Python: vectorization over numpy array

Question

X and Y are both 3d arrays with dimensions (a,b,c). My goal is to do a dot product.

Consider that case where index i and j are scalar, and (X[i,:,j].T).dot(Y[i,:,j]) would be simple and return a scalar.

However, if I try to do vectorization, i and j become 1d arrays, and (X[i,:,j].T).dot(Y[i,:,j]) return a matrix but I am expecting a 1d array as result. How do I get around this problem ?

you can use np.einsum to calculate the product. if you can, add some random values with expected output — Aly Hosny
– Aly Hosny, Commented Mar 20, 2020 at 12:49

FBruzzesi · Accepted Answer · 2020-03-20 13:25:26Z

1

Naive implementation using list comprehension:

a,b,c = X.shape

r1 = [(X[i,:,j].T).dot(Y[i,:,j]) for i in range(a) for j in range(c)]

Implementation using np.einsum:

r2 = np.einsum('ijk,ijk->ik', X,Y).flatten()

answered Mar 20, 2020 at 13:25

FBruzzesi

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