fix deprecations in Julia 0.7 (#96)

* replace `uninitialized` by `undef` * fix deprecation warnings for Julia 0.7 * Compat versions of sum, mean for 0.6 * Breaking with Julia 0.6, test with 0.7 This reverts commit d632a58 and updates REQUIRE and .travis.yml * more 0.7 updates
JuliaStats · Jun 27, 2018 · c4431c0 · c4431c0
1 parent 840b285
commit c4431c0
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Showing 13 changed files with 47 additions and 40 deletions.
diff --git a/.gitignore b/.gitignore
@@ -2,4 +2,5 @@
 *.jl.*.cov
 *.jl.mem
 *.ji
-benchmark/params.jld
+benchmark/params.jld
+Manifest.toml
diff --git a/.travis.yml b/.travis.yml
@@ -5,7 +5,7 @@ os:
     - osx
 
 julia:
-    - 0.6
+    - 0.7
     - nightly
 
 matrix:
@@ -18,9 +18,7 @@ notifications:
 sudo: false
 
 script:
-    - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-    - julia -e 'Pkg.clone(pwd()); Pkg.build("Distances")'
-    - julia -e 'Pkg.test("Distances", coverage=true)'
+    - julia -e 'import Pkg; Pkg.build(); Pkg.test(; coverage=true)'
 
 after_success:
-    - julia -e 'cd(Pkg.dir("Distances")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder())'
+    - julia -e 'import Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder())'
diff --git a/Project.toml b/Project.toml
@@ -0,0 +1,10 @@
+name = "Distances"
+uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+version = "0.7.0"
+
+[deps]
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+
+[targets.test.deps]
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
diff --git a/REQUIRE b/REQUIRE
@@ -1,2 +1 @@
-julia 0.6
-Compat 0.54.0
+julia 0.7-
diff --git a/src/Distances.jl b/src/Distances.jl
@@ -2,8 +2,7 @@ __precompile__()
 
 module Distances
 
-using Compat
-using Compat.LinearAlgebra
+using LinearAlgebra
 
 export
     # generic types/functions

diff --git a/src/common.jl b/src/common.jl
@@ -101,7 +101,7 @@ end
 function sumsq_percol(a::AbstractMatrix{T}) where {T}
     m = size(a, 1)
     n = size(a, 2)
-    r = Vector{T}(uninitialized, n)
+    r = Vector{T}(undef, n)
     for j = 1:n
         aj = view(a, :, j)
         r[j] = dot(aj, aj)
@@ -113,7 +113,7 @@ function wsumsq_percol(w::AbstractArray{T1}, a::AbstractMatrix{T2}) where {T1, T
     m = size(a, 1)
     n = size(a, 2)
     T = typeof(one(T1) * one(T2))
-    r = Vector{T}(uninitialized, n)
+    r = Vector{T}(undef, n)
     for j = 1:n
         aj = view(a, :, j)
         s = zero(T)
@@ -138,4 +138,4 @@ function dot_percol!(r::AbstractArray, a::AbstractMatrix, b::AbstractMatrix)
     return r
 end
 
-dot_percol(a::AbstractMatrix, b::AbstractMatrix) = dot_percol!(Vector{Float64}(size(a, 2)), a, b)
+dot_percol(a::AbstractMatrix, b::AbstractMatrix) = dot_percol!(Vector{Float64}(undef, size(a, 2)), a, b)
diff --git a/src/generic.jl b/src/generic.jl
@@ -62,19 +62,19 @@ end
 
 function colwise(metric::PreMetric, a::AbstractMatrix, b::AbstractMatrix)
     n = get_common_ncols(a, b)
-    r = Vector{result_type(metric, a, b)}(uninitialized, n)
+    r = Vector{result_type(metric, a, b)}(undef, n)
     colwise!(r, metric, a, b)
 end
 
 function colwise(metric::PreMetric, a::AbstractVector, b::AbstractMatrix)
     n = size(b, 2)
-    r = Vector{result_type(metric, a, b)}(uninitialized, n)
+    r = Vector{result_type(metric, a, b)}(undef, n)
     colwise!(r, metric, a, b)
 end
 
 function colwise(metric::PreMetric, a::AbstractMatrix, b::AbstractVector)
     n = size(a, 2)
-    r = Vector{result_type(metric, a, b)}(uninitialized, n)
+    r = Vector{result_type(metric, a, b)}(undef, n)
     colwise!(r, metric, a, b)
 end
 
@@ -117,12 +117,12 @@ end
 function pairwise(metric::PreMetric, a::AbstractMatrix, b::AbstractMatrix)
     m = size(a, 2)
     n = size(b, 2)
-    r = Matrix{result_type(metric, a, b)}(uninitialized, m, n)
+    r = Matrix{result_type(metric, a, b)}(undef, m, n)
     pairwise!(r, metric, a, b)
 end
 
 function pairwise(metric::PreMetric, a::AbstractMatrix)
     n = size(a, 2)
-    r = Matrix{result_type(metric, a, a)}(uninitialized, n, n)
+    r = Matrix{result_type(metric, a, a)}(undef, n, n)
     pairwise!(r, metric, a)
 end
diff --git a/src/mahalanobis.jl b/src/mahalanobis.jl
@@ -17,7 +17,7 @@ function evaluate(dist::SqMahalanobis{T}, a::AbstractVector, b::AbstractVector)
     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
-    
+
     Q = dist.qmat
     z = a - b
     return dot(z, Q * z)
@@ -48,7 +48,7 @@ function pairwise!(r::AbstractMatrix, dist::SqMahalanobis{T}, a::AbstractMatrix,
     Qb = Q * b
     sa2 = dot_percol(a, Qa)
     sb2 = dot_percol(b, Qb)
-    At_mul_B!(r, a, Qb)
+    mul!(r, a', Qb)
 
     for j = 1:nb
         @simd for i = 1:na
@@ -64,7 +64,7 @@ function pairwise!(r::AbstractMatrix, dist::SqMahalanobis{T}, a::AbstractMatrix)
 
     Qa = Q * a
     sa2 = dot_percol(a, Qa)
-    At_mul_B!(r, a, Qa)
+    mul!(r, a', Qa)
 
     for j = 1:n
         for i = 1:(j - 1)

diff --git a/src/metrics.jl b/src/metrics.jl
@@ -455,9 +455,9 @@ nrmsd(a, b) = evaluate(NormRMSDeviation(), a, b)
 
 # SqEuclidean
 function pairwise!(r::AbstractMatrix, dist::SqEuclidean, a::AbstractMatrix, b::AbstractMatrix)
-    At_mul_B!(r, a, b)
-    sa2 = sum(abs2, a, 1)
-    sb2 = sum(abs2, b, 1)
+    mul!(r, a', b)
+    sa2 = sum(abs2, a, dims=1)
+    sb2 = sum(abs2, b, dims=1)
     threshT = convert(eltype(r), dist.thresh)
     if threshT <= 0
         # If there's no chance of triggering the threshold, we can use @simd
@@ -492,7 +492,7 @@ end
 
 function pairwise!(r::AbstractMatrix, dist::SqEuclidean, a::AbstractMatrix)
     m, n = get_pairwise_dims(r, a)
-    At_mul_B!(r, a, a)
+    mul!(r, a', a)
     sa2 = sumsq_percol(a)
     threshT = convert(eltype(r), dist.thresh)
     @inbounds for j = 1:n
@@ -525,7 +525,7 @@ end
 # Euclidean
 function pairwise!(r::AbstractMatrix, dist::Euclidean, a::AbstractMatrix, b::AbstractMatrix)
     m, na, nb = get_pairwise_dims(r, a, b)
-    At_mul_B!(r, a, b)
+    mul!(r, a', b)
     sa2 = sumsq_percol(a)
     sb2 = sumsq_percol(b)
     threshT = convert(eltype(r), dist.thresh)
@@ -552,7 +552,7 @@ end
 
 function pairwise!(r::AbstractMatrix, dist::Euclidean, a::AbstractMatrix)
     m, n = get_pairwise_dims(r, a)
-    At_mul_B!(r, a, a)
+    mul!(r, a', a)
     sa2 = sumsq_percol(a)
     threshT = convert(eltype(r), dist.thresh)
     @inbounds for j = 1:n
@@ -580,7 +580,7 @@ end
 
 function pairwise!(r::AbstractMatrix, dist::CosineDist, a::AbstractMatrix, b::AbstractMatrix)
     m, na, nb = get_pairwise_dims(r, a, b)
-    At_mul_B!(r, a, b)
+    mul!(r, a', b)
     ra = sqrt!(sumsq_percol(a))
     rb = sqrt!(sumsq_percol(b))
     for j = 1:nb
@@ -592,7 +592,7 @@ function pairwise!(r::AbstractMatrix, dist::CosineDist, a::AbstractMatrix, b::Ab
 end
 function pairwise!(r::AbstractMatrix, dist::CosineDist, a::AbstractMatrix)
     m, n = get_pairwise_dims(r, a)
-    At_mul_B!(r, a, a)
+    mul!(r, a', a)
     ra = sqrt!(sumsq_percol(a))
     @inbounds for j = 1:n
         @simd for i = j + 1:n
@@ -608,7 +608,7 @@ end
 
 # CorrDist
 _centralize_colwise(x::AbstractVector) = x .- mean(x)
-_centralize_colwise(x::AbstractMatrix) = x .- mean(x, 1)
+_centralize_colwise(x::AbstractMatrix) = x .- mean(x, dims=1)
 function colwise!(r::AbstractVector, dist::CorrDist, a::AbstractMatrix, b::AbstractMatrix)
     colwise!(r, CosineDist(), _centralize_colwise(a), _centralize_colwise(b))
 end

diff --git a/src/wmetrics.jl b/src/wmetrics.jl
@@ -123,7 +123,7 @@ function pairwise!(r::AbstractMatrix, dist::WeightedSqEuclidean, a::AbstractMatr
 
     sa2 = wsumsq_percol(w, a)
     sb2 = wsumsq_percol(w, b)
-    At_mul_B!(r, a, b .* w)
+    mul!(r, a', b .* w)
     for j = 1:nb
         @simd for i = 1:na
             @inbounds r[i, j] = sa2[i] + sb2[j] - 2 * r[i, j]
@@ -136,7 +136,7 @@ function pairwise!(r::AbstractMatrix, dist::WeightedSqEuclidean, a::AbstractMatr
     m, n = get_pairwise_dims(length(w), r, a)
 
     sa2 = wsumsq_percol(w, a)
-    At_mul_B!(r, a, a .* w)
+    mul!(r, a', a .* w)
 
     for j = 1:n
         for i = 1:(j - 1)

diff --git a/test/REQUIRE b/test/REQUIRE
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,7 +1,8 @@
 using Distances
-using Compat.Test
-using Compat.LinearAlgebra
-using Compat.Random
+
+using Test
+using LinearAlgebra
+using Random
 
 include("F64.jl")
 include("test_dists.jl")
diff --git a/test/test_dists.jl b/test/test_dists.jl
@@ -97,7 +97,7 @@ end
     p = rand(T, n)
     q = rand(T, n)
     r = rand(T, n)
-    p[p .< median(p)] = 0
+    p[p .< median(p)] .= 0
     p /= sum(p)
     q /= sum(q)
     r /= sum(r)
@@ -189,7 +189,7 @@ end
         # Test KL, Renyi and JS divergences
         r = rand(T, 12)
         p = copy(r)
-        p[p .< median(p)] = 0.0
+        p[p .< median(p)] .= 0.0
         scale = sum(p) / sum(r)
         r /= sum(r)
         p /= sum(p)
@@ -310,7 +310,7 @@ end
         a = T.([1.0, 2.0, 1.0, 3.0, 2.0, 1.0])
         b = T.([1.0, 3.0, 0.0, 2.0, 2.0, 0.0])
         p = rand(T, 12)
-        p[p .< median(p)] = 0.0
+        p[p .< median(p)] .= 0.0
         q = rand(T, 12)
 
         # Bhattacharyya and Hellinger distances are defined for discrete
@@ -373,7 +373,7 @@ end
     Q = rand(T, m, n)
     # Make sure not to remove all of the non-zeros from any column
     for i in 1:n
-        P[P[:, i] .< median(P[:, i]) / 2, i] = 0.0
+        P[P[:, i] .< median(P[:, i]) / 2, i] .= 0.0
     end
 
     test_colwise(SqEuclidean(), X, Y, T)