Add KernelTensorSum

docs/src/kernels.md

@@ -124,6 +124,7 @@ TransformedKernel
 ScaledKernel
 KernelSum
 KernelProduct
+KernelTensorSum
 KernelTensorProduct
 NormalizedKernel
 ```

src/KernelFunctions.jl

@@ -15,9 +15,10 @@ export LinearKernel, PolynomialKernel
 export RationalKernel, RationalQuadraticKernel, GammaRationalKernel
 export PiecewisePolynomialKernel
 export PeriodicKernel, NeuralNetworkKernel
-export KernelSum, KernelProduct, KernelTensorProduct
+export KernelSum, KernelProduct, KernelTensorSum, KernelTensorProduct
 export TransformedKernel, ScaledKernel, NormalizedKernel
 export GibbsKernel
+export ⊕
 
 export Transform,
     SelectTransform,
@@ -108,6 +109,7 @@ include("kernels/normalizedkernel.jl")
 include("matrix/kernelmatrix.jl")
 include("kernels/kernelsum.jl")
 include("kernels/kernelproduct.jl")
+include("kernels/kerneltensorsum.jl")
 include("kernels/kerneltensorproduct.jl")
 include("kernels/overloads.jl")
 include("kernels/neuralkernelnetwork.jl")

src/kernels/kerneltensorsum.jl

@@ -0,0 +1,107 @@
+"""
+    KernelTensorSum
+
+Tensor sum of kernels.
+
+# Definition
+
+For inputs ``x = (x_1, \\ldots, x_n)`` and ``x' = (x'_1, \\ldots, x'_n)``, the tensor
+sum of kernels ``k_1, \\ldots, k_n`` is defined as
+```math
+k(x, x'; k_1, \\ldots, k_n) = \\sum_{i=1}^n k_i(x_i, x'_i).
+```
+
+# Construction
+
+The simplest way to specify a `KernelTensorSum` is to use the `⊕` operator (can be typed by `\\oplus<tab>`).
+```jldoctest tensorproduct
+julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5, 2);
+
+julia> kernelmatrix(k1 ⊕ k2, RowVecs(X)) == kernelmatrix(k1, X[:, 1]) + kernelmatrix(k2, X[:, 2])
+true
+```
+
+You can also specify a `KernelTensorSum` by providing kernels as individual arguments
+or as an iterable data structure such as a `Tuple` or a `Vector`. Using a tuple or
+individual arguments guarantees that `KernelTensorSum` is concretely typed but might
+lead to large compilation times if the number of kernels is large.
+```jldoctest tensorproduct
+julia> KernelTensorSum(k1, k2) == k1 ⊕ k2
+true
+
+julia> KernelTensorSum((k1, k2)) == k1 ⊕ k2
+true
+
+julia> KernelTensorSum([k1, k2]) == k1 ⊕ k2
+true
+```
+"""
+struct KernelTensorSum{K} <: Kernel
+    kernels::K
+end
+
+function KernelTensorSum(kernel::Kernel, kernels::Kernel...)
+    return KernelTensorSum((kernel, kernels...))
+end
+
+@functor KernelTensorSum
+
+Base.length(kernel::KernelTensorSum) = length(kernel.kernels)
+
+function (kernel::KernelTensorSum)(x, y)
+    if !((nx = length(x)) == (ny = length(y)) == (nkernerls = length(k)))
+        throw(DimensionMismatch("number of kernels ($nkernels) and number of features 
+(x=$nx, y=$ny) are not consistent"))
+    end
+    return sum(k(xi, yi) for (k, xi, yi) in zip(kernel.kernels, x, y))
+end
+
+function validate_domain(k::KernelTensorSum, x::AbstractVector, y::AbstractVector)
+    return (dx = dim(x)) == (dy = dim(y)) == (nkernels = length(k)) || error(
+        "number of kernels ($nkernels) and group of features (x=$dx), y=$dy) are not consistent",
+    )
+end
+
+function validate_domain(k::KernelTensorSum, x::AbstractVector)
+    return validate_domain(k, x, x)
+end
+
+function kernelmatrix(k::KernelTensorSum, x::AbstractVector)
+    validate_domain(k, x)
+    return mapreduce(kernelmatrix, +, k.kernels, slices(x))
+end
+
+function kernelmatrix(k::KernelTensorSum, x::AbstractVector, y::AbstractVector)
+    validate_domain(k, x, y)
+    return mapreduce(kernelmatrix, +, k.kernels, slices(x), slices(y))
+end
+
+function kernelmatrix_diag(k::KernelTensorSum, x::AbstractVector)
+    validate_domain(k, x)
+    return mapreduce(kernelmatrix_diag, +, k.kernels, slices(x))
+end
+
+function kernelmatrix_diag(k::KernelTensorSum, x::AbstractVector, y::AbstractVector)
+    validate_domain(k, x, y)
+    return mapreduce(kernelmatrix_diag, +, k.kernels, slices(x), slices(y))
+end
+
+function Base.:(==)(x::KernelTensorSum, y::KernelTensorSum)
+    return (
+        length(x.kernels) == length(y.kernels) &&
+        all(kx == ky for (kx, ky) in zip(x.kernels, y.kernels))
+    )
+end
+
+Base.show(io::IO, kernel::KernelTensorSum) = printshifted(io, kernel, 0)
+
+function printshifted(io::IO, kernel::KernelTensorSum, shift::Int)
+    print(io, "Tensor sum of ", length(kernel), " kernels:")
+    for k in kernel.kernels
+        print(io, "\n")
+        for _ in 1:(shift + 1)
+            print(io, "\t")
+        end
+        printshifted(io, k, shift + 2)
+    end
+end

src/kernels/overloads.jl

@@ -1,7 +1,10 @@
+function ⊕ end
+
 for (M, op, T) in (
     (:Base, :+, :KernelSum),
     (:Base, :*, :KernelProduct),
     (:TensorCore, :tensor, :KernelTensorProduct),
+    (:KernelFunctions, :⊕, :KernelTensorSum),
 )
     @eval begin
         $M.$op(k1::Kernel, k2::Kernel) = $T(k1, k2)

test/kernels/kerneltensorsum.jl

@@ -0,0 +1,67 @@
+@testset "kerneltensorsum" begin
+    rng = MersenneTwister(123456)
+    u1 = rand(rng, 10)
+    u2 = rand(rng, 10)
+    v1 = rand(rng, 5)
+    v2 = rand(rng, 5)
+
+    # kernels
+    k1 = SqExponentialKernel()
+    k2 = ExponentialKernel()
+    kernel1 = KernelTensorSum(k1, k2)
+    kernel2 = KernelTensorSum([k1, k2])
+
+    @test kernel1 == kernel2
+    @test kernel1.kernels == (k1, k2) === KernelTensorSum((k1, k2)).kernels
+    for (_k1, _k2) in Iterators.product(
+        (k1, KernelTensorSum((k1,)), KernelTensorSum([k1])),
+        (k2, KernelTensorSum((k2,)), KernelTensorSum([k2])),
+    )
+        @test kernel1 == _k1 ⊕ _k2
+    end
+    @test length(kernel1) == length(kernel2) == 2
+    @test string(kernel1) == (
+        "Tensor sum of 2 kernels:\n" *
+        "\tSquared Exponential Kernel (metric = Euclidean(0.0))\n" *
+        "\tExponential Kernel (metric = Euclidean(0.0))"
+    )
+    @test_throws DimensionMismatch kernel1(rand(3), rand(3))
+
+    @testset "val" begin
+        for (x, y) in (((v1, u1), (v2, u2)), ([v1, u1], [v2, u2]))
+            val = k1(x[1], y[1]) + k2(x[2], y[2])
+
+            @test kernel1(x, y) == kernel2(x, y) == val
+        end
+    end
+
+    # Standardised tests.
+    TestUtils.test_interface(kernel1, ColVecs{Float64})
+    TestUtils.test_interface(kernel1, RowVecs{Float64})
+    TestUtils.test_interface(
+        KernelTensorSum(WhiteKernel(), ConstantKernel(; c=1.1)), ColVecs{String}
+    )
+    test_ADs(
+        x -> KernelTensorSum(SqExponentialKernel(), LinearKernel(; c=exp(x[1]))),
+        rand(1);
+        dims=[2, 2],
+    )
+    types = [ColVecs{Float64,Matrix{Float64}}, RowVecs{Float64,Matrix{Float64}}]
+    test_interface_ad_perf(2.1, StableRNG(123456), types) do c
+        KernelTensorSum(SqExponentialKernel(), LinearKernel(; c=c))
+    end
+    test_params(KernelTensorSum(k1, k2), (k1, k2))
+
+    @testset "single kernel" begin
+        kernel = KernelTensorSum(k1)
+        @test length(kernel) == 1
+
+        @testset "eval" begin
+            for (x, y) in (((v1,), (v2,)), ([v1], [v2]))
+                val = k1(x[1], y[1])
+
+                @test kernel(x, y) == val
+            end
+        end
+    end
+end

test/kernels/overloads.jl

@@ -5,8 +5,9 @@
     k2 = SqExponentialKernel()
     k3 = RationalQuadraticKernel()
 
-    for (op, T) in ((+, KernelSum), (*, KernelProduct), (⊗, KernelTensorProduct))
-        if T === KernelTensorProduct
+    for (op, T) in
+        ((+, KernelSum), (*, KernelProduct), (⊗, KernelTensorProduct), (⊕, KernelTensorSum))
+        if T === KernelTensorProduct || T === KernelTensorSum
             v2_1 = rand(rng, 2)
             v2_2 = rand(rng, 2)
             v3_1 = rand(rng, 3)

test/runtests.jl

@@ -125,6 +125,7 @@ include("test_utils.jl")
             include("kernels/kernelproduct.jl")
             include("kernels/kernelsum.jl")
             include("kernels/kerneltensorproduct.jl")
+            include("kernels/kerneltensorsum.jl")
             include("kernels/overloads.jl")
             include("kernels/scaledkernel.jl")
             include("kernels/transformedkernel.jl")