001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     * 
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     * 
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    
018    package org.apache.commons.math.distribution;
019    
020    import org.apache.commons.math.TestUtils;
021    
022    /**
023     * Test cases for HyperGeometriclDistribution.
024     * Extends IntegerDistributionAbstractTest.  See class javadoc for
025     * IntegerDistributionAbstractTest for details.
026     * 
027     * @version $Revision: 762087 $ $Date: 2009-04-05 10:20:18 -0400 (Sun, 05 Apr 2009) $
028     */
029    public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest {
030    
031        /**
032         * Constructor for ChiSquareDistributionTest.
033         * @param name
034         */
035        public HypergeometricDistributionTest(String name) {
036            super(name);
037        }
038    
039    //-------------- Implementations for abstract methods -----------------------
040        
041        /** Creates the default discrete distribution instance to use in tests. */
042        @Override
043        public IntegerDistribution makeDistribution() {
044            return new HypergeometricDistributionImpl(10,5, 5);
045        }
046        
047        /** Creates the default probability density test input values */
048        @Override
049        public int[] makeDensityTestPoints() {
050            return new int[] {-1, 0, 1, 2, 3, 4, 5, 10};
051        }
052        
053        /** Creates the default probability density test expected values */
054        @Override
055        public double[] makeDensityTestValues() {
056            return new double[] {0d, 0.003968d, 0.099206d, 0.396825d, 0.396825d, 
057                    0.099206d, 0.003968d, 0d};
058        }
059        
060        /** Creates the default cumulative probability density test input values */
061        @Override
062        public int[] makeCumulativeTestPoints() {
063            return makeDensityTestPoints();
064        }
065        
066        /** Creates the default cumulative probability density test expected values */
067        @Override
068        public double[] makeCumulativeTestValues() {
069            return new double[] {0d, .003968d, .103175d, .50000d, .896825d, .996032d,
070                    1.00000d, 1d};
071        }
072        
073        /** Creates the default inverse cumulative probability test input values */
074        @Override
075        public double[] makeInverseCumulativeTestPoints() {
076            return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d,
077                    0.990d, 0.975d, 0.950d, 0.900d, 1d}; 
078        }
079        
080        /** Creates the default inverse cumulative probability density test expected values */
081        @Override
082        public int[] makeInverseCumulativeTestValues() {
083            return new int[] {-1, -1, 0, 0, 0, 0, 4, 3, 3, 3, 3, 5};
084        }
085        
086        //-------------------- Additional test cases ------------------------------
087        
088        /** Verify that if there are no failures, mass is concentrated on sampleSize */
089        public void testDegenerateNoFailures() throws Exception {
090            setDistribution(new HypergeometricDistributionImpl(5,5,3));
091            setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
092            setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
093            setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
094            setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
095            setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
096            setInverseCumulativeTestValues(new int[] {2, 2});
097            verifyDensities();
098            verifyCumulativeProbabilities();
099            verifyInverseCumulativeProbabilities();     
100        }
101        
102        /** Verify that if there are no successes, mass is concentrated on 0 */
103        public void testDegenerateNoSuccesses() throws Exception {
104            setDistribution(new HypergeometricDistributionImpl(5,0,3));
105            setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
106            setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d});
107            setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
108            setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d});
109            setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
110            setInverseCumulativeTestValues(new int[] {-1, -1});
111            verifyDensities();
112            verifyCumulativeProbabilities();
113            verifyInverseCumulativeProbabilities();     
114        }
115        
116        /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
117        public void testDegenerateFullSample() throws Exception {
118            setDistribution(new HypergeometricDistributionImpl(5,3,5));
119            setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 });
120            setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d});
121            setDensityTestPoints(new int[] {-1, 0, 1, 3, 10});
122            setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d});
123            setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d});
124            setInverseCumulativeTestValues(new int[] {2, 2});
125            verifyDensities();
126            verifyCumulativeProbabilities();
127            verifyInverseCumulativeProbabilities();     
128        }
129    
130        public void testPopulationSize() {
131            HypergeometricDistribution dist = new HypergeometricDistributionImpl(5,3,5);
132            try {
133                dist.setPopulationSize(-1);
134                fail("negative population size.  IllegalArgumentException expected");
135            } catch(IllegalArgumentException ex) {
136            }
137            
138            dist.setPopulationSize(10);
139            assertEquals(10, dist.getPopulationSize());
140        }
141        
142        public void testLargeValues() {
143            int populationSize = 3456;
144            int sampleSize = 789;
145            int numberOfSucceses = 101;
146            double[][] data = {
147                {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0},
148                {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244},
149                {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673},
150                {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792},
151                {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375},
152                {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203},
153                {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781}, 
154                {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701}, 
155                {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381}, 
156                {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199}, 
157                {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718}, 
158                {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418}, 
159                {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57},
160                {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59}, 
161                {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61},
162                {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63}, 
163                {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65},
164                {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68},
165            };
166            
167            testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
168        }
169    
170        private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) {
171            HypergeometricDistributionImpl dist = new HypergeometricDistributionImpl(populationSize, numberOfSucceses, sampleSize);
172            for (int i = 0; i < data.length; ++i) {
173                int x = (int)data[i][0];
174                double pdf = data[i][1];
175                double actualPdf = dist.probability(x);
176                TestUtils.assertRelativelyEquals(pdf, actualPdf, 1.0e-9);
177    
178                double cdf = data[i][2];
179                double actualCdf = dist.cumulativeProbability(x);
180                TestUtils.assertRelativelyEquals(cdf, actualCdf, 1.0e-9);
181    
182                double cdf1 = data[i][3];
183                double actualCdf1 = dist.upperCumulativeProbability(x);
184                TestUtils.assertRelativelyEquals(cdf1, actualCdf1, 1.0e-9);
185            }
186        }
187        
188        public void testMoreLargeValues() {
189            int populationSize = 26896;
190            int sampleSize = 895;
191            int numberOfSucceses = 55;
192            double[][] data = {
193                {0.0, 0.155168304750504, 0.155168304750504, 1.0}, 
194                {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496}, 
195                {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036}, 
196                {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033}, 
197                {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247}, 
198                {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237}, 
199                {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16}, 
200                {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17}, 
201                {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18}, 
202                {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20}, 
203                {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21}, 
204                {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23}, 
205                {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69}, 
206                {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71}, 
207                {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74}, 
208                {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76}, 
209                {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79}, 
210                {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82},        
211            };
212            testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
213        }
214    }