diff --git a/src/UnitTests/DistributionTests/CommonDistributionTests.cs b/src/UnitTests/DistributionTests/CommonDistributionTests.cs index 843c4314..42021271 100644 --- a/src/UnitTests/DistributionTests/CommonDistributionTests.cs +++ b/src/UnitTests/DistributionTests/CommonDistributionTests.cs @@ -3,7 +3,9 @@ // http://numerics.mathdotnet.com // http://github.com/mathnet/mathnet-numerics // http://mathnetnumerics.codeplex.com -// Copyright (c) 2009-2010 Math.NET +// +// Copyright (c) 2009-2013 Math.NET +// // Permission is hereby granted, free of charge, to any person // obtaining a copy of this software and associated documentation // files (the "Software"), to deal in the Software without @@ -12,8 +14,10 @@ // copies of the Software, and to permit persons to whom the // Software is furnished to do so, subject to the following // conditions: +// // The above copyright notice and this permission notice shall be // included in all copies or substantial portions of the Software. +// // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, // EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES // OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND @@ -34,102 +38,58 @@ using NUnit.Framework; namespace MathNet.Numerics.UnitTests.DistributionTests { - using Random = System.Random; - /// - /// This class will perform various tests on discrete and continuous univariate distributions. The multivariate distributions - /// will implement these respective tests in their local unit test classes as they do not adhere to the same interfaces. + /// This class will perform various tests on discrete and continuous univariate distributions. + /// The multivariate distributions will implement these respective tests in their local unit + /// test classes as they do not adhere to the same interfaces. /// [TestFixture, Category("Distributions")] public class CommonDistributionTests { - /// - /// Gets or sets the number of samples we want. - /// - public static int NumberOfTestSamples - { - get; - set; - } - - /// - /// Gets or sets the number of buckets in the histogram for the sampling function tests. - /// - public static int NumberOfBuckets - { - get; - set; - } - - /// - /// Gets or sets the error we want to tolerate for sampling functions. - /// - public static double Error - { - get; - set; - } - - /// - /// Gets or sets the error probability we want to tolerate for sampling functions. - /// - public static double ErrorProbability - { - get; - set; - } - - /// - /// The list of discrete distributions which we test. - /// - private List _discreteDistributions; - - /// - /// The list of continuous distributions which we test. - /// - private List _continuousDistributions; - - /// - /// Initializes static members of the CommonDistributionTests class. - /// - static CommonDistributionTests() - { - NumberOfTestSamples = 3500000; - NumberOfBuckets = 100; - Error = 0.01; - ErrorProbability = 0.001; - } - - /// - /// Set-up test parameters. - /// - [SetUp] - public void SetupDistributions() - { - _discreteDistributions = new List - { - new Bernoulli(0.6), - new Binomial(0.7, 10), - new Categorical(new[] { 0.7, 0.3 }), - new DiscreteUniform(1, 10) - }; + public const int NumberOfTestSamples = 3500000; + public const int NumberOfHistogramBuckets = 100; + public const double ErrorTolerance = 0.01; + public const double ErrorProbability = 0.001; - _continuousDistributions = new List - { - new Beta(1.0, 1.0), - new ContinuousUniform(0.0, 1.0), - new Gamma(1.0, 1.0), - new Normal(0.0, 1.0), - new Weibull(1.0, 1.0), - new LogNormal(1.0, 1.0), - new Triangular(0, 1, 0.7), - new StudentT(0.0, 1.0, 5.0) - }; - } + readonly List _discreteDistributions = + new List + { + new Bernoulli(0.6), + new Binomial(0.7, 10), + new Categorical(new[] { 0.7, 0.3 }), + //new ConwayMaxwellPoisson(0.2, 0.4), + new DiscreteUniform(1, 10), + //new Geometric(0.1), + new Hypergeometric(20, 3, 5), + //new NegativeBinomial(4, 0.6), + //new Poisson(0.4), + new Zipf(0.6, 10), + }; + + readonly List _continuousDistributions = + new List + { + new Beta(1.0, 1.0), + new Cauchy(1.0, 1.0), + new Chi(3.0), + new ChiSquared(3.0), + new ContinuousUniform(0.0, 1.0), + new Erlang(3, 0.4), + new Exponential(0.4), + new FisherSnedecor(0.3, 0.4), + new Gamma(1.0, 1.0), + new InverseGamma(1.0, 1.0), + new Laplace(1.0, 0.5), + new LogNormal(1.0, 1.0), + new Normal(0.0, 1.0), + new Pareto(1.0, 0.5), + new Rayleigh(0.8), + new Stable(0.5, 1.0, 0.5, 1.0), + new StudentT(0.0, 1.0, 5.0), + new Triangular(0, 1, 0.7), + new Weibull(1.0, 1.0), + }; - /// - /// Validate that univariate distributions have random source. - /// [Test] public void ValidateThatUnivariateDistributionsHaveRandomSource() { @@ -144,9 +104,6 @@ namespace MathNet.Numerics.UnitTests.DistributionTests } } - /// - /// Can set random source. - /// [Test] public void CanSetRandomSource() { @@ -183,30 +140,28 @@ namespace MathNet.Numerics.UnitTests.DistributionTests [Test] public void SampleFollowsCorrectDistribution() { - Random rnd = new SystemRandomSource(1); - foreach (var dd in _discreteDistributions) { - dd.RandomSource = rnd; + dd.RandomSource = new SystemRandomSource(1, false); var samples = new double[NumberOfTestSamples]; for (var i = 0; i < NumberOfTestSamples; i++) { samples[i] = dd.Sample(); } - VapnikChervonenkisTest(Error, ErrorProbability, samples, dd); + VapnikChervonenkisTest(ErrorTolerance, ErrorProbability, samples, dd); } foreach (var cd in _continuousDistributions) { - cd.RandomSource = rnd; + cd.RandomSource = new SystemRandomSource(1, false); var samples = new double[NumberOfTestSamples]; for (var i = 0; i < NumberOfTestSamples; i++) { samples[i] = cd.Sample(); } - VapnikChervonenkisTest(Error, ErrorProbability, samples, cd); + VapnikChervonenkisTest(ErrorTolerance, ErrorProbability, samples, cd); } } @@ -216,18 +171,18 @@ namespace MathNet.Numerics.UnitTests.DistributionTests [Test] public void SamplesFollowsCorrectDistribution() { - Random rnd = new SystemRandomSource(1); - foreach (var dd in _discreteDistributions) { - dd.RandomSource = rnd; - VapnikChervonenkisTest(Error, ErrorProbability, dd.Samples().Select(x => (double)x).Take(NumberOfTestSamples), dd); + dd.RandomSource = new SystemRandomSource(1, false); + var samples = dd.Samples().Select(x => (double)x).Take(NumberOfTestSamples).ToArray(); + VapnikChervonenkisTest(ErrorTolerance, ErrorProbability, samples, dd); } foreach (var cd in _continuousDistributions) { - cd.RandomSource = rnd; - VapnikChervonenkisTest(Error, ErrorProbability, cd.Samples().Take(NumberOfTestSamples), cd); + cd.RandomSource = new SystemRandomSource(1, false); + var samples = cd.Samples().Take(NumberOfTestSamples).ToArray(); + VapnikChervonenkisTest(ErrorTolerance, ErrorProbability, samples, cd); } } @@ -238,20 +193,19 @@ namespace MathNet.Numerics.UnitTests.DistributionTests /// The error probability we are willing to tolerate. /// The samples to use for testing. /// The distribution we are testing. - public static void VapnikChervonenkisTest(double epsilon, double delta, IEnumerable s, IUnivariateDistribution dist) + public static void VapnikChervonenkisTest(double epsilon, double delta, double[] s, IUnivariateDistribution dist) { // Using VC-dimension, we can bound the probability of making an error when estimating empirical probability // distributions. We are using Theorem 2.41 in "All Of Nonparametric Statistics". // http://books.google.com/books?id=MRFlzQfRg7UC&lpg=PP1&dq=all%20of%20nonparametric%20statistics&pg=PA22#v=onepage&q=%22shatter%20coe%EF%AC%83cients%20do%20not%22&f=false . // For intervals on the real line the VC-dimension is 2. - double n = s.Count(); - Assert.Greater(n, Math.Ceiling(32.0 * Math.Log(16.0 / delta) / epsilon / epsilon)); + Assert.Greater(s.Length, Math.Ceiling(32.0 * Math.Log(16.0 / delta) / epsilon / epsilon)); - var histogram = new Histogram(s, NumberOfBuckets); - for (var i = 0; i < NumberOfBuckets; i++) + var histogram = new Histogram(s, NumberOfHistogramBuckets); + for (var i = 0; i < NumberOfHistogramBuckets; i++) { var p = dist.CumulativeDistribution(histogram[i].UpperBound) - dist.CumulativeDistribution(histogram[i].LowerBound); - var pe = histogram[i].Count / n; + var pe = histogram[i].Count / s.Length; Assert.Less(Math.Abs(p - pe), epsilon, dist.ToString()); } } diff --git a/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs b/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs index 9ddf97ee..6e7462c8 100644 --- a/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs +++ b/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs @@ -343,21 +343,21 @@ namespace MathNet.Numerics.UnitTests.DistributionTests.Multivariate } // Extract the mean and precisions. - var means = samples.Select(mp => mp.Mean); - var precs = samples.Select(mp => mp.Precision); + var means = samples.Select(mp => mp.Mean).ToArray(); + var precs = samples.Select(mp => mp.Precision).ToArray(); var meanMarginal = cd.MeanMarginal(); var precMarginal = cd.PrecisionMarginal(); // Check the precision distribution. CommonDistributionTests.VapnikChervonenkisTest( - CommonDistributionTests.Error, + CommonDistributionTests.ErrorTolerance, CommonDistributionTests.ErrorProbability, precs, precMarginal); // Check the mean distribution. CommonDistributionTests.VapnikChervonenkisTest( - CommonDistributionTests.Error, + CommonDistributionTests.ErrorTolerance, CommonDistributionTests.ErrorProbability, means, meanMarginal); @@ -375,21 +375,21 @@ namespace MathNet.Numerics.UnitTests.DistributionTests.Multivariate var samples = cd.Samples().Take(CommonDistributionTests.NumberOfTestSamples).ToArray(); // Extract the mean and precisions. - var means = samples.Select(mp => mp.Mean); - var precs = samples.Select(mp => mp.Precision); + var means = samples.Select(mp => mp.Mean).ToArray(); + var precs = samples.Select(mp => mp.Precision).ToArray(); var meanMarginal = cd.MeanMarginal(); var precMarginal = cd.PrecisionMarginal(); // Check the precision distribution. CommonDistributionTests.VapnikChervonenkisTest( - CommonDistributionTests.Error, + CommonDistributionTests.ErrorTolerance, CommonDistributionTests.ErrorProbability, precs, precMarginal); // Check the mean distribution. CommonDistributionTests.VapnikChervonenkisTest( - CommonDistributionTests.Error, + CommonDistributionTests.ErrorTolerance, CommonDistributionTests.ErrorProbability, means, meanMarginal);