python - Numpy arrays: row/column wise argmax with random ties

Question

Here is what I am trying to do with Numpy in Python 2.7. Suppose I have an array a defined by the following:

a = np.array([[1,3,3],[4,5,6],[7,8,1]])

I can do a.argmax(0) or a.argmax(1) to get the row/column wise argmax:

a.argmax(0)
Out[329]: array([2, 2, 1], dtype=int64)
a.argmax(1)
Out[330]: array([1, 2, 1], dtype=int64)

However, when there is a tie like in a's first row, I would like to get the argmax decided randomly between the ties (by default, Numpy returns the first element whenever a tie occurs in argmax or argmin).

Last year, someone put a question on solving Numpy argmax/argmin ties randomly: Select One Element in Each Row of a Numpy Array by Column Indices

However, the question aimed at uni-dimensional arrays. There, the most voted answer works well for that. There is a second answer that attempts to solve the problem also for multidimensional arrays but doesn't work - i.e. it does not return, for each row/column the index of the maximum value with ties solved randomly.

What would be the most performent way to do that, since I am working with big arrays?

Answer 1

A simple way is to add a small random number to all the values at the start, so your data would be like this:

a = np.array([[1.1827,3.1734,3.9187],[4.8172,5.7101,6.9182],[7.1834,8.5012,1.9818]])

That can be done by a = a + np.random.random(a.shape).

If you later need to get the original values back, you can do a.astype(int) to drop the fractional parts.