fix test seeds

doc/src/devdocs/subarrays.md

@@ -19,14 +19,14 @@ julia> A = rand(2,3,4);
 
 julia> S1 = view(A, :, 1, 2:3)
 2×2 view(::Array{Float64, 3}, :, 1, 2:3) with eltype Float64:
- 0.762657  0.545403
- 0.66338   0.866199
+ 0.98627   0.991588
+ 0.523355  0.105192
 
 julia> S2 = view(A, 1, :, 2:3)
 3×2 view(::Array{Float64, 3}, 1, :, 2:3) with eltype Float64:
- 0.762657  0.545403
- 0.704125  0.0802701
- 0.886174  0.535537
+ 0.98627    0.991588
+ 0.0890391  0.904316
+ 0.134982   0.299203
 ```
 ```@meta
 DocTestSetup = nothing

doc/src/manual/performance-tips.md

@@ -77,12 +77,12 @@ julia> function sum_global()
        end;
 
 julia> @time sum_global()
-  0.011001 seconds (9.30 k allocations: 417.044 KiB, 98.77% compilation time)
-503.4466694614652
+  0.026328 seconds (9.30 k allocations: 416.747 KiB, 36.50% gc time, 99.48% compilation time)
+508.39048990953665
 
 julia> @time sum_global()
   0.000075 seconds (3.49 k allocations: 70.156 KiB)
-503.4466694614652
+508.39048990953665
 ```
 
 On the first call (`@time sum_global()`) the function gets compiled. (If you've not yet used [`@time`](@ref)
@@ -113,12 +113,12 @@ julia> function sum_arg(x)
        end;
 
 julia> @time sum_arg(x)
-  0.007258 seconds (4.24 k allocations: 226.052 KiB, 99.74% compilation time)
-503.4466694614652
+  0.010298 seconds (4.23 k allocations: 226.021 KiB, 99.81% compilation time)
+508.39048990953665
 
 julia> @time sum_arg(x)
   0.000005 seconds (1 allocation: 16 bytes)
-503.4466694614652
+508.39048990953665
 ```
 
 The 1 allocation seen is from running the `@time` macro itself in global scope. If we instead run
@@ -128,8 +128,8 @@ the timing in a function, we can see that indeed no allocations are performed:
 julia> time_sum(x) = @time sum_arg(x);
 
 julia> time_sum(x)
-  0.000002 seconds
-503.4466694614652
+  0.000001 seconds
+508.39048990953665
 ```
 
 In some situations, your function may need to allocate memory as part of its operation, and this

stdlib/LinearAlgebra/test/dense.jl

@@ -15,17 +15,17 @@ n = 10
 n1 = div(n, 2)
 n2 = 2*n1
 
-Random.seed!(1234321)
+Random.seed!(1234323)
 
 @testset "Matrix condition number" begin
     ainit = rand(n,n)
     @testset "for $elty" for elty in (Float32, Float64, ComplexF32, ComplexF64)
         ainit = convert(Matrix{elty}, ainit)
         for a in (copy(ainit), view(ainit, 1:n, 1:n))
-            @test cond(a,1) ≈ 11860.833898970453 atol=0.5
-            @test cond(a,2) ≈ 5578.947735096807 atol=0.5
-            @test cond(a,Inf) ≈ 9974.454657908922 atol=0.4
-            @test cond(a[:,1:5]) ≈ 5.956698046316083 atol=0.01
+            @test cond(a,1) ≈ 162.93076496229205 atol=0.5
+            @test cond(a,2) ≈ 97.7362909028399 atol=0.5
+            @test cond(a,Inf) ≈ 196.00888526411242 atol=0.4
+            @test cond(a[:,1:5]) ≈ 10.466183798451308 atol=0.01
             @test_throws ArgumentError cond(a,3)
         end
     end

stdlib/LinearAlgebra/test/eigen.jl

@@ -11,7 +11,7 @@ n = 10
 n1 = div(n, 2)
 n2 = 2*n1
 
-Random.seed!(1234324)
+Random.seed!(12343215)
 
 areal = randn(n,n)/2
 aimg  = randn(n,n)/2

stdlib/LinearAlgebra/test/lu.jl

@@ -11,7 +11,7 @@ n = 10
 n1 = div(n, 2)
 n2 = 2*n1
 
-Random.seed!(1234322)
+Random.seed!(1234323)
 
 areal = randn(n,n)/2
 aimg  = randn(n,n)/2

stdlib/LinearAlgebra/test/qr.jl

@@ -11,7 +11,7 @@ n = 10
 n1 = div(n, 2)
 n2 = 2*n1
 
-Random.seed!(1234324)
+Random.seed!(1234325)
 
 areal = randn(n,n)/2
 aimg  = randn(n,n)/2

stdlib/LinearAlgebra/test/uniformscaling.jl

@@ -10,7 +10,7 @@ using .Main.Quaternions
 isdefined(Main, :OffsetArrays) || @eval Main include(joinpath($(BASE_TEST_PATH), "testhelpers", "OffsetArrays.jl"))
 using .Main.OffsetArrays
 
-Random.seed!(1234)
+Random.seed!(1234543)
 
 @testset "basic functions" begin
     @test I === I' # transpose

stdlib/SparseArrays/src/sparsematrix.jl

@@ -1598,13 +1598,14 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(1234))
 julia> sprand(Bool, 2, 2, 0.5)
-2×2 SparseMatrixCSC{Bool, Int64} with 4 stored entries:
- 1  1
+2×2 SparseMatrixCSC{Bool, Int64} with 2 stored entries:
+ ⋅  ⋅
  1  1
 
 julia> sprand(Float64, 3, 0.75)
-3-element SparseVector{Float64, Int64} with 1 stored entry:
-  [1]  =  0.66338
+3-element SparseVector{Float64, Int64} with 2 stored entries:
+  [1]  =  0.523355
+  [2]  =  0.0890391
 ```
 """
 function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}=eltype(rfn(r, 1))) where T
@@ -1645,9 +1646,9 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(0))
 julia> sprandn(2, 2, 0.75)
-2×2 SparseMatrixCSC{Float64, Int64} with 4 stored entries:
-  1.49137  1.09378
- -1.56692  1.1035
+2×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
+ -1.92631  -0.858041
+   ⋅        0.0213808
 ```
 """
 sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) =

stdlib/SparseArrays/test/sparse.jl

@@ -1724,7 +1724,7 @@ end
     local A = guardseed(1234321) do
         triu(sprand(10, 10, 0.2))
     end
-    @test getcolptr(SparseArrays.droptol!(A, 0.01)) == [1, 1, 1, 1, 4, 5, 6, 6, 9, 11, 14]
+    @test getcolptr(SparseArrays.droptol!(A, 0.01)) == [1, 1, 1, 1, 3, 3, 4, 5, 7, 10, 11]
     @test isequal(SparseArrays.droptol!(sparse([1], [1], [1]), 1), SparseMatrixCSC(1, 1, Int[1, 1], Int[], Int[]))
 end