public void ConstructorTest1()
{
var log = new LogisticDistribution(location: 0.42, scale: 1.2);
double mean = log.Mean; // 0.42
double median = log.Median; // 0.42
double mode = log.Mode; // 0.42
double var = log.Variance; // 4.737410112522892
double cdf = log.DistributionFunction(x: 1.4); // 0.693528308197921
double pdf = log.ProbabilityDensityFunction(x: 1.4); // 0.17712232827170876
double lpdf = log.LogProbabilityDensityFunction(x: 1.4); // -1.7309146649427332
double ccdf = log.ComplementaryDistributionFunction(x: 1.4); // 0.306471691802079
double icdf = log.InverseDistributionFunction(p: cdf); // 1.3999999999999997
double hf = log.HazardFunction(x: 1.4); // 0.57794025683160088
double chf = log.CumulativeHazardFunction(x: 1.4); // 1.1826298874077226
string str = log.ToString(CultureInfo.InvariantCulture); // Logistic(x; μ = 0.42, scale = 1.2)
Assert.AreEqual(0.41999999999999998, mean);
Assert.AreEqual(0.41999999999999998, median);
Assert.AreEqual(4.737410112522892, var);
Assert.AreEqual(1.1826298874077226, chf);
Assert.AreEqual(0.693528308197921, cdf);
Assert.AreEqual(0.17712232827170876, pdf);
Assert.AreEqual(-1.7309146649427332, lpdf);
Assert.AreEqual(0.57794025683160088, hf);
Assert.AreEqual(0.306471691802079, ccdf);
Assert.AreEqual(1.3999999999999997, icdf);
Assert.AreEqual("Logistic(x; μ = 0.42, scale = 1.2)", str);
}
public void LogisticTest()
{
var target = new TukeyLambdaDistribution(lambda: 0);
var logistic = new LogisticDistribution();
compare(target, logistic, 1e-5);
}
public void EquivalencyTest3()
{
// when ksi -> 0, the shifted log-logistic reduces to the logistic distribution.
double sigma = 0.42; // scale
double ksi = 0; // shape
double mu = 2.4; // location
var target = new ShiftedLogLogisticDistribution(location: mu, scale: sigma, shape: ksi);
var log = new LogisticDistribution(mu, sigma);
Assert.AreEqual(log.Mean, target.Mean);
Assert.AreEqual(log.Median, target.Median);
Assert.AreEqual(log.Mode, target.Mode);
Assert.AreEqual(log.Variance, target.Variance);
double actual, expected;
for (double i = -10; i < 10; i += 0.1)
{
expected = log.DistributionFunction(i);
actual = target.DistributionFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-15));
expected = log.ProbabilityDensityFunction(i);
actual = target.ProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-15));
expected = log.LogProbabilityDensityFunction(i);
actual = target.LogProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-10));
expected = log.ComplementaryDistributionFunction(i);
actual = target.ComplementaryDistributionFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-15));
double p = log.DistributionFunction(i);
expected = log.InverseDistributionFunction(p);
actual = target.InverseDistributionFunction(p);
Assert.AreEqual(expected, actual, 1e-5);
expected = log.HazardFunction(i);
actual = target.HazardFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-15));
expected = log.CumulativeHazardFunction(i);
actual = target.CumulativeHazardFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-15));
}
}