On the use of numpy.random.RandomState

Question

What is the difference in

a = numpy.random.RandomState(1)
b = a.rand()

compared to

b = numpy.random.rand()

I'm not really sure if this has to do with seeding, and I'm new to the concept of seeding. If this is a case of seeding could somebody explain how the RandomState method does that, and maybe any tips on when it's useful to seed. Thank you.

Duplicate: https://stackoverflow.com/questions/22994423/difference-between-np-random-seed-and-np-random-randomstate — Dinesh, Oct 08 '18 at 15:51

score 4 · Accepted Answer · edited Oct 08 '18 at 16:54

4

The first sentence (a = numpy.random.RandomState(1)) start the pseudo random seed, in your case 1. That means that, no mater how many times you run the script, always get the same "random" number.

In the second case (b = numpy.random.rand()), the seed is predefined, you don't know what seed was used. So, every time you run a script, you obtain the next pseudo random of the unknown seed.

edited Oct 08 '18 at 16:54

bereket gebredingle

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answered Oct 08 '18 at 16:37

Eduardo López

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On the use of numpy.random.RandomState

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