Modified to extend ContinuousDistributionAbstractTest, improved coverage.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@141251 13f79535-47bb-0310-9956-ffa450edef68
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Phil Steitz
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package org.apache.commons.math.distribution;
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package org.apache.commons.math.distribution;
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import org.apache.commons.math.MathException;
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import org.apache.commons.math.TestUtils;
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import junit.framework.TestCase;
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/**
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/**
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* Tests for NormalDistribution implementation
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* Test cases for NormalDistribution.
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*
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* Extends ContinuousDistributionAbstractTest. See class javadoc for
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* "True" results are taken from R - the same as in Mathematica
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* ContinuousDistributionAbstractTest for details.
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*
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*
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* @version $Revision: 1.7 $ $Date: 2004/05/30 05:54:43 $
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*/
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*/
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public class NormalDistributionTest extends TestCase {
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public class NormalDistributionTest extends ContinuousDistributionAbstractTest {
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private NormalDistribution z;
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/**
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private static final double PRECISION = 10e-6;
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* Constructor for NormalDistributionTest.
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private static final double M = 2.1;
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* @param arg0
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private static final double SD = 1.4;
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*/
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public NormalDistributionTest(String arg0) {
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super(arg0);
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}
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/**
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public static void main(String[] args) {
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* Constructor for NormalDistributionTest.
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junit.swingui.TestRunner.run(NormalDistributionTest.class);
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* @param arg0
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}
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*/
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public NormalDistributionTest(String arg0) {
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super(arg0);
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}
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public static void main(String[] args) {
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//-------------- Implementations for abstract methods -----------------------
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junit.swingui.TestRunner.run(NormalDistributionTest.class);
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}
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protected void setUp() throws Exception {
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/** Creates the default continuous distribution instance to use in tests. */
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super.setUp();
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public ContinuousDistribution makeDistribution() {
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z = DistributionFactory.newInstance().createNormalDistribution(M, SD);
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return DistributionFactory.newInstance().createNormalDistribution(2.1, 1.4);
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}
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}
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protected void tearDown() throws Exception {
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/** Creates the default cumulative probability distribution test input values */
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super.tearDown();
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public double[] makeCumulativeTestPoints() {
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z = null;
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// quantiles computed using R
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}
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return new double[] {-2.226325d, -1.156887d, -0.6439496d, -0.2027951d, 0.3058278d,
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6.426325d, 5.356887d, 4.84395d, 4.402795d, 3.894172d};
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}
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public void testCumulativeProbabilitydoubleM_MINUS_2SD() throws MathException {
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/** Creates the default cumulative probability density test expected values */
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testProbability(M - 2*SD, 0.02275013);
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public double[] makeCumulativeTestValues() {
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}
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return new double[] {0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
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0.990d, 0.975d, 0.950d, 0.900d};
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}
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public void testCumulativeProbabilitydoubleM_MINUS_SD() throws MathException {
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// --------------------- Override tolerance --------------
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testProbability(M - SD, 0.1586553);
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protected void setup() throws Exception {
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}
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super.setUp();
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setTolerance(1E-6);
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}
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public void testCumulativeProbabilitydoubleM() throws MathException {
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//---------------------------- Additional test cases -------------------------
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testProbability(M, 0.5);
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}
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public void testCumulativeProbabilitydoubleM_PLUS_SD() throws MathException {
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private void verifyQuantiles() throws Exception {
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testProbability(M + SD, 0.8413447);
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NormalDistribution distribution = (NormalDistribution) getDistribution();
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}
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double mu = distribution.getMean();
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double sigma = distribution.getStandardDeviation();
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setCumulativeTestPoints( new double[] {mu - 2 *sigma, mu - sigma,
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mu, mu + sigma, mu +2 * sigma, mu +3 * sigma, mu + 4 * sigma,
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mu + 5 * sigma});
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// Quantiles computed using R (same as Mathematica)
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setCumulativeTestValues(new double[] {0.02275013, 0.1586553, 0.5, 0.8413447,
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0.9772499, 0.9986501, 0.9999683, 0.9999997});
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verifyCumulativeProbabilities();
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}
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public void testCumulativeProbabilitydoubleM_PLUS_2SD() throws MathException {
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public void testQuantiles() throws Exception {
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testProbability(M + 2*SD, 0.9772499);
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verifyQuantiles();
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}
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setDistribution(DistributionFactory.newInstance().createNormalDistribution(0, 1));
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verifyQuantiles();
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setDistribution(DistributionFactory.newInstance().createNormalDistribution(0, 0.1));
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verifyQuantiles();
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}
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public void testCumulativeProbabilitydoubleM_PLUS_3SD() throws MathException {
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public void testGetMean() {
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testProbability(M + 3*SD, 0.9986501);
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NormalDistribution distribution = (NormalDistribution) getDistribution();
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}
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assertEquals(2.1, distribution.getMean(), 0);
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}
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public void testCumulativeProbabilitydoubleM_PLUS_4SD() throws MathException {
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public void testSetMean() throws Exception {
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testProbability(M + 4*SD, 0.9999683);
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double mu = Math.random();
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}
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NormalDistribution distribution = (NormalDistribution) getDistribution();
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distribution.setMean(mu);
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verifyQuantiles();
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}
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public void testCumulativeProbabilitydoubleM_PLUS_5SD() throws MathException {
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public void testGetStandardDeviation() {
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testProbability(M + 5*SD, 0.9999997);
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NormalDistribution distribution = (NormalDistribution) getDistribution();
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}
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assertEquals(1.4, distribution.getStandardDeviation(), 0);
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}
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public void testInverseCumulativeProbability001() throws MathException {
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testValue(-2.226325, .001);
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}
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public void testInverseCumulativeProbability010() throws MathException{
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testValue(-1.156887, .010);
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}
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public void testInverseCumulativeProbability025() throws MathException{
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testValue(-0.6439496, .025);
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}
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public void testInverseCumulativeProbability050() throws MathException{
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testValue(-0.2027951, .050);
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}
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public void testInverseCumulativeProbability100() throws MathException{
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testValue(0.3058278, .100);
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}
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public void testInverseCumulativeProbability900() throws MathException{
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testValue(3.894172, .900);
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}
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public void testInverseCumulativeProbability950() throws MathException{
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testValue(4.402795, .950);
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}
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public void testInverseCumulativeProbability975() throws MathException{
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testValue(4.84395, .975);
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}
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public void testInverseCumulativeProbability990() throws MathException{
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testValue(5.356887, .990);
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}
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public void testInverseCumulativeProbability999() throws MathException{
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testValue(6.426325, .999);
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}
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public void testGetMean() {
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assertEquals(M, z.getMean(), 0);
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}
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public void testSetMean() throws MathException {
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double mu = Math.random();
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z.setMean(mu);
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assertEquals(mu, z.getMean(), 0);
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assertEquals(0.5d, z.cumulativeProbability(mu), PRECISION);
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}
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public void testGetStandardDeviation() {
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assertEquals(SD, z.getStandardDeviation(), 0);
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}
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public void testSetStandardDeviation() throws MathException{
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double sigma = 0.1d + Math.random();
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z.setStandardDeviation(sigma);
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assertEquals(sigma, z.getStandardDeviation(), 0);
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assertEquals(0.84134475, z.cumulativeProbability(z.getMean() + z.getStandardDeviation()), PRECISION );
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}
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private void testProbability(double x, double expected) throws MathException {
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double actual = Double.NaN;
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actual = z.cumulativeProbability(x);
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assertEquals(expected, actual, PRECISION);
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}
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private void testValue(double expected, double p) throws MathException {
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double actual = z.inverseCumulativeProbability(p);
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TestUtils.assertEquals(expected, actual, PRECISION);
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}
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public void testSetStandardDeviation() throws Exception {
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double sigma = 0.1d + Math.random();
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NormalDistribution distribution = (NormalDistribution) getDistribution();
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distribution.setStandardDeviation(sigma);
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assertEquals(sigma, distribution.getStandardDeviation(), 0);
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verifyQuantiles();
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try {
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distribution.setStandardDeviation(0);
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fail("Expecting IllegalArgumentException for sd = 0");
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} catch (IllegalArgumentException ex) {
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// Expected
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}
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}
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}
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}
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