Comments (6)
I really can't see what the difference is from the user's perspective
In one case, obsdim
is pretty obviously the observation dimension, on the other side with ColVecs
I will need to go through the docs, understand what it is and what it does and how to correctly use it.
In any case, I'll concede this point if you're really keen on it being a thing, and given that it's consistent with KF
Thanks! I will take care of making the PR
On the other hand, I have to say the structure is already pretty nice to use and I am looking forward to integrate it to AGP.jl
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Yup, requires either a ColVecs
or RowVecs
otherwise how do you know what convention the user is employing?
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Shouldn't we do like in KF.jl and put a wrapper for matrices by adding a obsdim
keyword? Or decide of an arbitrary convention?
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I really don't see what the advantage is over just using ColVecs
/ RowVecs
. One way of doing things is strictly better than two unless there's a really good reason to have two ways of doing things.
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This is probably going to be the same dialog again but :
I really don't believe we should force users to "convert" their data in a different format. I agree that it should be used in the internals but the user should not have to see it.
Again as a lambda user if I want to use a package I would just like to throw my matrix to the model eventually giving what is the dimensionality without having to wrap it first into some format.
from abstractgps.jl.
Agreed, this is going to go down the same line again.
Again as a lambda user if I want to use a package I would just like to throw my matrix to the model eventually giving what is the dimensionality without having to wrap it first into some format.
At the risk of labouring the point, wrapping your matrix is telling the package how to treat the dimensions of your data, nothing more. I really can't see what the difference is from the user's perspective between writing
f(ColVecs(X))
and
f(X; obsdim=2)
, particularly if we provide a good error message for f(X)
that says how to handle multi-input data.
In any case, I'll concede this point if you're really keen on it being a thing, and given that it's consistent with KF
from abstractgps.jl.
Related Issues (20)
- VFE/DTC should be moved to ApproximateGPs.jl HOT 3
- Should LatentFiniteGP contain the noise?
- Documentation build is ~ 55 min HOT 3
- Cross tests for SurrogateAbstractGPs
- 1D regression inconsistent with other package HOT 3
- Kernel for multidimensional output HOT 3
- ERROR: MethodError: no method matching Int64(::Irrational{:log2π}) HOT 7
- Zygote errors with parameterized mean functions and multidimensional input HOT 10
- Tidy up example 0
- Latency HOT 6
- Zygote v0.6.56 breaks tests HOT 6
- Stabilize `MeanFunction` API HOT 2
- An abstraction for the realization of a GP HOT 11
- Feature parity with GaussianProcesses.jl
- Hyperparameter optimization & maintanance HOT 7
- Noise parameter `Sigma` can't be recovered from `PosteriorGPs` HOT 5
- Log-likelihood is lower after fitting the process? HOT 5
- AbstractGP shares array data - impossible to add new points to grid (bayesian optimisation) HOT 6
- GP for functions with 2D input HOT 5
- Conflict between Zygote and AbstractGPs HOT 5
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