Damn, hmm. This just seems so so heavy to calculate reconstruction_err,
which isn't even used inside the algorithm. I don't even use it in the
pipeline. My current best idea is just to subclass ProjectedGradientNMF()
and overload fit_transform(), not computing reconstruction_err at all....
On Thu, Aug 29, 2013 at 9:24 AM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> 2013/8/29 Lars Buitinck <l.j.buiti...@uva.nl>:
> > W, H = csr_matrix(W), csr_matrix(H)
> > reconstruction_err = euclidean_distances(X, W * H).sum()
>
> Never mind. Even after fixing the formula, W * H is actually too dense
> for this to be any good, regardless of the initialization and
> sparseness parameter.
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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