move Random to stdlib

base/deprecated.jl

@@ -204,20 +204,6 @@ end
 import .LinAlg: cond
 @deprecate cond(F::LinAlg.LU, p::Integer) cond(convert(AbstractArray, F), p)
 
-# PR #21359
-import .Random: srand, randjump
-
-@deprecate srand(r::MersenneTwister, filename::AbstractString, n::Integer=4) srand(r, read!(filename, Vector{UInt32}(uninitialized, Int(n))))
-@deprecate srand(filename::AbstractString, n::Integer=4) srand(read!(filename, Vector{UInt32}(uninitialized, Int(n))))
-@deprecate MersenneTwister(filename::AbstractString)  srand(MersenneTwister(0), read!(filename, Vector{UInt32}(uninitialized, Int(4))))
-
-function randjump(mt::MersenneTwister, jumps::Integer, jumppoly::AbstractString)
-    depwarn("`randjump(rng, jumps, jumppoly::AbstractString)` is deprecated; use `randjump(rng, steps, jumps)` instead", :randjump)
-    Base.Random._randjump(mt, dSFMT.GF2X(jumppoly), jumps)
-end
-
-@deprecate randjump(mt::MersenneTwister, jumps::Integer)  randjump(mt, big(10)^20, jumps)
-
 # PR #21974
 @deprecate versioninfo(verbose::Bool) versioninfo(verbose=verbose)
 @deprecate versioninfo(io::IO, verbose::Bool) versioninfo(io, verbose=verbose)
@@ -663,8 +649,6 @@ end
 @deprecate convert(::Type{S}, g::Unicode.GraphemeIterator) where {S<:AbstractString}  convert(S, g.s)
 @deprecate convert(::Type{String}, v::AbstractVector{Char})   String(v)
 
-@deprecate convert(::Type{UInt128},     u::Random.UUID)     UInt128(u)
-@deprecate convert(::Type{Random.UUID}, s::AbstractString)  Random.UUID(s)
 @deprecate convert(::Type{Libc.FILE}, s::IO)  Libc.FILE(s)
 @deprecate convert(::Type{VersionNumber}, v::Integer)         VersionNumber(v)
 @deprecate convert(::Type{VersionNumber}, v::Tuple)           VersionNumber(v)
@@ -2548,6 +2532,28 @@ end
 # issue #24822
 @deprecate_binding Display AbstractDisplay
 
+# PR #24874
+@deprecate_moved rand! "Random" true true
+@deprecate_moved srand "Random" true true
+@deprecate_moved AbstractRNG "Random" true true
+@deprecate_moved randcycle  "Random" true true
+@deprecate_moved randcycle!  "Random" true true
+@deprecate_moved randperm  "Random" true true
+@deprecate_moved randperm! "Random" true true
+@deprecate_moved shuffle  "Random" true true
+@deprecate_moved shuffle! "Random" true true
+@deprecate_moved randsubseq "Random" true true
+@deprecate_moved randsubseq! "Random" true true
+@deprecate_moved randstring "Random" true true
+@deprecate_moved MersenneTwister  "Random" true true
+@deprecate_moved RandomDevice  "Random" true true
+@deprecate_moved randn! "Random" true true
+@deprecate_moved randexp "Random" true true
+@deprecate_moved randexp! "Random" true true
+@deprecate_moved bitrand "Random" true true
+@deprecate_moved randjump "Random" true true
+@deprecate_moved GLOBAL_RNG "Random" false true
+
 # 24595
 @deprecate falses(A::AbstractArray) falses(size(A))
 @deprecate trues(A::AbstractArray) trues(size(A))

base/error.jl

@@ -172,7 +172,7 @@ start(ebo::ExponentialBackOff) = (ebo.n, min(ebo.first_delay, ebo.max_delay))
 function next(ebo::ExponentialBackOff, state)
     next_n = state[1]-1
     curr_delay = state[2]
-    next_delay = min(ebo.max_delay, state[2] * ebo.factor * (1.0 - ebo.jitter + (rand() * 2.0 * ebo.jitter)))
+    next_delay = min(ebo.max_delay, state[2] * ebo.factor * (1.0 - ebo.jitter + (rand(Float64) * 2.0 * ebo.jitter)))
     (curr_delay, (next_n, next_delay))
 end
 done(ebo::ExponentialBackOff, state) = state[1]<1

base/exports.jl

@@ -449,12 +449,6 @@ export
     prod!,
     prod,
     promote_shape,
-    randcycle,
-    randcycle!,
-    randperm,
-    randperm!,
-    randsubseq!,
-    randsubseq,
     range,
     reducedim,
     repmat,
@@ -718,7 +712,6 @@ export
     print_shortest,
     print_with_color,
     println,
-    randstring,
     repeat,
     replace,
     replace!,
@@ -746,20 +739,6 @@ export
     @warn,
     @error,
 
-# random numbers
-    AbstractRNG,
-    MersenneTwister,
-    RandomDevice,
-    rand!,
-    rand,
-    randn!,
-    randn,
-    randexp!,
-    randexp,
-    srand,
-    bitrand,
-    randjump,
-
 # bigfloat & precision
     precision,
     rounding,
@@ -1196,9 +1175,13 @@ export
     sparse,
     sparsevec,
     spdiagm,
-    sprand,
-    sprandn,
     spzeros,
     rowvals,
     nzrange,
-    nnz
+    nnz,
+    sprand,
+    sprandn,
+
+# implemented in Random module
+    rand,
+    randn

base/file.jl

@@ -285,7 +285,7 @@ function mktemp(parent=tempdir())
 end
 
 function mktempdir(parent=tempdir())
-    seed::UInt32 = rand(UInt32)
+    seed::UInt32 = Base.Crand(UInt32)
     while true
         if (seed & typemax(UInt16)) == 0
             seed += 1

base/serialize.jl

@@ -1195,14 +1195,4 @@ function deserialize(s::AbstractSerializer, t::Type{Regex})
     Regex(pattern, compile_options, match_options)
 end
 
-if !Sys.iswindows()
-    function serialize(s::AbstractSerializer, rd::RandomDevice)
-        serialize_type(s, typeof(rd))
-        serialize(s, rd.unlimited)
-    end
-    function deserialize(s::AbstractSerializer, t::Type{RandomDevice})
-        unlimited = deserialize(s)
-        return RandomDevice(unlimited)
-    end
-end
 end

base/sparse/linalg.jl

@@ -601,12 +601,6 @@ function normestinv(A::SparseMatrixCSC{T}, t::Integer = min(2,maximum(size(A))))
 
     S = zeros(T <: Real ? Int : Ti, n, t)
 
-    function _rand_pm1!(v)
-        for i in eachindex(v)
-            v[i] = rand()<0.5 ? 1 : -1
-        end
-    end
-
     function _any_abs_eq(v,n::Int)
         for vv in v
             if abs(vv)==n

base/sparse/sparse.jl

@@ -24,12 +24,12 @@ import Base: @get!, acos, acosd, acot, acotd, acsch, asech, asin, asind, asinh,
     vcat, hcat, hvcat, cat, imag, indmax, ishermitian, kron, length, log, log1p, max, min,
     maximum, minimum, norm, one, promote_eltype, real, reshape, rot180,
     rotl90, rotr90, round, scale!, setindex!, similar, size, transpose, tril,
-    triu, vec, permute!, map, map!, Array
+    triu, vec, permute!, map, map!, Array, _rand_pm1!
 
 export AbstractSparseArray, AbstractSparseMatrix, AbstractSparseVector,
     SparseMatrixCSC, SparseVector, blkdiag, droptol!, dropzeros!, dropzeros,
     issparse, nonzeros, nzrange, rowvals, sparse, sparsevec, spdiagm,
-    sprand, sprandn, spzeros, nnz, permute
+    spzeros, nnz, permute
 
 include("abstractsparse.jl")
 include("sparsematrix.jl")

base/sparse/sparsematrix.jl

@@ -1353,118 +1353,6 @@ function _sparse_findprevnz(m::SparseMatrixCSC, i::Integer)
     return LinearIndices(m)[m.rowval[prevhi-1], prevcol]
 end
 
-import Base.Random.GLOBAL_RNG
-function sprand_IJ(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat)
-    ((m < 0) || (n < 0)) && throw(ArgumentError("invalid Array dimensions"))
-    0 <= density <= 1 || throw(ArgumentError("$density not in [0,1]"))
-    N = n*m
-
-    I, J = Vector{Int}(), Vector{Int}() # indices of nonzero elements
-    sizehint!(I, round(Int,N*density))
-    sizehint!(J, round(Int,N*density))
-
-    # density of nonzero columns:
-    L = log1p(-density)
-    coldensity = -expm1(m*L) # = 1 - (1-density)^m
-    colsparsity = exp(m*L) # = 1 - coldensity
-    iL = 1/L
-
-    rows = Vector{Int}()
-    for j in randsubseq(r, 1:n, coldensity)
-        # To get the right statistics, we *must* have a nonempty column j
-        # even if p*m << 1.   To do this, we use an approach similar to
-        # the one in randsubseq to compute the expected first nonzero row k,
-        # except given that at least one is nonzero (via Bayes' rule);
-        # carefully rearranged to avoid excessive roundoff errors.
-        k = ceil(log(colsparsity + rand(r)*coldensity) * iL)
-        ik = k < 1 ? 1 : k > m ? m : Int(k) # roundoff-error/underflow paranoia
-        randsubseq!(r, rows, 1:m-ik, density)
-        push!(rows, m-ik+1)
-        append!(I, rows)
-        nrows = length(rows)
-        Jlen = length(J)
-        resize!(J, Jlen+nrows)
-        @inbounds for i = Jlen+1:length(J)
-            J[i] = j
-        end
-    end
-    I, J
-end
-
-"""
-    sprand([rng],[type],m,[n],p::AbstractFloat,[rfn])
-
-Create a random length `m` sparse vector or `m` by `n` sparse matrix, in
-which the probability of any element being nonzero is independently given by
-`p` (and hence the mean density of nonzeros is also exactly `p`). Nonzero
-values are sampled from the distribution specified by `rfn` and have the type `type`. The uniform
-distribution is used in case `rfn` is not specified. The optional `rng`
-argument specifies a random number generator, see [Random Numbers](@ref).
-
-# Examples
-```jldoctest
-julia> rng = MersenneTwister(1234);
-
-julia> sprand(rng, Bool, 2, 2, 0.5)
-2×2 SparseMatrixCSC{Bool,Int64} with 2 stored entries:
-  [1, 1]  =  true
-  [2, 1]  =  true
-
-julia> sprand(rng, Float64, 3, 0.75)
-3-element SparseVector{Float64,Int64} with 1 stored entry:
-  [3]  =  0.298614
-```
-"""
-function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat,
-                rfn::Function, ::Type{T}=eltype(rfn(r,1))) where T
-    N = m*n
-    N == 0 && return spzeros(T,m,n)
-    N == 1 && return rand(r) <= density ? sparse([1], [1], rfn(r,1)) : spzeros(T,1,1)
-
-    I,J = sprand_IJ(r, m, n, density)
-    sparse_IJ_sorted!(I, J, rfn(r,length(I)), m, n, +)  # it will never need to combine
-end
-
-function sprand(m::Integer, n::Integer, density::AbstractFloat,
-                rfn::Function, ::Type{T}=eltype(rfn(1))) where T
-    N = m*n
-    N == 0 && return spzeros(T,m,n)
-    N == 1 && return rand() <= density ? sparse([1], [1], rfn(1)) : spzeros(T,1,1)
-
-    I,J = sprand_IJ(GLOBAL_RNG, m, n, density)
-    sparse_IJ_sorted!(I, J, rfn(length(I)), m, n, +)  # it will never need to combine
-end
-
-truebools(r::AbstractRNG, n::Integer) = fill(true, n)
-
-sprand(m::Integer, n::Integer, density::AbstractFloat) = sprand(GLOBAL_RNG,m,n,density)
-
-sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density,rand,Float64)
-sprand(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} = sprand(r,m,n,density,(r, i) -> rand(r, T, i), T)
-sprand(r::AbstractRNG, ::Type{Bool}, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density, truebools, Bool)
-sprand(::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} = sprand(GLOBAL_RNG, T, m, n, density)
-
-"""
-    sprandn([rng], m[,n],p::AbstractFloat)
-
-Create a random sparse vector of length `m` or sparse matrix of size `m` by `n`
-with the specified (independent) probability `p` of any entry being nonzero,
-where nonzero values are sampled from the normal distribution. The optional `rng`
-argument specifies a random number generator, see [Random Numbers](@ref).
-
-# Examples
-```jldoctest
-julia> rng = MersenneTwister(1234);
-
-julia> sprandn(rng, 2, 2, 0.75)
-2×2 SparseMatrixCSC{Float64,Int64} with 3 stored entries:
-  [1, 1]  =  0.532813
-  [2, 1]  =  -0.271735
-  [2, 2]  =  0.502334
-```
-"""
-sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density,randn,Float64)
-sprandn(m::Integer, n::Integer, density::AbstractFloat) = sprandn(GLOBAL_RNG,m,n,density)
 
 LinAlg.fillstored!(S::SparseMatrixCSC, x) = (fill!(view(S.nzval, 1:(S.colptr[S.n + 1] - 1)), x); S)
 

base/sparse/sparsevector.jl

@@ -471,31 +471,6 @@ copyto!(A::SparseMatrixCSC, B::SparseVector{TvB,TiB}) where {TvB,TiB} =
     copyto!(A, SparseMatrixCSC{TvB,TiB}(B.n, 1, TiB[1, length(B.nzind)+1], B.nzind, B.nzval))
 
 
-### Rand Construction
-sprand(n::Integer, p::AbstractFloat, rfn::Function, ::Type{T}) where {T} = sprand(GLOBAL_RNG, n, p, rfn, T)
-function sprand(r::AbstractRNG, n::Integer, p::AbstractFloat, rfn::Function, ::Type{T}) where T
-    I = randsubseq(r, 1:convert(Int, n), p)
-    V = rfn(r, T, length(I))
-    SparseVector(n, I, V)
-end
-
-sprand(n::Integer, p::AbstractFloat, rfn::Function) = sprand(GLOBAL_RNG, n, p, rfn)
-function sprand(r::AbstractRNG, n::Integer, p::AbstractFloat, rfn::Function)
-    I = randsubseq(r, 1:convert(Int, n), p)
-    V = rfn(r, length(I))
-    SparseVector(n, I, V)
-end
-
-sprand(n::Integer, p::AbstractFloat) = sprand(GLOBAL_RNG, n, p, rand)
-
-sprand(r::AbstractRNG, n::Integer, p::AbstractFloat) = sprand(r, n, p, rand)
-sprand(r::AbstractRNG, ::Type{T}, n::Integer, p::AbstractFloat) where {T} = sprand(r, n, p, (r, i) -> rand(r, T, i))
-sprand(r::AbstractRNG, ::Type{Bool}, n::Integer, p::AbstractFloat) = sprand(r, n, p, truebools)
-sprand(::Type{T}, n::Integer, p::AbstractFloat) where {T} = sprand(GLOBAL_RNG, T, n, p)
-
-sprandn(n::Integer, p::AbstractFloat) = sprand(GLOBAL_RNG, n, p, randn)
-sprandn(r::AbstractRNG, n::Integer, p::AbstractFloat) = sprand(r, n, p, randn)
-
 ## Indexing into Matrices can return SparseVectors
 
 # Column slices