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Merged
merged 4 commits into from
Oct 11, 2023
Merged

Stop using deprecated signatures from Distances.jl #529

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4d237b6
Stop using deprecated signatures from Distances.jl
simsurace Oct 6, 2023
5e089e7
Delete file that is not needed
simsurace Oct 6, 2023
624f7ad
Bump version and compat
simsurace Oct 6, 2023
043bab8
Change signature of `KernelFunctions.pairwise!`
simsurace Oct 6, 2023
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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "KernelFunctions"
uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
version = "0.10.57"
version = "0.10.58"

[deps]
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
Expand All @@ -25,7 +25,7 @@ ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
ChainRulesCore = "1"
Compat = "3.7, 4"
CompositionsBase = "0.1"
Distances = "0.10"
Distances = "0.10.9"
FillArrays = "0.10, 0.11, 0.12, 0.13, 1"
Functors = "0.1, 0.2, 0.3, 0.4"
IrrationalConstants = "0.1, 0.2"
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4 changes: 2 additions & 2 deletions src/basekernels/fbm.jl
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Expand Up @@ -57,7 +57,7 @@ end

function kernelmatrix!(K::AbstractMatrix, κ::FBMKernel, x::AbstractVector)
modx = _mod(x)
pairwise!(K, SqEuclidean(), x)
pairwise!(SqEuclidean(), K, x)
K .= _fbm.(modx, modx', K, κ.h)
return K
end
Expand All @@ -72,7 +72,7 @@ end
function kernelmatrix!(
K::AbstractMatrix, κ::FBMKernel, x::AbstractVector, y::AbstractVector
)
pairwise!(K, SqEuclidean(), x, y)
pairwise!(SqEuclidean(), K, x, y)
K .= _fbm.(_mod(x), _mod(y)', K, κ.h)
return K
end
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12 changes: 6 additions & 6 deletions src/distances/pairwise.jl
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Expand Up @@ -6,11 +6,11 @@ end

pairwise(d::PreMetric, X::AbstractVector) = pairwise(d, X, X)

function pairwise!(out::AbstractMatrix, d::PreMetric, X::AbstractVector, Y::AbstractVector)
function pairwise!(d::PreMetric, out::AbstractMatrix, X::AbstractVector, Y::AbstractVector)
return broadcast!(d, out, X, permutedims(Y))
end

pairwise!(out::AbstractMatrix, d::PreMetric, X::AbstractVector) = pairwise!(out, d, X, X)
pairwise!(d::PreMetric, out::AbstractMatrix, X::AbstractVector) = pairwise!(d, out, X, X)

function pairwise(d::PreMetric, x::AbstractVector{<:Real})
return Distances_pairwise(d, reshape(x, :, 1); dims=1)
Expand All @@ -20,14 +20,14 @@ function pairwise(d::PreMetric, x::AbstractVector{<:Real}, y::AbstractVector{<:R
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)
function pairwise!(d::PreMetric, out::AbstractMatrix, x::AbstractVector{<:Real})
return Distances.pairwise!(d, out, reshape(x, :, 1); dims=1)
end

function pairwise!(
out::AbstractMatrix, d::PreMetric, x::AbstractVector{<:Real}, y::AbstractVector{<:Real}
d::PreMetric, out::AbstractMatrix, x::AbstractVector{<:Real}, y::AbstractVector{<:Real}
)
return Distances.pairwise!(out, d, reshape(x, :, 1), reshape(y, :, 1); dims=1)
return Distances.pairwise!(d, out, reshape(x, :, 1), reshape(y, :, 1); dims=1)
end

# Also defines the colwise method for abstractvectors
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4 changes: 2 additions & 2 deletions src/matrix/kernelmatrix.jl
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Expand Up @@ -134,15 +134,15 @@ kernelmatrix_diag(κ::Kernel, x::AbstractVector, y::AbstractVector) = map(κ, x,

function kernelmatrix!(K::AbstractMatrix, κ::SimpleKernel, x::AbstractVector)
validate_inplace_dims(K, x)
pairwise!(K, metric(κ), x)
pairwise!(metric(κ), K, x)
return map!(x -> kappa(κ, x), K, K)
end

function kernelmatrix!(
K::AbstractMatrix, κ::SimpleKernel, x::AbstractVector, y::AbstractVector
)
validate_inplace_dims(K, x, y)
pairwise!(K, metric(κ), x, y)
pairwise!(metric(κ), K, x, y)
return map!(x -> kappa(κ, x), K, K)
end

Expand Down
16 changes: 8 additions & 8 deletions src/utils.jl
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Expand Up @@ -107,11 +107,11 @@ end
function pairwise(d::PreMetric, x::ColVecs, y::AbstractVector{<:AbstractVector{<:Real}})
return Distances_pairwise(d, x.X, reduce(hcat, y); dims=2)
end
function pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs)
return Distances.pairwise!(out, d, x.X; dims=2)
function pairwise!(d::PreMetric, out::AbstractMatrix, x::ColVecs)
return Distances.pairwise!(d, out, x.X; dims=2)
end
function pairwise!(out::AbstractMatrix, d::PreMetric, x::ColVecs, y::ColVecs)
return Distances.pairwise!(out, d, x.X, y.X; dims=2)
function pairwise!(d::PreMetric, out::AbstractMatrix, x::ColVecs, y::ColVecs)
return Distances.pairwise!(d, out, x.X, y.X; dims=2)
end

"""
Expand Down Expand Up @@ -178,11 +178,11 @@ end
function pairwise(d::PreMetric, x::RowVecs, y::AbstractVector{<:AbstractVector{<:Real}})
return Distances_pairwise(d, x.X, permutedims(reduce(hcat, y)); dims=1)
end
function pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs)
return Distances.pairwise!(out, d, x.X; dims=1)
function pairwise!(d::PreMetric, out::AbstractMatrix, x::RowVecs)
return Distances.pairwise!(d, out, x.X; dims=1)
end
function pairwise!(out::AbstractMatrix, d::PreMetric, x::RowVecs, y::RowVecs)
return Distances.pairwise!(out, d, x.X, y.X; dims=1)
function pairwise!(d::PreMetric, out::AbstractMatrix, x::RowVecs, y::RowVecs)
return Distances.pairwise!(d, out, x.X, y.X; dims=1)
end

# Resolve ambiguity error for ColVecs vs RowVecs. #346
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6 changes: 3 additions & 3 deletions test/distances/pairwise.jl
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Expand Up @@ -11,10 +11,10 @@

@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)
KernelFunctions.pairwise!(d, K, x, y)
@test K ≈ pairwise(d, X, Y; dims=2)
K = zeros(Ns[1], Ns[1])
KernelFunctions.pairwise!(K, d, x)
KernelFunctions.pairwise!(d, K, x)
@test K ≈ pairwise(d, X; dims=2)

x = randn(rng, 10)
Expand All @@ -24,6 +24,6 @@
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)
KernelFunctions.pairwise!(d, K, x, y)
@test K ≈ pairwise(d, X, Y; dims=1)
end
8 changes: 4 additions & 4 deletions test/utils.jl
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Expand Up @@ -47,10 +47,10 @@
@test vcat(DX, DY) isa ColVecs
@test vcat(DX, DY).X == hcat(X, Y)
K = zeros(N, N)
KernelFunctions.pairwise!(K, SqEuclidean(), DX)
KernelFunctions.pairwise!(SqEuclidean(), K, DX)
@test K ≈ pairwise(SqEuclidean(), X; dims=2)
K = zeros(N, N + 1)
KernelFunctions.pairwise!(K, SqEuclidean(), DX, DY)
KernelFunctions.pairwise!(SqEuclidean(), K, DX, DY)
@test K ≈ pairwise(SqEuclidean(), X, Y; dims=2)

let
Expand Down Expand Up @@ -105,10 +105,10 @@
@test vcat(DX, DY) isa RowVecs
@test vcat(DX, DY).X == vcat(X, Y)
K = zeros(D, D)
KernelFunctions.pairwise!(K, SqEuclidean(), DX)
KernelFunctions.pairwise!(SqEuclidean(), K, DX)
@test K ≈ pairwise(SqEuclidean(), X; dims=1)
K = zeros(D, D + 1)
KernelFunctions.pairwise!(K, SqEuclidean(), DX, DY)
KernelFunctions.pairwise!(SqEuclidean(), K, DX, DY)
@test K ≈ pairwise(SqEuclidean(), X, Y; dims=1)

let
Expand Down