On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran <bbu...@alum.mit.edu> wrote: > Given the following: > > from numpy import random > rs = random.RandomState(seed=1) > # skip the first X billion samples > x = rs.uniform(0, 10) > > How do I accomplish "skip the first X billion samples" (e.g. 7.2 > billion)? I see that there's a numpy.random.RandomState.set_state > which accepts (among other parameters) a value called "pos". This > sounds promising, but the other parameters I'm not sure how to compute > (e.g. the 1D array of 624 unsigned integers, etc.). I need to be able > to skip ahead in the sequence to reproduce some signals that were > generated for experiments. I could certainly consume and discard the > first X billion samples; however, that seems to be computationally > inefficient.
Unfortunately, it requires some significant number-theoretical precomputation for any given N number of steps that you want to skip. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/JUMP/index.html It's also unreliable for your purpose. You don't know how many random integers were actually pulled from the core PRNG if you ever used any of the nonuniform distributions (they usually will consume a variable number of uniform pseudorandom numbers to give you a single nonuniform number). Instead, you should just pickle the RandomState object just before you start using it for anything that you want to reproduce. The unpickled RandomState will reproduce the same numbers the first one did. -- Robert Kern _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
