/*
* 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.BinomialDistribution;
import org.apache.commons.math4.distribution.IntegerDistribution;
import org.junit.Assert;
import org.junit.Test;
/**
* Test cases for BinomialDistribution. Extends IntegerDistributionAbstractTest.
* See class javadoc for IntegerDistributionAbstractTest for details.
*
*/
public class BinomialDistributionTest extends IntegerDistributionAbstractTest {
/**
* Constructor to override default tolerance.
*/
public BinomialDistributionTest() {
setTolerance(1e-12);
}
// -------------- Implementations for abstract methods
// -----------------------
/** Creates the default discrete distribution instance to use in tests. */
@Override
public IntegerDistribution makeDistribution() {
return new BinomialDistribution(10, 0.70);
}
/** Creates the default probability density test input values. */
@Override
public int[] makeDensityTestPoints() {
return new int[] { -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
}
/**
* Creates the default probability density test expected values.
* Reference values are from R, version 2.15.3.
*/
@Override
public double[] makeDensityTestValues() {
return new double[] { 0d, 0.0000059049d, 0.000137781d, 0.0014467005,
0.009001692, 0.036756909, 0.1029193452, 0.200120949, 0.266827932,
0.2334744405, 0.121060821, 0.0282475249, 0d };
}
/** Creates the default cumulative probability density test input values */
@Override
public int[] makeCumulativeTestPoints() {
return makeDensityTestPoints();
}
/**
* Creates the default cumulative probability density test expected values.
* Reference values are from R, version 2.15.3.
*/
@Override
public double[] makeCumulativeTestValues() {
return new double[] { 0d, 5.9049e-06, 0.0001436859, 0.0015903864, 0.0105920784, 0.0473489874,
0.1502683326, 0.3503892816, 0.6172172136, 0.8506916541, 0.9717524751, 1d, 1d };
}
/** Creates the default inverse cumulative probability test input values */
@Override
public double[] makeInverseCumulativeTestPoints() {
return new double[] { 0, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d,
0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 };
}
/**
* Creates the default inverse cumulative probability density test expected
* values
*/
@Override
public int[] makeInverseCumulativeTestValues() {
return new int[] { 0, 2, 3, 4, 5, 5, 10, 10, 10, 9, 9, 10 };
}
// ----------------- Additional test cases ---------------------------------
/** Test degenerate case p = 0 */
@Test
public void testDegenerate0() {
BinomialDistribution dist = new BinomialDistribution(5, 0.0d);
setDistribution(dist);
setCumulativeTestPoints(new int[] { -1, 0, 1, 5, 10 });
setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d });
setDensityTestPoints(new int[] { -1, 0, 1, 10, 11 });
setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d });
setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
setInverseCumulativeTestValues(new int[] { 0, 0 });
verifyDensities();
verifyCumulativeProbabilities();
verifyInverseCumulativeProbabilities();
Assert.assertEquals(dist.getSupportLowerBound(), 0);
Assert.assertEquals(dist.getSupportUpperBound(), 0);
}
/** Test degenerate case p = 1 */
@Test
public void testDegenerate1() {
BinomialDistribution dist = new BinomialDistribution(5, 1.0d);
setDistribution(dist);
setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
setCumulativeTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 1d });
setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
setDensityTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 0d });
setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
setInverseCumulativeTestValues(new int[] { 5, 5 });
verifyDensities();
verifyCumulativeProbabilities();
verifyInverseCumulativeProbabilities();
Assert.assertEquals(dist.getSupportLowerBound(), 5);
Assert.assertEquals(dist.getSupportUpperBound(), 5);
}
/** Test degenerate case n = 0 */
@Test
public void testDegenerate2() {
BinomialDistribution dist = new BinomialDistribution(0, 0.01d);
setDistribution(dist);
setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d, 1d });
setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d, 0d });
setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
setInverseCumulativeTestValues(new int[] { 0, 0 });
verifyDensities();
verifyCumulativeProbabilities();
verifyInverseCumulativeProbabilities();
Assert.assertEquals(dist.getSupportLowerBound(), 0);
Assert.assertEquals(dist.getSupportUpperBound(), 0);
}
@Test
public void testMoments() {
final double tol = 1e-9;
BinomialDistribution dist;
dist = new BinomialDistribution(10, 0.5);
Assert.assertEquals(dist.getNumericalMean(), 10d * 0.5d, tol);
Assert.assertEquals(dist.getNumericalVariance(), 10d * 0.5d * 0.5d, tol);
dist = new BinomialDistribution(30, 0.3);
Assert.assertEquals(dist.getNumericalMean(), 30d * 0.3d, tol);
Assert.assertEquals(dist.getNumericalVariance(), 30d * 0.3d * (1d - 0.3d), tol);
}
@Test
public void testMath718() {
// for large trials the evaluation of ContinuedFraction was inaccurate
// do a sweep over several large trials to test if the current implementation is
// numerically stable.
for (int trials = 500000; trials < 20000000; trials += 100000) {
BinomialDistribution dist = new BinomialDistribution(trials, 0.5);
int p = dist.inverseCumulativeProbability(0.5);
Assert.assertEquals(trials / 2, p);
}
}
}