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Commons Math example source code file (NormalDistributionTest.java)

This example Commons Math source code file (NormalDistributionTest.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

continuousdistributionabstracttest, exception, exception, expecting, illegalargumentexception, mathexception, normaldistribution, normaldistribution, normaldistributionimpl, normaldistributionimpl, normaldistributiontest, override, override

The Commons Math NormalDistributionTest.java 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.math.distribution;

import org.apache.commons.math.MathException;

/**
 * Test cases for NormalDistribution.
 * Extends ContinuousDistributionAbstractTest.  See class javadoc for
 * ContinuousDistributionAbstractTest for details.
 *
 * @version $Revision: 924362 $ $Date: 2010-03-17 12:45:31 -0400 (Wed, 17 Mar 2010) $
 */
public class NormalDistributionTest extends ContinuousDistributionAbstractTest  {

    /**
     * Constructor for NormalDistributionTest.
     * @param arg0
     */
    public NormalDistributionTest(String arg0) {
        super(arg0);
    }

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

    /** Creates the default continuous distribution instance to use in tests. */
    @Override
    public NormalDistribution makeDistribution() {
        return new NormalDistributionImpl(2.1, 1.4);
    }

    /** Creates the default cumulative probability distribution test input values */
    @Override
    public double[] makeCumulativeTestPoints() {
        // quantiles computed using R
        return new double[] {-2.226325228634938d, -1.156887023657177d, -0.643949578356075d, -0.2027950777320613d, 0.305827808237559d,
                6.42632522863494d, 5.35688702365718d, 4.843949578356074d, 4.40279507773206d, 3.89417219176244d};
    }

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

    /** Creates the default probability density test expected values */
    @Override
    public double[] makeDensityTestValues() {
        return new double[] {0.00240506434076, 0.0190372444310, 0.0417464784322, 0.0736683145538, 0.125355951380,
                0.00240506434076, 0.0190372444310, 0.0417464784322, 0.0736683145538, 0.125355951380};
    }

    // --------------------- Override tolerance  --------------
    protected double defaultTolerance = NormalDistributionImpl.DEFAULT_INVERSE_ABSOLUTE_ACCURACY;
    @Override
    protected void setUp() throws Exception {
        super.setUp();
        setTolerance(defaultTolerance);
    }

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

    private void verifyQuantiles() throws Exception {
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        double mu = distribution.getMean();
        double sigma = distribution.getStandardDeviation();
        setCumulativeTestPoints( new double[] {mu - 2 *sigma, mu - sigma,
                mu, mu + sigma, mu + 2 * sigma,  mu + 3 * sigma, mu + 4 * sigma,
                mu + 5 * sigma});
        // Quantiles computed using R (same as Mathematica)
        setCumulativeTestValues(new double[] {0.02275013194817921, 0.158655253931457, 0.5, 0.841344746068543,
                0.977249868051821, 0.99865010196837, 0.999968328758167,  0.999999713348428});
        verifyCumulativeProbabilities();
    }

    public void testQuantiles() throws Exception {
        setDensityTestValues(new double[] {0.0385649760808, 0.172836231799, 0.284958771715, 0.172836231799, 0.0385649760808,
                0.00316560600853, 9.55930184035e-05, 1.06194251052e-06});
        verifyQuantiles();
        verifyDensities();

        setDistribution(new NormalDistributionImpl(0, 1));
        setDensityTestValues(new double[] {0.0539909665132, 0.241970724519, 0.398942280401, 0.241970724519, 0.0539909665132,
                0.00443184841194, 0.000133830225765, 1.48671951473e-06});
        verifyQuantiles();
        verifyDensities();

        setDistribution(new NormalDistributionImpl(0, 0.1));
        setDensityTestValues(new double[] {0.539909665132, 2.41970724519, 3.98942280401, 2.41970724519,
                0.539909665132, 0.0443184841194, 0.00133830225765, 1.48671951473e-05});
        verifyQuantiles();
        verifyDensities();
    }

    public void testInverseCumulativeProbabilityExtremes() throws Exception {
        setInverseCumulativeTestPoints(new double[] {0, 1});
        setInverseCumulativeTestValues(
                new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY});
        verifyInverseCumulativeProbabilities();
    }

    public void testGetMean() {
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        assertEquals(2.1, distribution.getMean(), 0);
    }

    public void testSetMean() throws Exception {
        double mu = Math.random();
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        distribution.setMean(mu);
        verifyQuantiles();
    }

    public void testGetStandardDeviation() {
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        assertEquals(1.4, distribution.getStandardDeviation(), 0);
    }

    public void testSetStandardDeviation() throws Exception {
        double sigma = 0.1d + Math.random();
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        distribution.setStandardDeviation(sigma);
        assertEquals(sigma, distribution.getStandardDeviation(), 0);
        verifyQuantiles();
        try {
            distribution.setStandardDeviation(0);
            fail("Expecting IllegalArgumentException for sd = 0");
        } catch (IllegalArgumentException ex) {
            // Expected
        }
    }

    public void testDensity() {
        double [] x = new double[]{-2, -1, 0, 1, 2};
        // R 2.5: print(dnorm(c(-2,-1,0,1,2)), digits=10)
        checkDensity(0, 1, x, new double[]{0.05399096651, 0.24197072452, 0.39894228040, 0.24197072452, 0.05399096651});
        // R 2.5: print(dnorm(c(-2,-1,0,1,2), mean=1.1), digits=10)
        checkDensity(1.1, 1, x, new double[]{0.003266819056,0.043983595980,0.217852177033,0.396952547477,0.266085249899});
    }

    private void checkDensity(double mean, double sd, double[] x, double[] expected) {
        NormalDistribution d = new NormalDistributionImpl(mean, sd);
        for (int i = 0; i < x.length; i++) {
            assertEquals(expected[i], d.density(x[i]), 1e-9);
        }
    }

    /**
     * Check to make sure top-coding of extreme values works correctly.
     * Verifies fix for JIRA MATH-167
     */
    public void testExtremeValues() throws Exception {
        NormalDistribution distribution = (NormalDistribution) getDistribution();
        distribution.setMean(0);
        distribution.setStandardDeviation(1);
        for (int i = 0; i < 100; i+=5) { // make sure no convergence exception
            double lowerTail = distribution.cumulativeProbability(-i);
            double upperTail = distribution.cumulativeProbability(i);
            if (i < 10) { // make sure not top-coded
                assertTrue(lowerTail > 0.0d);
                assertTrue(upperTail < 1.0d);
            }
            else { // make sure top coding not reversed
                assertTrue(lowerTail < 0.00001);
                assertTrue(upperTail > 0.99999);
            }
        }
   }

    public void testMath280() throws MathException {
        NormalDistribution normal = new NormalDistributionImpl(0,1);
        double result = normal.inverseCumulativeProbability(0.9986501019683698);
        assertEquals(3.0, result, defaultTolerance);
        result = normal.inverseCumulativeProbability(0.841344746068543);
        assertEquals(1.0, result, defaultTolerance);
        result = normal.inverseCumulativeProbability(0.9999683287581673);
        assertEquals(4.0, result, defaultTolerance);
        result = normal.inverseCumulativeProbability(0.9772498680518209);
        assertEquals(2.0, result, defaultTolerance);
    }

}

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