On 2 Oct 2014 16:52, "Robert Kern" <robert.k...@gmail.com> wrote: > > 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
If someone really wanted this functionality then I suppose it would be possible to precompute the special jump coefficients for lengths 2, 4, 8, 16, 32, ..., and then perform arbitrary jumps using a sequence of smaller jumps. (The coefficient table could be shipped with the source code.) -n
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