Made corrections on the FBM kernel

Author: theogf

src/trainable.jl

@@ -4,6 +4,8 @@ import .Flux.trainable
 
 trainable(k::ConstantKernel) = (k.c,)
 
+trainable(k::FBMKernel) = (k.h,)
+
 trainable(k::GammaExponentialKernel) = (k.γ,)
 
 trainable(k::GammaRationalQuadraticKernel) = (k.α, k.γ)

test/kernels/fbm.jl

@@ -0,0 +1,17 @@
+@testset "FBM" begin
+    h = 0.3
+    k = FBMKernel(h = h)
+    v1 = rand(3); v2 = rand(3)
+    @test k(v1,v2) ≈ (sqeuclidean(v1, zero(v1))^h + sqeuclidean(v2, zero(v2))^h - sqeuclidean(v1-v2, zero(v1-v2))^h)/2 atol=1e-5
+
+    # kernelmatrix tests
+    m1 = rand(3,3)
+    m2 = rand(3,3)
+    @test kernelmatrix(k, m1, m1) ≈ kernelmatrix(k, m1) atol=1e-5
+    @test kernelmatrix(k, m1, m2) ≈ k(m1, m2) atol=1e-5
+
+
+    x1 = rand()
+    x2 = rand()
+    @test kernelmatrix(k, x1*ones(1,1), x2*ones(1,1))[1] ≈ k(x1, x2) atol=1e-5
+end

test/runtests.jl

@@ -62,17 +62,18 @@ using KernelFunctions: metric
     end
 
     @testset "kernels" begin
+        include(joinpath("kernels", "constant.jl"))
+        include(joinpath("kernels", "cosine.jl"))
         include(joinpath("kernels", "exponential.jl"))
+        include(joinpath("kernels", "exponentiated.jl"))
+        include(joinpath("kernels", "fbm.jl"))
+        include(joinpath("kernels", "kernelproduct.jl"))
+        include(joinpath("kernels", "kernelsum.jl"))
         include(joinpath("kernels", "matern.jl"))
         include(joinpath("kernels", "polynomial.jl"))
-        include(joinpath("kernels", "constant.jl"))
         include(joinpath("kernels", "rationalquad.jl"))
-        include(joinpath("kernels", "exponentiated.jl"))
-        include(joinpath("kernels", "cosine.jl"))
-        include(joinpath("kernels", "transformedkernel.jl"))
         include(joinpath("kernels", "scaledkernel.jl"))
-        include(joinpath("kernels", "kernelsum.jl"))
-        include(joinpath("kernels", "kernelproduct.jl"))
+        include(joinpath("kernels", "transformedkernel.jl"))
 
         # Legacy tests that don't correspond to anything meaningful in src. Unclear how
         # helpful these are.

test/trainable.jl

@@ -1,17 +1,27 @@
 @testset "trainable" begin
-    ν = 2.0; c = 3.0; d = 2.0; γ = 2.0; α = 2.5
+    ν = 2.0; c = 3.0; d = 2.0; γ = 2.0; α = 2.5; h = 0.5
+
     kc = ConstantKernel(c=c)
     @test all(params(kc) .== params([c]))
-    km = MaternKernel(ν=ν)
-    @test all(params(km) .== params([ν]))
-    kl = LinearKernel(c=c)
-    @test all(params(kl) .== params([c]))
+
+    kfbm = FBMKernel(h = h)
+    @test all(params(kfbm) .== params([h]))
+
     kge = GammaExponentialKernel(γ=γ)
     @test all(params(kge) .== params([γ]))
+
     kgr = GammaRationalQuadraticKernel(γ=γ, α=α)
     @test all(params(kgr) .== params([α], [γ]))
+
+    kl = LinearKernel(c=c)
+    @test all(params(kl) .== params([c]))
+
+    km = MaternKernel(ν=ν)
+    @test all(params(km) .== params([ν]))
+
     kp = PolynomialKernel(c=c, d=d)
     @test all(params(kp) .== params([d], [c]))
+
     kr = RationalQuadraticKernel(α=α)
     @test all(params(kr) .== params([α]))