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sprand and SparseMatrixCSC constructor simplifications #30617

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merged 6 commits into from
Jan 22, 2019
Merged

sprand and SparseMatrixCSC constructor simplifications #30617

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e11cd40
* Factorize out sparse_sorted_unique!
abraunst Dec 22, 2018
d46c137
remove extra blank line
abraunst Jan 6, 2019
d651070
remove useless @inbounds
abraunst Jan 6, 2019
75990e5
reintroduce two @inbounds that were not useless
abraunst Jan 6, 2019
e22ed98
faster loop by @stevengj; rename sorted_unique -> increasing
abraunst Jan 7, 2019
8c9653a
method renaming, add explicit returns
abraunst Jan 7, 2019
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108 changes: 27 additions & 81 deletions stdlib/SparseArrays/src/sparsematrix.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -373,12 +373,19 @@ SparseMatrixCSC(M::AbstractMatrix{Tv}) where {Tv} = SparseMatrixCSC{Tv,Int}(M)
SparseMatrixCSC{Tv}(M::AbstractMatrix{Tv}) where {Tv} = SparseMatrixCSC{Tv,Int}(M)
function SparseMatrixCSC{Tv,Ti}(M::AbstractMatrix) where {Tv,Ti}
require_one_based_indexing(M)
I = findall(x -> x != 0, M)
eltypeTiI = Ti[i[1] for i in I]
eltypeTiJ = Ti[i[2] for i in I]
eltypeTvV = Tv[M[i] for i in I]
return sparse_IJ_sorted!(eltypeTiI, eltypeTiJ, eltypeTvV, size(M)...)
I = Ti[]
V = Tv[]
i = 0
for v in M
i += 1
if !iszero(v)
push!(I, i)
push!(V, v)
end
end
return sparse_sortedlinearindices!(I, V, size(M)...)
end

function SparseMatrixCSC{Tv,Ti}(M::StridedMatrix) where {Tv,Ti}
nz = count(t -> t != 0, M)
colptr = zeros(Ti, size(M, 2) + 1)
Expand Down Expand Up @@ -456,56 +463,6 @@ sparse(A::AbstractMatrix{Tv}) where {Tv} = convert(SparseMatrixCSC{Tv,Int}, A)

sparse(S::SparseMatrixCSC) = copy(S)

sparse_IJ_sorted!(I,J,V,m,n) = sparse_IJ_sorted!(I,J,V,m,n,+)

sparse_IJ_sorted!(I,J,V::AbstractVector{Bool},m,n) = sparse_IJ_sorted!(I,J,V,m,n,|)

function sparse_IJ_sorted!(I::AbstractVector{Ti}, J::AbstractVector{Ti},
V::AbstractVector,
m::Integer, n::Integer, combine::Function) where Ti<:Integer
require_one_based_indexing(I, J, V)
m = m < 0 ? 0 : m
n = n < 0 ? 0 : n
if isempty(V); return spzeros(eltype(V),Ti,m,n); end

cols = zeros(Ti, n+1)
cols[1] = 1 # For cumsum purposes
cols[J[1] + 1] = 1

lastdup = 1
ndups = 0
I_lastdup = I[1]
J_lastdup = J[1]
L = length(I)

@inbounds for k=2:L
if I[k] == I_lastdup && J[k] == J_lastdup
V[lastdup] = combine(V[lastdup], V[k])
ndups += 1
else
cols[J[k] + 1] += 1
lastdup = k-ndups
I_lastdup = I[k]
J_lastdup = J[k]
if ndups != 0
I[lastdup] = I_lastdup
V[lastdup] = V[k]
end
end
end

colptr = cumsum!(similar(cols), cols)

# Allow up to 20% slack
if ndups > 0.2*L
numnz = L-ndups
deleteat!(I, (numnz+1):L)
deleteat!(V, (numnz+1):length(V))
end

return SparseMatrixCSC(m, n, colptr, I, V)
end

"""
sparse(I, J, V,[ m, n, combine])

Expand Down Expand Up @@ -1383,23 +1340,20 @@ 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)
function sparse_sortedlinearindices!(I::Vector{Ti}, V::Vector, m::Int, n::Int) where Ti
length(I) == length(V) || throw(ArgumentError("I and V should have the same length"))
nnz = length(V)
colptr = Vector{Ti}(undef, n + 1)
j, colm = 1, 0
@inbounds for col = 1:n+1
colptr[col] = j
while j <= nnz && (rowval[j] -= colm) <= m
while j <= nnz && (I[j] -= colm) <= m
j += 1
end
j <= nnz && (rowval[j] += colm)
j <= nnz && (I[j] += colm)
colm += m
end
return SparseMatrixCSC(m, n, colptr, rowval, rfn(nnz))
return SparseMatrixCSC(m, n, colptr, I, V)
end

"""
Expand All @@ -1423,23 +1377,15 @@ 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))
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)
(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)
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)
end
sprand(m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T} = eltype(rfn(1))) where T = sprand(GLOBAL_RNG,m,n,density,(r, i) -> rfn(i))

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

Expand Down