Recently, listening to Robert Kern taught a new useful trick to use when you write functions that use the numpy random number generator:
As always, when using random number generation in code, the problem is to get ‘repeatable results’. Of course, you want only repeatable statistics, and with statistics, the problem is to define what repeatable is. Anyhow, for various reasons, it is useful to be able to reproduce exactly runs, for instance when testing, fine tuning, or debugging. That is why you would like to be able to control the seed of your random number generation. Robert Kern showed us (at the SciPy conference tutorial) a way to control the pseudo random number generator (PRNG) in a function, without affecting the rest of the code. This does not involve setting the seed of the global PRNG, as this is evil, because it has global effects. The idea is to pass in to your functions a PRNG instance (by default the global one):
def test(prng=np.random):
print pnrg.rand(10)
if you want to use your function with a controlled PRNG, you can instantiate one with a specific seed:
prng = np.random.RandomState(seed=0)
and pass it to your function.
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