Adapted PR to the new pkg structure

Author: theogf

Project.toml

@@ -15,7 +15,7 @@ ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
 Compat = "2.2, 3"
-Distances = "0.8"
+Distances = "0.8.2"
 Requires = "1.0.1"
 SpecialFunctions = "0.8, 0.9, 0.10"
 StatsBase = "0.32, 0.33"

src/distances/delta.jl

@@ -5,8 +5,8 @@ end
     @boundscheck if length(a) != length(b)
         throw(DimensionMismatch("first array has length $(length(a)) which does not match the length of the second, $(length(b))."))
     end
-    return a==b
+    return a == b
 end
 
-@inline (dist::Delta)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist,a,b)
-@inline (dist::Delta)(a::Number,b::Number) = a==b
+@inline (dist::Delta)(a::AbstractArray, b::AbstractArray) = Distances._evaluate(dist, a, b)
+@inline (dist::Delta)(a::Number,b::Number) = a == b

src/distances/dotproduct.jl

@@ -1,4 +1,5 @@
 struct DotProduct <: Distances.PreMetric end
+# struct DotProduct <: Distances.UnionSemiMetric end
 
 @inline function Distances._evaluate(::DotProduct, a::AbstractVector{T}, b::AbstractVector{T}) where {T}
     @boundscheck if length(a) != length(b)
@@ -7,8 +8,6 @@ struct DotProduct <: Distances.PreMetric end
     return dot(a,b)
 end
 
-# For later convenience once Distances.jl open their API
 @inline Distances.eval_op(::DotProduct, a::Real, b::Real) = a * b
-
 @inline (dist::DotProduct)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist, a, b)
-@inline (dist::DotProduct)(a::Number,b::Number) = Distances.eval_op(dist, a, b)
+@inline (dist::DotProduct)(a::Number,b::Number) = a * b

src/distances/sinus.jl

@@ -1,18 +1,16 @@
 struct Sinus{T} <: Distances.SemiMetric
+# struct Sinus{T} <: Distances.UnionSemiMetric
     r::Vector{T}
 end
 
 Distances.parameters(d::Sinus) = d.r
+@inline Distances.eval_op(::Sinus, a::Real, b::Real, p::Real) = abs2(sinpi(a - b) / p)
+@inline (dist::Sinus)(a::AbstractArray, b::AbstractArray) = Distances._evaluate(dist, a, b)
+@inline (dist::Sinus)(a::Number, b::Number) = abs2(sinpi(a - b) / first(dist.r))
 
 @inline function Distances._evaluate(d::Sinus, a::AbstractVector{T}, b::AbstractVector{T}) where {T}
     @boundscheck if (length(a) != length(b)) || length(a) != length(d.r)
         throw(DimensionMismatch("Dimensions of the inputs are not matching : a = $(length(a)), b = $(length(b)), r = $(length(d.r))"))
     end
     return sum(abs2, sinpi.(a - b) ./ d.r)
 end
-
-# For later convenience once Distances.jl open their API
-@inline Distances.eval_op(::Sinus, a::Real, b::Real, p::Real) = abs2(sinpi(a - b) / p)
-
-@inline (dist::Sinus)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist, a, b)
-@inline (dist::Sinus)(a::Number,b::Number) = abs2(sinpi(a - b) / first(dist.r))

src/kernels/periodic.jl

@@ -13,7 +13,7 @@ struct PeriodicKernel{T} <: BaseKernel
     end
 end
 
-PeriodicKernel(dims::Int = 1) = PeriodicKernel(Float64, dims)
+PeriodicKernel(dims::Int) = PeriodicKernel(Float64, dims)
 
 PeriodicKernel(T::DataType, dims::Int = 1) = PeriodicKernel(r = ones(T, dims))
 

test/distances/sinus.jl

@@ -0,0 +1,9 @@
+@testset "sinus" begin
+    A = rand(10)
+    B = rand(10)
+    p = rand(10)
+    d = KernelFunctions.Sinus(p)
+    @test Distances.parameters(d) == p
+    @test evaluate(d, A, B) == sum(abs2.(sinpi.(A - B) ./ p))
+    @test d(3.0, 2.0) == abs2(sinpi(3.0 - 2.0) / first(p))
+end

test/kernels/periodic.jl

@@ -0,0 +1,9 @@
+@testset "Periodic Kernel" begin
+    x = rand()*2; v1 = rand(3); v2 = rand(3);
+    r = rand(3)
+    k = PeriodicKernel(r = r)
+    @test kappa(k, x) ≈ exp(-0.5x)
+    @test k(v1, v2) ≈ exp(-0.5 * sum(abs2, sinpi.(v1 - v2) ./ r))
+    @test k(v1, v2) == k(v2, v1)
+    @test PeriodicKernel(3)(v1, v2) == PeriodicKernel(r = ones(3))(v1, v2)
+end

test/runtests.jl

@@ -49,6 +49,7 @@ using KernelFunctions: metric
     @testset "distances" begin
         include(joinpath("distances", "dotproduct.jl"))
         include(joinpath("distances", "delta.jl"))
+        include(joinpath("distances", "sinus.jl"))
     end
 
     @testset "transform" begin
@@ -71,6 +72,7 @@ using KernelFunctions: metric
         include(joinpath("kernels", "kernelproduct.jl"))
         include(joinpath("kernels", "kernelsum.jl"))
         include(joinpath("kernels", "matern.jl"))
+        include(joinpath("kernels", "periodic.jl"))
         include(joinpath("kernels", "polynomial.jl"))
         include(joinpath("kernels", "piecewisepolynomial.jl"))
         include(joinpath("kernels", "rationalquad.jl"))

test/trainable.jl

@@ -1,5 +1,5 @@
 @testset "trainable" begin
-    ν = 2.0; c = 3.0; d = 2.0; γ = 2.0; α = 2.5; h = 0.5
+    ν = 2.0; c = 3.0; d = 2.0; γ = 2.0; α = 2.5; h = 0.5; r = rand(3)
 
     kc = ConstantKernel(c=c)
     @test all(params(kc) .== params([c]))
@@ -22,6 +22,9 @@
     kp = PolynomialKernel(c=c, d=d)
     @test all(params(kp) .== params([d], [c]))
 
+    kpe = PeriodicKernel(r = r)
+    @test all(params(kpe) .== params(r))
+
     kr = RationalQuadraticKernel(α=α)
     @test all(params(kr) .== params([α]))