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22 package org.hipparchus.distribution.continuous;
23
24 import org.hipparchus.exception.MathIllegalArgumentException;
25 import org.hipparchus.exception.MathRuntimeException;
26 import org.hipparchus.util.Pair;
27 import org.junit.jupiter.api.Test;
28
29 import java.util.HashMap;
30 import java.util.List;
31 import java.util.Map;
32
33 import static org.junit.jupiter.api.Assertions.assertEquals;
34 import static org.junit.jupiter.api.Assertions.assertNull;
35 import static org.junit.jupiter.api.Assertions.assertTrue;
36 import static org.junit.jupiter.api.Assertions.fail;
37
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40
41 public class EnumeratedRealDistributionTest {
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45
46 private final EnumeratedRealDistribution testDistribution;
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50
51 public EnumeratedRealDistributionTest() {
52
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54 testDistribution = new EnumeratedRealDistribution(
55 new double[]{3.0, -1.0, 3.0, 7.0, -2.0, 8.0},
56 new double[]{0.2, 0.2, 0.3, 0.3, 0.0, 0.0});
57 }
58
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62
63 @Test
64 void testExceptions() {
65 EnumeratedRealDistribution invalid = null;
66 try {
67 invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0});
68 fail("Expected MathIllegalArgumentException");
69 } catch (MathIllegalArgumentException e) {
70 }
71 try{
72 invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, -1.0});
73 fail("Expected MathIllegalArgumentException");
74 } catch (MathIllegalArgumentException e) {
75 }
76 try {
77 invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, 0.0});
78 fail("Expected MathRuntimeException");
79 } catch (MathRuntimeException e) {
80 }
81 try {
82 invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.NaN});
83 fail("Expected MathIllegalArgumentException");
84 } catch (MathIllegalArgumentException e) {
85 }
86 try {
87 invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.POSITIVE_INFINITY});
88 fail("Expected MathIllegalArgumentException");
89 } catch (MathIllegalArgumentException e) {
90 }
91 assertNull(invalid, "Expected non-initialized DiscreteRealDistribution");
92 }
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96
97 @Test
98 void testProbability() {
99 double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
100 double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
101 for (int p = 0; p < points.length; p++) {
102 double density = testDistribution.probability(points[p]);
103 assertEquals(results[p], density, 0.0);
104 }
105 }
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110 @Test
111 void testDensity() {
112 double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
113 double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
114 for (int p = 0; p < points.length; p++) {
115 double density = testDistribution.density(points[p]);
116 assertEquals(results[p], density, 0.0);
117 }
118 }
119
120
121
122
123 @Test
124 void testCumulativeProbability() {
125 double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
126 double[] results = new double[]{0, 0.2, 0.2, 0.2, 0.2, 0.7, 0.7, 0.7, 0.7, 1.0, 1.0};
127 for (int p = 0; p < points.length; p++) {
128 double probability = testDistribution.cumulativeProbability(points[p]);
129 assertEquals(results[p], probability, 1e-10);
130 }
131 }
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136 @Test
137 void testGetNumericalMean() {
138 assertEquals(3.4, testDistribution.getNumericalMean(), 1e-10);
139 }
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144 @Test
145 void testGetNumericalVariance() {
146 assertEquals(7.84, testDistribution.getNumericalVariance(), 1e-10);
147 }
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152 @Test
153 void testGetSupportLowerBound() {
154 assertEquals(-1, testDistribution.getSupportLowerBound(), 0);
155 }
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160 @Test
161 void testGetSupportUpperBound() {
162 assertEquals(7, testDistribution.getSupportUpperBound(), 0);
163 }
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168 @Test
169 void testIsSupportConnected() {
170 assertTrue(testDistribution.isSupportConnected());
171 }
172
173
174 @Test
175 void testIssue1065() {
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201 EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(
202 new double[] { 14.0, 18.0, 21.0, 28.0, 31.0, 33.0 },
203 new double[] { 4.0 / 16.0, 5.0 / 16.0, 0.0 / 16.0, 3.0 / 16.0, 1.0 / 16.0, 3.0 / 16.0 });
204
205 assertEquals(14.0, distribution.inverseCumulativeProbability(0.0000), 0.0);
206 assertEquals(14.0, distribution.inverseCumulativeProbability(0.2500), 0.0);
207 assertEquals(33.0, distribution.inverseCumulativeProbability(1.0000), 0.0);
208
209 assertEquals(18.0, distribution.inverseCumulativeProbability(0.5000), 0.0);
210 assertEquals(18.0, distribution.inverseCumulativeProbability(0.5624), 0.0);
211 assertEquals(28.0, distribution.inverseCumulativeProbability(0.5626), 0.0);
212 assertEquals(31.0, distribution.inverseCumulativeProbability(0.7600), 0.0);
213 assertEquals(18.0, distribution.inverseCumulativeProbability(0.5625), 0.0);
214 assertEquals(28.0, distribution.inverseCumulativeProbability(0.7500), 0.0);
215 }
216
217 @Test
218 void testCreateFromDoubles() {
219 final double[] data = new double[] {0, 1, 1, 2, 2, 2};
220 EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(data);
221 assertEquals(0.5, distribution.probability(2), 0);
222 assertEquals(0.5, distribution.cumulativeProbability(1), 0);
223 }
224
225 @Test
226 void testGetPmf() {
227 final double[] data = new double[] {0,0,1,1,2,2,2,2,3,4};
228 final EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(data);
229 final List<Pair<Double, Double>> pmf = distribution.getPmf();
230 assertEquals(5, pmf.size());
231 final Map<Double, Double> pmfMap = new HashMap<Double, Double>();
232 pmfMap.put(0d, 0.2);
233 pmfMap.put(1d, 0.2);
234 pmfMap.put(2d, 0.4);
235 pmfMap.put(3d, 0.1);
236 pmfMap.put(4d, 0.1);
237 for (int i = 0; i < 5; i++) {
238 assertEquals(pmf.get(i).getSecond(), pmfMap.get(pmf.get(i).getFirst()), 0);
239 }
240 }
241 }