public void ValidateDensityLn(int shape, double invScale, double x, double pdfln)
{
var n = new Erlang(shape, invScale);
AssertHelpers.AlmostEqual(pdfln, n.DensityLn(x), 14);
AssertHelpers.AlmostEqual(pdfln, Erlang.PDFLn(shape, invScale, x), 14);
}
public void ValidateDensityLn(
[Values(0, 0, 0, 1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 10, 10, 10)] int shape,
[Values(0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double invScale,
[Values(0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0)] double x,
[Values(Double.NegativeInfinity, Double.NegativeInfinity, Double.NegativeInfinity, -2.3025850929940456285068402234265387271634735938763824, -2.402585092994045634057955346552321429281631934330484, -3.3025850929940456285068402234265387271634735938763824, 0.0, -1.0, -10.0, Double.NegativeInfinity, 0.22402344985898722897219667227693591172986563062456522, -69.052710713194601614865880235563786219860220971716511, Double.NegativeInfinity, -13.801827480081469611207717874566706164281149255663166, -2.0785616431350584550457947824074282958712358580042068, Double.NegativeInfinity, Double.NegativeInfinity, Double.PositiveInfinity)] double pdfln)
{
var n = new Erlang(shape, invScale);
AssertHelpers.AlmostEqual(pdfln, n.DensityLn(x), 14);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Erlang_distribution">Erlang distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Erlang distribution class with parameters Shape = 1, Scale = 2.
var erlang = new Erlang(1, 2.0);
Console.WriteLine(@"1. Initialize the new instance of the Erlang distribution class with parameters Shape = {0}, Scale = {1}", erlang.Shape, erlang.Scale);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", erlang);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", erlang.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", erlang.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", erlang.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", erlang.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", erlang.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", erlang.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", erlang.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", erlang.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", erlang.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", erlang.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", erlang.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Erlang distribution
Console.WriteLine(@"3. Generate 10 samples of the Erlang distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(erlang.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = erlang.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram");
erlang.Shape = 3;
for (var i = 0; i < data.Length; i++)
{
data[i] = erlang.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram");
erlang.Shape = 9;
erlang.Scale = 0.5;
for (var i = 0; i < data.Length; i++)
{
data[i] = erlang.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
public void ValidateDensityLn(
[Values(0, 0, 0, 1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 10, 10, 10)] int shape,
[Values(0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double invScale,
[Values(0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0, 0.0, 1.0, 10.0)] double x,
[Values(Double.NegativeInfinity, Double.NegativeInfinity, Double.NegativeInfinity, -2.3025850929940456285068402234265387271634735938763824, -2.402585092994045634057955346552321429281631934330484, -3.3025850929940456285068402234265387271634735938763824, 0.0, -1.0, -10.0, Double.NegativeInfinity, 0.22402344985898722897219667227693591172986563062456522, -69.052710713194601614865880235563786219860220971716511, Double.NegativeInfinity, -13.801827480081469611207717874566706164281149255663166, -2.0785616431350584550457947824074282958712358580042068, Double.NegativeInfinity, Double.NegativeInfinity, Double.PositiveInfinity)] double pdfln)
{
var n = new Erlang(shape, invScale);
AssertHelpers.AlmostEqual(pdfln, n.DensityLn(x), 14);
}
public void ValidateDensityLn(int shape, double invScale, double x, double pdfln)
{
var n = new Erlang(shape, invScale);
AssertHelpers.AlmostEqual(pdfln, n.DensityLn(x), 14);
}