| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.commons.math3.distribution; |
| |
| import org.apache.commons.math3.TestUtils; |
| import org.apache.commons.math3.exception.NotPositiveException; |
| import org.apache.commons.math3.exception.NotStrictlyPositiveException; |
| import org.apache.commons.math3.exception.NumberIsTooLargeException; |
| import org.apache.commons.math3.util.Precision; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * Test cases for HyperGeometriclDistribution. |
| * Extends IntegerDistributionAbstractTest. See class javadoc for |
| * IntegerDistributionAbstractTest for details. |
| * |
| */ |
| public class HypergeometricDistributionTest extends IntegerDistributionAbstractTest { |
| |
| /** |
| * Constructor to override default tolerance. |
| */ |
| public HypergeometricDistributionTest() { |
| setTolerance(1e-12); |
| } |
| |
| //-------------- Implementations for abstract methods ----------------------- |
| |
| /** Creates the default discrete distribution instance to use in tests. */ |
| @Override |
| public IntegerDistribution makeDistribution() { |
| return new HypergeometricDistribution(10, 5, 5); |
| } |
| |
| /** Creates the default probability density test input values */ |
| @Override |
| public int[] makeDensityTestPoints() { |
| return new int[] {-1, 0, 1, 2, 3, 4, 5, 10}; |
| } |
| |
| /** |
| * Creates the default probability density test expected values |
| * Reference values are from R, version 2.15.3. |
| */ |
| @Override |
| public double[] makeDensityTestValues() { |
| return new double[] {0d, 0.00396825396825, 0.0992063492063, 0.396825396825, 0.396825396825, |
| 0.0992063492063, 0.00396825396825, 0d}; |
| } |
| |
| /** |
| * Creates the default probability log density test expected values |
| * Reference values are from R, version 2.14.1. |
| */ |
| @Override |
| public double[] makeLogDensityTestValues() { |
| //-Inf -Inf |
| return new double[] {Double.NEGATIVE_INFINITY, -5.52942908751142, -2.31055326264322, -0.924258901523332, |
| -0.924258901523332, -2.31055326264322, -5.52942908751142, Double.NEGATIVE_INFINITY}; |
| } |
| |
| /** Creates the default cumulative probability density test input values */ |
| @Override |
| public int[] makeCumulativeTestPoints() { |
| return makeDensityTestPoints(); |
| } |
| |
| /** |
| * Creates the default cumulative probability density test expected values |
| * Reference values are from R, version 2.15.3. |
| */ |
| @Override |
| public double[] makeCumulativeTestValues() { |
| return new double[] {0d, 0.00396825396825, 0.103174603175, .5, 0.896825396825, 0.996031746032, |
| 1, 1}; |
| } |
| |
| /** Creates the default inverse cumulative probability test input values */ |
| @Override |
| public double[] makeInverseCumulativeTestPoints() { |
| return new double[] {0d, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, 0.999d, |
| 0.990d, 0.975d, 0.950d, 0.900d, 1d}; |
| } |
| |
| /** Creates the default inverse cumulative probability density test expected values */ |
| @Override |
| public int[] makeInverseCumulativeTestValues() { |
| return new int[] {0, 0, 1, 1, 1, 1, 5, 4, 4, 4, 4, 5}; |
| } |
| |
| //-------------------- Additional test cases ------------------------------ |
| |
| /** Verify that if there are no failures, mass is concentrated on sampleSize */ |
| @Test |
| public void testDegenerateNoFailures() { |
| HypergeometricDistribution dist = new HypergeometricDistribution(5,5,3); |
| setDistribution(dist); |
| setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 }); |
| setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d}); |
| setDensityTestPoints(new int[] {-1, 0, 1, 3, 10}); |
| setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d}); |
| setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d}); |
| setInverseCumulativeTestValues(new int[] {3, 3}); |
| verifyDensities(); |
| verifyCumulativeProbabilities(); |
| verifyInverseCumulativeProbabilities(); |
| Assert.assertEquals(dist.getSupportLowerBound(), 3); |
| Assert.assertEquals(dist.getSupportUpperBound(), 3); |
| } |
| |
| /** Verify that if there are no successes, mass is concentrated on 0 */ |
| @Test |
| public void testDegenerateNoSuccesses() { |
| HypergeometricDistribution dist = new HypergeometricDistribution(5,0,3); |
| setDistribution(dist); |
| setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 }); |
| setCumulativeTestValues(new double[] {0d, 1d, 1d, 1d, 1d}); |
| setDensityTestPoints(new int[] {-1, 0, 1, 3, 10}); |
| setDensityTestValues(new double[] {0d, 1d, 0d, 0d, 0d}); |
| setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d}); |
| setInverseCumulativeTestValues(new int[] {0, 0}); |
| verifyDensities(); |
| verifyCumulativeProbabilities(); |
| verifyInverseCumulativeProbabilities(); |
| Assert.assertEquals(dist.getSupportLowerBound(), 0); |
| Assert.assertEquals(dist.getSupportUpperBound(), 0); |
| } |
| |
| /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */ |
| @Test |
| public void testDegenerateFullSample() { |
| HypergeometricDistribution dist = new HypergeometricDistribution(5,3,5); |
| setDistribution(dist); |
| setCumulativeTestPoints(new int[] {-1, 0, 1, 3, 10 }); |
| setCumulativeTestValues(new double[] {0d, 0d, 0d, 1d, 1d}); |
| setDensityTestPoints(new int[] {-1, 0, 1, 3, 10}); |
| setDensityTestValues(new double[] {0d, 0d, 0d, 1d, 0d}); |
| setInverseCumulativeTestPoints(new double[] {0.1d, 0.5d}); |
| setInverseCumulativeTestValues(new int[] {3, 3}); |
| verifyDensities(); |
| verifyCumulativeProbabilities(); |
| verifyInverseCumulativeProbabilities(); |
| Assert.assertEquals(dist.getSupportLowerBound(), 3); |
| Assert.assertEquals(dist.getSupportUpperBound(), 3); |
| } |
| |
| @Test |
| public void testPreconditions() { |
| try { |
| new HypergeometricDistribution(0, 3, 5); |
| Assert.fail("negative population size. NotStrictlyPositiveException expected"); |
| } catch(NotStrictlyPositiveException ex) { |
| // Expected. |
| } |
| try { |
| new HypergeometricDistribution(5, -1, 5); |
| Assert.fail("negative number of successes. NotPositiveException expected"); |
| } catch(NotPositiveException ex) { |
| // Expected. |
| } |
| try { |
| new HypergeometricDistribution(5, 3, -1); |
| Assert.fail("negative sample size. NotPositiveException expected"); |
| } catch(NotPositiveException ex) { |
| // Expected. |
| } |
| try { |
| new HypergeometricDistribution(5, 6, 5); |
| Assert.fail("numberOfSuccesses > populationSize. NumberIsTooLargeException expected"); |
| } catch(NumberIsTooLargeException ex) { |
| // Expected. |
| } |
| try { |
| new HypergeometricDistribution(5, 3, 6); |
| Assert.fail("sampleSize > populationSize. NumberIsTooLargeException expected"); |
| } catch(NumberIsTooLargeException ex) { |
| // Expected. |
| } |
| } |
| |
| @Test |
| public void testAccessors() { |
| HypergeometricDistribution dist = new HypergeometricDistribution(5, 3, 4); |
| Assert.assertEquals(5, dist.getPopulationSize()); |
| Assert.assertEquals(3, dist.getNumberOfSuccesses()); |
| Assert.assertEquals(4, dist.getSampleSize()); |
| } |
| |
| @Test |
| public void testLargeValues() { |
| int populationSize = 3456; |
| int sampleSize = 789; |
| int numberOfSucceses = 101; |
| double[][] data = { |
| {0.0, 2.75646034603961e-12, 2.75646034603961e-12, 1.0}, |
| {1.0, 8.55705370142386e-11, 8.83269973602783e-11, 0.999999999997244}, |
| {2.0, 1.31288129219665e-9, 1.40120828955693e-9, 0.999999999911673}, |
| {3.0, 1.32724172984193e-8, 1.46736255879763e-8, 0.999999998598792}, |
| {4.0, 9.94501711734089e-8, 1.14123796761385e-7, 0.999999985326375}, |
| {5.0, 5.89080768883643e-7, 7.03204565645028e-7, 0.999999885876203}, |
| {20.0, 0.0760051397707708, 0.27349758476299, 0.802507555007781}, |
| {21.0, 0.087144222047629, 0.360641806810619, 0.72650241523701}, |
| {22.0, 0.0940378846881819, 0.454679691498801, 0.639358193189381}, |
| {23.0, 0.0956897500614809, 0.550369441560282, 0.545320308501199}, |
| {24.0, 0.0919766921922999, 0.642346133752582, 0.449630558439718}, |
| {25.0, 0.083641637261095, 0.725987771013677, 0.357653866247418}, |
| {96.0, 5.93849188852098e-57, 1.0, 6.01900244560712e-57}, |
| {97.0, 7.96593036832547e-59, 1.0, 8.05105570861321e-59}, |
| {98.0, 8.44582921934367e-61, 1.0, 8.5125340287733e-61}, |
| {99.0, 6.63604297068222e-63, 1.0, 6.670480942963e-63}, |
| {100.0, 3.43501099007557e-65, 1.0, 3.4437972280786e-65}, |
| {101.0, 8.78623800302957e-68, 1.0, 8.78623800302957e-68}, |
| }; |
| |
| testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data); |
| } |
| |
| private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize, int numberOfSucceses, double[][] data) { |
| HypergeometricDistribution dist = new HypergeometricDistribution(populationSize, numberOfSucceses, sampleSize); |
| for (int i = 0; i < data.length; ++i) { |
| int x = (int)data[i][0]; |
| double pmf = data[i][1]; |
| double actualPmf = dist.probability(x); |
| TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> pmf",pmf, actualPmf, 1.0e-9); |
| |
| double cdf = data[i][2]; |
| double actualCdf = dist.cumulativeProbability(x); |
| TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf",cdf, actualCdf, 1.0e-9); |
| |
| double cdf1 = data[i][3]; |
| double actualCdf1 = dist.upperCumulativeProbability(x); |
| TestUtils.assertRelativelyEquals("Expected equals for <"+x+"> cdf1",cdf1, actualCdf1, 1.0e-9); |
| } |
| } |
| |
| @Test |
| public void testMoreLargeValues() { |
| int populationSize = 26896; |
| int sampleSize = 895; |
| int numberOfSucceses = 55; |
| double[][] data = { |
| {0.0, 0.155168304750504, 0.155168304750504, 1.0}, |
| {1.0, 0.29437545000746, 0.449543754757964, 0.844831695249496}, |
| {2.0, 0.273841321577003, 0.723385076334967, 0.550456245242036}, |
| {3.0, 0.166488572570786, 0.889873648905753, 0.276614923665033}, |
| {4.0, 0.0743969744713231, 0.964270623377076, 0.110126351094247}, |
| {5.0, 0.0260542785784855, 0.990324901955562, 0.0357293766229237}, |
| {20.0, 3.57101101678792e-16, 1.0, 3.78252101622096e-16}, |
| {21.0, 2.00551638598312e-17, 1.0, 2.11509999433041e-17}, |
| {22.0, 1.04317070180562e-18, 1.0, 1.09583608347287e-18}, |
| {23.0, 5.03153504903308e-20, 1.0, 5.266538166725e-20}, |
| {24.0, 2.2525984149695e-21, 1.0, 2.35003117691919e-21}, |
| {25.0, 9.3677424515947e-23, 1.0, 9.74327619496943e-23}, |
| {50.0, 9.83633962945521e-69, 1.0, 9.8677629437617e-69}, |
| {51.0, 3.13448949497553e-71, 1.0, 3.14233143064882e-71}, |
| {52.0, 7.82755221928122e-74, 1.0, 7.84193567329055e-74}, |
| {53.0, 1.43662126065532e-76, 1.0, 1.43834540093295e-76}, |
| {54.0, 1.72312692517348e-79, 1.0, 1.7241402776278e-79}, |
| {55.0, 1.01335245432581e-82, 1.0, 1.01335245432581e-82}, |
| }; |
| testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data); |
| } |
| |
| @Test |
| public void testMoments() { |
| final double tol = 1e-9; |
| HypergeometricDistribution dist; |
| |
| dist = new HypergeometricDistribution(1500, 40, 100); |
| Assert.assertEquals(dist.getNumericalMean(), 40d * 100d / 1500d, tol); |
| Assert.assertEquals(dist.getNumericalVariance(), ( 100d * 40d * (1500d - 100d) * (1500d - 40d) ) / ( (1500d * 1500d * 1499d) ), tol); |
| |
| dist = new HypergeometricDistribution(3000, 55, 200); |
| Assert.assertEquals(dist.getNumericalMean(), 55d * 200d / 3000d, tol); |
| Assert.assertEquals(dist.getNumericalVariance(), ( 200d * 55d * (3000d - 200d) * (3000d - 55d) ) / ( (3000d * 3000d * 2999d) ), tol); |
| } |
| |
| @Test |
| public void testMath644() { |
| int N = 14761461; // population |
| int m = 1035; // successes in population |
| int n = 1841; // number of trials |
| |
| int k = 0; |
| final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n); |
| |
| Assert.assertTrue(Precision.compareTo(1.0, dist.upperCumulativeProbability(k), 1) == 0); |
| Assert.assertTrue(Precision.compareTo(dist.cumulativeProbability(k), 0.0, 1) > 0); |
| |
| // another way to calculate the upper cumulative probability |
| double upper = 1.0 - dist.cumulativeProbability(k) + dist.probability(k); |
| Assert.assertTrue(Precision.compareTo(1.0, upper, 1) == 0); |
| } |
| |
| @Test |
| public void testMath1021() { |
| final int N = 43130568; |
| final int m = 42976365; |
| final int n = 50; |
| final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n); |
| |
| for (int i = 0; i < 100; i++) { |
| final int sample = dist.sample(); |
| Assert.assertTrue("sample=" + sample, 0 <= sample); |
| Assert.assertTrue("sample=" + sample, sample <= n); |
| } |
| } |
| } |