1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * https://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 /*
19 * This is not the original file distributed by the Apache Software Foundation
20 * It has been modified by the Hipparchus project
21 */
22
23 package org.hipparchus.distribution.continuous;
24
25 import org.hipparchus.exception.MathIllegalArgumentException;
26 import org.junit.Assert;
27 import org.junit.Test;
28
29 /**
30 * Test cases for {@link TriangularDistribution}.
31 */
32 public class TriangularDistributionTest extends RealDistributionAbstractTest {
33
34 // --- Override tolerance -------------------------------------------------
35
36 @Override
37 public void setUp() {
38 super.setUp();
39 setTolerance(1e-4);
40 }
41
42 //--- Implementations for abstract methods --------------------------------
43
44 /**
45 * Creates the default triangular distribution instance to use in tests.
46 */
47 @Override
48 public TriangularDistribution makeDistribution() {
49 // Left side 5 wide, right side 10 wide.
50 return new TriangularDistribution(-3, 2, 12);
51 }
52
53 /**
54 * Creates the default cumulative probability distribution test input
55 * values.
56 */
57 @Override
58 public double[] makeCumulativeTestPoints() {
59 return new double[] { -3.0001, // below lower limit
60 -3.0, // at lower limit
61 -2.0, -1.0, 0.0, 1.0, // on lower side
62 2.0, // at mode
63 3.0, 4.0, 10.0, 11.0, // on upper side
64 12.0, // at upper limit
65 12.0001 // above upper limit
66 };
67 }
68
69 /**
70 * Creates the default cumulative probability density test expected values.
71 */
72 @Override
73 public double[] makeCumulativeTestValues() {
74 // Top at 2 / (b - a) = 2 / (12 - -3) = 2 / 15 = 7.5
75 // Area left = 7.5 * 5 * 0.5 = 18.75 (1/3 of the total area)
76 // Area right = 7.5 * 10 * 0.5 = 37.5 (2/3 of the total area)
77 // Area total = 18.75 + 37.5 = 56.25
78 // Derivative left side = 7.5 / 5 = 1.5
79 // Derivative right side = -7.5 / 10 = -0.75
80 double third = 1 / 3.0;
81 double left = 18.75;
82 double area = 56.25;
83 return new double[] { 0.0,
84 0.0,
85 0.75 / area, 3 / area, 6.75 / area, 12 / area,
86 third,
87 (left + 7.125) / area, (left + 13.5) / area,
88 (left + 36) / area, (left + 37.125) / area,
89 1.0,
90 1.0
91 };
92 }
93
94 /**
95 * Creates the default inverse cumulative probability distribution test
96 * input values.
97 */
98 @Override
99 public double[] makeInverseCumulativeTestPoints() {
100 // Exclude the points outside the limits, as they have cumulative
101 // probability of zero and one, meaning the inverse returns the
102 // limits and not the points outside the limits.
103 double[] points = makeCumulativeTestValues();
104 double[] points2 = new double[points.length-2];
105 System.arraycopy(points, 1, points2, 0, points2.length);
106 return points2;
107 //return Arrays.copyOfRange(points, 1, points.length - 1);
108 }
109
110 /**
111 * Creates the default inverse cumulative probability density test expected
112 * values.
113 */
114 @Override
115 public double[] makeInverseCumulativeTestValues() {
116 // Exclude the points outside the limits, as they have cumulative
117 // probability of zero and one, meaning the inverse returns the
118 // limits and not the points outside the limits.
119 double[] points = makeCumulativeTestPoints();
120 double[] points2 = new double[points.length-2];
121 System.arraycopy(points, 1, points2, 0, points2.length);
122 return points2;
123 //return Arrays.copyOfRange(points, 1, points.length - 1);
124 }
125
126 /** Creates the default probability density test expected values. */
127 @Override
128 public double[] makeDensityTestValues() {
129 return new double[] { 0,
130 0,
131 2 / 75.0, 4 / 75.0, 6 / 75.0, 8 / 75.0,
132 10 / 75.0,
133 9 / 75.0, 8 / 75.0, 2 / 75.0, 1 / 75.0,
134 0,
135 0
136 };
137 }
138
139 //--- Additional test cases -----------------------------------------------
140
141 /** Test lower bound getter. */
142 @Test
143 public void testGetLowerBound() {
144 TriangularDistribution distribution = makeDistribution();
145 Assert.assertEquals(-3.0, distribution.getSupportLowerBound(), 0);
146 }
147
148 /** Test upper bound getter. */
149 @Test
150 public void testGetUpperBound() {
151 TriangularDistribution distribution = makeDistribution();
152 Assert.assertEquals(12.0, distribution.getSupportUpperBound(), 0);
153 }
154
155 /** Test pre-condition for equal lower/upper limit. */
156 @Test(expected=MathIllegalArgumentException.class)
157 public void testPreconditions1() {
158 new TriangularDistribution(0, 0, 0);
159 }
160
161 /** Test pre-condition for lower limit larger than upper limit. */
162 @Test(expected=MathIllegalArgumentException.class)
163 public void testPreconditions2() {
164 new TriangularDistribution(1, 1, 0);
165 }
166
167 /** Test pre-condition for mode larger than upper limit. */
168 @Test(expected=MathIllegalArgumentException.class)
169 public void testPreconditions3() {
170 new TriangularDistribution(0, 2, 1);
171 }
172
173 /** Test pre-condition for mode smaller than lower limit. */
174 @Test(expected=MathIllegalArgumentException.class)
175 public void testPreconditions4() {
176 new TriangularDistribution(2, 1, 3);
177 }
178
179 /** Test mean/variance. */
180 @Test
181 public void testMeanVariance() {
182 TriangularDistribution dist;
183
184 dist = new TriangularDistribution(0, 0.5, 1.0);
185 Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
186 Assert.assertEquals(dist.getNumericalVariance(), 1 / 24.0, 0);
187
188 dist = new TriangularDistribution(0, 1, 1);
189 Assert.assertEquals(dist.getNumericalMean(), 2 / 3.0, 0);
190 Assert.assertEquals(dist.getNumericalVariance(), 1 / 18.0, 0);
191
192 dist = new TriangularDistribution(-3, 2, 12);
193 Assert.assertEquals(dist.getNumericalMean(), 3 + (2 / 3.0), 0);
194 Assert.assertEquals(dist.getNumericalVariance(), 175 / 18.0, 0);
195 }
196 }