Hi Ralf,
You might find the following gist useful:
https://gist.github.com/mblondel/6230778
It should support the usual kernel="precomputed". However, my
implementation doesn't support the usual features that one would expect
from a GP implementation such as kernel parameter tuning or ARD. In fact,
this is essentially a kernel ridge implementation.
HTH,
Mathieu
On Tue, Dec 10, 2013 at 5:53 PM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
> 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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>
>
> --
> Peter Prettenhofer
>
>
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