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Java example source code file (AbstractRealDistributionTest.java)

This example Java source code file (AbstractRealDistributionTest.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

abstractrealdistribution, abstractrealdistributiontest, outofrangeexception, override, rombergintegrator, test, univariatefunction, univariateintegrator, unsupportedoperationexception

The AbstractRealDistributionTest.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.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.integration.RombergIntegrator;
import org.apache.commons.math3.analysis.integration.UnivariateIntegrator;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.junit.Assert;
import org.junit.Test;

/** Various tests related to MATH-699. */
public class AbstractRealDistributionTest {

    @Test
    public void testContinuous() {
        final double x0 = 0.0;
        final double x1 = 1.0;
        final double x2 = 2.0;
        final double x3 = 3.0;
        final double p12 = 0.5;
        final AbstractRealDistribution distribution;
        distribution = new AbstractRealDistribution(null) {
            private static final long serialVersionUID = 1L;

            public double cumulativeProbability(final double x) {
                if ((x < x0) || (x > x3)) {
                    throw new OutOfRangeException(x, x0, x3);
                }
                if (x <= x1) {
                    return p12 * (x - x0) / (x1 - x0);
                } else if (x <= x2) {
                    return p12;
                } else if (x <= x3) {
                    return p12 + (1.0 - p12) * (x - x2) / (x3 - x2);
                }
                return 0.0;
            }

            public double density(final double x) {
                if ((x < x0) || (x > x3)) {
                    throw new OutOfRangeException(x, x0, x3);
                }
                if (x <= x1) {
                    return p12 / (x1 - x0);
                } else if (x <= x2) {
                    return 0.0;
                } else if (x <= x3) {
                    return (1.0 - p12) / (x3 - x2);
                }
                return 0.0;
            }

            public double getNumericalMean() {
                return ((x0 + x1) * p12 + (x2 + x3) * (1.0 - p12)) / 2.0;
            }

            public double getNumericalVariance() {
                final double meanX = getNumericalMean();
                final double meanX2;
                meanX2 = ((x0 * x0 + x0 * x1 + x1 * x1) * p12 + (x2 * x2 + x2
                        * x3 + x3 * x3)
                        * (1.0 - p12)) / 3.0;
                return meanX2 - meanX * meanX;
            }

            public double getSupportLowerBound() {
                return x0;
            }

            public double getSupportUpperBound() {
                return x3;
            }

            public boolean isSupportConnected() {
                return false;
            }

            public boolean isSupportLowerBoundInclusive() {
                return true;
            }

            public boolean isSupportUpperBoundInclusive() {
                return true;
            }

            @Override
            public double probability(final double x) {
                throw new UnsupportedOperationException();
            }
        };
        final double expected = x1;
        final double actual = distribution.inverseCumulativeProbability(p12);
        Assert.assertEquals("", expected, actual,
                distribution.getSolverAbsoluteAccuracy());
    }

    @Test
    public void testDiscontinuous() {
        final double x0 = 0.0;
        final double x1 = 0.25;
        final double x2 = 0.5;
        final double x3 = 0.75;
        final double x4 = 1.0;
        final double p12 = 1.0 / 3.0;
        final double p23 = 2.0 / 3.0;
        final AbstractRealDistribution distribution;
        distribution = new AbstractRealDistribution(null) {
            private static final long serialVersionUID = 1L;

            public double cumulativeProbability(final double x) {
                if ((x < x0) || (x > x4)) {
                    throw new OutOfRangeException(x, x0, x4);
                }
                if (x <= x1) {
                    return p12 * (x - x0) / (x1 - x0);
                } else if (x <= x2) {
                    return p12;
                } else if (x <= x3) {
                    return p23;
                } else {
                    return (1.0 - p23) * (x - x3) / (x4 - x3) + p23;
                }
            }

            public double density(final double x) {
                if ((x < x0) || (x > x4)) {
                    throw new OutOfRangeException(x, x0, x4);
                }
                if (x <= x1) {
                    return p12 / (x1 - x0);
                } else if (x <= x2) {
                    return 0.0;
                } else if (x <= x3) {
                    return 0.0;
                } else {
                    return (1.0 - p23) / (x4 - x3);
                }
            }

            public double getNumericalMean() {
                final UnivariateFunction f = new UnivariateFunction() {

                    public double value(final double x) {
                        return x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                return integrator.integrate(Integer.MAX_VALUE, f, x0, x4);
            }

            public double getNumericalVariance() {
                final double meanX = getNumericalMean();
                final UnivariateFunction f = new UnivariateFunction() {

                    public double value(final double x) {
                        return x * x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                final double meanX2 = integrator.integrate(Integer.MAX_VALUE,
                        f, x0, x4);
                return meanX2 - meanX * meanX;
            }

            public double getSupportLowerBound() {
                return x0;
            }

            public double getSupportUpperBound() {
                return x4;
            }

            public boolean isSupportConnected() {
                return false;
            }

            public boolean isSupportLowerBoundInclusive() {
                return true;
            }

            public boolean isSupportUpperBoundInclusive() {
                return true;
            }

            @Override
            public double probability(final double x) {
                throw new UnsupportedOperationException();
            }
        };
        final double expected = x2;
        final double actual = distribution.inverseCumulativeProbability(p23);
        Assert.assertEquals("", expected, actual,
                distribution.getSolverAbsoluteAccuracy());

    }
}

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