/
Random.jl
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/
Random.jl
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# This file is a part of Julia. License is MIT: https://julialang.org/license
module Random
include("DSFMT.jl")
using .DSFMT
using Base.GMP.MPZ
using Base.GMP: Limb
using Base: BitInteger, BitInteger_types, BitUnsigned, has_offset_axes
import Base: copymutable, copy, copy!, ==, hash, convert
using Serialization
import Serialization: serialize, deserialize
import Base: rand, randn
export rand!, randn!,
randexp, randexp!,
bitrand,
randstring,
randsubseq, randsubseq!,
shuffle, shuffle!,
randperm, randperm!,
randcycle, randcycle!,
AbstractRNG, MersenneTwister, RandomDevice
## general definitions
abstract type AbstractRNG end
"""
Random.gentype(T)
Determine the type of the elements generated by calling `rand([rng], x)`,
where `x::T`, and `x` is not a type.
The definition `gentype(x) = gentype(typeof(x))` is provided for convenience,
and `gentype(T)` defaults to `eltype(T)`.
NOTE: `rand([rng], X)`, where `X` is a type, is always assumed to produce
an object of type `X`.
# Examples
```jldoctest
julia> gentype(1:10)
Int64
```
"""
gentype(::Type{X}) where {X} = eltype(X)
gentype(x) = gentype(typeof(x))
### integers
# we define types which encode the generation of a specific number of bits
# the "raw" version means that the unused bits are not zeroed
abstract type UniformBits{T<:BitInteger} end
struct UInt10{T} <: UniformBits{T} end
struct UInt10Raw{T} <: UniformBits{T} end
struct UInt23{T} <: UniformBits{T} end
struct UInt23Raw{T} <: UniformBits{T} end
struct UInt52{T} <: UniformBits{T} end
struct UInt52Raw{T} <: UniformBits{T} end
struct UInt104{T} <: UniformBits{T} end
struct UInt104Raw{T} <: UniformBits{T} end
struct UInt2x52{T} <: UniformBits{T} end
struct UInt2x52Raw{T} <: UniformBits{T} end
uint_sup(::Type{<:Union{UInt10,UInt10Raw}}) = UInt16
uint_sup(::Type{<:Union{UInt23,UInt23Raw}}) = UInt32
uint_sup(::Type{<:Union{UInt52,UInt52Raw}}) = UInt64
uint_sup(::Type{<:Union{UInt104,UInt104Raw}}) = UInt128
uint_sup(::Type{<:Union{UInt2x52,UInt2x52Raw}}) = UInt128
for UI = (:UInt10, :UInt10Raw, :UInt23, :UInt23Raw, :UInt52, :UInt52Raw,
:UInt104, :UInt104Raw, :UInt2x52, :UInt2x52Raw)
@eval begin
$UI(::Type{T}=uint_sup($UI)) where {T} = $UI{T}()
# useful for defining rand generically:
uint_default(::$UI) = $UI{uint_sup($UI)}()
end
end
gentype(::Type{<:UniformBits{T}}) where {T} = T
### floats
abstract type FloatInterval{T<:AbstractFloat} end
struct CloseOpen01{T<:AbstractFloat} <: FloatInterval{T} end # interval [0,1)
struct CloseOpen12{T<:AbstractFloat} <: FloatInterval{T} end # interval [1,2)
const FloatInterval_64 = FloatInterval{Float64}
const CloseOpen01_64 = CloseOpen01{Float64}
const CloseOpen12_64 = CloseOpen12{Float64}
CloseOpen01(::Type{T}=Float64) where {T<:AbstractFloat} = CloseOpen01{T}()
CloseOpen12(::Type{T}=Float64) where {T<:AbstractFloat} = CloseOpen12{T}()
gentype(::Type{<:FloatInterval{T}}) where {T<:AbstractFloat} = T
const BitFloatType = Union{Type{Float16},Type{Float32},Type{Float64}}
### Sampler
abstract type Sampler{E} end
gentype(::Type{<:Sampler{E}}) where {E} = E
# temporarily for BaseBenchmarks
RangeGenerator(x) = Sampler(GLOBAL_RNG, x)
# In some cases, when only 1 random value is to be generated,
# the optimal sampler can be different than if multiple values
# have to be generated. Hence a `Repetition` parameter is used
# to choose the best one depending on the need.
const Repetition = Union{Val{1},Val{Inf}}
# these default fall-back for all RNGs would be nice,
# but generate difficult-to-solve ambiguities
# Sampler(::AbstractRNG, X, ::Val{Inf}) = Sampler(X)
# Sampler(::AbstractRNG, ::Type{X}, ::Val{Inf}) where {X} = Sampler(X)
Sampler(rng::AbstractRNG, x, r::Repetition=Val(Inf)) = Sampler(typeof(rng), x, r)
Sampler(rng::AbstractRNG, ::Type{X}, r::Repetition=Val(Inf)) where {X} = Sampler(typeof(rng), X, r)
Sampler(::Type{<:AbstractRNG}, sp::Sampler, ::Repetition) =
throw(ArgumentError("Sampler for this object is not defined"))
# default shortcut for the general case
Sampler(::Type{RNG}, X) where {RNG<:AbstractRNG} = Sampler(RNG, X, Val(Inf))
Sampler(::Type{RNG}, ::Type{X}) where {RNG<:AbstractRNG,X} = Sampler(RNG, X, Val(Inf))
#### pre-defined useful Sampler types
# default fall-back for types
struct SamplerType{T} <: Sampler{T} end
Sampler(::Type{<:AbstractRNG}, ::Type{T}, ::Repetition) where {T} = SamplerType{T}()
Base.getindex(::SamplerType{T}) where {T} = T
# default fall-back for values
struct SamplerTrivial{T,E} <: Sampler{E}
self::T
end
SamplerTrivial(x::T) where {T} = SamplerTrivial{T,gentype(T)}(x)
Sampler(::Type{<:AbstractRNG}, x, ::Repetition) = SamplerTrivial(x)
Base.getindex(sp::SamplerTrivial) = sp.self
# simple sampler carrying data (which can be anything)
struct SamplerSimple{T,S,E} <: Sampler{E}
self::T
data::S
end
SamplerSimple(x::T, data::S) where {T,S} = SamplerSimple{T,S,gentype(T)}(x, data)
Base.getindex(sp::SamplerSimple) = sp.self
# simple sampler carrying a (type) tag T and data
struct SamplerTag{T,S,E} <: Sampler{E}
data::S
SamplerTag{T}(s::S) where {T,S} = new{T,S,gentype(T)}(s)
end
#### helper samplers
# TODO: make constraining constructors to enforce that those
# types are <: Sampler{T}
##### Adapter to generate a randome value in [0, n]
struct LessThan{T<:Integer,S} <: Sampler{T}
sup::T
s::S # the scalar specification/sampler to feed to rand
end
function rand(rng::AbstractRNG, sp::LessThan)
while true
x = rand(rng, sp.s)
x <= sp.sup && return x
end
end
struct Masked{T<:Integer,S} <: Sampler{T}
mask::T
s::S
end
rand(rng::AbstractRNG, sp::Masked) = rand(rng, sp.s) & sp.mask
##### Uniform
struct UniformT{T} <: Sampler{T} end
uniform(::Type{T}) where {T} = UniformT{T}()
rand(rng::AbstractRNG, ::UniformT{T}) where {T} = rand(rng, T)
### machinery for generation with Sampler
# This describes how to generate random scalars or arrays, by generating a Sampler
# and calling rand on it (which should be defined in "generation.jl").
# NOTE: this section could be moved into a separate file when more containers are supported.
#### scalars
rand(rng::AbstractRNG, X) = rand(rng, Sampler(rng, X, Val(1)))
rand(rng::AbstractRNG=GLOBAL_RNG, ::Type{X}=Float64) where {X} = rand(rng, Sampler(rng, X, Val(1)))
rand(X) = rand(GLOBAL_RNG, X)
rand(::Type{X}) where {X} = rand(GLOBAL_RNG, X)
#### arrays
rand!(A::AbstractArray{T}, X) where {T} = rand!(GLOBAL_RNG, A, X)
rand!(A::AbstractArray{T}, ::Type{X}=T) where {T,X} = rand!(GLOBAL_RNG, A, X)
rand!(rng::AbstractRNG, A::AbstractArray{T}, X) where {T} = rand!(rng, A, Sampler(rng, X))
rand!(rng::AbstractRNG, A::AbstractArray{T}, ::Type{X}=T) where {T,X} = rand!(rng, A, Sampler(rng, X))
function rand!(rng::AbstractRNG, A::AbstractArray{T}, sp::Sampler) where T
for i in eachindex(A)
@inbounds A[i] = rand(rng, sp)
end
A
end
rand(r::AbstractRNG, dims::Integer...) = rand(r, Float64, Dims(dims))
rand( dims::Integer...) = rand(Float64, Dims(dims))
rand(r::AbstractRNG, X, dims::Dims) = rand!(r, Array{gentype(X)}(undef, dims), X)
rand( X, dims::Dims) = rand(GLOBAL_RNG, X, dims)
rand(r::AbstractRNG, X, d::Integer, dims::Integer...) = rand(r, X, Dims((d, dims...)))
rand( X, d::Integer, dims::Integer...) = rand(X, Dims((d, dims...)))
# note: the above methods would trigger an ambiguity warning if d was not separated out:
# rand(r, ()) would match both this method and rand(r, dims::Dims)
# moreover, a call like rand(r, NotImplementedType()) would be an infinite loop
# this is needed to disambiguate
rand(r::AbstractRNG, dims::Dims) = error("rand(rng, dims) is discontinued; try rand(rng, Float64, dims)")
rand(r::AbstractRNG, ::Type{X}, dims::Dims) where {X} = rand!(r, Array{X}(undef, dims), X)
rand( ::Type{X}, dims::Dims) where {X} = rand(GLOBAL_RNG, X, dims)
rand(r::AbstractRNG, ::Type{X}, d::Integer, dims::Integer...) where {X} = rand(r, X, Dims((d, dims...)))
rand( ::Type{X}, d::Integer, dims::Integer...) where {X} = rand(X, Dims((d, dims...)))
## __init__ & include
function __init__()
try
seed!()
catch ex
Base.showerror_nostdio(ex,
"WARNING: Error during initialization of module Random")
end
end
include("RNGs.jl")
include("generation.jl")
include("normal.jl")
include("misc.jl")
## rand & rand! & seed! docstrings
"""
rand([rng=GLOBAL_RNG], [S], [dims...])
Pick a random element or array of random elements from the set of values specified by `S`;
`S` can be
* an indexable collection (for example `1:n` or `['x','y','z']`),
* an `AbstractDict` or `AbstractSet` object,
* a string (considered as a collection of characters), or
* a type: the set of values to pick from is then equivalent to `typemin(S):typemax(S)` for
integers (this is not applicable to [`BigInt`](@ref)), and to ``[0, 1)`` for floating
point numbers;
`S` defaults to [`Float64`](@ref)
(except when `dims` is a tuple of integers, in which case `S` must be specified).
# Examples
```julia-repl
julia> rand(Int, 2)
2-element Array{Int64,1}:
1339893410598768192
1575814717733606317
julia> rand(MersenneTwister(0), Dict(1=>2, 3=>4))
1=>2
```
!!! note
The complexity of `rand(rng, s::Union{AbstractDict,AbstractSet})`
is linear in the length of `s`, unless an optimized method with
constant complexity is available, which is the case for `Dict`,
`Set` and `BitSet`. For more than a few calls, use `rand(rng,
collect(s))` instead, or either `rand(rng, Dict(s))` or `rand(rng,
Set(s))` as appropriate.
"""
rand
"""
rand!([rng=GLOBAL_RNG], A, [S=eltype(A)])
Populate the array `A` with random values. If `S` is specified
(`S` can be a type or a collection, cf. [`rand`](@ref) for details),
the values are picked randomly from `S`.
This is equivalent to `copyto!(A, rand(rng, S, size(A)))`
but without allocating a new array.
# Examples
```jldoctest
julia> rng = MersenneTwister(1234);
julia> rand!(rng, zeros(5))
5-element Array{Float64,1}:
0.5908446386657102
0.7667970365022592
0.5662374165061859
0.4600853424625171
0.7940257103317943
```
"""
rand!
"""
seed!([rng=GLOBAL_RNG], seed) -> rng
seed!([rng=GLOBAL_RNG]) -> rng
Reseed the random number generator: `rng` will give a reproducible
sequence of numbers if and only if a `seed` is provided. Some RNGs
don't accept a seed, like `RandomDevice`.
After the call to `seed!`, `rng` is equivalent to a newly created
object initialized with the same seed.
# Examples
```julia-repl
julia> Random.seed!(1234);
julia> x1 = rand(2)
2-element Array{Float64,1}:
0.590845
0.766797
julia> Random.seed!(1234);
julia> x2 = rand(2)
2-element Array{Float64,1}:
0.590845
0.766797
julia> x1 == x2
true
julia> rng = MersenneTwister(1234); rand(rng, 2) == x1
true
julia> MersenneTwister(1) == Random.seed!(rng, 1)
true
julia> rand(Random.seed!(rng), Bool) # not reproducible
true
julia> rand(Random.seed!(rng), Bool)
false
julia> rand(MersenneTwister(), Bool) # not reproducible either
true
```
"""
seed!(rng::AbstractRNG, ::Nothing) = seed!(rng)
end # module