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- New distributions are - Three parameter Burr (Type XII) - Inverse Gaussian - Truncated Pareto - Unit tests added for all public methods - New distributions added to CommonDistributionTestsuap-experimental
Philip Gruber
7 years ago
10 changed files with 2661 additions and 0 deletions
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// <copyright file="BurrTests.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2019 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using MathNet.Numerics.Distributions; |
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using NUnit.Framework; |
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using System; |
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using System.Linq; |
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namespace MathNet.Numerics.UnitTests.DistributionTests.Continuous |
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{ |
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/// <summary>
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/// <c>Burr</c> distribution tests.
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/// </summary>
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[TestFixture, Category("Distributions")] |
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public class BurrTests |
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{ |
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private const int highPrecision = 12; |
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private const int lowPrecision = 8; |
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/// <summary>
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/// Can create <c>Burr</c>.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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[TestCase(1.0, 1.0, 0.5)] |
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[TestCase(10.0, 0.1, 0.2)] |
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[TestCase(5.0, 1.0, 3)] |
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[TestCase(2.0, 10.0, 2.0)] |
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[TestCase(10.0, 100.0, 1.0)] |
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public void CanCreateBurr(double a, double c, double k) |
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{ |
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var n = new Burr(a, c, k); |
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Assert.AreEqual(a, n.a); |
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Assert.AreEqual(c, n.c); |
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Assert.AreEqual(k, n.k); |
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} |
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/// <summary>
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/// Can create <c>Burr</c> with random source.
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/// </summary>
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[Test] |
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public void CanCreateBurrWithRandomSource() |
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{ |
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var randomSource = new Numerics.Random.MersenneTwister(100); |
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var n = new Burr(1.0, 1.0, 0.5, randomSource); |
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Assert.AreEqual(randomSource, n.RandomSource); |
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} |
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/// <summary>
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/// <c>Burr</c> create fails with bad parameters.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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[TestCase(5.0, 1.0, -1.0)] |
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[TestCase(-1.0, 1.0, 2.05)] |
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[TestCase(1.0, -1.0, 2.05)] |
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[TestCase(Double.NaN, Double.NaN, Double.NaN)] |
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[TestCase(Double.NaN, 1.0, 2.4)] |
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[TestCase(1.0, 1.0, double.PositiveInfinity)] |
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[TestCase(1.0, -1.0, Double.NegativeInfinity)] |
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public void BurrCreateFailsWithBadParameters(double a, double c, double k) |
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{ |
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Assert.That(() => new Burr(a, c, k), Throws.ArgumentException); |
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} |
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/// <summary>
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/// Validate IsValidParameterSet.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="validity">exoected validity of paremeter set.</param>
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[TestCase(1.0, 1.0, 0.5, true)] |
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[TestCase(10.0, 0.1, 0.2, true)] |
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[TestCase(5.0, 1.0, -1.0, false)] |
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[TestCase(1.0, 1.0, double.PositiveInfinity, false)] |
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public void ValidateIsValidParameterSet(double a, double c, double k, bool validity) |
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{ |
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Assert.AreEqual(Burr.IsValidParameterSet(a, c, k), validity); |
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} |
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/// <summary>
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/// Validate ToString.
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/// </summary>
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[Test] |
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public void ValidateToString() |
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{ |
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var n = new Burr(1d, 2d, 3d); |
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Assert.AreEqual("Burr(a = 1, c = 2, k = 3)", n.ToString()); |
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} |
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/// <summary>
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/// Validate mode.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="mode">Expected value.</param>
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[TestCase(2.000000, 2.00000, 1.0, 1.154700538379251)] |
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[TestCase(1.000000, 1.00000, 0.5, 0.0)] |
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[TestCase(3.000000, 4.00000, 1.0, 2.640335210380180)] |
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public void ValidateMode(double a, double c, double k, double mode) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(mode, n.Mode, highPrecision); |
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} |
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/// <summary>
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/// Validate median.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="median">Expected value.</param>
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[TestCase(1.000000, 1.00000, 0.5, 3.0)] |
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[TestCase(1.000000, 0.50000, 0.5, 9.0)] |
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[TestCase(1.000000, 1.00000, 5.0, 0.148698354997035)] |
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public void ValidateMedian(double a, double c, double k, double median) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(median, n.Median, highPrecision); |
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} |
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/// <summary>
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/// Validate mean.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="mean">Expected value.</param>
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[TestCase(1.000000, 1.00000, 1.5, 2)] |
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[TestCase(4.000000, 5.00000, 0.5, 6.198785110989412)] |
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[TestCase(4.000000, 5.00000, 5.0, 2.729694550490384)] |
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public void ValidateMean(double a, double c, double k, double mean) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(mean, n.Mean, highPrecision); |
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} |
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/// <summary>
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/// Validate variance.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="variance">Expected value.</param>
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[TestCase(4.0, 5.0, 2.0, 0.983559126843161)] |
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[TestCase(2.0, 3.5, 4.0, 0.207489170174404)] |
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public void ValidateVariance(double a, double c, double k, double variance) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(variance, n.Variance, highPrecision); |
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} |
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/// <summary>
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/// Validate standard deviation.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="std">Expected value.</param>
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[TestCase(4.0, 5.0, 2.0, 0.991745494995143)] |
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[TestCase(2.0, 3.5, 4.0, 0.455509791524182)] |
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public void ValidateStandardDeviation(double a, double c, double k, double std) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(std, n.StdDev, highPrecision); |
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} |
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/// <summary>
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/// Validate minimum.
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/// </summary>
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[Test] |
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public void ValidateMinimum() |
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{ |
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var n = new Burr(1.0, 2.0, 2.0); |
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Assert.AreEqual(0.0, n.Minimum); |
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} |
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/// <summary>
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/// Validate maximum.
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/// </summary>
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[Test] |
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public void ValidateMaximum() |
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{ |
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var n = new Burr(1.0, 2.0, 1.0); |
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Assert.AreEqual(Double.PositiveInfinity, n.Maximum); |
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} |
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/// <summary>
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/// Validate entropy throws not supported exception
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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[TestCase(1.0, 1.0, 0.5)] |
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public void ValidateEntropyFailsWithNotSupported(double a, double c, double k) |
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{ |
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var n = new Burr(a, c, k); |
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Assert.Throws<NotSupportedException>(() => { var x = n.Entropy; }); |
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} |
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/// <summary>
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/// Validate GetMoments.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="n"> order of the moment.</param>
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/// <param name="value">Expected value.</param>
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[TestCase(1.000000, 1.00000, 1.5, 1, 2)] |
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[TestCase(4.000000, 5.00000, 0.5, 1, 6.198785110989412)] |
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[TestCase(4.000000, 5.00000, 5.0, 1, 2.729694550490384)] |
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[TestCase(4.0, 5.0, 2.0, 3, 50.738165747621750)] |
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[TestCase(2.0, 3.5, 4.0, 3, 2.895046685294824)] |
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public void ValidateMoments(double a, double c, double k, int order, double value) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(value, n.GetMoment(order), lowPrecision); |
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} |
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/// <summary>
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/// Validate GetMoments throws when given bad parameters
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="order">the order of the moment.</param>
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[TestCase(1.0, 1.0, 0.5, 1)] |
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[TestCase(10.0, 1.0, 1.5, 2)] |
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[TestCase(5.0, 2, 1.4, 3)] |
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public void ValidateGetMomentsFailsWithBadParameters(double a, double c, double k, double order) |
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{ |
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var n = new Burr(a, c, k); |
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Assert.That(() => n.GetMoment(order), Throws.ArgumentException); |
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} |
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/// <summary>
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/// Validate skewness.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="skewness">Expected value.</param>
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[TestCase(4.0, 5.0, 2.0, 0.635277200891842)] |
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[TestCase(2.0, 3.5, 4.0, 0.483073007212360)] |
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public void ValidateSkewness(double a, double c, double k, double skewness) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(skewness, n.Skewness, lowPrecision); |
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} |
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/// <summary>
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/// Validate density.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="x">Input X value.</param>
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/// <param name="p">Expected value.</param>
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[TestCase(1.00000, 1.000000, 0.5, 0.100000, 0.433392086020724)] |
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[TestCase(1.000000, 1.000000, 5.0, 0.500000, 0.438957475994512)] |
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public void ValidateDensity(double a, double c, double k, double x, double p) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(p, n.Density(x), highPrecision); |
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AssertHelpers.AlmostEqualRelative(p, Burr.PDF(a, c, k, x), highPrecision); |
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} |
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/// <summary>
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/// Validate density log.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="x">Input X value.</param>
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/// <param name="p">Expected value.</param>
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[TestCase(1.000000, 1.000000, 0.5, 0.500000, -1.301344842722192)] |
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[TestCase(1.000000, 1.000000, 5.0, 0.500000, -0.823352736214886)] |
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[TestCase(4.000000, 5.000000, 1.0, 5.000000, -1.682583872895781)] |
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public void ValidateDensityLn(double a, double c, double k, double x, double p) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(p, n.DensityLn(x), highPrecision); |
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AssertHelpers.AlmostEqualRelative(p, Burr.PDFLn(a, c, k, x), highPrecision); |
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} |
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/// <summary>
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/// Validate cumulative distribution.
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/// </summary>
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/// <param name="a">a parameter.</param>
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/// <param name="c">c parameter.</param>
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/// <param name="k">k parameter.</param>
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/// <param name="x">The location at which to compute the density.</param>
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/// <param name="f">Expected value.</param>
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[TestCase(1.000000, 1.000000, 0.5, 0.500000, 0.183503419072274)] |
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[TestCase(4.000000, 5.000000, 0.5, 0.500000, 0.00001525843981653452)] |
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[TestCase(4.000000, 5.000000, 0.5, 5.00000, 0.503203804978536)] |
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[TestCase(4.000000, 5.000000, 5.0, 5.00000, 0.999084238019496)] |
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public void ValidateCumulativeDistribution(double a, double c, double k, double x, double f) |
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{ |
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var n = new Burr(a, c, k); |
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AssertHelpers.AlmostEqualRelative(f, n.CumulativeDistribution(x), lowPrecision); |
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AssertHelpers.AlmostEqualRelative(f, Burr.CDF(a, c, k, x), lowPrecision); |
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} |
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/// <summary>
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/// Can sample static.
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/// </summary>
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[Test] |
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public void CanSampleStatic() |
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{ |
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Burr.Sample(new Numerics.Random.MersenneTwister(100), 1.0, 2.0, 2.0); |
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} |
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/// <summary>
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/// Can fill array with samples static.
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/// </summary>
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[Test] |
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public void CanFillSampleArrayStatic() |
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{ |
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double[] samples = new double[100]; |
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Burr.Samples(new Numerics.Random.MersenneTwister(100), samples, 1.0, 2.0, 2.0); |
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Assert.IsTrue(!samples.Any(x => x == 0)); |
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} |
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/// <summary>
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/// Can sample sequence static.
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/// </summary>
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[Test] |
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public void CanSampleSequenceStatic() |
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{ |
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var ied = Burr.Samples(new Numerics.Random.MersenneTwister(100), 1.0, 2.0, 2.0); |
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GC.KeepAlive(ied.Take(5).ToArray()); |
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} |
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/// <summary>
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/// Fail sample static with bad parameters.
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/// </summary>
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[Test] |
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public void FailSampleStatic() |
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{ |
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Assert.That(() => { var d = Burr.Sample(new Numerics.Random.MersenneTwister(100), 1.0, -1.0, 2.0); }, Throws.ArgumentException); |
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} |
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/// <summary>
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/// Fail filling array with samples with bad parameters static.
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/// </summary>
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[Test] |
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public void FailFillingSampleArrayStatic() |
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{ |
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double[] samples = new double[100]; |
||||
|
Assert.That(() => { Burr.Samples(new Numerics.Random.MersenneTwister(100), samples, -1.0, 2.0, 2.0); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail sample sequence static with bad parameters.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailSampleSequenceStatic() |
||||
|
{ |
||||
|
Assert.That(() => { var ied = Burr.Samples(new Numerics.Random.MersenneTwister(100), 1.0, -1.0, 2.0).First(); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSample() |
||||
|
{ |
||||
|
var n = new Burr(1.0, 2.0, 2.0); |
||||
|
n.Sample(); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can fill array with samples.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanFillSampleArray() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
var n = new Burr(1.0, 2.0, 2.0, new Numerics.Random.MersenneTwister(100)); |
||||
|
n.Samples(samples); |
||||
|
Assert.IsTrue(!samples.Any(x => x == 0)); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample sequence.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleSequence() |
||||
|
{ |
||||
|
var n = new Burr(1.0, 2.0, 2.0); |
||||
|
var ied = n.Samples(); |
||||
|
GC.KeepAlive(ied.Take(5).ToArray()); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,419 @@ |
|||||
|
// <copyright file="InverseGaussianTests.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.Distributions; |
||||
|
using NUnit.Framework; |
||||
|
using System; |
||||
|
using System.Linq; |
||||
|
|
||||
|
namespace MathNet.Numerics.UnitTests.DistributionTests.Continuous |
||||
|
{ |
||||
|
/// <summary>
|
||||
|
/// <c>InverseGaussian</c> distribution tests.
|
||||
|
/// </summary>
|
||||
|
[TestFixture, Category("Distributions")] |
||||
|
public class InverseGaussianTests |
||||
|
{ |
||||
|
private const int precision = 12; |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can create <c>Inverse Gaussian</c>.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu value.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
[TestCase(0.1, 0.1)] |
||||
|
[TestCase(1.0, 1.0)] |
||||
|
[TestCase(10.0, 10.0)] |
||||
|
public void CanCreateInverseGaussian(double mu, double lambda) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
Assert.AreEqual(mu, n.Mu); |
||||
|
Assert.AreEqual(lambda, n.Lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can create <c>Inverse Gaussian</c> with random source.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanCreateInverseGaussianWithRandomSource() |
||||
|
{ |
||||
|
var randomSource = new Numerics.Random.MersenneTwister(100); |
||||
|
var n = new InverseGaussian(1.0, 1.0, randomSource); |
||||
|
Assert.AreEqual(randomSource, n.RandomSource); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// <c>InverseGaussian</c> create fails with bad parameters.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
[TestCase(Double.NaN, 1.0)] |
||||
|
[TestCase(1.0, Double.NaN)] |
||||
|
[TestCase(Double.NaN, Double.NaN)] |
||||
|
[TestCase(-1.0, -1.0)] |
||||
|
public void InverseGaussianCreateFailsWithBadParameters(double mu, double lambda) |
||||
|
{ |
||||
|
Assert.That(() => new LogNormal(mu, lambda), Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate IsValidParameterSet.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="validity">exoected validity of paremeter set.</param>
|
||||
|
[TestCase(1.0, 1.0, true)] |
||||
|
[TestCase(10.0, 10.0, true)] |
||||
|
[TestCase(-1.0, -1.0, false)] |
||||
|
public void ValidateIsValidParameterSet(double mu, double lambda, bool validity) |
||||
|
{ |
||||
|
Assert.AreEqual(InverseGaussian.IsValidParameterSet(mu, lambda), validity); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate ToString.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateToString() |
||||
|
{ |
||||
|
var n = new InverseGaussian(1d, 2d); |
||||
|
Assert.AreEqual("InverseGaussian(μ = 1, λ = 2)", n.ToString()); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate mode.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="mode">Expected value.</param>
|
||||
|
[TestCase(0.100000, 0.100000, 0.030277563773199)] |
||||
|
[TestCase(1.500000, 5.500000, 1.007026953273370)] |
||||
|
[TestCase(2.500000, 1.500000, 0.481456008918130)] |
||||
|
[TestCase(5.500000, 2.500000, 0.815033614523337)] |
||||
|
[TestCase(5.500000, 5.500000, 1.665266007525970)] |
||||
|
public void ValidateMode(double mu, double lambda, double mode) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(mode, n.Mode, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate median.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="median">Expected value.</param>
|
||||
|
[TestCase(0.100000, 0.100000, 0.067584130569524)] |
||||
|
[TestCase(1.500000, 0.100000, 0.190369896722453)] |
||||
|
[TestCase(2.500000, 0.100000, 0.201110669500521)] |
||||
|
[TestCase(5.500000, 5.500000, 3.717127181323815)] |
||||
|
public void ValidateMedian(double mu, double lambda, double median) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(median, n.Median, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate mean.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="mean">Expected value.</param>
|
||||
|
[TestCase(0.100000, 5.500000, 0.1)] |
||||
|
[TestCase(1.500000, 0.100000, 1.5)] |
||||
|
[TestCase(2.500000, 5.500000, 2.5)] |
||||
|
[TestCase(5.500000, 0.100000, 5.5)] |
||||
|
public void ValidateMean(double mu, double lambda, double mean) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(mean, n.Mean, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate variance.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="variance">Expected value.</param>
|
||||
|
[TestCase(0.100000, 5.500000, 1.818181818181819e-04)] |
||||
|
[TestCase(1.500000, 0.100000, 33.75)] |
||||
|
public void ValidateVariance(double mu, double lambda, double variance) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(variance, n.Variance, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate standard deviation.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="std">Expected value.</param>
|
||||
|
[TestCase(2.500000, 5.500000, 1.685499656158105)] |
||||
|
[TestCase(5.500000, 0.100000, 40.789091679026143)] |
||||
|
public void ValidateStandardDeviation(double mu, double lambda, double std) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(std, n.StdDev, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate minimum.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateMinimum() |
||||
|
{ |
||||
|
var n = new InverseGaussian(1.0, 2.0); |
||||
|
Assert.AreEqual(0.0, n.Minimum); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate maximum.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateMaximum() |
||||
|
{ |
||||
|
var n = new InverseGaussian(1.0, 2.0); |
||||
|
Assert.AreEqual(Double.PositiveInfinity, n.Maximum); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate entropy throws not supported exception
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
[TestCase(1.0, 1.0)] |
||||
|
public void ValidateEntropyFailsWithNotSupported(double mu, double lambda) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
Assert.Throws<NotSupportedException>(() => { var x = n.Entropy; }); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate skewness.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="´lambda">Lambda parameter.</param>
|
||||
|
/// <param name="skewness">Expected value.</param>
|
||||
|
[TestCase(0.100000, 1.500000, 0.774596669241483)] |
||||
|
[TestCase(1.500000, 2.500000, 2.323790007724450)] |
||||
|
[TestCase(1.500000, 5.500000, 1.566698903601281)] |
||||
|
[TestCase(2.500000, 0.100000, 15.0)] |
||||
|
[TestCase(2.500000, 1.500000, 3.872983346207417)] |
||||
|
[TestCase(5.500000, 2.500000, 4.449719092257398)] |
||||
|
[TestCase(5.500000, 5.500000, 3.0)] |
||||
|
public void ValidateSkewness(double mu, double lambda, double skewness) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(skewness, n.Skewness, precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate density.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <param name="p">Expected value.</param>
|
||||
|
[TestCase(1.500000, 2.500000, 0.500000, 0.587321148416470)] |
||||
|
[TestCase(1.500000, 2.500000, 0.800000, 0.627284170789435)] |
||||
|
[TestCase(2.500000, 2.500000, 0.500000, 0.360208446721537)] |
||||
|
[TestCase(2.500000, 2.500000, 0.800000, 0.428023216678204)] |
||||
|
public void ValidateDensity(double mu, double lambda, double x, double p) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(p, n.Density(x), precision); |
||||
|
AssertHelpers.AlmostEqualRelative(p, InverseGaussian.PDF(mu, lambda, x), precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate density log.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Sigma value.</param>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <param name="p">Expected value.</param>
|
||||
|
[TestCase(1.500000, 0.100000, 0.100000, 0.948091004233817)] |
||||
|
[TestCase(1.500000, 1.500000, 0.800000, -0.585657318845943)] |
||||
|
[TestCase(2.500000, 0.100000, 0.800000, -1.764415752730381)] |
||||
|
[TestCase(2.500000, 1.500000, 0.100000, -4.174328339659523)] |
||||
|
[TestCase(2.500000, 1.500000, 0.500000, -0.636485208310673)] |
||||
|
public void ValidateDensityLn(double mu, double lambda, double x, double p) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(p, n.DensityLn(x), precision); |
||||
|
AssertHelpers.AlmostEqualRelative(p, InverseGaussian.PDFLn(mu, lambda, x), precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate cumulative distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Sigma value.</param>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <param name="f">Expected value.</param>
|
||||
|
[TestCase(1.500000, 2.500000, 0.100000, 0.000002882116410867029)] |
||||
|
[TestCase(2.500000, 1.500000, 0.100000, 0.0001938001952257318)] |
||||
|
[TestCase(2.500000, 1.500000, 0.500000, 0.145457623130791)] |
||||
|
[TestCase(2.500000, 2.500000, 0.100000, 0.000001529605202470422)] |
||||
|
[TestCase(2.500000, 2.500000, 0.800000, 0.187168922781367)] |
||||
|
public void ValidateCumulativeDistribution(double mu, double lambda, double x, double f) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(f, n.CumulativeDistribution(x), precision); |
||||
|
AssertHelpers.AlmostEqualRelative(f, InverseGaussian.CDF(mu, lambda, x), precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate inverse cumulative distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Sigma value.</param>
|
||||
|
/// <param name="probability">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <param name="f">Expected value.</param>
|
||||
|
[TestCase(1.500000, 1.500000, 0.100000, 0.356437063090717)] |
||||
|
[TestCase(1.500000, 1.500000, 0.500000, 1.013761958542859)] |
||||
|
[TestCase(2.500000, 2.500000, 0.500000, 1.689603264238098)] |
||||
|
[TestCase(2.500000, 2.500000, 0.800000, 3.619719792074686)] |
||||
|
public void ValidateInverseCumulativeDistribution(double mu, double lambda, double probability, double f) |
||||
|
{ |
||||
|
var n = new InverseGaussian(mu, lambda); |
||||
|
AssertHelpers.AlmostEqualRelative(f, InverseGaussian.ICDF(mu, lambda, probability), precision); |
||||
|
AssertHelpers.AlmostEqualRelative(f, n.InvCDF(probability), precision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleStatic() |
||||
|
{ |
||||
|
InverseGaussian.Sample(new Numerics.Random.MersenneTwister(100), 1.0, 1.0); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can fill array with samples static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanFillSampleArrayStatic() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), samples, 1.0, 1.0); |
||||
|
Assert.IsTrue(!samples.Any(x => x == 0)); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample sequence static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleSequenceStatic() |
||||
|
{ |
||||
|
var ied = InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), 1.0, 1.0); |
||||
|
GC.KeepAlive(ied.Take(5).ToArray()); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail sample static with bad parameters.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailSampleStatic() |
||||
|
{ |
||||
|
Assert.That(() => { var d = InverseGaussian.Sample(new Numerics.Random.MersenneTwister(100), 0.0, -1.0); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail filling array with samples with bad parameters static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailFillingSampleArrayStatic() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
Assert.That(() => { InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), samples, -1.0, 1.0); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail sample sequence static with bad parameters.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailSampleSequenceStatic() |
||||
|
{ |
||||
|
Assert.That(() => { var ied = InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), 0.0, -1.0).First(); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSample() |
||||
|
{ |
||||
|
var n = new InverseGaussian(1.0, 2.0); |
||||
|
n.Sample(); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can fill array with samples.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanFillSampleArray() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
var n = new InverseGaussian(1.0, 2.0, new Numerics.Random.MersenneTwister(100)); |
||||
|
n.Samples(samples); |
||||
|
Assert.IsTrue(!samples.Any(x => x == 0)); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample sequence.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleSequence() |
||||
|
{ |
||||
|
var n = new InverseGaussian(1.0, 2.0); |
||||
|
var ied = n.Samples(); |
||||
|
GC.KeepAlive(ied.Take(5).ToArray()); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can estimate distribution parameters.
|
||||
|
/// </summary>
|
||||
|
[TestCase(10.0, 0.1)] |
||||
|
[TestCase(1.5, 1.5)] |
||||
|
[TestCase(2.5, 2.5)] |
||||
|
[TestCase(2.5, 5.0)] |
||||
|
[TestCase(10.0, 50.0)] |
||||
|
public void CanEstimateParameters(double mu, double lambda) |
||||
|
{ |
||||
|
var original = new InverseGaussian(mu, lambda, new Numerics.Random.MersenneTwister(100)); |
||||
|
var estimated = InverseGaussian.Estimate(original.Samples().Take(1000000)); |
||||
|
|
||||
|
AssertHelpers.AlmostEqualRelative(mu, estimated.Mu, 1); |
||||
|
AssertHelpers.AlmostEqualRelative(lambda, estimated.Lambda, 1); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,446 @@ |
|||||
|
// <copyright file="TruncatedParetoTests.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.Distributions; |
||||
|
using NUnit.Framework; |
||||
|
using System; |
||||
|
using System.Linq; |
||||
|
|
||||
|
namespace MathNet.Numerics.UnitTests.DistributionTests.Continuous |
||||
|
{ |
||||
|
/// <summary>
|
||||
|
/// <c>TruncatedPareto</c> distribution tests.
|
||||
|
/// </summary>
|
||||
|
[TestFixture, Category("Distributions")] |
||||
|
public class TruncatedParetoTests |
||||
|
{ |
||||
|
private const int highPrecision = 12; |
||||
|
private const int lowPrecision = 6; |
||||
|
private const double highTruncation = 1E8; |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can create <c>TruncatedPareto</c>.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// /// <param name="truncation">Truncation parameter.</param>
|
||||
|
[TestCase(2.0, 2.0, 500.0)] |
||||
|
[TestCase(10.0, 2.0, 1000.0)] |
||||
|
[TestCase(100.0, 4.0, 10000.0)] |
||||
|
[TestCase(3.0, 10.0, 5.0)] |
||||
|
[TestCase(10.0, 100.0, 20.0)] |
||||
|
public void CanCreateTruncatedPareto(double scale, double shape, double truncation) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
Assert.AreEqual(scale, n.Scale, highPrecision); |
||||
|
Assert.AreEqual(shape, n.Shape, highPrecision); |
||||
|
Assert.AreEqual(truncation, n.Truncation, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can create <c>TruncatedPareto</c> with random source.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanCreateTruncatedParetoWithRandomSource() |
||||
|
{ |
||||
|
var randomSource = new Numerics.Random.MersenneTwister(100); |
||||
|
var n = new TruncatedPareto(10.0, 10.0, 1000, randomSource); |
||||
|
Assert.AreEqual(randomSource, n.RandomSource); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// <c>TruncatedPareto</c> create fails with bad parameters.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// /// <param name="truncation">Truncation parameter.</param>
|
||||
|
[TestCase(1.0, -1.0, 15.0)] |
||||
|
[TestCase(1.0, 3.0, 0.5)] |
||||
|
[TestCase(-1.0, 2.0, 15.0)] |
||||
|
[TestCase(double.NaN, 1.0, 1.0)] |
||||
|
[TestCase(1.0, double.PositiveInfinity, 0.0)] |
||||
|
[TestCase(5.0, 2.0, double.PositiveInfinity)] |
||||
|
public void TruncatedParetoCreateFailsWithBadParameters(double scale, double shape, double truncation) |
||||
|
{ |
||||
|
Assert.That(() => new TruncatedPareto(scale, shape, truncation), Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate IsValidParameterSet.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// /// <param name="truncation">Truncation parameter.</param>
|
||||
|
[TestCase(2.0, 2.0, 500, true)] |
||||
|
[TestCase(10.0, 10.0, 1000, true)] |
||||
|
[TestCase(5.0, 3.0, 1, false)] |
||||
|
public void ValidateIsValidParameterSet(double scale, double shape, double truncation, bool validity) |
||||
|
{ |
||||
|
Assert.AreEqual(TruncatedPareto.IsValidParameterSet(scale, shape, truncation), validity); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate ToString.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateToString() |
||||
|
{ |
||||
|
var n = new TruncatedPareto(1d, 2d, 100d); |
||||
|
Assert.AreEqual("Truncated Pareto(Scale = 1, Shape = 2, Truncation = 100)", n.ToString()); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate mode throws not supported exception
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale value.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation)] |
||||
|
public void ValidateModeFailsWithNotSupported(double scale, double shape, double truncation) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
Assert.Throws<NotSupportedException>(() => { var x = n.Mode; }); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate median.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale value.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="median">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 10.717734625362931)] |
||||
|
[TestCase(100, 3.5, 10000, 1.219013619375516e+02)] |
||||
|
[TestCase(1000, 5.5, 100000, 1.134312522193400e+03)] |
||||
|
public void ValidateMedian(double scale, double shape, double truncation, double median) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(median, n.Median, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate mean.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="std">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 11.111111111111111)] |
||||
|
[TestCase(100, 3.5, 10000, 1.399986139998614e+02)] |
||||
|
public void ValidateMean(double scale, double shape, double truncation, double mean) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(mean, n.Mean, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate variance.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="variance">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 1.543209876543210)] |
||||
|
[TestCase(100, 3.5, 10000, 3.710390409118045e+03)] |
||||
|
[TestCase(1000, 5.5, 100000, 7.760125676537910e+04)] |
||||
|
public void ValidateVariance(double scale, double shape, double truncation, double variance) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(variance, n.Variance, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate standard deviation.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="std">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 1.242259987499883)] |
||||
|
[TestCase(100, 3.5, 10000, 60.912974062329653)] |
||||
|
[TestCase(1000, 5.5, 100000, 2.785700212969427e+02)] |
||||
|
public void ValidateStandardDeviation(double scale, double shape, double truncation, double std) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(std, n.StdDev, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate minimum.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateMinimum() |
||||
|
{ |
||||
|
var n = new TruncatedPareto(1.0, 2.0, 500.0); |
||||
|
Assert.AreEqual(1.0, n.Minimum); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate maximum.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void ValidateMaximum() |
||||
|
{ |
||||
|
var n = new TruncatedPareto(1.0, 2.0, 500.0); |
||||
|
Assert.AreEqual(500.0, n.Maximum); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate entropy throws not supported exception
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">Mu parameter.</param>
|
||||
|
/// <param name="lambda">Lambda parameter.</param>
|
||||
|
[TestCase(100, 3.5, 10000)] |
||||
|
public void ValidateEntropyFailsWithNotSupported(double scale, double shape, double truncation) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
Assert.Throws<NotSupportedException>(() => { var x = n.Entropy; }); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate GetMoments.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="n"> order of the moment.</param>
|
||||
|
/// <param name="value">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 1, 11.111111111111111)] |
||||
|
[TestCase(1.0, 6.0, 10000, 1, 1.2)] |
||||
|
[TestCase(100, 3.5, 10000, 1, 1.399986139998614e+02)] |
||||
|
[TestCase(100, 3.5, 10000, 2, 2.331000233100023e+04)] |
||||
|
[TestCase(100, 3.5, 10000, 3, 6.300000630000063e+06)] |
||||
|
[TestCase(1000, 5.5, 100000, 1, 1.222222221012222e+03)] |
||||
|
[TestCase(1000, 5.5, 100000, 2, 1.571428414301428e+06)] |
||||
|
[TestCase(1000, 5.5, 100000, 4, 3.663000000036630e+12)] |
||||
|
[TestCase(1000, 2.0, 100000, 2, 9.211261498125996e+06)] |
||||
|
[TestCase(1000, 3.0, 100000, 3, 1.381552437348865e+10)] |
||||
|
public void ValidateMoments(double scale, double shape, double truncation, int order, double value) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(value, n.GetMoment(order), highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate skewness.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="skewness">Expected value.</param>
|
||||
|
[TestCase(10.0, 10.0, highTruncation, 2.8110568859997356)] |
||||
|
public void ValidateSkewness(double scale, double shape, double truncation, double skewness) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(skewness, n.Skewness, highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate density.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <param name="p">Expected value.</param>
|
||||
|
[TestCase(1, 1, highTruncation, 1, 1)] |
||||
|
[TestCase(1, 1, highTruncation, 1.5, 4 / 9.0)] |
||||
|
[TestCase(1, 1, highTruncation, 5, 1 / 25.0)] |
||||
|
[TestCase(1, 1, highTruncation, 50, 1 / 2500.0)] |
||||
|
[TestCase(1, 4, highTruncation, 1, 4)] |
||||
|
[TestCase(1, 4, highTruncation, 1.5, 128 / 243.0)] |
||||
|
[TestCase(1, 4, highTruncation, 50, 1 / 78125000.0)] |
||||
|
[TestCase(3, 2, highTruncation, 3, 2 / 3.0)] |
||||
|
[TestCase(3, 2, highTruncation, 5, 18 / 125.0)] |
||||
|
[TestCase(25, 100, highTruncation, 50, 1.5777218104420236e-30)] |
||||
|
[TestCase(100, 25, highTruncation, 150, 6.6003546737276816e-6)] |
||||
|
[TestCase(100, 3.5, 10000, 1000, 1.106797291738662e-06)] |
||||
|
[TestCase(100, 3.5, 10000, 2000, 4.891399189920708e-08)] |
||||
|
[TestCase(1000, 5.5, 100000, 2000, 6.076698900882660e-05)] |
||||
|
public void ValidateDensity(double scale, double shape, double truncation, double x, double p) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(p, n.Density(x), lowPrecision); |
||||
|
AssertHelpers.AlmostEqualRelative(p, TruncatedPareto.PDF(scale, shape, truncation, x), lowPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate density log.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <param name="p">Expected value.</param>
|
||||
|
[TestCase(3, 2, highTruncation, 3, -0.405465108108164)] |
||||
|
[TestCase(100, 3.5, 10000, 1000, -13.714040035965924)] |
||||
|
[TestCase(100, 3.5, 10000, 2000, -16.833202348485678)] |
||||
|
public void ValidateDensityLn(double scale, double shape, double truncation, double x, double p) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(p, n.DensityLn(x), lowPrecision); |
||||
|
AssertHelpers.AlmostEqualRelative(p, TruncatedPareto.PDFLn(scale, shape, truncation, x), lowPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate cumulative distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="x">The location at which to compute the cumulative distribution function.</param>
|
||||
|
/// <param name="expected">Expected value.</param>
|
||||
|
[TestCase(1.0, 1.0, highTruncation, 1.0, 0)] |
||||
|
[TestCase(7.0, 7.0, highTruncation, 10.0, 0.9176457)] |
||||
|
[TestCase(10.0, 10.0, highTruncation, 12.0, 0.83849441711015427)] |
||||
|
[TestCase(100, 3.5, 10000, 102, 0.066961862452387)] |
||||
|
[TestCase(100, 3.5, 10000, 1000, 0.999683872202370)] |
||||
|
[TestCase(100, 3.5, 10000, 2000, 0.999972149147496)] |
||||
|
[TestCase(1000, 5.5, 100000, 2000, 0.977902913097699)] |
||||
|
public void ValidateCumulativeDistribution(double scale, double shape, double truncation, double x, double expected) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(expected, n.CumulativeDistribution(x), highPrecision); |
||||
|
AssertHelpers.AlmostEqualRelative(expected, TruncatedPareto.CDF(scale, shape, truncation, x), highPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Validate inverse cumulative distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">Scale parameter.</param>
|
||||
|
/// <param name="shape">Shape parameter.</param>
|
||||
|
/// <param name="truncation">Truncation parameter.</param>
|
||||
|
/// <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <param name="expected">Expected value.</param>
|
||||
|
[TestCase(1.0, 1.0, highTruncation, 0, 1.0)] |
||||
|
[TestCase(7.0, 7.0, highTruncation, 0.9176457, 10.0)] |
||||
|
[TestCase(10.0, 10.0, highTruncation, 0.83849441711015427, 12.0)] |
||||
|
[TestCase(100, 3.5, 10000, 0.066961862452387, 102)] |
||||
|
[TestCase(100, 3.5, 10000, 0.999683872202370, 1000)] |
||||
|
[TestCase(100, 3.5, 10000, 0.999972149147496, 2000)] |
||||
|
[TestCase(1000, 5.5, 100000, 0.977902913097699, 2000)] |
||||
|
public void ValidateInverseCumulativeDistribution(double scale, double shape, double truncation, double p, double expected) |
||||
|
{ |
||||
|
var n = new TruncatedPareto(scale, shape, truncation); |
||||
|
AssertHelpers.AlmostEqualRelative(expected, n.InvCDF(p), lowPrecision); |
||||
|
AssertHelpers.AlmostEqualRelative(expected, TruncatedPareto.ICDF(scale, shape, truncation, p), lowPrecision); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleStatic() |
||||
|
{ |
||||
|
TruncatedPareto.Sample(new Numerics.Random.MersenneTwister(100), 10.0, 10.0, 1000.0); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can fill array with samples static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanFillSampleArrayStatic() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
TruncatedPareto.Samples(new Numerics.Random.MersenneTwister(100), samples, 10.0, 10.0, 1000.0); |
||||
|
Assert.IsTrue(!samples.Any(x => x == 0)); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample sequence static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleSequenceStatic() |
||||
|
{ |
||||
|
var ied = TruncatedPareto.Samples(new Numerics.Random.MersenneTwister(100), 10.0, 10.0, 1000.0); |
||||
|
GC.KeepAlive(ied.Take(5).ToArray()); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail sample static with bad parameters.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailSampleStatic() |
||||
|
{ |
||||
|
Assert.That(() => { var d = TruncatedPareto.Sample(new Numerics.Random.MersenneTwister(100), 10.0, 10.0, 5.0); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail filling array with samples with bad parameters static.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailFillingSampleArrayStatic() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
Assert.That(() => { TruncatedPareto.Samples(new Numerics.Random.MersenneTwister(100), samples, 10.0, 10.0, 5.0); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fail sample sequence static with bad parameters.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void FailSampleSequenceStatic() |
||||
|
{ |
||||
|
Assert.That(() => { var ied = TruncatedPareto.Samples(new Numerics.Random.MersenneTwister(100), 10.0, 10.0, 5.0).First(); }, Throws.ArgumentException); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSample() |
||||
|
{ |
||||
|
var n = new TruncatedPareto(10.0, 10.0, 1000.0); |
||||
|
n.Sample(); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can fill array with samples.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanFillSampleArray() |
||||
|
{ |
||||
|
double[] samples = new double[100]; |
||||
|
var n = new TruncatedPareto(10.0, 10.0, 1000.0); |
||||
|
n.Samples(samples); |
||||
|
Assert.IsTrue(!samples.Any(x => x == 0)); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Can sample sequence.
|
||||
|
/// </summary>
|
||||
|
[Test] |
||||
|
public void CanSampleSequence() |
||||
|
{ |
||||
|
var n = new TruncatedPareto(10.0, 10.0, 1000.0); |
||||
|
var ied = n.Samples(); |
||||
|
GC.KeepAlive(ied.Take(5).ToArray()); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,429 @@ |
|||||
|
// <copyright file="Burr.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 |
||||
|
{ |
||||
|
public class Burr : IContinuousDistribution |
||||
|
{ |
||||
|
private System.Random _random; |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the scale (a) of the distribution. Range: a > 0.
|
||||
|
/// </summary>
|
||||
|
public double a { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the first shape parameter (c) of the distribution. Range: c > 0.
|
||||
|
/// </summary>
|
||||
|
public double c { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the second shape parameter (k) of the distribution. Range: k > 0.
|
||||
|
/// </summary>
|
||||
|
public double k { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the Burr Type XII class.
|
||||
|
/// </summary>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
/// <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
|
||||
|
public Burr(double a, double c, double k, System.Random randomSource = null) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
_random = randomSource ?? SystemRandomSource.Default; |
||||
|
this.a = a; |
||||
|
this.c = c; |
||||
|
this.k = k; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// A string representation of the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a string representation of the distribution.</returns>
|
||||
|
public override string ToString() |
||||
|
{ |
||||
|
return "Burr(a = " + a + ", c = " + c + ", k = " + k + ")"; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Tests whether the provided values are valid parameters for this distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
public static bool IsValidParameterSet(double a, double c, double k) |
||||
|
{ |
||||
|
var allFinite = a.IsFinite() && c.IsFinite() && k.IsFinite(); |
||||
|
return allFinite && a > 0.0 && c > 0.0 && k > 0.0; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets 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>
|
||||
|
/// Gets the mean of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Mean |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return (1 / SpecialFunctions.Gamma(k)) * a * SpecialFunctions.Gamma(1 + 1 / c) * SpecialFunctions.Gamma(k - 1 / c); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the variance of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Variance |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return (1 / SpecialFunctions.Gamma(k)) * Math.Pow(a, 2) * SpecialFunctions.Gamma(1 + 2 / c) * SpecialFunctions.Gamma(k - 2 / c) |
||||
|
- Math.Pow((1 / SpecialFunctions.Gamma(k)) * a * SpecialFunctions.Gamma(1 + 1 / c) * SpecialFunctions.Gamma(k - 1 / c), 2); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the standard deviation of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double StdDev |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Math.Sqrt(Variance); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the mode of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Mode |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return a * Math.Pow((c - 1) / (c * k + 1), 1 / c); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the minimum of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Minimum |
||||
|
{ |
||||
|
get { return 0.0; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the maximum of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Maximum |
||||
|
{ |
||||
|
get { return double.PositiveInfinity; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the entropy of the Burr distribution (currently not supported).
|
||||
|
/// </summary>
|
||||
|
public double Entropy |
||||
|
{ |
||||
|
get { throw new NotSupportedException(); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the skewness of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Skewness |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
var mean = Mean; |
||||
|
var variance = Variance; |
||||
|
var std = StdDev; |
||||
|
return (GetMoment(3) - 3 * mean * variance - mean * mean * mean) / (std * std * std); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the median of the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
public double Median |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return a * Math.Pow(Math.Pow(2, 1 / k) - 1, 1 / c); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public double Sample() |
||||
|
{ |
||||
|
return SampleUnchecked(_random, a, c, k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
public void Samples(double[] values) |
||||
|
{ |
||||
|
SamplesUnchecked(_random, values, a, c, k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public IEnumerable<double> Samples() |
||||
|
{ |
||||
|
return SamplesUnchecked(_random, a, c, k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public static double Sample(System.Random rnd, double a, double c, double k) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SampleUnchecked(rnd, a, c, k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
public static void Samples(System.Random rnd, double[] values, double a, double c, double k) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
SamplesUnchecked(rnd, values, a, c, k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public static IEnumerable<double> Samples(System.Random rnd, double a, double c, double k) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SamplesUnchecked(rnd, a, c, k); |
||||
|
} |
||||
|
|
||||
|
internal static double SampleUnchecked(System.Random rnd, double a, double c, double k) |
||||
|
{ |
||||
|
var k_inv = 1 / k; |
||||
|
var c_inv = 1 / c; |
||||
|
double u = rnd.NextDouble(); |
||||
|
return a * Math.Pow(Math.Pow(1 - u, -k_inv) - 1, c_inv); |
||||
|
} |
||||
|
|
||||
|
internal static void SamplesUnchecked(System.Random rnd, double[] values, double a, double c, double k) |
||||
|
{ |
||||
|
if (values.Length == 0) |
||||
|
{ |
||||
|
return; |
||||
|
} |
||||
|
var k_inv = 1 / k; |
||||
|
var c_inv = 1 / c; |
||||
|
double[] u = rnd.NextDoubles(values.Length); |
||||
|
|
||||
|
for (var j = 0; j < values.Length; ++j) |
||||
|
{ |
||||
|
values[j] = a * Math.Pow(Math.Pow(1 - u[j], -k_inv) - 1, c_inv); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
internal static IEnumerable<double> SamplesUnchecked(System.Random rnd, double a, double c, double k) |
||||
|
{ |
||||
|
while (true) |
||||
|
{ |
||||
|
yield return SampleUnchecked(rnd, a, c, k); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the n-th raw moment of the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="n">The order (n) of the moment. Range: n ≥ 1.</param>
|
||||
|
/// <returns>the n-th moment of the distribution.</returns>
|
||||
|
public double GetMoment(double n) |
||||
|
{ |
||||
|
if (n > k * c) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.ArgumentParameterSetInvalid); |
||||
|
} |
||||
|
var lambda_n = (n / c) * SpecialFunctions.Gamma(n / c) * SpecialFunctions.Gamma(k - n / c); |
||||
|
return Math.Pow(a, n) * lambda_n / SpecialFunctions.Gamma(k); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <returns>the density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDF"/>
|
||||
|
public double Density(double x) |
||||
|
{ |
||||
|
return DensityImpl(a, c, k, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDFLn"/>
|
||||
|
public double DensityLn(double x) |
||||
|
{ |
||||
|
return DensityLnImpl(a, c, k, 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>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CDF"/>
|
||||
|
public double CumulativeDistribution(double x) |
||||
|
{ |
||||
|
return CumulativeDistributionImpl(a, c, k, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 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 a, double c, double k, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityImpl(a, c, k, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="DensityLn"/>
|
||||
|
public static double PDFLn(double a, double c, double k, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityLnImpl(a, c, k, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
|
||||
|
/// <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
|
||||
|
/// <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
|
||||
|
/// <param name="x">The location at which to compute the cumulative distribution function.</param>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CumulativeDistribution"/>
|
||||
|
public static double CDF(double a, double c, double k, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(a, c, k)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return CumulativeDistributionImpl(a, c, k, x); |
||||
|
} |
||||
|
|
||||
|
internal static double DensityImpl(double a, double c, double k, double x) |
||||
|
{ |
||||
|
var numerator = (k * c / a) * Math.Pow(x / a, c - 1); |
||||
|
var denominator = Math.Pow(1 + Math.Pow(x / a, c), k + 1); |
||||
|
return numerator / denominator; |
||||
|
} |
||||
|
|
||||
|
internal static double DensityLnImpl(double a, double c, double k, double x) |
||||
|
{ |
||||
|
return Math.Log(DensityImpl(a, c, k, x)); |
||||
|
} |
||||
|
|
||||
|
internal static double CumulativeDistributionImpl(double a, double c, double k, double x) |
||||
|
{ |
||||
|
var denominator = Math.Pow(1 + Math.Pow(x / a, c), k); |
||||
|
return 1 - 1 / denominator; |
||||
|
} |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,449 @@ |
|||||
|
// <copyright file="InverseGaussian.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 MathNet.Numerics.Statistics; |
||||
|
using System; |
||||
|
using System.Collections.Generic; |
||||
|
using System.Linq; |
||||
|
|
||||
|
namespace MathNet.Numerics.Distributions |
||||
|
{ |
||||
|
public class InverseGaussian : IContinuousDistribution |
||||
|
{ |
||||
|
private System.Random _random; |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the mean (μ) of the distribution. Range: μ > 0.
|
||||
|
/// </summary>
|
||||
|
public double Mu { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the shape (λ) of the distribution. Range: λ > 0.
|
||||
|
/// </summary>
|
||||
|
public double Lambda { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the InverseGaussian class.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <param name="randomSource">The random number generator which is used to draw random samples.</param>
|
||||
|
public InverseGaussian(double mu, double lambda, System.Random randomSource = null) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
_random = randomSource ?? SystemRandomSource.Default; |
||||
|
Mu = mu; |
||||
|
Lambda = lambda; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// A string representation of the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a string representation of the distribution.</returns>
|
||||
|
public override string ToString() |
||||
|
{ |
||||
|
return "InverseGaussian(μ = " + Mu + ", λ = " + Lambda + ")"; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Tests whether the provided values are valid parameters for this distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
public static bool IsValidParameterSet(double mu, double lambda) |
||||
|
{ |
||||
|
var allFinite = mu.IsFinite() && lambda.IsFinite(); |
||||
|
return allFinite && mu > 0.0 && lambda > 0.0; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets 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>
|
||||
|
/// Gets the mean of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Mean |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Mu; |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the variance of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Variance |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Math.Pow(Mu, 3) / Lambda; |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the standard deviation of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double StdDev |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Math.Sqrt(Variance); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the median of the Inverse Gaussian distribution.
|
||||
|
/// No closed form analytical expression exists, so this value is approximated numerically and can throw an exception.
|
||||
|
/// </summary>
|
||||
|
public double Median |
||||
|
{ |
||||
|
get { return InvCDF(0.5); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the minimum of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Minimum |
||||
|
{ |
||||
|
get { return 0.0; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the maximum of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Maximum |
||||
|
{ |
||||
|
get { return double.PositiveInfinity; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the skewness of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Skewness |
||||
|
{ |
||||
|
get { return 3 * Math.Sqrt(Mu / Lambda); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the kurtosis of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Kurtosis |
||||
|
{ |
||||
|
get { return 15 * Mu / Lambda; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the mode of the Inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
public double Mode |
||||
|
{ |
||||
|
get { return Mu * (Math.Sqrt(1 + (9 * Mu * Mu) / (4 * Lambda * Lambda)) - (3 * Mu) / (2 * Lambda)); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the entropy of the Inverse Gaussian distribution (currently not supported).
|
||||
|
/// </summary>
|
||||
|
public double Entropy |
||||
|
{ |
||||
|
get { throw new NotSupportedException(); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public double Sample() |
||||
|
{ |
||||
|
return SampleUnchecked(_random, Mu, Lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
public void Samples(double[] values) |
||||
|
{ |
||||
|
SamplesUnchecked(_random, values, Mu, Lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public IEnumerable<double> Samples() |
||||
|
{ |
||||
|
return SamplesUnchecked(_random, Mu, Lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the inverse Gaussian distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public static double Sample(System.Random rnd, double mu, double lambda) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SampleUnchecked(rnd, mu, lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
public static void Samples(System.Random rnd, double[] values, double mu, double lambda) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
SamplesUnchecked(rnd, values, mu, lambda); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the Burr distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public static IEnumerable<double> Samples(System.Random rnd, double mu, double lambda) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SamplesUnchecked(rnd, mu, lambda); |
||||
|
} |
||||
|
|
||||
|
internal static double SampleUnchecked(System.Random rnd, double mu, double lambda) |
||||
|
{ |
||||
|
double v = MathNet.Numerics.Distributions.Normal.Sample(rnd, 0, 1); |
||||
|
double test = rnd.NextDouble(); |
||||
|
return InverseGaussianSampleImpl(mu, lambda, v, test); |
||||
|
} |
||||
|
|
||||
|
internal static void SamplesUnchecked(System.Random rnd, double[] values, double mu, double lambda) |
||||
|
{ |
||||
|
if (values.Length == 0) |
||||
|
{ |
||||
|
return; |
||||
|
} |
||||
|
double[] v = new double[values.Length]; |
||||
|
MathNet.Numerics.Distributions.Normal.Samples(rnd, v, 0, 1); |
||||
|
double[] test = rnd.NextDoubles(values.Length); |
||||
|
for (var j = 0; j < values.Length; ++j) |
||||
|
{ |
||||
|
values[j] = InverseGaussianSampleImpl(mu, lambda, v[j], test[j]); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
internal static IEnumerable<double> SamplesUnchecked(System.Random rnd, double mu, double lambda) |
||||
|
{ |
||||
|
while (true) |
||||
|
{ |
||||
|
yield return SampleUnchecked(rnd, mu, lambda); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
internal static double InverseGaussianSampleImpl(double mu, double lambda, double normalSample, double uniformSample) |
||||
|
{ |
||||
|
double y = normalSample * normalSample; |
||||
|
double x = mu + (mu * mu * y) / (2 * lambda) - (mu / (2 * lambda)) * Math.Sqrt(4 * mu * lambda * y + mu * mu * y * y); |
||||
|
if (uniformSample <= mu / (mu + x)) |
||||
|
return x; |
||||
|
else |
||||
|
return mu * mu / x; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <returns>the density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDF"/>
|
||||
|
public double Density(double x) |
||||
|
{ |
||||
|
return DensityImpl(Mu, Lambda, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDFLn"/>
|
||||
|
public double DensityLn(double x) |
||||
|
{ |
||||
|
return DensityLnImpl(Mu, Lambda, 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>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CDF"/>
|
||||
|
public double CumulativeDistribution(double x) |
||||
|
{ |
||||
|
return CumulativeDistributionImpl(Mu, Lambda, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
|
||||
|
/// </summary>
|
||||
|
/// <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
|
||||
|
public double InvCDF(double p) |
||||
|
{ |
||||
|
Func<double, double> equationToSolve = (x) => CumulativeDistribution(x) - p; |
||||
|
if (RootFinding.NewtonRaphson.TryFindRoot(equationToSolve, Density, Mode, 0, double.PositiveInfinity, 1e-8, 100, out double quantile)) |
||||
|
return quantile; |
||||
|
else |
||||
|
throw new NonConvergenceException(Resources.NumericalEstimationFailed); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 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 mu, double lambda, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityImpl(mu, lambda, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="DensityLn"/>
|
||||
|
public static double PDFLn(double mu, double lambda, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityLnImpl(mu, lambda, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <param name="x">The location at which to compute the cumulative distribution function.</param>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CumulativeDistribution"/>
|
||||
|
public static double CDF(double mu, double lambda, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return CumulativeDistributionImpl(mu, lambda, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
|
||||
|
/// <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
|
||||
|
/// <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
|
||||
|
/// <seealso cref="CumulativeDistribution"/>
|
||||
|
public static double ICDF(double mu, double lambda, double p) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(mu, lambda)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
var igDist = new InverseGaussian(mu, lambda); |
||||
|
return igDist.InvCDF(p); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Estimates the Inverse Gaussian parameters from sample data with maximum-likelihood.
|
||||
|
/// </summary>
|
||||
|
/// <param name="samples">The samples to estimate the distribution parameters from.</param>
|
||||
|
/// <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
|
||||
|
/// <returns>An Inverse Gaussian distribution.</returns>
|
||||
|
public static InverseGaussian Estimate(IEnumerable<double> samples, System.Random randomSource = null) |
||||
|
{ |
||||
|
var sampleVec = samples.ToArray(); |
||||
|
var muHat = sampleVec.Mean(); |
||||
|
var lambdahat = 1 / (1 / samples.HarmonicMean() - 1 / muHat); |
||||
|
return new InverseGaussian(muHat, lambdahat, randomSource); |
||||
|
} |
||||
|
|
||||
|
internal static double DensityImpl(double mu, double lambda, double x) |
||||
|
{ |
||||
|
return Math.Sqrt(lambda / (2 * Math.PI * Math.Pow(x, 3))) * Math.Exp(-((lambda * Math.Pow(x - mu, 2)) / (2 * mu * mu * x))); |
||||
|
} |
||||
|
|
||||
|
internal static double DensityLnImpl(double mu, double lambda, double x) |
||||
|
{ |
||||
|
return Math.Log(Math.Sqrt(lambda / (2 * Math.PI * Math.Pow(x, 3)))) - ((lambda * Math.Pow(x - mu, 2)) / (2 * mu * mu * x)); |
||||
|
} |
||||
|
|
||||
|
internal static double CumulativeDistributionImpl(double mu, double lambda, double x) |
||||
|
{ |
||||
|
return Normal.CDF(0, 1, Math.Sqrt(lambda / x) * (x / mu - 1)) + Math.Exp(2 * lambda / mu) * Normal.CDF(0, 1, -Math.Sqrt(lambda / x) * (x / mu + 1)); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,466 @@ |
|||||
|
// <copyright file="TruncatedPareto.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 |
||||
|
{ |
||||
|
public class TruncatedPareto : IContinuousDistribution |
||||
|
{ |
||||
|
private System.Random _random; |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the TruncatedPareto class.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <param name="randomSource">The random number generator which is used to draw random samples.</param>
|
||||
|
/// <exception cref="ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are non-positive or if T ≤ xm.</exception>
|
||||
|
public TruncatedPareto(double scale, double shape, double truncation, System.Random randomSource = null) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
_random = randomSource ?? SystemRandomSource.Default; |
||||
|
Scale = scale; |
||||
|
Shape = shape; |
||||
|
Truncation = truncation; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// A string representation of the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a string representation of the distribution.</returns>
|
||||
|
public override string ToString() |
||||
|
{ |
||||
|
return "Truncated Pareto(Scale = " + Scale + ", Shape = " + Shape + ", Truncation = " + Truncation + ")"; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Tests whether the provided values are valid parameters for this distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
public static bool IsValidParameterSet(double scale, double shape, double truncation) |
||||
|
{ |
||||
|
var allFinite = scale.IsFinite() && shape.IsFinite() && truncation.IsFinite(); |
||||
|
return allFinite && scale > 0.0 && shape > 0.0 && truncation > scale; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets 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>
|
||||
|
/// Gets the scale (xm) of the distribution. Range: xm > 0.
|
||||
|
/// </summary>
|
||||
|
public double Scale { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the shape (α) of the distribution. Range: α > 0.
|
||||
|
/// </summary>
|
||||
|
public double Shape { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the truncation (T) of the distribution. Range: T > 0.
|
||||
|
/// </summary>
|
||||
|
public double Truncation { get; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the n-th raw moment of the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="n">The order (n) of the moment. Range: n ≥ 1.</param>
|
||||
|
/// <returns>the n-th moment of the distribution.</returns>
|
||||
|
public double GetMoment(int n) |
||||
|
{ |
||||
|
double moment; |
||||
|
if (Shape.AlmostEqual(n)) |
||||
|
{ |
||||
|
moment = ((Shape * Math.Pow(Scale, n)) / (1 - Math.Pow(Scale / Truncation, Shape))) * Math.Log(Truncation / Scale); |
||||
|
} |
||||
|
else |
||||
|
{ |
||||
|
moment = ((Shape * Math.Pow(Scale, n)) / (Shape - n)) * ((1 - Math.Pow((Scale / Truncation), (Shape - n))) / (1 - Math.Pow(Scale / Truncation, Shape))); |
||||
|
} |
||||
|
|
||||
|
return moment; |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the mean of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Mean |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return GetMoment(1); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the variance of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Variance |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return GetMoment(2) - Math.Pow(GetMoment(1), 2); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the standard deviation of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double StdDev |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Math.Sqrt(Variance); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the mode of the truncated Pareto distribution (not supported).
|
||||
|
/// </summary>
|
||||
|
public double Mode |
||||
|
{ |
||||
|
get { throw new NotSupportedException(); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the minimum of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Minimum |
||||
|
{ |
||||
|
get { return Scale; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the maximum of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Maximum |
||||
|
{ |
||||
|
get { return Truncation; } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the entropy of the truncated Pareto distribution (not supported).
|
||||
|
/// </summary>
|
||||
|
public double Entropy |
||||
|
{ |
||||
|
get { throw new NotSupportedException(); } |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the skewness of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Skewness |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
var mean = Mean; |
||||
|
var variance = Variance; |
||||
|
var std = StdDev; |
||||
|
return (GetMoment(3) - 3.0 * mean * variance - mean * mean * mean) / (std * std * std); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the median of the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
public double Median |
||||
|
{ |
||||
|
get |
||||
|
{ |
||||
|
return Scale * Math.Pow(1.0 - (1.0 / 2.0) * (1.0 - Math.Pow(Scale / Truncation, Shape)), -(1.0 / Shape)); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public double Sample() |
||||
|
{ |
||||
|
return SampleUnchecked(_random, Scale, Shape, Truncation); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
public void Samples(double[] values) |
||||
|
{ |
||||
|
SamplesUnchecked(_random, values, Scale, Shape, Truncation); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public IEnumerable<double> Samples() |
||||
|
{ |
||||
|
return SamplesUnchecked(_random, Scale, Shape, Truncation); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sample from the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <returns>a sample from the distribution.</returns>
|
||||
|
public static double Sample(System.Random rnd, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SampleUnchecked(rnd, scale, shape, truncation); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Fills an array with samples generated from the distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="values">The array to fill with the samples.</param>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
public static void Samples(System.Random rnd, double[] values, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
SamplesUnchecked(rnd, values, scale, shape, truncation); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Generates a sequence of samples from the truncated Pareto distribution.
|
||||
|
/// </summary>
|
||||
|
/// <param name="rnd">The random number generator to use.</param>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <returns>a sequence of samples from the distribution.</returns>
|
||||
|
public static IEnumerable<double> Samples(System.Random rnd, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return SamplesUnchecked(rnd, scale, shape, truncation); |
||||
|
} |
||||
|
|
||||
|
internal static double SampleUnchecked(System.Random rnd, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
double uniform = rnd.NextDouble(); |
||||
|
return InvCDFUncheckedImpl(scale, shape, truncation, uniform); |
||||
|
} |
||||
|
|
||||
|
internal static void SamplesUnchecked(System.Random rnd, double[] values, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
if (values.Length == 0) |
||||
|
{ |
||||
|
return; |
||||
|
} |
||||
|
double[] uniforms = rnd.NextDoubles(values.Length); |
||||
|
for (var j = 0; j < values.Length; ++j) |
||||
|
{ |
||||
|
values[j] = InvCDFUncheckedImpl(scale, shape, truncation, uniforms[j]); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
internal static IEnumerable<double> SamplesUnchecked(System.Random rnd, double scale, double shape, double truncation) |
||||
|
{ |
||||
|
while (true) |
||||
|
{ |
||||
|
yield return SampleUnchecked(rnd, scale, shape, truncation); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the density.</param>
|
||||
|
/// <returns>the density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDF"/>
|
||||
|
public double Density(double x) |
||||
|
{ |
||||
|
return DensityImpl(Scale, Shape, Truncation, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="PDFLn"/>
|
||||
|
public double DensityLn(double x) |
||||
|
{ |
||||
|
return DensityLnImpl(Scale, Shape, Truncation, 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>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CDF"/>
|
||||
|
public double CumulativeDistribution(double x) |
||||
|
{ |
||||
|
return CumulativeDistributionImpl(Scale, Shape, Truncation, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
|
||||
|
/// </summary>
|
||||
|
/// <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
|
||||
|
public double InvCDF(double p) |
||||
|
{ |
||||
|
return InvCDFUncheckedImpl(Scale, Shape, Truncation, p); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
|
||||
|
/// <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
|
||||
|
/// <seealso cref="CumulativeDistribution"/>
|
||||
|
public static double ICDF(double scale, double shape, double truncation, double p) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return InvCDFUncheckedImpl(scale, shape, truncation, p); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</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 scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityImpl(scale, shape, truncation, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <param name="x">The location at which to compute the log density.</param>
|
||||
|
/// <returns>the log density at <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="DensityLn"/>
|
||||
|
public static double PDFLn(double scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return DensityLnImpl(scale, shape, truncation, x); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
|
||||
|
/// </summary>
|
||||
|
/// <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
|
||||
|
/// <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
|
||||
|
/// <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
|
||||
|
/// <param name="x">The location at which to compute the cumulative distribution function.</param>
|
||||
|
/// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
|
||||
|
/// <seealso cref="CumulativeDistribution"/>
|
||||
|
public static double CDF(double scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
if (!IsValidParameterSet(scale, shape, truncation)) |
||||
|
{ |
||||
|
throw new ArgumentException(Resources.InvalidDistributionParameters); |
||||
|
} |
||||
|
return CumulativeDistributionImpl(scale, shape, truncation, x); |
||||
|
} |
||||
|
|
||||
|
internal static double DensityImpl(double scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
if (x < scale || x > truncation) |
||||
|
return 0; |
||||
|
else |
||||
|
return (shape * Math.Pow(scale, shape) * Math.Pow(x, -shape - 1)) / (1 - Math.Pow(scale / truncation, shape)); |
||||
|
} |
||||
|
|
||||
|
internal static double DensityLnImpl(double scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
return Math.Log(DensityImpl(scale, shape, truncation, x)); |
||||
|
} |
||||
|
|
||||
|
internal static double CumulativeDistributionImpl(double scale, double shape, double truncation, double x) |
||||
|
{ |
||||
|
if (x <= scale) |
||||
|
return 0; |
||||
|
else if (x >= truncation) |
||||
|
return 1; |
||||
|
else |
||||
|
return (1 - Math.Pow(scale, shape) * Math.Pow(x, -shape)) / (1 - Math.Pow(scale / truncation, shape)); |
||||
|
} |
||||
|
|
||||
|
internal static double InvCDFUncheckedImpl(double scale, double shape, double truncation, double p) |
||||
|
{ |
||||
|
var numerator = p * Math.Pow(truncation, shape) - p * Math.Pow(scale, shape) - Math.Pow(truncation, shape); |
||||
|
var denominator = Math.Pow(truncation, shape) * Math.Pow(scale, shape); |
||||
|
return Math.Pow(-numerator / denominator, -(1 / shape)); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
Loading…
Reference in new issue