/*
* 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.ExponentialDistribution;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.numbers.core.Precision;
import org.junit.Assert;
import org.junit.Test;
/**
* Test cases for ExponentialDistribution.
* Extends ContinuousDistributionAbstractTest. See class javadoc for
* ContinuousDistributionAbstractTest for details.
*
*/
public class ExponentialDistributionTest extends RealDistributionAbstractTest {
// --------------------- Override tolerance --------------
@Override
public void setUp() {
super.setUp();
setTolerance(1E-9);
}
//-------------- Implementations for abstract methods -----------------------
/** Creates the default continuous distribution instance to use in tests. */
@Override
public ExponentialDistribution makeDistribution() {
return new ExponentialDistribution(5.0);
}
/** Creates the default cumulative probability distribution test input values */
@Override
public double[] makeCumulativeTestPoints() {
// quantiles computed using R version 2.9.2
return new double[] {0.00500250166792, 0.0502516792675, 0.126589039921, 0.256466471938,
0.526802578289, 34.5387763949, 23.0258509299, 18.4443972706, 14.9786613678, 11.5129254650};
}
/** 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[] {0.1998, 0.198, 0.195, 0.19, 0.18, 0.000200000000000,
0.00200000000002, 0.00499999999997, 0.00999999999994, 0.0199999999999};
}
//------------ Additional tests -------------------------------------------
@Test
public void testCumulativeProbabilityExtremes() {
setCumulativeTestPoints(new double[] {-2, 0});
setCumulativeTestValues(new double[] {0, 0});
verifyCumulativeProbabilities();
}
@Test
public void testInverseCumulativeProbabilityExtremes() {
setInverseCumulativeTestPoints(new double[] {0, 1});
setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY});
verifyInverseCumulativeProbabilities();
}
@Test
public void testCumulativeProbability2() {
double actual = getDistribution().probability(0.25, 0.75);
Assert.assertEquals(0.0905214, actual, 10e-4);
}
@Test
public void testDensity() {
ExponentialDistribution d1 = new ExponentialDistribution(1);
Assert.assertTrue(Precision.equals(0.0, d1.density(-1e-9), 1));
Assert.assertTrue(Precision.equals(1.0, d1.density(0.0), 1));
Assert.assertTrue(Precision.equals(0.0, d1.density(1000.0), 1));
Assert.assertTrue(Precision.equals(FastMath.exp(-1), d1.density(1.0), 1));
Assert.assertTrue(Precision.equals(FastMath.exp(-2), d1.density(2.0), 1));
ExponentialDistribution d2 = new ExponentialDistribution(3);
Assert.assertTrue(Precision.equals(1/3.0, d2.density(0.0), 1));
// computed using print(dexp(1, rate=1/3), digits=10) in R 2.5
Assert.assertEquals(0.2388437702, d2.density(1.0), 1e-8);
// computed using print(dexp(2, rate=1/3), digits=10) in R 2.5
Assert.assertEquals(0.1711390397, d2.density(2.0), 1e-8);
}
@Test
public void testMeanAccessors() {
ExponentialDistribution distribution = (ExponentialDistribution) getDistribution();
Assert.assertEquals(5d, distribution.getMean(), Double.MIN_VALUE);
}
@Test(expected=NotStrictlyPositiveException.class)
public void testPreconditions() {
new ExponentialDistribution(0);
}
@Test
public void testMoments() {
final double tol = 1e-9;
ExponentialDistribution dist;
dist = new ExponentialDistribution(11d);
Assert.assertEquals(dist.getNumericalMean(), 11d, tol);
Assert.assertEquals(dist.getNumericalVariance(), 11d * 11d, tol);
dist = new ExponentialDistribution(10.5d);
Assert.assertEquals(dist.getNumericalMean(), 10.5d, tol);
Assert.assertEquals(dist.getNumericalVariance(), 10.5d * 10.5d, tol);
}
}