On Mon, Jun 29, 2020 at 11:30 AM Robert Kern wrote:
> On Mon, Jun 29, 2020 at 11:10 AM Kevin Sheppard <
> kevin.k.shepp...@gmail.com> wrote:
>
>>
>>1. The total number of digits in the binary representation is
>>somewhere between 32 and 128.
>>
>>
> I like using the standard library
On Mon, Jun 29, 2020 at 11:10 AM Kevin Sheppard
wrote:
> It can be anything, but “good practice” is to use a number that would have
> 2 properties:
>
>
>
>1. When expressed as binary number, it would have a large number of
>both 0s and 1s
>
>
The properties of the SeedSequence algorithm
It can be anything, but “good practice” is to use a number that would have 2 properties: When expressed as binary number, it would have a large number of both 0s and 1sThe total number of digits in the binary representation is somewhere between 32 and 128. The binary representation of the one I
Thanks Kevin!
A possibly dumb follow-up question: in your example,
> entropy = 382193877439745928479635728
is it relevant that `entropy` is a long integer? I.e., what are the
constraints on its value, can one use
entropy = 1234 or
entropy = 0 or
entropy = 1
instead?
On Mon, Jun 29, 2020
On Mon, Jun 29, 2020 at 8:02 AM Neal Becker wrote:
> I was using this to reset the generator, in order to repeat the same
> sequence again for testing purposes.
>
In general, you should just pass in a new Generator that was created with
the same seed.
def function_to_test(rg):
x =
On 6/29/20 5:37 PM, Kevin Sheppard
wrote:
The best practice is to use a SeedSequence
to spawn child SeedSequences, and then to use these children
to initialize your generators or bit generators.
The best practice is to use a SeedSequence to spawn child SeedSequences, and then to use these children to initialize your generators or bit generators. from numpy.random import SeedSequence, Generator, PCG64, default_rng entropy = 382193877439745928479635728 seed_seq =
(apologies for jumping into a conversation)
So what is the recommendation for instantiating a number of generators
with manually controlled seeds?
The use case is running a series of MC simulations with reproducible
streams. The runs are independent and are run in parallel in separate
OS
If you want to use the same entropy-initialized generator for temporarily-reproducible experiments, then you can use gen = np.random.default_rng()state = gen.bit_generator.stategen.standard_normal()# 0.5644742559549797, will vary across runsgen.bit_generator.state = stategen.standard_normal()#
I was using this to reset the generator, in order to repeat the same
sequence again for testing purposes.
On Wed, Jun 24, 2020 at 6:40 PM Robert Kern wrote:
> On Wed, Jun 24, 2020 at 3:31 PM Neal Becker wrote:
>
>> Consider the following:
>>
>> from numpy.random import default_rng
>> rs =
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