Hi everybody.
Recently I tried to hack at the SVM implementation, motivated by Lars'
issue #918 <https://github.com/scikit-learn/scikit-learn/issues/918>.
While trying to figure out how to add support for callable kernels, I
found out
that precomputed kernels are not supported at all in the sparse svm, even
though they are documented and look like they are tested.
Trying to implement sparse kernels, I ran in a dead end somewhere in the
C code
(see my PR <https://github.com/scikit-learn/scikit-learn/pull/920>).
Do you think we should try to fix that? Or should we warn / disable this
"feature"
which gives arbitrary results and randomly segfaults?
The use-case that Lars was interested in doesn't actually need the
sparse SVM,
and I don't really see many applications for having a sparse matrix as a
kernel.
I guess we should rather change the checks such that callable kernels
can take
sparse input but have to produce dense kernel matrices.
What do you think?
Cheers,
Andy
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