The *problem is restricted to Sparse Matrices* for dense Matrix
everything is working fine.
On 04/18/2012 04:51 PM, Dimitrios Pritsos wrote:
Ok I ve created the Issue in Git ( I Guess this is the proper place to
do what Olivier suggested me in the last email in this list)
however what is CV an where I can enable indices=True, as it is
suggested to the following message:
ValueError: X and y have incompatible shapes: (180, 255) vs (0,)
Note: Sparse matrices cannot be indexed w/boolean masks (use
`indices=True` in CV).
OR anyone knows the last commit I should role-back with git for
running a OneClassSVM that working properly?
THNX!
Best Regards,
Dimitrios
On 04/17/2012 09:19 PM, Dimitrios Pritsos wrote:
On 04/17/2012 08:56 PM, Olivier Grisel wrote:
Ok so this is a real bug. Please open an issue.
I don't really know how to do that !?
Also I am curious: what does it mean to do OneClass SVM with a linear
kernel? I thought OneClass SVM was for density estimation and I don't
see how on could define a finite density function (even if not
normalized) with an hyperplane.
In fact I have used to use the libsvm pything interface and where giving
a parameter linear or rbf it was not matter so much at least for my
data, i.e. Automated Genre Identification for web pages in particular
the Santini's corpus. However, since I am trying to do some high scale
tests python structures like lists or indices used by this interfce are
not sufficient. So I had to go to numpy/scipy/pytables and of course
scikits way.
In addition, for more that 10000 feature in practice most of the time
linear kernel works better compare to other kernels. I seem that this
has to do with the overfishing issues and the data are so sparse due to
the huge space of features where they are projected that hyperplane
seems sufficient or even better choice. So, I just used this rule of
thumb for OneClassSVM. I am not sure thought and everything is under
investigation.
I will try the lowlevel svm I hope the bug will be fixed soon.
regards,
Dimitrios
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