Migrated gamma distribution to shape fitter
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using System;
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using System.Threading;
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using System.Threading.Tasks;
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namespace FastRng.Double.Distributions
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{
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public sealed class Gamma : IDistribution
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{
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private static readonly IDistribution NORMAL_DISTRIBUTION = new Normal();
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private double shape = 1.0;
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public IRandom Random { get; set; }
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public double Shape
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{
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get => this.shape;
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set
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{
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if(value <= 0.0)
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throw new ArgumentOutOfRangeException(message: "Shape must be greater than 0", null);
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this.shape = value;
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}
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}
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public double Scale { get; set; } = 1.0;
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public async ValueTask<double> GetDistributedValue(CancellationToken token = default)
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{
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if (this.Random == null)
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return double.NaN;
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// Implementation based on "A Simple Method for Generating Gamma Variables"
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// by George Marsaglia and Wai Wan Tsang. ACM Transactions on Mathematical Software
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// Vol 26, No 3, September 2000, pages 363-372.
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if (this.Shape >= 1.0)
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{
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var d = this.Shape - 1.0 / 3.0;
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var c = 1.0 / Math.Sqrt(9.0 * d);
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while(true)
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{
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double x, v;
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do
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{
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x = await this.Random.NextNumber(NORMAL_DISTRIBUTION, token);
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v = 1.0 + c * x;
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}
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while (v <= 0.0);
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v = v * v * v;
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var u = await this.Random.GetUniform(token);
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var xSquared = x * x;
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if (u < 1.0 - 0.0331 * xSquared * xSquared || Math.Log(u) < 0.5 * xSquared + d * (1.0 - v + Math.Log(v)))
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return this.Scale * d * v;
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}
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}
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else
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{
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var dist = new Gamma{ Scale = 1, Shape = 1 + this.Shape};
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var g = await this.Random.NextNumber(dist, token);
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var w = await this.Random.GetUniform(token);
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return this.Scale * g * Math.Pow(w, 1.0 / this.Shape);
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}
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}
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}
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}
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45
FastRng/Double/Distributions/GammaA5B15.cs
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45
FastRng/Double/Distributions/GammaA5B15.cs
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using System;
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using System.Threading;
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using System.Threading.Tasks;
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namespace FastRng.Double.Distributions
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{
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public sealed class GammaA5B15 : IDistribution
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{
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private const double ALPHA = 5.0;
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private const double BETA = 15.0;
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private const double CONSTANT = 0.341344210715475;
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private static readonly double GAMMA_ALPHA;
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private static readonly double BETA_TO_THE_ALPHA;
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private ShapeFitter fitter;
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private IRandom random;
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static GammaA5B15()
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{
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GAMMA_ALPHA = MathTools.Gamma(ALPHA);
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BETA_TO_THE_ALPHA = Math.Pow(BETA, ALPHA);
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}
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public IRandom Random
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{
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get => this.random;
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set
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{
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this.random = value;
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this.fitter = new ShapeFitter(GammaA5B15.ShapeFunction, this.random, 100);
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}
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}
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private static double ShapeFunction(double x) => CONSTANT * ((BETA_TO_THE_ALPHA * Math.Pow(x, ALPHA - 1.0d) * Math.Exp(-BETA * x)) / GAMMA_ALPHA);
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public async ValueTask<double> GetDistributedValue(CancellationToken token = default)
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{
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if (this.Random == null)
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return double.NaN;
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return await this.fitter.NextNumber(token);
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}
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}
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}
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@ -1,139 +0,0 @@
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using System;
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using System.Diagnostics.CodeAnalysis;
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using System.Linq;
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using System.Threading.Tasks;
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using FastRng.Double;
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using NUnit.Framework;
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namespace FastRngTests.Double.Distributions
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{
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[ExcludeFromCodeCoverage]
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public class Gamma
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{
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaDistribution01()
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{
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const double SHAPE = 10.0;
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const double SCALE = 2.0;
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const double MEAN = SHAPE * SCALE;
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const double VARIANCE = SHAPE * SCALE * SCALE;
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var dist = new FastRng.Double.Distributions.Gamma{ Shape = SHAPE, Scale = SCALE };
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var stats = new RunningStatistics();
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var rng = new MultiThreadedRng();
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for (var n = 0; n < 100_000; n++)
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stats.Push(await rng.NextNumber(dist));
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rng.StopProducer();
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TestContext.WriteLine($"mean={MEAN} vs. {stats.Mean}");
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TestContext.WriteLine($"variance={VARIANCE} vs {stats.Variance}");
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Assert.That(stats.Mean, Is.EqualTo(MEAN).Within(0.4), "Mean is out of range");
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Assert.That(stats.Variance, Is.EqualTo(VARIANCE).Within(0.4), "Variance is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaDistribution02()
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{
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const double SHAPE = 0.5;
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const double SCALE = 2.0;
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const double MEAN = SHAPE * SCALE;
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const double VARIANCE = SHAPE * SCALE * SCALE;
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var dist = new FastRng.Double.Distributions.Gamma{ Shape = SHAPE, Scale = SCALE };
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var stats = new RunningStatistics();
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var rng = new MultiThreadedRng();
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for (var n = 0; n < 100_000; n++)
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stats.Push(await rng.NextNumber(dist));
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rng.StopProducer();
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TestContext.WriteLine($"mean={MEAN} vs. {stats.Mean}");
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TestContext.WriteLine($"variance={VARIANCE} vs {stats.Variance}");
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Assert.That(stats.Mean, Is.EqualTo(MEAN).Within(0.1), "Mean is out of range");
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Assert.That(stats.Variance, Is.EqualTo(VARIANCE).Within(0.1), "Variance is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange03()
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{
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var rng = new MultiThreadedRng();
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var dist = new FastRng.Double.Distributions.Gamma { Random = rng }; // Test default parameters
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await dist.GetDistributedValue();
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(0.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange01()
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{
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var rng = new MultiThreadedRng();
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await rng.NextNumber(-1.0, 1.0, new FastRng.Double.Distributions.Gamma());
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(-1.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange02()
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{
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var rng = new MultiThreadedRng();
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await rng.NextNumber(0.0, 1.0, new FastRng.Double.Distributions.Gamma());
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(0.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public void ParameterTest01()
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{
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var dist = new FastRng.Double.Distributions.Gamma();
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Assert.DoesNotThrow(() => dist.Scale = -45);
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Assert.DoesNotThrow(() => dist.Scale = 15);
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Assert.DoesNotThrow(() => dist.Scale = 0);
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Assert.Throws<ArgumentOutOfRangeException>(() => dist.Shape = 0);
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Assert.Throws<ArgumentOutOfRangeException>(() => dist.Shape = -78);
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Assert.DoesNotThrow(() => dist.Shape = 0.0001);
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Assert.DoesNotThrow(() => dist.Shape = 4);
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task NoRandomNumberGenerator01()
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{
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var dist = new FastRng.Double.Distributions.Gamma();
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Assert.DoesNotThrowAsync(async () => await dist.GetDistributedValue());
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Assert.That(await dist.GetDistributedValue(), Is.NaN);
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}
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}
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}
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106
FastRngTests/Double/Distributions/GammaA5B15.cs
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106
FastRngTests/Double/Distributions/GammaA5B15.cs
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using System;
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using System.Diagnostics.CodeAnalysis;
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using System.Linq;
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using System.Threading.Tasks;
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using FastRng.Double;
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using NUnit.Framework;
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namespace FastRngTests.Double.Distributions
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{
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[ExcludeFromCodeCoverage]
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public class GammaA5B15
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{
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaDistribution01()
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{
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var dist = new FastRng.Double.Distributions.GammaA5B15();
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var fra = new FrequencyAnalysis();
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var rng = new MultiThreadedRng();
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for (var n = 0; n < 100_000; n++)
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fra.CountThis(await rng.NextNumber(dist));
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rng.StopProducer();
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var result = fra.NormalizeAndPlotEvents(TestContext.WriteLine);
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Assert.That(result[0], Is.EqualTo(0.0000929594237282).Within(0.0005));
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Assert.That(result[1], Is.EqualTo(0.0012801746797876).Within(0.002));
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Assert.That(result[2], Is.EqualTo(0.0055781488254349).Within(0.003));
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Assert.That(result[21], Is.EqualTo(0.9331608887752720).Within(0.09));
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Assert.That(result[22], Is.EqualTo(0.9594734828891280).Within(0.09));
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Assert.That(result[23], Is.EqualTo(0.9790895765535350).Within(0.09));
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Assert.That(result[50], Is.EqualTo(0.3478287795336570).Within(0.06));
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Assert.That(result[75], Is.EqualTo(0.0403399049422936).Within(0.009));
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Assert.That(result[85], Is.EqualTo(0.0163628388658126).Within(0.009));
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Assert.That(result[90], Is.EqualTo(0.0097147611446660).Within(0.005));
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Assert.That(result[97], Is.EqualTo(0.0041135143233153).Within(0.008));
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Assert.That(result[98], Is.EqualTo(0.0036872732029996).Within(0.008));
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Assert.That(result[99], Is.EqualTo(0.0033038503429554).Within(0.008));
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange03()
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{
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var rng = new MultiThreadedRng();
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var dist = new FastRng.Double.Distributions.GammaA5B15 { Random = rng }; // Test default parameters
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await dist.GetDistributedValue();
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(0.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange01()
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{
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var rng = new MultiThreadedRng();
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var dist = new FastRng.Double.Distributions.GammaA5B15();
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await rng.NextNumber(-1.0, 1.0, dist);
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(-1.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task TestGammaGeneratorWithRange02()
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{
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var rng = new MultiThreadedRng();
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var dist = new FastRng.Double.Distributions.GammaA5B15();
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var samples = new double[1_000];
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for (var n = 0; n < samples.Length; n++)
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samples[n] = await rng.NextNumber(0.0, 1.0, dist);
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rng.StopProducer();
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Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(0.0), "Min is out of range");
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Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
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}
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[Test]
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[Category(TestCategories.COVER)]
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[Category(TestCategories.NORMAL)]
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public async Task NoRandomNumberGenerator01()
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{
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var dist = new FastRng.Double.Distributions.GammaA5B15();
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Assert.DoesNotThrowAsync(async () => await dist.GetDistributedValue());
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Assert.That(await dist.GetDistributedValue(), Is.NaN);
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}
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}
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}
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