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Java example source code file (TestUtilsTest.java)

This example Java source code file (TestUtilsTest.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

arraylist, assert, dimensionmismatchexception, expecting, normaldistribution, notpositiveexception, notstrictlypositiveexception, nullargumentexception, numberistoosmallexception, onewayanova, outofrangeexception, summarystatistics, test, testutilstest, util

The TestUtilsTest.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.stat.inference;

import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;


/**
 * Test cases for the TestUtils class.
 *
 */
public class TestUtilsTest {

    @Test
    public void testChiSquare() {

        // Target values computed using R version 1.8.1
        // Some assembly required ;-)
        //      Use sum((obs - exp)^2/exp) for the chi-square statistic and
        //      1 - pchisq(sum((obs - exp)^2/exp), length(obs) - 1) for the p-value

        long[] observed = {10, 9, 11};
        double[] expected = {10, 10, 10};
        Assert.assertEquals("chi-square statistic", 0.2,  TestUtils.chiSquare(expected, observed), 10E-12);
        Assert.assertEquals("chi-square p-value", 0.904837418036, TestUtils.chiSquareTest(expected, observed), 1E-10);

        long[] observed1 = { 500, 623, 72, 70, 31 };
        double[] expected1 = { 485, 541, 82, 61, 37 };
        Assert.assertEquals( "chi-square test statistic", 9.023307936427388, TestUtils.chiSquare(expected1, observed1), 1E-10);
        Assert.assertEquals("chi-square p-value", 0.06051952647453607, TestUtils.chiSquareTest(expected1, observed1), 1E-9);
        Assert.assertTrue("chi-square test reject", TestUtils.chiSquareTest(expected1, observed1, 0.07));
        Assert.assertTrue("chi-square test accept", !TestUtils.chiSquareTest(expected1, observed1, 0.05));

        try {
            TestUtils.chiSquareTest(expected1, observed1, 95);
            Assert.fail("alpha out of range, OutOfRangeException expected");
        } catch (OutOfRangeException ex) {
            // expected
        }

        long[] tooShortObs = { 0 };
        double[] tooShortEx = { 1 };
        try {
            TestUtils.chiSquare(tooShortEx, tooShortObs);
            Assert.fail("arguments too short, DimensionMismatchException expected");
        } catch (DimensionMismatchException ex) {
            // expected
        }

        // unmatched arrays
        long[] unMatchedObs = { 0, 1, 2, 3 };
        double[] unMatchedEx = { 1, 1, 2 };
        try {
            TestUtils.chiSquare(unMatchedEx, unMatchedObs);
            Assert.fail("arrays have different lengths, DimensionMismatchException expected");
        } catch (DimensionMismatchException ex) {
            // expected
        }

        // 0 expected count
        expected[0] = 0;
        try {
            TestUtils.chiSquareTest(expected, observed, .01);
            Assert.fail("bad expected count, NotStrictlyPositiveException expected");
        } catch (NotStrictlyPositiveException ex) {
            // expected
        }

        // negative observed count
        expected[0] = 1;
        observed[0] = -1;
        try {
            TestUtils.chiSquareTest(expected, observed, .01);
            Assert.fail("bad expected count, NotPositiveException expected");
        } catch (NotPositiveException ex) {
            // expected
        }

    }

    @Test
    public void testChiSquareIndependence() {

        // Target values computed using R version 1.8.1

        long[][] counts = { {40, 22, 43}, {91, 21, 28}, {60, 10, 22}};
        Assert.assertEquals( "chi-square test statistic", 22.709027688, TestUtils.chiSquare(counts), 1E-9);
        Assert.assertEquals("chi-square p-value", 0.000144751460134, TestUtils.chiSquareTest(counts), 1E-9);
        Assert.assertTrue("chi-square test reject", TestUtils.chiSquareTest(counts, 0.0002));
        Assert.assertTrue("chi-square test accept", !TestUtils.chiSquareTest(counts, 0.0001));

        long[][] counts2 = {{10, 15}, {30, 40}, {60, 90} };
        Assert.assertEquals( "chi-square test statistic", 0.168965517241, TestUtils.chiSquare(counts2), 1E-9);
        Assert.assertEquals("chi-square p-value",0.918987499852, TestUtils.chiSquareTest(counts2), 1E-9);
        Assert.assertTrue("chi-square test accept", !TestUtils.chiSquareTest(counts2, 0.1));

        // ragged input array
        long[][] counts3 = { {40, 22, 43}, {91, 21, 28}, {60, 10}};
        try {
            TestUtils.chiSquare(counts3);
            Assert.fail("Expecting DimensionMismatchException");
        } catch (DimensionMismatchException ex) {
            // expected
        }

        // insufficient data
        long[][] counts4 = {{40, 22, 43}};
        try {
            TestUtils.chiSquare(counts4);
            Assert.fail("Expecting DimensionMismatchException");
        } catch (DimensionMismatchException ex) {
            // expected
        }
        long[][] counts5 = {{40}, {40}, {30}, {10}};
        try {
            TestUtils.chiSquare(counts5);
            Assert.fail("Expecting DimensionMismatchException");
        } catch (DimensionMismatchException ex) {
            // expected
        }

        // negative counts
        long[][] counts6 = {{10, -2}, {30, 40}, {60, 90} };
        try {
            TestUtils.chiSquare(counts6);
            Assert.fail("Expecting NotPositiveException");
        } catch (NotPositiveException ex) {
            // expected
        }

        // bad alpha
        try {
            TestUtils.chiSquareTest(counts, 0);
            Assert.fail("Expecting OutOfRangeException");
        } catch (OutOfRangeException ex) {
            // expected
        }
    }

    @Test
    public void testChiSquareLargeTestStatistic() {
        double[] exp = new double[] {
                3389119.5, 649136.6, 285745.4, 25357364.76, 11291189.78, 543628.0,
                232921.0, 437665.75
        };

        long[] obs = new long[] {
                2372383, 584222, 257170, 17750155, 7903832, 489265, 209628, 393899
        };
        org.apache.commons.math3.stat.inference.ChiSquareTest csti =
            new org.apache.commons.math3.stat.inference.ChiSquareTest();
        double cst = csti.chiSquareTest(exp, obs);
        Assert.assertEquals("chi-square p-value", 0.0, cst, 1E-3);
        Assert.assertEquals( "chi-square test statistic",
                114875.90421929007, TestUtils.chiSquare(exp, obs), 1E-9);
    }

    /** Contingency table containing zeros - PR # 32531 */
    @Test
    public void testChiSquareZeroCount() {
        // Target values computed using R version 1.8.1
        long[][] counts = { {40, 0, 4}, {91, 1, 2}, {60, 2, 0}};
        Assert.assertEquals( "chi-square test statistic", 9.67444662263,
                TestUtils.chiSquare(counts), 1E-9);
        Assert.assertEquals("chi-square p-value", 0.0462835770603,
                TestUtils.chiSquareTest(counts), 1E-9);
    }

    private double[] tooShortObs = { 1.0 };
    private double[] emptyObs = {};
    private SummaryStatistics emptyStats = new SummaryStatistics();

    @Test
    public void testOneSampleT() {
        double[] observed =
            {93.0, 103.0, 95.0, 101.0, 91.0, 105.0, 96.0, 94.0, 101.0,  88.0, 98.0, 94.0, 101.0, 92.0, 95.0 };
        double mu = 100.0;
        SummaryStatistics sampleStats = null;
        sampleStats = new SummaryStatistics();
        for (int i = 0; i < observed.length; i++) {
            sampleStats.addValue(observed[i]);
        }

        // Target comparison values computed using R version 1.8.1 (Linux version)
        Assert.assertEquals("t statistic",  -2.81976445346,
                TestUtils.t(mu, observed), 10E-10);
        Assert.assertEquals("t statistic",  -2.81976445346,
                TestUtils.t(mu, sampleStats), 10E-10);
        Assert.assertEquals("p value", 0.0136390585873,
                TestUtils.tTest(mu, observed), 10E-10);
        Assert.assertEquals("p value", 0.0136390585873,
                TestUtils.tTest(mu, sampleStats), 10E-10);

        try {
            TestUtils.t(mu, (double[]) null);
            Assert.fail("arguments too short, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }

        try {
            TestUtils.t(mu, (SummaryStatistics) null);
            Assert.fail("arguments too short, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }

        try {
            TestUtils.t(mu, emptyObs);
            Assert.fail("arguments too short, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.t(mu, emptyStats);
            Assert.fail("arguments too short, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.t(mu, tooShortObs);
            Assert.fail("insufficient data to compute t statistic, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }
        try {
            TestUtils.tTest(mu, tooShortObs);
            Assert.fail("insufficient data to perform t test, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.t(mu, (SummaryStatistics) null);
            Assert.fail("insufficient data to compute t statistic, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }
        try {
            TestUtils.tTest(mu, (SummaryStatistics) null);
            Assert.fail("insufficient data to perform t test, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }
    }

    @Test
    public void testOneSampleTTest() {
        double[] oneSidedP =
            {2d, 0d, 6d, 6d, 3d, 3d, 2d, 3d, -6d, 6d, 6d, 6d, 3d, 0d, 1d, 1d, 0d, 2d, 3d, 3d };
        SummaryStatistics oneSidedPStats = new SummaryStatistics();
        for (int i = 0; i < oneSidedP.length; i++) {
            oneSidedPStats.addValue(oneSidedP[i]);
        }
        // Target comparison values computed using R version 1.8.1 (Linux version)
        Assert.assertEquals("one sample t stat", 3.86485535541,
                TestUtils.t(0d, oneSidedP), 10E-10);
        Assert.assertEquals("one sample t stat", 3.86485535541,
                TestUtils.t(0d, oneSidedPStats),1E-10);
        Assert.assertEquals("one sample p value", 0.000521637019637,
                TestUtils.tTest(0d, oneSidedP) / 2d, 10E-10);
        Assert.assertEquals("one sample p value", 0.000521637019637,
                TestUtils.tTest(0d, oneSidedPStats) / 2d, 10E-5);
        Assert.assertTrue("one sample t-test reject", TestUtils.tTest(0d, oneSidedP, 0.01));
        Assert.assertTrue("one sample t-test reject", TestUtils.tTest(0d, oneSidedPStats, 0.01));
        Assert.assertTrue("one sample t-test accept", !TestUtils.tTest(0d, oneSidedP, 0.0001));
        Assert.assertTrue("one sample t-test accept", !TestUtils.tTest(0d, oneSidedPStats, 0.0001));

        try {
            TestUtils.tTest(0d, oneSidedP, 95);
            Assert.fail("alpha out of range, OutOfRangeException expected");
        } catch (OutOfRangeException ex) {
            // expected
        }

        try {
            TestUtils.tTest(0d, oneSidedPStats, 95);
            Assert.fail("alpha out of range, OutOfRangeException expected");
        } catch (OutOfRangeException ex) {
            // expected
        }

    }

    @Test
    public void testTwoSampleTHeterscedastic() {
        double[] sample1 = { 7d, -4d, 18d, 17d, -3d, -5d, 1d, 10d, 11d, -2d };
        double[] sample2 = { -1d, 12d, -1d, -3d, 3d, -5d, 5d, 2d, -11d, -1d, -3d };
        SummaryStatistics sampleStats1 = new SummaryStatistics();
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = new SummaryStatistics();
        for (int i = 0; i < sample2.length; i++) {
            sampleStats2.addValue(sample2[i]);
        }

        // Target comparison values computed using R version 1.8.1 (Linux version)
        Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768,
                TestUtils.t(sample1, sample2), 1E-10);
        Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768,
                TestUtils.t(sampleStats1, sampleStats2), 1E-10);
        Assert.assertEquals("two sample heteroscedastic p value", 0.128839369622,
                TestUtils.tTest(sample1, sample2), 1E-10);
        Assert.assertEquals("two sample heteroscedastic p value", 0.128839369622,
                TestUtils.tTest(sampleStats1, sampleStats2), 1E-10);
        Assert.assertTrue("two sample heteroscedastic t-test reject",
                TestUtils.tTest(sample1, sample2, 0.2));
        Assert.assertTrue("two sample heteroscedastic t-test reject",
                TestUtils.tTest(sampleStats1, sampleStats2, 0.2));
        Assert.assertTrue("two sample heteroscedastic t-test accept",
                !TestUtils.tTest(sample1, sample2, 0.1));
        Assert.assertTrue("two sample heteroscedastic t-test accept",
                !TestUtils.tTest(sampleStats1, sampleStats2, 0.1));

        try {
            TestUtils.tTest(sample1, sample2, .95);
            Assert.fail("alpha out of range, OutOfRangeException expected");
        } catch (OutOfRangeException ex) {
            // expected
        }

        try {
            TestUtils.tTest(sampleStats1, sampleStats2, .95);
            Assert.fail("alpha out of range, OutOfRangeException expected");
        } catch (OutOfRangeException ex) {
            // expected
        }

        try {
            TestUtils.tTest(sample1, tooShortObs, .01);
            Assert.fail("insufficient data, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.tTest(sampleStats1, (SummaryStatistics) null, .01);
            Assert.fail("insufficient data, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }

        try {
            TestUtils.tTest(sample1, tooShortObs);
            Assert.fail("insufficient data, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.tTest(sampleStats1, (SummaryStatistics) null);
            Assert.fail("insufficient data, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }

        try {
            TestUtils.t(sample1, tooShortObs);
            Assert.fail("insufficient data, NumberIsTooSmallException expected");
        } catch (NumberIsTooSmallException ex) {
            // expected
        }

        try {
            TestUtils.t(sampleStats1, (SummaryStatistics) null);
            Assert.fail("insufficient data, NullArgumentException expected");
        } catch (NullArgumentException ex) {
            // expected
        }
    }
    @Test
    public void testTwoSampleTHomoscedastic() {
        double[] sample1 ={2, 4, 6, 8, 10, 97};
        double[] sample2 = {4, 6, 8, 10, 16};
        SummaryStatistics sampleStats1 = new SummaryStatistics();
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = new SummaryStatistics();
        for (int i = 0; i < sample2.length; i++) {
            sampleStats2.addValue(sample2[i]);
        }

        // Target comparison values computed using R version 1.8.1 (Linux version)
        Assert.assertEquals("two sample homoscedastic t stat", 0.73096310086,
                TestUtils.homoscedasticT(sample1, sample2), 10E-11);
        Assert.assertEquals("two sample homoscedastic p value", 0.4833963785,
                TestUtils.homoscedasticTTest(sampleStats1, sampleStats2), 1E-10);
        Assert.assertTrue("two sample homoscedastic t-test reject",
                TestUtils.homoscedasticTTest(sample1, sample2, 0.49));
        Assert.assertTrue("two sample homoscedastic t-test accept",
                !TestUtils.homoscedasticTTest(sample1, sample2, 0.48));
    }

    @Test
    public void testSmallSamples() {
        double[] sample1 = {1d, 3d};
        double[] sample2 = {4d, 5d};

        // Target values computed using R, version 1.8.1 (linux version)
        Assert.assertEquals(-2.2360679775, TestUtils.t(sample1, sample2),
                1E-10);
        Assert.assertEquals(0.198727388935, TestUtils.tTest(sample1, sample2),
                1E-10);
    }

    @Test
    public void testPaired() {
        double[] sample1 = {1d, 3d, 5d, 7d};
        double[] sample2 = {0d, 6d, 11d, 2d};
        double[] sample3 = {5d, 7d, 8d, 10d};

        // Target values computed using R, version 1.8.1 (linux version)
        Assert.assertEquals(-0.3133, TestUtils.pairedT(sample1, sample2), 1E-4);
        Assert.assertEquals(0.774544295819, TestUtils.pairedTTest(sample1, sample2), 1E-10);
        Assert.assertEquals(0.001208, TestUtils.pairedTTest(sample1, sample3), 1E-6);
        Assert.assertFalse(TestUtils.pairedTTest(sample1, sample3, .001));
        Assert.assertTrue(TestUtils.pairedTTest(sample1, sample3, .002));
    }

    private double[] classA =
      {93.0, 103.0, 95.0, 101.0};
    private double[] classB =
      {99.0, 92.0, 102.0, 100.0, 102.0};
    private double[] classC =
      {110.0, 115.0, 111.0, 117.0, 128.0};

    private List<double[]> classes = new ArrayList();
    private OneWayAnova oneWayAnova = new OneWayAnova();

    @Test
    public void testOneWayAnovaUtils() {
        classes.add(classA);
        classes.add(classB);
        classes.add(classC);
        Assert.assertEquals(oneWayAnova.anovaFValue(classes),
                TestUtils.oneWayAnovaFValue(classes), 10E-12);
        Assert.assertEquals(oneWayAnova.anovaPValue(classes),
                TestUtils.oneWayAnovaPValue(classes), 10E-12);
        Assert.assertEquals(oneWayAnova.anovaTest(classes, 0.01),
                TestUtils.oneWayAnovaTest(classes, 0.01));
    }
    @Test
    public void testGTestGoodnesOfFit() throws Exception {
        double[] exp = new double[]{
            0.54d, 0.40d, 0.05d, 0.01d
        };

        long[] obs = new long[]{
            70, 79, 3, 4
        };
        Assert.assertEquals("G test statistic",
                13.144799, TestUtils.g(exp, obs), 1E-5);
        double p_gtgf = TestUtils.gTest(exp, obs);
        Assert.assertEquals("g-Test p-value", 0.004333, p_gtgf, 1E-5);

        Assert.assertTrue(TestUtils.gTest(exp, obs, 0.05));
}

    @Test
    public void testGTestIndependance() throws Exception {
        long[] obs1 = new long[]{
            268, 199, 42
        };

        long[] obs2 = new long[]{
            807, 759, 184
        };

        double g = TestUtils.gDataSetsComparison(obs1, obs2);

        Assert.assertEquals("G test statistic",
                7.3008170, g, 1E-4);
        double p_gti = TestUtils.gTestDataSetsComparison(obs1, obs2);

        Assert.assertEquals("g-Test p-value", 0.0259805, p_gti, 1E-4);
        Assert.assertTrue(TestUtils.gTestDataSetsComparison(obs1, obs2, 0.05));
    }

    @Test
    public void testRootLogLikelihood() {
        // positive where k11 is bigger than expected.
        Assert.assertTrue(TestUtils.rootLogLikelihoodRatio(904, 21060, 1144, 283012) > 0.0);

        // negative because k11 is lower than expected
        Assert.assertTrue(TestUtils.rootLogLikelihoodRatio(36, 21928, 60280, 623876) < 0.0);

        Assert.assertEquals(FastMath.sqrt(2.772589), TestUtils.rootLogLikelihoodRatio(1, 0, 0, 1), 0.000001);
        Assert.assertEquals(-FastMath.sqrt(2.772589), TestUtils.rootLogLikelihoodRatio(0, 1, 1, 0), 0.000001);
        Assert.assertEquals(FastMath.sqrt(27.72589), TestUtils.rootLogLikelihoodRatio(10, 0, 0, 10), 0.00001);

        Assert.assertEquals(FastMath.sqrt(39.33052), TestUtils.rootLogLikelihoodRatio(5, 1995, 0, 100000), 0.00001);
        Assert.assertEquals(-FastMath.sqrt(39.33052), TestUtils.rootLogLikelihoodRatio(0, 100000, 5, 1995), 0.00001);

        Assert.assertEquals(FastMath.sqrt(4730.737), TestUtils.rootLogLikelihoodRatio(1000, 1995, 1000, 100000), 0.001);
        Assert.assertEquals(-FastMath.sqrt(4730.737), TestUtils.rootLogLikelihoodRatio(1000, 100000, 1000, 1995), 0.001);

        Assert.assertEquals(FastMath.sqrt(5734.343), TestUtils.rootLogLikelihoodRatio(1000, 1000, 1000, 100000), 0.001);
        Assert.assertEquals(FastMath.sqrt(5714.932), TestUtils.rootLogLikelihoodRatio(1000, 1000, 1000, 99000), 0.001);
    }

    @Test
    public void testKSOneSample() throws Exception {
       final NormalDistribution unitNormal = new NormalDistribution(0d, 1d);
       final double[] sample = KolmogorovSmirnovTestTest.gaussian;
       final double tol = KolmogorovSmirnovTestTest.TOLERANCE;
       Assert.assertEquals(0.3172069207622391, TestUtils.kolmogorovSmirnovTest(unitNormal, sample), tol);
       Assert.assertEquals(0.0932947561266756, TestUtils.kolmogorovSmirnovStatistic(unitNormal, sample), tol);
    }

    @Test
    public void testKSTwoSample() throws Exception {
        final double tol = KolmogorovSmirnovTestTest.TOLERANCE;
        final double[] smallSample1 = {
            6, 7, 9, 13, 19, 21, 22, 23, 24
        };
        final double[] smallSample2 = {
            10, 11, 12, 16, 20, 27, 28, 32, 44, 54
        };
        Assert
            .assertEquals(0.105577085453247, TestUtils.kolmogorovSmirnovTest(smallSample1, smallSample2, false), tol);
        final double d = TestUtils.kolmogorovSmirnovStatistic(smallSample1, smallSample2);
        Assert.assertEquals(0.5, d, tol);
        Assert
        .assertEquals(0.105577085453247, TestUtils.exactP(d, smallSample1.length,smallSample2.length, false), tol);
    }
}

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