[Numpy-discussion] efficient usage of tensordot
Nadav Horesh
nadavh@visionsense....
Sun Jan 18 02:38:31 CST 2009
This is not the first time this issue is raised here.
You may try this piece of code, which may take less memory:
(A*x).sum(axis=1).T
Nadav
-----הודעה מקורית-----
מאת: numpy-discussion-bounces@scipy.org בשם Gideon Simpson
נשלח: א 18-ינואר-09 07:30
אל: Discussion of Numerical Python
נושא: [Numpy-discussion] efficient usage of tensordot
This is related to a question I posted earlier.
Suppose I have array A with dimensions n x m x l and array x with
dimensions m x l. Interpret this as an array of l nxm matrices and
and array of l m dimensional vectors. I wish to compute the matrix-
vector product A[:,:,k] x[:,k] for each k = 0,... l -1. I discovered
that I could accomplish this with the command
np.diagonal(np.tensordot(A, k, axes=(1,0)), axis1= 1, axis2 = 2)
The tensordot command gives me
A_{ijk}x_{jl} = C_{ikl}
And the diagonal command grabs the entries in array C where k=l.
Is this the "optimal" way to make this calculation in numpy? It
certainly makes for nice, clean code, but is it the fastest I can get?
-gideon
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
-------------- next part --------------
A non-text attachment was scrubbed...
Name: not available
Type: application/ms-tnef
Size: 3412 bytes
Desc: not available
Url : http://projects.scipy.org/pipermail/numpy-discussion/attachments/20090118/9997a328/attachment.bin
More information about the Numpy-discussion
mailing list