/// <summary>
/// Creates a OxyPlot's graph for the Quantile Function.
/// </summary>
///
public PlotModel CreateICDF()
{
try
{
double[] y;
try { y = probabilities.Apply(instance.InverseDistributionFunction); }
catch
{
try
{
var general = GeneralContinuousDistribution
.FromDensityFunction(instance.Support, instance.ProbabilityFunction);
y = probabilities.Apply(general.InverseDistributionFunction);
}
catch
{
var general = GeneralContinuousDistribution
.FromDistributionFunction(instance.Support, instance.DistributionFunction);
y = probabilities.Apply(general.InverseDistributionFunction);
}
}
return(createBaseModel(unit, "QDF", probabilities, y, false));
}
catch
{
return(null);
}
}
/// <summary>
/// Creates a OxyPlot's graph for the Hazard Function.
/// </summary>
///
public PlotModel CreateHF()
{
try
{
double[] y;
try { y = supportPoints.Apply(instance.HazardFunction); }
catch
{
try
{
var general = GeneralContinuousDistribution
.FromDensityFunction(instance.Support, instance.ProbabilityFunction);
y = supportPoints.Apply(general.HazardFunction);
}
catch
{
var general = GeneralContinuousDistribution
.FromDistributionFunction(instance.Support, instance.DistributionFunction);
y = supportPoints.Apply(general.HazardFunction);
}
}
return(createBaseModel(range, "HF", supportPoints, y, instance is UnivariateDiscreteDistribution));
}
catch
{
return(null);
}
}
public void ConstructorTest9()
{
var original = new LognormalDistribution(location: 0.42, shape: 1.1);
var log = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testLognormal(log);
}
public void ConstructorTest17()
{
var original = new VonMisesDistribution(mean: 0.42, concentration: 1.2);
var vonMises = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testVonMises(vonMises);
}
public void ConstructorTest1()
{
var original = new NormalDistribution(mean: 4, stdDev: 4.2);
var normal = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testNormal(normal);
}
public void ConstructorTest14()
{
var original = new NakagamiDistribution(shape: 2.4, spread: 4.2);
var nakagami = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testNakagami(nakagami);
}
public void ConstructorTest13()
{
var original = new GompertzDistribution(eta: 4.2, b: 1.1);
var gompertz = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testGompertz(gompertz);
}
public void ConstructorTest10()
{
var original = new ChiSquareDistribution(degreesOfFreedom: 7);
var chisq = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testChiSquare(chisq);
}
public void ConstructorTest4()
{
var original = new InverseGaussianDistribution(mean: 0.42, shape: 1.2);
var invGaussian = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testInvGaussian(invGaussian);
}
public void ConstructorTest6()
{
var original = new LaplaceDistribution(location: 4, scale: 2);
var laplace = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
testLaplace(laplace);
}
/// <summary>
/// Creates a OxyPlot's graph for the Probability Density Function.
/// </summary>
///
public PlotModel CreatePDF()
{
try { pdf = supportPoints.Apply(instance.ProbabilityFunction); }
catch
{
var general = GeneralContinuousDistribution
.FromDistributionFunction(instance.Support, instance.DistributionFunction);
pdf = supportPoints.Apply(general.ProbabilityDensityFunction);
}
return(createBaseModel(range, "PDF", supportPoints, pdf, instance is UnivariateDiscreteDistribution));
}
/// <summary>
/// Creates a OxyPlot's graph for the Log Probability Density Function.
/// </summary>
///
public PlotModel CreateLPDF()
{
double[] y;
try { y = supportPoints.Apply(instance.LogProbabilityFunction); }
catch
{
var general = GeneralContinuousDistribution
.FromDistributionFunction(instance.Support, instance.DistributionFunction);
y = supportPoints.Apply(general.LogProbabilityDensityFunction);
}
return(createBaseModel(range, "Log-PDF", supportPoints, y, instance is UnivariateDiscreteDistribution));
}
public void MedianTest2()
{
NormalDistribution original = new NormalDistribution(0.4, 2.2);
var target = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
Assert.AreEqual(target.Median, original.Median, 1e-10);
target = GeneralContinuousDistribution.FromDensityFunction(
original.Support, original.ProbabilityDensityFunction);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5), 1e-10);
Assert.AreEqual(target.Median, original.Median, 1e-10);
}
public void MedianTest()
{
var laplace = new LaplaceDistribution(location: 2, scale: 0.42);
var target = GeneralContinuousDistribution.FromDensityFunction(
laplace.Support, laplace.ProbabilityDensityFunction);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
Assert.AreEqual(laplace.Median, target.Median, 1e-10);
target = GeneralContinuousDistribution.FromDistributionFunction(
laplace.Support, laplace.DistributionFunction);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5), 1e-10);
Assert.AreEqual(laplace.Median, target.Median, 1e-10);
}
public void ConstructorTest6()
{
var original = new LaplaceDistribution(location: 4, scale: 2);
var laplace = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = laplace.ProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-4));
}
testLaplace(laplace);
}
public void ConstructorTest17()
{
var original = new VonMisesDistribution(mean: 0.42, concentration: 1.2);
var vonMises = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = vonMises.ProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-4));
}
testVonMises(vonMises, 1);
}
public void ConstructorTest9()
{
var original = new LognormalDistribution(location: 0.42, shape: 1.1);
var log = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = log.ProbabilityDensityFunction(i);
double diff = Math.Abs(expected - actual);
Assert.AreEqual(expected, actual, 1e-8);
}
testLognormal(log);
}
public void ConstructorTest6()
{
var original = new LaplaceDistribution(location: 4, scale: 2);
var laplace = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = laplace.ProbabilityDensityFunction(i);
double diff = Math.Abs(expected - actual);
Assert.AreEqual(expected, actual, 1e-6);
}
testLaplace(laplace);
}
public void ConstructorTest1()
{
var original = new NormalDistribution(mean: 4, stdDev: 4.2);
var normal = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = normal.ProbabilityDensityFunction(i);
double diff = Math.Abs(expected - actual);
Assert.AreEqual(expected, actual, 1e-6);
}
testNormal(normal, 1e3);
}
public void ConstructorTest10()
{
var original = new ChiSquareDistribution(degreesOfFreedom: 7);
var chisq = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = chisq.ProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-7));
Assert.IsFalse(Double.IsNaN(actual));
Assert.IsFalse(Double.IsNaN(expected));
}
testChiSquare(chisq);
}
public void ConstructorTest14()
{
var original = new NakagamiDistribution(shape: 2.4, spread: 4.2);
var nakagami = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.ProbabilityDensityFunction(i);
double actual = nakagami.ProbabilityDensityFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 0.8));
Assert.IsFalse(double.IsNaN(expected));
Assert.IsFalse(double.IsNaN(actual));
}
testNakagami(nakagami);
}
public void ConstructorTest13()
{
var original = new GompertzDistribution(eta: 4.2, b: 1.1);
var gompertz = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (double i = -10; i < +10; i += 0.1)
{
double expected = original.DistributionFunction(i);
double actual = gompertz.DistributionFunction(i);
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-7));
Assert.IsFalse(double.IsNaN(expected));
Assert.IsFalse(double.IsNaN(actual));
}
testGompertz(gompertz);
}
public void InverseDistributionFunctionTest()
{
double[] expected =
{
Double.NegativeInfinity, -4.38252, -2.53481, -1.20248,
-0.0640578, 1.0, 2.06406, 3.20248,4.53481, 6.38252,
Double.PositiveInfinity
};
NormalDistribution original = new NormalDistribution(1.0, 4.2);
var target = GeneralContinuousDistribution.FromDistributionFunction(
original.Support, original.DistributionFunction);
for (int i = 0; i < expected.Length; i++)
{
double x = i / 10.0;
double actual = target.InverseDistributionFunction(x);
Assert.AreEqual(expected[i], actual, 1e-5);
Assert.IsFalse(Double.IsNaN(actual));
}
}
