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On Wed, Dec 10, 2008 at 4:18 PM, Peter Skomoroch <peter.skomoroch@gmail.com> wrote:
Nathan,
Thanks for the pointer, I had missed that wiki page.
It's fairly recent, so don't feel bad :)
The bottleneck now seems to be this for-loop, which takes the majority of the remaining time (1.82258105278 seconds):
for index, (i,j) in enumerate(nonzero_indices): data[index] = dot(W[i,:],H[:,j])
Is there a better approach for this assignment block?
You could vectorize the loop: W = random([n,r]).astype(float32) H = random([m,r]).astype(float32) # note, shape is (m,r) I,J = V.nonzero() X = (W[I,:] * H[J,:]).sum(axis=1) V_approx = sparse.coo_matrix((X,(I,J)), shape=(n,m)) If memory usage of the above is too costly, you could use the same approach, but on fixed-sized chunks of the arrays. -- Nathan Bell wnbell@gmail.com http://graphics.cs.uiuc.edu/~wnbell/