Is there a function for calculating all the pairwise dot products of the columns of a matrix, or between all pairs in a list of vectors?

Question

Say you have n. vectors of arbitrary (but equal) length m each. Is there a (numpy?) function, or a quick way, of calculating all pairwise dot products between these n. vectors?

My initial thought was that you could calculate A ^T A and take the upper triangular portion, but I'm not sure if that matrix multiplication is slow/computationally intensive. Is there a quicker/efficient way? Or should I just define a function using a for loop for all combinations of pairs?

Answer 1

As per @Brenila's comment, use tensordot :

np.tensordot(arr, arr, axes=(0,0))

Result shape is (n, n) for n = arr.shape[-1]