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How to linearize the nested parfor loop?

3 ビュー (過去 30 日間)
I have two variables i,j, where j index start depends on i index.
n = 3;
for i = 1:n
for j = i+1:n
% Feature matching
matches = getMatches(input, allDescriptors{i}, allDescriptors{j});
nf = size(matches, 2);
numMatches(i,j) = nf;
end
end
I am trying to linearize it using the below code:
n = 3;
M = n;
N = n;
parfor i = 1:M*N
% IND2SUB converts from a "linear" index into individual
% subscripts
[ii,jj] = ind2sub([M,N], i);
if (ii~=jj)
matches = getMatches(input, allDescriptors{ii}, allDescriptors{jj});
nf = size(matches, 2);
numMatches(i) = nf;
end
end
But have some entries on the lower part of the square matrix.
Any help is appreciated!

採用された回答

Jeff Miller
Jeff Miller 2021 年 7 月 18 日
One approach is to set out all the desired pairs in advance. A crude way to do that is
pairs = zeros(0,2);
n = 3;
for i = 1:n % or reallyl just to n-1?
for j = i+1:n
pairs(end+1,:) = [i, j];
end
end
Then your parfor loop can just go through the preset pairs:
npairs = size(pairs,1);
parfor i = 1:npairs
ii = pairs(i,1);
jj = pairs(i,2);
matches = getMatches(input, allDescriptors{ii}, allDescriptors{jj});
nf = size(matches, 2);
numMatches(i) = nf;
end
  5 件のコメント
Preetham Manjunatha
Preetham Manjunatha 2021 年 7 月 18 日
Thanks, it works! But there are way too many for loops, which I was trying to avoid.

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