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 HyperGeometriclDistribution.
21   * Extends IntegerDistributionAbstractTest.  See class javadoc for
22   * IntegerDistributionAbstractTest for details.
23   * 
24   * @version $Revision: 1.13 $ $Date: 2004/11/07 03:32:49 $
25   */
26  public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {
27  
28      /***
29       * Constructor for ChiSquareDistributionTest.
30       * @param name
31       */
32      public HypergeometricDistributionTest(String name) {
33          super(name);
34      }
35  
36  //-------------- Implementations for abstract methods -----------------------
37      
38      /*** Creates the default discrete distribution instance to use in tests. */
39      public IntegerDistribution makeDistribution() {
40          return DistributionFactory.newInstance().createHypergeometricDistribution(10,5, 5);
41      }
42      
43      /*** Creates the default probability density test input values */
44      public int[] makeDensityTestPoints() {
45          return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
46      }
47      
48      /*** Creates the default probability density test expected values */
49      public double[] makeDensityTestValues() {
50          return new double[] {0d, 0.003968d, 0.099206d, 0.396825d, 0.396825d, 
51                  0.099206d, 0.003968d, 0d};
52      }
53      
54      /*** Creates the default cumulative probability density test input values */
55      public int[] makeCumulativeTestPoints() {
56          return makeDensityTestPoints();
57      }
58      
59      /*** Creates the default cumulative probability density test expected values */
60      public double[] makeCumulativeTestValues() {
61          return new double[] {0d, .003968d, .103175d, .50000d, .896825d, .996032d,
62                  1.00000d, 1d};
63      }
64      
65      /*** Creates the default inverse cumulative probability test input values */
66      public double[] makeInverseCumulativeTestPoints() {
67          return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
68                  0.990d, 0.975d, 0.950d, 0.900d, 1d}; 
69      }
70      
71      /*** Creates the default inverse cumulative probability density test expected values */
72      public int[] makeInverseCumulativeTestValues() {
73          return new int[] {-1, -1, 0, 0, 0, 0, 4, 3, 3, 3, 3, 5};
74      }
75      
76      //-------------------- Additional test cases ------------------------------
77      
78      /*** Verify that if there are no failures, mass is concentrated on sampleSize */
79      public void testDegenerateNoFailures() throws Exception {
80          setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,5,3));
81          setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
82          setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
83          setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
84          setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
85          setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
86          setInverseCumulativeTestValues(new int[] {2, 2});
87          verifyDensities();
88          verifyCumulativeProbabilities();
89          verifyInverseCumulativeProbabilities();     
90      }
91      
92      /*** Verify that if there are no successes, mass is concentrated on 0 */
93      public void testDegenerateNoSuccesses() throws Exception {
94          setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,0,3));
95          setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
96          setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
97          setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
98          setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
99          setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
100         setInverseCumulativeTestValues(new int[] {-1, -1});
101         verifyDensities();
102         verifyCumulativeProbabilities();
103         verifyInverseCumulativeProbabilities();     
104     }
105     
106     /*** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
107     public void testDegenerateFullSample() throws Exception {
108         setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,3,5));
109         setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
110         setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
111         setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
112         setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
113         setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
114         setInverseCumulativeTestValues(new int[] {2, 2});
115         verifyDensities();
116         verifyCumulativeProbabilities();
117         verifyInverseCumulativeProbabilities();     
118     }
119 
120 }