/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
public void Run()
{
// 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
var conwayMaxwellPoisson = new ConwayMaxwellPoisson(2, 1);
Console.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", conwayMaxwellPoisson.Lambda, conwayMaxwellPoisson.Nu);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", conwayMaxwellPoisson);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", conwayMaxwellPoisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", conwayMaxwellPoisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", conwayMaxwellPoisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", conwayMaxwellPoisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", conwayMaxwellPoisson.Mean.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", conwayMaxwellPoisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", conwayMaxwellPoisson.StdDev.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
Console.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(conwayMaxwellPoisson.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram");
var data = new int[100000];
ConwayMaxwellPoisson.Samples(data, 4, 1);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram");
ConwayMaxwellPoisson.Samples(data, 2, 1);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram");
ConwayMaxwellPoisson.Samples(data, 5, 2);
ConsoleHelper.DisplayHistogram(data);
}
public void ValidateSkewnessThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var s = d.Skewness; });
}
public void ValidateEntropyThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var e = d.Entropy; });
}
public void ValidateToString()
{
var d = new ConwayMaxwellPoisson(1d, 2d);
Assert.AreEqual("ConwayMaxwellPoisson(λ = 1, ν = 2)", d.ToString());
}
public void CanCreateConwayMaxwellPoisson(double lambda, double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
Assert.AreEqual(lambda, d.Lambda);
Assert.AreEqual(nu, d.Nu);
}
public void ValidateCumulativeDistribution(double lambda, double nu, int x, double cdf)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(cdf, d.CumulativeDistribution(x), 12);
}
public void CanSampleSequence()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var ied = d.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
public void CanSample()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Sample();
}
public void ValidateProbabilityLn(double lambda, double nu, int x, double pln)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(pln, d.ProbabilityLn(x), 12);
}
public void ValidateMaximumThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var max = d.Maximum; });
}
public void ValidateMinimum()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.AreEqual(0.0, d.Minimum);
}
public void ValidateMean(int lambda, int nu, double mean)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(mean, d.Mean, 10);
}
