public void ValidateDensity(int shape, double invScale, double x, double pdf) { var n = new Erlang(shape, invScale); AssertHelpers.AlmostEqual(pdf, n.Density(x), 14); AssertHelpers.AlmostEqual(pdf, Erlang.PDF(shape, invScale, 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); }