public void CanCreateFisherSnedecor(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(d1, n.DegreeOfFreedom1);
Assert.AreEqual(d2, n.DegreeOfFreedom2);
}
public void ValidateSkewness(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 6)
{
Assert.AreEqual((((2.0 * d1) + d2 - 2.0) * Math.Sqrt(8.0 * (d2 - 4.0))) / ((d2 - 6.0) * Math.Sqrt(d1 * (d1 + d2 - 2.0))), n.Skewness);
}
}
public void ValidateMean(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 2)
{
Assert.AreEqual(d2 / (d2 - 2.0), n.Mean);
}
}
public void ValidateMode(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d1 > 2)
{
Assert.AreEqual((d2 * (d1 - 2.0)) / (d1 * (d2 + 2.0)), n.Mode);
}
}
public void ValidateVariance(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual((2.0 * d2 * d2 * (d1 + d2 - 2.0)) / (d1 * (d2 - 2.0) * (d2 - 2.0) * (d2 - 4.0)), n.Variance);
}
}
public void ValidateStdDev(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
}
public void CanCreateFisherSnedecor(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(d1, n.DegreeOfFreedom1);
Assert.AreEqual(d2, n.DegreeOfFreedom2);
}
public void ValidateDensity(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(Math.Sqrt(Math.Pow(d1 * x, d1) * Math.Pow(d2, d2) / Math.Pow((d1 * x) + d2, d1 + d2)) / (x * SpecialFunctions.Beta(d1 / 2.0, d2 / 2.0)), n.Density(x));
}
public void ValidateCumulativeDistribution(
[Values(0.1, 1.0, 10.0, 0.1, 1.0, 10.0, 0.1, 1.0, 10.0, 0.1, 1.0, 10.0)] double d1,
[Values(0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(SpecialFunctions.BetaRegularized(d1 / 2.0, d2 / 2.0, d1 * x / (d2 + (x * d1))), n.CumulativeDistribution(x));
}
public void ValidateDensityLn(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(Math.Log(n.Density(x)), n.DensityLn(x));
}
public void ValidateMode(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d1 > 2)
{
Assert.AreEqual((d2 * (d1 - 2.0)) / (d1 * (d2 + 2.0)), n.Mode);
}
}
public void ValidateMean(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 2)
{
Assert.AreEqual(d2 / (d2 - 2.0), n.Mean);
}
}
public void ValidateStdDev(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
}
public void ValidateVariance(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual((2.0 * d2 * d2 * (d1 + d2 - 2.0)) / (d1 * (d2 - 2.0) * (d2 - 2.0) * (d2 - 4.0)), n.Variance);
}
}
public void ValidateSkewness(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 6)
{
Assert.AreEqual((((2.0 * d1) + d2 - 2.0) * Math.Sqrt(8.0 * (d2 - 4.0))) / ((d2 - 6.0) * Math.Sqrt(d1 * (d1 + d2 - 2.0))), n.Skewness);
}
}
public static double TestStatic(IList <double> sample_0, IList <double> sample_1)
{
// as in https://en.wikipedia.org/wiki/F-test_of_equality_of_variances
// and
//Larsen Marx 4th edition P569
double variance_0 = ToolsMathStatistics.Variance(sample_0);
double variance_1 = ToolsMathStatistics.Variance(sample_1);
double f_statistic = variance_0 / variance_1;
double degrees_of_freedom_0 = (sample_0.Count - 1);
double degrees_of_freedom_1 = (sample_1.Count - 1);
return(FisherSnedecor.CDF(degrees_of_freedom_0, degrees_of_freedom_1, f_statistic));
}
public void CanSample()
{
var n = new FisherSnedecor(1.0, 2.0);
n.Sample();
}
public void ValidateMinimum()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.AreEqual(0.0, n.Minimum);
}
public void ValidateEntropyThrowsNotSupportedException()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws <NotSupportedException>(() => { var ent = n.Entropy; });
}
public void SetDegreesOfFreedom2FailsWithNegativeDegreeOfFreedom()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws <ArgumentOutOfRangeException>(() => n.DegreesOfFreedom2 = -1.0);
}
public void CanCreateFisherSnedecor(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(d1, n.DegreeOfFreedom1);
Assert.AreEqual(d2, n.DegreeOfFreedom2);
}
public void ValidateSkewness(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 6)
{
Assert.AreEqual((((2.0 * d1) + d2 - 2.0) * Math.Sqrt(8.0 * (d2 - 4.0))) / ((d2 - 6.0) * Math.Sqrt(d1 * (d1 + d2 - 2.0))), n.Skewness);
}
}
public void ValidateEntropy()
{
var n = new FisherSnedecor(1.0, 2.0);
var ent = n.Entropy;
}
public void ValidateStdDev(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
}
public void ValidateEntropyThrowsNotSupportedException()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var ent = n.Entropy; });
}
public void ValidateVariance(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual((2.0 * d2 * d2 * (d1 + d2 - 2.0)) / (d1 * (d2 - 2.0) * (d2 - 2.0) * (d2 - 4.0)), n.Variance);
}
}
public void ValidateMean(
[Values(0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 2)
{
Assert.AreEqual(d2 / (d2 - 2.0), n.Mean);
}
}
public void SetDegreeOfFreedom2FailsWithNegativeDegreeOfFreedom()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws<ArgumentOutOfRangeException>(() => n.DegreeOfFreedom2 = -1.0);
}
public void ValidateDensity(double d1, double d2, double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual<double>(Math.Sqrt(Math.Pow(d1 * x, d1) * Math.Pow(d2, d2) / (Math.Pow(d1 * x + d2, d1 + d2))) / (x * SpecialFunctions.Beta(d1 / 2.0, d2 / 2.0)), n.Density(x));
}
public (double residualDispersion, double explainedDispersion, double fEmpirical, double fTheoretical, bool isModelAdecuate) CheckAdequacyOfModel()
{
//If the adjustedValues are correct continue with the analysis
if (areAdjustedYValuesAlright == true)
{
double alpha = 0.05D;
double numberOfEquationParameters = 2.0D;
double explainedDeviation = this.GetExplainedDeviation();
double residualDeviation = this.GetResidualDeviation();
double explainedDisperssion = explainedDeviation / (numberOfEquationParameters - 1.0D);
if (Double.IsNaN(explainedDisperssion) || Double.IsInfinity(explainedDisperssion))
{
explainedDisperssion = 1;
}
double residualDisperssion = residualDeviation / (this.YValues.Count - numberOfEquationParameters);
if (Double.IsNaN(residualDisperssion) || Double.IsInfinity(residualDisperssion))
{
residualDisperssion = 1;
}
double FEmpirical = (explainedDisperssion >= residualDisperssion) ? (explainedDisperssion / residualDisperssion) : (residualDisperssion / explainedDisperssion);
if (Double.IsNaN(FEmpirical) || Double.IsInfinity(FEmpirical))
{
FEmpirical = 1;
}
double firstDegreeOfFreedom = 0.0;
double secondDegreeOfFreedom = 0.0;
if (explainedDisperssion > residualDisperssion)
{
firstDegreeOfFreedom = (numberOfEquationParameters - 1);
secondDegreeOfFreedom = (this.Count - numberOfEquationParameters);
}
else if (residualDisperssion > explainedDisperssion)
{
firstDegreeOfFreedom = (this.Count - numberOfEquationParameters);
secondDegreeOfFreedom = (numberOfEquationParameters - 1);
}
else
{
firstDegreeOfFreedom = (numberOfEquationParameters - 1);
secondDegreeOfFreedom = (this.Count - numberOfEquationParameters);
}
if (firstDegreeOfFreedom <= 0)
{
firstDegreeOfFreedom = 1;
}
if (secondDegreeOfFreedom <= 0)
{
secondDegreeOfFreedom = 1;
}
FisherSnedecor fDistibution = new FisherSnedecor(1, 1);
double FTheoretical = FisherSnedecor.InvCDF(firstDegreeOfFreedom, secondDegreeOfFreedom, (1.00 - alpha));
bool isModelAdequate = false;
if (FEmpirical <= FTheoretical)
{
isModelAdequate = false;
}
else if (FEmpirical > FTheoretical)
{
isModelAdequate = true;
}
return(residualDisperssion, explainedDisperssion, FEmpirical, FTheoretical, isModelAdequate);
}
else
{
//Else return default values.
return(0.0, 0.0, 0.0, 0.0, false);
}
}
public void ValidateCumulativeDistribution(
[Values(0.1, 1.0, 10.0, 0.1, 1.0, 10.0, 0.1, 1.0, 10.0, 0.1, 1.0, 10.0)] double d1,
[Values(0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(SpecialFunctions.BetaRegularized(d1 / 2.0, d2 / 2.0, d1 * x / (d2 + (x * d1))), n.CumulativeDistribution(x));
}
public void FisherSnedecorCreateFailsWithBadParameters(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
}
public void ValidateToString()
{
var n = new FisherSnedecor(2.0, 1.0);
Assert.AreEqual("FisherSnedecor(DegreeOfFreedom1 = 2, DegreeOfFreedom2 = 1)", n.ToString());
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/F-distribution">FisherSnedecor distribution</a>
public void Run()
{
// 1. Initialize the new instance of the FisherSnedecor distribution class with parameter DegreeOfFreedom1 = 50, DegreeOfFreedom2 = 20.
var fisherSnedecor = new FisherSnedecor(50, 20);
Console.WriteLine(@"1. Initialize the new instance of the FisherSnedecor distribution class with parameters DegreeOfFreedom1 = {0}, DegreeOfFreedom2 = {1}", fisherSnedecor.DegreeOfFreedom1, fisherSnedecor.DegreeOfFreedom2);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", fisherSnedecor);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", fisherSnedecor.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", fisherSnedecor.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", fisherSnedecor.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", fisherSnedecor.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", fisherSnedecor.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", fisherSnedecor.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", fisherSnedecor.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", fisherSnedecor.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", fisherSnedecor.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", fisherSnedecor.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the FisherSnedecor distribution
Console.WriteLine(@"3. Generate 10 samples of the FisherSnedecor distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(fisherSnedecor.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = fisherSnedecor.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram");
fisherSnedecor.DegreeOfFreedom1 = 20;
fisherSnedecor.DegreeOfFreedom2 = 10;
for (var i = 0; i < data.Length; i++)
{
data[i] = fisherSnedecor.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram");
fisherSnedecor.DegreeOfFreedom1 = 100;
fisherSnedecor.DegreeOfFreedom2 = 100;
for (var i = 0; i < data.Length; i++)
{
data[i] = fisherSnedecor.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
public void ValidateMode(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d1 > 2)
{
Assert.AreEqual((d2 * (d1 - 2.0)) / (d1 * (d2 + 2.0)), n.Mode);
}
}
public void ValidateCumulativeDistribution(double d1, double d2, double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual <double>(SpecialFunctions.BetaRegularized(d1 / 2.0, d2 / 2.0, d1 * x / (d2 + x * d1)), n.CumulativeDistribution(x));
}
public void ValidateSkewness(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 6)
{
Assert.AreEqual((((2.0 * d1) + d2 - 2.0) * Math.Sqrt(8.0 * (d2 - 4.0))) / ((d2 - 6.0) * Math.Sqrt(d1 * (d1 + d2 - 2.0))), n.Skewness);
}
}
public void ValidateStdDev(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
}
}
public void ValidateMode(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d1 > 2)
{
Assert.AreEqual((d2 * (d1 - 2.0)) / (d1 * (d2 + 2.0)), n.Mode);
}
}
public void ValidateDensityLn(double d1, double d2, double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(Math.Log(n.Density(x)), n.DensityLn(x));
}
public void ValidateToString()
{
var n = new FisherSnedecor(2.0, 1.0);
Assert.AreEqual("FisherSnedecor(DegreeOfFreedom1 = 2, DegreeOfFreedom2 = 1)", n.ToString());
}
public void ValidateMean(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 2)
{
Assert.AreEqual(d2 / (d2 - 2.0), n.Mean);
}
}
public void CanSetDegreeOfFreedom1(double d1)
{
var n = new FisherSnedecor(1.0, 2.0);
n.DegreeOfFreedom1 = d1;
}
public void ValidateVariance(double d1, double d2)
{
var n = new FisherSnedecor(d1, d2);
if (d2 > 4)
{
Assert.AreEqual((2.0 * d2 * d2 * (d1 + d2 - 2.0)) / (d1 * (d2 - 2.0) * (d2 - 2.0) * (d2 - 4.0)), n.Variance);
}
}
public static double FInv(double probability, int degreesFreedom1, int degreesFreedom2)
{
return(FisherSnedecor.InvCDF(degreesFreedom1, degreesFreedom2, 1d - probability));
}
public void CanSetDegreeOfFreedom2(double d2)
{
var n = new FisherSnedecor(1.0, 2.0);
n.DegreeOfFreedom2 = d2;
}
public void ValidateMedianThrowsNotSupportedException()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var m = n.Median; });
}
public void ValidateMedian()
{
var n = new FisherSnedecor(1.0, 2.0);
var m = n.Median;
}
public void ValidateMinimum()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.AreEqual(0.0, n.Minimum);
}
public void ValidateMaximum()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.AreEqual(Double.PositiveInfinity, n.Maximum);
}
public static double FDist(double x, int degreesFreedom1, int degreesFreedom2)
{
return(1d - FisherSnedecor.CDF(degreesFreedom1, degreesFreedom2, x));
}
public void ValidateDensity(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(Math.Sqrt(Math.Pow(d1 * x, d1) * Math.Pow(d2, d2) / Math.Pow((d1 * x) + d2, d1 + d2)) / (x * SpecialFunctions.Beta(d1 / 2.0, d2 / 2.0)), n.Density(x));
}
public void SetDegreesOfFreedom2FailsWithNegativeDegreeOfFreedom()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.That(() => n.DegreesOfFreedom2 = -1.0, Throws.ArgumentException);
}
public void ValidateDensityLn(
[Values(0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0, 0.1, 1.0, 10.0, 100.0)] double d1,
[Values(0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 100.0, 100.0)] double d2,
[Values(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0)] double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual(Math.Log(n.Density(x)), n.DensityLn(x));
}
public void ValidateMedianThrowsNotSupportedException()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.Throws <NotSupportedException>(() => { var m = n.Median; });
}
public void CanSample()
{
var n = new FisherSnedecor(1.0, 2.0);
n.Sample();
}
public void ValidateMaximum()
{
var n = new FisherSnedecor(1.0, 2.0);
Assert.AreEqual(Double.PositiveInfinity, n.Maximum);
}
public void CanSampleSequence()
{
var n = new FisherSnedecor(1.0, 2.0);
var ied = n.Samples();
ied.Take(5).ToArray();
}
public void ValidateToString()
{
var n = new FisherSnedecor(2d, 1d);
Assert.AreEqual("FisherSnedecor(d1 = 2, d2 = 1)", n.ToString());
}
public void ValidateCumulativeDistribution(double d1, double d2, double x)
{
var n = new FisherSnedecor(d1, d2);
Assert.AreEqual<double>(SpecialFunctions.BetaRegularized(d1 / 2.0, d2 / 2.0, d1 * d2 / (d1 + d1 * d2)), n.CumulativeDistribution(x));
}
