commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/HypergeometricDistributionTest.java - commons-statistics - Git at Google

 /*
  * 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.statistics.distribution;

 import org.apache.commons.numbers.core.Precision;
 import org.apache.commons.rng.simple.RandomSource;
 import org.junit.jupiter.api.Assertions;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;

 /**
  * Test cases for HyperGeometriclDistribution.
  * Extends DiscreteDistributionAbstractTest.  See class javadoc for
  * DiscreteDistributionAbstractTest for details.
  */
 public class HypergeometricDistributionTest extends DiscreteDistributionAbstractTest {

     //---------------------- Override tolerance --------------------------------

     @BeforeEach
     public void customSetUp() {
         setTolerance(1e-12);
     }

     //-------------- Implementations for abstract methods ----------------------

     /** Creates the default discrete distribution instance to use in tests. */
     @Override
     public DiscreteDistribution 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() {
         final 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();
         Assertions.assertEquals(3, dist.getSupportLowerBound());
         Assertions.assertEquals(3, dist.getSupportUpperBound());
     }

     /** Verify that if there are no successes, mass is concentrated on 0 */
     @Test
     public void testDegenerateNoSuccesses() {
         final 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();
         Assertions.assertEquals(0, dist.getSupportLowerBound());
         Assertions.assertEquals(0, dist.getSupportUpperBound());
     }

     /** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
     @Test
     public void testDegenerateFullSample() {
         final 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();
         Assertions.assertEquals(3, dist.getSupportLowerBound());
         Assertions.assertEquals(3, dist.getSupportUpperBound());
     }

     @Test
     public void testParameterAccessors() {
         final HypergeometricDistribution dist = new HypergeometricDistribution(5, 3, 4);
         Assertions.assertEquals(5, dist.getPopulationSize());
         Assertions.assertEquals(3, dist.getNumberOfSuccesses());
         Assertions.assertEquals(4, dist.getSampleSize());
     }

     @Test
     public void testConstructorPrecondition1() {
         Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(0, 3, 5));
     }
     @Test
     public void testConstructorPrecondition2() {
         Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, -1, 5));
     }
     @Test
     public void testConstructorPrecondition3() {
         Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 3, -1));
     }
     @Test
     public void testConstructorPrecondition4() {
         Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 6, 5));
     }
     @Test
     public void testConstructorPrecondition5() {
         Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 3, 6));
     }

     @Test
     public void testMoments() {
         final double tol = 1e-9;
         HypergeometricDistribution dist;

         dist = new HypergeometricDistribution(1500, 40, 100);
         Assertions.assertEquals(40d * 100d / 1500d, dist.getMean(), tol);
         Assertions.assertEquals((100d * 40d * (1500d - 100d) * (1500d - 40d)) / ((1500d * 1500d * 1499d)), dist.getVariance(), tol);

         dist = new HypergeometricDistribution(3000, 55, 200);
         Assertions.assertEquals(55d * 200d / 3000d, dist.getMean(), tol);
         Assertions.assertEquals((200d * 55d * (3000d - 200d) * (3000d - 55d)) / ((3000d * 3000d * 2999d)), dist.getVariance(), tol);
     }

     @Test
     public void testLargeValues() {
         final int populationSize = 3456;
         final int sampleSize = 789;
         final int numberOfSucceses = 101;
         final 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},
             // Out of domain
             {sampleSize + 1, 0, 1.0, 0},
         };

         testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
     }

     private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize,
         int numberOfSucceses, double[][] data) {
         final HypergeometricDistribution dist = new HypergeometricDistribution(populationSize, numberOfSucceses, sampleSize);
         for (int i = 0; i < data.length; ++i) {
             final int x = (int)data[i][0];
             final double pmf = data[i][1];
             final double actualPmf = dist.probability(x);
             TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> pmf", pmf, actualPmf, 1.0e-9);

             final double cdf = data[i][2];
             final double actualCdf = dist.cumulativeProbability(x);
             TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> cdf", cdf, actualCdf, 1.0e-9);

             final double cdf1 = data[i][3];
             final double actualCdf1 = dist.upperCumulativeProbability(x);
             TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> cdf1", cdf1, actualCdf1, 1.0e-9);
         }
     }

     @Test
     public void testMoreLargeValues() {
         final int populationSize = 26896;
         final int sampleSize = 895;
         final int numberOfSucceses = 55;
         final 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 testMath644() {
         final int N = 14761461;  // population
         final int m = 1035;      // successes in population
         final int n = 1841;      // number of trials

         final int k = 0;
         final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n);

         Assertions.assertEquals(0, Precision.compareTo(1.0, dist.upperCumulativeProbability(k), 1));
         Assertions.assertTrue(Precision.compareTo(dist.cumulativeProbability(k), 0.0, 1) > 0);

         // another way to calculate the upper cumulative probability
         final double upper = 1.0 - dist.cumulativeProbability(k) + dist.probability(k);
         Assertions.assertEquals(0, Precision.compareTo(1.0, upper, 1));
     }

     @Test
     public void testZeroTrial() {
         final int n = 11; // population
         final int m = 4;  // successes in population
         final int s = 0;  // number of trials

         final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, 0);

         for (int i = 1; i <= n; i++) {
             final double p = dist.probability(i);
             Assertions.assertEquals(0, p, 0d, () -> "p=" + p);
         }
     }

     @Test
     public void testMath1356() {
         final int n = 11;  // population
         final int m = 11;  // successes in population

         for (int s = 0; s <= n; s++) {
             final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, s);
             final double p = dist.probability(s);
             Assertions.assertEquals(1, p, 0d, () -> "p=" + p);
         }
     }

     @Test
     public void testMath1021() {
         final int N = 43130568;
         final int m = 42976365;
         final int n = 50;
         final DiscreteDistribution.Sampler dist =
             new HypergeometricDistribution(N, m, n).createSampler(RandomSource.create(RandomSource.WELL_512_A));

         for (int i = 0; i < 100; i++) {
             final int sample = dist.sample();
             Assertions.assertTrue(0 <= sample, () -> "sample=" + sample);
             Assertions.assertTrue(sample <= n, () -> "sample=" + sample);
         }
     }
 }
	/*
	* 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.statistics.distribution;

	import org.apache.commons.numbers.core.Precision;
	import org.apache.commons.rng.simple.RandomSource;
	import org.junit.jupiter.api.Assertions;
	import org.junit.jupiter.api.BeforeEach;
	import org.junit.jupiter.api.Test;

	/**
	* Test cases for HyperGeometriclDistribution.
	* Extends DiscreteDistributionAbstractTest. See class javadoc for
	* DiscreteDistributionAbstractTest for details.
	*/
	public class HypergeometricDistributionTest extends DiscreteDistributionAbstractTest {

	//---------------------- Override tolerance --------------------------------

	@BeforeEach
	public void customSetUp() {
	setTolerance(1e-12);
	}

	//-------------- Implementations for abstract methods ----------------------

	/** Creates the default discrete distribution instance to use in tests. */
	@Override
	public DiscreteDistribution 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() {
	final 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();
	Assertions.assertEquals(3, dist.getSupportLowerBound());
	Assertions.assertEquals(3, dist.getSupportUpperBound());
	}

	/** Verify that if there are no successes, mass is concentrated on 0 */
	@Test
	public void testDegenerateNoSuccesses() {
	final 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();
	Assertions.assertEquals(0, dist.getSupportLowerBound());
	Assertions.assertEquals(0, dist.getSupportUpperBound());
	}

	/** Verify that if sampleSize = populationSize, mass is concentrated on numberOfSuccesses */
	@Test
	public void testDegenerateFullSample() {
	final 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();
	Assertions.assertEquals(3, dist.getSupportLowerBound());
	Assertions.assertEquals(3, dist.getSupportUpperBound());
	}

	@Test
	public void testParameterAccessors() {
	final HypergeometricDistribution dist = new HypergeometricDistribution(5, 3, 4);
	Assertions.assertEquals(5, dist.getPopulationSize());
	Assertions.assertEquals(3, dist.getNumberOfSuccesses());
	Assertions.assertEquals(4, dist.getSampleSize());
	}

	@Test
	public void testConstructorPrecondition1() {
	Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(0, 3, 5));
	}
	@Test
	public void testConstructorPrecondition2() {
	Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, -1, 5));
	}
	@Test
	public void testConstructorPrecondition3() {
	Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 3, -1));
	}
	@Test
	public void testConstructorPrecondition4() {
	Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 6, 5));
	}
	@Test
	public void testConstructorPrecondition5() {
	Assertions.assertThrows(DistributionException.class, () -> new HypergeometricDistribution(5, 3, 6));
	}

	@Test
	public void testMoments() {
	final double tol = 1e-9;
	HypergeometricDistribution dist;

	dist = new HypergeometricDistribution(1500, 40, 100);
	Assertions.assertEquals(40d * 100d / 1500d, dist.getMean(), tol);
	Assertions.assertEquals((100d * 40d * (1500d - 100d) * (1500d - 40d)) / ((1500d * 1500d * 1499d)), dist.getVariance(), tol);

	dist = new HypergeometricDistribution(3000, 55, 200);
	Assertions.assertEquals(55d * 200d / 3000d, dist.getMean(), tol);
	Assertions.assertEquals((200d * 55d * (3000d - 200d) * (3000d - 55d)) / ((3000d * 3000d * 2999d)), dist.getVariance(), tol);
	}

	@Test
	public void testLargeValues() {
	final int populationSize = 3456;
	final int sampleSize = 789;
	final int numberOfSucceses = 101;
	final 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},
	// Out of domain
	{sampleSize + 1, 0, 1.0, 0},
	};

	testHypergeometricDistributionProbabilities(populationSize, sampleSize, numberOfSucceses, data);
	}

	private void testHypergeometricDistributionProbabilities(int populationSize, int sampleSize,
	int numberOfSucceses, double[][] data) {
	final HypergeometricDistribution dist = new HypergeometricDistribution(populationSize, numberOfSucceses, sampleSize);
	for (int i = 0; i < data.length; ++i) {
	final int x = (int)data[i][0];
	final double pmf = data[i][1];
	final double actualPmf = dist.probability(x);
	TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> pmf", pmf, actualPmf, 1.0e-9);

	final double cdf = data[i][2];
	final double actualCdf = dist.cumulativeProbability(x);
	TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> cdf", cdf, actualCdf, 1.0e-9);

	final double cdf1 = data[i][3];
	final double actualCdf1 = dist.upperCumulativeProbability(x);
	TestUtils.assertRelativelyEquals("Expected equals for <" + x + "> cdf1", cdf1, actualCdf1, 1.0e-9);
	}
	}

	@Test
	public void testMoreLargeValues() {
	final int populationSize = 26896;
	final int sampleSize = 895;
	final int numberOfSucceses = 55;
	final 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 testMath644() {
	final int N = 14761461; // population
	final int m = 1035; // successes in population
	final int n = 1841; // number of trials

	final int k = 0;
	final HypergeometricDistribution dist = new HypergeometricDistribution(N, m, n);

	Assertions.assertEquals(0, Precision.compareTo(1.0, dist.upperCumulativeProbability(k), 1));
	Assertions.assertTrue(Precision.compareTo(dist.cumulativeProbability(k), 0.0, 1) > 0);

	// another way to calculate the upper cumulative probability
	final double upper = 1.0 - dist.cumulativeProbability(k) + dist.probability(k);
	Assertions.assertEquals(0, Precision.compareTo(1.0, upper, 1));
	}

	@Test
	public void testZeroTrial() {
	final int n = 11; // population
	final int m = 4; // successes in population
	final int s = 0; // number of trials

	final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, 0);

	for (int i = 1; i <= n; i++) {
	final double p = dist.probability(i);
	Assertions.assertEquals(0, p, 0d, () -> "p=" + p);
	}
	}

	@Test
	public void testMath1356() {
	final int n = 11; // population
	final int m = 11; // successes in population

	for (int s = 0; s <= n; s++) {
	final HypergeometricDistribution dist = new HypergeometricDistribution(n, m, s);
	final double p = dist.probability(s);
	Assertions.assertEquals(1, p, 0d, () -> "p=" + p);
	}
	}

	@Test
	public void testMath1021() {
	final int N = 43130568;
	final int m = 42976365;
	final int n = 50;
	final DiscreteDistribution.Sampler dist =
	new HypergeometricDistribution(N, m, n).createSampler(RandomSource.create(RandomSource.WELL_512_A));

	for (int i = 0; i < 100; i++) {
	final int sample = dist.sample();
	Assertions.assertTrue(0 <= sample, () -> "sample=" + sample);
	Assertions.assertTrue(sample <= n, () -> "sample=" + sample);
	}
	}
	}