How to make this code faster?
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I have coded a formulation using for loops but it takes forever when my matrices are very large. Can anyone help me make this code more efficient without for loops?
I'm coding the formulations in page two (in the lower half page) of this document: http://www-stat.wharton.upenn.edu/~tcai/paper/Testing-2-Covariance-Matrices.pdf
And here are my codes:
if true
% Input: X, Y
p=size(X,2);
n1=size(X,1); n2=size(Y,1);
sigma1=cov(X); sigma2=cov(Y);
for i=1:p
for j=1:p
for k=1:n1
A(k,:)=((X(k,i)-mean(X(:,i))).*(X(k,j)-mean(X(:,j)))-sigma1(i,j)).^2;
end
Teta_bar1(i,j)=1./n1 .* sum(A);
for k=1:n2
B(k,:)=((Y(k,i)-mean(Y(:,i))).*(Y(k,j)-mean(Y(:,j)))-sigma2(i,j)).^2;
end
Teta_bar2(i,j)=1./n2 .* sum(B);
M(i,j)=((sigma1(i,j)-sigma2(i,j)).^2)./(Teta_bar1(i,j)/n1+Teta_bar2(i,j)/n2);
end
end
end
采纳的回答
Geoff Hayes
2014-10-15
MJ - what does it takes forever when my matrices are very large mean? Seconds, minutes, hours? Please provide an example of a matrix (perhaps just the dimensions) along with the time it takes to evaluate the above.
Before looking at vectorizing your code, you may want to profile your code to see where the bottlenecks are.
A couple things that you may want to do (for your optimization) is to allocate or pre-size matrices before you start to use them. For example, M appears to be a pxp matrix, so why not size it as such before you start to use it
p=size(X,2);
n1=size(X,1);
n2=size(Y,1);
sigma1=cov(X);
sigma2=cov(Y);
% pre-size matrices
M = zeros(p,p);
Teta_bar1 = zeros(p,p);
Teta_bar2 = zeros(p,p);
A = ??
B = ??
Look also at your inner loops that iterate over k
for k=1:n1
A(k,:)=((X(k,i)-mean(X(:,i))).*(X(k,j)-mean(X(:,j)))-sigma1(i,j)).^2;
end
Note that you calculate
mean(X(:,i))
on each iteration of k even though this average does not depend on k at all. It doesn't even depend upon j, so the code is calculating the same average p*n1 times when it only needs to do this once. Similarly, the
mean(X(:,j)
is calculated n1 times when it is only needed once. Try changing this to
for i=1:p
meanXi = mean(X(:,i)); % <--- calculate the mean
for j=1:p
meanXj = mean(X(:,j)); % <--- calculate the mean
for k=1:n1
A(k,:)=((X(k,i)-meanXi).*(X(k,j)-meanXj)-sigma1(i,j)).^2;
end
You can apply the same logic to the second inner loop that iterates over k to avoid the duplicate mean calculations.
4 个评论
Pierre Benoit
2014-10-15
编辑：Pierre Benoit
2014-10-15
Even better than to calculate the mean in the loop, just do it before the loop :
meanX = mean(X);
then access it with i and j
for k=1:n1
A(k,:)=((X(k,i)-meanX(i)).*(X(k,j)-meanX(j))-sigma1(i,j)).^2;
end
MJ
2014-10-15
MJ
2014-10-15
Geoff Hayes
2014-10-15
Glad it worked out!
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