On Mon, Sep 8, 2014 at 6:05 PM, Pierre-Andre Noel <noel.pierre.andre@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