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[DO NOT MERGE] sprand sanity with rfn argument #30637

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[DO NOT MERGE] sprand sanity with rfn argument #30637

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78 changes: 31 additions & 47 deletions stdlib/SparseArrays/src/sparsematrix.jl
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Expand Up @@ -1383,34 +1383,20 @@ function _sparse_findprevnz(m::SparseMatrixCSC, i::Integer)
end


function _sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn)
m, n = Int(m), Int(n)
(m < 0 || n < 0) && throw(ArgumentError("invalid Array dimensions"))
0 <= density <= 1 || throw(ArgumentError("$density not in [0,1]"))
j, colm = 1, 0
rowval = randsubseq(r, 1:(m*n), density)
nnz = length(rowval)
colptr = Vector{Int}(undef, n + 1)
@inbounds for col = 1:n+1
colptr[col] = j
while j <= nnz && (rowval[j] -= colm) <= m
j += 1
end
j <= nnz && (rowval[j] += colm)
colm += m
end
return SparseMatrixCSC(m, n, colptr, rowval, rfn(nnz))
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).
`p` (and hence the mean density of nonzeros is also exactly `p`). The
optional `rng` argument specifies a random number generator that defaults
to the global one, see [Random Numbers](@ref).

Nonzero values are taken from the function `rfn`. `rfn(i)` should
return a vector of `i` values. In case `rfn` is not specified,
it defaults to `rnf(i)=rand(rng, i)` (uniform distribution in ``[0,1)``) or, If
the parameter `type` is specified, to `rng(i)=rand(rng, type, i)`.
The special case of `type==Bool` corresponds to `rfn(i)=fill(true, i)`

# Examples
```jldoctest; setup = :(using Random; Random.seed!(1234))
Expand All @@ -1423,31 +1409,29 @@ julia> sprand(Float64, 3, 0.75)
[3] = 0.298614
```
"""
function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat,
rfn::Function, ::Type{T}=eltype(rfn(r,1))) where T
m,n = Int(m), Int(n)
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)
_sprand(r,m,n,density,i->rfn(r,i))
end

function sprand(m::Integer, n::Integer, density::AbstractFloat,
rfn::Function, ::Type{T}=eltype(rfn(1))) where T
m,n = Int(m), Int(n)
N = m*n
N == 0 && return spzeros(T,m,n)
N == 1 && return rand() <= density ? sparse([1], [1], rfn(1)) : spzeros(T,1,1)
_sprand(GLOBAL_RNG,m,n,density,rfn)
function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function = i->rand(r,i))
m, n = Int(m), Int(n)
(m < 0 || n < 0) && throw(ArgumentError("invalid Array dimensions"))
0 <= density <= 1 || throw(ArgumentError("$density not in [0,1]"))
j, colm = 1, 0
rowval = randsubseq(r, 1:(m*n), density)
nnz = length(rowval)
colptr = Vector{Int}(undef, n + 1)
@inbounds for col = 1:n+1
colptr[col] = j
while j <= nnz && (rowval[j] -= colm) <= m
j += 1
end
j <= nnz && (rowval[j] += colm)
colm += m
end
return SparseMatrixCSC(m, n, colptr, rowval, rfn(nnz))
end

truebools(r::AbstractRNG, n::Integer) = fill(true, n)

sprand(m::Integer, n::Integer, density::AbstractFloat) = sprand(GLOBAL_RNG,m,n,density)
sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function = rand) = sprand(GLOBAL_RNG,m,n,density,rfn)

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(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} = sprand(r,m,n,density, i->rand(r,T,i))
sprand(r::AbstractRNG, ::Type{Bool}, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density, i->fill(true,i))
sprand(::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where {T} = sprand(GLOBAL_RNG, T, m, n, density)

"""
Expand All @@ -1469,9 +1453,9 @@ julia> sprandn(2, 2, 0.75)
[2, 2] = 0.297336
```
"""
sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density,randn,Float64)
sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) = sprand(r,m,n,density,i->randn(r,i))
sprandn(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where T = sprand(r,m,n,density,i->randn(r,T,i))
sprandn(m::Integer, n::Integer, density::AbstractFloat) = sprandn(GLOBAL_RNG,m,n,density)
sprandn(r::AbstractRNG, ::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where T = sprand(r,m,n,density,(r,i) -> randn(r,T,i), T)
sprandn(::Type{T}, m::Integer, n::Integer, density::AbstractFloat) where T = sprandn(GLOBAL_RNG,T,m,n,density)

LinearAlgebra.fillstored!(S::SparseMatrixCSC, x) = (fill!(nzvalview(S), x); S)
Expand Down
21 changes: 7 additions & 14 deletions stdlib/SparseArrays/src/sparsevector.jl
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Expand Up @@ -492,31 +492,24 @@ copyto!(A::SparseMatrixCSC, B::SparseVector{TvB,TiB}) where {TvB,TiB} =


### 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))
V = rfn(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(r::AbstractRNG, n::Integer, p::AbstractFloat) = sprand(r, n, p, i->rand(r,i))
sprand(r::AbstractRNG, ::Type{Bool}, n::Integer, p::AbstractFloat) = sprand(r, n, p, i->fill(true,i))
sprand(::Type{T}, n::Integer, p::AbstractFloat) where {T} = sprand(GLOBAL_RNG, T, n, p)
sprand(r::AbstractRNG, ::Type{T}, n::Integer, p::AbstractFloat) where T = sprand(r, n, p, i->rand(r, T, i))

sprandn(n::Integer, p::AbstractFloat) = sprand(GLOBAL_RNG, n, p, randn)
sprandn(r::AbstractRNG, n::Integer, p::AbstractFloat) = sprand(r, n, p, randn)
sprandn(::Type{T}, n::Integer, p::AbstractFloat) where T = sprand(GLOBAL_RNG, n, p, (r, i) -> randn(r, T, i))
sprandn(r::AbstractRNG, ::Type{T}, n::Integer, p::AbstractFloat) where T = sprand(r, n, p, (r, i) -> randn(r, T, i))
sprandn(r::AbstractRNG, n::Integer, p::AbstractFloat) = sprand(r, n, p, i->randn(r,i))
sprandn(::Type{T}, n::Integer, p::AbstractFloat) where T = sprand(GLOBAL_RNG, n, p, i->randn(T, i))
sprandn(r::AbstractRNG, ::Type{T}, n::Integer, p::AbstractFloat) where T = sprand(r, n, p, i->randn(r, T, i))

## Indexing into Matrices can return SparseVectors

Expand Down
2 changes: 1 addition & 1 deletion stdlib/SparseArrays/test/sparse.jl
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Expand Up @@ -1921,7 +1921,7 @@ end

@testset "issue #16073" begin
@inferred sprand(1, 1, 1.0)
@inferred sprand(1, 1, 1.0, rand, Float64)
@inferred sprand(1, 1, 1.0, rand)
@inferred sprand(1, 1, 1.0, x -> round.(Int, rand(x) * 100))
end

Expand Down
2 changes: 1 addition & 1 deletion stdlib/SparseArrays/test/sparsevector.jl
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Expand Up @@ -179,7 +179,7 @@ end
@test all(nonzeros(xr) .> 0.0)
end
end
let xr = sprand(1000, 0.9, rand, Float32)
let xr = sprand(1000, 0.9, i->rand(Float32,i))
@test isa(xr, SparseVector{Float32,Int})
@test length(xr) == 1000
if !isempty(nonzeros(xr))
Expand Down