Comments (2)
You're right, krr
should use the same kernel
as krr_train
. This really only affects ExactKernel
s (you'll notice that krr
doesn't use kernel
for the RFFKernel
case, so in that case users are already constrained to use the same kernel
as used in krr_train
).
So I agree that for consistency and practicality krr
should always use the same kernel
as krr_train
. I think I originally wrote it the way it is currently because I wasn't sure of a good way to enforce using the same kernel
. But now that I think about it more, I guess a way to enforce it would be to include the kernel
in the ExactTrainingData
object (would I then need to change the name, because the kernel
isn't really data?) and then not have the kernel
argument to krr
.
Let me know what you think, or if you had your own idea of how to enforce consistency (or not enforce it but give a consistent default).
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Yes I was thinking of the training stage as analogous to a "Plan" stage
(cf https://juliamath.github.io/AbstractFFTs.jl/stable/api/#AbstractFFTs.plan_fft)
where the struct would store all the relevant stuff that went into the training,
none of which is allowed to change at testing time (AFAIK), including \rho, \Lambda, and the kernel.
The name with Data
is OK to me because parameters are a kind of data :)
BTW, this is a lower priority than my other PRs.
from perk.jl.
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