What are efficient ways to extract lower dimensional slices from a high-dimensional array ?
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I am looking for an efficient way to extract sub-arrays from a larger array. There are (at least) two standard solutions both having certain disadvantages.
First solution: Use a loop Disadvantage: Might be slow if nidx (see code below) is large
Second solution: use sub2ind Disadvantage: Interim indices created are large if nidx * ndim3 is large
Any other (especially better) ideas?
Kind regards, gg
%create 3dim testarray
ndim1 = 3;
ndim2 = 4;
ndim3 = 5;
testarr = rand([ndim1,ndim2,ndim3]);
%pick some indices into the first two dimensions
nidx = 10;
idx1 = randi([1,ndim1],[nidx,1]);
idx2 = randi([1,ndim2],[nidx,1]);
%now extract slices from testarr according to the indices
%First solution: by loop
newarr = zeros(nidx, ndim3);
for kidx = 1:nidx
newarr(kidx,:) = testarr(idx1(kidx), idx2(kidx), :);
end
%Second solution using sub2ind
refidx1 = repmat(idx1, [ndim3,1]);
refidx2 = repmat(idx2, [ndim3,1]);
tmp = repmat((1:ndim3),[nidx, 1]);
refidx3 = tmp(:);
lidx = sub2ind(size(testarr), refidx1, refidx2, refidx3);
newarr_alt = reshape(testarr(lidx), nidx, ndim3);
%are they equal this time?
isequal(newarr, newarr_alt)
4 Comments
James Tursa
on 14 Dec 2011
Copying the data can easily dominate the run time over the calculations involved, particularly for something as simple as squaring elements or setting elements to a value. E.g., a mex routine can often significantly outperform m-code for these cases.
Accepted Answer
Andrei Bobrov
on 14 Dec 2011
s = size(testarr);
m = prod(s(1:2));
out = testarr(bsxfun(@plus,idx2*(s(1)-1)+idx1,0:m:(s(3)-1)*m))
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