# [Numpy-discussion] skip samples in random number generator

Nathaniel Smith njs at pobox.com
Thu Oct 2 12:28:47 EDT 2014

```On 2 Oct 2014 16:52, "Robert Kern" <robert.kern at gmail.com> wrote:
>
> On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran <bburan at 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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