Dotnet/FastRng
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Refactored to use shape fitter

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This commit is contained in:
Thorsten Sommer 2020-09-30 20:21:46 +02:00
parent 243a4c044b
commit 9ea1b43b87
1 changed files with 16 additions and 28 deletions
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44
FastRng/Double/Distributions/Beta.cs
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@ -6,47 +6,35 @@ namespace FastRng.Double.Distributions
{ {
public sealed class Beta : IDistribution public sealed class Beta : IDistribution
{ {
private double a = 1.0; private const double ALPHA = 2;
private double b = 1.0; private const double BETA = 2;
public IRandom Random { get; set; } private const double CONSTANT = 4;
private ShapeFitter fitter;
private IRandom random;
public double A public Beta()
{ {
get => this.a; }
public IRandom Random
{
get => this.random;
set set
{ {
if(value <= 0.0) this.random = value;
throw new ArgumentOutOfRangeException(message: "Parameter must be greater than 0", null); this.fitter = new ShapeFitter(Beta.ShapeFunction, this.random, 50, 0.99);
this.a = value;
} }
} }
public double B private static double ShapeFunction(double x) => CONSTANT * Math.Pow(x, ALPHA - 1) * Math.Pow(1 - x, BETA - 1);
{
get => this.b;
set
{
if(value <= 0.0)
throw new ArgumentOutOfRangeException(message: "Parameter must be greater than 0", null);
this.b = value;
}
}
public async ValueTask<double> GetDistributedValue(CancellationToken token = default) public async ValueTask<double> GetDistributedValue(CancellationToken token = default)
{ {
if (this.Random == null) if (this.Random == null)
return double.NaN; return double.NaN;
// There are more efficient methods for generating beta samples. return await this.fitter.NextNumber(token);
// However such methods are a little more efficient and much more complicated.
// For an explanation of why the following method works, see
// http://www.johndcook.com/distribution_chart.html#gamma_beta
var u = await this.Random.NextNumber(new Gamma{Shape = this.A, Scale = 1.0}, token);
var v = await this.Random.NextNumber(new Gamma{Shape = this.B, Scale = 1.0}, token);
return u / (u + v);
} }
} }
} }