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Java example source code file (EnumeratedRealDistributionTest.java)

This example Java source code file (EnumeratedRealDistributionTest.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

dimensionmismatchexception, discreterealdistribution, double, enumeratedrealdistribution, enumeratedrealdistributiontest, expected, list, matharithmeticexception, notanumberexception, notfinitenumberexception, notpositiveexception, object, pair, test, util

The EnumeratedRealDistributionTest.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 static org.junit.Assert.assertEquals;

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

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathArithmeticException;
import org.apache.commons.math3.exception.NotANumberException;
import org.apache.commons.math3.exception.NotFiniteNumberException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Pair;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test class for {@link EnumeratedRealDistribution}.
 *
 */
public class EnumeratedRealDistributionTest {

    /**
     * The distribution object used for testing.
     */
    private final EnumeratedRealDistribution testDistribution;

    /**
     * Creates the default distribution object used for testing.
     */
    public EnumeratedRealDistributionTest() {
        // Non-sorted singleton array with duplicates should be allowed.
        // Values with zero-probability do not extend the support.
        testDistribution = new EnumeratedRealDistribution(
                new double[]{3.0, -1.0, 3.0, 7.0, -2.0, 8.0},
                new double[]{0.2, 0.2, 0.3, 0.3, 0.0, 0.0});
    }

    /**
     * Tests if the {@link EnumeratedRealDistribution} constructor throws
     * exceptions for invalid data.
     */
    @Test
    public void testExceptions() {
        EnumeratedRealDistribution invalid = null;
        try {
            invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0});
            Assert.fail("Expected DimensionMismatchException");
        } catch (DimensionMismatchException e) {
        }
        try{
        invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, -1.0});
            Assert.fail("Expected NotPositiveException");
        } catch (NotPositiveException e) {
        }
        try {
            invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, 0.0});
            Assert.fail("Expected MathArithmeticException");
        } catch (MathArithmeticException e) {
        }
        try {
            invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.NaN});
            Assert.fail("Expected NotANumberException");
        } catch (NotANumberException e) {
        }
        try {
            invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.POSITIVE_INFINITY});
            Assert.fail("Expected NotFiniteNumberException");
        } catch (NotFiniteNumberException e) {
        }
        Assert.assertNull("Expected non-initialized DiscreteRealDistribution", invalid);
    }

    /**
     * Tests if the distribution returns proper probability values.
     */
    @Test
    public void testProbability() {
        double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
        double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
        for (int p = 0; p < points.length; p++) {
            double density = testDistribution.probability(points[p]);
            Assert.assertEquals(results[p], density, 0.0);
        }
    }

    /**
     * Tests if the distribution returns proper density values.
     */
    @Test
    public void testDensity() {
        double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
        double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
        for (int p = 0; p < points.length; p++) {
            double density = testDistribution.density(points[p]);
            Assert.assertEquals(results[p], density, 0.0);
        }
    }

    /**
     * Tests if the distribution returns proper cumulative probability values.
     */
    @Test
    public void testCumulativeProbability() {
        double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
        double[] results = new double[]{0, 0.2, 0.2, 0.2, 0.2, 0.7, 0.7, 0.7, 0.7, 1.0, 1.0};
        for (int p = 0; p < points.length; p++) {
            double probability = testDistribution.cumulativeProbability(points[p]);
            Assert.assertEquals(results[p], probability, 1e-10);
        }
    }

    /**
     * Tests if the distribution returns proper mean value.
     */
    @Test
    public void testGetNumericalMean() {
        Assert.assertEquals(3.4, testDistribution.getNumericalMean(), 1e-10);
    }

    /**
     * Tests if the distribution returns proper variance.
     */
    @Test
    public void testGetNumericalVariance() {
        Assert.assertEquals(7.84, testDistribution.getNumericalVariance(), 1e-10);
    }

    /**
     * Tests if the distribution returns proper lower bound.
     */
    @Test
    public void testGetSupportLowerBound() {
        Assert.assertEquals(-1, testDistribution.getSupportLowerBound(), 0);
    }

    /**
     * Tests if the distribution returns proper upper bound.
     */
    @Test
    public void testGetSupportUpperBound() {
        Assert.assertEquals(7, testDistribution.getSupportUpperBound(), 0);
    }

    /**
     * Tests if the distribution returns properly that the support includes the
     * lower bound.
     */
    @Test
    public void testIsSupportLowerBoundInclusive() {
        Assert.assertTrue(testDistribution.isSupportLowerBoundInclusive());
    }

    /**
     * Tests if the distribution returns properly that the support includes the
     * upper bound.
     */
    @Test
    public void testIsSupportUpperBoundInclusive() {
        Assert.assertTrue(testDistribution.isSupportUpperBoundInclusive());
    }

    /**
     * Tests if the distribution returns properly that the support is connected.
     */
    @Test
    public void testIsSupportConnected() {
        Assert.assertTrue(testDistribution.isSupportConnected());
    }

    /**
     * Tests sampling.
     */
    @Test
    public void testSample() {
        final int n = 1000000;
        testDistribution.reseedRandomGenerator(-334759360); // fixed seed
        final double[] samples = testDistribution.sample(n);
        Assert.assertEquals(n, samples.length);
        double sum = 0;
        double sumOfSquares = 0;
        for (int i = 0; i < samples.length; i++) {
            sum += samples[i];
            sumOfSquares += samples[i] * samples[i];
        }
        Assert.assertEquals(testDistribution.getNumericalMean(),
                sum / n, 1e-2);
        Assert.assertEquals(testDistribution.getNumericalVariance(),
                sumOfSquares / n - FastMath.pow(sum / n, 2), 1e-2);
    }

    @Test
    public void testIssue942() {
        List<Pair list = new ArrayList>();
        list.add(new Pair<Object, Double>(new Object() {}, new Double(0)));
        list.add(new Pair<Object, Double>(new Object() {}, new Double(1)));
        Assert.assertEquals(1, new EnumeratedDistribution<Object>(list).sample(1).length);
    }

    @Test
    public void testIssue1065() {
        // Test Distribution for inverseCumulativeProbability
        //
        //         ^
        //         |
        // 1.000   +--------------------------------o===============
        //         |                               3|
        //         |                                |
        //         |                             1o=
        // 0.750   +-------------------------> o==  .
        //         |                          3|  . .
        //         |                   0       |  . .
        // 0.5625  +---------------> o==o======   . .
        //         |                 |  .      .  . .
        //         |                 |  .      .  . .
        //         |                5|  .      .  . .
        //         |                 |  .      .  . .
        //         |             o===   .      .  . .
        //         |             |   .  .      .  . .
        //         |            4|   .  .      .  . .
        //         |             |   .  .      .  . .
        // 0.000   +=============----+--+------+--+-+--------------->
        //                      14  18 21     28 31 33
        //
        // sum  = 4+5+0+3+1+3 = 16

        EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(
                new double[] { 14.0, 18.0, 21.0, 28.0, 31.0, 33.0 },
                new double[] { 4.0 / 16.0, 5.0 / 16.0, 0.0 / 16.0, 3.0 / 16.0, 1.0 / 16.0, 3.0 / 16.0 });

        assertEquals(14.0, distribution.inverseCumulativeProbability(0.0000), 0.0);
        assertEquals(14.0, distribution.inverseCumulativeProbability(0.2500), 0.0);
        assertEquals(33.0, distribution.inverseCumulativeProbability(1.0000), 0.0);

        assertEquals(18.0, distribution.inverseCumulativeProbability(0.5000), 0.0);
        assertEquals(18.0, distribution.inverseCumulativeProbability(0.5624), 0.0);
        assertEquals(28.0, distribution.inverseCumulativeProbability(0.5626), 0.0);
        assertEquals(31.0, distribution.inverseCumulativeProbability(0.7600), 0.0);
        assertEquals(18.0, distribution.inverseCumulativeProbability(0.5625), 0.0);
        assertEquals(28.0, distribution.inverseCumulativeProbability(0.7500), 0.0);
    }

    @Test
    public void testCreateFromDoubles() {
        final double[] data = new double[] {0, 1, 1, 2, 2, 2};
        EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(data);
        assertEquals(0.5, distribution.probability(2), 0);
        assertEquals(0.5, distribution.cumulativeProbability(1), 0);
    }
}

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