public void ConstructorTest()
{
var ks = new KolmogorovSmirnovDistribution(samples: 42);
double mean = ks.Mean; // 0.13404812830261556
double median = ks.Median; // 0.12393613519421857
double var = ks.Variance; // 0.019154717445778062
double cdf = ks.DistributionFunction(x: 0.27); // 0.99659863602996079
double ccdf = ks.ComplementaryDistributionFunction(x: 0.27); // 0.0034013639700392062
double icdf = ks.InverseDistributionFunction(p: cdf); // 0.26999997446092017
double chf = ks.CumulativeHazardFunction(x: 0.27); // 5.6835787601476619
string str = ks.ToString(); // "KS(x; n = 42)"
Assert.AreEqual(0.13404812830261556, mean);
Assert.AreEqual(0.12393613519421857, median, 1e-6);
Assert.AreEqual(0.019154717445778062, var);
Assert.AreEqual(5.6835787601476619, chf);
Assert.AreEqual(0.99659863602996079, cdf);
Assert.AreEqual(0.0034013639700392062, ccdf);
Assert.AreEqual(0.27, icdf, 1e-6);
Assert.AreEqual("KS(x; n = 42)", str);
}
public void ConstructorTest()
{
var ks = new KolmogorovSmirnovDistribution(samples: 42);
double mean = ks.Mean; // 0.13404812830261556
double median = ks.Median; // 0.12393613519421857
double var = ks.Variance; // 0.019154717445778062
try { double mode = ks.Mode; Assert.Fail(); }
catch { }
double cdf = ks.DistributionFunction(x: 0.27); // 0.99659863602996079
double ccdf = ks.ComplementaryDistributionFunction(x: 0.27); // 0.0034013639700392062
double icdf = ks.InverseDistributionFunction(p: cdf); // 0.26999997446092017
double chf = ks.CumulativeHazardFunction(x: 0.27); // 5.6835787601476619
string str = ks.ToString(); // "KS(x; n = 42)"
Assert.AreEqual(0.13404812830261556, mean);
Assert.AreEqual(0.12393613519421857, median, 1e-6);
Assert.AreEqual(0.019154717445778062, var);
Assert.AreEqual(5.6835787601476619, chf);
Assert.AreEqual(0.99659863602996079, cdf);
Assert.AreEqual(0.0034013639700392062, ccdf);
Assert.AreEqual(0.27, icdf, 1e-6);
Assert.AreEqual("KS(x; n = 42)", str);
var range1 = ks.GetRange(0.95);
var range2 = ks.GetRange(0.99);
var range3 = ks.GetRange(0.01);
Assert.AreEqual(0.076658557134805502, range1.Min);
Assert.AreEqual(0.20517007413251875, range1.Max);
Assert.AreEqual(0.064613698945741663, range2.Min);
Assert.AreEqual(0.24612731591151149, range2.Max);
Assert.AreEqual(0.064613698945741649, range3.Min);
Assert.AreEqual(0.24612731591151149, range3.Max);
}
public void NaN_Test()
{
var ks = new KolmogorovSmirnovDistribution(samples: 42);
double nan = double.NaN;
try
{
ks.DistributionFunction(x: nan);
Assert.Fail();
}
catch (ArgumentOutOfRangeException) { }
try
{
ks.ComplementaryDistributionFunction(x: nan);
Assert.Fail();
}
catch (ArgumentOutOfRangeException) { }
try
{
ks.InverseDistributionFunction(p: nan);
Assert.Fail();
}
catch (ArgumentOutOfRangeException) { }
try
{
ks.CumulativeHazardFunction(x: nan);
Assert.Fail();
}
catch (ArgumentOutOfRangeException) { }
}