Correlation (cor) vs. Covariance (cov)
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Hi all,
does someone know why to compute a loop with the cor function takes way longer than doing it with the cov function?
I've the followin loop
covC = cell(szX(1),1);
CovM = zeros(szX(1),szX(2));
for n = ws1+1:szX(1)
covC{n} = cov(((logr(n-ws1:n,:))));
CovM(n,:)= (covC{n}(1,:));
end
the matrix is ca. (1580x32)... if u run the posted loop it takes just a few seconds. However If I want to compute the correlation (and substitute cov with cor) it takes about 5 minutes
corC = cell(szX(1),1);
CorM = zeros(szX(1),szX(2));
for n = ws1+1:szX(1)
corC{n} = corr(((logr(n-ws1:n,:))));
CorM(n,:)= (corC{n}(1,:));
end
. I want to do it also with a 3000x1200 matrix
can someone suggest me a quicker prodecure to achive my goal?
thaks for ur hepl
2 个评论
Newuser
2011-5-1
Oleg Komarov
2011-5-1
You can use the compiler to verify where are the bottlenecks in corr as compared to corrcoeff.
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