Reputation: 427
Just came across this:
Vectorized way of calculating row-wise dot product two matrices with Scipy
This numpy.einsum is really awesome but its a little confusing to use. Suppose I have:
import numpy as np
a = np.array([[1,2,3], [3,4,5]])
b = np.array([[0,1,2], [1,1,7]])
How would i use the "ij" in einsum to get a "cross dot product" between a and b?
Using the example basically I would like to compute dot product of
[1,2,3] and [0,1,2]
[1,2,3] and [1,2,7]
[3,4,5] and [0,1,2]
[3,4,5] and [1,1,7]
and end up with [[8,26],[14,42]]
I know if I use
np.einsum("ij,ij->i",a,b)
I would just end up with [8, 42] which means I am missing the "cross" elements
Upvotes: 6
Views: 10448
Reputation: 214957
Your result is still 2 dimensional, so you need two indices. What you need is a matrix multiplication with the second array transposed, so instead of normal ij,jk->ik
, you transpose the second matrix by ij,kj->ik
:
np.einsum('ij,kj->ik', a, b)
#array([[ 8, 24],
# [14, 42]])
which is equivalent to:
np.dot(a, b.T)
#array([[ 8, 24],
# [14, 42]])
import numpy as np
a = np.array([[1,2,3], [3,4,5]])
b = np.array([[0,1,2], [1,1,7]])
Upvotes: 8