| /* |
| * 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.exception.NotStrictlyPositiveException; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * Test cases for CauchyDistribution. |
| * Extends ContinuousDistributionAbstractTest. See class javadoc for |
| * ContinuousDistributionAbstractTest for details. |
| * |
| */ |
| public class CauchyDistributionTest extends RealDistributionAbstractTest { |
| |
| // --------------------- Override tolerance -------------- |
| protected double defaultTolerance = NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY; |
| @Override |
| public void setUp() { |
| super.setUp(); |
| setTolerance(defaultTolerance); |
| } |
| |
| //-------------- Implementations for abstract methods ----------------------- |
| |
| /** Creates the default continuous distribution instance to use in tests. */ |
| @Override |
| public CauchyDistribution makeDistribution() { |
| return new CauchyDistribution(1.2, 2.1); |
| } |
| |
| /** Creates the default cumulative probability distribution test input values */ |
| @Override |
| public double[] makeCumulativeTestPoints() { |
| // quantiles computed using R 2.9.2 |
| return new double[] {-667.24856187, -65.6230835029, -25.4830299460, -12.0588781808, |
| -5.26313542807, 669.64856187, 68.0230835029, 27.8830299460, 14.4588781808, 7.66313542807}; |
| } |
| |
| /** Creates the default cumulative probability density test expected values */ |
| @Override |
| public double[] makeCumulativeTestValues() { |
| return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, |
| 0.990, 0.975, 0.950, 0.900}; |
| } |
| |
| /** Creates the default probability density test expected values */ |
| @Override |
| public double[] makeDensityTestValues() { |
| return new double[] {1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437, |
| 1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437}; |
| } |
| |
| //---------------------------- Additional test cases ------------------------- |
| |
| @Test |
| public void testInverseCumulativeProbabilityExtremes() { |
| setInverseCumulativeTestPoints(new double[] {0.0, 1.0}); |
| setInverseCumulativeTestValues( |
| new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY}); |
| verifyInverseCumulativeProbabilities(); |
| } |
| |
| @Test |
| public void testMedian() { |
| CauchyDistribution distribution = (CauchyDistribution) getDistribution(); |
| Assert.assertEquals(1.2, distribution.getMedian(), 0.0); |
| } |
| |
| @Test |
| public void testScale() { |
| CauchyDistribution distribution = (CauchyDistribution) getDistribution(); |
| Assert.assertEquals(2.1, distribution.getScale(), 0.0); |
| } |
| |
| @Test |
| public void testPreconditions() { |
| try { |
| new CauchyDistribution(0, 0); |
| Assert.fail("Cannot have zero scale"); |
| } catch (NotStrictlyPositiveException ex) { |
| // Expected. |
| } |
| try { |
| new CauchyDistribution(0, -1); |
| Assert.fail("Cannot have negative scale"); |
| } catch (NotStrictlyPositiveException ex) { |
| // Expected. |
| } |
| } |
| |
| @Test |
| public void testMoments() { |
| CauchyDistribution dist; |
| |
| dist = new CauchyDistribution(10.2, 0.15); |
| Assert.assertTrue(Double.isNaN(dist.getNumericalMean())); |
| Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); |
| |
| dist = new CauchyDistribution(23.12, 2.12); |
| Assert.assertTrue(Double.isNaN(dist.getNumericalMean())); |
| Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); |
| } |
| } |