public void ValidateDensityLn(double lambda, double x)
{
var n = new Exponential(lambda);
Assert.AreEqual(Math.Log(lambda) - (lambda*x), n.DensityLn(x));
Assert.AreEqual(Math.Log(lambda) - (lambda*x), Exponential.PDFLn(lambda, x));
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Exponential_distribution">Exponential distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Exponential distribution class with parameter Lambda = 1.
var exponential = new Exponential(1);
Console.WriteLine(@"1. Initialize the new instance of the Exponential distribution class with parameter Lambda = {0}", exponential.Lambda);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", exponential);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", exponential.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", exponential.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", exponential.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", exponential.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", exponential.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", exponential.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", exponential.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", exponential.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", exponential.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", exponential.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", exponential.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", exponential.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Exponential distribution
Console.WriteLine(@"3. Generate 10 samples of the Exponential distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(exponential.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Exponential(1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Exponential(1) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = exponential.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Exponential(9) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Exponential(9) distribution and display histogram");
exponential.Lambda = 9;
for (var i = 0; i < data.Length; i++)
{
data[i] = exponential.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram");
exponential.Lambda = 0.01;
for (var i = 0; i < data.Length; i++)
{
data[i] = exponential.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}