1   /*
2    * Copyright 2003-2004 The Apache Software Foundation.
3    * 
4    * Licensed under the Apache License, Version 2.0 (the "License");
5    * you may not use this file except in compliance with the License.
6    * You may obtain a copy of the License at
7    * 
8    *      http://www.apache.org/licenses/LICENSE-2.0
9    * 
10   * Unless required by applicable law or agreed to in writing, software
11   * distributed under the License is distributed on an "AS IS" BASIS,
12   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13   * See the License for the specific language governing permissions and
14   * limitations under the License.
15   */
16  
17  package org.apache.commons.math.distribution;
18  
19  /**
20   * Test cases for ChiSquareDistribution.
21   * Extends ContinuousDistributionAbstractTest.  See class javadoc for
22   * ContinuousDistributionAbstractTest for details.
23   * 
24   * @version $Revision: 155427 $ $Date: 2005-02-26 06:11:52 -0700 (Sat, 26 Feb 2005) $
25   */
26  public class ChiSquareDistributionTest extends ContinuousDistributionAbstractTest {
27      
28      /**
29       * Constructor for ChiSquareDistributionTest.
30       * @param name
31       */
32      public ChiSquareDistributionTest(String name) {
33          super(name);
34      }
35      
36      //-------------- Implementations for abstract methods -----------------------
37      
38      /** Creates the default continuous distribution instance to use in tests. */
39      public ContinuousDistribution makeDistribution() {
40          return DistributionFactory.newInstance().createChiSquareDistribution(5.0);
41      }   
42      
43      /** Creates the default cumulative probability distribution test input values */
44      public double[] makeCumulativeTestPoints() {
45          // quantiles computed using R version 1.8.1 (linux version)
46          return new double[] {0.210216d, 0.5542981d, 0.8312116d, 1.145476d, 1.610308d, 
47                  20.51501d, 15.08627d, 12.83250d, 11.07050d, 9.236357d};
48      }
49      
50      /** Creates the default cumulative probability density test expected values */
51      public double[] makeCumulativeTestValues() {
52          return new double[] {0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
53                  0.990d, 0.975d, 0.950d, 0.900d}; 
54      }
55      
56      /** Creates the default inverse cumulative probability test input values */
57      public double[] makeInverseCumulativeTestPoints() {
58          return new double[] {0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d,
59                  0.990d, 0.975d, 0.950d, 0.900d, 1};     
60      }
61      
62      /** Creates the default inverse cumulative probability density test expected values */
63      public double[] makeInverseCumulativeTestValues() {
64          return new double[] {0, 0.210216d, 0.5542981d, 0.8312116d, 1.145476d, 1.610308d, 
65                  20.51501d, 15.08627d, 12.83250d, 11.07050d, 9.236357d, 
66                  Double.POSITIVE_INFINITY};
67      }
68      
69   // --------------------- Override tolerance  --------------
70      protected void setup() throws Exception {
71          super.setUp();
72          setTolerance(1E-6);
73      }
74  
75   //---------------------------- Additional test cases -------------------------
76      
77      public void testSmallDf() throws Exception {
78          setDistribution(DistributionFactory.newInstance().createChiSquareDistribution(0.1d));
79          setTolerance(1E-4);
80          // quantiles computed using R version 1.8.1 (linux version)
81          setCumulativeTestPoints(new double[] {1.168926E-60, 1.168926E-40, 1.063132E-32, 
82                  1.144775E-26, 1.168926E-20, 5.472917, 2.175255, 1.13438, 
83                  0.5318646, 0.1526342});
84          setInverseCumulativeTestValues(getCumulativeTestPoints());
85          setInverseCumulativeTestPoints(getCumulativeTestValues());
86          verifyCumulativeProbabilities();
87          verifyInverseCumulativeProbabilities();
88      }
89      
90      public void testDfAccessors() {
91          ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
92          assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
93          distribution.setDegreesOfFreedom(4d);
94          assertEquals(4d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
95          try {
96              distribution.setDegreesOfFreedom(0d);
97              fail("Expecting IllegalArgumentException for df = 0");
98          } catch (IllegalArgumentException ex) {
99              // expected
100         }
101     } 
102     
103 }