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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]`.
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