Mast - 1 year ago 236

Python 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 more in reading it.

`random.get_state()`

How do I retrieve the current seed used by

`numpy.random`

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

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

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

The Mersenne Twister RNG used by numpy has 2^{19937}-1 possible internal states, whereas a single 64 bit integer has only 2^{64} 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 (2^{624 * 32} > 2^{19937}-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]
```

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**