Dotnet/FastRng
Dotnet/FastRng
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# FastRng
FastRng is a multi-threaded 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.
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 multi-threaded. Consumers can use a token to cancel e.g. timeout an operation.
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 easy to define discontinuous, arbitrary functions as shapes. Any consumer can define and use own distributions.
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](https://www.codeproject.com/Articles/25172/Simple-Random-Number-Generation). Thanks John for the inspiration.
Please notice: When using the debug environment, MultiThreadedRng might uses a smaller buffer size. Please ensure, that the production environment uses a release build, though.
The class `MultiThreadedRng` uses the George Marsaglia's MWC algorithm. The algorithm's implementation based loosely on John D. Cook's (johndcook.com) [implementation](https://www.codeproject.com/Articles/25172/Simple-Random-Number-Generation). Thanks, John, for the inspiration.
## Usage
Example code:
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- `MultiThreadedRng` fills some buffers after creation. Thus, create and reuse it as long as needed. Avoid useless re-creation.
- Distributions need some time at creation to calculate probabilities. Thus, just create a distribution once and use reuse it. 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