# 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](https://www.codeproject.com/Articles/25172/Simple-Random-Number-Generation). 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)
![](images/normal.png)

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


### Beta Distribution (alpha=2, beta=2)
![](images/beta-a2b2.png)

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


### Beta Distribution (alpha=2, beta=5)
![](images/beta-a2b5.png)

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


### Beta Distribution (alpha=5, beta=2)
![](images/beta-a5b2.png)

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


### Cauchy / Lorentz Distribution (x0=0)
![](images/cauchy-lorentz-x0.png)

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


### Cauchy / Lorentz Distribution (x0=1)
![](images/cauchy-lorentz-x1.png)

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


### Chi-Square Distribution (k=1)
![](images/chi-square-k1.png)

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


### Chi-Square Distribution (k=4)
![](images/chi-square-k4.png)

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


### Chi-Square Distribution (k=10)
![](images/chi-square-k10.png)

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


### Exponential Distribution (lambda=5)
![](images/exponential-la5.png)

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


### Exponential Distribution (lambda=10)
![](images/exponential-la10.png)

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


### Inverse Exponential Distribution (lambda=5)
![](images/inverse-exponential-la5.png)

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


### Inverse Exponential Distribution (lambda=10)
![](images/inverse-exponential-la10.png)

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


### Gamma Distribution (alpha=5, beta=15)
![](images/gamma-a5b15.png)

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


### Inverse Gamma Distribution (alpha=3, beta=0.5)
![](images/inverse-gamma-a3b05.png)

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


### Laplace Distribution (b=0.1, mu=0)
![](images/laplace-b01m0.png)

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


### Laplace Distribution (b=0.1, mu=0.5)
![](images/laplace-b01m05.png)

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


### Log-Normal Distribution (sigma=1, mu=0)
![](images/log-normal-s1m0.png)

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


### StudentT Distribution (nu=1)
![](images/student-t-nu1.png)

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


### Weibull Distribution (k=0.5, lambda=1)
![](images/weibull-k05la1.png)

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