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Commons Math example source code file (NormalDistributionTest.java)
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); } } Other Commons Math examples (source code examples)Here is a short list of links related to this Commons Math NormalDistributionTest.java source code file: |
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