remove usage of eachcol

JuliaOptimalTransport · Sep 22, 2021 · cee80a6 · cee80a6
1 parent 73ff802
commit cee80a6
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Showing 4 changed files with 13 additions and 14 deletions.
diff --git a/Project.toml b/Project.toml
@@ -4,7 +4,6 @@ authors = ["zsteve <[email protected]>"]
 version = "0.3.17"
 
 [deps]
-Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ExactOptimalTransport = "24df6009-d856-477c-ac5c-91f668376b31"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -13,7 +12,6 @@ NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [compat]
-Compat = "2.2, 3"
 ExactOptimalTransport = "0.1"
 IterativeSolvers = "0.8.4, 0.9"
 LogExpFunctions = "0.2, 0.3"

diff --git a/src/OptimalTransport.jl b/src/OptimalTransport.jl
@@ -13,7 +13,6 @@ using LinearAlgebra
 using IterativeSolvers
 using LogExpFunctions: LogExpFunctions
 using NNlib: NNlib
-using Compat
 
 export SinkhornGibbs, SinkhornStabilized, SinkhornEpsilonScaling
 export SinkhornBarycenterGibbs

diff --git a/src/utils.jl b/src/utils.jl
@@ -25,7 +25,10 @@ end
 dot_matwise(x::AbstractMatrix, y::AbstractArray) = dot_matwise(y, x)
 
 function dot_vecwise(x::AbstractMatrix, y::AbstractMatrix)
-    Compat.@compat return [dot(u, v) for (u, v) in zip(eachcol(x), eachcol(y))]
+    return [
+        dot(u, v) for (u, v) in
+        zip((view(x, :, i) for i in axes(x, 2)), (view(y, :, i) for i in axes(y, 2)))
+    ]
 end
 
 dot_vecwise(x::AbstractMatrix, y::AbstractVector) = x' * y

diff --git a/test/entropic/sinkhorn_divergence.jl b/test/entropic/sinkhorn_divergence.jl
@@ -8,7 +8,6 @@ using PythonOT: PythonOT
 using LinearAlgebra
 using Random
 using Test
-using Compat
 
 const POT = PythonOT
 
@@ -53,19 +52,19 @@ Random.seed!(100)
             ν = hcat([normalize!(f.(x; μ=randn(), σ=0.5), 1) for _ in 1:M]...)
             for reg in (true, false)
                 loss_batch = sinkhorn_divergence(μ, ν, C, ε; regularization=reg)
-                @compat @test loss_batch ≈ [
-                    sinkhorn_divergence(x, y, C, ε; regularization=reg) for
-                    (x, y) in zip(eachcol(μ), eachcol(ν))
+                @test loss_batch ≈ [
+                    sinkhorn_divergence(μ[:, i], ν[:, i], C, ε; regularization=reg) for
+                    i in 1:M
                 ]
                 loss_batch_μ = sinkhorn_divergence(μ, ν[:, 1], C, ε; regularization=reg)
-                @compat @test loss_batch_μ ≈ [
-                    sinkhorn_divergence(x, ν[:, 1], C, ε; regularization=reg) for
-                    x in eachcol(μ)
+                @test loss_batch_μ ≈ [
+                    sinkhorn_divergence(μ[:, i], ν[:, 1], C, ε; regularization=reg) for
+                    i in 1:M
                 ]
                 loss_batch_ν = sinkhorn_divergence(μ[:, 1], ν, C, ε; regularization=reg)
-                @compat @test loss_batch_ν ≈ [
-                    sinkhorn_divergence(μ[:, 1], y, C, ε; regularization=reg) for
-                    y in eachcol(ν)
+                @test loss_batch_ν ≈ [
+                    sinkhorn_divergence(μ[:, 1], ν[:, i], C, ε; regularization=reg) for
+                    i in 1:M
                 ]
             end
         end