OK, the issue is resolved. My dictionary was still in 32bit float from
saving. When I convert it to 64float before calling fit it works fine.
Sorry to bother.
On 14.02.2017 11:00, Benjamin Merkt wrote:
Hi,
I tried that with no effect. The fit still breaks after two iterations.
If I set precompute=True I get three coefficients instead of only two.
My Dictionary is fairly large (currently 128x42000). Is it even feasible
to use OMP with such a big Matrix (even with ~120GB ram)?
-Ben
On 13.02.2017 23:31, Vlad Niculae wrote:
Hi,
Are the columns of your matrix normalized? Try setting `normalized=True`.
Yours,
Vlad
On Mon, Feb 13, 2017 at 6:55 PM, Benjamin Merkt
<[email protected]> wrote:
Hi everyone,
I'm using OrthogonalMatchingPursuit to get a sparse coding of a
signal using
a dictionary learned by a KSVD algorithm (pyksvd). However, during
the fit I
get the following RuntimeWarning:
/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/omp.py:391:
RuntimeWarning: Orthogonal matching pursuit ended prematurely due to
linear
dependence in the dictionary. The requested precision might not have
been
met.
copy_X=copy_X, return_path=return_path)
In those cases the results are indeed not satisfactory. I don't get the
point of this warning as it is common in sparse coding to have an
overcomplete dictionary an thus also linear dependency within it. That
should not be an issue for OMP. In fact, the warning is also raised
if the
dictionary is a square matrix.
Might this Warning also point to other issues in the application?
Thanks, Ben
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