Wolfgang Schwanghart

Thanks for this great contribution! Would it, however, be possible that ipdm (in case only data1 is provided) only returns the row and column indices and distances so that they are arranged in the order (distance(rowix,colix)): (2,1), (3,1), ..., (n,1), (3,2), ..., (n,2), ..., (n,n–1)). That is the way pdist (part of the statistics toolbox) returns the distance matrix in a vector to be more memory efficient. pdist, however, does not support distance subsets.

So far, I use this workaround in my code.

% remove distances were d.columnindex = d.rowindex
I = d.columnindex==d.rowindex;
d.columnindex(I) = [];
d.rowindex(I) = [];
d.distance(I) = [];

% remove double entry of pairs
[dtemp, m] = unique(sort([d.rowindex d.columnindex],2),'rows');
d.rowindex = dtemp(:,1);
d.columnindex = dtemp(:,2);
d.distance = d.distance(m);

This reduces the size of d by more than a half. But it doesn't avoid the generation of the huge struct returned by ipdm, which is a real memory bottleneck for me. Would it be possible to calculate this more efficient output within ipdm? So far I don't see a straightforward way, but perhaps you do?

Best regards,
Wolfgang

Now that's much better and much faster. Two minor things that can still be improved. First, if the adjacency matrix is supplied as full matrix, you don't really need to convert it to sparse. It should work without converting. Returning the incidence matrix as sparse however, is always a good idea since it likely contains many more zeros than the adjacency matrix.

Second, the function fails in case of nonzeros on the main diagonal. While this may rarely happen (except when your graph has 1-cycles), it may be worth an a-priori check using the function spdiags.

By the way, it might be nicer to write ~issparse(mAdj) instead of (issparse(mAdj)==0), but that's really not so important. And... providing a minimal example in the help block is always good.

Thanks for the update.

Matt, your screenshot gives credit to one of the most beloved contributors of the FEX :-) (Soccer_infinite_teams).
Nice tool though!

that was what I meant, thank you :)
one more question, how to pass the data that has been plot? i mean here:
plot(out.distance,out.val,marker)
I need to do some curve fitting on those values...

very nice program indeed. I had a question by the way. how the distance is calculated? direct distance or along X and Y streets?