| /* |
| * 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.distribution; |
| |
| import org.apache.commons.math3.exception.NumberIsTooLargeException; |
| import org.apache.commons.math3.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); |
| } |
| } |