SyncRNG
=======
A synchronized Tausworthe RNG usable in R and Python.


Why?
----

This program was created because it was desired to have the same random 
numbers in both R and Python programs. 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.

How
---

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

```python
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:

```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.

Installation
------------

Installing the R package can be done through devtools:

```R
library(devtools)
devtools::install_github("GjjvdBurg/SyncRNG")
```

To install SyncRNG as a Python module, first clone the repository. The Python 
module can then be installed locally for the user using:

```sh
python setup.py install --user
```
or system-wide through:

```sh
sudo python setup.py install
```

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]`.