blob: 8d7510521312288ac23ae05fb586d026fa292710 [file] [log] [blame]
/*
* 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.math3.random;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.stat.StatUtils;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
import org.junit.Assert;
import org.junit.Test;
/**
* The class <code>StableRandomGeneratorTest</code> contains tests for the class
* {@link StableRandomGenerator}
*
*/
public class StableRandomGeneratorTest {
private RandomGenerator rg = new Well19937c(100);
private final static int sampleSize = 10000;
/**
* Run the double nextDouble() method test Due to leptokurtic property the
* acceptance range is widened.
*
* TODO: verify that tolerance this wide is really OK
*/
@Test
public void testNextDouble() {
StableRandomGenerator generator = new StableRandomGenerator(rg, 1.3,
0.1);
double[] sample = new double[2 * sampleSize];
for (int i = 0; i < sample.length; ++i) {
sample[i] = generator.nextNormalizedDouble();
}
Assert.assertEquals(0.0, StatUtils.mean(sample), 0.3);
}
/**
* If alpha = 2, than it must be Gaussian distribution
*/
@Test
public void testGaussianCase() {
StableRandomGenerator generator = new StableRandomGenerator(rg, 2d, 0.0);
double[] sample = new double[sampleSize];
for (int i = 0; i < sample.length; ++i) {
sample[i] = generator.nextNormalizedDouble();
}
Assert.assertEquals(0.0, StatUtils.mean(sample), 0.02);
Assert.assertEquals(1.0, StatUtils.variance(sample), 0.02);
}
/**
* If alpha = 1, than it must be Cauchy distribution
*/
@Test
public void testCauchyCase() {
StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
DescriptiveStatistics summary = new DescriptiveStatistics();
for (int i = 0; i < sampleSize; ++i) {
double sample = generator.nextNormalizedDouble();
summary.addValue(sample);
}
// Standard Cauchy distribution should have zero median and mode
double median = summary.getPercentile(50);
Assert.assertEquals(0.0, median, 0.2);
}
/**
* Input parameter range tests
*/
@Test
public void testAlphaRangeBelowZero() {
try {
new StableRandomGenerator(rg,
-1.0, 0.0);
Assert.fail("Expected OutOfRangeException");
} catch (OutOfRangeException e) {
Assert.assertEquals(-1.0, e.getArgument());
}
}
@Test
public void testAlphaRangeAboveTwo() {
try {
new StableRandomGenerator(rg,
3.0, 0.0);
Assert.fail("Expected OutOfRangeException");
} catch (OutOfRangeException e) {
Assert.assertEquals(3.0, e.getArgument());
}
}
@Test
public void testBetaRangeBelowMinusOne() {
try {
new StableRandomGenerator(rg,
1.0, -2.0);
Assert.fail("Expected OutOfRangeException");
} catch (OutOfRangeException e) {
Assert.assertEquals(-2.0, e.getArgument());
}
}
@Test
public void testBetaRangeAboveOne() {
try {
new StableRandomGenerator(rg,
1.0, 2.0);
Assert.fail("Expected OutOfRangeException");
} catch (OutOfRangeException e) {
Assert.assertEquals(2.0, e.getArgument());
}
}
}