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using System;
using System.Linq;
using IStation.Numerics.Random;
namespace IStation.Numerics.Distributions
{
///
/// Multivariate Dirichlet distribution. For details about this distribution, see
/// Wikipedia - Dirichlet distribution.
///
public class Dirichlet : IDistribution
{
System.Random _random;
readonly double[] _alpha;
///
/// Initializes a new instance of the Dirichlet class. The distribution will
/// be initialized with the default random number generator.
///
/// An array with the Dirichlet parameters.
public Dirichlet(double[] alpha)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(alpha))
{
throw new ArgumentException("Invalid parametrization for the distribution.");
}
_random = SystemRandomSource.Default;
_alpha = (double[])alpha.Clone();
}
///
/// Initializes a new instance of the Dirichlet class. The distribution will
/// be initialized with the default random number generator.
///
/// An array with the Dirichlet parameters.
/// The random number generator which is used to draw random samples.
public Dirichlet(double[] alpha, System.Random randomSource)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(alpha))
{
throw new ArgumentException("Invalid parametrization for the distribution.");
}
_random = randomSource ?? SystemRandomSource.Default;
_alpha = (double[])alpha.Clone();
}
///
/// Initializes a new instance of the class.
/// random number generator.
/// The value of each parameter of the Dirichlet distribution.
/// The dimension of the Dirichlet distribution.
public Dirichlet(double alpha, int k)
{
// Create a parameter structure.
var parm = new double[k];
for (var i = 0; i < k; i++)
{
parm[i] = alpha;
}
_random = SystemRandomSource.Default;
if (Control.CheckDistributionParameters && !IsValidParameterSet(parm))
{
throw new ArgumentException("Invalid parametrization for the distribution.");
}
_alpha = (double[])parm.Clone();
}
///
/// Initializes a new instance of the class.
/// random number generator.
/// The value of each parameter of the Dirichlet distribution.
/// The dimension of the Dirichlet distribution.
/// The random number generator which is used to draw random samples.
public Dirichlet(double alpha, int k, System.Random randomSource)
{
// Create a parameter structure.
var parm = new double[k];
for (var i = 0; i < k; i++)
{
parm[i] = alpha;
}
_random = randomSource ?? SystemRandomSource.Default;
if (Control.CheckDistributionParameters && !IsValidParameterSet(parm))
{
throw new ArgumentException("Invalid parametrization for the distribution.");
}
_alpha = (double[])parm.Clone();
}
///
/// Returns a that represents this instance.
///
///
/// A that represents this instance.
///
public override string ToString()
{
return $"Dirichlet(Dimension = {Dimension})";
}
///
/// Tests whether the provided values are valid parameters for this distribution.
/// No parameter can be less than zero and at least one parameter should be larger than zero.
///
/// The parameters of the Dirichlet distribution.
public static bool IsValidParameterSet(double[] alpha)
{
var allzero = true;
for (int i = 0; i < alpha.Length; i++)
{
var t = alpha[i];
if (t < 0.0)
{
return false;
}
if (t > 0.0)
{
allzero = false;
}
}
return !allzero;
}
///
/// Gets or sets the parameters of the Dirichlet distribution.
///
public double[] Alpha => _alpha;
///
/// Gets or sets the random number generator which is used to draw random samples.
///
public System.Random RandomSource
{
get => _random;
set => _random = value ?? SystemRandomSource.Default;
}
///
/// Gets the dimension of the Dirichlet distribution.
///
public int Dimension => _alpha.Length;
///
/// Gets the sum of the Dirichlet parameters.
///
double AlphaSum => _alpha.Sum();
///
/// Gets the mean of the Dirichlet distribution.
///
public double[] Mean
{
get
{
var sum = AlphaSum;
var parm = new double[Dimension];
for (var i = 0; i < Dimension; i++)
{
parm[i] = _alpha[i]/sum;
}
return parm;
}
}
///
/// Gets the variance of the Dirichlet distribution.
///
public double[] Variance
{
get
{
var s = AlphaSum;
var v = new double[_alpha.Length];
for (var i = 0; i < _alpha.Length; i++)
{
v[i] = _alpha[i]*(s - _alpha[i])/(s*s*(s + 1.0));
}
return v;
}
}
///
/// Gets the entropy of the distribution.
///
public double Entropy
{
get
{
var num = _alpha.Sum(t => (t - 1)*SpecialFunctions.DiGamma(t));
return SpecialFunctions.GammaLn(AlphaSum) + ((AlphaSum - Dimension)*SpecialFunctions.DiGamma(AlphaSum)) - num;
}
}
///
/// Computes the density of the distribution.
///
/// The locations at which to compute the density.
/// the density at .
/// The Dirichlet distribution requires that the sum of the components of x equals 1.
/// You can also leave out the last component, and it will be computed from the others.
public double Density(double[] x)
{
return Math.Exp(DensityLn(x));
}
///
/// Computes the log density of the distribution.
///
/// The locations at which to compute the density.
/// the density at .
public double DensityLn(double[] x)
{
if (x == null)
{
throw new ArgumentNullException(nameof(x));
}
var shortVersion = x.Length == (_alpha.Length - 1);
if ((x.Length != _alpha.Length) && !shortVersion)
{
throw new ArgumentException("x");
}
var term = 0.0;
var sumxi = 0.0;
var sumalpha = 0.0;
for (var i = 0; i < x.Length; i++)
{
var xi = x[i];
if ((xi <= 0.0) || (xi >= 1.0))
{
return 0.0;
}
term += (_alpha[i] - 1.0)*Math.Log(xi) - SpecialFunctions.GammaLn(_alpha[i]);
sumxi += xi;
sumalpha += _alpha[i];
}
// Calculate x[Length - 1] element, if needed
if (shortVersion)
{
if (sumxi >= 1.0)
{
return 0.0;
}
term += (_alpha[_alpha.Length - 1] - 1.0)*Math.Log(1.0 - sumxi) - SpecialFunctions.GammaLn(_alpha[_alpha.Length - 1]);
sumalpha += _alpha[_alpha.Length - 1];
}
else if (!sumxi.AlmostEqualRelative(1.0, 8))
{
return 0.0;
}
return term + SpecialFunctions.GammaLn(sumalpha);
}
///
/// Samples a Dirichlet distributed random vector.
///
/// A sample from this distribution.
public double[] Sample()
{
return Sample(_random, _alpha);
}
///
/// Samples a Dirichlet distributed random vector.
///
/// The random number generator to use.
/// The Dirichlet distribution parameter.
/// a sample from the distribution.
public static double[] Sample(System.Random rnd, double[] alpha)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(alpha))
{
throw new ArgumentException("Invalid parametrization for the distribution.");
}
var n = alpha.Length;
var gv = new double[n];
var sum = 0.0;
for (var i = 0; i < n; i++)
{
if (alpha[i] == 0.0)
{
gv[i] = 0.0;
}
else
{
gv[i] = Gamma.Sample(rnd, alpha[i], 1.0);
}
sum += gv[i];
}
for (var i = 0; i < n; i++)
{
gv[i] /= sum;
}
return gv;
}
}
}