Python vectorization of matrix-vector operation
I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)
I want to vectorize the following:
F = np.zeros(N)
for k in xrange(N):
F[k] = np.dot( A[:,:,k], V[:,k] ).sum()
Any way this can be done with either tensordot or any other numpy function without explicit looping?
python performance numpy vectorization tensordot
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
add a comment |
I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)
I want to vectorize the following:
F = np.zeros(N)
for k in xrange(N):
F[k] = np.dot( A[:,:,k], V[:,k] ).sum()
Any way this can be done with either tensordot or any other numpy function without explicit looping?
python performance numpy vectorization tensordot
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
add a comment |
I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)
I want to vectorize the following:
F = np.zeros(N)
for k in xrange(N):
F[k] = np.dot( A[:,:,k], V[:,k] ).sum()
Any way this can be done with either tensordot or any other numpy function without explicit looping?
python performance numpy vectorization tensordot
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)
I want to vectorize the following:
F = np.zeros(N)
for k in xrange(N):
F[k] = np.dot( A[:,:,k], V[:,k] ).sum()
Any way this can be done with either tensordot or any other numpy function without explicit looping?
python performance numpy vectorization tensordot
python performance numpy vectorization tensordot
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
edited Nov 22 '18 at 12:22
jpp
98.7k2159110
98.7k2159110
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
asked Nov 22 '18 at 12:20
fmonegagliafmonegaglia
1,62711726
1,62711726
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
With np.einsum -
F = np.einsum('ijk,jk->k',A,V)
We can optimize it further with optimize flag (check docs) set as True.
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
With np.einsum -
F = np.einsum('ijk,jk->k',A,V)
We can optimize it further with optimize flag (check docs) set as True.
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
add a comment |
With np.einsum -
F = np.einsum('ijk,jk->k',A,V)
We can optimize it further with optimize flag (check docs) set as True.
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
add a comment |
With np.einsum -
F = np.einsum('ijk,jk->k',A,V)
We can optimize it further with optimize flag (check docs) set as True.
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
With np.einsum -
F = np.einsum('ijk,jk->k',A,V)
We can optimize it further with optimize flag (check docs) set as True.
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
answered Nov 22 '18 at 12:27
DivakarDivakar
155k1485174
155k1485174
add a comment |
add a comment |
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