Fixes #115

src/KernelFunctions.jl

@@ -50,6 +50,7 @@ abstract type BaseKernel <: Kernel end
 abstract type SimpleKernel <: BaseKernel end
 
 include("utils.jl")
+include("distances/pairwise.jl")
 include("distances/dotproduct.jl")
 include("distances/delta.jl")
 include("distances/sinus.jl")

src/distances/pairwise.jl

@@ -0,0 +1,41 @@
+# Add our own pairwise function to be able to apply it on vectors
+
+pairwise(d::PreMetric, X::AbstractVector, Y::AbstractVector) = broadcast(d, X, Y')
+
+pairwise(d::PreMetric, X::AbstractVector) = pairwise(d, X, X)
+
+function pairwise!(
+    out::AbstractMatrix,
+    d::PreMetric,
+    X::AbstractVector,
+    Y::AbstractVector,
+)
+    broadcast!(d, out, X, Y')
+end
+
+pairwise!(out::AbstractMatrix, d::PreMetric, X::AbstractVector) = pairwise!(out, d, X, X)
+
+function pairwise(d::PreMetric, x::AbstractVector{<:Real})
+    return Distances.pairwise(d, reshape(x, :, 1); dims = 1)
+end
+
+function pairwise(
+    d::PreMetric,
+    x::AbstractVector{<:Real},
+    y::AbstractVector{<:Real},
+)
+    return Distances.pairwise(d, reshape(x, :, 1), reshape(y, :, 1); dims = 1)
+end
+
+function pairwise!(out::AbstractMatrix, d::PreMetric, x::AbstractVector{<:Real})
+    return Distances.pairwise!(out, d, reshape(x, :, 1); dims = 1)
+end
+
+function pairwise!(
+    out::AbstractMatrix,
+    d::PreMetric,
+    x::AbstractVector{<:Real},
+    y::AbstractVector{<:Real},
+)
+    return Distances.pairwise!(out, d, reshape(x, :, 1), reshape(y, :, 1); dims=1)
+end

src/generic.jl

@@ -20,29 +20,3 @@ end
 
 # Fallback implementation of evaluate for `SimpleKernel`s.
 (k::SimpleKernel)(x, y) = kappa(k, evaluate(metric(k), x, y))
-
-# This is type piracy. We should not doing this.
-function Distances.pairwise(d::PreMetric, x::AbstractVector{<:Real})
-    return pairwise(d, reshape(x, :, 1); dims=1)
-end
-
-function Distances.pairwise(
-    d::PreMetric,
-    x::AbstractVector{<:Real},
-    y::AbstractVector{<:Real},
-)
-    return pairwise(d, reshape(x, :, 1), reshape(y, :, 1); dims=1)
-end
-
-function Distances.pairwise!(out::AbstractMatrix, d::PreMetric, x::AbstractVector{<:Real})
-    return pairwise!(out, d, reshape(x, :, 1); dims=1)
-end
-
-function Distances.pairwise!(
-    out::AbstractMatrix,
-    d::PreMetric,
-    x::AbstractVector{<:Real},
-    y::AbstractVector{<:Real},
-)
-    return pairwise!(out, d, reshape(x, :, 1), reshape(y, :, 1); dims=1)
-end

src/utils.jl

@@ -43,13 +43,13 @@ Base.getindex(D::ColVecs, i) = ColVecs(view(D.X, :, i))
 
 dim(x::ColVecs) = size(x.X, 1)
 
-Distances.pairwise(d::PreMetric, x::ColVecs) = pairwise(d, x.X; dims=2)
-Distances.pairwise(d::PreMetric, x::ColVecs, y::ColVecs) = pairwise(d, x.X, y.X; dims=2)
-function Distances.pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs)
-    return pairwise!(out, d, x.X; dims=2)
+pairwise(d::PreMetric, x::ColVecs) = Distances.pairwise(d, x.X; dims=2)
+pairwise(d::PreMetric, x::ColVecs, y::ColVecs) = Distances.pairwise(d, x.X, y.X; dims=2)
+function pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs)
+    return Distances.pairwise!(out, d, x.X; dims=2)
 end
-function Distances.pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs, y::ColVecs)
-    return pairwise!(out, d, x.X, y.X; dims=2)
+function pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs, y::ColVecs)
+    return Distances.pairwise!(out, d, x.X, y.X; dims=2)
 end
 
 """
@@ -73,13 +73,13 @@ Base.getindex(D::RowVecs, i) = RowVecs(view(D.X, i, :))
 
 dim(x::RowVecs) = size(x.X, 2)
 
-Distances.pairwise(d::PreMetric, x::RowVecs) = pairwise(d, x.X; dims=1)
-Distances.pairwise(d::PreMetric, x::RowVecs, y::RowVecs) = pairwise(d, x.X, y.X; dims=1)
-function Distances.pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs)
-    return pairwise!(out, d, x.X; dims=1)
+pairwise(d::PreMetric, x::RowVecs) = Distances.pairwise(d, x.X; dims=1)
+pairwise(d::PreMetric, x::RowVecs, y::RowVecs) = Distances.pairwise(d, x.X, y.X; dims=1)
+function pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs)
+    return Distances.pairwise!(out, d, x.X; dims=1)
 end
-function Distances.pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs, y::RowVecs)
-    return pairwise!(out, d, x.X, y.X; dims=1)
+function pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs, y::RowVecs)
+    return Distances.pairwise!(out, d, x.X, y.X; dims=1)
 end
 
 """

src/zygote_adjoints.jl

@@ -5,17 +5,17 @@
   end
 end
 
-@adjoint function pairwise(d::Delta, X::AbstractMatrix, Y::AbstractMatrix; dims=2)
-  D = pairwise(d, X, Y; dims = dims)
+@adjoint function Distances.pairwise(d::Delta, X::AbstractMatrix, Y::AbstractMatrix; dims=2)
+  D = Distances.pairwise(d, X, Y; dims = dims)
   if dims == 1
       return D, Δ -> (nothing, nothing, nothing)
   else
       return D, Δ -> (nothing, nothing, nothing)
   end
 end
 
-@adjoint function pairwise(d::Delta, X::AbstractMatrix; dims=2)
-  D = pairwise(d, X; dims = dims)
+@adjoint function Distances.pairwise(d::Delta, X::AbstractMatrix; dims=2)
+  D = Distances.pairwise(d, X; dims = dims)
   if dims == 1
       return D, Δ -> (nothing, nothing)
   else
@@ -30,17 +30,17 @@ end
   end
 end
 
-@adjoint function pairwise(d::DotProduct, X::AbstractMatrix, Y::AbstractMatrix; dims=2)
-  D = pairwise(d, X, Y; dims = dims)
+@adjoint function Distances.pairwise(d::DotProduct, X::AbstractMatrix, Y::AbstractMatrix; dims=2)
+  D = Distances.pairwise(d, X, Y; dims = dims)
   if dims == 1
       return D, Δ -> (nothing, Δ * Y, (X' * Δ)')
   else
       return D, Δ -> (nothing, (Δ * Y')', X * Δ)
   end
 end
 
-@adjoint function pairwise(d::DotProduct, X::AbstractMatrix; dims=2)
-  D = pairwise(d, X; dims = dims)
+@adjoint function Distances.pairwise(d::DotProduct, X::AbstractMatrix; dims=2)
+  D = Distances.pairwise(d, X; dims = dims)
   if dims == 1
       return D, Δ -> (nothing, 2 * Δ * X)
   else

test/distances/pairwise.jl

@@ -0,0 +1,29 @@
+@testset "pairwise" begin
+    rng = MersenneTwister(123456)
+    d = SqEuclidean()
+    Ns = (4, 5)
+    D = 3
+    x = [randn(rng, D) for _ in 1:Ns[1]]
+    y = [randn(rng, D) for _ in 1:Ns[2]]
+    X = hcat(x...)
+    Y = hcat(y...)
+    K = zeros(Ns)
+
+    @test KernelFunctions.pairwise(d, x, y) ≈ pairwise(d, X, Y, dims=2)
+    @test KernelFunctions.pairwise(d, x) ≈ pairwise(d, X, dims=2)
+    KernelFunctions.pairwise!(K, d, x, y)
+    @test K ≈ pairwise(d, X, Y, dims=2)
+    K = zeros(Ns[1], Ns[1])
+    KernelFunctions.pairwise!(K, d, x)
+    @test K ≈ pairwise(d, X, dims=2)
+
+    x = randn(rng, 10)
+    X = reshape(x, :, 1)
+    y = randn(rng, 11)
+    Y = reshape(y, :, 1)
+    K = zeros(10, 11)
+    @test KernelFunctions.pairwise(d, x, y) ≈ pairwise(d, X, Y; dims=1)
+    @test KernelFunctions.pairwise(d, x) ≈ pairwise(d, X; dims=1)
+    KernelFunctions.pairwise!(K, d, x, y)
+    @test K ≈ pairwise(d, X, Y, dims=1)
+end

test/generic.jl

@@ -3,12 +3,4 @@
     @test length(k) == 1
     @test iterate(k) == (k,nothing)
     @test iterate(k,1) == nothing
-
-    rng = MersenneTwister(123456)
-    x = randn(rng, 10)
-    X = reshape(x, :, 1)
-    y = randn(rng, 11)
-    Y = reshape(y, :, 1)
-    @test pairwise(SqEuclidean(), x, y) ≈ pairwise(SqEuclidean(), X, Y; dims=1)
-    @test pairwise(SqEuclidean(), x) ≈ pairwise(SqEuclidean(), X; dims=1)
 end

test/runtests.jl

@@ -48,6 +48,7 @@ using KernelFunctions: metric, kappa, ColVecs, RowVecs
     include("utils_AD.jl")
 
     @testset "distances" begin
+        include(joinpath("distances", "pairwise.jl"))
         include(joinpath("distances", "dotproduct.jl"))
         include(joinpath("distances", "delta.jl"))
         include(joinpath("distances", "sinus.jl"))

test/utils.jl

@@ -22,13 +22,13 @@
 
         Y = randn(rng, D, N + 1)
         DY = ColVecs(Y)
-        @test pairwise(SqEuclidean(), DX) ≈ pairwise(SqEuclidean(), X; dims=2)
-        @test pairwise(SqEuclidean(), DX, DY) ≈ pairwise(SqEuclidean(), X, Y; dims=2)
+        @test KernelFunctions.pairwise(SqEuclidean(), DX) ≈ pairwise(SqEuclidean(), X; dims=2)
+        @test KernelFunctions.pairwise(SqEuclidean(), DX, DY) ≈ pairwise(SqEuclidean(), X, Y; dims=2)
         K = zeros(N, N)
-        pairwise!(K, SqEuclidean(), DX)
+        KernelFunctions.pairwise!(K, SqEuclidean(), DX)
         @test K ≈ pairwise(SqEuclidean(), X; dims=2)
         K = zeros(N, N + 1)
-        pairwise!(K, SqEuclidean(), DX, DY)
+        KernelFunctions.pairwise!(K, SqEuclidean(), DX, DY)
         @test K ≈ pairwise(SqEuclidean(), X, Y; dims=2)
 
         let
@@ -56,13 +56,13 @@
 
         Y = randn(rng, D + 1, N)
         DY = RowVecs(Y)
-        @test pairwise(SqEuclidean(), DX) ≈ pairwise(SqEuclidean(), X; dims=1)
-        @test pairwise(SqEuclidean(), DX, DY) ≈ pairwise(SqEuclidean(), X, Y; dims=1)
+        @test KernelFunctions.pairwise(SqEuclidean(), DX) ≈ pairwise(SqEuclidean(), X; dims=1)
+        @test KernelFunctions.pairwise(SqEuclidean(), DX, DY) ≈ pairwise(SqEuclidean(), X, Y; dims=1)
         K = zeros(D, D)
-        pairwise!(K, SqEuclidean(), DX)
+        KernelFunctions.pairwise!(K, SqEuclidean(), DX)
         @test K ≈ pairwise(SqEuclidean(), X; dims=1)
         K = zeros(D, D + 1)
-        pairwise!(K, SqEuclidean(), DX, DY)
+        KernelFunctions.pairwise!(K, SqEuclidean(), DX, DY)
         @test K ≈ pairwise(SqEuclidean(), X, Y; dims=1)
 
         let