public void CanSampleSequence()
{
var n = new Chi(1.0);
var ied = n.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
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 ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var n = new Chi(1.0);
Assert.AreEqual("Chi(k = 1)", n.ToString());
}
public void SetDofFailsWithNonPositiveDoF(double dof)
{
{
var n = new Chi(1.0);
n.DegreesOfFreedom = dof;
}
}
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(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual(SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x * x / 2.0) / SpecialFunctions.Gamma(dof / 2.0), n.CumulativeDistribution(x));
}
public void ValidateDensityLn(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual(((1.0 - (dof / 2.0)) * Math.Log(2.0)) + ((dof - 1.0) * Math.Log(x)) - (x * (x / 2.0)) - SpecialFunctions.GammaLn(dof / 2.0), n.DensityLn(x));
}
public void ValidateDensity(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual((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), n.Density(x));
}
public void ValidateMode(double dof)
{
var n = new Chi(dof);
if (dof >= 1)
{
Assert.AreEqual(Math.Sqrt(dof - 1), n.Mode);
}
}
public void ValidateMode([Values(1.0, 2.0, 2.5, 3.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
if (dof >= 1)
{
Assert.AreEqual(Math.Sqrt(dof - 1), n.Mode);
}
}
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 chi()
{
var rand = new Random(Seed);
var r = 10;
var chi = new Chi(r, rand);
Assert.True(Math.Abs(0.05 - chi.GetCdf(3.94)) < 0.001);
Assert.True(Math.Abs(0.999 - chi.GetCdf(29.59)) < 0.001);
}
public void inverse_chi()
{
var rand = new Random(Seed);
var n = 1000;
var r = 10;
var chi = new Chi(r, rand);
var samples = chi.GenerateSample(n);
Assert.True(Math.Abs(r - samples.Mean()) < 0.5);
Assert.True(Math.Abs(2 * r - samples.Variance()) < 1.5);
}
public static void chi_square_noncentral_cdf_values_test()
//****************************************************************************80
//
// Purpose:
//
// CHI_SQUARE_NONCENTRAL_CDF_VALUES_TEST tests CHI_SQUARE_NONCENTRAL_CDF_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 February 2007
//
// Author:
//
// John Burkardt
//
{
int df = 0;
double fx = 0;
double lambda = 0;
double x = 0;
Console.WriteLine("");
Console.WriteLine("CHI_SQUARE_NONCENTRAL_CDF_VALUES_TEST:");
Console.WriteLine(" CHI_SQUARE_NONCENTRAL_CDF_VALUES returns values of");
Console.WriteLine(" the noncentral Chi-Squared Cumulative Density Function.");
Console.WriteLine("");
Console.WriteLine(" X LAMBDA DF CDF");
Console.WriteLine("");
int n_data = 0;
for (;;)
{
Chi.chi_square_noncentral_cdf_values(ref n_data, ref df, ref lambda, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(10) + " "
+ lambda.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ df.ToString(CultureInfo.InvariantCulture).PadLeft(4) + " "
+ fx.ToString(CultureInfo.InvariantCulture).PadLeft(12) + "");
}
}
public static void scaled_inverse_chi_square_pdf_values_test( )
//****************************************************************************80
//
// Purpose:
//
// SCALED_INVERSE_CHI_SQUARE_PDF_VALUES_TEST tests SCALED_INVERSE_CHI_SQUARE_PDF_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 05 August 2015
//
// Author:
//
// John Burkardt
//
{
double df = 0;
double fx = 0;
double x = 0;
double xi = 0;
Console.WriteLine("");
Console.WriteLine("SCALED_INVERSE_CHI_SQUARE_PDF_VALUES_TEST:");
Console.WriteLine(" SCALED_INVERSE_CHI_SQUARE_PDF_VALUES returns values of ");
Console.WriteLine(" the scaled inverse Chi-Square Probability Density Function.");
Console.WriteLine("");
Console.WriteLine(" DF XI X PDF");
Console.WriteLine("");
int n_data = 0;
for ( ; ;)
{
Chi.scaled_inverse_chi_square_pdf_values(ref n_data, ref df, ref xi, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ df.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ xi.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ fx.ToString(CultureInfo.InvariantCulture).PadLeft(12) + "");
}
}
public static void chi_values_test()
//****************************************************************************80
//
// Purpose:
//
// CHI_VALUES_TEST tests CHI_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 11 June 2007
//
// Author:
//
// John Burkardt
//
{
double fx = 0;
double x = 0;
Console.WriteLine("");
Console.WriteLine("CHI_VALUES_TEST:");
Console.WriteLine(" CHI_VALUES stores values of");
Console.WriteLine(" the Hyperbolic Cosine Integral function CHI(X).");
Console.WriteLine("");
Console.WriteLine(" X CHI(X)");
Console.WriteLine("");
int n_data = 0;
for (;;)
{
Chi.chi_values(ref n_data, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ fx.ToString(CultureInfo.InvariantCulture).PadLeft(12) + "");
}
}
public static double maxwell_sample(double a, ref int seed)
//****************************************************************************80
//
// Purpose:
//
// MAXWELL_SAMPLE samples the Maxwell PDF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 16 October 2004
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, double A, the parameter of the PDF.
// 0 < A.
//
// Input/output, int &SEED, a seed for the random number generator.
//
// Output, double MAXWELL_SAMPLE, a sample of the PDF.
//
{
const double a2 = 3.0;
double x = Chi.chi_square_sample(a2, ref seed);
x = a * Math.Sqrt(x);
return(x);
}
public void ValidateCumulativeDistribution(double dof, double x)
{
var n = new Chi(dof);
Assert.AreEqual<double>(SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x * x / 2.0) / SpecialFunctions.Gamma(dof / 2.0), n.CumulativeDistribution(x));
}
public void ValidateMode(double dof)
{
var n = new Chi(dof);
if (dof >= 1)
{
Assert.AreEqual<double>(Math.Sqrt(dof - 1), n.Mode);
}
}
public void ValidateVariance(double dof)
{
var n = new Chi(dof);
Assert.AreEqual(dof - (n.Mean * n.Mean), n.Variance);
}
public void ValidateDensityLn(double dof, double x)
{
var n = new Chi(dof);
Assert.AreEqual<double>((1.0 - dof / 2.0) * Math.Log(2.0) + (dof - 1.0) * Math.Log(x) - x * x / 2.0 - SpecialFunctions.GammaLn(dof / 2.0), n.DensityLn(x));
}
public void ValidateToString()
{
var n = new Chi(1.0);
Assert.AreEqual("Chi(DoF = 1)", n.ToString());
}
public void ValidateCumulativeDistribution(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual(SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x * x / 2.0) / SpecialFunctions.Gamma(dof / 2.0), n.CumulativeDistribution(x));
}
public void CanSample()
{
var n = new Chi(1.0);
n.Sample();
}
public IActionResult Status()
{
Chi myChi = new Chi();
return(View("Status", myChi));
}
public void SetDofFailsWithNonPositiveDoF(double dof)
{
{
var n = new Chi(1.0);
n.DegreesOfFreedom = dof;
}
}
public void ChiCreateFailsWithBadParameters(double dof)
{
var n = new Chi(dof);
}
public void CanSetDoF(double dof)
{
var n = new Chi(1.0);
n.DegreesOfFreedom = dof;
}
public void CanCreateChi(double dof)
{
var n = new Chi(dof);
Assert.AreEqual<double>(dof, n.DegreesOfFreedom);
}
public void ValidateVariance(double dof)
{
var n = new Chi(dof);
Assert.AreEqual<double>(dof - n.Mean * n.Mean, n.Variance);
}
public void ValidateStdDev(double dof)
{
var n = new Chi(dof);
Assert.AreEqual<double>(Math.Sqrt(n.Variance), n.StdDev);
}
public void ValidateDensity(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual((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), n.Density(x));
}
public void ValidateDensityLn(
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double dof,
[Values(0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity, 0.0, 0.1, 1.0, 5.5, 110.1, Double.PositiveInfinity)] double x)
{
var n = new Chi(dof);
Assert.AreEqual(((1.0 - (dof / 2.0)) * Math.Log(2.0)) + ((dof - 1.0) * Math.Log(x)) - (x * (x / 2.0)) - SpecialFunctions.GammaLn(dof / 2.0), n.DensityLn(x));
}
public void CanCreateChi(double dof)
{
var n = new Chi(dof);
Assert.AreEqual(dof, n.DegreesOfFreedom);
}
public void CanSampleSequence()
{
var n = new Chi(1.0);
var ied = n.Samples();
ied.Take(5).ToArray();
}
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 CanCreateChi([Values(1.0, 3.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
Assert.AreEqual(dof, n.DegreesOfFreedom);
}
public void ValidateMean(double dof)
{
var n = new Chi(dof);
Assert.AreEqual<double>(Math.Sqrt(2) * (SpecialFunctions.Gamma((dof + 1.0) / 2.0) / SpecialFunctions.Gamma(dof / 2.0)), n.Mean);
}
public void SetDofFailsWithNonPositiveDoF([Values(-1.0, -0.0, 0.0)] 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 ValidateDensity(double dof, double x)
{
var n = new Chi(dof);
Assert.AreEqual<double>((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), n.Density(x));
}
public void ValidateDensityLn(double dof, double x)
{
var n = new Chi(dof);
Assert.AreEqual(((1.0 - (dof / 2.0)) * Math.Log(2.0)) + ((dof - 1.0) * Math.Log(x)) - (x * (x / 2.0)) - SpecialFunctions.GammaLn(dof / 2.0), n.DensityLn(x));
}
public void ValidateStdDev(double dof)
{
var n = new Chi(dof);
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
public void ValidateMedian()
{
var n = new Chi(1.0);
var median = n.Median;
}
public void ValidateMedianThrowsNotSupportedException()
{
var n = new Chi(1.0);
Assert.Throws <NotSupportedException>(() => { var median = n.Median; });
}
public void ValidateMode([Values(1.0, 2.0, 2.5, 3.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
if (dof >= 1)
{
Assert.AreEqual(Math.Sqrt(dof - 1), n.Mode);
}
}
public void ValidateMaximum()
{
var n = new Chi(1.0);
Assert.AreEqual(Double.PositiveInfinity, n.Maximum);
}
public void ValidateMedianThrowsNotSupportedException()
{
var n = new Chi(1.0);
Assert.Throws<NotSupportedException>(() => { var median = n.Median; });
}
public void CanSample()
{
var n = new Chi(1.0);
n.Sample();
}
public void ValidateMinimum()
{
var n = new Chi(1.0);
Assert.AreEqual(0.0, n.Minimum);
}
public void ValidateMean([Values(1.0, 2.0, 2.5, 5.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
Assert.AreEqual(Math.Sqrt(2) * (SpecialFunctions.Gamma((dof + 1.0) / 2.0) / SpecialFunctions.Gamma(dof / 2.0)), n.Mean);
}
public void ValidateMaximum()
{
var n = new Chi(1.0);
Assert.AreEqual(Double.PositiveInfinity, n.Maximum);
}
public void ValidateToString()
{
var n = new Chi(1.0);
Assert.AreEqual("Chi(k = 1)", n.ToString());
}
public void ValidateVariance([Values(1.0, 2.0, 2.5, 3.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
Assert.AreEqual(dof - (n.Mean * n.Mean), n.Variance);
}
/// <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 ValidateStdDev([Values(1.0, 2.0, 2.5, 3.0, Double.PositiveInfinity)] double dof)
{
var n = new Chi(dof);
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
