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17 package org.apache.commons.math.distribution;
18
19 import org.apache.commons.math.TestUtils;
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28 public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {
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33
34 public HypergeometricDistributionTest(String name) {
35 super(name);
36 }
37
38
39
40
41 public IntegerDistribution makeDistribution() {
42 return DistributionFactory.newInstance().createHypergeometricDistribution(10,5, 5);
43 }
44
45
46 public int[] makeDensityTestPoints() {
47 return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
48 }
49
50
51 public double[] makeDensityTestValues() {
52 return new double[] {0d, 0.003968d, 0.099206d, 0.396825d, 0.396825d,
53 0.099206d, 0.003968d, 0d};
54 }
55
56
57 public int[] makeCumulativeTestPoints() {
58 return makeDensityTestPoints();
59 }
60
61
62 public double[] makeCumulativeTestValues() {
63 return new double[] {0d, .003968d, .103175d, .50000d, .896825d, .996032d,
64 1.00000d, 1d};
65 }
66
67
68 public double[] makeInverseCumulativeTestPoints() {
69 return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
70 0.990d, 0.975d, 0.950d, 0.900d, 1d};
71 }
72
73
74 public int[] makeInverseCumulativeTestValues() {
75 return new int[] {-1, -1, 0, 0, 0, 0, 4, 3, 3, 3, 3, 5};
76 }
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80
81 public void testDegenerateNoFailures() throws Exception {
82 setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,5,3));
83 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
84 setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
85 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
86 setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
87 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
88 setInverseCumulativeTestValues(new int[] {2, 2});
89 verifyDensities();
90 verifyCumulativeProbabilities();
91 verifyInverseCumulativeProbabilities();
92 }
93
94
95 public void testDegenerateNoSuccesses() throws Exception {
96 setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,0,3));
97 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
98 setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
99 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
100 setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
101 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
102 setInverseCumulativeTestValues(new int[] {-1, -1});
103 verifyDensities();
104 verifyCumulativeProbabilities();
105 verifyInverseCumulativeProbabilities();
106 }
107
108
109 public void testDegenerateFullSample() throws Exception {
110 setDistribution(DistributionFactory.newInstance().createHypergeometricDistribution(5,3,5));
111 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
112 setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
113 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
114 setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
115 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
116 setInverseCumulativeTestValues(new int[] {2, 2});
117 verifyDensities();
118 verifyCumulativeProbabilities();
119 verifyInverseCumulativeProbabilities();
120 }
121
122 public void testPopulationSize() {
123 HypergeometricDistribution dist = DistributionFactory.newInstance().createHypergeometricDistribution(5,3,5);
124 try {
125 dist.setPopulationSize(-1);
126 fail("negative population size. IllegalArgumentException expected");
127 } catch(IllegalArgumentException ex) {
128 }
129
130 dist.setPopulationSize(10);
131 assertEquals(10, dist.getPopulationSize());
132 }
133
134 public void testLargeValues() {
135 int populationSize = 3456;
136 int sampleSize = 789;
137 int numberOfSucceses = 101;
138 double[][] data = {
139 {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0},
140 {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244},
141 {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673},
142 {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792},
143 {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375},
144 {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203},
145 {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781},
146 {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701},
147 {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381},
148 {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199},
149 {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718},
150 {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418},
151 {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57},
152 {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59},
153 {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61},
154 {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63},
155 {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65},
156 {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68},
157 };
158
159 testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
160 }
161
162 private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) {
163 HypergeometricDistributionImpl dist = new HypergeometricDistributionImpl(populationSize, numberOfSucceses, sampleSize);
164 for (int i = 0; i < data.length; ++i) {
165 int x = (int)data[i][0];
166 double pdf = data[i][1];
167 double actualPdf = dist.probability(x);
168 TestUtils.assertRelativelyEquals(pdf, actualPdf, 1.0e-9);
169
170 double cdf = data[i][2];
171 double actualCdf = dist.cumulativeProbability(x);
172 TestUtils.assertRelativelyEquals(cdf, actualCdf, 1.0e-9);
173
174 double cdf1 = data[i][3];
175 double actualCdf1 = dist.upperCumulativeProbability(x);
176 TestUtils.assertRelativelyEquals(cdf1, actualCdf1, 1.0e-9);
177 }
178 }
179
180 public void testMoreLargeValues() {
181 int populationSize = 26896;
182 int sampleSize = 895;
183 int numberOfSucceses = 55;
184 double[][] data = {
185 {0.0, 0.155168304750504, 0.155168304750504, 1.0},
186 {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496},
187 {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036},
188 {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033},
189 {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247},
190 {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237},
191 {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16},
192 {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17},
193 {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18},
194 {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20},
195 {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21},
196 {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23},
197 {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69},
198 {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71},
199 {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74},
200 {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76},
201 {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79},
202 {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82},
203 };
204 testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
205 }
206 }