Removed BaseKernel

Author: theogf

src/KernelFunctions.jl

@@ -48,8 +48,7 @@ Abstract type defining a slice-wise transformation on an input matrix
 abstract type Transform end
 
 abstract type Kernel end
-abstract type BaseKernel <: Kernel end
-abstract type SimpleKernel <: BaseKernel end
+abstract type SimpleKernel <: Kernel end
 
 include("utils.jl")
 include("distances/pairwise.jl")

src/basekernels/fbm.jl

@@ -9,7 +9,7 @@ Fractional Brownian motion kernel with Hurst index `h` from (0,1) given by
 For `h=1/2`, this is the Wiener Kernel, for `h>1/2`, the increments are
 positively correlated and for `h<1/2` the increments are negatively correlated.
 """
-struct FBMKernel{T<:Real} <: BaseKernel
+struct FBMKernel{T<:Real} <: Kernel
     h::Vector{T}
     function FBMKernel(; h::T=0.5) where {T<:Real}
         @assert 0.0 <= h <= 1.0 "FBMKernel: Given Hurst index h is invalid."

src/basekernels/gabor.jl

@@ -7,7 +7,7 @@ Gabor kernel with lengthscale `ell` and period `p`. Given by
 ```
 
 """
-struct GaborKernel{K<:Kernel} <: BaseKernel
+struct GaborKernel{K<:Kernel} <: Kernel
     kernel::K
     function GaborKernel(;ell=nothing, p=nothing)
         k = _gabor(ell=ell, p=p)

src/basekernels/nn.jl

@@ -17,7 +17,7 @@ Bayesian neural network with erf (Error Function) as activation function.
 - [Neal(1996)](https://www.cs.toronto.edu/~radford/bnn.book.html)
 - [Andrew Gordon's Thesis Pg 45](http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf)
 """
-struct NeuralNetworkKernel <: BaseKernel end
+struct NeuralNetworkKernel <: Kernel end
 
 function (κ::NeuralNetworkKernel)(x, y)
     return asin(dot(x, y) / sqrt((1 + sum(abs2, x)) * (1 + sum(abs2, y))))

src/basekernels/wiener.jl

@@ -32,7 +32,7 @@ See the paper *Probabilistic ODE Solvers with Runge-Kutta Means* by Schober, Duv
 Hennig, NIPS, 2014, for more details.
 
 """
-struct WienerKernel{I} <: BaseKernel
+struct WienerKernel{I} <: Kernel
     function WienerKernel{I}() where I
         @assert I ∈ (-1, 0, 1, 2, 3) "Invalid parameter i=$(I). Should be -1, 0, 1, 2 or 3."
         if I == -1

src/generic.jl

@@ -11,12 +11,5 @@ function concretetypes(k, ktypes::Vector)
     return ktypes
 end
 
-for k in nameof.(subtypes(BaseKernel))
-    @eval begin
-        @deprecate($k(ρ::Real;args...),transform($k(args...),ρ))
-        @deprecate($k(ρ::AbstractVector{<:Real};args...),transform($k(args...),ρ))
-    end
-end
-
 # Fallback implementation of evaluate for `SimpleKernel`s.
 (k::SimpleKernel)(x, y) = kappa(k, evaluate(metric(k), x, y))

test/matrix/kernelmatrix.jl

@@ -1,6 +1,6 @@
 # Custom Kernel implementation that only defines how to evaluate itself. This is used to
 # test that fallback kernelmatrix / kerneldiagmatrix methods work properly.
-struct BaseSE <: KernelFunctions.BaseKernel end
+struct BaseSE <: KernelFunctions.Kernel end
 (k::BaseSE)(x, y) = exp(-evaluate(SqEuclidean(), x, y))
 
 # Custom kernel to test `SimpleKernel` interface on, independently the `SimpleKernel`s that
@@ -90,7 +90,7 @@ KernelFunctions.kappa(::ToySimpleKernel, d) = exp(-d / 2)
 
             tmp_diag = Vector{Float64}(undef, length(x))
             @test kerneldiagmatrix!(tmp_diag, k, x) ≈ kerneldiagmatrix(k, x)
-            @test_throws DimensionMismatch kerneldiagmatrix!(tmp_diag, k, y)            
+            @test_throws DimensionMismatch kerneldiagmatrix!(tmp_diag, k, y)
         end
     end
 

test/mokernels/independent.jl

@@ -5,7 +5,7 @@
     k = IndependentMOKernel(GaussianKernel())
     @test k isa IndependentMOKernel
     @test k isa Kernel
-    @test k.kernel isa KernelFunctions.BaseKernel
+    @test k.kernel isa KernelFunctions.Kernel
     @test k(x[2], y[2]) isa Real
 
     @test kernelmatrix(k, x, y) == kernelmatrix(k, collect(x), collect(y))