pha3z/rng

Random Number Generators in C#

C# Random Number Generators

Written by Conrad Shyu
revised on June 17, 2015
last updated on July 5, 2020

Synopsis

This C# implementation generates random number of various, well-known probability distributions. Specifically, this implementation supports the following continuous probability distributions:

• Beta, with default parameters shape 2 and 5
• Cauchy, with default parameters location 0.0 and scale 1.0
• Chi Square, with default degree of freedom 10
• Erlang, with default parameters shape 2 and rate 0.5
• Exponential, with default parameters lambda 1.0
• F, with default degrees of freedom 4 and 6
• Gamma, with default parameters shape 2.0 and rate 0.5
• Gaussian or Normal, with default mean 0.0 and standard deviation 1.0
• Pareto, with default parameters scale 2.0 and shape 3.0
• Rayleigh, with default parameter sigma 0.5
• Student-t, with default degree of freedom 10
• Uniform, with default minimum 0.0 and maximum 1.0
• Weibull, with default parameters scale 1.0 and shape 1.0

All the equations for generators, except Gamma, are analytical derived either using the inverse CDF (cumulative distribution function) or transformation. The random numbers generated based on the inverse CDF are guaranteed to follow the designated distribution. The Gamma random number generator, on the other hand, is based on the Marsaglia and Tsang's method. This method is also implemented in GSL (Gnu Scientific Library). The equation for Normal or Gaussian distribution is derived from the bivariate normal distribution, instead of univariate. The derivation is based on the Box-Muller transformation.

Filename	Description
random.cs	C# implementation of random number generators
README.md	this file

To compile the source code, simply type:

csc /out:random.exe random.cs

or, alternatively, on Ubuntu with Mono

mcs /out:random.exe random.cs

Author's Comments

Please report any problems or send comments to me.

Reference

• GEP Box, Muller M (1958) A note on the generation of random normal deviates. Annual of Mathematical Statistics, 29(2): 610-611. doi:10.1214/aoms/1177706645.
• Marsaglia G, Tsang WW (2000) The Ziggurat method for generating random variables. Journal of Statistical Software, 5(8). http://www.jstatsoft.org/v05/i08.

Copyright (C) 2015 Conrad Shyu
Richmond, Virginia 23223

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.