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Java example source code file (ChiSquaredDistributionTest.java)

This example Java source code file (ChiSquaredDistributionTest.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

chisquareddistribution, chisquareddistributiontest, override, realdistributionabstracttest, test

The ChiSquaredDistributionTest.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.junit.Assert;
import org.junit.Test;

/**
 * Test cases for {@link ChiSquaredDistribution}.
 *
 * @see RealDistributionAbstractTest
 */
public class ChiSquaredDistributionTest extends RealDistributionAbstractTest {

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

    /** Creates the default continuous distribution instance to use in tests. */
    @Override
    public ChiSquaredDistribution makeDistribution() {
        return new ChiSquaredDistribution(5.0);
    }

    /** Creates the default cumulative probability distribution test input values */
    @Override
    public double[] makeCumulativeTestPoints() {
        // quantiles computed using R version 2.9.2
        return new double[] {0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
                20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978};
    }

    /** Creates the default cumulative probability density test expected values */
    @Override
    public double[] makeCumulativeTestValues() {
        return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
    }

    /** Creates the default inverse cumulative probability test input values */
    @Override
    public double[] makeInverseCumulativeTestPoints() {
        return new double[] {0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
                0.990d, 0.975d, 0.950d, 0.900d, 1};
    }

    /** Creates the default inverse cumulative probability density test expected values */
    @Override
    public double[] makeInverseCumulativeTestValues() {
        return new double[] {0, 0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
                20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978,
                Double.POSITIVE_INFINITY};
    }

    /** Creates the default probability density test expected values */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0.0115379817652, 0.0415948507811, 0.0665060119842, 0.0919455953114, 0.121472591024,
                0.000433630076361, 0.00412780610309, 0.00999340341045, 0.0193246438937, 0.0368460089216};
    }

 // --------------------- Override tolerance  --------------
    @Override
    public void setUp() {
        super.setUp();
        setTolerance(1e-9);
    }

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

    @Test
    public void testSmallDf() {
        setDistribution(new ChiSquaredDistribution(0.1d));
        setTolerance(1E-4);
        // quantiles computed using R version 1.8.1 (linux version)
        setCumulativeTestPoints(new double[] {1.168926E-60, 1.168926E-40, 1.063132E-32,
                1.144775E-26, 1.168926E-20, 5.472917, 2.175255, 1.13438,
                0.5318646, 0.1526342});
        setInverseCumulativeTestValues(getCumulativeTestPoints());
        setInverseCumulativeTestPoints(getCumulativeTestValues());
        verifyCumulativeProbabilities();
        verifyInverseCumulativeProbabilities();
    }

    @Test
    public void testDfAccessors() {
        ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
        Assert.assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
    }

    @Test
    public void testDensity() {
        double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
        //R 2.5: print(dchisq(x, df=1), digits=10)
        checkDensity(1, x, new double[]{0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436, 0.01464498256});
        //R 2.5: print(dchisq(x, df=0.1), digits=10)
        checkDensity(0.1, x, new double[]{0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02, 9.447299159e-03, 8.827199396e-04});
        //R 2.5: print(dchisq(x, df=2), digits=10)
        checkDensity(2, x, new double[]{0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059, 0.04104249931});
        //R 2.5: print(dchisq(x, df=10), digits=10)
        checkDensity(10, x, new double[]{0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04, 7.664155024e-03, 6.680094289e-02});
    }

    private void checkDensity(double df, double[] x, double[] expected) {
        ChiSquaredDistribution d = new ChiSquaredDistribution(df);
        for (int i = 0; i < x.length; i++) {
            Assert.assertEquals(expected[i], d.density(x[i]), 1e-5);
        }
    }

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

        dist = new ChiSquaredDistribution(1500);
        Assert.assertEquals(dist.getNumericalMean(), 1500, tol);
        Assert.assertEquals(dist.getNumericalVariance(), 3000, tol);

        dist = new ChiSquaredDistribution(1.12);
        Assert.assertEquals(dist.getNumericalMean(), 1.12, tol);
        Assert.assertEquals(dist.getNumericalVariance(), 2.24, tol);
    }
}

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