Perhaps avoid validating input dims on AbstractVector{<:AbstractVector{<:Real}}

[grero]

I would suggest either changing this, or perhaps better, skipping it all together. https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/ef6d4591b36194fca069d8bc7ae8c1e2ee288080/src/utils.jl#L194

I'm developing kernels that work on pairs of Vector{Float64}, where the kernel essentially sums over all pairwise distances in x and y. For this kernel, the check above does not really make sense, and indeed passes if I construct the kernel matrix like this:

K = kernelmatrix(kernel, x,x)

However it fails if I instead do

K = kernelmatrix(kernel, x,y)

unless length(first(x))==length(first(y)). I realise that I can just create my own type essentially wrapping Vector{Vector{Float64}}, but that seems like unnecessary work, especially since the kernel machinery works beautifully as it is, as long as the inputs pass the validation.

[willtebbutt]

Ah, interesting. So you've got a case where the dimensionality of the input changes from element to element?

[willtebbutt]

Are you hitting the checks in the various methods in https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/src/matrix/kernelmatrix.jl ? I agree that they're making overly strong assumptions about input dimensionality, but I would like to check that they're actually the source of your problem.

[grero]

Yes, that's right. For context, I'm working on using kernel methods for analysing spike trains, which are basically sets of event timestamps. The obvious way to represent this is as a vector of vector, where each inner vector represents a spike train for one trial. Since the number of events changes from trial to trial, the dimension of the inner vectors also change.

[willtebbutt]

Excellent. This makes complete sense. Would you mind opening a PR which removes the checks, and adds tests which fail without the removal of the checks? I imagine we can have this sorted by the end of the day.

[grero]

Are you hitting the checks in the various methods in https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/src/matrix/kernelmatrix.jl ? I agree that they're making overly strong assumptions about input dimensionality, but I would like to check that they're actually the source of your problem.

Yes, as I indicated at the top, the validate_inputs method fails on my inputs, unless the first element of x and y happens to have the same number of items.

[willtebbutt]

Great -- I just wasn't sure if that was the only place where we use validate_inputs erroneously, or if there was something else.

[grero]

PR submitted.