CODEWITHSUNDEEP
pythonpython-3.xnumpynumpy-random
Atimaharjun
Atimaharjun

Reputation: 409

How to access numpy default global random number generator

I need to create a class which takes in a random number generator (i.e. a numpy.random.RandomState object) as a parameter. In the case this argument is not specified, I would like to assign it to the random generator that numpy uses when we run numpy.random.<random-method>. How do I access this global generator? Currently I am doing this by just assigning the module object as the random generator (since they share methods / duck typing). However this causes issues when pickling (unable to pickle module object) and deep-copying. I would like to use the RandomState object behind numpy.random

PS: I'm using python-3.4

Upvotes: 9

Views: 2757

Answers (3)

Pierre de Buyl
Pierre de Buyl

Reputation: 7293

I don't know how to access the global state. However, you can use a RandomState object and pass it along. Random distributions are attached to it, so you call them as methods.

Example:

import numpy as np

def computation(parameter, rs):
    return parameter*np.sum(rs.uniform(size=5)-0.5)

my_state = np.random.RandomState(seed=3)

print(computation(3, my_state))

Upvotes: 0

user2357112
user2357112

Reputation: 280778

As well as what kazemakase suggests, we can take advantage of the fact that module-level functions like numpy.random.random are really methods of a hidden numpy.random.RandomState by pulling the __self__ directly from one of those methods:

numpy_default_rng = numpy.random.random.__self__

Upvotes: 5

MB-F
MB-F

Reputation: 23637

numpy.random imports * from numpy.random.mtrand, which is an extension module written in Cython. The source code shows that the global state is stored in the variable _rand. This variable is not imported into the numpy.random scope but you can get it directly from mtrand.

import numpy as np
from numpy.random.mtrand import _rand as global_randstate

np.random.seed(42)
print(np.random.rand())
# 0.3745401188473625

np.random.RandomState().seed(42)  # Different object, does not influence global state
print(np.random.rand())
# 0.9507143064099162

global_randstate.seed(42)  # this changes the global state
print(np.random.rand())
# 0.3745401188473625

Upvotes: 2

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