FastRng 1.2.0

dotnet add package FastRng --version 1.2.0                
NuGet\Install-Package FastRng -Version 1.2.0                
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="FastRng" Version="1.2.0" />                
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add FastRng --version 1.2.0                
#r "nuget: FastRng, 1.2.0"                
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install FastRng as a Cake Addin
#addin nuget:?package=FastRng&version=1.2.0

// Install FastRng as a Cake Tool
#tool nuget:?package=FastRng&version=1.2.0                

FastRng

FastRng is a multithreaded pseudo-random number generator. Besides the generation of uniformly distributed random numbers, there are several other distributions to choose from. For performance reasons, the parameters of the distributions are not user-definable. For some distributions, therefore, different parameter variations are available. If a different combination is desired, a separate class can be created.

Please note, that Math.NET's (https://www.mathdotnet.com/) random number generator is in some situations faster. Unlike Math.NET, MultiThreadedRng is multithreaded. Consumers can use a token to cancel an operation.

FastRng (class MultiThreadedRng) using a shape fitter (a rejection sampler) to enforce arbitrary shapes of probabilities for desired distributions. By using the shape fitter, it is even easier to define discontinuous, arbitrary functions as shapes. Any consumer can define and use own distributions.

The class MultiThreadedRng uses the George Marsaglia's MWC algorithm. The algorithm's implementation based loosely on John D. Cook's (johndcook.com) implementation. Thanks, John, for the inspiration.

Usage

Example code:

using FastRng.Float;
using FastRng.Float.Distributions;

[...]

using var rng = new MultiThreadedRng<float>();
var dist = new ChiSquareK1<float>(rng);

var value1 = dist.NextNumber();
var value2 = dist.NextNumber(rangeStart: -1.0f, rangeEnd: 1.0f);
if(dist.HasDecisionBeenMade(above: 0.8f, below: 0.9f))
{
    // Decision has been made
}

Notes:

  • MultiThreadedRng and all distributions are using generic math types: you might use float, double, or Half.

  • MultiThreadedRng is IDisposable. It is important to call Dispose, when the generator is not needed anymore. Otherwise, the supporting background threads are still running.

  • MultiThreadedRng fills some buffers after creation. Thus, create and reuse it as long as needed. Avoid useless re-creation.

  • Distributions need some time in creation to calculate probabilities. Thus, create a distribution once and use reuse it. Avoid useless re-creation.

Available Distributions

Normal Distribution (std. dev.=0.2, mean=0.5)

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Wikipedia: https://en.wikipedia.org/wiki/Normal_distribution

Beta Distribution (alpha=2, beta=2)

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Wikipedia: https://en.wikipedia.org/wiki/Beta_distribution

Beta Distribution (alpha=2, beta=5)

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Wikipedia: https://en.wikipedia.org/wiki/Beta_distribution

Beta Distribution (alpha=5, beta=2)

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Wikipedia: https://en.wikipedia.org/wiki/Beta_distribution

Cauchy / Lorentz Distribution (x0=0)

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Wikipedia: https://en.wikipedia.org/wiki/Cauchy_distribution

Cauchy / Lorentz Distribution (x0=1)

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Wikipedia: https://en.wikipedia.org/wiki/Cauchy_distribution

Chi-Square Distribution (k=1)

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Wikipedia: https://en.wikipedia.org/wiki/Chi-square_distribution

Chi-Square Distribution (k=4)

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Wikipedia: https://en.wikipedia.org/wiki/Chi-square_distribution

Chi-Square Distribution (k=10)

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Wikipedia: https://en.wikipedia.org/wiki/Chi-square_distribution

Exponential Distribution (lambda=5)

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Wikipedia: https://en.wikipedia.org/wiki/Exponential_distribution

Exponential Distribution (lambda=10)

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Wikipedia: https://en.wikipedia.org/wiki/Exponential_distribution

Inverse Exponential Distribution (lambda=5)

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Wikipedia: https://en.wikipedia.org/wiki/Inverse_distribution#Inverse_exponential_distribution

Inverse Exponential Distribution (lambda=10)

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Wikipedia: https://en.wikipedia.org/wiki/Inverse_distribution#Inverse_exponential_distribution

Gamma Distribution (alpha=5, beta=15)

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Wikipedia: https://en.wikipedia.org/wiki/Gamma_distribution

Inverse Gamma Distribution (alpha=3, beta=0.5)

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Wikipedia: https://en.wikipedia.org/wiki/Inverse-gamma_distribution

Laplace Distribution (b=0.1, mu=0)

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Wikipedia: https://en.wikipedia.org/wiki/Laplace_distribution

Laplace Distribution (b=0.1, mu=0.5)

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Wikipedia: https://en.wikipedia.org/wiki/Laplace_distribution

Log-Normal Distribution (sigma=1, mu=0)

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Wikipedia: https://en.wikipedia.org/wiki/Log-normal_distribution

StudentT Distribution (nu=1)

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Wikipedia: https://en.wikipedia.org/wiki/Student%27s_t-distribution

Weibull Distribution (k=0.5, lambda=1)

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Wikipedia: https://en.wikipedia.org/wiki/Weibull_distribution

Product Compatible and additional computed target framework versions.
.NET net8.0 is compatible.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.
  • net8.0

    • No dependencies.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
1.2.0 205 5/29/2024
1.1.2 276 11/14/2023
1.1.1 159 10/26/2023
1.1.0 173 7/10/2023
1.0.0 521 11/7/2020