fixed pairwise for non-symmetric kernels (fixed #143)

JuliaStats · Mar 14, 2021 · a405c5d · a405c5d
1 parent febc53f
commit a405c5d
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Showing 2 changed files with 24 additions and 12 deletions.
diff --git a/src/common.jl b/src/common.jl
@@ -122,29 +122,30 @@ end
 
 # calculate pairwise kernel
 function pairwise!(K::AbstractVecOrMat{<:Real}, kernel::Function,
-                   X::AbstractVecOrMat{<:Real}, Y::AbstractVecOrMat{<:Real})
+                   X::AbstractVecOrMat{<:Real}, Y::AbstractVecOrMat{<:Real};
+                   force_symmetry=false)
     n = size(X, 2)
     m = size(Y, 2)
     for j = 1:m
         aj = view(Y, :, j)
-        for i in j:n
+        for i in (force_symmetry ? j : 1):n
             @inbounds K[i, j] = kernel(view(X, :, i), aj)[]
         end
-        j <= n && for i in 1:(j - 1)
+        force_symmetry && j <= n && for i in 1:(j - 1)
             @inbounds K[i, j] = K[j, i]   # leveraging the symmetry
         end
     end
     K
 end
 
-pairwise!(K::AbstractVecOrMat{<:Real}, kernel::Function, X::AbstractVecOrMat{<:Real}) =
-    pairwise!(K, kernel, X, X)
+pairwise!(K::AbstractVecOrMat{<:Real}, kernel::Function, X::AbstractVecOrMat{<:Real}; kwargs...) =
+    pairwise!(K, kernel, X, X; force_symmetry=true)
 
-function pairwise(kernel::Function, X::AbstractVecOrMat{<:Real}, Y::AbstractVecOrMat{<:Real})
+function pairwise(kernel::Function, X::AbstractVecOrMat{<:Real}, Y::AbstractVecOrMat{<:Real}; kwargs...)
     n = size(X, 2)
     m = size(Y, 2)
     K = similar(X, n, m)
-    pairwise!(K, kernel, X, Y)
+    pairwise!(K, kernel, X, Y; kwargs...)
 end
 
-pairwise(kernel::Function, X::AbstractVecOrMat{<:Real}) = pairwise(kernel, X, X)
+pairwise(kernel::Function, X::AbstractVecOrMat{<:Real}) = pairwise(kernel, X, X; force_symmetry=true)
diff --git a/test/kpca.jl b/test/kpca.jl
@@ -34,18 +34,29 @@ import Random
     end
 
     # kernel calculations
-    K = MultivariateStats.pairwise((x,y)->norm(x-y), X, X[:,1:2])
+    ker1  = (x,y)->x'y
+    ker2  = (x,y)->norm(x-y)
+
+    K = MultivariateStats.pairwise(ker1, X)
+    @test size(K) == (n,n)
+    @test K[1,2] == K[2,1]
+
+    K = MultivariateStats.pairwise(ker1, X, X.+1)
+    @test size(K) == (n,n)
+    @test K[1,2] != K[2,1]
+
+    K = MultivariateStats.pairwise(ker2, X, X[:,1:2])
     @test size(K) == (n, 2)
     @test K[1,1] == 0
     @test K[3,2] == norm(X[:,3] - X[:,2])
 
-    K = MultivariateStats.pairwise((x,y)->norm(x-y), X[:,1:3], X)
+    K = MultivariateStats.pairwise(ker2, X[:,1:3], X)
     @test size(K) == (3, n)
     @test K[1,1] == 0
     @test K[3,2] == norm(X[:,2] - X[:,3])
 
     K = similar(X, n, n)
-    MultivariateStats.pairwise!(K, (x,y)->norm(x-y), X)
+    MultivariateStats.pairwise!(K, ker2, X)
     @test size(K) == (n, n)
     @test K[1,1] == 0
     @test K[2,1] == norm(X[:,2] - X[:,1])
@@ -54,7 +65,7 @@ import Random
     Iₙ = ones(n,n)/n
     @test MultivariateStats.transform!(KC, copy(K)) ≈ K - Iₙ*K - K*Iₙ + Iₙ*K*Iₙ
 
-    K = MultivariateStats.pairwise((x,y)->norm(x-y), X, X[:,1])
+    K = MultivariateStats.pairwise(ker2, X, X[:,1])
     @test size(K) == (n, 1)
     @test K[1,1] == 0
     @test K[2,1] == norm(X[:,2] - X[:,1])