How to calculate weighted average on a traingular similarity matrix

Question

I have a triangular similarity matrix like this.

[[3, 1, 2, 0],
 [1, 3, 0, 0],
 [1, 0, 0, 0],
 [0, 0, 0, 0]]

How do I calculate a weighted average for each row while discarding the zero elemets?

Answer 1

You could add along the second axis, and divide by the sum over the amount of non-zero values per row. Then with where in np.divide you can divide where a condition is satisfied, which by setting it to a mask specifying where non-zero values are, you can prevent getting a division by zero error:

a = np.array([[3, 1, 2, 0],
              [1, 3, 0, 0],
              [1, 0, 0, 0],
              [0, 0, 0, 0]])

m = (a!=0).sum(1)
np.divide(a.sum(1), m, where=m!=0)
# array([2., 2., 1., 0.])