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Update README.md for Julia 1.9 #24

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90 changes: 53 additions & 37 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -7,12 +7,12 @@

This package explores a possible extension of `rand`-related
functionalities (from the `Random` module); the code is initially
taken from https://github.com/JuliaLang/julia/pull/24912.
taken from <https://github.com/JuliaLang/julia/pull/24912>.
Note that type piracy is committed!
While hopefully useful, this package is still experimental, and
hence unstable. User feedback, and design or implementation contributions are welcome.

This does essentially 4 things:
This does essentially four things:

1) define distribution objects, to give first-class status to features
provided by `Random`; for example `rand(Normal(), 3)` is equivalent
Expand Down Expand Up @@ -63,7 +63,7 @@ For convenience, the following names from `Random` are re-exported
in this package: `rand!`, `AbstractRNG`, `MersenneTwister`,
`RandomDevice` (`rand` is in `Base`). Functions like `randn!` or
`randstring` are considered to be obsoleted by this package so are not
re-exported. It's still needed to import `Random` separately in order
re-exported. It is still necessary to import `Random` separately in order
to use functions which don't extend the `rand` API, namely
`randsubseq`, `shuffle`, `randperm`, `randcycle`, and their mutating
variants.
Expand Down Expand Up @@ -92,7 +92,7 @@ julia> rand(make(Pair, 1:10, Normal())) # random Pair, where both members have d
5 => 0.674375

julia> rand(make(Pair{Number,Any}, 1:10, Normal())) # specify the Pair type
Pair{Number,Any}(1, -0.131617)
Pair{Number, Any}(1, -0.131617)

julia> rand(Pair{Float64,Int}) # equivalent to rand(make(Pair, Float64, Int))
0.321676 => -4583276276690463733
Expand Down Expand Up @@ -122,35 +122,46 @@ julia> rand(Normal(ComplexF64)) # equivalent to randn(ComplexF64)
0.9322376894079347 + 0.2812214248483498im

julia> rand(Set, 3)
Set([0.717172, 0.78481, 0.86901])
Set{Float64} with 3 elements:
0.0675168818514279
0.31058418699493895
0.15029104540378424

julia> rand!(ans, Exponential())
Set([0.7935073925105659, 2.593684878770254, 1.629181233597078])
julia> rand!(ans, Exponential())
Set{Float64} with 3 elements:
1.082312697650858
1.2984094155972015
0.016146678329819485

julia> rand(1:9, Set, 3) # if you try `rand(1:3, Set, 9)`, it will take a while ;-)
Set([3, 5, 8])
Set{Int64} with 3 elements:
4
7
1

julia> rand(Dict{String,Int8}, 2)
Dict{String,Int8} with 3 entries:
Dict{String, Int8} with 2 entries:
"vxybIbae" => 42
"bO2fTwuq" => -13

julia> rand(make(Pair, 1:9, Normal()), Dict, 3)
Dict{Int64,Float64} with 3 entries:
Dict{Int64, Float64} with 3 entries:
9 => 0.916406
3 => -2.44958
8 => -0.703348

julia> using SparseArrays

julia> rand(SparseVector, 0.3, 9) # equivalent to sprand(9, 0.3)
9-element SparseVector{Float64,Int64} with 3 stored entries:
9-element SparseVector{Float64, Int64} with 3 stored entries:
[1] = 0.173858
[6] = 0.568631
[8] = 0.297207

julia> rand(Normal(), SparseMatrixCSC, 0.3, 2, 3) # equivalent to sprandn(2, 3, 0.3)
2×3 SparseMatrixCSC{Float64,Int64} with 2 stored entries:
[2, 1] = 0.448981
[1, 2] = 0.730103
2×3 SparseMatrixCSC{Float64, Int64} with 2 stored entries:
⋅ -1.5617 ⋅
0.572305 ⋅ ⋅

# like for Array, sparse arrays enjoy to be special cased: `SparseVector` or `SparseMatrixCSC`
# can be omitted in the `rand` call (not in the `make` call):
Expand All @@ -168,26 +179,29 @@ julia> rand(make(String, 3, "123")) # ... which is as always equivalent to a cal
"211"

julia> rand(String, Set, 3) # String considered as a scalar
Set(["0Dfqj6Yr", "ILngfcRz", "HT5IEyK3"])
Set{String} with 3 elements:
"jDbjXu9b"
"0Lo75VKo"
"webpNhfY"

julia> rand(BitArray, 3) # equivalent to, but unfortunately more verbose than, bitrand(3)
3-element BitArray{1}:
true
true
false

julia> julia> rand(Bernoulli(0.2), BitVector, 10) # using the Bernoulli distribution
10-element BitArray{1}:
false
false
false
false
true
false
true
false
false
true
3-element BitVector:
1
1
0

julia> rand(Bernoulli(0.2), BitVector, 10) # using the Bernoulli distribution
10-element BitVector:
0
1
0
1
0
0
0
0
0
1

julia> rand(1:3, NTuple{3}) # NTuple{3} considered as a container, equivalent to rand(make(NTuple{3}, 1:3))
(3, 3, 1)
Expand All @@ -208,13 +222,15 @@ julia> rand(make(MVector{2,AbstractString}, String), SMatrix{3, 2})
["fFJuUtJQ", "H2mAQrIV"] ["pt0OYFJw", "O0fCfjjR"]

julia> Set(Iterators.take(Rand(RandomDevice(), 1:10), 3)) # RNG defaults to Random.GLOBAL_RNG
Set([9, 2, 6]) # note that the set could end up with less than 3 elements if `Rand` generates duplicates
Set{Int64} with 2 elements: # note that the set can end up with less than 3 elements if `Rand` generates duplicates
5
9

julia> collect(Iterators.take(Uniform(1:10), 3)) # distributions can be iterated over, using Random.GLOBAL_RNG implicitly
3-element Array{Int64,1}:
7
10
5
3-element Vector{Int64}:
9
6
8

julia> rand(Complex => Int) # equivalent to rand(make(Complex, Int)) (experimental)
4610038282330316390 + 4899086469899572461im
Expand Down