Difference between np.random.seed() and np.random.RandomState()

Question

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?

Answer 1

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()