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Java example source code file (WeibullDistributionTest.java)

This example Java source code file (WeibullDistributionTest.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

notstrictlypositiveexception, override, realdistributionabstracttest, test, weibulldistribution, weibulldistributiontest

The WeibullDistributionTest.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.special.Gamma;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test cases for WeibullDistribution.
 * Extends ContinuousDistributionAbstractTest.  See class javadoc for
 * ContinuousDistributionAbstractTest for details.
 *
 */
public class WeibullDistributionTest extends RealDistributionAbstractTest {

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

    /** Creates the default continuous distribution instance to use in tests. */
    @Override
    public WeibullDistribution makeDistribution() {
        return new WeibullDistribution(1.2, 2.1);
    }

    /** Creates the default cumulative probability distribution test input values */
    @Override
    public double[] makeCumulativeTestPoints() {
        // quantiles computed using R version 2.9.2
        return new double[] {0.00664355180993, 0.0454328283309, 0.0981162737374, 0.176713524579, 0.321946865392,
                10.5115496887, 7.4976304671, 6.23205600701, 5.23968436955, 4.2079028257};
    }

    /** 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 probability density test expected values */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0.180535929306, 0.262801138133, 0.301905425199, 0.330899152971,
          0.353441418887, 0.000788590320203, 0.00737060094841, 0.0177576041516, 0.0343043442574, 0.065664589369};
    }

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

    @Test
    public void testInverseCumulativeProbabilitySmallPAccuracy() {
        WeibullDistribution dist = new WeibullDistribution(2, 3);
        double t = dist.inverseCumulativeProbability(1e-17);
        // Analytically, answer is solution to 1e-17 = 1-exp(-(x/3)^2)
        // x = sqrt(-9*log(1-1e-17))
        // If we're not careful, answer will be 0. Answer below is computed with care in Octave:
        Assert.assertEquals(9.48683298050514e-9, t, 1e-17);
    }

    @Test
    public void testInverseCumulativeProbabilityExtremes() {
        setInverseCumulativeTestPoints(new double[] {0.0, 1.0});
        setInverseCumulativeTestValues(
                new double[] {0.0, Double.POSITIVE_INFINITY});
        verifyInverseCumulativeProbabilities();
    }

    @Test
    public void testAlpha() {
        WeibullDistribution dist = new WeibullDistribution(1, 2);
        Assert.assertEquals(1, dist.getShape(), 0);
        try {
            new WeibullDistribution(0, 2);
            Assert.fail("NotStrictlyPositiveException expected");
        } catch (NotStrictlyPositiveException e) {
            // Expected.
        }
    }

    @Test
    public void testBeta() {
        WeibullDistribution dist = new WeibullDistribution(1, 2);
        Assert.assertEquals(2, dist.getScale(), 0);
        try {
            new WeibullDistribution(1, 0);
            Assert.fail("NotStrictlyPositiveException expected");
        } catch (NotStrictlyPositiveException e) {
            // Expected.
        }
    }

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

        dist = new WeibullDistribution(2.5, 3.5);
        // In R: 3.5*gamma(1+(1/2.5)) (or emperically: mean(rweibull(10000, 2.5, 3.5)))
        Assert.assertEquals(dist.getNumericalMean(), 3.5 * FastMath.exp(Gamma.logGamma(1 + (1 / 2.5))), tol);
        Assert.assertEquals(dist.getNumericalVariance(), (3.5 * 3.5) *
                FastMath.exp(Gamma.logGamma(1 + (2 / 2.5))) -
                (dist.getNumericalMean() * dist.getNumericalMean()), tol);

        dist = new WeibullDistribution(10.4, 2.222);
        Assert.assertEquals(dist.getNumericalMean(), 2.222 * FastMath.exp(Gamma.logGamma(1 + (1 / 10.4))), tol);
        Assert.assertEquals(dist.getNumericalVariance(), (2.222 * 2.222) *
                FastMath.exp(Gamma.logGamma(1 + (2 / 10.4))) -
                (dist.getNumericalMean() * dist.getNumericalMean()), tol);
    }
}

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