public void CanSampleSequence()
{
var d = new NegativeBinomial(1.0, 0.5);
var ied = d.Samples();
ied.Take(5).ToArray();
}
public void CanSampleSequence()
{
var d = new NegativeBinomial(1.0, 0.5);
var ied = d.Samples();
ied.Take(5).ToArray();
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Negative_binomial">NegativeBinomial distribution</a>
public void Run()
{
// 1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = 0.2, R = 20
var negativeBinomial = new NegativeBinomial(20, 0.2);
Console.WriteLine(@"1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = {0}, N = {1}", negativeBinomial.P, negativeBinomial.R);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", negativeBinomial);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", negativeBinomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", negativeBinomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", negativeBinomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", negativeBinomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", negativeBinomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", negativeBinomial.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", negativeBinomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", negativeBinomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", negativeBinomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", negativeBinomial.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the NegativeBinomial distribution
Console.WriteLine(@"3. Generate 10 samples of the NegativeBinomial distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(negativeBinomial.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the NegativeBinomial(0.2, 20) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the NegativeBinomial(20, 0.2) distribution and display histogram");
var data = new int[100000];
NegativeBinomial.Samples(data, 20, 0.2);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the NegativeBinomial(0.7, 20) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the NegativeBinomial(20, 0.7) distribution and display histogram");
NegativeBinomial.Samples(data, 20, 0.7);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the NegativeBinomial(0.5, 1) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the NegativeBinomial(1, 0.5) distribution and display histogram");
NegativeBinomial.Samples(data, 1, 0.5);
ConsoleHelper.DisplayHistogram(data);
}