Added tests and corrected some

Author: theogf

src/KernelFunctions.jl

@@ -1,6 +1,6 @@
 module KernelFunctions
 
-export kernelmatrix, kernelmatrix!, kerneldiagmatrix, kerneldiagmatrix!, kappa
+export kernel, kernelmatrix, kernelmatrix!, kerneldiagmatrix, kerneldiagmatrix!, kappa
 export Kernel
 export ConstantKernel, WhiteKernel, ZeroKernel
 export SqExponentialKernel, ExponentialKernel, GammaExponentialKernel

src/kernels/polynomial.jl

@@ -24,7 +24,7 @@ function LinearKernel(ρ::A,c::T=zero(eltype(ρ))) where {A<:AbstractVector{<:Re
     LinearKernel{eltype(A),ScaleTransform{A},T}(ScaleTransform(ρ),c)
 end
 
-function LinearKernel(t::Tr,c::T=zero(eltype(t))) where {Tr<:Transform,T<:Real}
+function LinearKernel(t::Tr,c::T=zero(Float64)) where {Tr<:Transform,T<:Real}
     LinearKernel{eltype(t),Tr,T}(t,c)
 end
 
@@ -59,7 +59,7 @@ function PolynomialKernel(ρ::A,d::T₁=2.0,c::T₂=zero(eltype(ρ))) where {A<:
     PolynomialKernel{eltype(A),ScaleTransform{A},T₁,T₂}(ScaleTransform(ρ),c,d)
 end
 
-function PolynomialKernel(t::Tr,d::T₁=2.0,c::T₂=zero(eltype(t))) where {Tr<:Transform,T₁<:Real,T₂<:Real}
+function PolynomialKernel(t::Tr,d::T₁=2.0,c::T₂=zero(eltype(T₁))) where {Tr<:Transform,T₁<:Real,T₂<:Real}
     @check_args(PolynomialKernel, d, d >= one(T₁), "d >= 1")
     PolynomialKernel{eltype(Tr),Tr,T₁,T₂}(t,c,d)
 end

test/runtests.jl

@@ -1,12 +1,12 @@
 using Test
 using KernelFunctions
 using Distances
-using FiniteDifferences
+# using FiniteDifferences
 using Random
 using Zygote
 
 # Helpful functionality for writing tests.
-include("test_util.jl")
+# include("test_util.jl")
 
 @testset "KernelFunctions" begin
 include("test_kernelmatrix.jl")

test/test_distances.jl

@@ -7,7 +7,7 @@ B = rand(20,5)
 @testset "Distance" begin
     @testset "Dot Product" begin
         d = KernelFunctions.DotProduct()
-        @test diag(pairwise(d,A,dims=2)) == dot.(eachcol(A),eachcol(A))
+        @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_kernelmatrix.jl

@@ -12,6 +12,7 @@ k = SqExponentialKernel()
 @testset "Inplace Kernel Matrix" begin
     for obsdim in [1,2]
         @test kernelmatrix!(K[obsdim],k,A,B,obsdim=obsdim) == kernelmatrix(k,A,B,obsdim=obsdim)
+        @test kernelmatrix!(K[obsdim],k,A,obsdim=obsdim) == kernelmatrix(k,A,obsdim=obsdim)
         @test kerneldiagmatrix!(Kdiag[obsdim],k,A,obsdim=obsdim) == kerneldiagmatrix(k,A,obsdim=obsdim)
     end
 end
@@ -22,5 +23,6 @@ end
         @test kernelmatrix(k,A,obsdim=obsdim) == kappa.([k],pairwise(KernelFunctions.metric(k),A,dims=obsdim))
         @test k(A,B,obsdim=obsdim) == kernelmatrix(k,A,B,obsdim=obsdim)
         @test k(A,obsdim=obsdim) == kernelmatrix(k,A,obsdim=obsdim)
+        @test kernel(k,1.0,2.0) == kernel(k,[1.0],[2.0])
     end
 end

test/test_kernels.jl

@@ -3,7 +3,7 @@ using LinearAlgebra
 using KernelFunctions
 using SpecialFunctions
 
-x = rand()*2; v1 = rand(3); v2 = rand(3)
+x = rand()*2; v1 = rand(3); v2 = rand(3); id = IdentityTransform()
 @testset "Kappa functions of kernels" begin
     @testset "Constant" begin
         @testset "ZeroKernel" begin
@@ -32,6 +32,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = SqExponentialKernel()
             @test kappa(k,x) ≈ exp(-x)
             @test k(v1,v2) ≈ exp(-norm(v1-v2)^2)
+            @test kappa(SqExponentialKernel(id),x) == kappa(k,x)
             l = 0.5
             k = SqExponentialKernel(l)
             @test k(v1,v2) ≈ exp(-l^2*norm(v1-v2)^2)
@@ -43,6 +44,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = ExponentialKernel()
             @test kappa(k,x) ≈ exp(-x)
             @test k(v1,v2) ≈ exp(-norm(v1-v2))
+            @test kappa(ExponentialKernel(id),x) == kappa(k,x)
             l = 0.5
             k = ExponentialKernel(l)
             @test k(v1,v2) ≈ exp(-l*norm(v1-v2))
@@ -54,12 +56,16 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = GammaExponentialKernel(1.0,2.0)
             @test kappa(k,x) ≈ exp(-(x)^(k.γ))
             @test k(v1,v2) ≈ exp(-norm(v1-v2)^(2k.γ))
+            @test kappa(GammaExponentialKernel(id),x) == kappa(k,x)
             l = 0.5
             k = GammaExponentialKernel(l,1.5)
             @test k(v1,v2) ≈ exp(-l^(3.0)*norm(v1-v2)^(3.0))
             v = rand(3)
             k = GammaExponentialKernel(v,3.0)
             @test k(v1,v2) ≈ exp(-norm(v.*(v1-v2)).^6.0)
+            #Coherence :
+            @test kernel(GammaExponentialKernel(1.0,1.0),v1,v2) ≈ kernel(SqExponentialKernel(),v1,v2)
+            @test kernel(GammaExponentialKernel(1.0,0.5),v1,v2) ≈ kernel(ExponentialKernel(),v1,v2)
         end
     end
     @testset "Exponentiated" begin
@@ -83,6 +89,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             matern(x,ν) = 2^(1-ν)/gamma(ν)*(sqrt(2ν)*x)^ν*besselk(ν,sqrt(2ν)*x)
             @test kappa(k,x) ≈ matern(x,ν)
             @test kappa(k,0.0) == 1.0
+            @test kappa(MaternKernel(id,ν),x) == kappa(k,x)
             l = 0.5; ν = 3.0
             k = MaternKernel(l,ν)
             @test k(v1,v2) ≈ matern(l*norm(v1-v2),ν)
@@ -94,6 +101,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = Matern32Kernel()
             @test kappa(k,x) ≈ (1+sqrt(3)*x)exp(-sqrt(3)*x)
             @test k(v1,v2) ≈ (1+sqrt(3)*norm(v1-v2))exp(-sqrt(3)*norm(v1-v2))
+            @test kappa(Matern32Kernel(id),x) == kappa(k,x)
             l = 0.5
             k = Matern32Kernel(l)
             @test k(v1,v2) ≈ (1+l*sqrt(3)*norm(v1-v2))exp(-l*sqrt(3)*norm(v1-v2))
@@ -105,6 +113,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = Matern52Kernel()
             @test kappa(k,x) ≈ (1+sqrt(5)*x+5/3*x^2)exp(-sqrt(5)*x)
             @test k(v1,v2) ≈ (1+sqrt(5)*norm(v1-v2)+5/3*norm(v1-v2)^2)exp(-sqrt(5)*norm(v1-v2))
+            @test kappa(Matern52Kernel(id),x) == kappa(k,x)
             l = 0.5
             k = Matern52Kernel(l)
             @test k(v1,v2) ≈ (1+l*sqrt(5)*norm(v1-v2)+l^2*5/3*norm(v1-v2)^2)exp(-l*sqrt(5)*norm(v1-v2))
@@ -124,6 +133,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = LinearKernel()
             @test kappa(k,x) ≈ x
             @test k(v1,v2) ≈ dot(v1,v2)
+            @test kappa(LinearKernel(id),x) == kappa(k,x)
             l = 0.5
             k = LinearKernel(l,c)
             @test k(v1,v2) ≈ l^2*dot(v1,v2) + c
@@ -135,6 +145,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = PolynomialKernel()
             @test kappa(k,x) ≈ x^2
             @test k(v1,v2) ≈ dot(v1,v2)^2
+            @test kappa(PolynomialKernel(id),x) == kappa(k,x)
             d = 3.0
             l = 0.5
             k = PolynomialKernel(l,d,c)
@@ -151,6 +162,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = RationalQuadraticKernel()
             @test kappa(k,x) ≈ (1.0+x/2.0)^-2
             @test k(v1,v2) ≈ (1.0+norm(v1-v2)^2/2.0)^-2
+            @test kappa(RationalQuadraticKernel(id),x) == kappa(k,x)
             l = 0.5
             a = 1.0 + rand()
             k = RationalQuadraticKernel(l,a)
@@ -163,6 +175,7 @@ x = rand()*2; v1 = rand(3); v2 = rand(3)
             k = GammaRationalQuadraticKernel()
             @test kappa(k,x) ≈ (1.0+x^2.0/2.0)^-2
             @test k(v1,v2) ≈ (1.0+norm(v1-v2)^4.0/2.0)^-2
+            @test kappa(GammaRationalQuadraticKernel(id),x) == kappa(k,x)
             l = 0.5
             a = 1.0 + rand()
             g = 4.0