forked from tsai/mathnet-numerics
Jurgen Van Gael
17 years ago
7 changed files with 816 additions and 0 deletions
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// <copyright file="StudentT.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://mathnet.opensourcedotnet.info
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//
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// Copyright (c) 2009 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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namespace MathNet.Numerics.Distributions |
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{ |
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using System; |
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using System.Collections.Generic; |
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using Properties; |
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/// <summary>
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/// Implements the univariate Student t-distribution. For details about this distribution, see
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/// <a href="http://en.wikipedia.org/wiki/Student%27s_t-distribution">Wikipedia - Student's t-distribution</a>.
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/// </summary>
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/// <remarks><para>We use a slightly generalized version (compared to Wikipedia) of the Student t-distribution.
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/// Namely, one which also parameterizes the location and scale. See the book "Bayesian Data Analysis" for more
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/// details.</para>
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/// <para>The distribution will use the <see cref="System.Random"/> by default.
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/// Users can get/set the random number generator by using the <see cref="RandomSource"/> property.</para>
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/// <para>The statistics classes will check all the incoming parameters whether they are in the allowed
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/// range. This might involve heavy computation. Optionally, by setting Control.CheckDistributionParameters
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/// to false, all parameter checks can be turned off.</para></remarks>
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public class StudentT : IContinuousDistribution |
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{ |
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/// <summary>
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/// Keeps track of the location of the Student t-distribution.
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/// </summary>
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private double _location; |
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/// <summary>
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/// Keeps track of the degrees of freedom for the Student t-distribution.
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/// </summary>
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private double _dof; |
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/// <summary>
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/// Keeps track of the scale for the Student t-distribution.
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/// </summary>
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private double _scale; |
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/// <summary>
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/// The distribution's random number generator.
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/// </summary>
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private Random _random; |
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/// <summary>
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/// Initializes a new instance of the StudentT class. This is a Student t-distribution with location 0.0
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/// scale 1.0 and degrees of freedom 1. The distribution will
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/// be initialized with the default <seealso cref="System.Random"/> random number generator.
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/// </summary>
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public StudentT() : this(0.0, 1.0, 1.0) |
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{ |
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} |
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/// <summary>
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/// Initializes a new instance of the StudentT class with a particular location, scale and degrees of
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/// freedom. The distribution will
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/// be initialized with the default <seealso cref="System.Random"/> random number generator.
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/// </summary>
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/// <param name="location">The location of the Student t-distribution.</param>
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/// <param name="scale">The scale of the Student t-distribution.</param>
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/// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
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public StudentT(double location, double scale, double dof) |
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{ |
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SetParameters(location, scale, dof); |
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RandomSource = new Random(); |
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} |
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/// <summary>
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/// A string representation of the distribution.
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/// </summary>
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/// <returns>a string representation of the distribution.</returns>
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public override string ToString() |
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{ |
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return "StudentT(Location = " + _location + ", Scale = " + _scale + ", DoF = " + _dof + ")"; |
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} |
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/// <summary>
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/// Checks whether the parameters of the distribution are valid.
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/// </summary>
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/// <param name="location">The location of the Student t-distribution.</param>
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/// <param name="scale">The scale of the Student t-distribution.</param>
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/// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
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/// <returns>True when the parameters are valid, false otherwise.</returns>
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private static bool IsValidParameterSet(double location, double scale, double dof) |
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{ |
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if (scale <= 0.0 || dof <= 0.0 || Double.IsNaN(scale) || Double.IsNaN(location) || Double.IsNaN(dof)) |
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{ |
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return false; |
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} |
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return true; |
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} |
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/// <summary>
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/// Sets the parameters of the distribution after checking their validity.
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/// </summary>
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/// <param name="location">The location of the Student t-distribution.</param>
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/// <param name="scale">The scale of the Student t-distribution.</param>
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/// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
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/// <exception cref="ArgumentOutOfRangeException">When the parameters don't pass the <see cref="IsValidParameterSet"/> function.</exception>
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private void SetParameters(double location, double scale, double dof) |
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{ |
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if (Control.CheckDistributionParameters && !IsValidParameterSet(location, scale, dof)) |
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{ |
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throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters); |
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} |
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_location = location; |
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_scale = scale; |
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_dof = dof; |
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} |
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/// <summary>
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/// Gets or sets the location of the Student t-distribution.
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/// </summary>
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public double Location |
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{ |
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get |
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{ |
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return _location; |
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} |
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set |
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{ |
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SetParameters(value, _scale, _dof); |
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} |
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} |
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/// <summary>
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/// Gets or sets the scale of the Student t-distribution.
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/// </summary>
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public double Scale |
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{ |
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get |
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{ |
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return _scale; |
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} |
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set |
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{ |
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SetParameters(_location, value, _dof); |
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} |
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} |
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/// <summary>
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/// Gets or sets the degrees of freedom of the Student t-distribution.
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/// </summary>
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public double DegreesOfFreedom |
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{ |
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get |
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{ |
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return _dof; |
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} |
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set |
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{ |
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SetParameters(_location, _scale, value); |
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} |
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} |
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#region IDistribution implementation
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/// <summary>
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/// Gets or sets the random number generator which is used to draw random samples.
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/// </summary>
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public Random RandomSource |
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{ |
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get |
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{ |
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return _random; |
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} |
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set |
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{ |
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if (value == null) |
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{ |
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throw new ArgumentNullException(); |
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} |
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_random = value; |
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} |
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} |
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/// <summary>
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/// Gets or sets the mean of the Student t-distribution.
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/// </summary>
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public double Mean |
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{ |
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get { return _location; } |
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} |
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/// <summary>
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/// Gets or sets the variance of the Student t-distribution.
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/// </summary>
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public double Variance |
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{ |
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get |
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{ |
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if (_dof > 2.0) |
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{ |
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return _dof / (_dof - 2.0) / _scale; |
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} |
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else if (_dof > 1.0) |
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{ |
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return Double.PositiveInfinity; |
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} |
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else |
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{ |
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throw new Exception(Resources.UndefinedMoment); |
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} |
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} |
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} |
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/// <summary>
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/// Gets or sets the standard deviation of the Student t-distribution.
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/// </summary>
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public double StdDev |
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{ |
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get |
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{ |
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if (_dof > 2.0) |
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{ |
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return Math.Sqrt(_dof / (_dof - 2.0)); |
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} |
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else if (_dof > 1.0) |
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{ |
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return Double.PositiveInfinity; |
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} |
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else |
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{ |
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throw new Exception(Resources.UndefinedMoment); |
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} |
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} |
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} |
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/// <summary>
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/// Gets the entropy of the Student t-distribution.
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/// </summary>
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public double Entropy |
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{ |
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get { throw new NotImplementedException(); } |
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} |
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/// <summary>
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/// Gets the skewness of the Student t-distribution.
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/// </summary>
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public double Skewness |
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{ |
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get { throw new NotImplementedException(); } |
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} |
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#endregion
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#region IContinuousDistribution implementation
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/// <summary>
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/// Gets the mode of the Student t-distribution.
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/// </summary>
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public double Mode |
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{ |
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get { return _location; } |
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} |
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/// <summary>
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/// Gets the median of the Student t-distribution.
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/// </summary>
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public double Median |
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{ |
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get { return _location; } |
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} |
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/// <summary>
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/// Gets the minimum of the Student t-distribution.
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/// </summary>
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public double Minimum |
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{ |
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get { return Double.NegativeInfinity; } |
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} |
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/// <summary>
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/// Gets the maximum of the Student t-distribution.
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/// </summary>
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public double Maximum |
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{ |
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get { return Double.PositiveInfinity; } |
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} |
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/// <summary>
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/// Computes the density of the Student t-distribution.
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/// </summary>
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/// <param name="x">The location at which to compute the density.</param>
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/// <returns>the density at <paramref name="x"/>.</returns>
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public double Density(double x) |
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{ |
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double d = (x - _location) / _scale; |
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return SpecialFunctions.Gamma((_dof + 1.0) / 2.0) |
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* Math.Pow(1.0 + d * d / _dof, -0.5 * (_dof + 1.0)) |
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/ SpecialFunctions.Gamma(_dof / 2.0) |
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/ Math.Sqrt(_dof * Math.PI) |
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/ _scale; |
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} |
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/// <summary>
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/// Computes the log density of the Student t-distribution.
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/// </summary>
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/// <param name="x">The location at which to compute the log density.</param>
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/// <returns>the log density at <paramref name="x"/>.</returns>
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public double DensityLn(double x) |
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{ |
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double d = (x - _location) / _scale; |
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return SpecialFunctions.GammaLn((_dof + 1.0) / 2.0) |
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- 0.5 * (_dof + 1.0) * Math.Log(1.0 + d * d / _dof) |
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- SpecialFunctions.GammaLn(_dof / 2.0) |
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-0.5 * Math.Log(_dof * Math.PI) |
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- Math.Log(_scale); |
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} |
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/// <summary>
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/// Computes the cumulative distribution function of the Student t-distribution.
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/// </summary>
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/// <param name="x">The location at which to compute the cumulative density.</param>
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/// <returns>the cumulative density at <paramref name="x"/>.</returns>
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public double CumulativeDistribution(double x) |
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{ |
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throw new NotImplementedException(); |
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} |
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/// <summary>
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/// Generates a sample from the Student t-distribution.
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/// </summary>
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/// <returns>a sample from the distribution.</returns>
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public double Sample() |
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{ |
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throw new NotImplementedException(); |
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} |
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/// <summary>
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/// Generates a sequence of samples from the Student t-distribution.
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/// </summary>
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/// <returns>a sequence of samples from the distribution.</returns>
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public IEnumerable<double> Samples() |
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{ |
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throw new NotImplementedException(); |
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} |
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#endregion
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/// <summary>
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/// Generates a sample from the Student t-distribution.
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/// </summary>
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/// <param name="rng">The random number generator to use.</param>
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/// <param name="location">The location of the Student t-distribution.</param>
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/// <param name="scale">The scale of the Student t-distribution.</param>
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/// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
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/// <returns>a sample from the distribution.</returns>
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public static double Sample(Random rng, double location, double scale, double dof) |
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{ |
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if (Control.CheckDistributionParameters && !IsValidParameterSet(location, scale, dof)) |
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{ |
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throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters); |
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} |
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throw new NotImplementedException(); |
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} |
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/// <summary>
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/// Generates a sequence of samples from the Student t-distribution using the <i>Box-Muller</i> algorithm.
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/// </summary>
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/// <param name="rng">The random number generator to use.</param>
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/// <param name="location">The location of the Student t-distribution.</param>
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/// <param name="scale">The scale of the Student t-distribution.</param>
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/// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
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/// <returns>a sequence of samples from the distribution.</returns>
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public static IEnumerable<double> Samples(Random rng, double location, double scale, double dof) |
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{ |
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if (Control.CheckDistributionParameters && !IsValidParameterSet(location, scale, dof)) |
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{ |
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throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters); |
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} |
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throw new NotImplementedException(); |
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} |
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} |
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} |
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@ -0,0 +1,393 @@ |
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// <copyright file="StudentTTests.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://mathnet.opensourcedotnet.info
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//
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// Copyright (c) 2009 Math.NET
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//
|
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// 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>
|
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|
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namespace MathNet.Numerics.UnitTests.DistributionTests |
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{ |
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using System; |
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using System.Linq; |
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using MbUnit.Framework; |
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using MathNet.Numerics.Distributions; |
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[TestFixture] |
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public class StudentTTests |
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{ |
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[SetUp] |
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public void SetUp() |
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{ |
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Control.CheckDistributionParameters = true; |
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} |
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[Test, MultipleAsserts] |
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public void CanCreateStandardStudentT() |
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{ |
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var n = new StudentT(); |
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AssertEx.AreEqual<double>(0.0, n.Location); |
||||
|
AssertEx.AreEqual<double>(1.0, n.Scale); |
||||
|
AssertEx.AreEqual<double>(1.0, n.DegreesOfFreedom); |
||||
|
} |
||||
|
|
||||
|
/*[Test, MultipleAsserts] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void CanCreateNormal(double mean, double sdev) |
||||
|
{ |
||||
|
var n = new Normal(mean, sdev); |
||||
|
AssertEx.AreEqual<double>(mean, n.Mean); |
||||
|
AssertEx.AreEqual<double>(sdev, n.StdDev); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
[Row(Double.NaN, 1.0)] |
||||
|
[Row(1.0, Double.NaN)] |
||||
|
[Row(Double.NaN, Double.NaN)] |
||||
|
[Row(1.0, -1.0)] |
||||
|
public void NormalCreateFailsWithBadParameters(double mean, double sdev) |
||||
|
{ |
||||
|
var n = new Normal(mean, sdev); |
||||
|
} |
||||
|
|
||||
|
[Test, MultipleAsserts] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void CanCreateNormalFromMeanAndStdDev(double mean, double sdev) |
||||
|
{ |
||||
|
var n = Normal.WithMeanStdDev(mean, sdev); |
||||
|
AssertEx.AreEqual<double>(mean, n.Mean); |
||||
|
AssertEx.AreEqual<double>(sdev, n.StdDev); |
||||
|
} |
||||
|
|
||||
|
[Test, MultipleAsserts] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void CanCreateNormalFromMeanAndVariance(double mean, double var) |
||||
|
{ |
||||
|
var n = Normal.WithMeanVariance(mean, var); |
||||
|
AssertHelpers.AlmostEqual(mean, n.Mean, 16); |
||||
|
AssertHelpers.AlmostEqual(var, n.Variance, 16); |
||||
|
} |
||||
|
|
||||
|
[Test, MultipleAsserts] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void CanCreateNormalFromMeanAndPrecision(double mean, double prec) |
||||
|
{ |
||||
|
var n = Normal.WithMeanPrecision(mean, prec); |
||||
|
AssertHelpers.AlmostEqual(mean, n.Mean, 15); |
||||
|
AssertHelpers.AlmostEqual(prec, n.Precision, 15); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void ValidateToString() |
||||
|
{ |
||||
|
var n = new Normal(1.0, 2.0); |
||||
|
AssertEx.AreEqual<string>("Normal(Mean = 1, StdDev = 2)", n.ToString()); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void CanSetPrecision(double prec) |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.Precision = prec; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
public void SetPrecisionFailsWithNegativePrecision() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.Precision = -1.0; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void CanSetVariance(double var) |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.Variance = var; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
public void SetVarianceFailsWithNegativeVariance() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.Variance = -1.0; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void CanSetStdDev(double sdev) |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.StdDev = sdev; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
public void SetStdDevFailsWithNegativeStdDev() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.StdDev = -1.0; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(Double.NegativeInfinity)] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void CanSetMean(double mean) |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
n.Mean = mean; |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void ValidateEntropy(double sdev) |
||||
|
{ |
||||
|
var n = new Normal(1.0, sdev); |
||||
|
AssertEx.AreEqual<double>(MathNet.Numerics.Constants.LogSqrt2PiE + Math.Log(n.StdDev), n.Entropy); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void ValidateSkewness(double sdev) |
||||
|
{ |
||||
|
var n = new Normal(1.0, sdev); |
||||
|
AssertEx.AreEqual<double>(0.0, n.Skewness); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(Double.NegativeInfinity)] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void ValidateMode(double mean) |
||||
|
{ |
||||
|
var n = new Normal(mean, 1.0); |
||||
|
AssertEx.AreEqual<double>(mean, n.Mode); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(Double.NegativeInfinity)] |
||||
|
[Row(-0.0)] |
||||
|
[Row(0.0)] |
||||
|
[Row(0.1)] |
||||
|
[Row(1.0)] |
||||
|
[Row(10.0)] |
||||
|
[Row(Double.PositiveInfinity)] |
||||
|
public void ValidateMedian(double mean) |
||||
|
{ |
||||
|
var n = new Normal(mean, 1.0); |
||||
|
AssertEx.AreEqual<double>(mean, n.Median); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void ValidateMinimum() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
AssertEx.AreEqual<double>(System.Double.NegativeInfinity, n.Minimum); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void ValidateMaximum() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
AssertEx.AreEqual<double>(System.Double.PositiveInfinity, n.Maximum); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void ValidateDensity(double mean, double sdev) |
||||
|
{ |
||||
|
var n = Normal.WithMeanStdDev(mean, sdev); |
||||
|
for(int i = 0; i < 11; i++) |
||||
|
{ |
||||
|
double x = i - 5.0; |
||||
|
double d = (mean - x)/sdev; |
||||
|
double pdf = Math.Exp(-0.5*d*d)/(sdev*Constants.Sqrt2Pi); |
||||
|
AssertEx.AreEqual<double>(pdf, n.Density(x)); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(0.0, 0.0)] |
||||
|
[Row(0.0, 0.1)] |
||||
|
[Row(0.0, 1.0)] |
||||
|
[Row(0.0, 10.0)] |
||||
|
[Row(10.0, 1.0)] |
||||
|
[Row(-5.0, 100.0)] |
||||
|
[Row(0.0, Double.PositiveInfinity)] |
||||
|
public void ValidateDensityLn(double mean, double sdev) |
||||
|
{ |
||||
|
var n = Normal.WithMeanStdDev(mean, sdev); |
||||
|
for (int i = 0; i < 11; i++) |
||||
|
{ |
||||
|
double x = i - 5.0; |
||||
|
double d = (mean - x) / sdev; |
||||
|
double pdfln = -0.5 * d * d - Math.Log(sdev) - Constants.LogSqrt2Pi; |
||||
|
AssertEx.AreEqual<double>(pdfln, n.DensityLn(x)); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void CanSampleStatic() |
||||
|
{ |
||||
|
var d = Normal.Sample(new Random(), 0.0, 1.0); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void CanSampleSequenceStatic() |
||||
|
{ |
||||
|
var ied = Normal.Samples(new Random(), 0.0, 1.0); |
||||
|
var arr = ied.Take(5).ToArray(); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
public void FailSampleStatic() |
||||
|
{ |
||||
|
var d = Normal.Sample(new Random(), 0.0, -1.0); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[ExpectedException(typeof(ArgumentOutOfRangeException))] |
||||
|
public void FailSampleSequenceStatic() |
||||
|
{ |
||||
|
var ied = Normal.Samples(new Random(), 0.0, -1.0).First(); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void CanSample() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
var d = n.Sample(); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void CanSampleSequence() |
||||
|
{ |
||||
|
var n = new Normal(); |
||||
|
var ied = n.Samples(); |
||||
|
var e = ied.Take(5).ToArray(); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(Double.NegativeInfinity, 0.0)] |
||||
|
[Row(-5.0, 0.00000028665157187919391167375233287464535385442301361187883)] |
||||
|
[Row(-2.0, 0.0002326290790355250363499258867279847735487493358890356)] |
||||
|
[Row(-0.0, 0.0062096653257761351669781045741922211278977469230927036)] |
||||
|
[Row(0.0, 0.0062096653257761351669781045741922211278977469230927036)] |
||||
|
[Row(4.0, 0.30853753872598689636229538939166226011639782444542207)] |
||||
|
[Row(5.0, 0.5)] |
||||
|
[Row(6.0, 0.69146246127401310363770461060833773988360217555457859)] |
||||
|
[Row(10.0, 0.9937903346742238648330218954258077788721022530769078)] |
||||
|
[Row(Double.PositiveInfinity, 1.0)] |
||||
|
public void ValidateCumulativeDistribution(double x, double f) |
||||
|
{ |
||||
|
var n = Normal.WithMeanStdDev(5.0, 2.0); |
||||
|
AssertHelpers.AlmostEqual(f, n.CumulativeDistribution(x), 10); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
[Row(Double.NegativeInfinity, 0.0)] |
||||
|
[Row(-5.0, 0.00000028665157187919391167375233287464535385442301361187883)] |
||||
|
[Row(-2.0, 0.0002326290790355250363499258867279847735487493358890356)] |
||||
|
[Row(-0.0, 0.0062096653257761351669781045741922211278977469230927036)] |
||||
|
[Row(0.0, 0.0062096653257761351669781045741922211278977469230927036)] |
||||
|
[Row(4.0, 0.30853753872598689636229538939166226011639782444542207)] |
||||
|
[Row(5.0, 0.5)] |
||||
|
[Row(6.0, 0.69146246127401310363770461060833773988360217555457859)] |
||||
|
[Row(10.0, 0.9937903346742238648330218954258077788721022530769078)] |
||||
|
[Row(Double.PositiveInfinity, 1.0)] |
||||
|
public void ValidateInverseCumulativeDistribution(double x, double f) |
||||
|
{ |
||||
|
var n = Normal.WithMeanStdDev(5.0, 2.0); |
||||
|
AssertHelpers.AlmostEqual(x, n.InverseCumulativeDistribution(f), 15); |
||||
|
}*/ |
||||
|
} |
||||
|
} |
||||
Loading…
Reference in new issue