[Numpy-discussion] SFMT (faster mersenne twister)

[Robert Kern]

On Mon, Sep 8, 2014 at 6:05 PM, Pierre-Andre Noel
<noel.pierre.andre at gmail.com> wrote:
>  > I think we could add new generators to NumPy though,
>  > perhaps with a keyword to control the algorithm (defaulting to the
> current
>  > Mersenne Twister).
>
> Why not do something like the C++11 <random>? In <random>, a "generator"
> is the engine producing randomness, and a "distribution" decides what is
> the type of outputs that you want. Here is the example on
> http://www.cplusplus.com/reference/random/ .
>
>      std::default_random_engine generator;
>      std::uniform_int_distribution<int> distribution(1,6);
>      int dice_roll = distribution(generator);  // generates number in
> the range 1..6
>
> For convenience, you can bind the generator with the distribution (still
> from the web page above).
>
>      auto dice = std::bind(distribution, generator);
>      int wisdom = dice()+dice()+dice();
>
> Here is how I propose to adapt this scheme to numpy. First, there would
> be a global generator defaulting to the current implementation of
> Mersene Twister. Calls to numpy's "RandomState", "seed", "get_state" and
> "set_state" would affect this global generator.
>
> All numpy functions associated to random number generation (i.e.,
> everything listed on
> http://docs.scipy.org/doc/numpy/reference/routines.random.html except
> for "RandomState", "seed", "get_state" and "set_state") would accept the
> kwarg "generator", which defaults to the global generator (by default
> the current Mersene Twister).
>
> Now there could be other generator objects: the new Mersene Twister,
> some lightweight-but-worse generator, or some cryptographically-safe
> random generator. Each such generator would have "RandomState", "seed",
> "get_state" and "set_state" methods (except perhaps the
> criptographically-safe ones). When calling a numpy function with
> generator=my_generator, that function uses this generator instead the
> global one. Moreover, there would be be a function, say
> select_default_random_engine(generator), which changes the global
> generator to a user-specified one.

I think the Python standard library's example is more instructive. We
have new classes for each new core uniform generator. They will share
a common superclass to share the implementation of the non-uniform
distributions. numpy.random.RandomState will continue to be the
current Mersenne Twister implementation, and so will the underlying
global RandomState for all of the convenience functions in
numpy.random. If you want the SFMT variant, you instantiate
numpy.random.SFMT() and call its methods directly.

-- 
Robert Kern