KernelFunctions.jl
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JuliaGaussianProcesses
Julia package for kernel functions for machine learning
https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/pull/263 by @david-vicente introduced separate methods for test_AD for MOKernel - I'm concerned this means that we won't be running the same set of tests for MOKernels. Maybe that's how...
# Proposal 1 ```julia # Euclidean domain with D dimensions. struct Euclidean D::Int end dim(domain::Euclidean) = domain.D """ DomainKernel{Tkernel
I'd like to have a function `spectral_distribution` which takes a kernel defined over R^d and returns in spectral distribution. For example ```julia function spectral_distribution(k::SqExponentialKernel) MvNormal(dim(k), ones(dim(k))) end ``` However, there...
There are mainly two things that can be improved in the current implementations of `show`: one can remove string interpolation and implement `show(::IO, ::MIME"text/plain", x)` for more verbose multi-line output...
This PR implements https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/issues/299#issuecomment-943761371. See the comment and the additional explanations in the issue for more details. This PR is still WIP and tests will fail (I assume).
**Summary** @st-- in issue https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/issues/372 suggested modifying the GibbsKernel to take as an argument a base kernel function. This is a first cut at trying to implement this to get...
Hi, I'm new to Julia and Gaussian processes but recently came across a couple of interesting papers about non-stationary spectral mixture kernels [1], [2]. I can see that you have...
It could be convenient to have a `show(MIME("text/latex"), x)` or `Latexify.latexify(x)` for kernel functions.
I don't believe there is any reason for `check_args` to be a macro, we can just turn this into a function.
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Julia package for kernel functions for machine learning