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Java > Open Source Codes > org > apache > commons > math > distribution > HypergeometricDistributionTest


1 /*
2  * Copyright 2003-2005 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 $Revision$ $Date: 2005-02-26 05:11:52 -0800 (Sat, 26 Feb 2005) $
25  */
26 public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {
27
28     /**
29      * Constructor for ChiSquareDistributionTest.
30      * @param name
31      */
32     public HypergeometricDistributionTest(String JavaDoc 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 JavaDoc {
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 JavaDoc {
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 JavaDoc {
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     public void testPopulationSize() {
121         HypergeometricDistribution dist = DistributionFactory.newInstance().createHypergeometricDistribution(5,3,5);
122         try {
123             dist.setPopulationSize(-1);
124             fail("negative population size. IllegalArgumentException expected");
125         } catch(IllegalArgumentException JavaDoc ex) {
126         }
127         
128         dist.setPopulationSize(10);
129         assertEquals(10, dist.getPopulationSize());
130     }
131 }
132
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