Hi Ralf,
unfortunately, I cannot answer your question but it would be indeed very
valuabe to allow custom correlation functions.
best,
Peter
2013/12/9 Ralf Gunter <ralfgun...@gmail.com>
> Hi all,
>
> We're trying to use a custom correlation kernel with GP in the usual
> form K(x, x'). However, by looking at the built-in correlation models
> (and how they're used by gaussian_process.py) it seems sklearn only
> takes models in the form K(theta, dx). There may very well be a
> reformulation of our K that depends only on (x-x'), but if so it would
> probably be highly non-trivial as it depends on e.g. modified
> spherical bessel functions evaluated at a scaled product of the xs. Is
> there any way to have the GP module take our kernel without modifying
> the GP code?
>
> I apologize if this has been asked/answered before -- some searching
> on google only led me to models that also depend only on (x-x').
>
> Thanks!
>
>
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