/*
* 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.TriangularDistribution;
import org.apache.commons.math4.exception.NumberIsTooLargeException;
import org.apache.commons.math4.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Test;
/**
* Test cases for {@link TriangularDistribution}. See class javadoc for
* {@link RealDistributionAbstractTest} for further details.
*/
public class TriangularDistributionTest extends RealDistributionAbstractTest {
// --- Override tolerance -------------------------------------------------
@Override
public void setUp() {
super.setUp();
setTolerance(1e-4);
}
//--- Implementations for abstract methods --------------------------------
/**
* Creates the default triangular distribution instance to use in tests.
*/
@Override
public TriangularDistribution makeDistribution() {
// Left side 5 wide, right side 10 wide.
return new TriangularDistribution(-3, 2, 12);
}
/**
* Creates the default cumulative probability distribution test input
* values.
*/
@Override
public double[] makeCumulativeTestPoints() {
return new double[] { -3.0001, // below lower limit
-3.0, // at lower limit
-2.0, -1.0, 0.0, 1.0, // on lower side
2.0, // at mode
3.0, 4.0, 10.0, 11.0, // on upper side
12.0, // at upper limit
12.0001 // above upper limit
};
}
/**
* Creates the default cumulative probability density test expected values.
*/
@Override
public double[] makeCumulativeTestValues() {
// Top at 2 / (b - a) = 2 / (12 - -3) = 2 / 15 = 7.5
// Area left = 7.5 * 5 * 0.5 = 18.75 (1/3 of the total area)
// Area right = 7.5 * 10 * 0.5 = 37.5 (2/3 of the total area)
// Area total = 18.75 + 37.5 = 56.25
// Derivative left side = 7.5 / 5 = 1.5
// Derivative right side = -7.5 / 10 = -0.75
double third = 1 / 3.0;
double left = 18.75;
double area = 56.25;
return new double[] { 0.0,
0.0,
0.75 / area, 3 / area, 6.75 / area, 12 / area,
third,
(left + 7.125) / area, (left + 13.5) / area,
(left + 36) / area, (left + 37.125) / area,
1.0,
1.0
};
}
/**
* Creates the default inverse cumulative probability distribution test
* input values.
*/
@Override
public double[] makeInverseCumulativeTestPoints() {
// Exclude the points outside the limits, as they have cumulative
// probability of zero and one, meaning the inverse returns the
// limits and not the points outside the limits.
double[] points = makeCumulativeTestValues();
double[] points2 = new double[points.length-2];
System.arraycopy(points, 1, points2, 0, points2.length);
return points2;
//return Arrays.copyOfRange(points, 1, points.length - 1);
}
/**
* Creates the default inverse cumulative probability density test expected
* values.
*/
@Override
public double[] makeInverseCumulativeTestValues() {
// Exclude the points outside the limits, as they have cumulative
// probability of zero and one, meaning the inverse returns the
// limits and not the points outside the limits.
double[] points = makeCumulativeTestPoints();
double[] points2 = new double[points.length-2];
System.arraycopy(points, 1, points2, 0, points2.length);
return points2;
//return Arrays.copyOfRange(points, 1, points.length - 1);
}
/** Creates the default probability density test expected values. */
@Override
public double[] makeDensityTestValues() {
return new double[] { 0,
0,
2 / 75.0, 4 / 75.0, 6 / 75.0, 8 / 75.0,
10 / 75.0,
9 / 75.0, 8 / 75.0, 2 / 75.0, 1 / 75.0,
0,
0
};
}
//--- Additional test cases -----------------------------------------------
/** Test lower bound getter. */
@Test
public void testGetLowerBound() {
TriangularDistribution distribution = makeDistribution();
Assert.assertEquals(-3.0, distribution.getSupportLowerBound(), 0);
}
/** Test upper bound getter. */
@Test
public void testGetUpperBound() {
TriangularDistribution distribution = makeDistribution();
Assert.assertEquals(12.0, distribution.getSupportUpperBound(), 0);
}
/** Test pre-condition for equal lower/upper limit. */
@Test(expected=NumberIsTooLargeException.class)
public void testPreconditions1() {
new TriangularDistribution(0, 0, 0);
}
/** Test pre-condition for lower limit larger than upper limit. */
@Test(expected=NumberIsTooLargeException.class)
public void testPreconditions2() {
new TriangularDistribution(1, 1, 0);
}
/** Test pre-condition for mode larger than upper limit. */
@Test(expected=NumberIsTooLargeException.class)
public void testPreconditions3() {
new TriangularDistribution(0, 2, 1);
}
/** Test pre-condition for mode smaller than lower limit. */
@Test(expected=NumberIsTooSmallException.class)
public void testPreconditions4() {
new TriangularDistribution(2, 1, 3);
}
/** Test mean/variance. */
@Test
public void testMeanVariance() {
TriangularDistribution dist;
dist = new TriangularDistribution(0, 0.5, 1.0);
Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
Assert.assertEquals(dist.getNumericalVariance(), 1 / 24.0, 0);
dist = new TriangularDistribution(0, 1, 1);
Assert.assertEquals(dist.getNumericalMean(), 2 / 3.0, 0);
Assert.assertEquals(dist.getNumericalVariance(), 1 / 18.0, 0);
dist = new TriangularDistribution(-3, 2, 12);
Assert.assertEquals(dist.getNumericalMean(), 3 + (2 / 3.0), 0);
Assert.assertEquals(dist.getNumericalVariance(), 175 / 18.0, 0);
}
}