/*
* 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.ChiSquaredDistribution;
import org.junit.Assert;
import org.junit.Test;
/**
* Test cases for {@link ChiSquaredDistribution}.
*
* @see RealDistributionAbstractTest
*/
public class ChiSquaredDistributionTest extends RealDistributionAbstractTest {
//-------------- Implementations for abstract methods -----------------------
/** Creates the default continuous distribution instance to use in tests. */
@Override
public ChiSquaredDistribution makeDistribution() {
return new ChiSquaredDistribution(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[] {0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978};
}
/** 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 inverse cumulative probability test input values */
@Override
public double[] makeInverseCumulativeTestPoints() {
return new double[] {0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
0.990d, 0.975d, 0.950d, 0.900d, 1};
}
/** Creates the default inverse cumulative probability density test expected values */
@Override
public double[] makeInverseCumulativeTestValues() {
return new double[] {0, 0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978,
Double.POSITIVE_INFINITY};
}
/** Creates the default probability density test expected values */
@Override
public double[] makeDensityTestValues() {
return new double[] {0.0115379817652, 0.0415948507811, 0.0665060119842, 0.0919455953114, 0.121472591024,
0.000433630076361, 0.00412780610309, 0.00999340341045, 0.0193246438937, 0.0368460089216};
}
// --------------------- Override tolerance --------------
@Override
public void setUp() {
super.setUp();
setTolerance(1e-9);
}
//---------------------------- Additional test cases -------------------------
@Test
public void testSmallDf() {
setDistribution(new ChiSquaredDistribution(0.1d));
setTolerance(1E-4);
// quantiles computed using R version 1.8.1 (linux version)
setCumulativeTestPoints(new double[] {1.168926E-60, 1.168926E-40, 1.063132E-32,
1.144775E-26, 1.168926E-20, 5.472917, 2.175255, 1.13438,
0.5318646, 0.1526342});
setInverseCumulativeTestValues(getCumulativeTestPoints());
setInverseCumulativeTestPoints(getCumulativeTestValues());
verifyCumulativeProbabilities();
verifyInverseCumulativeProbabilities();
}
@Test
public void testDfAccessors() {
ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
Assert.assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
}
@Test
public void testDensity() {
double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
//R 2.5: print(dchisq(x, df=1), digits=10)
checkDensity(1, x, new double[]{0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436, 0.01464498256});
//R 2.5: print(dchisq(x, df=0.1), digits=10)
checkDensity(0.1, x, new double[]{0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02, 9.447299159e-03, 8.827199396e-04});
//R 2.5: print(dchisq(x, df=2), digits=10)
checkDensity(2, x, new double[]{0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059, 0.04104249931});
//R 2.5: print(dchisq(x, df=10), digits=10)
checkDensity(10, x, new double[]{0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04, 7.664155024e-03, 6.680094289e-02});
}
private void checkDensity(double df, double[] x, double[] expected) {
ChiSquaredDistribution d = new ChiSquaredDistribution(df);
for (int i = 0; i < x.length; i++) {
Assert.assertEquals(expected[i], d.density(x[i]), 1e-5);
}
}
@Test
public void testMoments() {
final double tol = 1e-9;
ChiSquaredDistribution dist;
dist = new ChiSquaredDistribution(1500);
Assert.assertEquals(dist.getNumericalMean(), 1500, tol);
Assert.assertEquals(dist.getNumericalVariance(), 3000, tol);
dist = new ChiSquaredDistribution(1.12);
Assert.assertEquals(dist.getNumericalMean(), 1.12, tol);
Assert.assertEquals(dist.getNumericalVariance(), 2.24, tol);
}
}