speed up the calculation in multidimensional array
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Array:RP_bin
Matrix: matrix
[~,c]=size(matrix);
xx=find(RP_bin);
q=length(xx);
[~,c]=size(matrix);
cor=zeros(c,c,q);
for i=1:q
for gg=1:c
for yy=1:c
a=matrix(1:xx(i),gg);
b=matrix(1:xx(i),yy);
cc=sum((a>0 & b>0)|(a<0 & b<0));
cc1=sum(a~=0 & b~=0);
cor(yy,gg,i)=round(100 * cc/cc1) / 100; %cc/cc1; %%array multidimension
end
end
end
>> size(cor)
ans =
82 82 188
to execution this code is :
Elapsed time is 21.425685 seconds.
is possibile to speed loop?
2 Comments
Answers (1)
Dyuman Joshi
on 15 Jul 2023
You can reduce a for loop by vectorizing the code -
load('RP_BIN.mat')
load('matlab.mat')
[~,c]=size(matrix);
xx=find(RP_bin);
q=numel(xx);
%Preallocation
[cor,COR]=deal(zeros(c,c,q));
%%Original approach
tic
for i=1:q
for gg=1:c
for yy=1:c
a=matrix(1:xx(i),gg);
b=matrix(1:xx(i),yy);
cc=sum((a>0 & b>0)|(a<0 & b<0));
cc1=sum(a~=0 & b~=0);
cor(yy,gg,i)=round(100 * cc./cc1)/100; %cc/cc1; %%array multidimension
end
end
end
toc
%%Modified approach
tic
gg=1:c;
for i=1:q
%As the array a is only dependent on the outer loop, bring it out of
%the inner loop, so that it isn't re-calculated in every iteration of inner loop
%thereby increasing the speed of the code
a=matrix(1:xx(i),gg);
for yy=1:c
b=matrix(1:xx(i),yy);
cc=sum((a>0 & b>0)|(a<0 & b<0),1);
cc1=sum(a~=0 & b~=0,1);
COR(yy,gg,i)=round(100 * cc./cc1)/100; %cc/cc1; %%array multidimension
end
end
toc
%%Comparing the output obtained
%As the calculations have NaN data, use isequaln()
isequaln(cor,COR)
As you can see that there is significant improvement in speed (more than 50%)
3 Comments
Dyuman Joshi
on 15 Jul 2023
Edited: Dyuman Joshi
on 15 Jul 2023
Then why not use that only?
You asked if the loop can be sped up, I responded accordingly. You did not say that you were comparing it to an inbuilt MATLAB function.
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