public void CanCreateHypergeometric(int population, int success, int draws) { var d = new Hypergeometric(population, success, draws); Assert.AreEqual(population, d.Population); Assert.AreEqual(success, d.Success); Assert.AreEqual(draws, d.Draws); }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Hypergeometric distribution</a> public void Run() { // 1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = 10, M = 2, N = 8 var hypergeometric = new Hypergeometric(30, 15, 10); Console.WriteLine(@"1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = {0}, M = {1}, N = {2}", hypergeometric.PopulationSize, hypergeometric.M, hypergeometric.N); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", hypergeometric); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '3'", hypergeometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability mass at location '3'", hypergeometric.Probability(3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability mass at location '3'", hypergeometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", hypergeometric.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", hypergeometric.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", hypergeometric.Mean.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", hypergeometric.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", hypergeometric.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", hypergeometric.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", hypergeometric.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the Hypergeometric distribution Console.WriteLine(@"3. Generate 10 samples of the Hypergeometric distribution"); for (var i = 0; i < 10; i++) { Console.Write(hypergeometric.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = hypergeometric.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram"); hypergeometric.PopulationSize = 52; hypergeometric.M = 13; hypergeometric.N = 5; for (var i = 0; i < data.Length; i++) { data[i] = hypergeometric.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void ValidateEntropyThrowsNotSupportedException() { var d = new Hypergeometric(10, 1, 1); Assert.Throws<NotSupportedException>(() => { var e = d.Entropy; }); }
public void ValidateToString() { var d = new Hypergeometric(10, 1, 1); Assert.AreEqual("Hypergeometric(N = 10, M = 1, n = 1)", d.ToString()); }
public void CumulativeDistributionMustNotOverflow_CodePlexIssue5729() { var d = new Hypergeometric(10000, 2, 9800); Assert.That(d.CumulativeDistribution(0.0), Is.Not.NaN); Assert.That(d.CumulativeDistribution(0.1), Is.Not.NaN); }
public void CanSampleSequence() { var d = new Hypergeometric(10, 1, 1); var ied = d.Samples(); GC.KeepAlive(ied.Take(5).ToArray()); }
public void ValidateCumulativeDistribution(int population, int success, int draws, double x, double cdf) { var d = new Hypergeometric(population, success, draws); AssertHelpers.AlmostEqualRelative(cdf, d.CumulativeDistribution(x), 9); }
public void ValidateProbabilityLn(int population, int success, int draws, int x) { var d = new Hypergeometric(population, success, draws); Assert.That(d.ProbabilityLn(x), Is.EqualTo(Math.Log(d.Probability(x))).Within(1e-14)); }
public void CanSample() { var d = new Hypergeometric(10, 1, 1); d.Sample(); }
public void ValidateProbability(int population, int success, int draws, int x) { var d = new Hypergeometric(population, success, draws); Assert.That(d.Probability(x), Is.EqualTo(SpecialFunctions.Binomial(success, x)*SpecialFunctions.Binomial(population - success, draws - x)/SpecialFunctions.Binomial(population, draws))); }
public void ValidateMaximum(int population, int success, int draws) { var d = new Hypergeometric(population, success, draws); Assert.AreEqual(Math.Min(success, draws), d.Maximum); }
public void ValidateMedianThrowsNotSupportedException() { var d = new Hypergeometric(10, 1, 1); Assert.Throws<NotSupportedException>(() => { var m = d.Median; }); }
public void ValidateMode(int population, int success, int draws) { var d = new Hypergeometric(population, success, draws); Assert.AreEqual((draws + 1)*(success + 1)/(population + 2), d.Mode); }
public void ValidateSkewness(int population, int success, int draws) { var d = new Hypergeometric(population, success, draws); Assert.AreEqual((Math.Sqrt(population - 1.0)*(population - (2*draws))*(population - (2*success)))/(Math.Sqrt(draws*success*(population - success)*(population - draws))*(population - 2.0)), d.Skewness); }