Rewrite isolated ' calls in test/ to preserve behavior through ' lowe…

…ring changes.

test/arrayops.jl

@@ -48,7 +48,7 @@ using Main.TestHelpers.OAs
     a[1,2] = 2
     a[2,1] = 3
     a[2,2] = 4
-    b = a'
+    b = adjoint(a)
     @test a[1,1] == 1. && a[1,2] == 2. && a[2,1] == 3. && a[2,2] == 4.
     @test b[1,1] == 1. && b[2,1] == 2. && b[1,2] == 3. && b[2,2] == 4.
     a[[1 2 3 4]] = 0
@@ -624,7 +624,7 @@ end
 @testset "large matrices transpose" begin
     for i = 1 : 3
         a = rand(200, 300)
-        @test isequal(a', permutedims(a, [2, 1]))
+        @test isequal(adjoint(a), permutedims(a, [2, 1]))
     end
 end
 
@@ -2050,7 +2050,7 @@ end # module AutoRetType
     @test isa([[1,2,3]'; [1,2,3]'], Matrix{Int})
     @test isa([[1,2,3]' [1,2,3]'], Transpose{Int, Vector{Int}})
     @test isa([Any[1.0, 2]'; Any[2.0, 2]'], Matrix{Any})
-    @test isa([Any[1.0, 2]' Any[2.0, 2']'], Adjoint{Any, Vector{Any}})
+    @test isa([Any[1.0, 2]' Any[2.0, 2]'], Adjoint{Any, Vector{Any}})
     # Test that concatenations of heterogeneous Matrix-Vector pairs yield dense matrices
     @test isa(hcat(densemat, densevec), Array)
     @test isa(hcat(densevec, densemat), Array)

test/examples.jl

@@ -24,9 +24,9 @@ include(joinpath(dir, "ndgrid.jl"))
 r = repmat(1:10,1,10)
 r1, r2 = ndgrid(1:10, 1:10)
 @test r1 == r
-@test r2 == r'
+@test r2 == adjoint(r)
 r3, r4 = meshgrid(1:10,1:10)
-@test r3 == r'
+@test r3 == adjoint(r)
 @test r4 == r
 
 include(joinpath(dir, "queens.jl"))

test/linalg/blas.jl

@@ -141,7 +141,7 @@ srand(100)
 
             if elty <: Complex
                 A = rand(elty,n,n)
-                A = A + A'
+                A = A + adjoint(A)
                 α = real(α)
                 @test triu(BLAS.her!('U',α,x,copy(A))) ≈ triu(A + α*x*x')
                 @test_throws DimensionMismatch BLAS.her!('U',α,ones(elty,n+1),copy(A))
@@ -278,7 +278,7 @@ srand(100)
         @test_throws DimensionMismatch BLAS.gemm!('N','N', one(elty), I43, I4, elm1, I4)
         @test_throws DimensionMismatch BLAS.gemm!('T','N', one(elty), I43, I4, elm1, I43)
         @test_throws DimensionMismatch BLAS.gemm!('N','T', one(elty), I43, I43, elm1, I43)
-        @test_throws DimensionMismatch BLAS.gemm!('T','T', one(elty), I43, I43, elm1, I43')
+        @test_throws DimensionMismatch BLAS.gemm!('T','T', one(elty), I43, I43, elm1, adjoint(I43))
     end
     @testset "gemm compared to (sy)(he)rk" begin
         if eltype(elm1) <: Complex

test/linalg/cholesky.jl

@@ -219,7 +219,7 @@ end
     for uplo in (:U, :L)
         AcA = A'A
         BcB = AcA + v*v'
-        BcB = (BcB + BcB')/2
+        BcB = (BcB + adjoint(BcB))/2
         F = cholfact(Hermitian(AcA, uplo))
         G = cholfact(Hermitian(BcB, uplo))
         @test LinAlg.lowrankupdate(F, v)[uplo] ≈ G[uplo]

test/linalg/conjarray.jl

@@ -6,7 +6,7 @@
     @test cm[1,1] == 1-im
     @test cm[1] == 1-im
     @test trace(cm*m) == 27
-    @test cm' == m
+    @test adjoint(cm) == m
 
     cm[:,2] = [3; 3-im] #setindex! with a vector
     @test conj(cm) == [1+im 3; 2 3+im]

test/linalg/dense.jl

@@ -66,7 +66,7 @@ bimg  = randn(n,2)/2
             end
 
             @testset "Test nullspace" begin
-                a15null = nullspace(a[:,1:n1]')
+                a15null = nullspace(adjoint(a[:,1:n1]))
                 @test rank([a[:,1:n1] a15null]) == 10
                 @test norm(a[:,1:n1]'a15null,Inf) ≈ zero(eltya) atol=300ε
                 @test norm(a15null'a[:,1:n1],Inf) ≈ zero(eltya) atol=400ε
@@ -95,7 +95,7 @@ bimg  = randn(n,2)/2
         @testset "Matrix square root" begin
             asq = sqrt(a)
             @test asq*asq ≈ a
-            asym = a'+a # symmetric indefinite
+            asym = adjoint(a)+a # symmetric indefinite
             asymsq = sqrt(asym)
             @test asymsq*asymsq ≈ asym
         end
@@ -349,9 +349,9 @@ end
 @testset "Matrix exponential" begin
     @testset "Tests for $elty" for elty in (Float32, Float64, ComplexF32, ComplexF64)
         A1  = convert(Matrix{elty}, [4 2 0; 1 4 1; 1 1 4])
-        eA1 = convert(Matrix{elty}, [147.866622446369 127.781085523181  127.781085523182;
+        eA1 = convert(Matrix{elty}, adjoint([147.866622446369 127.781085523181  127.781085523182;
                                      183.765138646367 183.765138646366  163.679601723179;
-                                     71.797032399996  91.8825693231832 111.968106246371]')
+                                     71.797032399996  91.8825693231832 111.968106246371]))
         @test exp(A1) ≈ eA1
 
         A2  = convert(Matrix{elty},
@@ -365,9 +365,9 @@ end
         @test exp(A2) ≈ eA2
 
         A3  = convert(Matrix{elty}, [-131 19 18;-390 56 54;-387 57 52])
-        eA3 = convert(Matrix{elty}, [-1.50964415879218 -5.6325707998812  -4.934938326092;
+        eA3 = convert(Matrix{elty}, adjoint([-1.50964415879218 -5.6325707998812  -4.934938326092;
                                      0.367879439109187 1.47151775849686  1.10363831732856;
-                                     0.135335281175235 0.406005843524598 0.541341126763207]')
+                                     0.135335281175235 0.406005843524598 0.541341126763207]))
         @test exp(A3) ≈ eA3
 
         A4 = convert(Matrix{elty}, [0.25 0.25; 0 0])
@@ -790,7 +790,7 @@ end
 @testset "/ and \\ consistency with pinv for vectors" begin
     @testset "Tests for type $elty" for elty in (Float32, Float64, ComplexF32, ComplexF64)
         c = rand(elty, 5)
-        r = rand(elty, 5)'
+        r = (elty <: Complex ? Adjoint : Transpose)(rand(elty, 5))
         cm = rand(elty, 5, 1)
         rm = rand(elty, 1, 5)
         @testset "inner prodcuts" begin

test/linalg/diagonal.jl

@@ -182,7 +182,7 @@ srand(1)
         r  = VV * Transpose(Array(D))
         @test Array(mul!(VV, Transpose(DD))) ≈ r
         DD = copy(D)
-        r  = VV * (Array(D)')
+        r  = VV * Array(D)'
         @test Array(mul!(VV, Adjoint(DD))) ≈ r
     end
     @testset "triu/tril" begin

test/linalg/eigen.jl

@@ -17,7 +17,7 @@ aimg  = randn(n,n)/2
 
 @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, Int)
     aa = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
-    asym = aa'+aa                  # symmetric indefinite
+    asym = adjoint(aa)+aa                  # symmetric indefinite
     apd  = aa'*aa                 # symmetric positive-definite
     for (a, asym, apd) in ((aa, asym, apd),
                            (view(aa, 1:n, 1:n),
@@ -49,8 +49,8 @@ aimg  = randn(n,n)/2
             h = asym
             @test minimum(eigvals(h)) ≈ eigmin(h)
             @test maximum(eigvals(h)) ≈ eigmax(h)
-            @test_throws DomainError eigmin(a - a')
-            @test_throws DomainError eigmax(a - a')
+            @test_throws DomainError eigmin(a - adjoint(a))
+            @test_throws DomainError eigmax(a - adjoint(a))
         end
         @testset "symmetric generalized eigenproblem" begin
             if isa(a, Array)

test/linalg/lapack.jl

@@ -167,7 +167,7 @@ end
         lU,lS,lVt = LAPACK.gesvd!('S','S',A)
         @test U ≈ lU
         @test S ≈ lS
-        @test V' ≈ lVt
+        @test adjoint(V) ≈ lVt
         B = rand(elty,10,10)
         # xggsvd3 replaced xggsvd in LAPACK 3.6.0
         if LAPACK.version() < v"3.6.0"
@@ -391,7 +391,7 @@ end
 @testset "hetrf, hetrs" begin
     @testset for elty in (ComplexF32, ComplexF64)
         A = rand(elty,10,10)
-        A = A + A' #hermitian!
+        A = A + adjoint(A) #hermitian!
         B = copy(A)
         B,ipiv = LAPACK.hetrf!('U',B)
         @test_throws DimensionMismatch LAPACK.hetrs!('U',B,ipiv,rand(elty,11,5))
@@ -449,14 +449,14 @@ end
     @testset for elty in (ComplexF32, ComplexF64)
         srand(935)
         A = rand(elty,10,10)
-        A = A + A' #hermitian!
+        A = A + adjoint(A) #hermitian!
         b = rand(elty,10)
         c = A \ b
         b,A = LAPACK.hesv!('U',A,b)
         @test b ≈ c
         @test_throws DimensionMismatch LAPACK.hesv!('U',A,rand(elty,11))
         A = rand(elty,10,10)
-        A = A + A' #hermitian!
+        A = A + adjoint(A) #hermitian!
         b = rand(elty,10)
         c = A \ b
         b,A = LAPACK.hesv_rook!('U',A,b)

test/linalg/lq.jl

@@ -27,7 +27,7 @@ rectangularQ(Q::LinAlg.LQPackedQ) = convert(Array, Q)
 @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64)
     a = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
     a2 = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(a2real, a2img) : a2real)
-    asym = a'+a                  # symmetric indefinite
+    asym = adjoint(a)+a                  # symmetric indefinite
     apd  = a'*a                 # symmetric positive-definite
     ε = εa = eps(abs(float(one(eltya))))
 
@@ -54,7 +54,7 @@ rectangularQ(Q::LinAlg.LQPackedQ) = convert(Array, Q)
                     @test size(lqa[:Q],3) == 1
                     @test Base.LinAlg.getq(lqa) == lqa[:Q]
                     @test_throws KeyError lqa[:Z]
-                    @test Array(lqa') ≈ a'
+                    @test Array(adjoint(lqa)) ≈ adjoint(a)
                     @test lqa * lqa' ≈ a * a'
                     @test lqa' * lqa ≈ a' * a
                     @test q*squareQ(q)' ≈ Matrix(I, n, n)

test/linalg/lu.jl

@@ -106,7 +106,7 @@ dimg  = randn(n)/2
                 for (bb, cc) in ((Bs, Cs), (view(Bs, 1:n, 1), view(Cs, 1:n)))
                     @test norm(a*(lua\bb) - bb, 1) < ε*κ*n*2 # Two because the right hand side has two columns
                     @test norm(a'*(lua'\bb) - bb, 1) < ε*κ*n*2 # Two because the right hand side has two columns
-                    @test norm(a'*(lua'\a') - a', 1) < ε*κ*n^2
+                    @test norm(a'*(lua'\a') - adjoint(a), 1) < ε*κ*n^2
                     @test norm(a*(lua\cc) - cc, 1) < ε*κ*n # cc is a vector
                     @test norm(a'*(lua'\cc) - cc, 1) < ε*κ*n # cc is a vector
                     @test AbstractArray(lua) ≈ a

test/linalg/matmul.jl

@@ -179,7 +179,7 @@ end
     res = Float64[135228751 9979252 -115270247; 9979252 10481254 10983256; -115270247 10983256 137236759]
     for A in (copy(AA), view(AA, 1:501, 1:3))
         @test *(Transpose(A), A) == res
-        @test *(A', Transpose(A')) == res
+        @test *(Adjoint(A), Transpose(adjoint(A))) == res
     end
     cutoff = 501
     A = reshape(1:6*cutoff,2*cutoff,3).-(6*cutoff)/2

test/linalg/qr.jl

@@ -26,7 +26,7 @@ rectangularQ(Q::LinAlg.AbstractQ) = convert(Array, Q)
 @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, BigFloat, Int)
     raw_a = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
     raw_a2 = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(a2real, a2img) : a2real)
-    asym = raw_a' + raw_a                  # symmetric indefinite
+    asym = adjoint(raw_a) + raw_a                  # symmetric indefinite
     apd  = raw_a' * raw_a                 # symmetric positive-definite
     ε = εa = eps(abs(float(one(eltya))))
 
@@ -53,9 +53,9 @@ rectangularQ(Q::LinAlg.AbstractQ) = convert(Array, Q)
                 @test_throws KeyError qra[:Z]
                 @test q'*squareQ(q) ≈ Matrix(I, a_1, a_1)
                 @test q*squareQ(q)' ≈ Matrix(I, a_1, a_1)
-                @test q'*Matrix(1.0I, a_1, a_1)' ≈ squareQ(q)'
+                @test q'*Matrix(1.0I, a_1, a_1)' ≈ adjoint(squareQ(q))
                 @test squareQ(q)'q ≈ Matrix(I, a_1, a_1)
-                @test Matrix(1.0I, a_1, a_1)'q' ≈ squareQ(q)'
+                @test Matrix(1.0I, a_1, a_1)'q' ≈ adjoint(squareQ(q))
                 @test q*r ≈ a
                 @test a*(qra\b) ≈ b atol=3000ε
                 @test Array(qra) ≈ a
@@ -135,7 +135,7 @@ rectangularQ(Q::LinAlg.AbstractQ) = convert(Array, Q)
                 a = raw_a
                 qrpa = factorize(a[:,1:n1])
                 q, r = qrpa[:Q], qrpa[:R]
-                @test mul!(squareQ(q)', q) ≈ Matrix(I, n, n)
+                @test mul!(adjoint(squareQ(q)), q) ≈ Matrix(I, n, n)
                 @test_throws DimensionMismatch mul!(Matrix{eltya}(I, n+1, n+1),q)
                 @test mul!(squareQ(q), Adjoint(q)) ≈ Matrix(I, n, n)
                 @test_throws DimensionMismatch mul!(Matrix{eltya}(I, n+1, n+1), Adjoint(q))
@@ -145,7 +145,7 @@ rectangularQ(Q::LinAlg.AbstractQ) = convert(Array, Q)
 
                 qra = qrfact(a[:,1:n1], Val(false))
                 q, r = qra[:Q], qra[:R]
-                @test mul!(squareQ(q)', q) ≈ Matrix(I, n, n)
+                @test mul!(adjoint(squareQ(q)), q) ≈ Matrix(I, n, n)
                 @test_throws DimensionMismatch mul!(Matrix{eltya}(I, n+1, n+1),q)
                 @test mul!(squareQ(q), Adjoint(q)) ≈ Matrix(I, n, n)
                 @test_throws DimensionMismatch mul!(Matrix{eltya}(I, n+1, n+1),Adjoint(q))

test/linalg/schur.jl

@@ -17,7 +17,7 @@ aimg  = randn(n,n)/2
 
 @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, Int)
     a = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
-    asym = a'+a                  # symmetric indefinite
+    asym = adjoint(a)+a                  # symmetric indefinite
     apd  = a'*a                 # symmetric positive-definite
     for (a, asym, apd) in ((a, asym, apd),
                            (view(a, 1:n, 1:n),

test/linalg/svd.jl

@@ -46,7 +46,7 @@ a2img  = randn(n,n)/2
 @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, Int)
     aa = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
     aa2 = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(a2real, a2img) : a2real)
-    asym = aa'+aa                  # symmetric indefinite
+    asym = adjoint(aa)+aa                  # symmetric indefinite
     apd  = aa'*aa                 # symmetric positive-definite
     for (a, a2) in ((aa, aa2), (view(aa, 1:n, 1:n), view(aa2, 1:n, 1:n)))
         ε = εa = eps(abs(float(one(eltya))))
@@ -56,7 +56,7 @@ a2img  = randn(n,n)/2
             @test usv[:S] === svdvals(usv)
             @test usv[:U] * (Diagonal(usv[:S]) * usv[:Vt]) ≈ a
             @test convert(Array, usv) ≈ a
-            @test usv[:Vt]' ≈ usv[:V]
+            @test adjoint(usv[:Vt]) ≈ usv[:V]
             @test_throws KeyError usv[:Z]
             b = rand(eltya,n)
             @test usv\b ≈ a\b
@@ -73,7 +73,7 @@ a2img  = randn(n,n)/2
             gsvd = svdfact(a,a_svd)
             @test gsvd[:U]*gsvd[:D1]*gsvd[:R]*gsvd[:Q]' ≈ a
             @test gsvd[:V]*gsvd[:D2]*gsvd[:R]*gsvd[:Q]' ≈ a_svd
-            @test usv[:Vt]' ≈ usv[:V]
+            @test adjoint(usv[:Vt]) ≈ usv[:V]
             @test_throws KeyError usv[:Z]
             @test_throws KeyError gsvd[:Z]
             @test gsvd[:vals] ≈ svdvals(a,a_svd)

test/linalg/symmetric.jl

@@ -11,15 +11,15 @@ end
 
 @testset "Hermitian matrix exponential/log" begin
     A1 = randn(4,4) + im*randn(4,4)
-    A2 = A1 + A1'
+    A2 = A1 + adjoint(A1)
     @test exp(A2) ≈ exp(Hermitian(A2))
     @test log(A2) ≈ log(Hermitian(A2))
-    A3 = A1 * A1' # posdef
+    A3 = A1 * adjoint(A1) # posdef
     @test exp(A3) ≈ exp(Hermitian(A3))
     @test log(A3) ≈ log(Hermitian(A3))
 
     A1 = randn(4,4)
-    A3 = A1 * A1'
+    A3 = A1 * adjoint(A1)
     A4 = A1 + transpose(A1)
     @test exp(A4) ≈ exp(Symmetric(A4))
     @test log(A3) ≈ log(Symmetric(A3))
@@ -121,7 +121,7 @@ end
                 elseif eltya <: Complex
                     # test that zero imaginary component is
                     # handled properly
-                    @test ishermitian(Symmetric(b + b'))
+                    @test ishermitian(Symmetric(b + adjoint(b)))
                 end
             end
 
@@ -255,7 +255,7 @@ end
                     let A = a[:,1:5]*a[:,1:5]'
                         # Make sure A is Hermitian even in the presence of rounding error
                         # xianyi/OpenBLAS#729
-                        A = (A' + A) / 2
+                        A = (adjoint(A) + A) / 2
                         @test rank(A) == rank(Hermitian(A))
                     end
                 end
@@ -383,7 +383,7 @@ end
     @test conj(c) == conj(Array(c))
     cc = copy(c)
     @test conj!(c) == conj(Array(cc))
-    c = Hermitian(b + b')
+    c = Hermitian(b + adjoint(b))
     @test conj(c) == conj(Array(c))
     cc = copy(c)
     @test conj!(c) == conj(Array(cc))

test/linalg/triangular.jl

@@ -136,14 +136,14 @@ for elty1 in (Float32, Float64, BigFloat, ComplexF32, ComplexF64, Complex{BigFlo
             @test transpose(A1) == transpose(Matrix(A1))
             @test transpose(viewA1) == transpose(Matrix(viewA1))
             # adjoint
-            @test A1' == Matrix(A1)'
-            @test viewA1' == Matrix(viewA1)'
+            @test adjoint(A1) == adjoint(Matrix(A1))
+            @test adjoint(viewA1) == adjoint(Matrix(viewA1))
             # transpose!
             @test transpose!(copy(A1)) == transpose(A1)
             @test transpose!(t1(view(copy(A1).data, vrange, vrange))) == transpose(viewA1)
             # adjoint!
-            @test adjoint!(copy(A1)) == A1'
-            @test adjoint!(t1(view(copy(A1).data, vrange, vrange))) == viewA1'
+            @test adjoint!(copy(A1)) == adjoint(A1)
+            @test adjoint!(t1(view(copy(A1).data, vrange, vrange))) == adjoint(viewA1)
         end
 
         # diag

test/mod2pi.jl

@@ -222,7 +222,7 @@ testModPi()
 
      # ieee754_rem_pio2_return contains the returned value from the ieee754_rem_pio2
      # function in openlibm: https://github.com/JuliaLang/openlibm/blob/0598080ca09468490a13ae393ba17d8620c1b201/src/e_rem_pio2.c
-     ieee754_rem_pio2_return = [1.5707963267948966 1.5707963267948966;
+     ieee754_rem_pio2_return = adjoint([1.5707963267948966 1.5707963267948966;
                                 1.0471975511965979 -1.0471975511965979;
                                 0.10000000000000014 -0.10000000000000014;
                                 6.123233995736766e-17 -6.123233995736766e-17;
@@ -238,7 +238,7 @@ testModPi()
                                 -0.6853981633974484 0.6853981633974484;
                                 3.135095805817224e-14 -3.135095805817224e-14;
                                 3.287386219680602e-8 -3.287386219680602e-8;
-                                -0.1757159771004682 0.1757159771004682]'
+                                -0.1757159771004682 0.1757159771004682])
 
     for (i, case) in enumerate(cases)
         # negative argument

test/offsetarray.jl

@@ -327,8 +327,8 @@ cv = copy(v)
 
 A = OffsetArray(rand(4,4), (-3,5))
 @test A ≈ A
-@test axes(A') === (6:9, -2:1)
-@test parent(A') == parent(A)'
+@test axes(adjoint(A)) === (6:9, -2:1)
+@test parent(adjoint(A)) == adjoint(parent(A))
 @test collect(A) == parent(A)
 @test maximum(A) == maximum(parent(A))
 @test minimum(A) == minimum(parent(A))