public void CanCreateInverseGamma(double a, double b) { var n = new InverseGamma(a, b); Assert.AreEqual(a, n.Shape); Assert.AreEqual(b, n.Scale); }
public void ValidateToString() { var n = new InverseGamma(1.1d, 2.1d); Assert.AreEqual("InverseGamma(α = 1.1, β = 2.1)", n.ToString()); }
public void ValidateVariance(double a, double b) { var n = new InverseGamma(a, b); if (a > 2) { Assert.AreEqual(b * b / ((a - 1.0) * (a - 1.0) * (a - 2.0)), n.Variance); } }
public void ValidateMode(double a, double b) { var n = new InverseGamma(a, b); Assert.AreEqual(b / (a + 1.0), n.Mode); }
public void ValidateStdDev(double a, double b) { var n = new InverseGamma(a, b); if (a > 2) { Assert.AreEqual(b / ((a - 1.0) * Math.Sqrt(a - 2.0)), n.StdDev); } }
public void SetBFailsWithNonPositiveB(double b) { var n = new InverseGamma(1.0, 1.0); Assert.That(() => n.Scale = b, Throws.ArgumentException); }
public void ValidateMinimum() { var n = new InverseGamma(1.0, 1.0); Assert.AreEqual(0.0, n.Minimum); }
public void ValidateMaximum() { var n = new InverseGamma(1.0, 1.0); Assert.AreEqual(Double.PositiveInfinity, n.Maximum); }
public void ValidateMean(double a, double b) { var n = new InverseGamma(a, b); if (a > 1) { Assert.AreEqual(b / (a - 1.0), n.Mean); } }
public void ValidateCumulativeDistribution(double a, double b, double x) { var n = new InverseGamma(a, b); double expected = SpecialFunctions.GammaUpperRegularized(a, b/x); Assert.AreEqual(expected, n.CumulativeDistribution(x)); Assert.AreEqual(expected, InverseGamma.CDF(a, b, x)); }
public void ValidateDensityLn(double a, double b, double x) { var n = new InverseGamma(a, b); double expected = Math.Log(Math.Pow(b, a)*Math.Pow(x, -a - 1.0)*Math.Exp(-b/x)/SpecialFunctions.Gamma(a)); Assert.AreEqual(expected, n.DensityLn(x)); Assert.AreEqual(expected, InverseGamma.PDFLn(a, b, x)); }
public void SetBFailsWithNonPositiveB(double b) { var n = new InverseGamma(1.0, 1.0); Assert.Throws<ArgumentOutOfRangeException>(() => n.Scale = b); }
public void SetAFailsWithNonPositiveA(double a) { var n = new InverseGamma(1.0, 1.0); Assert.Throws<ArgumentOutOfRangeException>(() => n.Shape = a); }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Inverse-gamma_distribution">InverseGamma distribution</a> public void Run() { // 1. Initialize the new instance of the InverseGamma distribution class with parameters shape = 4, scale = 0.5 var inverseGamma = new InverseGamma(4, 0.5); Console.WriteLine(@"1. Initialize the new instance of the InverseGamma distribution class with parameters Shape = {0}, Scale = {1}", inverseGamma.Shape, inverseGamma.Scale); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", inverseGamma); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", inverseGamma.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability density at location '0.3'", inverseGamma.Density(0.3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability density at location '0.3'", inverseGamma.DensityLn(0.3).ToString(" #0.00000;-#0.00000")); // Entropy Console.WriteLine(@"{0} - Entropy", inverseGamma.Entropy.ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", inverseGamma.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", inverseGamma.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", inverseGamma.Mean.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", inverseGamma.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", inverseGamma.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", inverseGamma.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", inverseGamma.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the InverseGamma distribution Console.WriteLine(@"3. Generate 10 samples of the InverseGamma distribution"); for (var i = 0; i < 10; i++) { Console.Write(inverseGamma.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = inverseGamma.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram"); inverseGamma.Shape = 8; for (var i = 0; i < data.Length; i++) { data[i] = inverseGamma.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 6. Generate 100000 samples of the InverseGamma(2, 1) distribution and display histogram Console.WriteLine(@"6. Generate 100000 samples of the InverseGamma(8, 2) distribution and display histogram"); inverseGamma.Scale = 2; for (var i = 0; i < data.Length; i++) { data[i] = inverseGamma.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void CanSample() { var n = new InverseGamma(1.0, 1.0); n.Sample(); }
public void ValidateMedianThrowsNotSupportedException() { var n = new InverseGamma(1.0, 1.0); Assert.Throws<NotSupportedException>(() => { var median = n.Median; }); }
public void CanSampleSequence() { var n = new InverseGamma(1.0, 1.0); var ied = n.Samples(); ied.Take(5).ToArray(); }
public void SetAFailsWithNonPositiveA(double a) { var n = new InverseGamma(1.0, 1.0); Assert.That(() => n.Shape = a, Throws.ArgumentException); }