lready did basic
regression testing. Thanks again!
-Will
On Fri, Aug 30, 2013 at 1:32 AM, Lars Buitinck wrote:
> 2013/8/30 Will Buckner :
> > Damn, hmm. This just seems so so heavy to calculate reconstruction_err,
> > which isn't even used inside the algorithm. I don't
Thanks so much for spending some time on this. I'll give it a try first
thing tomorrow and report back. Thanks Lars!
-Will
On Fri, Aug 30, 2013 at 1:32 AM, Lars Buitinck wrote:
> 2013/8/30 Will Buckner :
> > Damn, hmm. This just seems so so heavy to calculate reconstruction_
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ason, and I've gotta make this
work. safe_sparse_dot()
returning dense if inputs are dense makes sense; the idea is to make W and
H sparse as Lars suggested, sorry.
Thanks,
Will
On Thu, Aug 29, 2013 at 1:58 AM, Olivier Grisel wrote:
> 2013/8/29 Will Buckner :
> >> the motivat
> the motivation for these lines is that even if X is sparse
safe_sparse_dot(W, H)
will not be. So you will allocate a matrix of size X but dense which is
unacceptable in many cases.
Er, it looks like safe_sparse_dot() returns sparse unless dense_output=True.
And, I'm confused as to how this would
Hey guys,
I have a couple of questions about decomposition.nmf with respect to sparse
matrices:
nmf.py@527:
if not sp.issparse(X):
self.reconstruction_err_ = norm(X - np.dot(W, H))
else:
norm2X = np.sum(X.data ** 2) # Ok because X is C
