On Tue, May 17, 2016 at 2:40 PM, Sturla Molden <sturla.mol...@gmail.com> wrote: > > Stephan Hoyer <sho...@gmail.com> wrote: > > I have recently encountered several use cases for randomly generate random > > number seeds: > > > > 1. When writing a library of stochastic functions that take a seed as an > > input argument, and some of these functions call multiple other such > > stochastic functions. Dask is one such example [1]. > > > > 2. When a library needs to produce results that are reproducible after > > calling numpy.random.seed, but that do not want to use the functions in > > numpy.random directly. This came up recently in a pandas pull request [2], > > because we want to allow using RandomState objects as an alternative to > > global state in numpy.random. A major advantage of this approach is that it > > provides an obvious alternative to reusing the private numpy.random._mtrand > > [3]. > > What about making numpy.random a finite state machine, and keeping a stack > of RandomState seeds? That is, something similar to what OpenGL does for > its matrices? Then we get two functions, numpy.random.push_seed and > numpy.random.pop_seed.
I don't think that addresses the issues brought up here. It's just more global state to worry about. -- Robert Kern
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