using broadcasting Julia for converting vector of vectors to matrices

Question

I am a julia newbie, and have a baby assignment to write a function which converts a vector of vectors to a matrix. This was pretty easy to do by iterating over the elements.

However, I have read that broadcasting tends to be more efficient. But I wasn't sure how to do it here, because a .= operation cannot work, as it would read the vector as a 1 by n array, and thus be trying to broadcast on two arrays of different length.

Is there a way to broadcast?

My code is below

function vecvec_to_matrix(vecvec)
    dim1 = length(vecvec)
    dim2 = length(vecvec[1])
    my_array = zeros(Int64, dim1, dim2)
    for i in 1:dim1
        for j in 1:dim2
            my_array[i,j] = vecvec[i][j]
        end
    end
    return my_array
end

The biggest performance bottleneck in your code is memory layout. You store the vectors that are in vecvec vector as rows of my_array, and in Julia it is more efficient to process data by columns (so if you stored them in columns instead it would be faster with e.g. reduce(hcat, vecvec) as commented below). — Bogumił Kamiński
Broadcasting is not more efficient than iterating. Broadcasting does iteration behind the scenes anyway. — DNF

tholy tholy · Accepted Answer · 2020-09-15T09:40:20

If your vectors are short and of fixed size (e.g., a list of points in 3 dimensions), then you should strongly consider using the StaticArrays package and then calling reinterpret. Demo:

julia> using StaticArrays

julia> A = rand(3, 8)
3×8 Array{Float64,2}:
 0.153872  0.361708  0.39703   0.405625  0.0881371  0.390133  0.185328  0.585539
 0.467841  0.846298  0.884588  0.798848  0.14218    0.156283  0.232487  0.22629
 0.390566  0.897737  0.569882  0.491681  0.499163   0.377012  0.140902  0.513979

julia> reinterpret(SVector{3,Float64}, A)
1×8 reinterpret(SArray{Tuple{3},Float64,1,3}, ::Array{Float64,2}):
 [0.153872, 0.467841, 0.390566]  [0.361708, 0.846298, 0.897737]  [0.39703, 0.884588, 0.569882]  …  [0.390133, 0.156283, 0.377012]  [0.185328, 0.232487, 0.140902]  [0.585539, 0.22629, 0.513979]

julia> B = vec(copy(ans))
8-element Array{SArray{Tuple{3},Float64,1,3},1}:
 [0.1538721224514592, 0.467840786943454, 0.39056612358281706]
 [0.3617079493961777, 0.8462982350893753, 0.8977366743282564]
 [0.3970299970547111, 0.884587972864584, 0.5698823030478959]
 [0.40562472747685074, 0.7988484677138279, 0.49168126614394647]
 [0.08813706434793178, 0.14218012559727544, 0.499163319341982]
 [0.3901332827772166, 0.15628284837250006, 0.3770117394226711]
 [0.18532803309577517, 0.23248748941275688, 0.14090166962667428]
 [0.5855387782654986, 0.22628968661452897, 0.5139790762185006]

julia> reshape(reinterpret(Float64, B), (3, 8))
3×8 reshape(reinterpret(Float64, ::Array{SArray{Tuple{3},Float64,1,3},1}), 3, 8) with eltype Float64:
 0.153872  0.361708  0.39703   0.405625  0.0881371  0.390133  0.185328  0.585539
 0.467841  0.846298  0.884588  0.798848  0.14218    0.156283  0.232487  0.22629
 0.390566  0.897737  0.569882  0.491681  0.499163   0.377012  0.140902  0.513979

using broadcasting Julia for converting vector of vectors to matrices

2 Answers