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

Question

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 m

Answer 1

This answer complements important details others missed. First, to rephrase the conclusion:

Original random seeds (set via np.random.seed) cannot be retrieved after generating numbers, but intermediates (current state) can.

Refer to @vestland's answer; it may, however, mislead: the generated numbers differ not due to inability to map states, but that an incomplete encoding is used: get_state()[1]. The complete representation includes pos = get_state()[2]. To illustrate:

import numpy as np

state0 = np.random.get_state()
rand0  = np.random.randint(0, 10, 1)
state1 = np.random.get_state()
rand1  = np.random.randint(0, 10, 1)

assert all(s0 == s1 for s0, s1 in zip(state0[1], state1[1]))

We generated a number, yet get_state()[1] remained identical. However:

np.random.set_state(state0)
assert np.random.randint(0, 10, 1) == rand0

and likewise for state1 & rand1. Hence, @vestland's numbers differ because when not setting a seed, pos = 623 - whereas if we use np.random.seed, pos = 624. Why the inconvenient discrepancy? No clue.

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In summary on np.random.seed(s):

• get_state()[1][0] immediately after setting: retrieves s that exactly recreates the state
• get_state()[1][0] after generating numbers: may or may not retrieve s, but it will not recreate the current state (at get_state())
• get_state()[1][0] after generating many numbers: will not retrieve s. This is because pos exhausted its representation.
• get_state() at any point: will exactly recreate that point.

Lastly, behavior may also differ due to get_state()[3:] (and of course [0]).