public void CanSample()
{
var n = new InverseGamma(1.0, 1.0);
n.Sample();
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Inverse-gamma_distribution">InverseGamma distribution</a>
public void Run()
{
// 1. Initialize the new instance of the InverseGamma distribution class with parameters shape = 4, scale = 0.5
var inverseGamma = new InverseGamma(4, 0.5);
Console.WriteLine(@"1. Initialize the new instance of the InverseGamma distribution class with parameters Shape = {0}, Scale = {1}", inverseGamma.Shape, inverseGamma.Scale);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", inverseGamma);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", inverseGamma.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", inverseGamma.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", inverseGamma.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", inverseGamma.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", inverseGamma.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", inverseGamma.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", inverseGamma.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", inverseGamma.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", inverseGamma.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", inverseGamma.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", inverseGamma.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the InverseGamma distribution
Console.WriteLine(@"3. Generate 10 samples of the InverseGamma distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(inverseGamma.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = inverseGamma.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram");
inverseGamma.Shape = 8;
for (var i = 0; i < data.Length; i++)
{
data[i] = inverseGamma.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the InverseGamma(2, 1) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the InverseGamma(8, 2) distribution and display histogram");
inverseGamma.Scale = 2;
for (var i = 0; i < data.Length; i++)
{
data[i] = inverseGamma.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
