http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/ExponentialDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/ExponentialDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/ExponentialDistributionTest.java deleted file mode 100644 index 00fa614..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/ExponentialDistributionTest.java +++ /dev/null @@ -1,135 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.ExponentialDistribution; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.apache.commons.math4.util.FastMath; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for ExponentialDistribution. - * Extends ContinuousDistributionAbstractTest. See class javadoc for - * ContinuousDistributionAbstractTest for details. - * - */ -public class ExponentialDistributionTest extends RealDistributionAbstractTest { - - // --------------------- Override tolerance -------------- - @Override - public void setUp() { - super.setUp(); - setTolerance(1E-9); - } - - //-------------- Implementations for abstract methods ----------------------- - - /** Creates the default continuous distribution instance to use in tests. */ - @Override - public ExponentialDistribution makeDistribution() { - return new ExponentialDistribution(5.0); - } - - /** Creates the default cumulative probability distribution test input values */ - @Override - public double[] makeCumulativeTestPoints() { - // quantiles computed using R version 2.9.2 - return new double[] {0.00500250166792, 0.0502516792675, 0.126589039921, 0.256466471938, - 0.526802578289, 34.5387763949, 23.0258509299, 18.4443972706, 14.9786613678, 11.5129254650}; - } - - /** Creates the default cumulative probability density test expected values */ - @Override - public double[] makeCumulativeTestValues() { - return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, - 0.990, 0.975, 0.950, 0.900}; - } - - /** Creates the default probability density test expected values */ - @Override - public double[] makeDensityTestValues() { - return new double[] {0.1998, 0.198, 0.195, 0.19, 0.18, 0.000200000000000, - 0.00200000000002, 0.00499999999997, 0.00999999999994, 0.0199999999999}; - } - - //------------ Additional tests ------------------------------------------- - - @Test - public void testCumulativeProbabilityExtremes() { - setCumulativeTestPoints(new double[] {-2, 0}); - setCumulativeTestValues(new double[] {0, 0}); - verifyCumulativeProbabilities(); - } - - @Test - public void testInverseCumulativeProbabilityExtremes() { - setInverseCumulativeTestPoints(new double[] {0, 1}); - setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY}); - verifyInverseCumulativeProbabilities(); - } - - @Test - public void testCumulativeProbability2() { - double actual = getDistribution().probability(0.25, 0.75); - Assert.assertEquals(0.0905214, actual, 10e-4); - } - - @Test - public void testDensity() { - ExponentialDistribution d1 = new ExponentialDistribution(1); - Assert.assertTrue(Precision.equals(0.0, d1.density(-1e-9), 1)); - Assert.assertTrue(Precision.equals(1.0, d1.density(0.0), 1)); - Assert.assertTrue(Precision.equals(0.0, d1.density(1000.0), 1)); - Assert.assertTrue(Precision.equals(FastMath.exp(-1), d1.density(1.0), 1)); - Assert.assertTrue(Precision.equals(FastMath.exp(-2), d1.density(2.0), 1)); - - ExponentialDistribution d2 = new ExponentialDistribution(3); - Assert.assertTrue(Precision.equals(1/3.0, d2.density(0.0), 1)); - // computed using print(dexp(1, rate=1/3), digits=10) in R 2.5 - Assert.assertEquals(0.2388437702, d2.density(1.0), 1e-8); - - // computed using print(dexp(2, rate=1/3), digits=10) in R 2.5 - Assert.assertEquals(0.1711390397, d2.density(2.0), 1e-8); - } - - @Test - public void testMeanAccessors() { - ExponentialDistribution distribution = (ExponentialDistribution) getDistribution(); - Assert.assertEquals(5d, distribution.getMean(), Double.MIN_VALUE); - } - - @Test(expected=NotStrictlyPositiveException.class) - public void testPreconditions() { - new ExponentialDistribution(0); - } - - @Test - public void testMoments() { - final double tol = 1e-9; - ExponentialDistribution dist; - - dist = new ExponentialDistribution(11d); - Assert.assertEquals(dist.getNumericalMean(), 11d, tol); - Assert.assertEquals(dist.getNumericalVariance(), 11d * 11d, tol); - - dist = new ExponentialDistribution(10.5d); - Assert.assertEquals(dist.getNumericalMean(), 10.5d, tol); - Assert.assertEquals(dist.getNumericalVariance(), 10.5d * 10.5d, tol); - } -}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/FDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/FDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/FDistributionTest.java deleted file mode 100644 index 21ae3ca..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/FDistributionTest.java +++ /dev/null @@ -1,159 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.FDistribution; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for FDistribution. - * Extends ContinuousDistributionAbstractTest. See class javadoc for - * ContinuousDistributionAbstractTest for details. - * - */ -public class FDistributionTest extends RealDistributionAbstractTest { - - //-------------- Implementations for abstract methods ----------------------- - - /** Creates the default continuous distribution instance to use in tests. */ - @Override - public FDistribution makeDistribution() { - return new FDistribution(5.0, 6.0); - } - - /** Creates the default cumulative probability distribution test input values */ - @Override - public double[] makeCumulativeTestPoints() { - // quantiles computed using R version 2.9.2 - return new double[] {0.0346808448626, 0.0937009113303, 0.143313661184, 0.202008445998, 0.293728320107, - 20.8026639595, 8.74589525602, 5.98756512605, 4.38737418741, 3.10751166664}; - } - - /** Creates the default cumulative probability density test expected values */ - @Override - public double[] makeCumulativeTestValues() { - return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900}; - } - - /** Creates the default probability density test expected values */ - @Override - public double[] makeDensityTestValues() { - return new double[] {0.0689156576706, 0.236735653193, 0.364074131941, 0.481570789649, 0.595880479994, - 0.000133443915657, 0.00286681303403, 0.00969192007502, 0.0242883861471, 0.0605491314658}; - } - - // --------------------- Override tolerance -------------- - @Override - public void setUp() { - super.setUp(); - setTolerance(1e-9); - } - - //---------------------------- Additional test cases ------------------------- - - @Test - public void testCumulativeProbabilityExtremes() { - setCumulativeTestPoints(new double[] {-2, 0}); - setCumulativeTestValues(new double[] {0, 0}); - verifyCumulativeProbabilities(); - } - - @Test - public void testInverseCumulativeProbabilityExtremes() { - setInverseCumulativeTestPoints(new double[] {0, 1}); - setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY}); - verifyInverseCumulativeProbabilities(); - } - - @Test - public void testDfAccessors() { - FDistribution dist = (FDistribution) getDistribution(); - Assert.assertEquals(5d, dist.getNumeratorDegreesOfFreedom(), Double.MIN_VALUE); - Assert.assertEquals(6d, dist.getDenominatorDegreesOfFreedom(), Double.MIN_VALUE); - } - - @Test - public void testPreconditions() { - try { - new FDistribution(0, 1); - Assert.fail("Expecting NotStrictlyPositiveException for df = 0"); - } catch (NotStrictlyPositiveException ex) { - // Expected. - } - try { - new FDistribution(1, 0); - Assert.fail("Expecting NotStrictlyPositiveException for df = 0"); - } catch (NotStrictlyPositiveException ex) { - // Expected. - } - } - - @Test - public void testLargeDegreesOfFreedom() { - FDistribution fd = new FDistribution(100000, 100000); - double p = fd.cumulativeProbability(.999); - double x = fd.inverseCumulativeProbability(p); - Assert.assertEquals(.999, x, 1.0e-5); - } - - @Test - public void testSmallDegreesOfFreedom() { - FDistribution fd = new FDistribution(1, 1); - double p = fd.cumulativeProbability(0.975); - double x = fd.inverseCumulativeProbability(p); - Assert.assertEquals(0.975, x, 1.0e-5); - - fd = new FDistribution(1, 2); - p = fd.cumulativeProbability(0.975); - x = fd.inverseCumulativeProbability(p); - Assert.assertEquals(0.975, x, 1.0e-5); - } - - @Test - public void testMoments() { - final double tol = 1e-9; - FDistribution dist; - - dist = new FDistribution(1, 2); - Assert.assertTrue(Double.isNaN(dist.getNumericalMean())); - Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); - - dist = new FDistribution(1, 3); - Assert.assertEquals(dist.getNumericalMean(), 3d / (3d - 2d), tol); - Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); - - dist = new FDistribution(1, 5); - Assert.assertEquals(dist.getNumericalMean(), 5d / (5d - 2d), tol); - Assert.assertEquals(dist.getNumericalVariance(), (2d * 5d * 5d * 4d) / 9d, tol); - } - - @Test - public void testMath785() { - // this test was failing due to inaccurate results from ContinuedFraction. - - try { - double prob = 0.01; - FDistribution f = new FDistribution(200000, 200000); - double result = f.inverseCumulativeProbability(prob); - Assert.assertTrue(result < 1.0); - } catch (Exception e) { - Assert.fail("Failing to calculate inverse cumulative probability"); - } - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/GammaDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/GammaDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/GammaDistributionTest.java deleted file mode 100644 index d796f7e..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/GammaDistributionTest.java +++ /dev/null @@ -1,358 +0,0 @@ -/* - * 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.math4.distribution; - -import java.io.BufferedReader; -import java.io.IOException; -import java.io.InputStream; -import java.io.InputStreamReader; - -import org.apache.commons.math4.distribution.GammaDistribution; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.apache.commons.numbers.gamma.LanczosApproximation; -import org.apache.commons.math4.stat.descriptive.SummaryStatistics; -import org.apache.commons.math4.util.FastMath; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for GammaDistribution. - * Extends ContinuousDistributionAbstractTest. See class javadoc for - * ContinuousDistributionAbstractTest for details. - * - */ -public class GammaDistributionTest extends RealDistributionAbstractTest { - - //-------------- Implementations for abstract methods ----------------------- - - /** Creates the default continuous distribution instance to use in tests. */ - @Override - public GammaDistribution makeDistribution() { - return new GammaDistribution(4d, 2d); - } - - /** Creates the default cumulative probability distribution test input values */ - @Override - public double[] makeCumulativeTestPoints() { - // quantiles computed using R version 2.9.2 - return new double[] {0.857104827257, 1.64649737269, 2.17973074725, 2.7326367935, 3.48953912565, - 26.1244815584, 20.0902350297, 17.5345461395, 15.5073130559, 13.3615661365}; - } - - /** Creates the default cumulative probability density test expected values */ - @Override - public double[] makeCumulativeTestValues() { - return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900}; - } - - /** Creates the default probability density test expected values */ - @Override - public double[] makeDensityTestValues() { - return new double[] {0.00427280075546, 0.0204117166709, 0.0362756163658, 0.0542113174239, 0.0773195272491, - 0.000394468852816, 0.00366559696761, 0.00874649473311, 0.0166712508128, 0.0311798227954}; - } - - // --------------------- Override tolerance -------------- - @Override - public void setUp() { - super.setUp(); - setTolerance(1e-9); - } - - //---------------------------- Additional test cases ------------------------- - @Test - public void testParameterAccessors() { - GammaDistribution distribution = (GammaDistribution) getDistribution(); - Assert.assertEquals(4d, distribution.getShape(), 0); - Assert.assertEquals(2d, distribution.getScale(), 0); - } - - @Test - public void testPreconditions() { - try { - new GammaDistribution(0, 1); - Assert.fail("Expecting NotStrictlyPositiveException for alpha = 0"); - } catch (NotStrictlyPositiveException ex) { - // Expected. - } - try { - new GammaDistribution(1, 0); - Assert.fail("Expecting NotStrictlyPositiveException for alpha = 0"); - } catch (NotStrictlyPositiveException ex) { - // Expected. - } - } - - @Test - public void testProbabilities() { - testProbability(-1.000, 4.0, 2.0, .0000); - testProbability(15.501, 4.0, 2.0, .9499); - testProbability(0.504, 4.0, 1.0, .0018); - testProbability(10.011, 1.0, 2.0, .9933); - testProbability(5.000, 2.0, 2.0, .7127); - } - - @Test - public void testValues() { - testValue(15.501, 4.0, 2.0, .9499); - testValue(0.504, 4.0, 1.0, .0018); - testValue(10.011, 1.0, 2.0, .9933); - testValue(5.000, 2.0, 2.0, .7127); - } - - private void testProbability(double x, double a, double b, double expected) { - GammaDistribution distribution = new GammaDistribution( a, b ); - double actual = distribution.cumulativeProbability(x); - Assert.assertEquals("probability for " + x, expected, actual, 10e-4); - } - - private void testValue(double expected, double a, double b, double p) { - GammaDistribution distribution = new GammaDistribution( a, b ); - double actual = distribution.inverseCumulativeProbability(p); - Assert.assertEquals("critical value for " + p, expected, actual, 10e-4); - } - - @Test - public void testDensity() { - double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5}; - // R2.5: print(dgamma(x, shape=1, rate=1), digits=10) - checkDensity(1, 1, x, new double[]{0.000000000000, 0.999999000001, 0.606530659713, 0.367879441171, 0.135335283237, 0.006737946999}); - // R2.5: print(dgamma(x, shape=2, rate=1), digits=10) - checkDensity(2, 1, x, new double[]{0.000000000000, 0.000000999999, 0.303265329856, 0.367879441171, 0.270670566473, 0.033689734995}); - // R2.5: print(dgamma(x, shape=4, rate=1), digits=10) - checkDensity(4, 1, x, new double[]{0.000000000e+00, 1.666665000e-19, 1.263605541e-02, 6.131324020e-02, 1.804470443e-01, 1.403738958e-01}); - // R2.5: print(dgamma(x, shape=4, rate=10), digits=10) - checkDensity(4, 10, x, new double[]{0.000000000e+00, 1.666650000e-15, 1.403738958e+00, 7.566654960e-02, 2.748204830e-05, 4.018228850e-17}); - // R2.5: print(dgamma(x, shape=.1, rate=10), digits=10) - checkDensity(0.1, 10, x, new double[]{0.000000000e+00, 3.323953832e+04, 1.663849010e-03, 6.007786726e-06, 1.461647647e-10, 5.996008322e-24}); - // R2.5: print(dgamma(x, shape=.1, rate=20), digits=10) - checkDensity(0.1, 20, x, new double[]{0.000000000e+00, 3.562489883e+04, 1.201557345e-05, 2.923295295e-10, 3.228910843e-19, 1.239484589e-45}); - // R2.5: print(dgamma(x, shape=.1, rate=4), digits=10) - checkDensity(0.1, 4, x, new double[]{0.000000000e+00, 3.032938388e+04, 3.049322494e-02, 2.211502311e-03, 2.170613371e-05, 5.846590589e-11}); - // R2.5: print(dgamma(x, shape=.1, rate=1), digits=10) - checkDensity(0.1, 1, x, new double[]{0.000000000e+00, 2.640334143e+04, 1.189704437e-01, 3.866916944e-02, 7.623306235e-03, 1.663849010e-04}); - } - - private void checkDensity(double alpha, double rate, double[] x, double[] expected) { - GammaDistribution d = new GammaDistribution(alpha, 1 / rate); - for (int i = 0; i < x.length; i++) { - Assert.assertEquals(expected[i], d.density(x[i]), 1e-5); - } - } - - @Test - public void testInverseCumulativeProbabilityExtremes() { - setInverseCumulativeTestPoints(new double[] {0, 1}); - setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY}); - verifyInverseCumulativeProbabilities(); - } - - @Test - public void testMoments() { - final double tol = 1e-9; - GammaDistribution dist; - - dist = new GammaDistribution(1, 2); - Assert.assertEquals(dist.getNumericalMean(), 2, tol); - Assert.assertEquals(dist.getNumericalVariance(), 4, tol); - - dist = new GammaDistribution(1.1, 4.2); - Assert.assertEquals(dist.getNumericalMean(), 1.1d * 4.2d, tol); - Assert.assertEquals(dist.getNumericalVariance(), 1.1d * 4.2d * 4.2d, tol); - } - - private static final double HALF_LOG_2_PI = 0.5 * FastMath.log(2.0 * FastMath.PI); - - public static double logGamma(double x) { - /* - * This is a copy of - * double Gamma.logGamma(double) - * prior to MATH-849 - */ - double ret; - - if (Double.isNaN(x) || (x <= 0.0)) { - ret = Double.NaN; - } else { - double sum = LanczosApproximation.value(x); - double tmp = x + LanczosApproximation.g() + .5; - ret = ((x + .5) * FastMath.log(tmp)) - tmp + - HALF_LOG_2_PI + FastMath.log(sum / x); - } - - return ret; - } - - public static double density(final double x, final double shape, - final double scale) { - /* - * This is a copy of - * double GammaDistribution.density(double) - * prior to MATH-753. - */ - if (x < 0) { - return 0; - } - return FastMath.pow(x / scale, shape - 1) / scale * - FastMath.exp(-x / scale) / FastMath.exp(logGamma(shape)); - } - - /* - * MATH-753: large values of x or shape parameter cause density(double) to - * overflow. Reference data is generated with the Maxima script - * gamma-distribution.mac, which can be found in - * src/test/resources/org/apache/commons/math4/distribution. - */ - - private void doTestMath753(final double shape, - final double meanNoOF, final double sdNoOF, - final double meanOF, final double sdOF, - final String resourceName) throws IOException { - final GammaDistribution distribution = new GammaDistribution(shape, 1.0); - final SummaryStatistics statOld = new SummaryStatistics(); - final SummaryStatistics statNewNoOF = new SummaryStatistics(); - final SummaryStatistics statNewOF = new SummaryStatistics(); - - final InputStream resourceAsStream; - resourceAsStream = this.getClass().getResourceAsStream(resourceName); - Assert.assertNotNull("Could not find resource " + resourceName, - resourceAsStream); - final BufferedReader in; - in = new BufferedReader(new InputStreamReader(resourceAsStream)); - - try { - for (String line = in.readLine(); line != null; line = in.readLine()) { - if (line.startsWith("#")) { - continue; - } - final String[] tokens = line.split(", "); - Assert.assertTrue("expected two floating-point values", - tokens.length == 2); - final double x = Double.parseDouble(tokens[0]); - final String msg = "x = " + x + ", shape = " + shape + - ", scale = 1.0"; - final double expected = Double.parseDouble(tokens[1]); - final double ulp = FastMath.ulp(expected); - final double actualOld = density(x, shape, 1.0); - final double actualNew = distribution.density(x); - final double errOld, errNew; - errOld = FastMath.abs((actualOld - expected) / ulp); - errNew = FastMath.abs((actualNew - expected) / ulp); - - if (Double.isNaN(actualOld) || Double.isInfinite(actualOld)) { - Assert.assertFalse(msg, Double.isNaN(actualNew)); - Assert.assertFalse(msg, Double.isInfinite(actualNew)); - statNewOF.addValue(errNew); - } else { - statOld.addValue(errOld); - statNewNoOF.addValue(errNew); - } - } - if (statOld.getN() != 0) { - /* - * If no overflow occurs, check that new implementation is - * better than old one. - */ - final StringBuilder sb = new StringBuilder("shape = "); - sb.append(shape); - sb.append(", scale = 1.0\n"); - sb.append("Old implementation\n"); - sb.append("------------------\n"); - sb.append(statOld.toString()); - sb.append("New implementation\n"); - sb.append("------------------\n"); - sb.append(statNewNoOF.toString()); - final String msg = sb.toString(); - - final double oldMin = statOld.getMin(); - final double newMin = statNewNoOF.getMin(); - Assert.assertTrue(msg, newMin <= oldMin); - - final double oldMax = statOld.getMax(); - final double newMax = statNewNoOF.getMax(); - Assert.assertTrue(msg, newMax <= oldMax); - - final double oldMean = statOld.getMean(); - final double newMean = statNewNoOF.getMean(); - Assert.assertTrue(msg, newMean <= oldMean); - - final double oldSd = statOld.getStandardDeviation(); - final double newSd = statNewNoOF.getStandardDeviation(); - Assert.assertTrue(msg, newSd <= oldSd); - - Assert.assertTrue(msg, newMean <= meanNoOF); - Assert.assertTrue(msg, newSd <= sdNoOF); - } - if (statNewOF.getN() != 0) { - final double newMean = statNewOF.getMean(); - final double newSd = statNewOF.getStandardDeviation(); - - final StringBuilder sb = new StringBuilder("shape = "); - sb.append(shape); - sb.append(", scale = 1.0"); - sb.append(", max. mean error (ulps) = "); - sb.append(meanOF); - sb.append(", actual mean error (ulps) = "); - sb.append(newMean); - sb.append(", max. sd of error (ulps) = "); - sb.append(sdOF); - sb.append(", actual sd of error (ulps) = "); - sb.append(newSd); - final String msg = sb.toString(); - - Assert.assertTrue(msg, newMean <= meanOF); - Assert.assertTrue(msg, newSd <= sdOF); - } - } catch (IOException e) { - Assert.fail(e.getMessage()); - } finally { - in.close(); - } - } - - - @Test - public void testMath753Shape1() throws IOException { - doTestMath753(1.0, 1.5, 0.5, 0.0, 0.0, "gamma-distribution-shape-1.csv"); - } - - @Test - public void testMath753Shape8() throws IOException { - doTestMath753(8.0, 1.5, 1.0, 0.0, 0.0, "gamma-distribution-shape-8.csv"); - } - - @Test - public void testMath753Shape10() throws IOException { - doTestMath753(10.0, 1.0, 1.0, 0.0, 0.0, "gamma-distribution-shape-10.csv"); - } - - @Test - public void testMath753Shape100() throws IOException { - doTestMath753(100.0, 1.5, 1.0, 0.0, 0.0, "gamma-distribution-shape-100.csv"); - } - - @Test - public void testMath753Shape142() throws IOException { - doTestMath753(142.0, 3.3, 1.6, 40.0, 40.0, "gamma-distribution-shape-142.csv"); - } - - @Test - public void testMath753Shape1000() throws IOException { - doTestMath753(1000.0, 1.0, 1.0, 160.0, 220.0, "gamma-distribution-shape-1000.csv"); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/GeometricDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/GeometricDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/GeometricDistributionTest.java deleted file mode 100644 index 80a034e..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/GeometricDistributionTest.java +++ /dev/null @@ -1,172 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.GeometricDistribution; -import org.apache.commons.math4.distribution.IntegerDistribution; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for GeometricDistribution. - * <p> - * See class javadoc for IntegerDistributionAbstractTest for details. - * - * @since 3.3 - */ -public class GeometricDistributionTest extends IntegerDistributionAbstractTest { - - /** - * Constructor to override default tolerance. - */ - public GeometricDistributionTest() { - setTolerance(1e-12); - } - - // -------------- Implementations for abstract methods -------------------- - - /** Creates the default discrete distribution instance to use in tests. */ - @Override - public IntegerDistribution makeDistribution() { - return new GeometricDistribution(0.40); - } - - /** Creates the default probability density test input values */ - @Override - public int[] makeDensityTestPoints() { - return new int[] { -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, - 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 }; - } - - /** - * Creates the default probability density test expected values. - * Reference values are from R, version version 2.15.3. - */ - @Override - public double[] makeDensityTestValues() { - return new double[] { - 0d, 0.4, 0.24, 0.144, 0.0864, 0.05184, 0.031104, 0.0186624, - 0.01119744, 0.006718464, 0.0040310784, 0.00241864704, - 0.001451188224,0.0008707129344, 0.00052242776064, 0.000313456656384, - 0.00018807399383, 0.000112844396298, 6.77066377789e-05, 4.06239826674e-05, - 2.43743896004e-05, 1.46246337603e-05, 8.77478025615e-06, 5.26486815369e-06, - 3.15892089221e-06, 1.89535253533e-06, 1.1372115212e-06, 6.82326912718e-07, - 4.09396147631e-07, 2.45637688579e-07 - }; - } - - /** - * Creates the default log probability density test expected values. - * Reference values are from R, version version 2.14.1. - */ - @Override - public double[] makeLogDensityTestValues() { - return new double[] { - Double.NEGATIVE_INFINITY, -0.916290731874155, -1.42711635564015, -1.93794197940614, - -2.44876760317213, -2.95959322693812, -3.47041885070411, -3.9812444744701, - -4.49207009823609, -5.00289572200208, -5.51372134576807, -6.02454696953406, - -6.53537259330005, -7.04619821706604, -7.55702384083203, -8.06784946459802, - -8.57867508836402, -9.08950071213001, -9.600326335896, -10.111151959662, - -10.621977583428, -11.132803207194, -11.64362883096, -12.154454454726, - -12.6652800784919, -13.1761057022579, -13.6869313260239, -14.1977569497899, - -14.7085825735559, -15.2194081973219 - }; - } - - /** Creates the default cumulative probability density test input values */ - @Override - public int[] makeCumulativeTestPoints() { - return makeDensityTestPoints(); - } - - /** Creates the default cumulative probability density test expected values */ - @Override - public double[] makeCumulativeTestValues() { - final double[] densities = makeDensityTestValues(); - final int n = densities.length; - final double[] ret = new double[n]; - ret[0] = densities[0]; - for (int i = 1; i < n; i++) { - ret[i] = ret[i - 1] + densities[i]; - } - return ret; - } - - /** Creates the default inverse cumulative probability test input values */ - @Override - public double[] makeInverseCumulativeTestPoints() { - return new double[] { - 0.000, 0.005, 0.010, 0.015, 0.020, 0.025, 0.030, 0.035, 0.040, - 0.045, 0.050, 0.055, 0.060, 0.065, 0.070, 0.075, 0.080, 0.085, - 0.090, 0.095, 0.100, 0.105, 0.110, 0.115, 0.120, 0.125, 0.130, - 0.135, 0.140, 0.145, 0.150, 0.155, 0.160, 0.165, 0.170, 0.175, - 0.180, 0.185, 0.190, 0.195, 0.200, 0.205, 0.210, 0.215, 0.220, - 0.225, 0.230, 0.235, 0.240, 0.245, 0.250, 0.255, 0.260, 0.265, - 0.270, 0.275, 0.280, 0.285, 0.290, 0.295, 0.300, 0.305, 0.310, - 0.315, 0.320, 0.325, 0.330, 0.335, 0.340, 0.345, 0.350, 0.355, - 0.360, 0.365, 0.370, 0.375, 0.380, 0.385, 0.390, 0.395, 0.400, - 0.405, 0.410, 0.415, 0.420, 0.425, 0.430, 0.435, 0.440, 0.445, - 0.450, 0.455, 0.460, 0.465, 0.470, 0.475, 0.480, 0.485, 0.490, - 0.495, 0.500, 0.505, 0.510, 0.515, 0.520, 0.525, 0.530, 0.535, - 0.540, 0.545, 0.550, 0.555, 0.560, 0.565, 0.570, 0.575, 0.580, - 0.585, 0.590, 0.595, 0.600, 0.605, 0.610, 0.615, 0.620, 0.625, - 0.630, 0.635, 0.640, 0.645, 0.650, 0.655, 0.660, 0.665, 0.670, - 0.675, 0.680, 0.685, 0.690, 0.695, 0.700, 0.705, 0.710, 0.715, - 0.720, 0.725, 0.730, 0.735, 0.740, 0.745, 0.750, 0.755, 0.760, - 0.765, 0.770, 0.775, 0.780, 0.785, 0.790, 0.795, 0.800, 0.805, - 0.810, 0.815, 0.820, 0.825, 0.830, 0.835, 0.840, 0.845, 0.850, - 0.855, 0.860, 0.865, 0.870, 0.875, 0.880, 0.885, 0.890, 0.895, - 0.900, 0.905, 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, - 0.945, 0.950, 0.955, 0.960, 0.965, 0.970, 0.975, 0.980, 0.985, - 0.990, 0.995, 1.000 - }; - } - - /** - * Creates the default inverse cumulative probability density test expected - * values - */ - @Override - public int[] makeInverseCumulativeTestValues() { - return new int[] { - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, - 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, - 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, - 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, - 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, - 5, 5, 6, 6, 6, 6, 7, 7, 8, 9, 10, Integer.MAX_VALUE - }; - } - - // ----------------- Additional test cases --------------------------------- - - @Test - public void testMoments() { - final double tol = 1e-9; - GeometricDistribution dist; - - dist = new GeometricDistribution(0.5); - Assert.assertEquals(dist.getNumericalMean(), (1.0d - 0.5d) / 0.5d, tol); - Assert.assertEquals(dist.getNumericalVariance(), (1.0d - 0.5d) / (0.5d * 0.5d), tol); - - dist = new GeometricDistribution(0.3); - Assert.assertEquals(dist.getNumericalMean(), (1.0d - 0.3d) / 0.3d, tol); - Assert.assertEquals(dist.getNumericalVariance(), (1.0d - 0.3d) / (0.3d * 0.3d), tol); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/GumbelDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/GumbelDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/GumbelDistributionTest.java deleted file mode 100644 index d041e26..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/GumbelDistributionTest.java +++ /dev/null @@ -1,72 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.GumbelDistribution; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for GumbelDistribution. - */ -public class GumbelDistributionTest extends RealDistributionAbstractTest { - - @Test - public void testParameters() { - GumbelDistribution d = makeDistribution(); - Assert.assertEquals(0.5, d.getLocation(), Precision.EPSILON); - Assert.assertEquals(2, d.getScale(), Precision.EPSILON); - } - - @Test - public void testSupport() { - GumbelDistribution d = makeDistribution(); - Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound())); - Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound())); - Assert.assertTrue(d.isSupportConnected()); - } - - @Override - public GumbelDistribution makeDistribution() { - return new GumbelDistribution(0.5, 2); - } - - @Override - public double[] makeCumulativeTestPoints() { - return new double[] { - -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 - }; - } - - @Override - public double[] makeDensityTestValues() { - return new double[] { - 1.258262e-06, 3.594689e-04, 9.115766e-03, 5.321100e-02, 1.274352e-01, 1.777864e-01, - 1.787177e-01, 1.472662e-01, 1.075659e-01, 7.302736e-02, 4.742782e-02 - }; - } - - @Override - public double[] makeCumulativeTestValues() { - return new double[] { - 1.608760e-07, 7.577548e-05, 3.168165e-03, 3.049041e-02, 1.203923e-01, 2.769203e-01, - 4.589561e-01, 6.235249e-01, 7.508835e-01, 8.404869e-01, 8.999652e-01 - }; - } - -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/HypergeometricDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/HypergeometricDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/HypergeometricDistributionTest.java deleted file mode 100644 index 85b73f4..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/HypergeometricDistributionTest.java +++ /dev/null @@ -1,354 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.TestUtils; -import org.apache.commons.math4.distribution.HypergeometricDistribution; -import org.apache.commons.math4.distribution.IntegerDistribution; -import org.apache.commons.math4.exception.NotPositiveException; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.apache.commons.math4.exception.NumberIsTooLargeException; -import org.apache.commons.numbers.core.Precision; -import org.apache.commons.rng.simple.RandomSource; -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 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); - Assert.assertEquals("p=" + p, 0, p, 0d); - } - } - - @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); - Assert.assertEquals("p=" + p, 1, p, 0d); - } - } - - @Test - public void testMath1021() { - final int N = 43130568; - final int m = 42976365; - final int n = 50; - final IntegerDistribution.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(); - Assert.assertTrue("sample=" + sample, 0 <= sample); - Assert.assertTrue("sample=" + sample, sample <= n); - } - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/LaplaceDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/LaplaceDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/LaplaceDistributionTest.java deleted file mode 100644 index 099ce72..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/LaplaceDistributionTest.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.LaplaceDistribution; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for LaplaceDistribution. - */ -public class LaplaceDistributionTest extends RealDistributionAbstractTest { - - @Test - public void testParameters() { - LaplaceDistribution d = makeDistribution(); - Assert.assertEquals(0, d.getLocation(), Precision.EPSILON); - Assert.assertEquals(1, d.getScale(), Precision.EPSILON); - } - - @Test - public void testSupport() { - LaplaceDistribution d = makeDistribution(); - Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound())); - Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound())); - Assert.assertTrue(d.isSupportConnected()); - } - - @Override - public LaplaceDistribution makeDistribution() { - return new LaplaceDistribution(0, 1); - } - - @Override - public double[] makeCumulativeTestPoints() { - return new double[] { - -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 - }; - } - - @Override - public double[] makeDensityTestValues() { - return new double[] { - 0.003368973, 0.009157819, 0.024893534, 0.067667642, 0.183939721, - 0.500000000, 0.183939721, 0.067667642, 0.024893534, 0.009157819, 0.003368973 - }; - } - - @Override - public double[] makeCumulativeTestValues() { - return new double[] { - 0.003368973, 0.009157819, 0.024893534, 0.067667642, 0.183939721, - 0.500000000, 0.816060279, 0.932332358, 0.975106466, 0.990842181, 0.996631027 - }; - } - -} - http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/LevyDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/LevyDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/LevyDistributionTest.java deleted file mode 100644 index 0e25259..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/LevyDistributionTest.java +++ /dev/null @@ -1,82 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.LevyDistribution; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -public class LevyDistributionTest extends RealDistributionAbstractTest { - - @Test - public void testParameters() { - LevyDistribution d = makeDistribution(); - Assert.assertEquals(1.2, d.getLocation(), Precision.EPSILON); - Assert.assertEquals(0.4, d.getScale(), Precision.EPSILON); - } - - @Test - public void testSupport() { - LevyDistribution d = makeDistribution(); - Assert.assertEquals(d.getLocation(), d.getSupportLowerBound(), Precision.EPSILON); - Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound())); - Assert.assertTrue(d.isSupportConnected()); - } - - @Override - public LevyDistribution makeDistribution() { - return new LevyDistribution(1.2, 0.4); - } - - @Override - public double[] makeCumulativeTestPoints() { - return new double[] { - 1.2001, 1.21, 1.225, 1.25, 1.3, 1.9, 3.4, 5.6 - }; - } - - @Override - public double[] makeCumulativeTestValues() { - // values computed with R and function plevy from rmutil package - return new double[] { - 0, 2.53962850749e-10, 6.33424836662e-05, 0.00467773498105, - 0.0455002638964, 0.449691797969, 0.669815357599, 0.763024600553 - }; - } - - @Override - public double[] makeDensityTestValues() { - // values computed with R and function dlevy from rmutil package - return new double[] { - 0, 5.20056373765e-07, 0.0214128361224, 0.413339707082, 1.07981933026, - 0.323749319161, 0.0706032550094, 0.026122839884 - }; - } - - /** - * Creates the default logarithmic probability density test expected values. - * Reference values are from R, version 2.14.1. - */ - @Override - public double[] makeLogDensityTestValues() { - return new double[] { - -1987.561573341398d, -14.469328620160d, -3.843764717971d, - -0.883485488811d, 0.076793740349d, -1.127785768948d, - -2.650679030597d, -3.644945255983d}; - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/LogNormalDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/LogNormalDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/LogNormalDistributionTest.java deleted file mode 100644 index 2ea431d..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/LogNormalDistributionTest.java +++ /dev/null @@ -1,254 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.LogNormalDistribution; -import org.apache.commons.math4.exception.NotStrictlyPositiveException; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for {@link LogNormalDistribution}. Extends - * {@link RealDistributionAbstractTest}. See class javadoc of that class - * for details. - * - * @since 3.0 - */ -public class LogNormalDistributionTest extends RealDistributionAbstractTest { - - //-------------- Implementations for abstract methods ----------------------- - - /** Creates the default real distribution instance to use in tests. */ - @Override - public LogNormalDistribution makeDistribution() { - return new LogNormalDistribution(2.1, 1.4); - } - - /** Creates the default cumulative probability distribution test input values */ - @Override - public double[] makeCumulativeTestPoints() { - // quantiles computed using R - return new double[] { -2.226325228634938, -1.156887023657177, - -0.643949578356075, -0.2027950777320613, - 0.305827808237559, 6.42632522863494, - 5.35688702365718, 4.843949578356074, - 4.40279507773206, 3.89417219176244 }; - } - - /** Creates the default cumulative probability density test expected values */ - @Override - public double[] makeCumulativeTestValues() { - return new double[] { 0, 0, 0, 0, 0.00948199951485, 0.432056525076, - 0.381648158697, 0.354555726206, 0.329513316888, - 0.298422824228 }; - } - - /** Creates the default probability density test expected values */ - @Override - public double[] makeDensityTestValues() { - return new double[] { 0, 0, 0, 0, 0.0594218160072, 0.0436977691036, - 0.0508364857798, 0.054873528325, 0.0587182664085, - 0.0636229042785 }; - } - - /** - * Creates the default inverse cumulative probability distribution test - * input values. - */ - @Override - public double[] makeInverseCumulativeTestPoints() { - // Exclude the test points less than zero, as they have cumulative - // probability of zero, meaning the inverse returns zero, and not the - // points less than zero. - double[] points = makeCumulativeTestValues(); - double[] points2 = new double[points.length - 4]; - System.arraycopy(points, 4, points2, 0, points2.length - 4); - return points2; - //return Arrays.copyOfRange(points, 4, points.length - 4); - } - - /** - * Creates the default inverse cumulative probability test expected - * values. - */ - @Override - public double[] makeInverseCumulativeTestValues() { - // Exclude the test points less than zero, as they have cumulative - // probability of zero, meaning the inverse returns zero, and not the - // points less than zero. - double[] points = makeCumulativeTestPoints(); - double[] points2 = new double[points.length - 4]; - System.arraycopy(points, 4, points2, 0, points2.length - 4); - return points2; - //return Arrays.copyOfRange(points, 1, points.length - 4); - } - - // --------------------- Override tolerance -------------- - @Override - public void setUp() { - super.setUp(); - setTolerance(LogNormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - //---------------------------- Additional test cases ------------------------- - - private void verifyQuantiles() { - LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); - double mu = distribution.getScale(); - double sigma = distribution.getShape(); - setCumulativeTestPoints( new double[] { mu - 2 *sigma, mu - sigma, - mu, mu + sigma, mu + 2 * sigma, - mu + 3 * sigma,mu + 4 * sigma, - mu + 5 * sigma }); - verifyCumulativeProbabilities(); - } - - @Test - public void testQuantiles() { - setCumulativeTestValues(new double[] {0, 0.0396495152787, - 0.16601209243, 0.272533253269, - 0.357618409638, 0.426488363093, - 0.483255136841, 0.530823013877}); - setDensityTestValues(new double[] {0, 0.0873055825147, 0.0847676303432, - 0.0677935186237, 0.0544105523058, - 0.0444614628804, 0.0369750288945, - 0.0312206409653}); - verifyQuantiles(); - verifyDensities(); - - setDistribution(new LogNormalDistribution(0, 1)); - setCumulativeTestValues(new double[] {0, 0, 0, 0.5, 0.755891404214, - 0.864031392359, 0.917171480998, - 0.946239689548}); - setDensityTestValues(new double[] {0, 0, 0, 0.398942280401, - 0.156874019279, 0.07272825614, - 0.0381534565119, 0.0218507148303}); - verifyQuantiles(); - verifyDensities(); - - setDistribution(new LogNormalDistribution(0, 0.1)); - setCumulativeTestValues(new double[] {0, 0, 0, 1.28417563064e-117, - 1.39679883412e-58, - 1.09839325447e-33, - 2.52587961726e-20, - 2.0824223487e-12}); - setDensityTestValues(new double[] {0, 0, 0, 2.96247992535e-114, - 1.1283370232e-55, 4.43812313223e-31, - 5.85346445002e-18, - 2.9446618076e-10}); - verifyQuantiles(); - verifyDensities(); - } - - @Test - public void testInverseCumulativeProbabilityExtremes() { - setInverseCumulativeTestPoints(new double[] {0, 1}); - setInverseCumulativeTestValues( - new double[] {0, Double.POSITIVE_INFINITY}); - verifyInverseCumulativeProbabilities(); - } - - @Test - public void testGetScale() { - LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); - Assert.assertEquals(2.1, distribution.getScale(), 0); - } - - @Test - public void testGetShape() { - LogNormalDistribution distribution = (LogNormalDistribution)getDistribution(); - Assert.assertEquals(1.4, distribution.getShape(), 0); - } - - @Test(expected=NotStrictlyPositiveException.class) - public void testPreconditions() { - new LogNormalDistribution(1, 0); - } - - @Test - public void testDensity() { - double [] x = new double[]{-2, -1, 0, 1, 2}; - // R 2.13: print(dlnorm(c(-2,-1,0,1,2)), digits=10) - checkDensity(0, 1, x, new double[] { 0.0000000000, 0.0000000000, - 0.0000000000, 0.3989422804, - 0.1568740193 }); - // R 2.13: print(dlnorm(c(-2,-1,0,1,2), mean=1.1), digits=10) - checkDensity(1.1, 1, x, new double[] { 0.0000000000, 0.0000000000, - 0.0000000000, 0.2178521770, - 0.1836267118}); - } - - private void checkDensity(double scale, double shape, double[] x, - double[] expected) { - LogNormalDistribution d = new LogNormalDistribution(scale, shape); - for (int i = 0; i < x.length; i++) { - Assert.assertEquals(expected[i], d.density(x[i]), 1e-9); - } - } - - /** - * Check to make sure top-coding of extreme values works correctly. - * Verifies fixes for JIRA MATH-167, MATH-414 - */ - @Test - public void testExtremeValues() { - LogNormalDistribution d = new LogNormalDistribution(0, 1); - for (int i = 0; i < 1e5; i++) { // make sure no convergence exception - double upperTail = d.cumulativeProbability(i); - if (i <= 72) { // make sure not top-coded - Assert.assertTrue(upperTail < 1.0d); - } - else { // make sure top coding not reversed - Assert.assertTrue(upperTail > 0.99999); - } - } - - Assert.assertEquals(d.cumulativeProbability(Double.MAX_VALUE), 1, 0); - Assert.assertEquals(d.cumulativeProbability(-Double.MAX_VALUE), 0, 0); - Assert.assertEquals(d.cumulativeProbability(Double.POSITIVE_INFINITY), 1, 0); - Assert.assertEquals(d.cumulativeProbability(Double.NEGATIVE_INFINITY), 0, 0); - } - - @Test - public void testMeanVariance() { - final double tol = 1e-9; - LogNormalDistribution dist; - - dist = new LogNormalDistribution(0, 1); - Assert.assertEquals(dist.getNumericalMean(), 1.6487212707001282, tol); - Assert.assertEquals(dist.getNumericalVariance(), - 4.670774270471604, tol); - - dist = new LogNormalDistribution(2.2, 1.4); - Assert.assertEquals(dist.getNumericalMean(), 24.046753552064498, tol); - Assert.assertEquals(dist.getNumericalVariance(), - 3526.913651880464, tol); - - dist = new LogNormalDistribution(-2000.9, 10.4); - Assert.assertEquals(dist.getNumericalMean(), 0.0, tol); - Assert.assertEquals(dist.getNumericalVariance(), 0.0, tol); - } - - @Test - public void testTinyVariance() { - LogNormalDistribution dist = new LogNormalDistribution(0, 1e-9); - double t = dist.getNumericalVariance(); - Assert.assertEquals(1e-18, t, 1e-20); - } - -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/LogisticsDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/LogisticsDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/LogisticsDistributionTest.java deleted file mode 100644 index b866dd9..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/LogisticsDistributionTest.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.LogisticDistribution; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for LogisticsDistribution. - */ -public class LogisticsDistributionTest extends RealDistributionAbstractTest { - - @Test - public void testParameters() { - LogisticDistribution d = makeDistribution(); - Assert.assertEquals(2, d.getLocation(), Precision.EPSILON); - Assert.assertEquals(5, d.getScale(), Precision.EPSILON); - } - - @Test - public void testSupport() { - LogisticDistribution d = makeDistribution(); - Assert.assertTrue(Double.isInfinite(d.getSupportLowerBound())); - Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound())); - Assert.assertTrue(d.isSupportConnected()); - } - - @Override - public LogisticDistribution makeDistribution() { - return new LogisticDistribution(2, 5); - } - - @Override - public double[] makeCumulativeTestPoints() { - return new double[] { - -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 - }; - } - - @Override - public double[] makeDensityTestValues() { - return new double[] { - 0.03173698, 0.03557889, 0.03932239, 0.04278194, 0.04575685, 0.04805215, - 0.04950331, 0.05000000, 0.04950331, 0.04805215, 0.04575685 - }; - } - - @Override - public double[] makeCumulativeTestValues() { - return new double[] { - 0.1978161, 0.2314752, 0.2689414, 0.3100255, 0.3543437, 0.4013123, - 0.4501660, 0.5000000, 0.5498340, 0.5986877, 0.6456563 - }; - } - -} - http://git-wip-us.apache.org/repos/asf/commons-math/blob/ef846813/src/test/java/org/apache/commons/math4/distribution/NakagamiDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/NakagamiDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/NakagamiDistributionTest.java deleted file mode 100644 index 75628de..0000000 --- a/src/test/java/org/apache/commons/math4/distribution/NakagamiDistributionTest.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * 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.math4.distribution; - -import org.apache.commons.math4.distribution.NakagamiDistribution; -import org.apache.commons.numbers.core.Precision; -import org.junit.Assert; -import org.junit.Test; - -/** - * Test cases for NakagamiDistribution. - */ -public class NakagamiDistributionTest extends RealDistributionAbstractTest { - - @Test - public void testParameters() { - NakagamiDistribution d = makeDistribution(); - Assert.assertEquals(0.5, d.getShape(), Precision.EPSILON); - Assert.assertEquals(1, d.getScale(), Precision.EPSILON); - } - - @Test - public void testSupport() { - NakagamiDistribution d = makeDistribution(); - Assert.assertEquals(d.getSupportLowerBound(), 0, Precision.EPSILON); - Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound())); - Assert.assertTrue(d.isSupportConnected()); - } - - @Override - public NakagamiDistribution makeDistribution() { - return new NakagamiDistribution(0.5, 1); - } - - @Override - public double[] makeCumulativeTestPoints() { - return new double[] { - 0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2 - }; - } - - @Override - public double[] makeDensityTestValues() { - return new double[] { - 0.0000000, 0.7820854, 0.7365403, 0.6664492, 0.5793831, 0.4839414, - 0.3883721, 0.2994549, 0.2218417, 0.1579003, 0.1079819 - }; - } - - @Override - public double[] makeCumulativeTestValues() { - return new double[] { - 0.0000000, 0.1585194, 0.3108435, 0.4514938, 0.5762892, 0.6826895, - 0.7698607, 0.8384867, 0.8904014, 0.9281394, 0.9544997 - }; - } - -} -
