Array elements value assignment

Question

I intend to create 100 binary arrays of shape (8, 8, 8) in a way that for each of its arrays about 40% of the elements are zero and the rest are 1. I have the following code:

num1=100
size = (num1, 8, 8, 8)
prob_0 = 0.4 # 40% of zeros
prob_1 = 1 - prob_0 # 60% of ones
P = np.random.choice([0, 1], size=size, p=[prob_0, prob_1])

However, it is probabilistic and I am looking for its deterministic version.

Further elaboration: In the above code, it is theoretically possible that I get an array with all the elements being 1 for instance. What I'm looking for is a method that controls value assignment in a way I only get to have arrays that only 40% of its elements are 0 and the rest are 1. In other words, each of the (8, 8, 8) arrays should have a constant number of ones and zeros.

Just to make it clear, the constraint is to have the same number of 0's and 1's between runs, but their positions are not necessarily deterministic — ronpi
– ronpi, Commented Aug 23, 2021 at 15:29
Output of first run, compared to the output from the second run of the same program — ronpi
– ronpi, Commented Aug 23, 2021 at 20:32

ronpi · Accepted Answer · 2021-08-23 02:04:09Z

1

Just use numpy's Generator to get deterministic results. This is how you can get the exact same array each run.

from numpy.random import default_rng

rng = default_rng(42)    # default random generator

num1=100
size = (num1, 8, 8, 8)
prob_0 = 0.4 # 40% of zeros
prob_1 = 1 - prob_0 # 60% of ones
P = rng.choice([0, 1], size=size, p=[prob_0, prob_1])

edited Aug 23, 2021 at 2:04

answered Aug 22, 2021 at 21:15

ronpi

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1 Comment

Liam Spring Over a year ago

Thank you for your response. But in fact, it is not what I'm after. I have added extra info to my question.

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