I know that to seed the randomness of numpy.random, and be able to reproduce it, I should us:
import numpy as np
np.random.seed(1234)but what does np.random.RandomState()do?
5 Answers
If you want to set the seed that calls to np.random... will use, use np.random.seed:
np.random.seed(1234)
np.random.uniform(0, 10, 5)
#array([ 1.9151945 , 6.22108771, 4.37727739, 7.85358584, 7.79975808])
np.random.rand(2,3)
#array([[ 0.27259261, 0.27646426, 0.80187218],
# [ 0.95813935, 0.87593263, 0.35781727]])Use the class to avoid impacting the global numpy state:
r = np.random.RandomState(1234)
r.uniform(0, 10, 5)
#array([ 1.9151945 , 6.22108771, 4.37727739, 7.85358584, 7.79975808])And it maintains the state just as before:
r.rand(2,3)
#array([[ 0.27259261, 0.27646426, 0.80187218],
# [ 0.95813935, 0.87593263, 0.35781727]])You can see the state of the sort of 'global' class with:
np.random.get_state()and of your own class instance with:
r.get_state()np.random.RandomState() constructs a random number generator. It does not have any effect on the freestanding functions in np.random, but must be used explicitly:
>>> rng = np.random.RandomState(42)
>>> rng.randn(4)
array([ 0.49671415, -0.1382643 , 0.64768854, 1.52302986])
>>> rng2 = np.random.RandomState(42)
>>> rng2.randn(4)
array([ 0.49671415, -0.1382643 , 0.64768854, 1.52302986])random.seed is a method to fill random.RandomState container.
from numpy docs:
numpy.random.seed(seed=None)Seed the generator.
This method is called when RandomState is initialized. It can be called again to re-seed the generator. For details, see RandomState.
class numpy.random.RandomState4Container for the Mersenne Twister pseudo-random number generator.
np.random.RandomState() - a class that provides several methods based on different probability distributions.
np.random.RandomState.seed() - called when RandomState() is initialised.
Seed is a global pseudo-random generator. However, randomstate is a pseudo-random generator isolated from others, which only impact specific variable.
rng = np.random.RandomState(0)
rng.rand(4)
# Out[1]: array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
rng = np.random.RandomState(0)
rng.rand(4)
# Out[2]: array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])It's basically as same as Seed, but as the following, We don't assign randomstate to a variable.
np.random.RandomState(0)
# Out[3]: <mtrand.RandomState at 0xddaa288>
np.random.rand(4)
# Out[4]: array([0.62395295, 0.1156184 , 0.31728548, 0.41482621])
np.random.RandomState(0)
# Out[5]: <mtrand.RandomState at 0xddaac38>
np.random.rand(4)
# Out[6]: array([0.86630916, 0.25045537, 0.48303426, 0.98555979])The latter is different from the former. It means that randomstate only avails inside specific variable.
