How can I retrieve the current seed of NumPy's random number generator?

Mast picture Mast · Aug 24, 2015 · Viewed 31.1k times · Source

The following imports NumPy and sets the seed.

import numpy as np
np.random.seed(42)

However, I'm not interested in setting the seed but more in reading it. random.get_state() does not seem to contain the seed. The documentation doesn't show an obvious answer.

How do I retrieve the current seed used by numpy.random, assuming I did not set it manually?

I want to use the current seed to carry over for the next iteration of a process.

Answer

ali_m picture ali_m · Aug 24, 2015

The short answer is that you simply can't (at least not in general).

The Mersenne Twister RNG used by numpy has 219937-1 possible internal states, whereas a single 64 bit integer has only 264 possible values. It's therefore impossible to map every RNG state to a unique integer seed.

You can get and set the internal state of the RNG directly using np.random.get_state and np.random.set_state. The output of get_state is a tuple whose second element is a (624,) array of 32 bit integers. This array has more than enough bits to represent every possible internal state of the RNG (2624 * 32 > 219937-1).

The tuple returned by get_state can be used much like a seed in order to create reproducible sequences of random numbers. For example:

import numpy as np

# randomly initialize the RNG from some platform-dependent source of entropy
np.random.seed(None)

# get the initial state of the RNG
st0 = np.random.get_state()

# draw some random numbers
print(np.random.randint(0, 100, 10))
# [ 8 76 76 33 77 26  3  1 68 21]

# set the state back to what it was originally
np.random.set_state(st0)

# draw again
print(np.random.randint(0, 100, 10))
# [ 8 76 76 33 77 26  3  1 68 21]