public void ValidateCumulativeDistribution(double d1, double d2, double x)
{
var n = new FisherSnedecor(d1, d2);
double expected = SpecialFunctions.BetaRegularized(d1/2.0, d2/2.0, d1*x/(d2 + (x*d1)));
Assert.That(n.CumulativeDistribution(x), Is.EqualTo(expected));
Assert.That(FisherSnedecor.CDF(d1, d2, x), Is.EqualTo(expected));
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/F-distribution">FisherSnedecor distribution</a>
public void Run()
{
// 1. Initialize the new instance of the FisherSnedecor distribution class with parameter DegreesOfFreedom1 = 50, DegreesOfFreedom2 = 20.
var fisherSnedecor = new FisherSnedecor(50, 20);
Console.WriteLine(@"1. Initialize the new instance of the FisherSnedecor distribution class with parameters DegreesOfFreedom1 = {0}, DegreesOfFreedom2 = {1}", fisherSnedecor.DegreesOfFreedom1, fisherSnedecor.DegreesOfFreedom2);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", fisherSnedecor);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", fisherSnedecor.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", fisherSnedecor.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", fisherSnedecor.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", fisherSnedecor.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", fisherSnedecor.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", fisherSnedecor.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", fisherSnedecor.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", fisherSnedecor.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", fisherSnedecor.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", fisherSnedecor.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the FisherSnedecor distribution
Console.WriteLine(@"3. Generate 10 samples of the FisherSnedecor distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(fisherSnedecor.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram");
var data = new double[100000];
FisherSnedecor.Samples(data, 50, 20);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram");
FisherSnedecor.Samples(data, 20, 10);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram");
FisherSnedecor.Samples(data, 100, 100);
ConsoleHelper.DisplayHistogram(data);
}