>
>
>>
>> - the seed/SeedSequence that was used to construct the BitGenerator
>>
>
> >>> rng = np.random.default_rng()
> >>> rng.bit_generator.seed_seq
> SeedSequence(
> entropy=186013007116029215180532390504704448637,
> )
>
> In some older versions of numpy, the attribute was semi-private as
>
If you missed the NumPy 2.0 birds-of-a-feather session at SciPy 2024, here
is your lucky second chance!
Join an AMA with Nathan Goldbaum, a NumPy maintainer from Quansight Labs,
hosted by OpenTeams to discuss the team’s work on NumPy 2.0 and future
direction of the project. This is also an opportu
The next NumPy Optimization Team meeting will be held this Monday, August
5th at 5 pm UTC.
Join us via Zoom:
https://numfocus-org.zoom.us/j/81261288210?pwd=iwV99tGSjR61RTGEERKM4QKxe46g1n.1
.
Everyone is welcome and encouraged to attend.
To add to the meeting agenda the topics you’d like to discuss,
On Fri, Aug 2, 2024 at 9:37 AM Robert Kern wrote:
> On Fri, Aug 2, 2024 at 1:28 AM Andrew Nelson wrote:
>
>> When using the new `Generator`s for stochastic optimisation I sometimes
>> find myself possessing a great solution, but am wondering what path the
>> random number generation took to get
On Fri, Aug 2, 2024 at 1:28 AM Andrew Nelson wrote:
> When using the new `Generator`s for stochastic optimisation I sometimes
> find myself possessing a great solution, but am wondering what path the
> random number generation took to get to that point.
>
> I know that I can get the current state
