Hi,
I often need to compute the equivalent of
np.diag(np.dot(A, B)).
Computing np.dot(A, B) is highly inefficient if you only need the diagonal
entries. Two more efficient ways of computing the same thing are
np.sum(A * B.T, axis=1)
and
np.einsum(ij,ji-i, A, B).
The first can allocate quite
On Fri, May 22, 2015 at 5:39 PM, Mathieu Blondel math...@mblondel.org
wrote:
Hi,
I often need to compute the equivalent of
np.diag(np.dot(A, B)).
Computing np.dot(A, B) is highly inefficient if you only need the diagonal
entries. Two more efficient ways of computing the same thing are
Right now I am using np.sum(A * B.T, axis=1) for dense data and I have
implemented a Cython routine for sparse data.
I haven't benched np.sum(A * B.T, axis=1) vs. np.einsum(ij,ji-i, A, B)
yet since I am mostly interested in the sparse case right now.
When A and B are C-style and Fortran-style,
There was an idea on this list to provide a function the run multiple dot on
several vectors/matrices. It seems to be a particular implementation of this
proposed function.
Nadav.
On 22 May 2015 11:58, David Cournapeau courn...@gmail.com wrote:
On Fri, May 22, 2015 at 5:39 PM, Mathieu
On 22 May 2015 at 12:15, Mathieu Blondel math...@mblondel.org wrote:
Right now I am using np.sum(A * B.T, axis=1) for dense data and I have
implemented a Cython routine for sparse data.
I haven't benched np.sum(A * B.T, axis=1) vs. np.einsum(ij,ji-i, A, B)
yet since I am mostly interested in
