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 of the BitGenerators. However, what I'd like to do is query the BitGenerator to figure out how the BitGenerator was setup in the first place. i.e. either: - the seed/SeedSequence that was used to construct the BitGenerator or - the state that was last applied to the BitGenerator One obvious way around this would be to do the seeding in the first place, but it would sure be nice to figure out how to replicate the BitGenerator without having to do that. I'm thinking of something like: ``` rng = np.random.default_rng(190828902908) rng.uniform(size=(100,)) # 190828902908 print(rng.initial_seed) ``` -- _____________________________________ Dr. Andrew Nelson _____________________________________
_______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
