Numpy Dot Product of two 2-d arrays in numpy to get 3-d array

Question

Sorry for the badly explained title. I am trying to parallelise a part of my code and got stuck on a dot product. I am looking for an efficient way of doing what the code below does, I'm sure there is a simple linear algebra solution but I'm very stuck:

puy = np.arange(8).reshape(2,4)
puy2 = np.arange(12).reshape(3,4)

print puy, '\n'
print puy2.T

zz = np.zeros([4,2,3])

for i in range(4):
    zz[i,:,:] = np.dot(np.array([puy[:,i]]).T,
                np.array([puy2.T[i,:]]))

DSM · Accepted Answer · 2016-02-14 21:32:46Z

6

One way would be to use np.einsum, which allows you to specify what you want to happen to the indices:

>>> np.einsum('ik,jk->kij', puy, puy2)
array([[[ 0,  0,  0],
        [ 0, 16, 32]],

       [[ 1,  5,  9],
        [ 5, 25, 45]],

       [[ 4, 12, 20],
        [12, 36, 60]],

       [[ 9, 21, 33],
        [21, 49, 77]]])
>>> np.allclose(np.einsum('ik,jk->kij', puy, puy2), zz)
True

answered Feb 14, 2016 at 21:32

DSM

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Divakar · Accepted Answer · 2016-02-15 08:21:00Z

3

Here's another way with broadcasting -

(puy[None,...]*puy2[:,None,:]).T

answered Feb 15, 2016 at 8:21

Divakar

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