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! ------------------------------------------------------------------------------ Sponsored by Intel(R) XDK Develop, test and display web and hybrid apps with a single code base. Download it for free now! http://pubads.g.doubleclick.net/gampad/clk?id=111408631&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
