Refactors the test suite (#63)

* Refactors test suite

* Includes Flux tests

* Formats test/utils.jl properly

* Remove redundant files

Author: willtebbutt

test/approximations/nystrom.jl

@@ -0,0 +1,9 @@
+@testset "nystrom" begin
+    dims = [10,5]
+    X = rand(dims...)
+    k = SqExponentialKernel()
+    for obsdim in [1, 2]
+        @test kernelmatrix(k, X; obsdim=obsdim) ≈ kernelmatrix(nystrom(k, X, 1.0; obsdim=obsdim))
+        @test kernelmatrix(k, X; obsdim=obsdim) ≈ kernelmatrix(nystrom(k, X, collect(1:dims[obsdim]); obsdim=obsdim))
+    end
+end

test/distances/delta.jl

@@ -0,0 +1,10 @@
+@testset "delta" begin
+    A = rand(10,5)
+    B = rand(20,5)
+    d = KernelFunctions.Delta()
+    @test pairwise(d,A,dims=1) == Matrix(I,size(A,1),size(A,1))
+    @test pairwise(d,A,B,dims=1) == zeros(size(A,1),size(B,1))
+    @test d(1,2) == 0
+    @test d(1,1) == 1
+    @test_throws DimensionMismatch d(rand(3),rand(4))
+end

test/distances/dotproduct.jl

@@ -0,0 +1,8 @@
+@testset "dotproduct" begin
+    A = rand(10,5)
+    B = rand(20,5)
+    d = KernelFunctions.DotProduct()
+    @test diag(pairwise(d,A,dims=2)) == [dot(A[:,i],A[:,i]) for i in 1:size(A,2)]
+    @test_throws DimensionMismatch d(rand(3),rand(4))
+    @test d(3.0,2.0) == 6.0
+end

test/test_generic.jl renamed to test/generic.jl

@@ -1,8 +1,5 @@
-using KernelFunctions
-
-k = SqExponentialKernel()
-
-@testset "Generic functions to test" begin
+@testset "generic" begin
+    k = SqExponentialKernel()
     @test length(k) == 1
     @test iterate(k) == (k,nothing)
     @test iterate(k,1) == nothing

test/kernels/constant.jl

@@ -0,0 +1,25 @@
+@testset "constant" begin
+    @testset "ZeroKernel" begin
+        k = ZeroKernel()
+        @test eltype(k) == Any
+        @test kappa(k,2.0) == 0.0
+        @test KernelFunctions.metric(ZeroKernel()) == KernelFunctions.Delta()
+    end
+    @testset "WhiteKernel" begin
+        k = WhiteKernel()
+        @test eltype(k) == Any
+        @test kappa(k,1.0) == 1.0
+        @test kappa(k,0.0) == 0.0
+        @test EyeKernel == WhiteKernel
+        @test metric(WhiteKernel()) == KernelFunctions.Delta()
+    end
+    @testset "ConstantKernel" begin
+        c = 2.0
+        k = ConstantKernel(c=c)
+        @test eltype(k) == Any
+        @test kappa(k,1.0) == c
+        @test kappa(k,0.5) == c
+        @test metric(ConstantKernel()) == KernelFunctions.Delta()
+        @test metric(ConstantKernel(c=2.0)) == KernelFunctions.Delta()
+    end
+end

test/kernels/cosine.jl

@@ -0,0 +1,14 @@
+@testset "cosine" begin
+    rng = MersenneTwister(123456)
+    x = rand(rng)*2
+    v1 = rand(rng, 3)
+    v2 = rand(rng, 3)
+
+    k = CosineKernel()
+    @test eltype(k) == Any
+    @test kappa(k, 1.0) ≈ -1.0 atol=1e-5
+    @test kappa(k, 2.0) ≈ 1.0 atol=1e-5
+    @test kappa(k, 1.5) ≈ 0.0 atol=1e-5
+    @test kappa(k,x) ≈ cospi(x) atol=1e-5
+    @test k(v1, v2) ≈ cospi(sqrt(sum(abs2.(v1-v2)))) atol=1e-5
+end

test/kernels/custom.jl

@@ -0,0 +1,24 @@
+# minimal definition of a custom kernel
+struct MyKernel <: Kernel end
+
+KernelFunctions.kappa(::MyKernel, d2::Real) = exp(-d2)
+KernelFunctions.metric(::MyKernel) = SqEuclidean()
+
+# some syntactic sugar
+(κ::MyKernel)(d::Real) = kappa(κ, d)
+(κ::MyKernel)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real}) = kappa(κ, x, y)
+(κ::MyKernel)(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real}; obsdim = 2) = kernelmatrix(κ, X, Y; obsdim = obsdim)
+(κ::MyKernel)(X::AbstractMatrix{<:Real}; obsdim = 2) = kernelmatrix(κ, X; obsdim = obsdim)
+
+@testset "custom" begin
+    @test kappa(MyKernel(), 3) == kappa(SqExponentialKernel(), 3)
+    @test kappa(MyKernel(), 1, 3) == kappa(SqExponentialKernel(), 1, 3)
+    @test kappa(MyKernel(), [1, 2], [3, 4]) == kappa(SqExponentialKernel(), [1, 2], [3, 4])
+    @test kernelmatrix(MyKernel(), [1 2; 3 4], [5 6; 7 8]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4], [5 6; 7 8])
+    @test kernelmatrix(MyKernel(), [1 2; 3 4]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4])
+
+    @test MyKernel()(3) == SqExponentialKernel()(3)
+    @test MyKernel()([1, 2], [3, 4]) == SqExponentialKernel()([1, 2], [3, 4])
+    @test MyKernel()([1 2; 3 4], [5 6; 7 8]) == SqExponentialKernel()([1 2; 3 4], [5 6; 7 8])
+    @test MyKernel()([1 2; 3 4]) == SqExponentialKernel()([1 2; 3 4])
+end

test/kernels/exponential.jl

@@ -0,0 +1,34 @@
+@testset "exponential" begin
+    rng = MersenneTwister(123456)
+    x = rand(rng)*2
+    v1 = rand(rng, 3)
+    v2 = rand(rng, 3)
+    @testset "SqExponentialKernel" begin
+        k = SqExponentialKernel()
+        @test kappa(k,x) ≈ exp(-x)
+        @test k(v1,v2) ≈ exp(-norm(v1-v2)^2)
+        @test kappa(SqExponentialKernel(),x) == kappa(k,x)
+        @test metric(SqExponentialKernel()) == SqEuclidean()
+    end
+    @testset "ExponentialKernel" begin
+        k = ExponentialKernel()
+        @test kappa(k,x) ≈ exp(-x)
+        @test k(v1,v2) ≈ exp(-norm(v1-v2))
+        @test kappa(ExponentialKernel(),x) == kappa(k,x)
+        @test metric(ExponentialKernel()) == Euclidean()
+    end
+    @testset "GammaExponentialKernel" begin
+        γ = 2.0
+        k = GammaExponentialKernel(γ=γ)
+        @test kappa(k,x) ≈ exp(-(x)^(γ))
+        @test k(v1,v2) ≈ exp(-norm(v1-v2)^(2γ))
+        @test kappa(GammaExponentialKernel(),x) == kappa(k,x)
+        @test GammaExponentialKernel(gamma=γ).γ == [γ]
+        @test metric(GammaExponentialKernel()) == SqEuclidean()
+        @test metric(GammaExponentialKernel(γ=2.0)) == SqEuclidean()
+
+        #Coherence :
+        @test KernelFunctions._kernel(GammaExponentialKernel(γ=1.0),v1,v2) ≈ KernelFunctions._kernel(SqExponentialKernel(),v1,v2)
+        @test KernelFunctions._kernel(GammaExponentialKernel(γ=0.5),v1,v2) ≈ KernelFunctions._kernel(ExponentialKernel(),v1,v2)
+    end
+end

test/kernels/exponentiated.jl

@@ -0,0 +1,12 @@
+@testset "exponentiated" begin
+    rng = MersenneTwister(123456)
+    x = rand(rng)*2
+    v1 = rand(rng, 3)
+    v2 = rand(rng, 3)
+
+    k = ExponentiatedKernel()
+    @test kappa(k,x) ≈ exp(x)
+    @test kappa(k,-x) ≈ exp(-x)
+    @test k(v1,v2) ≈ exp(dot(v1,v2))
+    @test metric(ExponentiatedKernel()) == KernelFunctions.DotProduct()
+end

test/kernels/kernelproduct.jl

@@ -0,0 +1,15 @@
+@testset "kernelproduct" begin
+    rng = MersenneTwister(123456)
+    v1 = rand(rng, 3)
+    v2 = rand(rng, 3)
+
+    k1 = LinearKernel()
+    k2 = SqExponentialKernel()
+    k3 = RationalQuadraticKernel()
+
+    k = KernelProduct([k1,k2])
+    @test length(k) == 2
+    @test kappa(k,v1,v2) == kappa(k1*k2,v1,v2)
+    @test kappa(k*k,v1,v2) ≈ kappa(k,v1,v2)^2
+    @test kappa(k*k3,v1,v2) ≈ kappa(k3*k,v1,v2)
+end