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# SyncRNG
[](https://github.com/GjjvdBurg/SyncRNG/actions)
[](https://cran.r-project.org/web/packages/SyncRNG/index.html)
[](https://cran.r-project.org/web/packages/SyncRNG/index.html)
[](https://pypi.org/project/SyncRNG)
[](https://pepy.tech/project/SyncRNG)
Generate the same random numbers 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 random number generator (RNG), the implementations are so
different that it is not possible to get the same random numbers, even with
the same seed.
SyncRNG is a "Tausworthe" RNG implemented in 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 when the same seed is used.
You can read more about my motivations for creating this
[here](https://gertjanvandenburg.com/blog/syncrng/).
## 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
After installing the package, you can use the basic ``SyncRNG`` random number
generator. In Python you can do:
```python
>>> from SyncRNG import SyncRNG
>>> s = SyncRNG(seed=123456)
>>> for i in range(10):
>>> print(s.randi())
```
And in R you can use:
```r
> library(SyncRNG)
> s <- SyncRNG(seed=123456)
> for (i in 1:10) {
> cat(s$randi(), '\n')
> }
```
You'll notice that the random numbers are indeed the same.
### R: User defined RNG
R allows the user to define a custom random number generator, which is then
used for the common ``runif`` and ``rnorm`` functions in R. This has also been
implemented in SyncRNG as of version 1.3.0. To enable this, run:
```r
> library(SyncRNG)
> set.seed(123456, 'user', 'user')
> runif(10)
```
These numbers are between [0, 1) and multiplying by ``2**32 - 1`` gives the
same results as above.
### Functionality
In both R and Python the following methods are available for the ``SyncRNG``
class:
1. ``randi()``: generate a random integer on the interval [0, 2^32).
2. ``rand()``: generate a random floating point number on the interval [0.0,
1.0)
3. ``randbelow(n)``: generate a random integer below a given integer ``n``.
4. ``shuffle(x)``: generate a permutation of a given list of numbers ``x``.
Functionality is deliberately kept minimal to make maintaining this library
easier. It is straightforward to build more advanced applications on the
existing methods, as the following example shows.
### Creating the same train/test splits
A common use case for this package is to create the same train and test splits
in R and Python. Below are some code examples that illustrate how to do this.
Both assume you have a matrix ``X`` with `100` rows.
In R:
```r
# This function creates a list with train and test indices for each fold
k.fold <- function(n, K, shuffle=TRUE, seed=0)
{
idxs <- c(1:n)
if (shuffle) {
rng <- SyncRNG(seed=seed)
idxs <- rng$shuffle(idxs)
}
# Determine fold sizes
fsizes <- c(1:K)*0 + floor(n / K)
mod <- n %% K
if (mod > 0)
fsizes[1:mod] <- fsizes[1:mod] + 1
out <- list(n=n, num.folds=K)
current <- 1
for (f in 1:K) {
fs <- fsizes[f]
startidx <- current
stopidx <- current + fs - 1
test.idx <- idxs[startidx:stopidx]
train.idx <- idxs[!(idxs %in% test.idx)]
out$testidxs[[f]] <- test.idx
out$trainidxs[[f]] <- train.idx
current <- stopidx
}
return(out)
}
# Which you can use as follows
folds <- k.fold(nrow(X), K=10, shuffle=T, seed=123)
for (f in 1:folds$num.folds) {
X.train <- X[folds$trainidx[[f]], ]
X.test <- X[folds$testidx[[f]], ]
# continue using X.train and X.test here
}
```
And in Python:
```python
def k_fold(n, K, shuffle=True, seed=0):
"""Generator for train and test indices"""
idxs = list(range(n))
if shuffle:
rng = SyncRNG(seed=seed)
idxs = rng.shuffle(idxs)
fsizes = [n // K]*K
mod = n % K
if mod > 0:
fsizes[:mod] = [x+1 for x in fsizes[:mod]]
current = 0
for fs in fsizes:
startidx = current
stopidx = current + fs
test_idx = idxs[startidx:stopidx]
train_idx = [x for x in idxs if not x in test_idx]
yield train_idx, test_idx
current = stopidx
# Which you can use as follows
kf = k_fold(X.shape[0], K=3, shuffle=True, seed=123)
for trainidx, testidx in kf:
X_train = X[trainidx, :]
X_test = X[testidx, :]
# continue using X_train and X_test here
```
## Notes
The random numbers are uniformly distributed on ``[0, 2^32 - 1]``. No
attention has been paid to thread-safety and you shouldn't use this random
number generator for cryptographic applications.
## Questions and Issues
If you have questions, comments, or suggestions about SyncRNG or you encounter
a problem, please open an issue [on
GitHub](https://github.com/GjjvdBurg/SyncRNG/). Please don't hesitate to
contact me, you're helping to make this project better for everyone! If you
prefer not to use Github you can email me at ``gertjanvandenburg at gmail dot
com``.
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