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);
}
/// <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 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);
}