Honour T and the documented rfn forms in sprand with a value function

src/constructors.jl

@@ -697,6 +697,7 @@ You can sample nonzero values from a different distribution by passing a
 custom `rfn` function instead of `rand`.   This should be a function `rfn(k)`
 that returns an array of `k` random numbers sampled from the desired distribution;
 alternatively, if `rng` is supplied, it should instead be a function `rfn(rng, k)`.
+Passing `T` after `rfn` converts the values that `rfn` returns to `T`.
 
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(1234))
@@ -711,16 +712,27 @@ julia> sprand(Float64, 3, 0.75)
   [2]  =  0.49425
 ```
 """
-function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}=eltype(rfn(r, 1))) where T
+function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}) where T
     m, n = Int(m), Int(n)
+    I, V = _sprandvalues(r, m, n, density, rfn)
+    return sparse_sortedlinearindices!(I, convert(Vector{T}, V), m, n)
+end
+function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function)
+    m, n = Int(m), Int(n)
+    I, V = _sprandvalues(r, m, n, density, rfn)
+    return sparse_sortedlinearindices!(I, convert(Vector{eltype(V)}, V), m, n)
+end
+function _sprandvalues(r::AbstractRNG, m::Int, n::Int, density::AbstractFloat, rfn::F) where {F}
     (m < 0 || n < 0) && throw(ArgumentError("invalid Array dimensions"))
     0 <= density <= 1 || throw(ArgumentError("$density not in [0,1]"))
     I = randsubseq(r, 1:(m*n), density)
-    return sparse_sortedlinearindices!(I, convert(Vector{T}, rfn(r,length(I))), m, n)
+    return I, rfn(r, length(I))
 end
 
-sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T} = eltype(rfn(1))) where {T} =
+sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function) =
     sprand(default_rng(), m, n, density, (r, i) -> rfn(i))
+sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}) where {T} =
+    sprand(default_rng(), m, n, density, (r, i) -> rfn(i), T)
 
 truebools(r::AbstractRNG, n::Integer) = fill(true, n)
 

src/sparsevector.jl

@@ -768,11 +768,16 @@ end
 
 
 ### Rand Construction
-sprand(n::Integer, p::AbstractFloat, rfn::Function, ::Type{T}) where {T} = sprand(default_rng(), n, p, rfn, T)
+# `rfn(rng, T, k)` predates the documented `rfn(k)` and `rfn(rng, k)` and is still accepted
+function sprand(n::Integer, p::AbstractFloat, rfn::Function, ::Type{T}) where {T}
+    r = default_rng()
+    applicable(rfn, r, T, 0) && return sprand(r, n, p, rfn, T)
+    return sprand(r, n, p, (r, i) -> rfn(i), T)
+end
 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)
+    V = applicable(rfn, r, T, length(I)) ? rfn(r, T, length(I)) : rfn(r, length(I))
+    SparseVector(n, I, convert(Vector{T}, V))
 end
 
 sprand(n::Integer, p::AbstractFloat, rfn::Function) = sprand(default_rng(), n, p, (r, i) -> rfn(i))

test/constructors.jl

@@ -290,6 +290,7 @@ end
 @static if COMPREHENSIVE
     m = sprand(Float32, 10, 10, 0.1)
     @test eltype(m) == Float32
+    @test eltype(sprand(10, 10, 0.5, k -> rand(Int8, k), Float64)) == Float64
 end
     m = sprand(Float64, 10, 10, 0.1)
     @test eltype(m) == Float64

test/sparsevector.jl

@@ -242,6 +242,14 @@ end
             @test xv == xr
             @test sprand(20, 1, 0.5, rfn1) isa SparseMatrixCSC{Int8,Int}
             @test sprand(MersenneTwister(5), 20, 1, 0.5, rfn2) isa SparseMatrixCSC{Int8,Int}
+            # and with `T`, which the older `rfn(rng, T, k)` still accepts
+            @test sprand(20, 0.5, rfn1, Float64) isa SparseVector{Float64,Int}
+            @test sprand(MersenneTwister(5), 20, 0.5, rfn2, Float64) isa SparseVector{Float64,Int}
+            @test sprand(MersenneTwister(5), 20, 0.5, (r, T, k) -> rand(r, T, k), Float64) isa SparseVector{Float64,Int}
+            # without `T`, `rfn` is called once, for the values
+            calls = Ref(0)
+            sprand(MersenneTwister(5), 4, 4, 0.5, (r, k) -> (calls[] += 1; rand(r, Int8, k)))
+            @test calls[] == 1
         end
         end
     end