/*
* 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 static org.junit.Assert.assertEquals;
import org.apache.commons.math4.distribution.EnumeratedIntegerDistribution;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.exception.MathArithmeticException;
import org.apache.commons.math4.exception.NotANumberException;
import org.apache.commons.math4.exception.NotFiniteNumberException;
import org.apache.commons.math4.exception.NotPositiveException;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.rng.simple.RandomSource;
import org.junit.Assert;
import org.junit.Test;
/**
* Test class for {@link EnumeratedIntegerDistribution}.
*
*/
public class EnumeratedIntegerDistributionTest {
/**
* The distribution object used for testing.
*/
private final EnumeratedIntegerDistribution testDistribution;
/**
* Creates the default distribution object used for testing.
*/
public EnumeratedIntegerDistributionTest() {
// Non-sorted singleton array with duplicates should be allowed.
// Values with zero-probability do not extend the support.
testDistribution = new EnumeratedIntegerDistribution(
new int[]{3, -1, 3, 7, -2, 8},
new double[]{0.2, 0.2, 0.3, 0.3, 0.0, 0.0});
}
/**
* Tests if the EnumeratedIntegerDistribution constructor throws
* exceptions for invalid data.
*/
@Test
public void testExceptions() {
EnumeratedIntegerDistribution invalid = null;
try {
new EnumeratedIntegerDistribution(new int[]{1, 2}, new double[]{0.0});
Assert.fail("Expected DimensionMismatchException");
} catch (DimensionMismatchException e) {
}
try {
new EnumeratedIntegerDistribution(new int[]{1, 2}, new double[]{0.0, -1.0});
Assert.fail("Expected NotPositiveException");
} catch (NotPositiveException e) {
}
try {
new EnumeratedIntegerDistribution(new int[]{1, 2}, new double[]{0.0, 0.0});
Assert.fail("Expected MathArithmeticException");
} catch (MathArithmeticException e) {
}
try {
new EnumeratedIntegerDistribution(new int[]{1, 2}, new double[]{0.0, Double.NaN});
Assert.fail("Expected NotANumberException");
} catch (NotANumberException e) {
}
try {
new EnumeratedIntegerDistribution(new int[]{1, 2}, new double[]{0.0, Double.POSITIVE_INFINITY});
Assert.fail("Expected NotFiniteNumberException");
} catch (NotFiniteNumberException e) {
}
Assert.assertNull("Expected non-initialized DiscreteRealDistribution", invalid);
}
/**
* Tests if the distribution returns proper probability values.
*/
@Test
public void testProbability() {
int[] points = new int[]{-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8};
double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
for (int p = 0; p < points.length; p++) {
double probability = testDistribution.probability(points[p]);
Assert.assertEquals(results[p], probability, 0.0);
}
}
/**
* Tests if the distribution returns proper cumulative probability values.
*/
@Test
public void testCumulativeProbability() {
int[] points = new int[]{-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8};
double[] results = new double[]{0, 0.2, 0.2, 0.2, 0.2, 0.7, 0.7, 0.7, 0.7, 1.0, 1.0};
for (int p = 0; p < points.length; p++) {
double probability = testDistribution.cumulativeProbability(points[p]);
Assert.assertEquals(results[p], probability, 1e-10);
}
}
/**
* Tests if the distribution returns proper mean value.
*/
@Test
public void testGetNumericalMean() {
Assert.assertEquals(3.4, testDistribution.getNumericalMean(), 1e-10);
}
/**
* Tests if the distribution returns proper variance.
*/
@Test
public void testGetNumericalVariance() {
Assert.assertEquals(7.84, testDistribution.getNumericalVariance(), 1e-10);
}
/**
* Tests if the distribution returns proper lower bound.
*/
@Test
public void testGetSupportLowerBound() {
Assert.assertEquals(-1, testDistribution.getSupportLowerBound());
}
/**
* Tests if the distribution returns proper upper bound.
*/
@Test
public void testGetSupportUpperBound() {
Assert.assertEquals(7, testDistribution.getSupportUpperBound());
}
/**
* Tests if the distribution returns properly that the support is connected.
*/
@Test
public void testIsSupportConnected() {
Assert.assertTrue(testDistribution.isSupportConnected());
}
/**
* Tests sampling.
*/
@Test
public void testSample() {
final int n = 1000000;
final IntegerDistribution.Sampler sampler
= testDistribution.createSampler(RandomSource.create(RandomSource.WELL_19937_C,
-334759360)); // fixed seed
final int[] samples = AbstractIntegerDistribution.sample(n, sampler);
Assert.assertEquals(n, samples.length);
double sum = 0;
double sumOfSquares = 0;
for (int i = 0; i < samples.length; i++) {
sum += samples[i];
sumOfSquares += samples[i] * samples[i];
}
Assert.assertEquals(testDistribution.getNumericalMean(),
sum / n, 1e-2);
Assert.assertEquals(testDistribution.getNumericalVariance(),
sumOfSquares / n - FastMath.pow(sum / n, 2), 1e-2);
}
@Test
public void testCreateFromIntegers() {
final int[] data = new int[] {0, 1, 1, 2, 2, 2};
EnumeratedIntegerDistribution distribution = new EnumeratedIntegerDistribution(data);
assertEquals(0.5, distribution.probability(2), 0);
assertEquals(0.5, distribution.cumulativeProbability(1), 0);
}
}