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18 package org.apache.commons.math.distribution;
19
20 import org.apache.commons.math.TestUtils;
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28
29 public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {
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34
35 public HypergeometricDistributionTest(String name) {
36 super(name);
37 }
38
39
40
41
42 public IntegerDistribution makeDistribution() {
43 return new HypergeometricDistributionImpl(10,5, 5);
44 }
45
46
47 public int[] makeDensityTestPoints() {
48 return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
49 }
50
51
52 public double[] makeDensityTestValues() {
53 return new double[] {0d, 0.003968d, 0.099206d, 0.396825d, 0.396825d,
54 0.099206d, 0.003968d, 0d};
55 }
56
57
58 public int[] makeCumulativeTestPoints() {
59 return makeDensityTestPoints();
60 }
61
62
63 public double[] makeCumulativeTestValues() {
64 return new double[] {0d, .003968d, .103175d, .50000d, .896825d, .996032d,
65 1.00000d, 1d};
66 }
67
68
69 public double[] makeInverseCumulativeTestPoints() {
70 return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
71 0.990d, 0.975d, 0.950d, 0.900d, 1d};
72 }
73
74
75 public int[] makeInverseCumulativeTestValues() {
76 return new int[] {-1, -1, 0, 0, 0, 0, 4, 3, 3, 3, 3, 5};
77 }
78
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80
81
82 public void testDegenerateNoFailures() throws Exception {
83 setDistribution(new HypergeometricDistributionImpl(5,5,3));
84 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
85 setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
86 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
87 setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
88 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
89 setInverseCumulativeTestValues(new int[] {2, 2});
90 verifyDensities();
91 verifyCumulativeProbabilities();
92 verifyInverseCumulativeProbabilities();
93 }
94
95
96 public void testDegenerateNoSuccesses() throws Exception {
97 setDistribution(new HypergeometricDistributionImpl(5,0,3));
98 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
99 setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
100 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
101 setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
102 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
103 setInverseCumulativeTestValues(new int[] {-1, -1});
104 verifyDensities();
105 verifyCumulativeProbabilities();
106 verifyInverseCumulativeProbabilities();
107 }
108
109
110 public void testDegenerateFullSample() throws Exception {
111 setDistribution(new HypergeometricDistributionImpl(5,3,5));
112 setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
113 setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
114 setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
115 setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
116 setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
117 setInverseCumulativeTestValues(new int[] {2, 2});
118 verifyDensities();
119 verifyCumulativeProbabilities();
120 verifyInverseCumulativeProbabilities();
121 }
122
123 public void testPopulationSize() {
124 HypergeometricDistribution dist = new HypergeometricDistributionImpl(5,3,5);
125 try {
126 dist.setPopulationSize(-1);
127 fail("negative population size. IllegalArgumentException expected");
128 } catch(IllegalArgumentException ex) {
129 }
130
131 dist.setPopulationSize(10);
132 assertEquals(10, dist.getPopulationSize());
133 }
134
135 public void testLargeValues() {
136 int populationSize = 3456;
137 int sampleSize = 789;
138 int numberOfSucceses = 101;
139 double[][] data = {
140 {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0},
141 {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244},
142 {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673},
143 {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792},
144 {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375},
145 {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203},
146 {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781},
147 {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701},
148 {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381},
149 {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199},
150 {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718},
151 {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418},
152 {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57},
153 {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59},
154 {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61},
155 {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63},
156 {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65},
157 {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68},
158 };
159
160 testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
161 }
162
163 private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) {
164 HypergeometricDistributionImpl dist = new HypergeometricDistributionImpl(populationSize, numberOfSucceses, sampleSize);
165 for (int i = 0; i < data.length; ++i) {
166 int x = (int)data[i][0];
167 double pdf = data[i][1];
168 double actualPdf = dist.probability(x);
169 TestUtils.assertRelativelyEquals(pdf, actualPdf, 1.0e-9);
170
171 double cdf = data[i][2];
172 double actualCdf = dist.cumulativeProbability(x);
173 TestUtils.assertRelativelyEquals(cdf, actualCdf, 1.0e-9);
174
175 double cdf1 = data[i][3];
176 double actualCdf1 = dist.upperCumulativeProbability(x);
177 TestUtils.assertRelativelyEquals(cdf1, actualCdf1, 1.0e-9);
178 }
179 }
180
181 public void testMoreLargeValues() {
182 int populationSize = 26896;
183 int sampleSize = 895;
184 int numberOfSucceses = 55;
185 double[][] data = {
186 {0.0, 0.155168304750504, 0.155168304750504, 1.0},
187 {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496},
188 {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036},
189 {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033},
190 {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247},
191 {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237},
192 {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16},
193 {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17},
194 {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18},
195 {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20},
196 {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21},
197 {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23},
198 {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69},
199 {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71},
200 {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74},
201 {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76},
202 {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79},
203 {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82},
204 };
205 testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
206 }
207 }