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Code is taking too much time for computation and I want to reduce number of iterations. I tried to use method suggested by @Albuquerque How to optimise the following for loop code?, but for this code I have 3d array. Please suggest how to optimize following code.

K=2
D=3
N=3
sigma=np.asarray([[1,2,3],[4,5,6]])
F=np.asarray([[1,2,3],[4,5,6]])
X=np.asarray([[1,2,3],[4,5,6],[7,8,9]])

W=
[
 [
  [1,2,3],
  [3,2,1]
 ],
 [
  [1,1,19],
  [1,2,1]
 ],
 [
  [2,2,2],
  [1,3,5]
 ]
]

result2= np.ones([N, D])
for i in range(N):
   for l in range(D):
     result2[i][l]=np.sum(W[i][k][l]*(F[k][l]+sigma[k][l]*X[i][l]) for k in range(K))


output-
array([[ 26.,  42.,  60.],
      [ 25.,  72., 441.],
      [ 48., 171., 360.]])
Improve this question
3
  • 1
    It would help if you explain what you are trying to do (with an input and output) and not just dump your code. Commented Oct 22, 2019 at 17:20
  • What is this code supposed to do? Right now it looks like some kind of product Commented Oct 22, 2019 at 17:20
  • thanks guys, I solved it. Using numpy einsum, we can perform multi-dimensional array operations. (docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html) thanks again. Commented Oct 22, 2019 at 23:27
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