Python numpy - Reproducibility of random numbers
numpy, prng, python, random
Solution
Okay, David was right. The PRNGs in numpy work correctly. Throughout every minimal example I created, they worked as they are supposed to.
My problem was a different one, but finally I solved it. Do never loop over a dictionary within a deterministic algorithm. It seems that Python orders the items arbitrarily when calling the .item() function for getting in iterator.
So I am not that disappointed that this was this kind of error, because it is a useful reminder of what to think about when trying to do reproducible simulations.
Problem
We have a very simple program (single-threaded) where we we do a bunch of random sample generation. For this we are using several calls of the `numpy` random functions (like `normal` or `random_sample`). Sometimes the result of one random call determines the number of times another random function is called. Now I want to set a seed in the beginning s.th. multiple runs of my program should yield the same result. For this I'm using an instance of the `numpy` class `RandomState`. While this is the case in the beginning, at some time the results become different and this is why I'm wondering. When I am doing everything correctly, having no concurrency and thereby a linear call of the functions AND no other random number generator involded, why does it not work?