Select random non zero elements from each row of a 2d numpy array

Question

I have a 2d array

a = array([[5, 0, 1, 0],
           [0, 1, 3, 5],
           [2, 3, 0, 0],
           [4, 0, 2, 4],
           [3, 2, 0, 3]])

and a 1d array

b = array([1, 2, 1, 2, 2])

which (b) tells how many non-zero elements we want to choose from each row of the array a.

For example, b[0] = 1 tells us that we have to choose 1 non-zero element from a[0], b[1] = 2 tells us that we have to choose 2 non-zero elements from a[1], and so on.

For a 1d array, it can be done using np.random.choice, but I can't find how to do it for a 2d array, so I have to use a for loop which slows the computation.

I want the result as 2d array as

array([[5, 0, 0, 0],
       [0, 1, 0, 5],
       [2, 0, 0, 0],
       [0, 0, 2, 4],
       [3, 2, 0, 0]])

Here, we have 1 element in row 1, 2 elements in row 2, 1 element in row 3 and so on as given in array b.

What if the number of non-zero items doesn't match with random number in b? — Kasravnd
– Kasravnd, Commented Jun 25, 2018 at 11:00
@Kasramvd The numbers in b are not random, it is the integer value of 80% of the number of non-zero elements in a for each row. Like for row 1 number of non-zero element is 2, 80% of 2 is 1.6, int(1.6) is 1. — Shivansh Kumar
– Shivansh Kumar, Commented Jun 25, 2018 at 11:06

MaJoR · Accepted Answer · 2018-06-25 11:02:40Z

0

It looks like a Competitive Programming problem.

I don't think that you can achieve the results using numpy.random.choice (I may be wrong).

Anyways, think of it like this. To select x number of non-zero elements from a 1D array of size n, it will be of O(n) complexity in the worst case. And, for a 2D array it will be O(n^2) if you follow the same naive approach.

this post is almost similar to your question, but numpy.nonzero also is an O(n^2) function.

answered Jun 25, 2018 at 11:02

MaJoR

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