protected final ChiSquareTest testStatistic = new ChiSquareTest();

    @Test
    public void testNextIntExtremeValues() {
        int x = randomData.nextInt(Integer.MIN_VALUE, Integer.MAX_VALUE);
        int y = randomData.nextInt(Integer.MIN_VALUE, Integer.MAX_VALUE);
        Assert.assertFalse(x == y);
    }

    @Test
    public void testNextLongExtremeValues() {
        long x = randomData.nextLong(Long.MIN_VALUE, Long.MAX_VALUE);
        long y = randomData.nextLong(Long.MIN_VALUE, Long.MAX_VALUE);
        Assert.assertFalse(x == y);
    }

    @Test
    public void testNextUniformExtremeValues() {
        double x = randomData.nextUniform(-Double.MAX_VALUE, Double.MAX_VALUE);
        double y = randomData.nextUniform(-Double.MAX_VALUE, Double.MAX_VALUE);
        Assert.assertFalse(x == y);
        Assert.assertFalse(Double.isNaN(x));
        Assert.assertFalse(Double.isNaN(y));
        Assert.assertFalse(Double.isInfinite(x));
        Assert.assertFalse(Double.isInfinite(y));
    }

    @Test
    public void testNextIntIAE() {
        try {
            randomData.nextInt(4, 3);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @Test
    public void testNextIntNegativeToPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextIntUniform(-3, 5);
            checkNextIntUniform(-3, 6);
        }
    }

    @Test
    public void testNextIntNegativeRange() {
        for (int i = 0; i < 5; i++) {
            checkNextIntUniform(-7, -4);
            checkNextIntUniform(-15, -2);
            checkNextIntUniform(Integer.MIN_VALUE + 1, Integer.MIN_VALUE + 12);
        }
    }

    @Test
    public void testNextIntPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextIntUniform(0, 3);
            checkNextIntUniform(2, 12);
            checkNextIntUniform(1,2);
            checkNextIntUniform(Integer.MAX_VALUE - 12, Integer.MAX_VALUE - 1);
        }
    }

    private void checkNextIntUniform(int min, int max) {
        final Frequency freq = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            final int value = randomData.nextInt(min, max);
            Assert.assertTrue("nextInt range", (value >= min) && (value <= max));
            freq.addValue(value);
        }
        final int len = max - min + 1;
        final long[] observed = new long[len];
        for (int i = 0; i < len; i++) {
            observed[i] = freq.getCount(min + i);
        }
        final double[] expected = new double[len];
        for (int i = 0; i < len; i++) {
            expected[i] = 1d / len;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.001);
    }

    @Test
    public void testNextIntWideRange() {
        int lower = -0x6543210F;
        int upper =  0x456789AB;
        int max   = Integer.MIN_VALUE;
        int min   = Integer.MAX_VALUE;
        for (int i = 0; i < 1000000; ++i) {
            int r = randomData.nextInt(lower, upper);
            max = FastMath.max(max, r);
            min = FastMath.min(min, r);
            Assert.assertTrue(r >= lower);
            Assert.assertTrue(r <= upper);
        }
        double ratio = (((double) max)   - ((double) min)) /
                       (((double) upper) - ((double) lower));
        Assert.assertTrue(ratio > 0.99999);
    }

    @Test
    public void testNextLongIAE() {
        try {
            randomData.nextLong(4, 3);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @Test
    public void testNextLongNegativeToPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextLongUniform(-3, 5);
            checkNextLongUniform(-3, 6);
        }
    }

    @Test
    public void testNextLongNegativeRange() {
        for (int i = 0; i < 5; i++) {
            checkNextLongUniform(-7, -4);
            checkNextLongUniform(-15, -2);
            checkNextLongUniform(Long.MIN_VALUE + 1, Long.MIN_VALUE + 12);
        }
    }

    @Test
    public void testNextLongPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextLongUniform(0, 3);
            checkNextLongUniform(2, 12);
            checkNextLongUniform(Long.MAX_VALUE - 12, Long.MAX_VALUE - 1);
        }
    }

    private void checkNextLongUniform(long min, long max) {
        final Frequency freq = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            final long value = randomData.nextLong(min, max);
            Assert.assertTrue("nextLong range: " + value + " " + min + " " + max,
                              (value >= min) && (value <= max));
            freq.addValue(value);
        }
        final int len = ((int) (max - min)) + 1;
        final long[] observed = new long[len];
        for (int i = 0; i < len; i++) {
            observed[i] = freq.getCount(min + i);
        }
        final double[] expected = new double[len];
        for (int i = 0; i < len; i++) {
            expected[i] = 1d / len;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.01);
    }

    @Test
    public void testNextLongWideRange() {
        long lower = -0x6543210FEDCBA987L;
        long upper =  0x456789ABCDEF0123L;
        long max = Long.MIN_VALUE;
        long min = Long.MAX_VALUE;
        for (int i = 0; i < 10000000; ++i) {
            long r = randomData.nextLong(lower, upper);
            max = FastMath.max(max, r);
            min = FastMath.min(min, r);
            Assert.assertTrue(r >= lower);
            Assert.assertTrue(r <= upper);
        }
        double ratio = (((double) max)   - ((double) min)) /
                       (((double) upper) - ((double) lower));
        Assert.assertTrue(ratio > 0.99999);
    }

    @Test
    public void testNextSecureLongIAE() {
        try {
            randomData.nextSecureLong(4, 3);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureLongNegativeToPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureLongUniform(-3, 5);
            checkNextSecureLongUniform(-3, 6);
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureLongNegativeRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureLongUniform(-7, -4);
            checkNextSecureLongUniform(-15, -2);
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureLongPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureLongUniform(0, 3);
            checkNextSecureLongUniform(2, 12);
        }
    }

    private void checkNextSecureLongUniform(int min, int max) {
        final Frequency freq = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            final long value = randomData.nextSecureLong(min, max);
            Assert.assertTrue("nextLong range", (value >= min) && (value <= max));
            freq.addValue(value);
        }
        final int len = max - min + 1;
        final long[] observed = new long[len];
        for (int i = 0; i < len; i++) {
            observed[i] = freq.getCount(min + i);
        }
        final double[] expected = new double[len];
        for (int i = 0; i < len; i++) {
            expected[i] = 1d / len;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.0001);
    }

    @Test
    public void testNextSecureIntIAE() {
        try {
            randomData.nextSecureInt(4, 3);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureIntNegativeToPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureIntUniform(-3, 5);
            checkNextSecureIntUniform(-3, 6);
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureIntNegativeRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureIntUniform(-7, -4);
            checkNextSecureIntUniform(-15, -2);
        }
    }

    @Test
    @Retry(3)
    public void testNextSecureIntPositiveRange() {
        for (int i = 0; i < 5; i++) {
            checkNextSecureIntUniform(0, 3);
            checkNextSecureIntUniform(2, 12);
        }
    }

    private void checkNextSecureIntUniform(int min, int max) {
        final Frequency freq = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            final int value = randomData.nextSecureInt(min, max);
            Assert.assertTrue("nextInt range", (value >= min) && (value <= max));
            freq.addValue(value);
        }
        final int len = max - min + 1;
        final long[] observed = new long[len];
        for (int i = 0; i < len; i++) {
            observed[i] = freq.getCount(min + i);
        }
        final double[] expected = new double[len];
        for (int i = 0; i < len; i++) {
            expected[i] = 1d / len;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.0001);
    }



    /**
     * Make sure that empirical distribution of random Poisson(4)'s has P(X <=
     * 5) close to actual cumulative Poisson probability and that nextPoisson
     * fails when mean is non-positive.
     */
    @Test
    public void testNextPoisson() {
        try {
            randomData.nextPoisson(0);
            Assert.fail("zero mean -- expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextPoisson(-1);
            Assert.fail("negative mean supplied -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextPoisson(0);
            Assert.fail("0 mean supplied -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }

        final double mean = 4.0d;
        final int len = 5;
        PoissonDistribution poissonDistribution = new PoissonDistribution(mean);
        Frequency f = new Frequency();
        randomData.reSeed(1000);
        for (int i = 0; i < largeSampleSize; i++) {
            f.addValue(randomData.nextPoisson(mean));
        }
        final long[] observed = new long[len];
        for (int i = 0; i < len; i++) {
            observed[i] = f.getCount(i + 1);
        }
        final double[] expected = new double[len];
        for (int i = 0; i < len; i++) {
            expected[i] = poissonDistribution.probability(i + 1) * largeSampleSize;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.0001);
    }

    @Test
    public void testNextPoissonConsistency() {

        // Small integral means
        for (int i = 1; i < 100; i++) {
            checkNextPoissonConsistency(i);
        }
        // non-integer means
        for (int i = 1; i < 10; i++) {
            checkNextPoissonConsistency(randomData.nextUniform(1, 1000));
        }
        // large means
        for (int i = 1; i < 10; i++) {
            checkNextPoissonConsistency(randomData.nextUniform(1000, 10000));
        }
    }

    /**
     * Verifies that nextPoisson(mean) generates an empirical distribution of values
     * consistent with PoissonDistributionImpl by generating 1000 values, computing a
     * grouped frequency distribution of the observed values and comparing this distribution
     * to the corresponding expected distribution computed using PoissonDistributionImpl.
     * Uses ChiSquare test of goodness of fit to evaluate the null hypothesis that the
     * distributions are the same. If the null hypothesis can be rejected with confidence
     * 1 - alpha, the check fails.
     */
    public void checkNextPoissonConsistency(double mean) {
        // Generate sample values
        final int sampleSize = 1000;        // Number of deviates to generate
        final int minExpectedCount = 7;     // Minimum size of expected bin count
        long maxObservedValue = 0;
        final double alpha = 0.001;         // Probability of false failure
        Frequency frequency = new Frequency();
        for (int i = 0; i < sampleSize; i++) {
            long value = randomData.nextPoisson(mean);
            if (value > maxObservedValue) {
                maxObservedValue = value;
            }
            frequency.addValue(value);
        }

        /*
         *  Set up bins for chi-square test.
         *  Ensure expected counts are all at least minExpectedCount.
         *  Start with upper and lower tail bins.
         *  Lower bin = [0, lower); Upper bin = [upper, +inf).
         */
        PoissonDistribution poissonDistribution = new PoissonDistribution(mean);
        int lower = 1;
        while (poissonDistribution.cumulativeProbability(lower - 1) * sampleSize < minExpectedCount) {
            lower++;
        }
        int upper = (int) (5 * mean);  // Even for mean = 1, not much mass beyond 5
        while ((1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize < minExpectedCount) {
            upper--;
        }

        // Set bin width for interior bins.  For poisson, only need to look at end bins.
        int binWidth = 0;
        boolean widthSufficient = false;
        double lowerBinMass = 0;
        double upperBinMass = 0;
        while (!widthSufficient) {
            binWidth++;
            lowerBinMass = poissonDistribution.cumulativeProbability(lower - 1, lower + binWidth - 1);
            upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth - 1, upper - 1);
            widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
        }

        /*
         *  Determine interior bin bounds.  Bins are
         *  [1, lower = binBounds[0]), [lower, binBounds[1]), [binBounds[1], binBounds[2]), ... ,
         *    [binBounds[binCount - 2], upper = binBounds[binCount - 1]), [upper, +inf)
         *
         */
        List<Integer> binBounds = new ArrayList();
        binBounds.add(lower);
        int bound = lower + binWidth;
        while (bound < upper - binWidth) {
            binBounds.add(bound);
            bound += binWidth;
        }
        binBounds.add(upper); // The size of bin [binBounds[binCount - 2], upper) satisfies binWidth <= size < 2*binWidth.

        // Compute observed and expected bin counts
        final int binCount = binBounds.size() + 1;
        long[] observed = new long[binCount];
        double[] expected = new double[binCount];

        // Bottom bin
        observed[0] = 0;
        for (int i = 0; i < lower; i++) {
            observed[0] += frequency.getCount(i);
        }
        expected[0] = poissonDistribution.cumulativeProbability(lower - 1) * sampleSize;

        // Top bin
        observed[binCount - 1] = 0;
        for (int i = upper; i <= maxObservedValue; i++) {
            observed[binCount - 1] += frequency.getCount(i);
        }
        expected[binCount - 1] = (1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize;

        // Interior bins
        for (int i = 1; i < binCount - 1; i++) {
            observed[i] = 0;
            for (int j = binBounds.get(i - 1); j < binBounds.get(i); j++) {
                observed[i] += frequency.getCount(j);
            } // Expected count is (mass in [binBounds[i-1], binBounds[i])) * sampleSize
            expected[i] = (poissonDistribution.cumulativeProbability(binBounds.get(i) - 1) -
                poissonDistribution.cumulativeProbability(binBounds.get(i - 1) -1)) * sampleSize;
        }

        // Use chisquare test to verify that generated values are poisson(mean)-distributed
        ChiSquareTest chiSquareTest = new ChiSquareTest();
            // Fail if we can reject null hypothesis that distributions are the same
        if (chiSquareTest.chiSquareTest(expected, observed, alpha)) {
            StringBuilder msgBuffer = new StringBuilder();
            DecimalFormat df = new DecimalFormat("#.##");
            msgBuffer.append("Chisquare test failed for mean = ");
            msgBuffer.append(mean);
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("bin\t\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append("[");
                msgBuffer.append(i == 0 ? 1: binBounds.get(i - 1));
                msgBuffer.append(",");
                msgBuffer.append(i == binBounds.size() ? "inf": binBounds.get(i));
                msgBuffer.append(")");
                msgBuffer.append("\t\t");
                msgBuffer.append(df.format(expected[i]));
                msgBuffer.append("\t\t");
                msgBuffer.append(observed[i]);
                msgBuffer.append("\n");
            }
            msgBuffer.append("This test can fail randomly due to sampling error with probability ");
            msgBuffer.append(alpha);
            msgBuffer.append(".");
            Assert.fail(msgBuffer.toString());
        }
    }

    /** test dispersion and failure modes for nextHex() */
    @Test
    public void testNextHex() {
        try {
            randomData.nextHexString(-1);
            Assert.fail("negative length supplied -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextHexString(0);
            Assert.fail("zero length supplied -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        String hexString = randomData.nextHexString(3);
        if (hexString.length() != 3) {
            Assert.fail("incorrect length for generated string");
        }
        hexString = randomData.nextHexString(1);
        if (hexString.length() != 1) {
            Assert.fail("incorrect length for generated string");
        }
        try {
            hexString = randomData.nextHexString(0);
            Assert.fail("zero length requested -- expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        Frequency f = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            hexString = randomData.nextHexString(100);
            if (hexString.length() != 100) {
                Assert.fail("incorrect length for generated string");
            }
            for (int j = 0; j < hexString.length(); j++) {
                f.addValue(hexString.substring(j, j + 1));
            }
        }
        double[] expected = new double[16];
        long[] observed = new long[16];
        for (int i = 0; i < 16; i++) {
            expected[i] = (double) smallSampleSize * 100 / 16;
            observed[i] = f.getCount(hex[i]);
        }
        TestUtils.assertChiSquareAccept(expected, observed, 0.001);
    }

    /** test dispersion and failure modes for nextHex() */
    @Test
    @Retry(3)
    public void testNextSecureHex() {
        try {
            randomData.nextSecureHexString(-1);
            Assert.fail("negative length -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextSecureHexString(0);
            Assert.fail("zero length -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        String hexString = randomData.nextSecureHexString(3);
        if (hexString.length() != 3) {
            Assert.fail("incorrect length for generated string");
        }
        hexString = randomData.nextSecureHexString(1);
        if (hexString.length() != 1) {
            Assert.fail("incorrect length for generated string");
        }
        try {
            hexString = randomData.nextSecureHexString(0);
            Assert.fail("zero length requested -- expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        Frequency f = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            hexString = randomData.nextSecureHexString(100);
            if (hexString.length() != 100) {
                Assert.fail("incorrect length for generated string");
            }
            for (int j = 0; j < hexString.length(); j++) {
                f.addValue(hexString.substring(j, j + 1));
            }
        }
        double[] expected = new double[16];
        long[] observed = new long[16];
        for (int i = 0; i < 16; i++) {
            expected[i] = (double) smallSampleSize * 100 / 16;
            observed[i] = f.getCount(hex[i]);
        }
        TestUtils.assertChiSquareAccept(expected, observed, 0.001);
    }

    @Test
    public void testNextUniformIAE() {
        try {
            randomData.nextUniform(4, 3);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextUniform(0, Double.POSITIVE_INFINITY);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextUniform(Double.NEGATIVE_INFINITY, 0);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextUniform(0, Double.NaN);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextUniform(Double.NaN, 0);
            Assert.fail("MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @Test
    public void testNextUniformUniformPositiveBounds() {
        for (int i = 0; i < 5; i++) {
            checkNextUniformUniform(0, 10);
        }
    }

    @Test
    public void testNextUniformUniformNegativeToPositiveBounds() {
        for (int i = 0; i < 5; i++) {
            checkNextUniformUniform(-3, 5);
        }
    }

    @Test
    public void testNextUniformUniformNegaiveBounds() {
        for (int i = 0; i < 5; i++) {
            checkNextUniformUniform(-7, -3);
        }
    }

    @Test
    public void testNextUniformUniformMaximalInterval() {
        for (int i = 0; i < 5; i++) {
            checkNextUniformUniform(-Double.MAX_VALUE, Double.MAX_VALUE);
        }
    }

    private void checkNextUniformUniform(double min, double max) {
        // Set up bin bounds - min, binBound[0], ..., binBound[binCount-2], max
        final int binCount = 5;
        final double binSize = max / binCount - min/binCount; // Prevent overflow in extreme value case
        final double[] binBounds = new double[binCount - 1];
        binBounds[0] = min + binSize;
        for (int i = 1; i < binCount - 1; i++) {
            binBounds[i] = binBounds[i - 1] + binSize;  // + instead of * to avoid overflow in extreme case
        }

        final Frequency freq = new Frequency();
        for (int i = 0; i < smallSampleSize; i++) {
            final double value = randomData.nextUniform(min, max);
            Assert.assertTrue("nextUniform range", (value > min) && (value < max));
            // Find bin
            int j = 0;
            while (j < binCount - 1 && value > binBounds[j]) {
                j++;
            }
            freq.addValue(j);
        }

        final long[] observed = new long[binCount];
        for (int i = 0; i < binCount; i++) {
            observed[i] = freq.getCount(i);
        }
        final double[] expected = new double[binCount];
        for (int i = 0; i < binCount; i++) {
            expected[i] = 1d / binCount;
        }

        TestUtils.assertChiSquareAccept(expected, observed, 0.01);
    }

    /** test exclusive endpoints of nextUniform **/
    @Test
    public void testNextUniformExclusiveEndpoints() {
        for (int i = 0; i < 1000; i++) {
            double u = randomData.nextUniform(0.99, 1);
            Assert.assertTrue(u > 0.99 && u < 1);
        }
    }

    /** test failure modes and distribution of nextGaussian() */
    @Test
    public void testNextGaussian() {
        try {
            randomData.nextGaussian(0, 0);
            Assert.fail("zero sigma -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        double[] quartiles = TestUtils.getDistributionQuartiles(new NormalDistribution(0,1));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGaussian(0, 1);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    /** test failure modes and distribution of nextExponential() */
    @Test
    public void testNextExponential() {
        try {
            randomData.nextExponential(-1);
            Assert.fail("negative mean -- expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        try {
            randomData.nextExponential(0);
            Assert.fail("zero mean -- expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        double[] quartiles;
        long[] counts;

        // Mean 1
        quartiles = TestUtils.getDistributionQuartiles(new ExponentialDistribution(1));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextExponential(1);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);

        // Mean 5
        quartiles = TestUtils.getDistributionQuartiles(new ExponentialDistribution(5));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextExponential(5);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    /** test reseeding, algorithm/provider games */
    @Test
    public void testConfig() {
        randomData.reSeed(1000);
        double v = randomData.nextUniform(0, 1);
        randomData.reSeed();
        Assert.assertTrue("different seeds", FastMath.abs(v - randomData.nextUniform(0, 1)) > 10E-12);
        randomData.reSeed(1000);
        Assert.assertEquals("same seeds", v, randomData.nextUniform(0, 1), 10E-12);
        randomData.reSeedSecure(1000);
        String hex = randomData.nextSecureHexString(40);
        randomData.reSeedSecure();
        Assert.assertTrue("different seeds", !hex.equals(randomData
                .nextSecureHexString(40)));
        randomData.reSeedSecure(1000);
        Assert.assertTrue("same seeds", !hex
                .equals(randomData.nextSecureHexString(40)));

        /*
         * remove this test back soon, since it takes about 4 seconds
         *
         * try { randomData.setSecureAlgorithm("SHA1PRNG","SUN"); } catch
         * (NoSuchProviderException ex) { ; } Assert.assertTrue("different seeds",
         * !hex.equals(randomData.nextSecureHexString(40))); try {
         * randomData.setSecureAlgorithm("NOSUCHTHING","SUN");
         * Assert.fail("expecting NoSuchAlgorithmException"); } catch
         * (NoSuchProviderException ex) { ; } catch (NoSuchAlgorithmException
         * ex) { ; }
         *
         * try { randomData.setSecureAlgorithm("SHA1PRNG","NOSUCHPROVIDER");
         * Assert.fail("expecting NoSuchProviderException"); } catch
         * (NoSuchProviderException ex) { ; }
         */

        // test reseeding without first using the generators
        RandomDataGenerator rd = new RandomDataGenerator();
        rd.reSeed(100);
        rd.nextLong(1, 2);
        RandomDataGenerator rd2 = new RandomDataGenerator();
        rd2.reSeedSecure(2000);
        rd2.nextSecureLong(1, 2);
        rd = new RandomDataGenerator();
        rd.reSeed();
        rd.nextLong(1, 2);
        rd2 = new RandomDataGenerator();
        rd2.reSeedSecure();
        rd2.nextSecureLong(1, 2);
    }

    /** tests for nextSample() sampling from Collection */
    @Test
    public void testNextSample() {
        Object[][] c = { { "0", "1" }, { "0", "2" }, { "0", "3" },
                { "0", "4" }, { "1", "2" }, { "1", "3" }, { "1", "4" },
                { "2", "3" }, { "2", "4" }, { "3", "4" } };
        long[] observed = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
        double[] expected = { 100, 100, 100, 100, 100, 100, 100, 100, 100, 100 };

        HashSet<Object> cPop = new HashSet(); // {0,1,2,3,4}
        for (int i = 0; i < 5; i++) {
            cPop.add(Integer.toString(i));
        }

        Object[] sets = new Object[10]; // 2-sets from 5
        for (int i = 0; i < 10; i++) {
            HashSet<Object> hs = new HashSet();
            hs.add(c[i][0]);
            hs.add(c[i][1]);
            sets[i] = hs;
        }

        for (int i = 0; i < 1000; i++) {
            Object[] cSamp = randomData.nextSample(cPop, 2);
            observed[findSample(sets, cSamp)]++;
        }

        /*
         * Use ChiSquare dist with df = 10-1 = 9, alpha = .001 Change to 21.67
         * for alpha = .01
         */
        Assert.assertTrue("chi-square test -- will fail about 1 in 1000 times",
                testStatistic.chiSquare(expected, observed) < 27.88);

        // Make sure sample of size = size of collection returns same collection
        HashSet<Object> hs = new HashSet();
        hs.add("one");
        Object[] one = randomData.nextSample(hs, 1);
        String oneString = (String) one[0];
        if ((one.length != 1) || !oneString.equals("one")) {
            Assert.fail("bad sample for set size = 1, sample size = 1");
        }

        // Make sure we fail for sample size > collection size
        try {
            one = randomData.nextSample(hs, 2);
            Assert.fail("sample size > set size, expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }

        // Make sure we fail for empty collection
        try {
            hs = new HashSet<Object>();
            one = randomData.nextSample(hs, 0);
            Assert.fail("n = k = 0, expecting MathIllegalArgumentException");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
    }

    @SuppressWarnings("unchecked")
    private int findSample(Object[] u, Object[] samp) {
        for (int i = 0; i < u.length; i++) {
            HashSet<Object> set = (HashSet) u[i];
            HashSet<Object> sampSet = new HashSet();
            for (int j = 0; j < samp.length; j++) {
                sampSet.add(samp[j]);
            }
            if (set.equals(sampSet)) {
                return i;
            }
        }
        Assert.fail("sample not found:{" + samp[0] + "," + samp[1] + "}");
        return -1;
    }

    /** tests for nextPermutation */
    @Test
    public void testNextPermutation() {
        int[][] p = { { 0, 1, 2 }, { 0, 2, 1 }, { 1, 0, 2 }, { 1, 2, 0 },
                { 2, 0, 1 }, { 2, 1, 0 } };
        long[] observed = { 0, 0, 0, 0, 0, 0 };
        double[] expected = { 100, 100, 100, 100, 100, 100 };

        for (int i = 0; i < 600; i++) {
            int[] perm = randomData.nextPermutation(3, 3);
            observed[findPerm(p, perm)]++;
        }

        String[] labels = {"{0, 1, 2}", "{ 0, 2, 1 }", "{ 1, 0, 2 }",
                "{ 1, 2, 0 }", "{ 2, 0, 1 }", "{ 2, 1, 0 }"};
        TestUtils.assertChiSquareAccept(labels, expected, observed, 0.001);

        // Check size = 1 boundary case
        int[] perm = randomData.nextPermutation(1, 1);
        if ((perm.length != 1) || (perm[0] != 0)) {
            Assert.fail("bad permutation for n = 1, sample k = 1");

            // Make sure we fail for k size > n
            try {
                perm = randomData.nextPermutation(2, 3);
                Assert.fail("permutation k > n, expecting MathIllegalArgumentException");
            } catch (MathIllegalArgumentException ex) {
                // ignored
            }

            // Make sure we fail for n = 0
            try {
                perm = randomData.nextPermutation(0, 0);
                Assert.fail("permutation k = n = 0, expecting MathIllegalArgumentException");
            } catch (MathIllegalArgumentException ex) {
                // ignored
            }

            // Make sure we fail for k < n < 0
            try {
                perm = randomData.nextPermutation(-1, -3);
                Assert.fail("permutation k < n < 0, expecting MathIllegalArgumentException");
            } catch (MathIllegalArgumentException ex) {
                // ignored
            }

        }
    }

    // Disable until we have equals
    //public void testSerial() {
    //    Assert.assertEquals(randomData, TestUtils.serializeAndRecover(randomData));
    //}

    private int findPerm(int[][] p, int[] samp) {
        for (int i = 0; i < p.length; i++) {
            boolean good = true;
            for (int j = 0; j < samp.length; j++) {
                if (samp[j] != p[i][j]) {
                    good = false;
                }
            }
            if (good) {
                return i;
            }
        }
        Assert.fail("permutation not found");
        return -1;
    }

    @Test
    public void testNextBeta() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new BetaDistribution(2,5));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextBeta(2, 5);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextCauchy() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new CauchyDistribution(1.2, 2.1));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextCauchy(1.2, 2.1);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextChiSquare() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new ChiSquaredDistribution(12));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextChiSquare(12);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextF() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new FDistribution(12, 5));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextF(12, 5);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextGamma() {
        double[] quartiles;
        long[] counts;

        // Tests shape > 1, one case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(4, 2));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(4, 2);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);

        // Tests shape <= 1, another case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(0.3, 3));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(0.3, 3);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextT() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new TDistribution(10));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextT(10);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextWeibull() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new WeibullDistribution(1.2, 2.1));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextWeibull(1.2, 2.1);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextBinomial() {
        BinomialDistributionTest testInstance = new BinomialDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        BinomialDistribution distribution = (BinomialDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextBinomial(distribution.getNumberOfTrials(),
                  distribution.getProbabilityOfSuccess());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
        }
        TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
    }

    @Test
    public void testNextHypergeometric() {
        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
        }
        TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
    }

    @Test
    public void testNextPascal() {
        PascalDistributionTest testInstance = new PascalDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        PascalDistribution distribution = (PascalDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextPascal(distribution.getNumberOfSuccesses(), distribution.getProbabilityOfSuccess());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
        }
        TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
    }

    @Test
    public void testNextZipf() {
        ZipfDistributionTest testInstance = new ZipfDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        ZipfDistribution distribution = (ZipfDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextZipf(distribution.getNumberOfElements(), distribution.getExponent());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
        }
        TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
    }

    @Test
    /**
     * MATH-720
     */
    public void testReseed() {
        PoissonDistribution x = new PoissonDistribution(3.0);
        x.reseedRandomGenerator(0);
        final double u = x.sample();
        PoissonDistribution y = new PoissonDistribution(3.0);
        y.reseedRandomGenerator(0);
        Assert.assertEquals(u, y.sample(), 0);
    }

}

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