/*
* 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.math4.distribution;
import org.apache.commons.math4.distribution.CauchyDistribution;
import org.apache.commons.math4.distribution.NormalDistribution;
import org.apache.commons.math4.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()));
}
}