public void ConstructorTest2()
{
var rayleigh = new RayleighDistribution(sigma: 0.42);
double mean = rayleigh.Mean; // 0.52639193767251
double median = rayleigh.Median; // 0.49451220943852386
double var = rayleigh.Variance; // 0.075711527953380237
double cdf = rayleigh.DistributionFunction(x: 1.4); // 0.99613407986052716
double pdf = rayleigh.ProbabilityDensityFunction(x: 1.4); // 0.030681905868831811
double lpdf = rayleigh.LogProbabilityDensityFunction(x: 1.4); // -3.4840821835248961
double ccdf = rayleigh.ComplementaryDistributionFunction(x: 1.4); // 0.0038659201394728449
double icdf = rayleigh.InverseDistributionFunction(p: cdf); // 1.4000000080222026
double hf = rayleigh.HazardFunction(x: 1.4); // 7.9365079365078612
double chf = rayleigh.CumulativeHazardFunction(x: 1.4); // 5.5555555555555456
string str = rayleigh.ToString(CultureInfo.InvariantCulture); // Rayleigh(x; σ = 0.42)
Assert.AreEqual(0.52639193767251, mean);
Assert.AreEqual(0.49451220943852386, median, 1e-8);
Assert.AreEqual(0.075711527953380237, var);
Assert.AreEqual(5.5555555555555456, chf);
Assert.AreEqual(0.99613407986052716, cdf);
Assert.AreEqual(0.030681905868831811, pdf);
Assert.AreEqual(-3.4840821835248961, lpdf);
Assert.AreEqual(7.9365079365078612, hf);
Assert.AreEqual(0.0038659201394728449, ccdf);
Assert.AreEqual(1.40000000, icdf, 1e-8);
Assert.AreEqual("Rayleigh(x; σ = 0.42)", str);
}
/// <summary>
/// Estimates a new Gamma distribution from a given set of observations.
/// </summary>
///
public static RayleighDistribution Estimate(double[] observations)
{
var n = new RayleighDistribution();
n.Fit(observations);
return(n);
}
public void ConstructorTest2()
{
var rayleigh = new RayleighDistribution(sigma: 0.42);
double mean = rayleigh.Mean; // 0.52639193767251
double median = rayleigh.Median; // 0.49451220943852386
double var = rayleigh.Variance; // 0.075711527953380237
double mode = rayleigh.Mode; // 0.42
double cdf = rayleigh.DistributionFunction(x: 1.4); // 0.99613407986052716
double pdf = rayleigh.ProbabilityDensityFunction(x: 1.4); // 0.030681905868831811
double lpdf = rayleigh.LogProbabilityDensityFunction(x: 1.4); // -3.4840821835248961
double ccdf = rayleigh.ComplementaryDistributionFunction(x: 1.4); // 0.0038659201394728449
double icdf = rayleigh.InverseDistributionFunction(p: cdf); // 1.4000000080222026
double hf = rayleigh.HazardFunction(x: 1.4); // 7.9365079365078612
double chf = rayleigh.CumulativeHazardFunction(x: 1.4); // 5.5555555555555456
string str = rayleigh.ToString(CultureInfo.InvariantCulture); // Rayleigh(x; σ = 0.42)
Assert.AreEqual(0.52639193767251, mean);
Assert.AreEqual(0.42, mode);
Assert.AreEqual(0.49451220943852386, median, 1e-8);
Assert.AreEqual(0.075711527953380237, var);
Assert.AreEqual(5.5555555555555456, chf);
Assert.AreEqual(0.99613407986052716, cdf);
Assert.AreEqual(0.030681905868831811, pdf);
Assert.AreEqual(-3.4840821835248961, lpdf);
Assert.AreEqual(7.9365079365078612, hf);
Assert.AreEqual(0.0038659201394728449, ccdf);
Assert.AreEqual(1.40000000, icdf, 1e-8);
Assert.AreEqual("Rayleigh(x; σ = 0.42)", str);
var range1 = rayleigh.GetRange(0.95);
Assert.AreEqual(0.13452243301684083, range1.Min);
Assert.AreEqual(1.0280536793538564, range1.Max);
var range2 = rayleigh.GetRange(0.99);
Assert.AreEqual(0.059546263061601511, range2.Min);
Assert.AreEqual(1.2746387879926619, range2.Max);
var range3 = rayleigh.GetRange(0.01);
Assert.AreEqual(0.059546263061601677, range3.Min);
Assert.AreEqual(1.2746387879926619, range3.Max);
}
public void ProbabilityDistributionTest()
{
RayleighDistribution n = new RayleighDistribution(0.807602);
double[] expected = { 0, 0.712311, 0.142855, 0.00463779, 0.0000288872 };
double[] actual = new double[expected.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = n.ProbabilityDensityFunction(i);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
public void ConstructorTest()
{
RayleighDistribution n = new RayleighDistribution(0.807602);
Assert.AreEqual(1.0121790039242726, n.Mean);
Assert.AreEqual(0.27993564482286737, n.Variance);
}
public void MedianTest()
{
RayleighDistribution target = new RayleighDistribution(0.52);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
}
public void GenerateTest2()
{
RayleighDistribution target = new RayleighDistribution(4.2);
double[] samples = new double[1000000];
for (int i = 0; i < samples.Length; i++)
samples[i] = target.Generate();
var actual = RayleighDistribution.Estimate(samples);
actual.Fit(samples);
Assert.AreEqual(4, actual.Mean, 0.01);
Assert.AreEqual(2, actual.Variance, 0.01);
}
public void GenerateTest()
{
RayleighDistribution target = new RayleighDistribution(2.5);
double[] samples = target.Generate(1000000);
var actual = RayleighDistribution.Estimate(samples);
actual.Fit(samples);
Assert.AreEqual(2, actual.Mean, 0.01);
Assert.AreEqual(5, actual.Variance, 0.01);
}
public void CumulativeDistributionTest()
{
RayleighDistribution n = new RayleighDistribution(0.807602);
double[] expected = { 0, 0.535415, 0.953414, 0.998992, 0.999995 };
double[] actual = new double[expected.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = n.DistributionFunction(i);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-6);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
public void GenerateTest2()
{
Accord.Math.Tools.SetupGenerator(0);
RayleighDistribution target = new RayleighDistribution(4.2);
double[] samples = new double[1000000];
for (int i = 0; i < samples.Length; i++)
samples[i] = target.Generate();
var actual = RayleighDistribution.Estimate(samples);
Assert.AreEqual(4.2, actual.Scale, 1e-3);
}
public void GenerateTest()
{
Accord.Math.Tools.SetupGenerator(0);
RayleighDistribution target = new RayleighDistribution(2.5);
double[] samples = target.Generate(1000000);
var actual = RayleighDistribution.Estimate(samples);
Assert.AreEqual(2.5, actual.Scale, 1e-3);
}
