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Java example source code file (RealDistributionAbstractTest.java)

This example Java source code file (RealDistributionAbstractTest.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.

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The RealDistributionAbstractTest.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 java.util.ArrayList;
import java.util.Collections;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;

import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator;
import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;

/**
 * Abstract base class for {@link RealDistribution} tests.
 * <p>
 * To create a concrete test class for a continuous distribution
 * implementation, first implement makeDistribution() to return a distribution
 * instance to use in tests. Then implement each of the test data generation
 * methods below.  In each case, the test points and test values arrays
 * returned represent parallel arrays of inputs and expected values for the
 * distribution returned by makeDistribution().  Default implementations
 * are provided for the makeInverseXxx methods that just invert the mapping
 * defined by the arrays returned by the makeCumulativeXxx methods.
 * <p>
 * makeCumulativeTestPoints() -- arguments used to test cumulative probabilities
 * makeCumulativeTestValues() -- expected cumulative probabilites
 * makeDensityTestValues() -- expected density values at cumulativeTestPoints
 * makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf
 * makeInverseCumulativeTestValues() -- expected inverse cdf values
 * <p>
 * To implement additional test cases with different distribution instances and
 * test data, use the setXxx methods for the instance data in test cases and
 * call the verifyXxx methods to verify results.
 * <p>
 * Error tolerance can be overriden by implementing getTolerance().
 * <p>
 * Test data should be validated against reference tables or other packages
 * where possible, and the source of the reference data and/or validation
 * should be documented in the test cases.  A framework for validating
 * distribution data against R is included in the /src/test/R source tree.
 * <p>
 * See {@link NormalDistributionTest} and {@link ChiSquaredDistributionTest}
 * for examples.
 *
 */
public abstract class RealDistributionAbstractTest {

//-------------------- Private test instance data -------------------------
    /**  Distribution instance used to perform tests */
    private RealDistribution distribution;

    /** Tolerance used in comparing expected and returned values */
    private double tolerance = 1E-4;

    /** Arguments used to test cumulative probability density calculations */
    private double[] cumulativeTestPoints;

    /** Values used to test cumulative probability density calculations */
    private double[] cumulativeTestValues;

    /** Arguments used to test inverse cumulative probability density calculations */
    private double[] inverseCumulativeTestPoints;

    /** Values used to test inverse cumulative probability density calculations */
    private double[] inverseCumulativeTestValues;

    /** Values used to test density calculations */
    private double[] densityTestValues;

    /** Values used to test logarithmic density calculations */
    private double[] logDensityTestValues;

    //-------------------- Abstract methods -----------------------------------

    /** Creates the default continuous distribution instance to use in tests. */
    public abstract RealDistribution makeDistribution();

    /** Creates the default cumulative probability test input values */
    public abstract double[] makeCumulativeTestPoints();

    /** Creates the default cumulative probability test expected values */
    public abstract double[] makeCumulativeTestValues();

    /** Creates the default density test expected values */
    public abstract double[] makeDensityTestValues();

    /** Creates the default logarithmic density test expected values.
     * The default implementation simply computes the logarithm
     * of each value returned by {@link #makeDensityTestValues()}.*/
    public double[] makeLogDensityTestValues() {
        final double[] densityTestValues = makeDensityTestValues();
        final double[] logDensityTestValues = new double[densityTestValues.length];
        for (int i = 0; i < densityTestValues.length; i++) {
            logDensityTestValues[i] = FastMath.log(densityTestValues[i]);
        }
        return logDensityTestValues;
    }

    //---- Default implementations of inverse test data generation methods ----

    /** Creates the default inverse cumulative probability test input values */
    public double[] makeInverseCumulativeTestPoints() {
        return makeCumulativeTestValues();
    }

    /** Creates the default inverse cumulative probability density test expected values */
    public double[] makeInverseCumulativeTestValues() {
        return makeCumulativeTestPoints();
    }

    //-------------------- Setup / tear down ----------------------------------

    /**
     * Setup sets all test instance data to default values
     */
    @Before
    public void setUp() {
        distribution = makeDistribution();
        cumulativeTestPoints = makeCumulativeTestPoints();
        cumulativeTestValues = makeCumulativeTestValues();
        inverseCumulativeTestPoints = makeInverseCumulativeTestPoints();
        inverseCumulativeTestValues = makeInverseCumulativeTestValues();
        densityTestValues = makeDensityTestValues();
        logDensityTestValues = makeLogDensityTestValues();
    }

    /**
     * Cleans up test instance data
     */
    @After
    public void tearDown() {
        distribution = null;
        cumulativeTestPoints = null;
        cumulativeTestValues = null;
        inverseCumulativeTestPoints = null;
        inverseCumulativeTestValues = null;
        densityTestValues = null;
        logDensityTestValues = null;
    }

    //-------------------- Verification methods -------------------------------

    /**
     * Verifies that cumulative probability density calculations match expected values
     * using current test instance data
     */
    @SuppressWarnings("deprecation")
    protected void verifyCumulativeProbabilities() {
        // verify cumulativeProbability(double)
        for (int i = 0; i < cumulativeTestPoints.length; i++) {
            TestUtils.assertEquals("Incorrect cumulative probability value returned for "
                + cumulativeTestPoints[i], cumulativeTestValues[i],
                distribution.cumulativeProbability(cumulativeTestPoints[i]),
                getTolerance());
        }
        // verify cumulativeProbability(double, double)
        // XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
        for (int i = 0; i < cumulativeTestPoints.length; i++) {
            for (int j = 0; j < cumulativeTestPoints.length; j++) {
                if (cumulativeTestPoints[i] <= cumulativeTestPoints[j]) {
                    TestUtils.assertEquals(cumulativeTestValues[j] - cumulativeTestValues[i],
                        distribution.cumulativeProbability(cumulativeTestPoints[i], cumulativeTestPoints[j]),
                        getTolerance());
                } else {
                    try {
                        distribution.cumulativeProbability(cumulativeTestPoints[i], cumulativeTestPoints[j]);
                    } catch (NumberIsTooLargeException e) {
                        continue;
                    }
                    Assert.fail("distribution.cumulativeProbability(double, double) should have thrown an exception that second argument is too large");
                }
            }
        }
    }

    /**
     * Verifies that inverse cumulative probability density calculations match expected values
     * using current test instance data
     */
    protected void verifyInverseCumulativeProbabilities() {
        for (int i = 0; i < inverseCumulativeTestPoints.length; i++) {
            TestUtils.assertEquals("Incorrect inverse cumulative probability value returned for "
                + inverseCumulativeTestPoints[i], inverseCumulativeTestValues[i],
                 distribution.inverseCumulativeProbability(inverseCumulativeTestPoints[i]),
                 getTolerance());
        }
    }

    /**
     * Verifies that density calculations match expected values
     */
    protected void verifyDensities() {
        for (int i = 0; i < cumulativeTestPoints.length; i++) {
            TestUtils.assertEquals("Incorrect probability density value returned for "
                + cumulativeTestPoints[i], densityTestValues[i],
                 distribution.density(cumulativeTestPoints[i]),
                 getTolerance());
        }
    }

    /**
     * Verifies that logarithmic density calculations match expected values
     */
    protected void verifyLogDensities() {
        // FIXME: when logProbability methods are added to RealDistribution in 4.0, remove cast below
        for (int i = 0; i < cumulativeTestPoints.length; i++) {
            TestUtils.assertEquals("Incorrect probability density value returned for "
                    + cumulativeTestPoints[i], logDensityTestValues[i],
                    ((AbstractRealDistribution) distribution).logDensity(cumulativeTestPoints[i]),
                    getTolerance());
        }
    }

    //------------------------ Default test cases -----------------------------

    /**
     * Verifies that cumulative probability density calculations match expected values
     * using default test instance data
     */
    @Test
    public void testCumulativeProbabilities() {
        verifyCumulativeProbabilities();
    }

    /**
     * Verifies that inverse cumulative probability density calculations match expected values
     * using default test instance data
     */
    @Test
    public void testInverseCumulativeProbabilities() {
        verifyInverseCumulativeProbabilities();
    }

    /**
     * Verifies that density calculations return expected values
     * for default test instance data
     */
    @Test
    public void testDensities() {
        verifyDensities();
    }

    /**
     * Verifies that logarithmic density calculations return expected values
     * for default test instance data
     */
    @Test
    public void testLogDensities() {
        verifyLogDensities();
    }

    /**
     * Verifies that probability computations are consistent
     */
    @SuppressWarnings("deprecation")
    @Test
    public void testConsistency() {
        for (int i=1; i < cumulativeTestPoints.length; i++) {

            // check that cdf(x, x) = 0
            // XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
            TestUtils.assertEquals(0d,
               distribution.cumulativeProbability
                 (cumulativeTestPoints[i], cumulativeTestPoints[i]), tolerance);

            // check that P(a < X <= b) = P(X <= b) - P(X <= a)
            double upper = FastMath.max(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
            double lower = FastMath.min(cumulativeTestPoints[i], cumulativeTestPoints[i -1]);
            double diff = distribution.cumulativeProbability(upper) -
                distribution.cumulativeProbability(lower);
            // XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
            double direct = distribution.cumulativeProbability(lower, upper);
            TestUtils.assertEquals("Inconsistent cumulative probabilities for ("
                    + lower + "," + upper + ")", diff, direct, tolerance);
        }
    }

    /**
     * Verifies that illegal arguments are correctly handled
     */
    @SuppressWarnings("deprecation")
    @Test
    public void testIllegalArguments() {
        try {
            // XXX In 4.0, "cumulativeProbability(double,double)" must be replaced with "probability" (MATH-839).
            distribution.cumulativeProbability(1, 0);
            Assert.fail("Expecting MathIllegalArgumentException for bad cumulativeProbability interval");
        } catch (MathIllegalArgumentException ex) {
            // expected
        }
        try {
            distribution.inverseCumulativeProbability(-1);
            Assert.fail("Expecting MathIllegalArgumentException for p = -1");
        } catch (MathIllegalArgumentException ex) {
            // expected
        }
        try {
            distribution.inverseCumulativeProbability(2);
            Assert.fail("Expecting MathIllegalArgumentException for p = 2");
        } catch (MathIllegalArgumentException ex) {
            // expected
        }
    }

    /**
     * Test sampling
     */
    @Test
    public void testSampling() {
        final int sampleSize = 1000;
        distribution.reseedRandomGenerator(1000); // Use fixed seed
        double[] sample = distribution.sample(sampleSize);
        double[] quartiles = TestUtils.getDistributionQuartiles(distribution);
        double[] expected = {250, 250, 250, 250};
        long[] counts = new long[4];
        for (int i = 0; i < sampleSize; i++) {
            TestUtils.updateCounts(sample[i], counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    /**
     * Verify that density integrals match the distribution.
     * The (filtered, sorted) cumulativeTestPoints array is used to source
     * integration limits. The integral of the density (estimated using a
     * Legendre-Gauss integrator) is compared with the cdf over the same
     * interval. Test points outside of the domain of the density function
     * are discarded.
     */
    @SuppressWarnings("deprecation")
    @Test
    public void testDensityIntegrals() {
        final double tol = 1.0e-9;
        final BaseAbstractUnivariateIntegrator integrator =
            new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
        final UnivariateFunction d = new UnivariateFunction() {
            public double value(double x) {
                return distribution.density(x);
            }
        };
        final ArrayList<Double> integrationTestPoints = new ArrayList();
        for (int i = 0; i < cumulativeTestPoints.length; i++) {
            if (Double.isNaN(cumulativeTestValues[i]) ||
                    cumulativeTestValues[i] < 1.0e-5 ||
                    cumulativeTestValues[i] > 1 - 1.0e-5) {
                continue; // exclude integrals outside domain.
            }
            integrationTestPoints.add(cumulativeTestPoints[i]);
        }
        Collections.sort(integrationTestPoints);
        for (int i = 1; i < integrationTestPoints.size(); i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability(  // FIXME @4.0 when rename happens
                            integrationTestPoints.get(0), integrationTestPoints.get(i)),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, integrationTestPoints.get(0),
                                    integrationTestPoints.get(i)), tol);
        }
    }

    /**
     * Verify that isSupportLowerBoundInclusvie returns true iff the lower bound
     * is finite and density is non-NaN, non-infinite there.
     */
    @SuppressWarnings("deprecation")
    @Test
    public void testIsSupportLowerBoundInclusive() {
        final double lowerBound = distribution.getSupportLowerBound();
        double result = Double.NaN;
        result = distribution.density(lowerBound);
        Assert.assertEquals(
                !Double.isInfinite(lowerBound) && !Double.isNaN(result) &&
                !Double.isInfinite(result),
                distribution.isSupportLowerBoundInclusive());

    }

    /**
     * Verify that isSupportUpperBoundInclusvie returns true iff the upper bound
     * is finite and density is non-NaN, non-infinite there.
     */
    @SuppressWarnings("deprecation")
    @Test
    public void testIsSupportUpperBoundInclusive() {
        final double upperBound = distribution.getSupportUpperBound();
        double result = Double.NaN;
        result = distribution.density(upperBound);
        Assert.assertEquals(
                !Double.isInfinite(upperBound) && !Double.isNaN(result) &&
                !Double.isInfinite(result),
                distribution.isSupportUpperBoundInclusive());

    }

    @Test
    public void testDistributionClone()
        throws IOException,
               ClassNotFoundException {
        // Construct a distribution and initialize its internal random
        // generator, using a fixed seed for deterministic results.
        distribution.reseedRandomGenerator(123);
        distribution.sample();

        // Clone the distribution.
        final RealDistribution cloned = deepClone();

        // Make sure they still produce the same samples.
        final double s1 = distribution.sample();
        final double s2 = cloned.sample();
        Assert.assertEquals(s1, s2, 0d);
    }

    //------------------ Getters / Setters for test instance data -----------
    /**
     * @return Returns the cumulativeTestPoints.
     */
    protected double[] getCumulativeTestPoints() {
        return cumulativeTestPoints;
    }

    /**
     * @param cumulativeTestPoints The cumulativeTestPoints to set.
     */
    protected void setCumulativeTestPoints(double[] cumulativeTestPoints) {
        this.cumulativeTestPoints = cumulativeTestPoints;
    }

    /**
     * @return Returns the cumulativeTestValues.
     */
    protected double[] getCumulativeTestValues() {
        return cumulativeTestValues;
    }

    /**
     * @param cumulativeTestValues The cumulativeTestValues to set.
     */
    protected void setCumulativeTestValues(double[] cumulativeTestValues) {
        this.cumulativeTestValues = cumulativeTestValues;
    }

    protected double[] getDensityTestValues() {
        return densityTestValues;
    }

    protected void setDensityTestValues(double[] densityTestValues) {
        this.densityTestValues = densityTestValues;
    }

    /**
     * @return Returns the distribution.
     */
    protected RealDistribution getDistribution() {
        return distribution;
    }

    /**
     * @param distribution The distribution to set.
     */
    protected void setDistribution(RealDistribution distribution) {
        this.distribution = distribution;
    }

    /**
     * @return Returns the inverseCumulativeTestPoints.
     */
    protected double[] getInverseCumulativeTestPoints() {
        return inverseCumulativeTestPoints;
    }

    /**
     * @param inverseCumulativeTestPoints The inverseCumulativeTestPoints to set.
     */
    protected void setInverseCumulativeTestPoints(double[] inverseCumulativeTestPoints) {
        this.inverseCumulativeTestPoints = inverseCumulativeTestPoints;
    }

    /**
     * @return Returns the inverseCumulativeTestValues.
     */
    protected double[] getInverseCumulativeTestValues() {
        return inverseCumulativeTestValues;
    }

    /**
     * @param inverseCumulativeTestValues The inverseCumulativeTestValues to set.
     */
    protected void setInverseCumulativeTestValues(double[] inverseCumulativeTestValues) {
        this.inverseCumulativeTestValues = inverseCumulativeTestValues;
    }

    /**
     * @return Returns the tolerance.
     */
    protected double getTolerance() {
        return tolerance;
    }

    /**
     * @param tolerance The tolerance to set.
     */
    protected void setTolerance(double tolerance) {
        this.tolerance = tolerance;
    }

    /**
     * Serialization and deserialization loop of the {@link #distribution}.
     */
    private RealDistribution deepClone()
        throws IOException,
               ClassNotFoundException {
        // Serialize to byte array.
        final ByteArrayOutputStream bOut = new ByteArrayOutputStream();
        final ObjectOutputStream oOut = new ObjectOutputStream(bOut);
        oOut.writeObject(distribution);
        final byte[] data = bOut.toByteArray();

        // Deserialize from byte array.
        final ByteArrayInputStream bIn = new ByteArrayInputStream(data);
        final ObjectInputStream oIn = new ObjectInputStream(bIn);
        final Object clone = oIn.readObject();
        oIn.close();

        return (RealDistribution) clone;
    }
}

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