SyncRNG

A synchronized Tausworthe RNG usable in R and Python.

Why?

This program was created because I needed to have the same random numbers in both R and Python. Although both languages implement a Mersenne-Twister RNG, the implementations are so different that it is not possible to get the same random numbers with the same seed.

SyncRNG is a Tausworthe RNG implemented in syncrng.c, and linked to both R and Python. Since both use the same underlying C code, the random numbers will be the same in both languages, provided the same seed is used.

You can read more about my motivations for creating this here.

How

First install the packages as stated under Installation. Then, in Python you can do::

from SyncRNG import SyncRNG

s = SyncRNG(seed=123456)
for i in range(10):
  print(s.randi())

Similarly, after installing the R library you can do in R::

library(SyncRNG)

s <- SyncRNG(seed=123456)
for (i in 1:10) {
   cat(s$randi(), '\n')
}

You’ll notice that the random numbers are indeed the same.

R - User defined RNG

R allows the user to define a custom random number generator, which is then used for the common runif and rnorm functions in R. This has also been implemented in SyncRNG as of version 1.3.0. To enable this, run::

library(SyncRNG)

set.seed(123456, 'user', 'user')
runif(10)

These numbers are between [0, 1) and multiplying by 2**32 - 1 gives the same results as above.

Installation

Installing the R package can be done through CRAN::

install.packages('SyncRNG')

The Python package can be installed using pip::

pip install syncrng

Usage

In both R and Python the following methods are available for the SyncRNG class:

1. randi(): generate a random integer on the interval [0, 2^32).
2. rand(): generate a random floating point number on the interval [0.0, 1.0)
3. randbelow(n): generate a random integer below a given integer n.
4. shuffle(x): generate a permutation of a given list of numbers x.

Notes

The random numbers are uniformly distributed on [0, 2^32 - 1].