public void GammaDistributionConstructorTest2()
{
var gamma = new GammaDistribution(theta: 4, k: 2);
double mean = gamma.Mean; // 8.0
double median = gamma.Median; // 6.7133878418421506
double var = gamma.Variance; // 32.0
double cdf = gamma.DistributionFunction(x: 0.27); // 0.002178158242390601
double pdf = gamma.ProbabilityDensityFunction(x: 0.27); // 0.015773530285395465
double lpdf = gamma.LogProbabilityDensityFunction(x: 0.27); // -4.1494220422235433
double ccdf = gamma.ComplementaryDistributionFunction(x: 0.27); // 0.99782184175760935
double icdf = gamma.InverseDistributionFunction(p: cdf); // 0.26999998689819171
double hf = gamma.HazardFunction(x: 0.27); // 0.015807962529274005
double chf = gamma.CumulativeHazardFunction(x: 0.27); // 0.0021805338793574793
string str = gamma.ToString(CultureInfo.InvariantCulture); // "Γ(x; k = 2, θ = 4)"
Assert.AreEqual(8.0, mean);
Assert.AreEqual(6.7133878418421506, median, 1e-6);
Assert.AreEqual(32.0, var);
Assert.AreEqual(0.0021805338793574793, chf);
Assert.AreEqual(0.002178158242390601, cdf);
Assert.AreEqual(0.015773530285395465, pdf);
Assert.AreEqual(-4.1494220422235433, lpdf);
Assert.AreEqual(0.015807962529274005, hf);
Assert.AreEqual(0.99782184175760935, ccdf);
Assert.AreEqual(0.26999998689819171, icdf, 1e-6);
Assert.AreEqual("Γ(x; k = 2, θ = 4)", str);
double p05 = gamma.DistributionFunction(median);
Assert.AreEqual(0.5, p05, 1e-6);
}
public void GammaDistributionConstructorTest2()
{
#region doc_ctor
// Create a Γ-distribution with k = 2 and θ = 4
var gamma = new GammaDistribution(theta: 4, k: 2);
// Common measures
double mean = gamma.Mean; // 8.0
double median = gamma.Median; // 6.7133878418421506
double var = gamma.Variance; // 32.0
double mode = gamma.Mode; // 4.0
// Cumulative distribution functions
double cdf = gamma.DistributionFunction(x: 0.27); // 0.002178158242390601
double ccdf = gamma.ComplementaryDistributionFunction(x: 0.27); // 0.99782184175760935
double icdf = gamma.InverseDistributionFunction(p: cdf); // 0.26999998689819171
// Probability density functions
double pdf = gamma.ProbabilityDensityFunction(x: 0.27); // 0.015773530285395465
double lpdf = gamma.LogProbabilityDensityFunction(x: 0.27); // -4.1494220422235433
// Hazard (failure rate) functions
double hf = gamma.HazardFunction(x: 0.27); // 0.015807962529274005
double chf = gamma.CumulativeHazardFunction(x: 0.27); // 0.0021805338793574793
// String representation
string str = gamma.ToString(CultureInfo.InvariantCulture); // "Γ(x; k = 2, θ = 4)"
#endregion
Assert.AreEqual(8.0, mean);
Assert.AreEqual(6.7133878418421506, median, 1e-6);
Assert.AreEqual(32.0, var);
Assert.AreEqual(4.0, mode);
Assert.AreEqual(0.0021805338793574793, chf, 1e-10);
Assert.AreEqual(0.002178158242390601, cdf, 1e-10);
Assert.AreEqual(0.015773530285395465, pdf, 1e-10);
Assert.AreEqual(-4.1494220422235433, lpdf, 1e-10);
Assert.AreEqual(0.015807962529274005, hf, 1e-10);
Assert.AreEqual(0.99782184175760935, ccdf, 1e-10);
Assert.AreEqual(0.26999998689819171, icdf, 1e-6);
Assert.AreEqual("Γ(x; k = 2, θ = 4)", str);
double p05 = gamma.DistributionFunction(median);
Assert.AreEqual(0.5, p05, 1e-6);
var range1 = gamma.GetRange(0.95);
var range2 = gamma.GetRange(0.99);
var range3 = gamma.GetRange(0.01);
Assert.AreEqual(1.4214460427946485, range1.Min, 1e-10);
Assert.AreEqual(18.975458073562308, range1.Max, 1e-10);
Assert.AreEqual(0.59421896101306348, range2.Min, 1e-10);
Assert.AreEqual(26.553408271975243, range2.Max, 1e-10);
Assert.AreEqual(0.59421896101306348, range3.Min, 1e-10);
Assert.AreEqual(26.553408271975243, range3.Max, 1e-10);
}
public void GammaDistributionConstructorTest2()
{
var gamma = new GammaDistribution(theta: 4, k: 2);
double mean = gamma.Mean; // 8.0
double median = gamma.Median; // 6.7133878418421506
double var = gamma.Variance; // 32.0
double cdf = gamma.DistributionFunction(x: 0.27); // 0.002178158242390601
double pdf = gamma.ProbabilityDensityFunction(x: 0.27); // 0.015773530285395465
double lpdf = gamma.LogProbabilityDensityFunction(x: 0.27); // -4.1494220422235433
double ccdf = gamma.ComplementaryDistributionFunction(x: 0.27); // 0.99782184175760935
double icdf = gamma.InverseDistributionFunction(p: cdf); // 0.26999998689819171
double hf = gamma.HazardFunction(x: 0.27); // 0.015807962529274005
double chf = gamma.CumulativeHazardFunction(x: 0.27); // 0.0021805338793574793
string str = gamma.ToString(CultureInfo.InvariantCulture); // "Γ(x; k = 2, θ = 4)"
Assert.AreEqual(8.0, mean);
Assert.AreEqual(6.7133878418421506, median, 1e-6);
Assert.AreEqual(32.0, var);
Assert.AreEqual(0.0021805338793574793, chf);
Assert.AreEqual(0.002178158242390601, cdf);
Assert.AreEqual(0.015773530285395465, pdf);
Assert.AreEqual(-4.1494220422235433, lpdf);
Assert.AreEqual(0.015807962529274005, hf);
Assert.AreEqual(0.99782184175760935, ccdf);
Assert.AreEqual(0.26999998689819171, icdf, 1e-6);
Assert.AreEqual("Γ(x; k = 2, θ = 4)", str);
double p05 = gamma.DistributionFunction(median);
Assert.AreEqual(0.5, p05, 1e-6);
}
public void GammaDistributionConstructorTest2()
{
var gamma = new GammaDistribution(theta: 4, k: 2);
double mean = gamma.Mean; // 8.0
double median = gamma.Median; // 6.7133878418421506
double var = gamma.Variance; // 32.0
double mode = gamma.Mode; // 4.0
double cdf = gamma.DistributionFunction(x: 0.27); // 0.002178158242390601
double pdf = gamma.ProbabilityDensityFunction(x: 0.27); // 0.015773530285395465
double lpdf = gamma.LogProbabilityDensityFunction(x: 0.27); // -4.1494220422235433
double ccdf = gamma.ComplementaryDistributionFunction(x: 0.27); // 0.99782184175760935
double icdf = gamma.InverseDistributionFunction(p: cdf); // 0.26999998689819171
double hf = gamma.HazardFunction(x: 0.27); // 0.015807962529274005
double chf = gamma.CumulativeHazardFunction(x: 0.27); // 0.0021805338793574793
string str = gamma.ToString(CultureInfo.InvariantCulture); // "Γ(x; k = 2, θ = 4)"
Assert.AreEqual(8.0, mean);
Assert.AreEqual(6.7133878418421506, median, 1e-6);
Assert.AreEqual(32.0, var);
Assert.AreEqual(4.0, mode);
Assert.AreEqual(0.0021805338793574793, chf);
Assert.AreEqual(0.002178158242390601, cdf);
Assert.AreEqual(0.015773530285395465, pdf);
Assert.AreEqual(-4.1494220422235433, lpdf);
Assert.AreEqual(0.015807962529274005, hf);
Assert.AreEqual(0.99782184175760935, ccdf);
Assert.AreEqual(0.26999998689819171, icdf, 1e-6);
Assert.AreEqual("Γ(x; k = 2, θ = 4)", str);
double p05 = gamma.DistributionFunction(median);
Assert.AreEqual(0.5, p05, 1e-6);
var range1 = gamma.GetRange(0.95);
var range2 = gamma.GetRange(0.99);
var range3 = gamma.GetRange(0.01);
Assert.AreEqual(1.4214460427946485, range1.Min);
Assert.AreEqual(18.975458073562308, range1.Max);
Assert.AreEqual(0.59421896101306348, range2.Min);
Assert.AreEqual(26.553408271975243, range2.Max);
Assert.AreEqual(0.59421896101306348, range3.Min);
Assert.AreEqual(26.553408271975243, range3.Max);
}
public void CumulativeFunctionTest()
{
double shape = 0.4;
double scale = 4.2;
double[] cdf =
{
0, 0.251017, 0.328997, 0.38435, 0.428371, 0.465289,
0.497226, 0.525426, 0.55069, 0.573571, 0.594469
};
GammaDistribution target = new GammaDistribution(scale, shape);
for (int i = 0; i < 11; i++)
{
double x = i / 10.0;
double actual = target.DistributionFunction(x);
double expected = cdf[i];
Assert.AreEqual(expected, actual, 1e-5);
Assert.IsFalse(double.IsNaN(actual));
}
}
public void CumulativeFunctionTest()
{
double shape = 0.4;
double scale = 4.2;
double[] cdf =
{
0, 0.251017, 0.328997, 0.38435, 0.428371, 0.465289,
0.497226, 0.525426, 0.55069, 0.573571, 0.594469
};
GammaDistribution target = new GammaDistribution(scale, shape);
for (int i = 0; i < 11; i++)
{
double x = i / 10.0;
double actual = target.DistributionFunction(x);
double expected = cdf[i];
Assert.AreEqual(expected, actual, 1e-5);
Assert.IsFalse(double.IsNaN(actual));
}
}
protected override double InnerDistributionFunction(double x)
{
return(baseGamma.DistributionFunction(x));
}