Use sampler-based Random API

This avoids the performance problem with `ReinterpretArray`.
On the other hand, the time for generating the random number array with the default RNG,
is significantly increased.

Author: kimikage

Project.toml

@@ -3,6 +3,7 @@ uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
 version = "0.8.4"
 
 [deps]
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]

src/FixedPointNumbers.jl

@@ -7,9 +7,10 @@ import Base: ==, <, <=, -, +, *, /, ~, isapprox,
              big, rationalize, float, trunc, round, floor, ceil, bswap, clamp,
              div, fld, rem, mod, mod1, fld1, min, max, minmax,
              signed, unsigned, copysign, flipsign, signbit,
-             rand, length
+             length
 
 import Statistics   # for _mean_promote
+import Random
 
 using Base.Checked: checked_add, checked_sub, checked_div
 
@@ -353,8 +354,9 @@ scaledual(::Type{Tdual}, x::AbstractArray{T}) where {Tdual, T <: FixedPoint} =
     throw(ArgumentError(String(take!(io))))
 end
 
-rand(::Type{T}) where {T <: FixedPoint} = reinterpret(T, rand(rawtype(T)))
-rand(::Type{T}, sz::Dims) where {T <: FixedPoint} = reinterpret(T, rand(rawtype(T), sz))
+function Random.rand(r::Random.AbstractRNG, ::Random.SamplerType{X}) where X <: FixedPoint
+    X(rand(r, rawtype(X)), 0)
+end
 
 if VERSION >= v"1.1" # work around https://github.com/JuliaLang/julia/issues/34121
     include("precompile.jl")

test/fixed.jl

@@ -1,4 +1,4 @@
-using FixedPointNumbers, Statistics, Test
+using FixedPointNumbers, Statistics, Random, Test
 using FixedPointNumbers: bitwidth
 
 function test_op(fun::F, ::Type{T}, fx, fy, fxf, fyf, tol) where {F,T}
@@ -371,12 +371,13 @@ end
 end
 
 @testset "rand" begin
-    for F in (Fixed{Int8,7}, Fixed{Int16,8}, Fixed{Int16,10}, Fixed{Int32,16})
+    for F in (Q0f7, Q7f8, Q5f10, Q15f16)
         @test isa(rand(F), F)
         a = rand(F, (3, 5))
-        @test ndims(a) == 2 && eltype(a) == F
+        @test ndims(a) == 2 && eltype(a) === F
         @test size(a) == (3,5)
     end
+    @test rand(MersenneTwister(1234), Q0f7) === -0.156Q0f7
 end
 
 @testset "floatmin" begin

test/normed.jl

@@ -1,4 +1,4 @@
-using FixedPointNumbers, Statistics, Test
+using FixedPointNumbers, Statistics, Random, Test
 using FixedPointNumbers: bitwidth
 
 @testset "domain of f" begin
@@ -552,16 +552,13 @@ end
 end
 
 @testset "rand" begin
-    for T in (Normed{UInt8,8}, Normed{UInt8,6},
-              Normed{UInt16,16}, Normed{UInt16,14},
-              Normed{UInt32,32}, Normed{UInt32,30},
-              Normed{UInt64,64}, Normed{UInt64,62})
-        a = rand(T)
-        @test isa(a, T)
-        a = rand(T, (3, 5))
-        @test ndims(a) == 2 && eltype(a) == T
+    for N in (N0f8, N2f6, N0f16, N2f14, N0f32, N2f30, N0f64, N2f62)
+        @test isa(rand(N), N)
+        a = rand(N, (3, 5))
+        @test ndims(a) == 2 && eltype(a) === N
         @test size(a) == (3,5)
     end
+    @test rand(MersenneTwister(1234), N0f8) === 0.925N0f8
 end
 
 @testset "Overflow with Float16" begin