/// <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); }