Is it possible to find mean and std dev. of every row in a huge matrix with double data and a lot of NaN interspersed without looping through every row ?
A = [1 2 NaN; 4 NaN 6 ; 8 9 10]
output = 1.5 5 9
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If you have the Statistics toolbox, you can use nanmean(A,2) and nanstd(A,2).
If you don't have the toolbox, you can still avoid looping by doing
B=A; map=~isnan(A); B(~map)=0; N=sum(map,2);
rowmeans = sum(B,2)./N; rowstds = sqrt( sum(B.^2,2)./N -rowmeans.^2 );
If A is of type sparse, however, the above may need to be modified for efficiency's sake.
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