public void ValidateCumulativeDistribution(double lambda, double x) { var n = new Exponential(lambda); if (x >= 0.0) { Assert.AreEqual(1.0 - Math.Exp(-lambda*x), n.CumulativeDistribution(x)); Assert.AreEqual(1.0 - Math.Exp(-lambda*x), Exponential.CDF(lambda, x)); } else { Assert.AreEqual(0.0, n.CumulativeDistribution(x)); Assert.AreEqual(0.0, Exponential.CDF(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); }