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Add Skewed Generalized T distribution and Skewed Generalized Error distribution

v4
mikael 7 years ago
committed by Christoph Ruegg
parent
982966f704
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332251d99c
4 changed files with 1164 additions and 0 deletions
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  1. 8
      src/FSharp/Distributions.fs
  2. 261
      src/Numerics.Tests/DistributionTests/Continuous/SkewGeneralizedTTests.cs
  3. 372
      src/Numerics/Distributions/SkewedGeneralizedError.cs
  4. 523
      src/Numerics/Distributions/SkewedGeneralizedT.cs

8
src/FSharp/Distributions.fs
View File

@ -150,6 +150,14 @@ module Sample =
let studentT location scale freedom (rng:System.Random) = StudentT.Sample(rng, location, scale, freedom)
let studentTSeq location scale freedom (rng:System.Random) = StudentT.Samples(rng, location, scale, freedom)
/// Skew Generalized T with location (μ), scale (σ), skew (λ), kurtosis param (p) and kurtosis param (q).
let skewGeneralizedT location scale skew p q (rng:System.Random) = SkewedGeneralizedT.Sample(rng, location, scale, skew, p, q)
let skewGeneralizedTSeq location scale skew p q (rng:System.Random) = SkewedGeneralizedT.Samples(rng, location, scale, skew, p, q)
/// Skew Generalized Error with location (μ), scale (σ), skew (λ) and kurtosis param (p).
let skewGeneralizedError location scale skew p (rng:System.Random) = SkewedGeneralizedError.Sample(rng, location, scale, skew, p)
let skewGeneralizedErrorSeq location scale skew p (rng:System.Random) = SkewedGeneralizedError.Samples(rng, location, scale, skew, p)
/// Weibull with shape (k) and scale (λ).
let weibull shape scale (rng:System.Random) = Weibull.Sample(rng, shape, scale)
let weibullSeq shape scale (rng:System.Random) = Weibull.Samples(rng, shape, scale)

261
src/Numerics.Tests/DistributionTests/Continuous/SkewGeneralizedTTests.cs
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@ -0,0 +1,261 @@
// <copyright file="StudentTTests.cs" company="Math.NET">
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
//
// Copyright (c) 2009-2016 Math.NET
//
// Permission is hereby granted, free of charge, to any person
// obtaining a copy of this software and associated documentation
// files (the "Software"), to deal in the Software without
// restriction, including without limitation the rights to use,
// copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the
// Software is furnished to do so, subject to the following
// conditions:
//
// The above copyright notice and this permission notice shall be
// included in all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
// OTHER DEALINGS IN THE SOFTWARE.
// </copyright>
using System;
using System.Linq;
using MathNet.Numerics.Distributions;
using NUnit.Framework;
namespace MathNet.Numerics.UnitTests.DistributionTests.Continuous
{
/// <summary>
/// <c>SkewedGeneralizedT</c> distribution tests.
/// Reference values are from the R package sgt 2.0 (run on Microsoft R Open v3.5.2)
/// </summary>
[TestFixture, Category("Distributions")]
public class SkewedGeneralizedTTests
{
[Test]
public void CanCreateStandardSkewedGeneralizedT()
{
var n = new SkewedGeneralizedT();
Assert.AreEqual(0.0, n.Location);
Assert.AreEqual(1.0, n.Scale);
Assert.AreEqual(0.0, n.Skew);
Assert.AreEqual(2.0, n.P);
Assert.AreEqual(double.PositiveInfinity, n.Q);
}
[TestCase(0.0, 1.0, 0.0, 2.0, double.PositiveInfinity)] // Standard Normal distribution
[TestCase(5.0, 1.0, 0.0, 2.0, double.PositiveInfinity)] // Mean shifted Normal distribution
[TestCase(0.0, 2.0, 0.0, 2.0, double.PositiveInfinity)] // Scaled Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, double.PositiveInfinity)] // Skewed Normal distribution
[TestCase(1.0, 1.5, 0.9, 2.0, double.PositiveInfinity)] // Mean shifted and scaled Skewed Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, 1.1)] // Skewed Student T distribution
[TestCase(0.0, 1.0, 0.0, 2.0, 1.1)] // Student T distribution
[TestCase(0.0, 1.0, -0.3, 2.2, double.PositiveInfinity)] // Skewed Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, 2.2, double.PositiveInfinity)] // Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0)] // Continuous Uniform
public void CanCreateSkewedGeneralizedT(double location, double scale, double skew, double p, double q)
{
var n = new SkewedGeneralizedT(location, scale, skew, p, q);
Assert.AreEqual(location, n.Location);
Assert.AreEqual(scale, n.Scale);
Assert.AreEqual(skew, n.Skew);
Assert.AreEqual(p, n.P);
Assert.AreEqual(q, n.Q);
}
[TestCase(0.0, 1.0, 0.0, 2.0, 1.0)] // pq <= 2
[TestCase(0.0, 1.0, 0.0, -2.0, -1.0)] // pq <= 2 and negative values
[TestCase(5.0, -1.0, 0.0, 2.0, double.PositiveInfinity)] // Negative scale
[TestCase(0.0, 2.0, 1.1, 2.0, double.PositiveInfinity)] // Invalid skew, too large
[TestCase(0.0, 1.0, -1.1, 2.0, double.PositiveInfinity)] // Invalid skew, too small
public void SkewedGeneralizedTCreateFailsWithBadParameters(double location, double scale, double skew, double p, double q)
{
Assert.That(() => new SkewedGeneralizedT(location, scale, skew, p, q), Throws.ArgumentException);
}
[TestCase(0.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 0.2631360242)] // Standard Normal distribution
[TestCase(5.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 4.9123, 0.3974110362)] // Mean shifted Normal distribution
[TestCase(0.0, 2.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 0.1797618922)] // Scaled Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, double.PositiveInfinity, 0.9123, 0.1958872375)] // Skewed Normal distribution
[TestCase(1.0, 1.5, 0.9, 2.0, double.PositiveInfinity, 0.9123, 0.2400040926)] // Mean shifted and scaled Skewed Normal distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, 0.9123, 0.523647666)] // Skewed Student T distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, -0.9123, 0.1799965988)] // Skewed Student T distribution
[TestCase(0.0, 1.0, 0.0, 2.0, 5.0, 0.9123, 0.2524160191)] // Student T distribution
[TestCase(0.0, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, 0.3323206895)] // Skewed Generalized Error Distribution
[TestCase(-1.5, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, 0.006297697854)] // Mean shifted Skewed Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, 2.2, double.PositiveInfinity, 0.9123, 0.2701962342)] // Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.5123, 0.2886751346)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.6123, 0.2886751346)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.77, 1.0, double.PositiveInfinity, 0.6123, 0.2016342715)] // Skewed Laplace
[TestCase(0.0, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 0.2974537422)] // Laplace
[TestCase(0.9, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 0.4707430703)] // Mean shifted Laplace
public void ValidateDensity(double location, double scale, double skew, double p, double q, double x, double d)
{
var n = new SkewedGeneralizedT(location, scale, skew, p, q);
var density = n.Density(x);
AssertHelpers.AlmostEqualRelative(d, density, 8);
}
[TestCase(0.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, -1.335084178)] // Standard Normal distribution
[TestCase(5.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 4.9123, -0.9227841782)] // Mean shifted Normal distribution
[TestCase(0.0, 2.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, -1.716122125)] // Scaled Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, double.PositiveInfinity, 0.9123, -1.630216104)] // Skewed Normal distribution
[TestCase(1.0, 1.5, 0.9, 2.0, double.PositiveInfinity, 0.9123, -1.427099303)] // Mean shifted and scaled Skewed Normal distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, 0.9123, -0.646936214)] // Skewed Student T distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, -0.9123, -1.714817324)] // Skewed Student T distribution
[TestCase(0.0, 1.0, 0.0, 2.0, 5.0, 0.9123, -1.376676683)] // Student T distribution
[TestCase(0.0, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, -1.101654844)] // Skewed Generalized Error Distribution
[TestCase(-1.5, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, -5.067571132)] // Mean shifted Skewed Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, 2.2, double.PositiveInfinity, 0.9123, -1.308606791)] // Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, -0.5123, -1.242453325)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, -0.6123, -1.242453325)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.77, 1.0, double.PositiveInfinity, 0.6123, -1.60129976)] // Skewed Laplace
[TestCase(0.0, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, -1.212496555)] // Laplace
[TestCase(0.9, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, -0.7534428322)] // Mean shifted Laplace
public void ValidateDensityLn(double location, double scale, double skew, double p, double q, double x, double d)
{
var n = new SkewedGeneralizedT(location, scale, skew, p, q);
var density = n.DensityLn(x);
AssertHelpers.AlmostEqualRelative(d, density, 8);
}
[TestCase(0.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 0.8191945928)] // Standard Normal distribution
[TestCase(5.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 4.123, 0.190243319)] // Mean shifted Normal distribution
[TestCase(0.0, 2.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 0.6758589413)] // Scaled Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, double.PositiveInfinity, 0.9123, 0.8216671619)] // Skewed Normal distribution
[TestCase(1.0, 1.5, 0.9, 2.0, double.PositiveInfinity, 0.9123, 0.5519973476)] // Mean shifted and scaled Skewed Normal distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, 0.9123, 0.8297526431)] // Skewed Student T distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, -0.9123, 0.1624618933)] // Skewed Student T distribution
[TestCase(0.0, 1.0, 0.0, 2.0, 5.0, 0.9123, 0.8341106883)] // Student T distribution
[TestCase(0.0, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, 0.8118701776)] // Skewed Generalized Error Distribution
[TestCase(-1.5, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, 0.9987893207)] // Mean shifted Skewed Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, 2.2, double.PositiveInfinity, 0.9123, 0.8140902875)] // Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.5123, 0.6478882715)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.6123, 0.6767557849)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.77, 1.0, double.PositiveInfinity, 0.6123, 0.8000467981)] // Skewed Laplace
[TestCase(0.0, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 0.7896684418)] // Laplace
[TestCase(0.9, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 0.3328656172)] // Mean shifted Laplace
public void ValidateCDF(double location, double scale, double skew, double p, double q, double x, double pr)
{
var n = new SkewedGeneralizedT(location, scale, skew, p, q);
var cpr = n.CumulativeDistribution(x);
AssertHelpers.AlmostEqualRelative(pr, cpr, 8);
}
[TestCase(0.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 1.355055108)] // Standard Normal distribution
[TestCase(5.0, 1.0, 0.0, 2.0, double.PositiveInfinity, 0.4123, 4.778367525)] // Mean shifted Normal distribution
[TestCase(0.0, 2.0, 0.0, 2.0, double.PositiveInfinity, 0.9123, 2.710110216)] // Scaled Normal distribution
[TestCase(0.0, 1.0, 0.9, 2.0, double.PositiveInfinity, 0.9123, 1.506912119)] // Skewed Normal distribution
[TestCase(1.0, 1.5, 0.9, 2.0, double.PositiveInfinity, 0.9123, 3.260368178)] // Mean shifted and scaled Skewed Normal distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, 0.9123, 1.068716999)] // Skewed Student T distribution
[TestCase(0.0, 1.0, -0.9, 2.0, 5.0, 0.123, -1.159154003)] // Skewed Student T distribution
[TestCase(0.0, 1.0, 0.0, 2.0, 5.0, 0.9123, 1.304471125)] // Student T distribution
[TestCase(0.0, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, 1.275451275)] // Skewed Generalized Error Distribution
[TestCase(-1.5, 1.0, -0.3, 2.2, double.PositiveInfinity, 0.9123, -0.2245487247)] // Mean shifted Skewed Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, 2.2, double.PositiveInfinity, 0.9123, 1.363253899)] // Generalized Error Distribution
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.5123, 0.04260844987)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.0, double.PositiveInfinity, 1.0, 0.6123, 0.3890186114)] // Continuous Uniform
[TestCase(0.0, 1.0, 0.77, 1.0, double.PositiveInfinity, 0.6123, -0.04432801722)] // Skewed Laplace
[TestCase(0.0, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 0.179871174)] // Laplace
[TestCase(0.9, 1.0, 0.0, 1.0, double.PositiveInfinity, 0.6123, 1.079871174)] // Mean shifted Laplace
public void ValidateInvCDF(double location, double scale, double skew, double p, double q, double quantile, double x)
{
var n = new SkewedGeneralizedT(location, scale, skew, p, q);
var xq = n.InverseCumulativeDistribution(quantile);
AssertHelpers.AlmostEqualRelative(x, xq, 8);
AssertHelpers.AlmostEqualRelative(quantile, n.CumulativeDistribution(xq), 8);
}
[TestCase(0, 1.0, 0.4123)]
[TestCase(1.5, 2.5, 0.5123)]
[TestCase(-0.5, 5, 0.6123)]
public void ValidateLaplaceDensityEquivalence(double location, double scale, double x)
{
var n = new SkewedGeneralizedT(location, scale, 0, 1, double.PositiveInfinity);
var b = scale / Math.Sqrt(2.0);
var l = new Laplace(location, b);
AssertHelpers.AlmostEqualRelative(l.Density(x), n.Density(x), 8);
AssertHelpers.AlmostEqualRelative(l.DensityLn(x), n.DensityLn(x), 8);
}
[TestCase(0, 1.0, 0.4123)]
[TestCase(1.5, 2.5, 0.5123)]
[TestCase(-0.5, 5, 0.6123)]
public void ValidateNormalDensityEquivalence(double location, double scale, double x)
{
var sgt = new SkewedGeneralizedT(location, scale, 0, 2, double.PositiveInfinity);
var n = new Normal(location, scale);
AssertHelpers.AlmostEqualRelative(n.Density(x), sgt.Density(x), 8);
AssertHelpers.AlmostEqualRelative(n.DensityLn(x), sgt.DensityLn(x), 8);
}
[TestCase(0, 1, -0.1, 0.5123)]
[TestCase(0, 1, 0.1, 0.6123)]
public void ValidateSkewedNormalDistribution(double location, double scale, double skew, double x)
{
var sn = new SkewedGeneralizedT(location, scale, skew, 2, double.PositiveInfinity);
var n = new Normal(location, scale);
var sp = sn.CumulativeDistribution(x);
var p = n.CumulativeDistribution(x);
if (skew > 0)
Assert.IsTrue(sp > p);
else
Assert.IsTrue(sp < p);
}
/// <summary>
/// Can sample static.
/// </summary>
[Test]
public void CanSampleStatic()
{
SkewedGeneralizedT.Sample(0.0, 1.0, 0.3, 2.2, 5.6);
}
/// <summary>
/// Can sample sequence static.
/// </summary>
[Test]
public void CanSampleSequenceStatic()
{
var ied = SkewedGeneralizedT.Samples(0.0, 1.0, 0.3, 2.2, 5.6);
GC.KeepAlive(ied.Take(5).ToArray());
}
/// <summary>
/// Can sample.
/// </summary>
[Test]
public void CanSample()
{
var n = new SkewedGeneralizedT();
n.Sample();
}
/// <summary>
/// Can sample sequence.
/// </summary>
[Test]
public void CanSampleSequence()
{
var n = new SkewedGeneralizedT();
var ied = n.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
}
}

372
src/Numerics/Distributions/SkewedGeneralizedError.cs
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@ -0,0 +1,372 @@
// <copyright file="StudentT.cs" company="Math.NET">
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
//
// Copyright (c) 2009-2019 Math.NET
//
// Permission is hereby granted, free of charge, to any person
// obtaining a copy of this software and associated documentation
// files (the "Software"), to deal in the Software without
// restriction, including without limitation the rights to use,
// copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the
// Software is furnished to do so, subject to the following
// conditions:
//
// The above copyright notice and this permission notice shall be
// included in all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
// OTHER DEALINGS IN THE SOFTWARE.
// </copyright>
using MathNet.Numerics.Properties;
using MathNet.Numerics.Random;
using System;
using System.Collections.Generic;
namespace MathNet.Numerics.Distributions
{
/// <summary>
/// Continuous Univariate Skewed Generalized Error Distribution (SGED).
/// Implements the univariate SSkewed Generalized Error Distribution. For details about this
/// distribution, see
/// <a href="https://en.wikipedia.org/wiki/Generalized_normal_distribution">
/// Wikipedia - Generalized Error Distribution</a>.
/// It includes Laplace, Normal and Student-t distributions.
/// This is the <see cref="SkewedGeneralizedT"/> distribution with q=Inf.
/// </summary>
/// <remarks><para>This implementation is based on the R package dsgt and corresponding viginette, see
/// <a href="">https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf</a>. Compared to that
/// implementation, the options for mean adjustment and variance adjustment are always true.
/// The location (μ) is the mean of the distribution.
/// The scale (σ) squared is the variance of the distribution.
/// </para>
/// <para>The distribution will use the <see cref="System.Random"/> by
/// default. Users can get/set the random number generator by using the
/// <see cref="RandomSource"/> property.</para>
/// <para>The statistics classes will check all the incoming parameters
/// whether they are in the allowed range.</para></remarks>
public class SkewedGeneralizedError : IContinuousDistribution
{
private System.Random _random;
/// <summary>
/// Initializes a new instance of the SkewedGeneralizedError class. This is a generalized error distribution
/// with location=0.0, scale=1.0, skew=0.0 and p=2.0 (a standard normal distribution).
/// </summary>
public SkewedGeneralizedError()
{
_random = SystemRandomSource.Default;
Location = 0.0;
Scale = 1.0;
Skew = 0.0;
P = 2.0;
}
/// <summary>
/// Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
/// and kurtosis parameters. Different parameterizations result in different distributions.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
public SkewedGeneralizedError(double location, double scale, double skew, double p)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
_random = SystemRandomSource.Default;
Location = location;
Scale = scale;
Skew = skew;
P = p;
}
/// <summary>
/// Gets or sets the random number generator which is used to draw random samples.
/// </summary>
public System.Random RandomSource
{
get { return _random; }
set { _random = value ?? SystemRandomSource.Default; }
}
/// <summary>
/// A string representation of the distribution.
/// </summary>
/// <returns>a string representation of the distribution.</returns>
public override string ToString()
{
return $"SkewedGeneralizedError(μ = {Location}, σ = {Scale}, λ = { Skew }, p = {P}";
}
/// <summary>
/// Tests whether the provided values are valid parameters for this distribution.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
public static bool IsValidParameterSet(double location, double scale, double skew, double p)
{
return scale > 0.0 && skew > -1.0 && skew < 1.0 && p > 0.0 && !double.IsNaN(location);
}
/// <summary>
/// Gets the location (μ) of the Skewed Generalized t-distribution.
/// </summary>
public double Location { get; private set; }
/// <summary>
/// Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
/// </summary>
public double Scale { get; private set; }
/// <summary>
/// Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
/// </summary>
public double Skew { get; private set; }
/// <summary>
/// Gets the parameter that controls the kurtosis of the distribution. Range: p > 0.
/// </summary>
public double P { get; private set; }
public double Mode => throw new NotImplementedException();
public double Minimum => double.NegativeInfinity;
public double Maximum => double.PositiveInfinity;
public double Mean => Location;
public double Variance => Scale * Scale;
public double StdDev => Scale;
public double Entropy => throw new NotImplementedException();
public double Skewness => throw new NotImplementedException();
public double Median => Location;
private static double AdjustScale(double scale, double skew, double p)
{
var g1 = SpecialFunctions.Gamma(3.0 / p);
var g2 = SpecialFunctions.Gamma(0.5 + 1.0 / p);
var g3 = SpecialFunctions.Gamma(1.0 / p);
var g4 = SpecialFunctions.Gamma(1.0 / p);
var n1 = Constants.Pi * (1.0 + 3.0 * skew * skew) * g1;
var n2 = Math.Pow(16.0, 1.0 / p) * skew * skew * Math.Pow(g2, 2) * g3;
var d = Constants.Pi * g4;
return scale / Math.Sqrt((n1 - n2) / d);
}
private static double AdjustX(double x, double scale, double skew, double p)
{
return x + AdjustAddend(scale, skew, p);
}
private static double AdjustAddend(double scale, double skew, double p)
{
return (Math.Pow(2.0, 2.0 / p) * scale * skew * SpecialFunctions.Gamma(1.0 / 2.0 + 1.0 / p)) /
Math.Sqrt(Constants.Pi);
}
public static double PDF(double location, double scale, double skew, double p, double x)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
scale = AdjustScale(scale, skew, p);
x = AdjustX(x, scale, skew, p);
// p / (2 * sigma * gamma(1 / p) * exp((abs(x - mu) / (sigma * (1 + lambda * sgn(x - mu)))) ^ p))
var d1 = Math.Abs(x - location);
var d2 = scale * (1.0 + skew * Math.Sign(x - location));
var d3 = 2.0 * scale * SpecialFunctions.Gamma(1.0 / p);
return p / (Math.Exp(Math.Pow(d1 / d2, p)) * d3);
}
public static double PDFLn(double location, double scale, double skew, double p, double x)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
scale = AdjustScale(scale, skew, p);
x = AdjustX(x, scale, skew, p);
return Math.Log(p) - Math.Log(2.0) - Math.Log(scale) - SpecialFunctions.GammaLn(1.0 / p) -
Math.Pow(Math.Abs(x - location) / (scale * (1.0 + skew * Math.Sign(x - location))), p);
}
public static double CDF(double location, double scale, double skew, double p, double x)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
scale = AdjustScale(scale, skew, p);
x = AdjustX(x, scale, skew, p) - location;
var flip = x < 0;
if (flip)
{
skew = -skew;
x = -x;
}
var res = (1.0 - skew) / 2.0 + (1.0 + skew) / 2.0 * Gamma.CDF(1.0 / p, 1.0, Math.Pow(x / (scale * (1.0 + skew)), p));
return flip ? 1.0 - res : res;
}
public static double InvCDF(double location, double scale, double skew, double p, double pr)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
scale = AdjustScale(scale, skew, p);
var flip = pr < (1.0 - skew) / 2.0;
var lambda = skew;
if (flip)
{
pr = 1.0 - pr;
lambda = -lambda;
}
var res = scale * (1.0 + lambda) * Math.Pow(Gamma.InvCDF(1.0 / p, 1.0, 2 * pr / (1.0 + lambda) + (lambda - 1.0) / (lambda + 1.0)), 1.0 / p);
if (flip)
res = -res;
res += location;
return res - AdjustAddend(scale, skew, p);
}
public double InverseCumulativeDistribution(double p)
{
return InvCDF(Location, Scale, Skew, P, p);
}
public double CumulativeDistribution(double x)
{
return CDF(Location, Scale, Skew, P, x);
}
public double Density(double x)
{
return PDF(Location, Scale, Skew, P, x);
}
public double DensityLn(double x)
{
return PDFLn(Location, Scale, Skew, P, x);
}
/// <summary>
/// Generates a sample from the Skew Generalized Error distribution.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
/// <returns>a sample from the distribution.</returns>
public static double Sample(System.Random rnd, double location, double scale, double skew, double p)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return SampleUnchecked(rnd, location, scale, skew, p);
}
/// <summary>
/// Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
/// <returns>a sequence of samples from the distribution.</returns>
public static IEnumerable<double> Samples(System.Random rnd, double location, double scale, double skew, double p)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
while (true)
{
yield return SampleUnchecked(rnd, location, scale, skew, p);
}
}
public static IEnumerable<double> Samples(double location, double scale, double skew, double p)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return Samples(SystemRandomSource.Default, location, scale, skew, p);
}
public static double Sample(double location, double scale, double skew, double p)
{
if (!IsValidParameterSet(location, scale, skew, p))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return SampleUnchecked(SystemRandomSource.Default, location, scale, skew, p);
}
private static double SampleUnchecked(System.Random rnd, double location, double scale, double skew, double p)
{
var u = ContinuousUniform.Sample(rnd, 0, 1);
return InvCDF(location, scale, skew, p, u);
}
public double Sample()
{
return SampleUnchecked(SystemRandomSource.Default, Location, Scale, Skew, P);
}
public void Samples(double[] values)
{
if (values == null)
return;
for (int i = 0; i < values.Length; i++)
{
values[i] = Sample();
}
}
public IEnumerable<double> Samples()
{
return Samples();
}
}
}

523
src/Numerics/Distributions/SkewedGeneralizedT.cs
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@ -0,0 +1,523 @@
// <copyright file="StudentT.cs" company="Math.NET">
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
//
// Copyright (c) 2009-2019 Math.NET
//
// Permission is hereby granted, free of charge, to any person
// obtaining a copy of this software and associated documentation
// files (the "Software"), to deal in the Software without
// restriction, including without limitation the rights to use,
// copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the
// Software is furnished to do so, subject to the following
// conditions:
//
// The above copyright notice and this permission notice shall be
// included in all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
// OTHER DEALINGS IN THE SOFTWARE.
// </copyright>
using System;
using System.Collections.Generic;
using MathNet.Numerics.Properties;
using MathNet.Numerics.Random;
namespace MathNet.Numerics.Distributions
{
/// <summary>
/// Continuous Univariate Skewed Generalized T-distribution.
/// Implements the univariate Skewed Generalized t-distribution. For details about this
/// distribution, see
/// <a href="https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution">
/// Wikipedia - Skewed generalized t-distribution</a>.
/// The skewed generalized t-distribution contains many different distributions within it
/// as special cases based on the parameterization chosen.
/// </summary>
/// <remarks><para>This implementation is based on the R package dsgt and corresponding viginette, see
/// <a href="">https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf</a>. Compared to that
/// implementation, the options for mean adjustment and variance adjustment are always true.
/// The location (μ) is the mean of the distribution.
/// The scale (σ) squared is the variance of the distribution.
/// </para>
/// <para>The distribution will use the <see cref="System.Random"/> by
/// default. Users can get/set the random number generator by using the
/// <see cref="RandomSource"/> property.</para>
/// <para>The statistics classes will check all the incoming parameters
/// whether they are in the allowed range.</para></remarks>
public class SkewedGeneralizedT : IContinuousDistribution
{
private System.Random _random;
// If the given parameterization is one of the recognized special cases, then
// this variable is non-null and the special case is used for all functions.
// Else this value is null and the full formulation of the generalized distribution is used.
private IContinuousDistribution _d;
/// <summary>
/// Initializes a new instance of the SkewedGeneralizedT class. This is a skewed generalized t-distribution
/// with location=0.0, scale=1.0, skew=0.0, p=2.0 and q=Inf (a standard normal distribution).
/// </summary>
public SkewedGeneralizedT()
{
_random = SystemRandomSource.Default;
Location = 0.0;
Scale = 1.0;
Skew = 0.0;
P = 2.0;
Q = double.PositiveInfinity;
_d = new Normal(Location, Scale, _random);
}
/// <summary>
/// Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
/// and kurtosis parameters. Different parameterizations result in different distributions.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
public SkewedGeneralizedT(double location, double scale, double skew, double p, double q)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
_random = SystemRandomSource.Default;
Location = location;
Scale = scale;
Skew = skew;
P = p;
Q = q;
_d = FindSpecializedDistribution(location, scale, skew, p, q);
}
/// <summary>
/// Given a parameter set, returns the distribution that matches this parameterization.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <returns>Null if no known distribution matches the parameterization, else the distribution.</returns>
public static IContinuousDistribution FindSpecializedDistribution(double location, double scale, double skew, double p, double q)
{
if (p == double.PositiveInfinity)
{
scale *= Math.Sqrt(3.0);
return new ContinuousUniform(location - scale, location + scale);
}
if (q == double.PositiveInfinity)
return new SkewedGeneralizedError(location, scale, skew, p);
return null;
}
/// <summary>
/// Gets or sets the random number generator which is used to draw random samples.
/// </summary>
public System.Random RandomSource
{
get { return _random; }
set { _random = value ?? SystemRandomSource.Default; }
}
/// <summary>
/// A string representation of the distribution.
/// </summary>
/// <returns>a string representation of the distribution.</returns>
public override string ToString()
{
return $"SkewedGeneralizedT(μ = {Location}, σ = {Scale}, λ = { Skew }, p = {P}, q = {Q})";
}
/// <summary>
/// Tests whether the provided values are valid parameters for this distribution.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
public static bool IsValidParameterSet(double location, double scale, double skew, double p, double q)
{
return scale > 0.0 && skew > -1.0 && skew < 1.0 && p > 0.0 && q > 0.0 && p*q> 2.0 && !double.IsNaN(location);
}
/// <summary>
/// Gets the location (μ) of the Skewed Generalized t-distribution.
/// </summary>
public double Location { get; private set; }
/// <summary>
/// Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
/// </summary>
public double Scale { get; private set; }
/// <summary>
/// Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
/// </summary>
public double Skew { get; private set; }
/// <summary>
/// Gets the first parameter that controls the kurtosis of the distribution. Range: p > 0.
/// </summary>
public double P { get; private set; }
/// <summary>
/// Gets the second parameter that controls the kurtosis of the distribution. Range: q > 0.
/// </summary>
public double Q { get; private set; }
public double Mode => throw new NotImplementedException();
public double Minimum => _d == null ? double.NegativeInfinity : _d.Minimum;
public double Maximum => _d == null ? double.PositiveInfinity : _d.Maximum;
public double Mean => _d == null ? Location : _d.Mean;
public double Variance => _d == null ? Scale * Scale : _d.Variance;
public double StdDev => _d == null ? Scale : _d.StdDev;
public double Entropy => _d == null ? throw new NotImplementedException() : _d.Entropy;
public double Skewness => _d == null ? throw new NotImplementedException() : _d.Skewness;
public double Median => _d == null ? Location : _d.Median;
private static double AdjustScale(double scale, double skew, double p, double q)
{
var b1 = SpecialFunctions.Beta(3.0 / p, q - 2.0 / p);
var b2 = SpecialFunctions.Beta(1.0 / p, q);
var b3 = SpecialFunctions.Beta(2.0 / p, q - 1.0 / p);
var b4 = SpecialFunctions.Beta(1.0 / p, q);
return scale / (Math.Pow(q, 1.0 / p) * Math.Sqrt((3.0 * skew * skew + 1.0) * b1 / b2 - 4.0 * skew * skew * ((b3 / b4) * (b3 / b4))));
}
// Note: Scale is assumed to be adjusted already when calling this function.
private static double AdjustX(double x, double scale, double skew, double p, double q)
{
return x + AdjustAddend(scale, skew, p, q);
}
// Note: Scale is assumed to be adjusted already when calling this function.
private static double AdjustAddend(double scale, double skew, double p, double q)
{
var b1 = SpecialFunctions.Beta(2.0 / p, q - 1.0 / p);
var b2 = SpecialFunctions.Beta(1.0 / p, q);
return (2.0 * scale * skew * Math.Pow(q, 1.0 / p) * b1) / b2;
}
/// <summary>
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <param name="x">The location at which to compute the density.</param>
/// <returns>the density at <paramref name="x"/>.</returns>
/// <seealso cref="Density"/>
public static double PDF(double location, double scale, double skew, double p, double q, double x)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
var fn = PDFunc(location, scale, skew, p, q, false);
return fn(x);
}
/// <summary>
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
/// </summary>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <param name="x">The location at which to compute the density.</param>
/// <returns>the density at <paramref name="x"/>.</returns>
/// <seealso cref="Density"/>
public static double PDFLn(double location, double scale, double skew, double p, double q, double x)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
var fn = PDFunc(location, scale, skew, p, q, true);
return fn(x);
}
private static double PDFull(double location, double scale, double skew, double p, double q, double x)
{
scale = AdjustScale(scale, skew, p, q);
x = AdjustX(x, scale, skew, p, q);
var b = SpecialFunctions.Beta(1.0 / p, q);
var skewSign = Math.Sign(x - location);
var d1 = Math.Pow(Math.Abs(x - location), p);
var d2 = q * Math.Pow(scale, p) * Math.Pow(skew * skewSign + 1.0, p);
var denominator = 2.0 * scale * Math.Pow(q, 1.0 / p) * b * Math.Pow(d1 / d2 + 1.0, 1.0 / p + q);
return p / denominator;
}
private static double PDFullLn(double location, double scale, double skew, double p, double q, double x)
{
scale = AdjustScale(scale, skew, p, q);
x = AdjustX(x, scale, skew, p, q);
var bLn = SpecialFunctions.BetaLn(1.0 / p, q);
return Math.Log(p) - Math.Log(2.0) - Math.Log(scale) - Math.Log(q) / p - bLn - (1.0 / p + q) *
Math.Log(1.0 + Math.Pow(Math.Abs(x - location), p) /
(q * Math.Pow(scale, p) * Math.Pow(1.0 + skew * Math.Sign(x - location), p)));
}
// For known parameterizations we just use the existing distributions as visualized
// by Hansen, McDonald and Newey (2010).
// Note that, for all cases where skew is required to be 0, if skew is non-zero, this
// simply gives the corresponding skewed version of the distribution.
private static Func<double, double> PDFunc(double location, double scale, double skew, double p, double q, bool ln)
{
if (p == double.PositiveInfinity)
{
scale *= Math.Sqrt(3.0);
return x => ln ? ContinuousUniform.PDFLn(location - scale, location + scale, x) :
ContinuousUniform.PDF(-1.0 * (Math.Sqrt(3.0) * scale + location), Math.Sqrt(3.0) * scale + location, x);
}
if (q == double.PositiveInfinity)
return x => ln ? SkewedGeneralizedError.PDFLn(location, scale, skew, p, x) :
SkewedGeneralizedError.PDF(location, scale, skew, p, x);
return x => ln ? PDFullLn(location, scale, skew, p, q, x) :
PDFull(location, scale, skew, p, q, x);
}
/// <summary>
/// Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
/// </summary>
/// <param name="x">The location at which to compute the cumulative distribution function.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
/// <seealso cref="CumulativeDistribution"/>
public static double CDF(double location, double scale, double skew, double p, double q, double x)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
// Note: Adapted from the R package,
// based on a transformation of the cumulative probability density function that uses the
// incomplete beta function or incomplete gamma function.
scale = AdjustScale(scale, skew, p, q);
x = AdjustX(x, scale, skew, p, q) - location;
var flip = x > 0;
if (flip)
{
skew = -skew;
x = -x;
}
var res = (1.0 - skew) / 2.0 + (skew - 1.0) / 2.0 * Beta.CDF(1.0 / p, q, 1.0 / (1.0 + q * Math.Pow(scale * (1.0 - skew) / -x, p)));
return flip ? 1.0 - res : res;
}
/// <summary>
/// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
/// at the given probability. This is also known as the quantile or percent point function.
/// </summary>
/// <param name="pr">The location at which to compute the inverse cumulative density.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
/// <seealso cref="InverseCumulativeDistribution"/>
public static double InvCDF(double location, double scale, double skew, double p, double q, double pr)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
// Note: Adapted from the R package,
// solving for the inverse of the CDF that uses the inverse of the incomplete beta function or
// incomplete gamma function
scale = AdjustScale(scale, skew, p, q);
var flip = pr > (1.0 - skew) / 2.0;
var lambda = skew;
if (flip)
{
pr = 1.0 - pr;
lambda = -lambda;
}
var res = scale * (lambda - 1.0) * Math.Pow(1.0 / (q * Beta.InvCDF(1.0 / p, q, 1.0 - 2.0 * pr / (1.0 - lambda))) - 1.0 / q, -1.0 / p);
if (flip)
res = -res;
res += location;
return res - AdjustAddend(scale, skew, p, q);
}
public double CumulativeDistribution(double x)
{
return _d == null ? CDF(Location, Scale, Skew, P, Q, x) : _d.CumulativeDistribution(x);
}
/// <summary>
/// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
/// at the given probability. This is also known as the quantile or percent point function.
/// </summary>
/// <param name="p">The location at which to compute the inverse cumulative density.</param>
/// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
/// <seealso cref="InvCDF"/>
public double InverseCumulativeDistribution(double p)
{
// InverseCumulativeDistribution is not a part of the interface, so resort to type-checking.
if (_d != null)
{
if (_d is SkewedGeneralizedError sge)
return sge.InverseCumulativeDistribution(p);
if (_d is ContinuousUniform u)
return u.InverseCumulativeDistribution(p);
}
return InvCDF(Location, Scale, Skew, P, Q, p);
}
public double Density(double x)
{
return _d == null ? PDF(Location, Scale, Skew, P, Q, x) : _d.Density(x);
}
public double DensityLn(double x)
{
return _d == null ? PDFLn(Location, Scale, Skew, P, Q, x) : _d.DensityLn(x);
}
/// <summary>
/// Generates a sample from the Skew Generalized t-distribution.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <returns>a sample from the distribution.</returns>
public static double Sample(System.Random rnd, double location, double scale, double skew, double p, double q)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return SampleUnchecked(rnd, location, scale, skew, p, q);
}
/// <summary>
/// Generates a sequence of samples from the Skew Generalized t-distribution using inverse transform.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="location">The location (μ) of the distribution.</param>
/// <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
/// <param name="skew">The skew, 1 > λ > -1</param>
/// <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
/// <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
/// <returns>a sequence of samples from the distribution.</returns>
public static IEnumerable<double> Samples(System.Random rnd, double location, double scale, double skew, double p, double q)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
while (true)
{
yield return SampleUnchecked(rnd, location, scale, skew, p, q);
}
}
public static IEnumerable<double> Samples(double location, double scale, double skew, double p, double q)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return Samples(SystemRandomSource.Default, location, scale, skew, p, q);
}
public static double Sample(double location, double scale, double skew, double p, double q)
{
if (!IsValidParameterSet(location, scale, skew, p, q))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
return SampleUnchecked(SystemRandomSource.Default, location, scale, skew, p, q);
}
private static double SampleUnchecked(System.Random rnd, double location, double scale, double skew, double p, double q)
{
var u = ContinuousUniform.Sample(rnd, 0, 1);
return InvCDF(location, scale, skew, p, q, u);
}
public double Sample()
{
return SampleUnchecked(SystemRandomSource.Default, Location, Scale, Skew, P, Q);
}
public void Samples(double[] values)
{
if (values == null)
return;
for (int i = 0; i < values.Length; i++)
{
values[i] = Sample();
}
}
public IEnumerable<double> Samples()
{
return Samples();
}
}
}
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