public void ConstructorTest2()
{
var invGaussian = new InverseGaussianDistribution(mean: 0.42, shape: 1.2);
double mean = invGaussian.Mean; // 0.42
double median = invGaussian.Median; // 0.35856861093990083
double var = invGaussian.Variance; // 0.061739999999999989
double mode = invGaussian.Mode; // 0.23793654141004067
double cdf = invGaussian.DistributionFunction(x: 0.27); // 0.30658791274125458
double pdf = invGaussian.ProbabilityDensityFunction(x: 0.27); // 2.3461495925760354
double lpdf = invGaussian.LogProbabilityDensityFunction(x: 0.27); // 0.85277551314980737
double ccdf = invGaussian.ComplementaryDistributionFunction(x: 0.27); // 0.69341208725874548
double icdf = invGaussian.InverseDistributionFunction(p: cdf); // 0.26999999957543408
double hf = invGaussian.HazardFunction(x: 0.27); // 3.383485283406336
double chf = invGaussian.CumulativeHazardFunction(x: 0.27); // 0.36613081401302111
string str = invGaussian.ToString(CultureInfo.InvariantCulture); // "N^-1(x; μ = 0.42, λ = 1.2)"
Assert.AreEqual(0.42, mean);
Assert.AreEqual(0.35856861093990083, median, 1e-6);
Assert.AreEqual(0.061739999999999989, var);
Assert.AreEqual(0.36613081401302111, chf);
Assert.AreEqual(0.23793654141004067, mode);
Assert.AreEqual(0.30658791274125458, cdf);
Assert.AreEqual(2.3461495925760354, pdf);
Assert.AreEqual(0.85277551314980737, lpdf);
Assert.AreEqual(3.383485283406336, hf);
Assert.AreEqual(0.69341208725874548, ccdf);
Assert.AreEqual(0.26999999957543408, icdf, 1e-7);
Assert.AreEqual("N^-1(x; μ = 0.42, λ = 1.2)", str);
var range1 = invGaussian.GetRange(0.95);
var range2 = invGaussian.GetRange(0.99);
var range3 = invGaussian.GetRange(0.01);
Assert.AreEqual(0.14769446268576839, range1.Min);
Assert.AreEqual(0.90166590229504751, range1.Max);
Assert.AreEqual(0.10646291322190742, range2.Min);
Assert.AreEqual(1.2855706686397079, range2.Max);
Assert.AreEqual(0.10646291322190739, range3.Min);
Assert.AreEqual(1.2855706686397079, range3.Max);
Assert.AreEqual(0, invGaussian.Support.Min);
Assert.AreEqual(double.PositiveInfinity, invGaussian.Support.Max);
Assert.AreEqual(invGaussian.InverseDistributionFunction(0), invGaussian.Support.Min);
Assert.AreEqual(invGaussian.InverseDistributionFunction(1), invGaussian.Support.Max);
}
public void ConstructorTest2()
{
var invGaussian = new InverseGaussianDistribution(mean: 0.42, shape: 1.2);
double mean = invGaussian.Mean; // 0.42
double median = invGaussian.Median; // 0.35856861093990083
double var = invGaussian.Variance; // 0.061739999999999989
double cdf = invGaussian.DistributionFunction(x: 0.27); // 0.30658791274125458
double pdf = invGaussian.ProbabilityDensityFunction(x: 0.27); // 2.3461495925760354
double lpdf = invGaussian.LogProbabilityDensityFunction(x: 0.27); // 0.85277551314980737
double ccdf = invGaussian.ComplementaryDistributionFunction(x: 0.27); // 0.69341208725874548
double icdf = invGaussian.InverseDistributionFunction(p: cdf); // 0.26999999957543408
double hf = invGaussian.HazardFunction(x: 0.27); // 3.383485283406336
double chf = invGaussian.CumulativeHazardFunction(x: 0.27); // 0.36613081401302111
string str = invGaussian.ToString(CultureInfo.InvariantCulture); // "N^-1(x; μ = 0.42, λ = 1.2)"
Assert.AreEqual(0.42, mean);
Assert.AreEqual(0.35856861093990083, median, 1e-7);
Assert.AreEqual(0.061739999999999989, var);
Assert.AreEqual(0.36613081401302111, chf);
Assert.AreEqual(0.30658791274125458, cdf);
Assert.AreEqual(2.3461495925760354, pdf);
Assert.AreEqual(0.85277551314980737, lpdf);
Assert.AreEqual(3.383485283406336, hf);
Assert.AreEqual(0.69341208725874548, ccdf);
Assert.AreEqual(0.26999999957543408, icdf, 1e-8);
Assert.AreEqual("N^-1(x; μ = 0.42, λ = 1.2)", str);
}
public void MedianTest()
{
InverseGaussianDistribution target = new InverseGaussianDistribution(6, 2);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
}
