add more methods rand(rng, ...)

And similarly for rand!, randbool.
Fixes #8360.

Author: rfourquet

base/bitarray.jl

@@ -331,10 +331,10 @@ bitpack{T,N}(A::AbstractArray{T,N}) = convert(BitArray{N}, A)
 
 ## Random ##
 
-function bitarray_rand_fill!(B::BitArray)
+function bitarray_rand_fill!(r, B::BitArray) # r is an AbstractRNG
     length(B) == 0 && return B
     Bc = B.chunks
-    rand!(Bc)
+    rand!(r, Bc)
     Bc[end] &= @_msk_end length(B)
     return B
 end

base/int.jl

@@ -8,6 +8,13 @@ typealias SmallSigned Union(Int8,Int16,Int32)
 typealias SmallUnsigned Union(Uint8,Uint16,Uint32)
 end
 
+typealias Signed64 Union(Int8,Int16,Int32,Int64)
+typealias Unsigned64 Union(Uint8,Uint16,Uint32,Uint64)
+typealias Integer64 Union(Signed64,Unsigned64)
+typealias Signed128 Union(Signed64, Int128)
+typealias Unsigned128 Union(Unsigned64, Uint128)
+typealias Integer128 Union(Signed128,Unsigned128)
+
 ## integer arithmetic ##
 
 -(x::SmallSigned) = -int(x)
@@ -93,9 +100,6 @@ cld(x::Unsigned, y::Signed) = div(x,y)+(!signbit(y)&(rem(x,y)!=0))
 
 # Don't promote integers for div/rem/mod since there no danger of overflow,
 # while there is a substantial performance penalty to 64-bit promotion.
-typealias Signed64 Union(Int8,Int16,Int32,Int64)
-typealias Unsigned64 Union(Uint8,Uint16,Uint32,Uint64)
-typealias Integer64 Union(Signed64,Unsigned64)
 
 div{T<:Signed64}  (x::T, y::T) = box(T,sdiv_int(unbox(T,x),unbox(T,y)))
 div{T<:Unsigned64}(x::T, y::T) = box(T,udiv_int(unbox(T,x),unbox(T,y)))
@@ -371,6 +375,23 @@ signed(x) = convert(Signed,x)
 unsigned(x) = convert(Unsigned,x)
 integer(x) = convert(Integer,x)
 
+as_signed{T<:Signed128}(::Type{T}) = T
+as_signed(::Type{Uint8}  ) = Int8
+as_signed(::Type{Uint16} ) = Int16
+as_signed(::Type{Uint32} ) = Int32
+as_signed(::Type{Uint64} ) = Int64
+as_signed(::Type{Uint128}) = Int128
+
+as_unsigned{T<:Unsigned128}(::Type{T}) = T
+as_unsigned(::Type{Int8}  ) = Uint8
+as_unsigned(::Type{Int16} ) = Uint16
+as_unsigned(::Type{Int32} ) = Uint32
+as_unsigned(::Type{Int64} ) = Uint64
+as_unsigned(::Type{Int128}) = Uint128
+
+as_signed{T<:Integer128}(x::T)   = convert(as_signed(T), x)
+as_unsigned{T<:Integer128}(x::T) = convert(as_unsigned(T), x)
+
 round(x::Integer) = x
 trunc(x::Integer) = x
 floor(x::Integer) = x

base/random.jl

@@ -1,6 +1,6 @@
 module Random
 
-using Base.dSFMT
+using Base: dSFMT, Integer128, Signed128, as_unsigned
 
 export srand,
        rand, rand!,
@@ -23,6 +23,25 @@ type MersenneTwister <: AbstractRNG
     MersenneTwister() = MersenneTwister(0)
 end
 
+immutable GlobalRNG <: AbstractRNG
+end
+
+typealias MT Union(GlobalRNG, MersenneTwister)
+
+# random numbers of the following types are implemented:
+MTRealTypes = Union(Bool, Integer128, Float16, Float32, Float64)
+MTTypes = Union(MTRealTypes, [Complex{T} for T in MTRealTypes.types]...)
+
+## rand: a non-specified RNG defaults to GlobalRNG()
+
+rand() = rand(GlobalRNG())
+rand{T<:MTTypes}(::Type{T}) = rand(GlobalRNG(), T)
+rand(dims::Dims) = rand(GlobalRNG(), dims)
+rand(dims::Int...) = rand(dims)
+rand{T<:MTTypes}(::Type{T}, dims::Dims) = rand(GlobalRNG(), T, dims)
+rand{T<:MTTypes}(::Type{T}, d1::Int, dims::Int...) = rand(T, tuple(d1, dims...))
+rand!{T<:MTTypes}(A::AbstractArray{T}) = rand!(GlobalRNG(), A)
+
 ## initialization
 
 __init__() = srand()
@@ -97,64 +116,49 @@ srand(r::MersenneTwister, filename::String, n::Integer=4) = srand(r, make_seed(f
 
 ## random floating point values
 
-rand(::Type{Float64}) = dsfmt_gv_genrand_close_open()
-rand() = dsfmt_gv_genrand_close_open()
-
-rand(::Type{Float32}) = float32(rand())
-rand(::Type{Float16}) = float16(rand())
-
-rand{T<:Real}(::Type{Complex{T}}) = complex(rand(T),rand(T))
+rand(r::AbstractRNG) = rand(r, Float64)
 
+# MersenneTwister
+rand(r::GlobalRNG, ::Type{Float64})       = dsfmt_gv_genrand_close_open()
+rand(r::MersenneTwister, ::Type{Float64}) = dsfmt_genrand_close_open(r.state)
 
-rand(r::MersenneTwister) = dsfmt_genrand_close_open(r.state)
+rand{T<:Union(Float16, Float32)}(r::MT, ::Type{T}) = convert(T, rand(r))
 
-## random integers
+## random integers (MersenneTwister)
 
-dsfmt_randui32() = dsfmt_gv_genrand_uint32()
-dsfmt_randui64() = uint64(dsfmt_randui32()) | (uint64(dsfmt_randui32())<<32)
+rand(::GlobalRNG, ::Type{Uint32})        = dsfmt_gv_genrand_uint32()
+rand(r::MersenneTwister, ::Type{Uint32}) = dsfmt_genrand_uint32(r.state)
 
-rand(::Type{Uint8})   = uint8(rand(Uint32))
-rand(::Type{Uint16})  = uint16(rand(Uint32))
-rand(::Type{Uint32})  = dsfmt_randui32()
-rand(::Type{Uint64})  = dsfmt_randui64()
-rand(::Type{Uint128}) = uint128(rand(Uint64))<<64 | rand(Uint64)
+rand(r::MT, ::Type{Bool})    = bool(   rand(r, Uint32) & 1)
+rand(r::MT, ::Type{Uint8})   = uint8(  rand(r, Uint32))
+rand(r::MT, ::Type{Uint16})  = uint16( rand(r, Uint32))
+rand(r::MT, ::Type{Uint64})  = uint64( rand(r, Uint32))<<32 | rand(r, Uint32)
+rand(r::MT, ::Type{Uint128}) = uint128(rand(r, Uint64))<<64 | rand(r, Uint64)
 
-rand(::Type{Int8})    = int8(rand(Uint8))
-rand(::Type{Int16})   = int16(rand(Uint16))
-rand(::Type{Int32})   = int32(rand(Uint32))
-rand(::Type{Int64})   = int64(rand(Uint64))
-rand(::Type{Int128})  = int128(rand(Uint128))
+rand{T<:Signed128}(r::MT, ::Type{T}) = convert(T, rand(r, as_unsigned(T)))
 
-# Arrays of random numbers
+## random complex values (AbstractRNG)
 
-rand(::Type{Float64}, dims::Dims) = rand!(Array(Float64, dims))
-rand(::Type{Float64}, dims::Int...) = rand(Float64, dims)
+rand{T<:Real}(r::AbstractRNG, ::Type{Complex{T}}) = complex(rand(r, T), rand(r, T))
 
-rand(dims::Dims) = rand(Float64, dims)
-rand(dims::Int...) = rand(Float64, dims)
+## Arrays of random numbers (AbstractRNG)
 
-rand(r::AbstractRNG, dims::Dims) = rand!(r, Array(Float64, dims))
+rand(r::AbstractRNG, dims::Dims) = rand(r, Float64, dims)
 rand(r::AbstractRNG, dims::Int...) = rand(r, dims)
 
-function rand!{T}(A::Array{T})
-    for i=1:length(A)
-        A[i] = rand(T)
-    end
-    A
-end
+rand(r::AbstractRNG, T::Type, dims::Dims) = rand!(r, Array(T, dims))
+rand(r::AbstractRNG, T::Type, d1::Int, dims::Int...) = rand(r, T, tuple(d1, dims...))
+# note: the above method would trigger an ambiguity warning if d1 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
 
-function rand!(r::AbstractRNG, A::AbstractArray)
+function rand!{T}(r::AbstractRNG, A::AbstractArray{T})
     for i=1:length(A)
-        @inbounds A[i] = rand(r)
+        @inbounds A[i] = rand(r, T)
     end
     A
 end
 
-rand(T::Type, dims::Dims) = rand!(Array(T, dims))
-rand{T<:Number}(::Type{T}) = error("no random number generator for type $T; try a more specific type")
-rand{T<:Number}(::Type{T}, dims::Int...) = rand(T, dims)
-
-
 ## Generate random integer within a range
 
 # remainder function according to Knuth, where rem_knuth(a, 0) = a
@@ -240,16 +244,18 @@ end
 rand{T}(r::Range{T}, dims::Dims) = rand!(r, Array(T, dims))
 rand(r::Range, dims::Int...) = rand(r, dims)
 
+## random BitArrays (AbstractRNG)
 
-## random Bools
+rand!(r::AbstractRNG, B::BitArray) = Base.bitarray_rand_fill!(r, B)
 
-rand!(B::BitArray) = Base.bitarray_rand_fill!(B)
+randbool(r::AbstractRNG, dims::Dims)   = rand!(r, BitArray(dims))
+randbool(r::AbstractRNG, dims::Int...) = rand!(r, BitArray(dims))
 
-randbool(dims::Dims) = rand!(BitArray(dims))
+randbool(dims::Dims)   = rand!(BitArray(dims))
 randbool(dims::Int...) = rand!(BitArray(dims))
 
-randbool() = ((dsfmt_randui32() & 1) == 1)
-rand(::Type{Bool}) = randbool()
+randbool(r::AbstractRNG=GlobalRNG()) = rand(r, Bool)
+
 
 ## randn() - Normally distributed random numbers using Ziggurat algorithm
 

doc/stdlib/base.rst

@@ -3932,43 +3932,35 @@ Random Numbers
 
 Random number generation in Julia uses the `Mersenne Twister library <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/#dSFMT>`_. Julia has a global RNG, which is used by default. Multiple RNGs can be plugged in using the ``AbstractRNG`` object, which can then be used to have multiple streams of random numbers. Currently, only ``MersenneTwister`` is supported.
 
+Most functions related to random generation accept an optional ``AbstractRNG`` as the first argument, ``rng`` , which defaults to the global one if not provided. Morever, some of them accept optionally dimension specifications ``dims...`` (which can be given as a tuple) to generate arrays of random values.
+
+A ``MersenneTwister`` RNG can generate random numbers of the following types: ``Float16, Float32, Float64, Bool, Int16, Uint16, Int32, Uint32, Int64, Uint64, Int128, Uint128`` (or complex numbers or arrays of those types). Random floating point numbers are generated uniformly in [0,1).
+
 .. function:: srand([rng], [seed])
 
-   Reseed the random number generator. If a ``seed`` is provided, the RNG will give a reproducible sequence of numbers, otherwise Julia will get entropy from the system. The ``seed`` may be a non-negative integer, a vector of ``Uint32`` integers or a filename, in which case the seed is read from a file. If the argument ``rng`` is not provided, the default global RNG is seeded.
+   Reseed the random number generator. If a ``seed`` is provided, the RNG will give a reproducible sequence of numbers, otherwise Julia will get entropy from the system. The ``seed`` may be a non-negative integer, a vector of ``Uint32`` integers or a filename, in which case the seed is read from a file.
 
 .. function:: MersenneTwister([seed])
 
    Create a ``MersenneTwister`` RNG object. Different RNG objects can have their own seeds, which may be useful for generating different streams of random numbers.
 
-.. function:: rand() -> Float64
+.. function:: rand([rng], [t::Type], [dims...])
 
-   Generate a ``Float64`` random number uniformly in [0,1)
+   Generate a random value or an array of random values of the given type, ``t``, which defaults to ``Float64``.
 
 .. function:: rand!([rng], A)
 
-   Populate the array A with random number generated from the specified RNG.
-
-.. function:: rand(rng::AbstractRNG, [dims...])
-
-   Generate a random ``Float64`` number or array of the size specified by dims, using the specified RNG object. Currently, ``MersenneTwister`` is the only available Random Number Generator (RNG), which may be seeded using srand.
-
-.. function:: rand(dims or [dims...])
-
-   Generate a random ``Float64`` array of the size specified by dims
-
-.. function:: rand(t::Type, [dims...])
-
-   Generate a random number or array of random numbes of the given type.
+   Populate the array A with random values.
 
 .. function:: rand(r, [dims...])
 
    Pick a random element or array of random elements from range ``r`` (for example, ``1:n`` or ``0:2:10``).
 
-.. function:: randbool([dims...])
+.. function:: randbool([rng], [dims...])
 
-   Generate a random boolean value. Optionally, generate an array of random boolean values.
+   Generate a random boolean value. Optionally, generate a ``BitArray`` of random boolean values.
 
-.. function:: randn([rng], dims or [dims...])
+.. function:: randn([rng], [dims...])
 
    Generate a normally-distributed random number with mean 0 and standard deviation 1. Optionally generate an array of normally-distributed random numbers.
 

test/random.jl

@@ -19,12 +19,17 @@ srand(0); rand(); x = rand(384);
 # Try a seed larger than 2^32
 @test rand(MersenneTwister(5294967296)) == 0.3498809918210497
 
-# Test array filling, Issue #7643
+# Test array filling, Issues #7643, #8360
 @test rand(MersenneTwister(0), 1) == [0.8236475079774124]
 A = zeros(2, 2)
 rand!(MersenneTwister(0), A)
 @test A == [0.8236475079774124  0.16456579813368521;
             0.9103565379264364  0.17732884646626457]
+@test rand(MersenneTwister(0), Int64, 1) == [172014471070449746]
+A = zeros(Int64, 2, 2)
+rand!(MersenneTwister(0), A)
+@test A == [  172014471070449746  -193283627354378518;
+            -4679130500738884555 -9008350441255501549]
 
 # randn
 @test randn(MersenneTwister(42)) == -0.5560268761463861