public void CanSample() { var n = new Chi(1.0); n.Sample(); }
public void CanSampleSequence() { var n = new Chi(1.0); var ied = n.Samples(); GC.KeepAlive(ied.Take(5).ToArray()); }
public void ValidateMaximum() { var n = new Chi(1.0); Assert.AreEqual(Double.PositiveInfinity, n.Maximum); }
public void ValidateDensityLn(double dof, double x, double expected) { var chi = new Chi(dof); Assert.That(chi.DensityLn(x), Is.EqualTo(expected).Within(13)); Assert.That(Chi.PDFLn(dof, x), Is.EqualTo(expected).Within(13)); }
public void SetDofFailsWithNonPositiveDoF(double dof) { var n = new Chi(1.0); Assert.Throws<ArgumentOutOfRangeException>(() => n.DegreesOfFreedom = dof); }
public void ValidateMinimum() { var n = new Chi(1.0); Assert.AreEqual(0.0, n.Minimum); }
public void ValidateStdDev(double dof) { var n = new Chi(dof); Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev); }
public void ValidateToString() { var n = new Chi(1.0); Assert.AreEqual("Chi(k = 1)", n.ToString()); }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Chi_distribution">Chi distribution</a> public void Run() { // 1. Initialize the new instance of the Chi distribution class with parameter dof = 1. var chi = new Chi(1); Console.WriteLine(@"1. Initialize the new instance of the Chi distribution class with parameter DegreesOfFreedom = {0}", chi.DegreesOfFreedom); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", chi); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", chi.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability density at location '0.3'", chi.Density(0.3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability density at location '0.3'", chi.DensityLn(0.3).ToString(" #0.00000;-#0.00000")); // Entropy Console.WriteLine(@"{0} - Entropy", chi.Entropy.ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", chi.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", chi.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", chi.Mean.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", chi.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", chi.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", chi.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", chi.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the Chi distribution Console.WriteLine(@"3. Generate 10 samples of the Chi distribution"); for (var i = 0; i < 10; i++) { Console.Write(chi.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the Chi(1) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the Chi(1) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = chi.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the Chi(2) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the Chi(2) distribution and display histogram"); chi.DegreesOfFreedom = 2; for (var i = 0; i < data.Length; i++) { data[i] = chi.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 6. Generate 100000 samples of the Chi(5) distribution and display histogram Console.WriteLine(@"6. Generate 100000 samples of the Chi(5) distribution and display histogram"); chi.DegreesOfFreedom = 5; for (var i = 0; i < data.Length; i++) { data[i] = chi.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void ValidateVariance(double dof) { var n = new Chi(dof); Assert.AreEqual(dof - (n.Mean * n.Mean), n.Variance); }
public void ValidateCumulativeDistribution(double dof, double x) { var n = new Chi(dof); double expected = SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x * x / 2.0) / SpecialFunctions.Gamma(dof / 2.0); Assert.AreEqual(expected, n.CumulativeDistribution(x)); Assert.AreEqual(expected, Chi.CDF(dof, x)); }
public void ValidateDensityLn(double dof, double x) { var n = new Chi(dof); double expected = ((1.0 - (dof / 2.0)) * Math.Log(2.0)) + ((dof - 1.0) * Math.Log(x)) - (x * (x / 2.0)) - SpecialFunctions.GammaLn(dof / 2.0); Assert.AreEqual(expected, n.DensityLn(x)); Assert.AreEqual(expected, Chi.PDFLn(dof, x)); }
public void ValidateDensity(double dof, double x) { var n = new Chi(dof); double expected = (Math.Pow(2.0, 1.0 - (dof / 2.0)) * Math.Pow(x, dof - 1.0) * Math.Exp(-x * (x / 2.0))) / SpecialFunctions.Gamma(dof / 2.0); Assert.AreEqual(expected, n.Density(x)); Assert.AreEqual(expected, Chi.PDF(dof, x)); }
public void ValidateCumulativeDistribution(double dof, double x, double expected) { var chi = new Chi(dof); Assert.That(chi.CumulativeDistribution(x), Is.EqualTo(expected).Within(13)); Assert.That(Chi.CDF(dof, x), Is.EqualTo(expected).Within(13)); //double expected = SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x * x / 2.0) / SpecialFunctions.Gamma(dof / 2.0); //Assert.AreEqual(expected, n.CumulativeDistribution(x)); //Assert.AreEqual(expected, Chi.CDF(dof, x)); }
public void ValidateMode(double dof) { var n = new Chi(dof); if (dof >= 1) { Assert.AreEqual(Math.Sqrt(dof - 1), n.Mode); } }
public void CanCreateChi(double dof) { var n = new Chi(dof); Assert.AreEqual(dof, n.DegreesOfFreedom); }
public void ValidateMedianThrowsNotSupportedException() { var n = new Chi(1.0); Assert.Throws<NotSupportedException>(() => { var median = n.Median; }); }
public void ValidateMean(double dof) { var n = new Chi(dof); Assert.AreEqual(Constants.Sqrt2 * (SpecialFunctions.Gamma((dof + 1.0) / 2.0) / SpecialFunctions.Gamma(dof / 2.0)), n.Mean); }
public void SetDofFailsWithNonPositiveDoF(double dof) { var n = new Chi(1.0); Assert.That(() => n.DegreesOfFreedom = dof, Throws.ArgumentException); }