Suppose I want to generate a bunch of binary number generators given the success probabilities. One concern I have is about the random seed. The random seeds for f1, f2, f3 are their creation time, right? And the seeds are fixed for each function regardless of the random seed in the global namespace, right?
def f(p):
import random, time
random.seed(time.time())
def g():
return 1 if random.random() < p else 0
return g
f1 = f(0.05)
f2 = f(0.65)
f3 = f(0.25)
You pass in a seed to the global random object each time you call f(), because all top-level functions in the random module feed into a singleton object. This means that by the time f3 is created, the seeds set in f2 and f1 have been superseded, the seeds are not independent from the global random object. Importing random again for each f() call does not give you a new state, as really only binds names anew each time once the module object itself is loaded (only on the first import).
If you want to have a seeded random generator per function, you need to create individual random.Random() instances:
import random
import time
def f(p):
seeded_random = random.Random(time.time())
def g():
return 1 if seeded_random.random() < p else 0
return g
From the random module documentation:
The functions supplied by this module are actually bound methods of a hidden instance of the
random.Randomclass. You can instantiate your own instances ofRandomto get generators that don’t share state.
f1, f2 and f3 all use a single global Random() object. You tell that one object to use a new seed three times, which simply means you discard whatever state was set for the first two calls.f1, f2 and f3 produce random numbers from that one global Random() object seeded to the last call. That means they'll produce values in the same sequence as if you used one random.Random() object with the same seed as that last random.seed() call.foo = random.Random(seed_foo) object, then called foo.random() a number of times, the values that that produces can be re-created exactly by using the exact same seed again in a different random.Random() object. Your f1, f2 and f3 simply draw from that sequence together, they are not backed by independent random.Random() objects. Their state is connected.
randommodule and therefore only one seed. You'll have to create arandom.Random(seed)object for each function.