http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java new file mode 100644 index 0000000..bcf8e7c --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/AbstractDiscreteDistributionTest.java @@ -0,0 +1,130 @@ +/* + * 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.junit.Assert; +import org.junit.Test; + +/** + * Test cases for AbstractDiscreteDistribution default implementations. + * + */ +public class AbstractDiscreteDistributionTest { + protected final DiceDistribution diceDistribution = new DiceDistribution(); + protected final double p = diceDistribution.probability(1); + + @Test + public void testInverseCumulativeProbabilityMethod() + { + double precision = 0.000000000000001; + Assert.assertEquals(1, diceDistribution.inverseCumulativeProbability(0)); + Assert.assertEquals(1, diceDistribution.inverseCumulativeProbability((1d-Double.MIN_VALUE)/6d)); + Assert.assertEquals(2, diceDistribution.inverseCumulativeProbability((1d+precision)/6d)); + Assert.assertEquals(2, diceDistribution.inverseCumulativeProbability((2d-Double.MIN_VALUE)/6d)); + Assert.assertEquals(3, diceDistribution.inverseCumulativeProbability((2d+precision)/6d)); + Assert.assertEquals(3, diceDistribution.inverseCumulativeProbability((3d-Double.MIN_VALUE)/6d)); + Assert.assertEquals(4, diceDistribution.inverseCumulativeProbability((3d+precision)/6d)); + Assert.assertEquals(4, diceDistribution.inverseCumulativeProbability((4d-Double.MIN_VALUE)/6d)); + Assert.assertEquals(5, diceDistribution.inverseCumulativeProbability((4d+precision)/6d)); + Assert.assertEquals(5, diceDistribution.inverseCumulativeProbability((5d-precision)/6d));//Can't use Double.MIN + Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((5d+precision)/6d)); + Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((6d-precision)/6d));//Can't use Double.MIN + Assert.assertEquals(6, diceDistribution.inverseCumulativeProbability((6d)/6d)); + } + + @Test + public void testCumulativeProbabilitiesSingleArguments() { + for (int i = 1; i < 7; i++) { + Assert.assertEquals(p * i, + diceDistribution.cumulativeProbability(i), Double.MIN_VALUE); + } + Assert.assertEquals(0.0, + diceDistribution.cumulativeProbability(0), Double.MIN_VALUE); + Assert.assertEquals(1.0, + diceDistribution.cumulativeProbability(7), Double.MIN_VALUE); + } + + @Test + public void testProbabilitiesRangeArguments() { + int lower = 0; + int upper = 6; + for (int i = 0; i < 2; i++) { + // cum(0,6) = p(0 < X <= 6) = 1, cum(1,5) = 4/6, cum(2,4) = 2/6 + Assert.assertEquals(1 - p * 2 * i, + diceDistribution.probability(lower, upper), 1E-12); + lower++; + upper--; + } + for (int i = 0; i < 6; i++) { + Assert.assertEquals(p, diceDistribution.probability(i, i+1), 1E-12); + } + } + + /** + * Simple distribution modeling a 6-sided die + */ + class DiceDistribution extends AbstractDiscreteDistribution { + public static final long serialVersionUID = 23734213; + + private final double p = 1d/6d; + + @Override + public double probability(int x) { + if (x < 1 || x > 6) { + return 0; + } else { + return p; + } + } + + @Override + public double cumulativeProbability(int x) { + if (x < 1) { + return 0; + } else if (x >= 6) { + return 1; + } else { + return p * x; + } + } + + @Override + public double getNumericalMean() { + return 3.5; + } + + @Override + public double getNumericalVariance() { + return 70/24; // E(X^2) - E(X)^2 + } + + @Override + public int getSupportLowerBound() { + return 1; + } + + @Override + public int getSupportUpperBound() { + return 6; + } + + @Override + public final boolean isSupportConnected() { + return true; + } + } +}
http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java new file mode 100644 index 0000000..f37961c --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java @@ -0,0 +1,381 @@ +/* + * 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 java.util.Arrays; + +import org.apache.commons.rng.simple.RandomSource; +import org.apache.commons.rng.UniformRandomProvider; +import org.apache.commons.math3.stat.StatUtils; +import org.apache.commons.math3.stat.inference.GTest; +import org.junit.Assert; +import org.junit.Test; + +public class BetaDistributionTest { + + static final double[] alphaBetas = {0.1, 1, 10, 100, 1000}; + static final double epsilon = StatUtils.min(alphaBetas); + + @Test + public void testCumulative() { + double[] x = new double[]{-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1}; + // all test data computed using R 2.5 + checkCumulative(0.1, 0.1, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.4063850939, 0.4397091902, 0.4628041861, + 0.4821200456, 0.5000000000, 0.5178799544, 0.5371958139, 0.5602908098, + 0.5936149061, 1.0000000000, 1.0000000000}); + checkCumulative(0.1, 0.5, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.7048336221, 0.7593042194, 0.7951765304, + 0.8234948385, 0.8480017124, 0.8706034370, 0.8926585878, 0.9156406404, + 0.9423662883, 1.0000000000, 1.0000000000}); + checkCumulative(0.1, 1.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.7943282347, 0.8513399225, 0.8865681506, + 0.9124435366, 0.9330329915, 0.9502002165, 0.9649610951, 0.9779327685, + 0.9895192582, 1.0000000000, 1.0000000000}); + checkCumulative(0.1, 2.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.8658177758, 0.9194471163, 0.9486279211, + 0.9671901487, 0.9796846411, 0.9882082252, 0.9939099280, 0.9974914239, + 0.9994144508, 1.0000000000, 1.0000000000}); + checkCumulative(0.1, 4.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.9234991121, 0.9661958941, 0.9842285085, + 0.9928444112, 0.9970040660, 0.9989112804, 0.9996895625, 0.9999440793, + 0.9999967829, 1.0000000000, 1.0000000000}); + checkCumulative(0.5, 0.1, + x, new double[]{ + 0.00000000000, 0.00000000000, 0.05763371168, 0.08435935962, + 0.10734141216, 0.12939656302, 0.15199828760, 0.17650516146, + 0.20482346963, 0.24069578055, 0.29516637795, 1.00000000000, 1.00000000000}); + + checkCumulative(0.5, 0.5, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.2048327647, 0.2951672353, 0.3690101196, + 0.4359057832, 0.5000000000, 0.5640942168, 0.6309898804, 0.7048327647, + 0.7951672353, 1.0000000000, 1.0000000000}); + checkCumulative(0.5, 1.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.3162277660, 0.4472135955, 0.5477225575, + 0.6324555320, 0.7071067812, 0.7745966692, 0.8366600265, 0.8944271910, + 0.9486832981, 1.0000000000, 1.0000000000}); + checkCumulative(0.5, 2.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.4585302607, 0.6260990337, 0.7394254526, + 0.8221921916, 0.8838834765, 0.9295160031, 0.9621590305, 0.9838699101, + 0.9961174630, 1.0000000000, 1.0000000000}); + checkCumulative(0.5, 4.0, + x, new double[]{ + 0.0000000000, 0.0000000000, 0.6266250826, 0.8049844719, 0.8987784842, + 0.9502644369, 0.9777960959, 0.9914837366, 0.9974556254, 0.9995223859, + 0.9999714889, 1.0000000000, 1.0000000000}); + checkCumulative(1.0, 0.1, + x, new double[]{ + 0.00000000000, 0.00000000000, 0.01048074179, 0.02206723146, + 0.03503890488, 0.04979978349, 0.06696700846, 0.08755646344, + 0.11343184943, 0.14866007748, 0.20567176528, 1.00000000000, 1.00000000000}); + checkCumulative(1.0, 0.5, + x, new double[]{ + 0.00000000000, 0.00000000000, 0.05131670195, 0.10557280900, + 0.16333997347, 0.22540333076, 0.29289321881, 0.36754446797, + 0.45227744249, 0.55278640450, 0.68377223398, 1.00000000000, 1.00000000000}); + checkCumulative(1, 1, + x, new double[]{ + 0.0, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.0}); + checkCumulative(1, 2, + x, new double[]{ + 0.00, 0.00, 0.19, 0.36, 0.51, 0.64, 0.75, 0.84, 0.91, 0.96, 0.99, 1.00, 1.00}); + checkCumulative(1, 4, + x, new double[]{ + 0.0000, 0.0000, 0.3439, 0.5904, 0.7599, 0.8704, 0.9375, 0.9744, 0.9919, + 0.9984, 0.9999, 1.0000, 1.0000}); + checkCumulative(2.0, 0.1, + x, new double[]{ + 0.0000000000000, 0.0000000000000, 0.0005855492117, 0.0025085760862, + 0.0060900720266, 0.0117917748341, 0.0203153588864, 0.0328098512512, + 0.0513720788952, 0.0805528836776, 0.1341822241505, 1.0000000000000, 1.0000000000000}); + checkCumulative(2, 1, + x, new double[]{ + 0.00, 0.00, 0.01, 0.04, 0.09, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81, 1.00, 1.00}); + checkCumulative(2.0, 0.5, + x, new double[]{ + 0.000000000000, 0.000000000000, 0.003882537047, 0.016130089900, + 0.037840969486, 0.070483996910, 0.116116523517, 0.177807808356, + 0.260574547368, 0.373900966300, 0.541469739276, 1.000000000000, 1.000000000000}); + checkCumulative(2, 2, + x, new double[]{ + 0.000, 0.000, 0.028, 0.104, 0.216, 0.352, 0.500, 0.648, 0.784, 0.896, 0.972, 1.000, 1.000}); + checkCumulative(2, 4, + x, new double[]{ + 0.00000, 0.00000, 0.08146, 0.26272, 0.47178, 0.66304, 0.81250, 0.91296, + 0.96922, 0.99328, 0.99954, 1.00000, 1.00000}); + checkCumulative(4.0, 0.1, + x, new double[]{ + 0.000000000e+00, 0.000000000e+00, 3.217128269e-06, 5.592070271e-05, + 3.104375474e-04, 1.088719595e-03, 2.995933981e-03, 7.155588777e-03, + 1.577149153e-02, 3.380410585e-02, 7.650088789e-02, 1.000000000e+00, 1.000000000e+00}); + checkCumulative(4.0, 0.5, + x, new double[]{ + 0.000000000e+00, 0.000000000e+00, 2.851114863e-05, 4.776140576e-04, + 2.544374616e-03, 8.516263371e-03, 2.220390414e-02, 4.973556312e-02, + 1.012215158e-01, 1.950155281e-01, 3.733749174e-01, 1.000000000e+00, 1.000000000e+00}); + checkCumulative(4, 1, + x, new double[]{ + 0.0000, 0.0000, 0.0001, 0.0016, 0.0081, 0.0256, 0.0625, 0.1296, 0.2401, + 0.4096, 0.6561, 1.0000, 1.0000}); + checkCumulative(4, 2, + x, new double[]{ + 0.00000, 0.00000, 0.00046, 0.00672, 0.03078, 0.08704, 0.18750, 0.33696, + 0.52822, 0.73728, 0.91854, 1.00000, 1.00000}); + checkCumulative(4, 4, + x, new double[]{ + 0.000000, 0.000000, 0.002728, 0.033344, 0.126036, 0.289792, 0.500000, + 0.710208, 0.873964, 0.966656, 0.997272, 1.000000, 1.000000}); + } + + private void checkCumulative(double alpha, double beta, double[] x, double[] cumes) { + BetaDistribution d = new BetaDistribution(alpha, beta); + for (int i = 0; i < x.length; i++) { + Assert.assertEquals(cumes[i], d.cumulativeProbability(x[i]), 1e-8); + } + + for (int i = 1; i < x.length - 1; i++) { + Assert.assertEquals(x[i], d.inverseCumulativeProbability(cumes[i]), 1e-5); + } + } + + @Test + public void testDensity() { + double[] x = new double[]{1e-6, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}; + checkDensity(0.1, 0.1, + x, new double[]{ + 12741.2357380649, 0.4429889586665234, 2.639378715e-01, 2.066393611e-01, + 1.832401831e-01, 1.766302780e-01, 1.832404579e-01, 2.066400696e-01, + 2.639396531e-01, 4.429925026e-01}); + checkDensity(0.1, 0.5, + x, new double[]{ + 2.218377102e+04, 7.394524202e-01, 4.203020268e-01, 3.119435533e-01, + 2.600787829e-01, 2.330648626e-01, 2.211408259e-01, 2.222728708e-01, + 2.414013907e-01, 3.070567405e-01}); + checkDensity(0.1, 1.0, + x, new double[]{ + 2.511886432e+04, 7.943210858e-01, 4.256680458e-01, 2.955218303e-01, + 2.281103709e-01, 1.866062624e-01, 1.583664652e-01, 1.378514078e-01, + 1.222414585e-01, 1.099464743e-01}); + checkDensity(0.1, 2.0, + x, new double[]{ + 2.763072312e+04, 7.863770012e-01, 3.745874120e-01, 2.275514842e-01, + 1.505525939e-01, 1.026332391e-01, 6.968107049e-02, 4.549081293e-02, + 2.689298641e-02, 1.209399123e-02}); + checkDensity(0.1, 4.0, + x, new double[]{ + 2.997927462e+04, 6.911058917e-01, 2.601128486e-01, 1.209774010e-01, + 5.880564714e-02, 2.783915474e-02, 1.209657335e-02, 4.442148268e-03, + 1.167143939e-03, 1.312171805e-04}); + checkDensity(0.5, 0.1, + x, new double[]{ + 88.3152184726, 0.3070542841, 0.2414007269, 0.2222727015, + 0.2211409364, 0.2330652355, 0.2600795198, 0.3119449793, + 0.4203052841, 0.7394649088}); + checkDensity(0.5, 0.5, + x, new double[]{ + 318.3100453389, 1.0610282383, 0.7957732234, 0.6946084565, + 0.6497470636, 0.6366197724, 0.6497476051, 0.6946097796, + 0.7957762075, 1.0610376697}); + checkDensity(0.5, 1.0, + x, new double[]{ + 500.0000000000, 1.5811309244, 1.1180311937, 0.9128694077, + 0.7905684268, 0.7071060741, 0.6454966865, 0.5976138778, + 0.5590166450, 0.5270459839}); + checkDensity(0.5, 2.0, + x, new double[]{ + 749.99925000000, 2.134537420613655, 1.34163575536, 0.95851150881, + 0.71151039830, 0.53032849490, 0.38729704363, 0.26892534859, + 0.16770415497, 0.07905610701}); + checkDensity(0.5, 4.0, + x, new double[]{ + 1.093746719e+03, 2.52142232809988, 1.252190241e+00, 6.849343920e-01, + 3.735417140e-01, 1.933481570e-01, 9.036885833e-02, 3.529621669e-02, + 9.782644546e-03, 1.152878503e-03}); + checkDensity(1.0, 0.1, + x, new double[]{ + 0.1000000900, 0.1099466942, 0.1222417336, 0.1378517623, 0.1583669403, + 0.1866069342, 0.2281113974, 0.2955236034, 0.4256718768, + 0.7943353837}); + checkDensity(1.0, 0.5, + x, new double[]{ + 0.5000002500, 0.5270465695, 0.5590173438, 0.5976147315, 0.6454977623, + 0.7071074883, 0.7905704033, 0.9128724506, + 1.1180367838, 1.5811467358}); + checkDensity(1, 1, + x, new double[]{ + 1, 1, 1, + 1, 1, 1, 1, + 1, 1, 1}); + checkDensity(1, 2, + x, new double[]{ + 1.999998, 1.799998, 1.599998, 1.399998, 1.199998, 0.999998, 0.799998, + 0.599998, 0.399998, + 0.199998}); + checkDensity(1, 4, + x, new double[]{ + 3.999988000012, 2.915990280011, 2.047992320010, 1.371994120008, + 0.863995680007, 0.499997000006, 0.255998080005, 0.107998920004, + 0.031999520002, 0.003999880001}); + checkDensity(2.0, 0.1, + x, new double[]{ + 1.100000990e-07, 1.209425730e-02, 2.689331586e-02, 4.549123318e-02, + 6.968162794e-02, 1.026340191e-01, 1.505537732e-01, 2.275534997e-01, + 3.745917198e-01, 7.863929037e-01}); + checkDensity(2.0, 0.5, + x, new double[]{ + 7.500003750e-07, 7.905777599e-02, 1.677060417e-01, 2.689275256e-01, + 3.872996256e-01, 5.303316769e-01, 7.115145488e-01, 9.585174425e-01, + 1.341645818e+00, 2.134537420613655}); + checkDensity(2, 1, + x, new double[]{ + 0.000002, 0.200002, 0.400002, 0.600002, 0.800002, 1.000002, 1.200002, + 1.400002, 1.600002, + 1.800002}); + checkDensity(2, 2, + x, new double[]{ + 5.9999940e-06, 5.4000480e-01, 9.6000360e-01, 1.2600024e+00, + 1.4400012e+00, 1.5000000e+00, 1.4399988e+00, 1.2599976e+00, + 9.5999640e-01, 5.3999520e-01}); + checkDensity(2, 4, + x, new double[]{ + 0.00001999994, 1.45800971996, 2.04800255997, 2.05799803998, + 1.72799567999, 1.24999500000, 0.76799552000, 0.37799676001, + 0.12799824001, 0.01799948000}); + checkDensity(4.0, 0.1, + x, new double[]{ + 1.193501074e-19, 1.312253162e-04, 1.167181580e-03, 4.442248535e-03, + 1.209679109e-02, 2.783958903e-02, 5.880649983e-02, 1.209791638e-01, + 2.601171405e-01, 6.911229392e-01}); + checkDensity(4.0, 0.5, + x, new double[]{ + 1.093750547e-18, 1.152948959e-03, 9.782950259e-03, 3.529697305e-02, + 9.037036449e-02, 1.933508639e-01, 3.735463833e-01, 6.849425461e-01, + 1.252205894e+00, 2.52142232809988}); + checkDensity(4, 1, + x, new double[]{ + 4.000000000e-18, 4.000120001e-03, 3.200048000e-02, 1.080010800e-01, + 2.560019200e-01, 5.000030000e-01, 8.640043200e-01, 1.372005880e+00, + 2.048007680e+00, 2.916009720e+00}); + checkDensity(4, 2, + x, new double[]{ + 1.999998000e-17, 1.800052000e-02, 1.280017600e-01, 3.780032400e-01, + 7.680044800e-01, 1.250005000e+00, 1.728004320e+00, 2.058001960e+00, + 2.047997440e+00, 1.457990280e+00}); + checkDensity(4, 4, + x, new double[]{ + 1.399995800e-16, 1.020627216e-01, 5.734464512e-01, 1.296547409e+00, + 1.935364838e+00, 2.187500000e+00, 1.935355162e+00, 1.296532591e+00, + 5.734335488e-01, 1.020572784e-01}); + } + + @SuppressWarnings("boxing") + private void checkDensity(double alpha, double beta, double[] x, double[] expected) { + BetaDistribution d = new BetaDistribution(alpha, beta); + for (int i = 0; i < x.length; i++) { + Assert.assertEquals(String.format("density at x=%.1f for alpha=%.1f, beta=%.1f", x[i], alpha, beta), expected[i], d.density(x[i]), 1e-5); + } + } + + @Test + public void testMoments() { + final double tol = 1e-9; + BetaDistribution dist; + + dist = new BetaDistribution(1, 1); + Assert.assertEquals(dist.getNumericalMean(), 0.5, tol); + Assert.assertEquals(dist.getNumericalVariance(), 1.0 / 12.0, tol); + + dist = new BetaDistribution(2, 5); + Assert.assertEquals(dist.getNumericalMean(), 2.0 / 7.0, tol); + Assert.assertEquals(dist.getNumericalVariance(), 10.0 / (49.0 * 8.0), tol); + } + + @Test + public void testMomentsSampling() { + final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_1024_A, + 123456789L); + final int numSamples = 1000; + for (final double alpha : alphaBetas) { + for (final double beta : alphaBetas) { + final BetaDistribution betaDistribution = new BetaDistribution(alpha, beta); + final double[] observed = AbstractContinuousDistribution.sample(numSamples, + betaDistribution.createSampler(rng)); + Arrays.sort(observed); + + final String distribution = String.format("Beta(%.2f, %.2f)", alpha, beta); + Assert.assertEquals(String.format("E[%s]", distribution), + betaDistribution.getNumericalMean(), + StatUtils.mean(observed), epsilon); + Assert.assertEquals(String.format("Var[%s]", distribution), + betaDistribution.getNumericalVariance(), + StatUtils.variance(observed), epsilon); + } + } + } + + @Test + public void testGoodnessOfFit() { + final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_19937_A, + 123456789L); + + final int numSamples = 1000; + final double level = 0.01; + for (final double alpha : alphaBetas) { + for (final double beta : alphaBetas) { + final BetaDistribution betaDistribution = new BetaDistribution(alpha, beta); + + final ContinuousDistribution.Sampler sampler = betaDistribution.createSampler(rng); + final double[] observed = AbstractContinuousDistribution.sample(numSamples, sampler); + + final double gT = gTest(betaDistribution, observed); + Assert.assertFalse("G goodness-of-fit (" + gT + ") test rejected null at alpha = " + level, + gT < level); + } + } + } + + private double gTest(final ContinuousDistribution expectedDistribution, final double[] values) { + final int numBins = values.length / 30; + final double[] breaks = new double[numBins]; + for (int b = 0; b < breaks.length; b++) { + breaks[b] = expectedDistribution.inverseCumulativeProbability((double) b / numBins); + } + + final long[] observed = new long[numBins]; + for (final double value : values) { + int b = 0; + do { + b++; + } while (b < numBins && value >= breaks[b]); + + observed[b - 1]++; + } + + final double[] expected = new double[numBins]; + Arrays.fill(expected, (double) values.length / numBins); + + return new GTest().gTest(expected, observed); + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java new file mode 100644 index 0000000..9d5a97e --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BinomialDistributionTest.java @@ -0,0 +1,173 @@ +/* + * 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.junit.Assert; +import org.junit.Test; + +/** + * Test cases for BinomialDistribution. Extends DiscreteDistributionAbstractTest. + * See class javadoc for DiscreteDistributionAbstractTest for details. + * + */ +public class BinomialDistributionTest extends DiscreteDistributionAbstractTest { + + /** + * Constructor to override default tolerance. + */ + public BinomialDistributionTest() { + setTolerance(1e-12); + } + + // -------------- Implementations for abstract methods + // ----------------------- + + /** Creates the default discrete distribution instance to use in tests. */ + @Override + public DiscreteDistribution makeDistribution() { + return new BinomialDistribution(10, 0.70); + } + + /** 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 }; + } + + /** + * 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, 0.0000059049d, 0.000137781d, 0.0014467005, + 0.009001692, 0.036756909, 0.1029193452, 0.200120949, 0.266827932, + 0.2334744405, 0.121060821, 0.0282475249, 0d }; + } + + /** 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, 5.9049e-06, 0.0001436859, 0.0015903864, 0.0105920784, 0.0473489874, + 0.1502683326, 0.3503892816, 0.6172172136, 0.8506916541, 0.9717524751, 1d, 1d }; + } + + /** Creates the default inverse cumulative probability test input values */ + @Override + public double[] makeInverseCumulativeTestPoints() { + return new double[] { 0, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d, + 0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 }; + } + + /** + * Creates the default inverse cumulative probability density test expected + * values + */ + @Override + public int[] makeInverseCumulativeTestValues() { + return new int[] { 0, 2, 3, 4, 5, 5, 10, 10, 10, 9, 9, 10 }; + } + + // ----------------- Additional test cases --------------------------------- + + /** Test degenerate case p = 0 */ + @Test + public void testDegenerate0() { + BinomialDistribution dist = new BinomialDistribution(5, 0.0d); + setDistribution(dist); + setCumulativeTestPoints(new int[] { -1, 0, 1, 5, 10 }); + setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d }); + setDensityTestPoints(new int[] { -1, 0, 1, 10, 11 }); + 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); + } + + /** Test degenerate case p = 1 */ + @Test + public void testDegenerate1() { + BinomialDistribution dist = new BinomialDistribution(5, 1.0d); + setDistribution(dist); + setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 }); + setCumulativeTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 1d }); + setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 }); + setDensityTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 0d }); + setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d }); + setInverseCumulativeTestValues(new int[] { 5, 5 }); + verifyDensities(); + verifyCumulativeProbabilities(); + verifyInverseCumulativeProbabilities(); + Assert.assertEquals(dist.getSupportLowerBound(), 5); + Assert.assertEquals(dist.getSupportUpperBound(), 5); + } + + /** Test degenerate case n = 0 */ + @Test + public void testDegenerate2() { + BinomialDistribution dist = new BinomialDistribution(0, 0.01d); + setDistribution(dist); + setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 }); + setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d, 1d }); + setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 }); + setDensityTestValues(new double[] { 0d, 1d, 0d, 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); + } + + @Test + public void testMoments() { + final double tol = 1e-9; + BinomialDistribution dist; + + dist = new BinomialDistribution(10, 0.5); + Assert.assertEquals(dist.getNumericalMean(), 10d * 0.5d, tol); + Assert.assertEquals(dist.getNumericalVariance(), 10d * 0.5d * 0.5d, tol); + + dist = new BinomialDistribution(30, 0.3); + Assert.assertEquals(dist.getNumericalMean(), 30d * 0.3d, tol); + Assert.assertEquals(dist.getNumericalVariance(), 30d * 0.3d * (1d - 0.3d), tol); + } + + @Test + public void testMath718() { + // for large trials the evaluation of ContinuedFraction was inaccurate + // do a sweep over several large trials to test if the current implementation is + // numerically stable. + + for (int trials = 500000; trials < 20000000; trials += 100000) { + BinomialDistribution dist = new BinomialDistribution(trials, 0.5); + int p = dist.inverseCumulativeProbability(0.5); + Assert.assertEquals(trials / 2, p); + } + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java new file mode 100644 index 0000000..4407976 --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/CauchyDistributionTest.java @@ -0,0 +1,111 @@ +/* + * 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.junit.Assert; +import org.junit.Test; + +/** + * Test cases for CauchyDistribution. + * Extends ContinuousDistributionAbstractTest. See class javadoc for + * ContinuousDistributionAbstractTest for details. + * + */ +public class CauchyDistributionTest extends ContinuousDistributionAbstractTest { + + // --------------------- Override tolerance -------------- + protected double defaultTolerance = 1e-7; + @Override + public void setUp() { + super.setUp(); + setTolerance(defaultTolerance); + } + + //-------------- Implementations for abstract methods ----------------------- + + /** Creates the default continuous distribution instance to use in tests. */ + @Override + public CauchyDistribution makeDistribution() { + return new CauchyDistribution(1.2, 2.1); + } + + /** Creates the default cumulative probability distribution test input values */ + @Override + public double[] makeCumulativeTestPoints() { + // quantiles computed using R 2.9.2 + return new double[] {-667.24856187, -65.6230835029, -25.4830299460, -12.0588781808, + -5.26313542807, 669.64856187, 68.0230835029, 27.8830299460, 14.4588781808, 7.66313542807}; + } + + /** 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[] {1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437, + 1.49599158008e-06, 0.000149550440335, 0.000933076881878, 0.00370933207799, 0.0144742330437}; + } + + //---------------------------- Additional test cases ------------------------- + + @Test + public void testInverseCumulativeProbabilityExtremes() { + setInverseCumulativeTestPoints(new double[] {0.0, 1.0}); + setInverseCumulativeTestValues(new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY}); + verifyInverseCumulativeProbabilities(); + } + + @Test + public void testMedian() { + CauchyDistribution distribution = (CauchyDistribution) getDistribution(); + Assert.assertEquals(1.2, distribution.getMedian(), 0.0); + } + + @Test + public void testScale() { + CauchyDistribution distribution = (CauchyDistribution) getDistribution(); + Assert.assertEquals(2.1, distribution.getScale(), 0.0); + } + + @Test(expected=IllegalArgumentException.class) + public void testPrecondition1() { + new CauchyDistribution(0, 0); + } + @Test(expected=IllegalArgumentException.class) + public void testPrecondition2() { + new CauchyDistribution(0, -1); + } + + @Test + public void testMoments() { + CauchyDistribution dist; + + dist = new CauchyDistribution(10.2, 0.15); + Assert.assertTrue(Double.isNaN(dist.getNumericalMean())); + Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); + + dist = new CauchyDistribution(23.12, 2.12); + Assert.assertTrue(Double.isNaN(dist.getNumericalMean())); + Assert.assertTrue(Double.isNaN(dist.getNumericalVariance())); + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java new file mode 100644 index 0000000..dc97f47 --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java @@ -0,0 +1,136 @@ +/* + * 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.junit.Assert; +import org.junit.Test; + +/** + * Test cases for {@link ChiSquaredDistribution}. + * + * @see ContinuousDistributionAbstractTest + */ +public class ChiSquaredDistributionTest extends ContinuousDistributionAbstractTest { + + //-------------- Implementations for abstract methods ----------------------- + + /** Creates the default continuous distribution instance to use in tests. */ + @Override + public ChiSquaredDistribution makeDistribution() { + return new ChiSquaredDistribution(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[] {0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696, + 20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978}; + } + + /** 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 inverse cumulative probability test input values */ + @Override + public double[] makeInverseCumulativeTestPoints() { + return new double[] {0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d, + 0.990d, 0.975d, 0.950d, 0.900d, 1}; + } + + /** Creates the default inverse cumulative probability density test expected values */ + @Override + public double[] makeInverseCumulativeTestValues() { + return new double[] {0, 0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696, + 20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978, + Double.POSITIVE_INFINITY}; + } + + /** Creates the default probability density test expected values */ + @Override + public double[] makeDensityTestValues() { + return new double[] {0.0115379817652, 0.0415948507811, 0.0665060119842, 0.0919455953114, 0.121472591024, + 0.000433630076361, 0.00412780610309, 0.00999340341045, 0.0193246438937, 0.0368460089216}; + } + + // --------------------- Override tolerance -------------- + @Override + public void setUp() { + super.setUp(); + setTolerance(1e-9); + } + + //---------------------------- Additional test cases ------------------------- + + @Test + public void testSmallDf() { + setDistribution(new ChiSquaredDistribution(0.1d)); + setTolerance(1E-4); + // quantiles computed using R version 1.8.1 (linux version) + setCumulativeTestPoints(new double[] {1.168926E-60, 1.168926E-40, 1.063132E-32, + 1.144775E-26, 1.168926E-20, 5.472917, 2.175255, 1.13438, + 0.5318646, 0.1526342}); + setInverseCumulativeTestValues(getCumulativeTestPoints()); + setInverseCumulativeTestPoints(getCumulativeTestValues()); + verifyCumulativeProbabilities(); + verifyInverseCumulativeProbabilities(); + } + + @Test + public void testDfAccessors() { + ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution(); + Assert.assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE); + } + + @Test + public void testDensity() { + double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5}; + //R 2.5: print(dchisq(x, df=1), digits=10) + checkDensity(1, x, new double[]{0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436, 0.01464498256}); + //R 2.5: print(dchisq(x, df=0.1), digits=10) + checkDensity(0.1, x, new double[]{0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02, 9.447299159e-03, 8.827199396e-04}); + //R 2.5: print(dchisq(x, df=2), digits=10) + checkDensity(2, x, new double[]{0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059, 0.04104249931}); + //R 2.5: print(dchisq(x, df=10), digits=10) + checkDensity(10, x, new double[]{0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04, 7.664155024e-03, 6.680094289e-02}); + } + + private void checkDensity(double df, double[] x, double[] expected) { + ChiSquaredDistribution d = new ChiSquaredDistribution(df); + for (int i = 0; i < x.length; i++) { + Assert.assertEquals(expected[i], d.density(x[i]), 1e-5); + } + } + + @Test + public void testMoments() { + final double tol = 1e-9; + ChiSquaredDistribution dist; + + dist = new ChiSquaredDistribution(1500); + Assert.assertEquals(dist.getNumericalMean(), 1500, tol); + Assert.assertEquals(dist.getNumericalVariance(), 3000, tol); + + dist = new ChiSquaredDistribution(1.12); + Assert.assertEquals(dist.getNumericalMean(), 1.12, tol); + Assert.assertEquals(dist.getNumericalVariance(), 2.24, tol); + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java new file mode 100644 index 0000000..152a6c2 --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ConstantContinuousDistributionTest.java @@ -0,0 +1,92 @@ +/* + * 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.junit.Assert; +import org.junit.Test; + +/** + * Test cases for ConstantContinuousDistribution. + */ +public class ConstantContinuousDistributionTest extends ContinuousDistributionAbstractTest { + + // --- Override tolerance ------------------------------------------------- + + @Override + public void setUp() { + super.setUp(); + setTolerance(0); + } + + //--- Implementations for abstract methods -------------------------------- + + /** Creates the default uniform real distribution instance to use in tests. */ + @Override + public ConstantContinuousDistribution makeDistribution() { + return new ConstantContinuousDistribution(1); + } + + /** Creates the default cumulative probability distribution test input values */ + @Override + public double[] makeCumulativeTestPoints() { + return new double[] {0, 0.5, 1}; + } + + /** Creates the default cumulative probability distribution test expected values */ + @Override + public double[] makeCumulativeTestValues() { + return new double[] {0, 0, 1}; + } + + /** Creates the default probability density test expected values */ + @Override + public double[] makeDensityTestValues() { + return new double[] {0, 0, 1}; + } + + /** Override default test, verifying that inverse cum is constant */ + @Override + @Test + public void testInverseCumulativeProbabilities() { + ContinuousDistribution dist = getDistribution(); + for (double x : getCumulativeTestValues()) { + Assert.assertEquals(1,dist.inverseCumulativeProbability(x), 0); + } + } + + //--- Additional test cases ----------------------------------------------- + + @Test + public void testMeanVariance() { + ConstantContinuousDistribution dist; + + dist = new ConstantContinuousDistribution(-1); + Assert.assertEquals(dist.getNumericalMean(), -1, 0d); + Assert.assertEquals(dist.getNumericalVariance(), 0, 0d); + } + + @Test + @Override + public void testSampler() { + final double value = 12.345; + final ContinuousDistribution.Sampler sampler = new ConstantContinuousDistribution(value).createSampler(null); + for (int i = 0; i < 10; i++) { + Assert.assertEquals(value, sampler.sample(), 0); + } + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java new file mode 100644 index 0000000..a6176f3 --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ContinuousDistributionAbstractTest.java @@ -0,0 +1,456 @@ +/* + * 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 java.util.ArrayList; +import java.util.Collections; + +import org.apache.commons.math3.analysis.UnivariateFunction; +import org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator; +import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator; +import org.apache.commons.rng.simple.RandomSource; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +/** + * Abstract base class for {@link ContinuousDistribution} tests. + * <p> + * To create a concrete test class for a continuous distribution + * implementation, first implement makeDistribution() to return a distribution + * instance to use in tests. Then implement each of the test data generation + * methods below. In each case, the test points and test values arrays + * returned represent parallel arrays of inputs and expected values for the + * distribution returned by makeDistribution(). Default implementations + * are provided for the makeInverseXxx methods that just invert the mapping + * defined by the arrays returned by the makeCumulativeXxx methods. + * <p> + * makeCumulativeTestPoints() -- arguments used to test cumulative probabilities + * makeCumulativeTestValues() -- expected cumulative probabilities + * makeDensityTestValues() -- expected density values at cumulativeTestPoints + * makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf + * makeInverseCumulativeTestValues() -- expected inverse cdf values + * <p> + * To implement additional test cases with different distribution instances and + * test data, use the setXxx methods for the instance data in test cases and + * call the verifyXxx methods to verify results. + * <p> + * Error tolerance can be overridden by implementing getTolerance(). + * <p> + * Test data should be validated against reference tables or other packages + * where possible, and the source of the reference data and/or validation + * should be documented in the test cases. A framework for validating + * distribution data against R is included in the /src/test/R source tree. + * <p> + * See {@link NormalDistributionTest} and {@link ChiSquaredDistributionTest} + * for examples. + * + */ +public abstract class ContinuousDistributionAbstractTest { + +//-------------------- Private test instance data ------------------------- + /** Distribution instance used to perform tests */ + private ContinuousDistribution distribution; + + /** Tolerance used in comparing expected and returned values */ + private double tolerance = 1e-4; + + /** Arguments used to test cumulative probability density calculations */ + private double[] cumulativeTestPoints; + + /** Values used to test cumulative probability density calculations */ + private double[] cumulativeTestValues; + + /** Arguments used to test inverse cumulative probability density calculations */ + private double[] inverseCumulativeTestPoints; + + /** Values used to test inverse cumulative probability density calculations */ + private double[] inverseCumulativeTestValues; + + /** Values used to test density calculations */ + private double[] densityTestValues; + + /** Values used to test logarithmic density calculations */ + private double[] logDensityTestValues; + + //-------------------- Abstract methods ----------------------------------- + + /** Creates the default continuous distribution instance to use in tests. */ + public abstract ContinuousDistribution makeDistribution(); + + /** Creates the default cumulative probability test input values */ + public abstract double[] makeCumulativeTestPoints(); + + /** Creates the default cumulative probability test expected values */ + public abstract double[] makeCumulativeTestValues(); + + /** Creates the default density test expected values */ + public abstract double[] makeDensityTestValues(); + + /** Creates the default logarithmic density test expected values. + * The default implementation simply computes the logarithm + * of each value returned by {@link #makeDensityTestValues()}.*/ + public double[] makeLogDensityTestValues() { + final double[] densityTestValues = makeDensityTestValues(); + final double[] logDensityTestValues = new double[densityTestValues.length]; + for (int i = 0; i < densityTestValues.length; i++) { + logDensityTestValues[i] = Math.log(densityTestValues[i]); + } + return logDensityTestValues; + } + + //---- Default implementations of inverse test data generation methods ---- + + /** Creates the default inverse cumulative probability test input values */ + public double[] makeInverseCumulativeTestPoints() { + return makeCumulativeTestValues(); + } + + /** Creates the default inverse cumulative probability density test expected values */ + public double[] makeInverseCumulativeTestValues() { + return makeCumulativeTestPoints(); + } + + //-------------------- Setup / tear down ---------------------------------- + + /** + * Setup sets all test instance data to default values + */ + @Before + public void setUp() { + distribution = makeDistribution(); + cumulativeTestPoints = makeCumulativeTestPoints(); + cumulativeTestValues = makeCumulativeTestValues(); + inverseCumulativeTestPoints = makeInverseCumulativeTestPoints(); + inverseCumulativeTestValues = makeInverseCumulativeTestValues(); + densityTestValues = makeDensityTestValues(); + logDensityTestValues = makeLogDensityTestValues(); + } + + /** + * Cleans up test instance data + */ + @After + public void tearDown() { + distribution = null; + cumulativeTestPoints = null; + cumulativeTestValues = null; + inverseCumulativeTestPoints = null; + inverseCumulativeTestValues = null; + densityTestValues = null; + logDensityTestValues = null; + } + + //-------------------- Verification methods ------------------------------- + + /** + * Verifies that cumulative probability density calculations match expected values + * using current test instance data + */ + protected void verifyCumulativeProbabilities() { + // verify cumulativeProbability(double) + for (int i = 0; i < cumulativeTestPoints.length; i++) { + TestUtils.assertEquals("Incorrect cumulative probability value returned for " + + cumulativeTestPoints[i], cumulativeTestValues[i], + distribution.cumulativeProbability(cumulativeTestPoints[i]), + getTolerance()); + } + // verify probability(double, double) + for (int i = 0; i < cumulativeTestPoints.length; i++) { + for (int j = 0; j < cumulativeTestPoints.length; j++) { + if (cumulativeTestPoints[i] <= cumulativeTestPoints[j]) { + TestUtils.assertEquals(cumulativeTestValues[j] - cumulativeTestValues[i], + distribution.probability(cumulativeTestPoints[i], cumulativeTestPoints[j]), + getTolerance()); + } else { + try { + distribution.probability(cumulativeTestPoints[i], cumulativeTestPoints[j]); + } catch (IllegalArgumentException e) { + continue; + } + Assert.fail("distribution.probability(double, double) should have thrown an exception that second argument is too large"); + } + } + } + } + + /** + * Verifies that inverse cumulative probability density calculations match expected values + * using current test instance data + */ + protected void verifyInverseCumulativeProbabilities() { + for (int i = 0; i < inverseCumulativeTestPoints.length; i++) { + TestUtils.assertEquals("Incorrect inverse cumulative probability value returned for " + + inverseCumulativeTestPoints[i], inverseCumulativeTestValues[i], + distribution.inverseCumulativeProbability(inverseCumulativeTestPoints[i]), + getTolerance()); + } + } + + /** + * Verifies that density calculations match expected values + */ + protected void verifyDensities() { + for (int i = 0; i < cumulativeTestPoints.length; i++) { + TestUtils.assertEquals("Incorrect probability density value returned for " + + cumulativeTestPoints[i], densityTestValues[i], + distribution.density(cumulativeTestPoints[i]), + getTolerance()); + } + } + + /** + * Verifies that logarithmic density calculations match expected values + */ + protected void verifyLogDensities() { + for (int i = 0; i < cumulativeTestPoints.length; i++) { + TestUtils.assertEquals("Incorrect probability density value returned for " + + cumulativeTestPoints[i], logDensityTestValues[i], + distribution.logDensity(cumulativeTestPoints[i]), + getTolerance()); + } + } + + //------------------------ Default test cases ----------------------------- + + /** + * Verifies that cumulative probability density calculations match expected values + * using default test instance data + */ + @Test + public void testCumulativeProbabilities() { + verifyCumulativeProbabilities(); + } + + /** + * Verifies that inverse cumulative probability density calculations match expected values + * using default test instance data + */ + @Test + public void testInverseCumulativeProbabilities() { + verifyInverseCumulativeProbabilities(); + } + + /** + * Verifies that density calculations return expected values + * for default test instance data + */ + @Test + public void testDensities() { + verifyDensities(); + } + + /** + * Verifies that logarithmic density calculations return expected values + * for default test instance data + */ + @Test + public void testLogDensities() { + verifyLogDensities(); + } + + /** + * Verifies that probability computations are consistent + */ + @Test + public void testConsistency() { + for (int i = 1; i < cumulativeTestPoints.length; i++) { + + // check that cdf(x, x) = 0 + TestUtils.assertEquals(0d, + distribution.probability + (cumulativeTestPoints[i], cumulativeTestPoints[i]), + tolerance); + + // check that P(a < X <= b) = P(X <= b) - P(X <= a) + double upper = Math.max(cumulativeTestPoints[i], cumulativeTestPoints[i -1]); + double lower = Math.min(cumulativeTestPoints[i], cumulativeTestPoints[i -1]); + double diff = distribution.cumulativeProbability(upper) - + distribution.cumulativeProbability(lower); + double direct = distribution.probability(lower, upper); + TestUtils.assertEquals("Inconsistent probability for (" + + lower + "," + upper + ")", diff, direct, tolerance); + } + } + + /** + * Verifies that illegal arguments are correctly handled + */ + @Test(expected=IllegalArgumentException.class) + public void testPrecondition1() { + distribution.probability(1, 0); + } + @Test(expected=IllegalArgumentException.class) + public void testPrecondition2() { + distribution.inverseCumulativeProbability(-1); + } + @Test(expected=IllegalArgumentException.class) + public void testPrecondition3() { + distribution.inverseCumulativeProbability(2); + } + + /** + * Test sampling + */ + @Test + public void testSampler() { + final int sampleSize = 1000; + final ContinuousDistribution.Sampler sampler = + distribution.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 123456789L)); + final double[] sample = AbstractContinuousDistribution.sample(sampleSize, sampler); + final double[] quartiles = TestUtils.getDistributionQuartiles(distribution); + final double[] expected = {250, 250, 250, 250}; + final long[] counts = new long[4]; + + for (int i = 0; i < sampleSize; i++) { + TestUtils.updateCounts(sample[i], counts, quartiles); + } + TestUtils.assertChiSquareAccept(expected, counts, 0.001); + } + + /** + * Verify that density integrals match the distribution. + * The (filtered, sorted) cumulativeTestPoints array is used to source + * integration limits. The integral of the density (estimated using a + * Legendre-Gauss integrator) is compared with the cdf over the same + * interval. Test points outside of the domain of the density function + * are discarded. + */ + @Test + public void testDensityIntegrals() { + final double tol = 1e-9; + final BaseAbstractUnivariateIntegrator integrator = + new IterativeLegendreGaussIntegrator(5, 1e-12, 1e-10); + final UnivariateFunction d = new UnivariateFunction() { + @Override + public double value(double x) { + return distribution.density(x); + } + }; + final ArrayList<Double> integrationTestPoints = new ArrayList<>(); + for (int i = 0; i < cumulativeTestPoints.length; i++) { + if (Double.isNaN(cumulativeTestValues[i]) || + cumulativeTestValues[i] < 1e-5 || + cumulativeTestValues[i] > 1 - 1e-5) { + continue; // exclude integrals outside domain. + } + integrationTestPoints.add(cumulativeTestPoints[i]); + } + Collections.sort(integrationTestPoints); + for (int i = 1; i < integrationTestPoints.size(); i++) { + Assert.assertEquals(distribution.probability(integrationTestPoints.get(0), integrationTestPoints.get(i)), + integrator.integrate(1000000, // Triangle integrals are very slow to converge + d, integrationTestPoints.get(0), + integrationTestPoints.get(i)), tol); + } + } + + //------------------ Getters / Setters for test instance data ----------- + /** + * @return Returns the cumulativeTestPoints. + */ + protected double[] getCumulativeTestPoints() { + return cumulativeTestPoints; + } + + /** + * @param cumulativeTestPoints The cumulativeTestPoints to set. + */ + protected void setCumulativeTestPoints(double[] cumulativeTestPoints) { + this.cumulativeTestPoints = cumulativeTestPoints; + } + + /** + * @return Returns the cumulativeTestValues. + */ + protected double[] getCumulativeTestValues() { + return cumulativeTestValues; + } + + /** + * @param cumulativeTestValues The cumulativeTestValues to set. + */ + protected void setCumulativeTestValues(double[] cumulativeTestValues) { + this.cumulativeTestValues = cumulativeTestValues; + } + + protected double[] getDensityTestValues() { + return densityTestValues; + } + + protected void setDensityTestValues(double[] densityTestValues) { + this.densityTestValues = densityTestValues; + } + + /** + * @return Returns the distribution. + */ + protected ContinuousDistribution getDistribution() { + return distribution; + } + + /** + * @param distribution The distribution to set. + */ + protected void setDistribution(ContinuousDistribution distribution) { + this.distribution = distribution; + } + + /** + * @return Returns the inverseCumulativeTestPoints. + */ + protected double[] getInverseCumulativeTestPoints() { + return inverseCumulativeTestPoints; + } + + /** + * @param inverseCumulativeTestPoints The inverseCumulativeTestPoints to set. + */ + protected void setInverseCumulativeTestPoints(double[] inverseCumulativeTestPoints) { + this.inverseCumulativeTestPoints = inverseCumulativeTestPoints; + } + + /** + * @return Returns the inverseCumulativeTestValues. + */ + protected double[] getInverseCumulativeTestValues() { + return inverseCumulativeTestValues; + } + + /** + * @param inverseCumulativeTestValues The inverseCumulativeTestValues to set. + */ + protected void setInverseCumulativeTestValues(double[] inverseCumulativeTestValues) { + this.inverseCumulativeTestValues = inverseCumulativeTestValues; + } + + /** + * @return Returns the tolerance. + */ + protected double getTolerance() { + return tolerance; + } + + /** + * @param tolerance The tolerance to set. + */ + protected void setTolerance(double tolerance) { + this.tolerance = tolerance; + } +} http://git-wip-us.apache.org/repos/asf/commons-statistics/blob/9c794a15/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java ---------------------------------------------------------------------- diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java new file mode 100644 index 0000000..ab0e0a1 --- /dev/null +++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/DiscreteDistributionAbstractTest.java @@ -0,0 +1,411 @@ +/* + * 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.rng.simple.RandomSource; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +/** + * Abstract base class for {@link DiscreteDistribution} tests. + * <p> + * To create a concrete test class for an integer distribution implementation, + * implement makeDistribution() to return a distribution instance to use in + * tests and each of the test data generation methods below. In each case, the + * test points and test values arrays returned represent parallel arrays of + * inputs and expected values for the distribution returned by makeDistribution(). + * <p> + * makeDensityTestPoints() -- arguments used to test probability density calculation + * makeDensityTestValues() -- expected probability densities + * makeCumulativeTestPoints() -- arguments used to test cumulative probabilities + * makeCumulativeTestValues() -- expected cumulative probabilites + * makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf evaluation + * makeInverseCumulativeTestValues() -- expected inverse cdf values + * <p> + * To implement additional test cases with different distribution instances and test data, + * use the setXxx methods for the instance data in test cases and call the verifyXxx methods + * to verify results. + * + */ +public abstract class DiscreteDistributionAbstractTest { + +//-------------------- Private test instance data ------------------------- + /** Discrete distribution instance used to perform tests */ + private DiscreteDistribution distribution; + + /** Tolerance used in comparing expected and returned values */ + private double tolerance = 1e-12; + + /** Arguments used to test probability density calculations */ + private int[] densityTestPoints; + + /** Values used to test probability density calculations */ + private double[] densityTestValues; + + /** Values used to test logarithmic probability density calculations */ + private double[] logDensityTestValues; + + /** Arguments used to test cumulative probability density calculations */ + private int[] cumulativeTestPoints; + + /** Values used to test cumulative probability density calculations */ + private double[] cumulativeTestValues; + + /** Arguments used to test inverse cumulative probability density calculations */ + private double[] inverseCumulativeTestPoints; + + /** Values used to test inverse cumulative probability density calculations */ + private int[] inverseCumulativeTestValues; + + //-------------------- Abstract methods ----------------------------------- + + /** Creates the default discrete distribution instance to use in tests. */ + public abstract DiscreteDistribution makeDistribution(); + + /** Creates the default probability density test input values */ + public abstract int[] makeDensityTestPoints(); + + /** Creates the default probability density test expected values */ + public abstract double[] makeDensityTestValues(); + + /** Creates the default logarithmic probability density test expected values. + * + * The default implementation simply computes the logarithm of all the values in + * {@link #makeDensityTestValues()}. + * + * @return double[] the default logarithmic probability density test expected values. + */ + public double[] makeLogDensityTestValues() { + final double[] densityTestValues = makeDensityTestValues(); + final double[] logDensityTestValues = new double[densityTestValues.length]; + for (int i = 0; i < densityTestValues.length; i++) { + logDensityTestValues[i] = Math.log(densityTestValues[i]); + } + return logDensityTestValues; + } + + /** Creates the default cumulative probability density test input values */ + public abstract int[] makeCumulativeTestPoints(); + + /** Creates the default cumulative probability density test expected values */ + public abstract double[] makeCumulativeTestValues(); + + /** Creates the default inverse cumulative probability test input values */ + public abstract double[] makeInverseCumulativeTestPoints(); + + /** Creates the default inverse cumulative probability density test expected values */ + public abstract int[] makeInverseCumulativeTestValues(); + + //-------------------- Setup / tear down ---------------------------------- + + /** + * Setup sets all test instance data to default values + */ + @Before + public void setUp() { + distribution = makeDistribution(); + densityTestPoints = makeDensityTestPoints(); + densityTestValues = makeDensityTestValues(); + logDensityTestValues = makeLogDensityTestValues(); + cumulativeTestPoints = makeCumulativeTestPoints(); + cumulativeTestValues = makeCumulativeTestValues(); + inverseCumulativeTestPoints = makeInverseCumulativeTestPoints(); + inverseCumulativeTestValues = makeInverseCumulativeTestValues(); + } + + /** + * Cleans up test instance data + */ + @After + public void tearDown() { + distribution = null; + densityTestPoints = null; + densityTestValues = null; + logDensityTestValues = null; + cumulativeTestPoints = null; + cumulativeTestValues = null; + inverseCumulativeTestPoints = null; + inverseCumulativeTestValues = null; + } + + //-------------------- Verification methods ------------------------------- + + /** + * Verifies that probability density calculations match expected values + * using current test instance data + */ + protected void verifyDensities() { + for (int i = 0; i < densityTestPoints.length; i++) { + Assert.assertEquals("Incorrect density value returned for " + densityTestPoints[i], + densityTestValues[i], + distribution.probability(densityTestPoints[i]), getTolerance()); + } + } + + /** + * Verifies that logarithmic probability density calculations match expected values + * using current test instance data. + */ + protected void verifyLogDensities() { + for (int i = 0; i < densityTestPoints.length; i++) { + // FIXME: when logProbability methods are added to DiscreteDistribution in 4.0, remove cast below + Assert.assertEquals("Incorrect log density value returned for " + densityTestPoints[i], + logDensityTestValues[i], + ((AbstractDiscreteDistribution) distribution).logProbability(densityTestPoints[i]), tolerance); + } + } + + /** + * Verifies that cumulative probability density calculations match expected values + * using current test instance data + */ + protected void verifyCumulativeProbabilities() { + for (int i = 0; i < cumulativeTestPoints.length; i++) { + Assert.assertEquals("Incorrect cumulative probability value returned for " + cumulativeTestPoints[i], + cumulativeTestValues[i], + distribution.cumulativeProbability(cumulativeTestPoints[i]), getTolerance()); + } + } + + + /** + * Verifies that inverse cumulative probability density calculations match expected values + * using current test instance data + */ + protected void verifyInverseCumulativeProbabilities() { + for (int i = 0; i < inverseCumulativeTestPoints.length; i++) { + Assert.assertEquals("Incorrect inverse cumulative probability value returned for " + + inverseCumulativeTestPoints[i], inverseCumulativeTestValues[i], + distribution.inverseCumulativeProbability(inverseCumulativeTestPoints[i])); + } + } + + //------------------------ Default test cases ----------------------------- + + /** + * Verifies that probability density calculations match expected values + * using default test instance data + */ + @Test + public void testDensities() { + verifyDensities(); + } + + /** + * Verifies that logarithmic probability density calculations match expected values + * using default test instance data + */ + @Test + public void testLogDensities() { + verifyLogDensities(); + } + + /** + * Verifies that cumulative probability density calculations match expected values + * using default test instance data + */ + @Test + public void testCumulativeProbabilities() { + verifyCumulativeProbabilities(); + } + + /** + * Verifies that inverse cumulative probability density calculations match expected values + * using default test instance data + */ + @Test + public void testInverseCumulativeProbabilities() { + verifyInverseCumulativeProbabilities(); + } + + @Test + public void testConsistencyAtSupportBounds() { + final int lower = distribution.getSupportLowerBound(); + Assert.assertEquals("Cumulative probability mmust be 0 below support lower bound.", + 0.0, distribution.cumulativeProbability(lower - 1), 0.0); + Assert.assertEquals("Cumulative probability of support lower bound must be equal to probability mass at this point.", + distribution.probability(lower), distribution.cumulativeProbability(lower), getTolerance()); + Assert.assertEquals("Inverse cumulative probability of 0 must be equal to support lower bound.", + lower, distribution.inverseCumulativeProbability(0.0)); + + final int upper = distribution.getSupportUpperBound(); + if (upper != Integer.MAX_VALUE) { + Assert.assertEquals("Cumulative probability of support upper bound must be equal to 1.", + 1.0, distribution.cumulativeProbability(upper), 0.0); + } + Assert.assertEquals("Inverse cumulative probability of 1 must be equal to support upper bound.", + upper, distribution.inverseCumulativeProbability(1.0)); + } + + @Test(expected=IllegalArgumentException.class) + public void testPrecondition1() { + distribution.probability(1, 0); + } + @Test(expected=IllegalArgumentException.class) + public void testPrecondition2() { + distribution.inverseCumulativeProbability(-1); + } + @Test(expected=IllegalArgumentException.class) + public void testPrecondition3() { + distribution.inverseCumulativeProbability(2); + } + + /** + * Test sampling + */ + @Test + public void testSampling() { + int[] densityPoints = makeDensityTestPoints(); + double[] densityValues = makeDensityTestValues(); + int sampleSize = 1000; + int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues); + AbstractDiscreteDistribution distribution = (AbstractDiscreteDistribution) makeDistribution(); + double[] expectedCounts = new double[length]; + long[] observedCounts = new long[length]; + for (int i = 0; i < length; i++) { + expectedCounts[i] = sampleSize * densityValues[i]; + } + // Use fixed seed. + final DiscreteDistribution.Sampler sampler = + distribution.createSampler(RandomSource.create(RandomSource.WELL_512_A, + 1000)); + int[] sample = AbstractDiscreteDistribution.sample(sampleSize, sampler); + for (int i = 0; i < sampleSize; i++) { + for (int j = 0; j < length; j++) { + if (sample[i] == densityPoints[j]) { + observedCounts[j]++; + } + } + } + TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001); + } + + //------------------ Getters / Setters for test instance data ----------- + /** + * @return Returns the cumulativeTestPoints. + */ + protected int[] getCumulativeTestPoints() { + return cumulativeTestPoints; + } + + /** + * @param cumulativeTestPoints The cumulativeTestPoints to set. + */ + protected void setCumulativeTestPoints(int[] cumulativeTestPoints) { + this.cumulativeTestPoints = cumulativeTestPoints; + } + + /** + * @return Returns the cumulativeTestValues. + */ + protected double[] getCumulativeTestValues() { + return cumulativeTestValues; + } + + /** + * @param cumulativeTestValues The cumulativeTestValues to set. + */ + protected void setCumulativeTestValues(double[] cumulativeTestValues) { + this.cumulativeTestValues = cumulativeTestValues; + } + + /** + * @return Returns the densityTestPoints. + */ + protected int[] getDensityTestPoints() { + return densityTestPoints; + } + + /** + * @param densityTestPoints The densityTestPoints to set. + */ + protected void setDensityTestPoints(int[] densityTestPoints) { + this.densityTestPoints = densityTestPoints; + } + + /** + * @return Returns the densityTestValues. + */ + protected double[] getDensityTestValues() { + return densityTestValues; + } + + /** + * @param densityTestValues The densityTestValues to set. + */ + protected void setDensityTestValues(double[] densityTestValues) { + this.densityTestValues = densityTestValues; + } + + /** + * @return Returns the distribution. + */ + protected DiscreteDistribution getDistribution() { + return distribution; + } + + /** + * @param distribution The distribution to set. + */ + protected void setDistribution(DiscreteDistribution distribution) { + this.distribution = distribution; + } + + /** + * @return Returns the inverseCumulativeTestPoints. + */ + protected double[] getInverseCumulativeTestPoints() { + return inverseCumulativeTestPoints; + } + + /** + * @param inverseCumulativeTestPoints The inverseCumulativeTestPoints to set. + */ + protected void setInverseCumulativeTestPoints(double[] inverseCumulativeTestPoints) { + this.inverseCumulativeTestPoints = inverseCumulativeTestPoints; + } + + /** + * @return Returns the inverseCumulativeTestValues. + */ + protected int[] getInverseCumulativeTestValues() { + return inverseCumulativeTestValues; + } + + /** + * @param inverseCumulativeTestValues The inverseCumulativeTestValues to set. + */ + protected void setInverseCumulativeTestValues(int[] inverseCumulativeTestValues) { + this.inverseCumulativeTestValues = inverseCumulativeTestValues; + } + + /** + * @return Returns the tolerance. + */ + protected double getTolerance() { + return tolerance; + } + + /** + * @param tolerance The tolerance to set. + */ + protected void setTolerance(double tolerance) { + this.tolerance = tolerance; + } +}
