On Thu, Oct 2, 2014 at 11:56 PM, Sturla Molden wrote:
> Robert Kern wrote:
>
>> Yes, but that would require rewriting much of numpy.random to allow
>> replacing the core generator. This would work out-of-box because it's
>> just manipulating the state of the current core generator.
>
> Yes, then
Robert Kern wrote:
> Yes, but that would require rewriting much of numpy.random to allow
> replacing the core generator. This would work out-of-box because it's
> just manipulating the state of the current core generator.
Yes, then we just need to sacrifice a year's worth of CPU time, and a PR
w
Thanks for the great input. The idea of implementing jump-ahead in
numpy.random would be a very nice feature, but I don't currently have
the time to work on implementing such a feature. For now, it seems
the simplest approach is to cache the RandomState and reuse that
later.
Brad
On Thu, Oct 2,
On Thu, Oct 2, 2014 at 9:52 PM, Sturla Molden wrote:
> Robert Kern wrote:
>
>> No one needs small jumps of arbitrary size. The real use case for
>> jumping is to make N parallel streams that won't overlap. You pick a
>> number, let's call it `jump_steps`, much larger than any single run of
>> you
Robert Kern wrote:
> No one needs small jumps of arbitrary size. The real use case for
> jumping is to make N parallel streams that won't overlap. You pick a
> number, let's call it `jump_steps`, much larger than any single run of
> your system could possibly consume (i.e. the number of core PRNG
On Thu, Oct 2, 2014 at 5:28 PM, Nathaniel Smith wrote:
> On 2 Oct 2014 16:52, "Robert Kern" wrote:
>>
>> On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran wrote:
>> > Given the following:
>> >
>> > from numpy import random
>> > rs = random.RandomState(seed=1)
>> > # skip the first X billion samples
>>
On 2 Oct 2014 16:52, "Robert Kern" wrote:
>
> On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran 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 firs
On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran 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
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 par
