Hi all,
I have a large sparse matrix A ( its size is (2823,2823 ) its type is SparceMatrixCSC{ Integer, Int64} ) with 0/1 values only.
I want to get a sparse matrix B = A^2, and get the largest value in this matrix.
For reasons beyond my understanding it looks like matrix B suddenly gets a type “Any” making it impossible to cast B back to an Array and/or get it min/max values.
see also screenshot below with minimum example and error message
I get the same errors for
Array( pairwiseComp)
minimum( pairwiseComp[ :, : ] )
Can someone either show me what I do wrong or give me a work around?
Do you really want the abstract type Integer as the element type, or can you use Int instead?
That’s the type Julia gave to this marix A/compMatrix.
(I first initialized a 2-Dim array with only 0/1 values, then converted it to sparse).
I don’t really care about Integer or Int. I just want to be able to get matrix B = A^2 and get its max/min.
I’m just surprised to see B gets type Any, instead of Integer ( like A has ); and that throwing errors.
Can you share the code you used to make A?
julia> using SparseArrays
julia> a = sprand(Int, 7, 7, 0.5)
7×7 SparseMatrixCSC{Int64, Int64} with 25 stored entries:
⋅ ⋅ … ⋅
2818502083055319317 4767726624010214836 ⋅
⋅ ⋅ 6887863942578783783
809827778114231296 8685491858074137979 2448067753728538581
⋅ ⋅ ⋅
⋅ 8945844710485273438 … -1574207559067161462
8530774627037061445 6557253109629682135 4738318991760729793
julia> a.nzval[findall(x -> x != 0, a.nzval)] .= 1
25-element view(::Vector{Int64}, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 … 16, 17, 18, 19,
20, 21, 22, 23, 24, 25]) with eltype Int64:
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
julia> a
7×7 SparseMatrixCSC{Int64, Int64} with 25 stored entries:
⋅ ⋅ 1 1 1 1 ⋅
1 1 1 ⋅ ⋅ ⋅ ⋅
⋅ ⋅ ⋅ 1 1 1 1
1 1 1 1 ⋅ ⋅ 1
⋅ ⋅ 1 ⋅ ⋅ ⋅ ⋅
⋅ 1 ⋅ ⋅ 1 ⋅ 1
1 1 ⋅ 1 1 ⋅ 1
julia> a*a
7×7 SparseMatrixCSC{Int64, Int64} with 44 stored entries:
1 2 2 2 2 1 3
1 1 2 2 2 2 1
2 3 2 2 2 ⋅ 3
3 3 3 4 3 2 3
⋅ ⋅ ⋅ 1 1 1 1
2 2 2 1 1 ⋅ 1
3 3 4 3 2 1 2
julia>
It’s based on data, but the following code results in the exact same issues
using SparseArrays
A = zeros( Integer, 2400, 2400 )
for i in 1:2400
if rand(1:4) == 1
for j in i+1:2400
if rand(1:15) == 1
A[i,j] = 1
A[j,i] = 1
end
end
end
end
sum( A)
A = sparse( A )
B = A^2
println( "some stats A: size ", size(A), " min value ", minimum( A ), " max value ", maximum( A ) )
println( "some stats B: size ", size(B), " min value ", minimum( B ), " max value ", maximum( B ) )
I’m not sure how or what this answers?
Do you have a reason for using Integer (an abstract type) here? If you use a concrete type like Int (or BigInt if you need the ability to store arbitrarily large numbers), it will not widen the element type to Any when multiplying.
4 Likes
As I pointed out, creating the matrix a from Int (concrete type) leads to stable calculation.
Ah, my apologies; I wasn’t aware there was a significant difference between Int and Integer.
Indeed the usage of Int solves my problem. Thank you.
1 Like
Why is the element type being widened at all? Why doesn’t it stay Integer?
1 Like
I think some of the sparse array code unfortunately relies on inference to compute some things such as the output element type and if the type is abstract, that might widen.
2 Likes
