While trying to get a minimal example to reproduce the error I found
that there it also occurred when both arrays where float64. However, I
then realized that my data vector has fairly small values (~1e-4 to
1e-8). If I normalize this as well it works for all combinations of 64
and 32 bit.
-Ben
On 17.02.2017 01:56, Vlad Niculae wrote:
I would consider this a bug. I'm not 100% sure what the conventions
for dtypes are. I'd appreciate it if you could open an issue, and even
better if you have a small reproducing example. I'll look into it this
weekend.
Vlad
On Fri, Feb 17, 2017 at 7:25 AM, Benjamin Merkt
<[email protected]> wrote:
Is this still considered a bug and therefore worth an issue?
On 14.02.2017 13:34, Benjamin Merkt wrote:
Yes, the data array y was already float64.
On 14.02.2017 12:28, Vlad Niculae wrote:
One possible issue I can see causing this is if X and y have different
dtypes... was this the case for you?
On Tue, Feb 14, 2017 at 8:26 PM, Vlad Niculae <[email protected]> wrote:
Hi Ben,
This actually sounds like a bug in this case! At a glance, the code
should use the correct BLAS calls for the data type you provide. Can
you reproduce this with a simple small example that gets different
results if the data is 32 vs 64 bit? Would you mind filing an issue?
Thanks,
Vlad
On Tue, Feb 14, 2017 at 8:19 PM, Benjamin Merkt
<[email protected]> wrote:
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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