Tensor product kernel

src/KernelFunctions.jl

@@ -18,6 +18,7 @@ export RationalQuadraticKernel, GammaRationalQuadraticKernel
 export MahalanobisKernel
 export KernelSum, KernelProduct
 export TransformedKernel, ScaledKernel
+export TensorProductKernel
 
 export Transform, SelectTransform, ChainTransform, ScaleTransform, LowRankTransform, IdentityTransform, FunctionTransform
 
@@ -46,14 +47,16 @@ include("distances/dotproduct.jl")
 include("distances/delta.jl")
 include("transform/transform.jl")
 
-for k in ["exponential","matern","polynomial","constant","rationalquad","exponentiated","cosine","maha","fbm"]
-    include(joinpath("kernels",k*".jl"))
+for f in readdir(joinpath(@__DIR__, "basekernels"))
+    endswith(f, ".jl") && include(joinpath("basekernels", f))
 end
+
 include("kernels/transformedkernel.jl")
 include("kernels/scaledkernel.jl")
 include("matrix/kernelmatrix.jl")
 include("kernels/kernelsum.jl")
 include("kernels/kernelproduct.jl")
+include("kernels/tensorproduct.jl")
 include("approximations/nystrom.jl")
 include("generic.jl")
 

src/generic.jl

@@ -16,7 +16,7 @@ Base.show(io::IO,κ::Kernel) = print(io,nameof(typeof(κ)))
 
 ### Syntactic sugar for creating matrices and using kernel functions
 for k in subtypes(BaseKernel)
-    if  k ∈ [FBMKernel] continue end #for kernels without `metric` or `kappa`
+    k === FBMKernel && continue #for kernels without `metric` or `kappa`
     @eval begin
         @inline (κ::$k)(d::Real) = kappa(κ,d) #TODO Add test
         @inline (κ::$k)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real}) = kappa(κ, x, y)

src/kernels/tensorproduct.jl

@@ -0,0 +1,27 @@
+"""
+    TensorProductKernel(kernel1, kernel2)
+
+A tensor product kernel of the form
+```math
+k((x₁, x₂), (y₁, y₂)) = kernel1(x₁, y₁) * kernel2(x₂, y₂)
+```
+"""
+struct TensorProductKernel{K1<:Kernel,K2<:Kernel} <: Kernel
+    kernel1::K1
+    kernel2::K2
+end
+
+kappa(kernel::TensorProductKernel, (x1, x2), (y1, y2)) =
+    kappa(kernel.kernel1, x1, y1) * kappa(kernel.kernel2, x2, y2)
+
+(kernel::TensorProductKernel)(x, y) = kappa(kernel, x, y)
+
+Base.show(io::IO, κ::TensorProductKernel) = printshifted(io, κ, 0)
+
+function printshifted(io::IO, κ::TensorProductKernel, shift::Int)
+    print(io,"Tensor product kernel:")
+    print(io,"\n"*("\t"^(shift+1))*"- ")
+    printshifted(io, κ.kernel1, shift+2)
+    print(io,"\n"*("\t"^(shift+1))*"- ")
+    printshifted(io, κ.kernel2, shift+2)
+end

test/test_kernels.jl

@@ -197,4 +197,14 @@ x = rand()*2; v1 = rand(3); v2 = rand(3); id = IdentityTransform()
             @test kappa(k*k3,v1,v2) ≈ kappa(k3*k,v1,v2)
         end
     end
+    @testset "Tensor product" begin
+        k1 = SqExponentialKernel()
+        k2 = ExponentialKernel()
+        kernel = TensorProductKernel(k1, k2)
+
+        u1, u2 = rand(10), rand(10)
+
+        @test kernel((v1, u1), (v2, u2)) == kappa(kernel, (v1, u1), (v2, u2))
+        @test kernel((v1, u1), (v2, u2)) == kappa(kernel.kernel1, v1, v2) * kappa(kernel.kernel2, u1, u2)
+    end
 end