I have made a significant revision. In this version, downstream projects like
scikit-learn should experience significantly less forced churn.
https://github.com/rkern/numpy/blob/nep/rng-clarification/doc/neps/nep-0019-rng-policy.rst
https://mail.python.org/pipermail/numpy-discussion/2018-June/078252.html
tl;dr RandomState lives! But its distributions are forever frozen. So maybe
"undead" is more apt. Anyways, RandomState will continue to provide the same
stream-compatibility that it always has. But it will be internally refactored to
use the same core uniform PRNG objects that the new RandomGenerator
distributions class will use underneath (defaulting to the current Mersenne
Twister, of course). The distribution methods on RandomGenerator will be allowed
to evolve with numpy versions and get better/faster implementations.
Your code can mix the usage of RandomState and RandomGenerator as needed, but
they can be made to share the same underlying RNG algorithm's state.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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