How to remove for loops in this code?

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Prativa sahoo
Prativa sahoo 2016-10-14
评论： Prativa sahoo 2016-10-17
Hi, I am new to matlab. i am running the following code .It is taking a very long time to process. Can anyone suggest how to rewrite it to reduce the computation time. arr1 = 4d array with size (512 512 122 4)
parfor n=1:slice
n
for i=1:width
for j=1:height
options = optimset('Algorithm', 'levenberg-marquardt', 'display','off');
warning('off');
St = squeeze(arr1(i,j,n,:));
if St(1)>50
St = St./St(1);
St = St(:)';
bat = 1;
c_bat = St(1);
%pl_parm=[TTP,slop1,slop2];
pl_parm=[2;0;.1];
xdata = [1:length(St)];
lb = [2;0;-1];
ub = [4;10;1];
[par_PL,resnorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(@PLmodel,pl_parm,xdata,St,lb,ub,options);
TTP(i,j,n)=par_PL(1);
Slope1(i,j,n)=par_PL(2);
Slope2(i,j,n)=par_PL(3);
end
end
end
end

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回答（2 个）

Marc Jakobi
Marc Jakobi 2016-10-14
If lsqcurvefit() can't be run on matrices, you may not be able to reduce the loop nesting. But if it is a lot of data, you can decrease time by a bit by taking variables that don't change out of the loop. Setting them every time is not efficient. Also, try to avoid displaying things (like n) in the command window. Command window outputs significantly increase the time it takes to perform computations.
options = optimset('Algorithm', 'levenberg-marquardt', 'display','off');
warning('off');
bat = 1;
%pl_parm=[TTP,slop1,slop2];
pl_parm=[2;0;.1];
lb = [2;0;-1];
ub = [4;10;1];
parfor n=1:slice
for i=1:width
for j=1:height
St = squeeze(arr1(i,j,n,:));
if St(1)>50
St = St./St(1);
St = St(:)';
c_bat = St(1);
xdata = [1:length(St)];
[par_PL,resnorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(@PLmodel,pl_parm,xdata,St,lb,ub,options);
TTP(i,j,n)=par_PL(1);
Slope1(i,j,n)=par_PL(2);
Slope2(i,j,n)=par_PL(3);
end
end
end
end
Walter Roberson
Walter Roberson 2016-10-14
lsqcurvefit is not itself vectorizable.
You have bounds constraints, so lsqcurvefit is going to turn this into a lsqnonlin call with trust-region-reflective selected.
You could pull out all the hyperpages where arr1(:,:,:,1) > 50, and arrange those as a series of rows or columns in a 2D array. The question then becomes whether there is a way that you could ask to perform the fitting over the entire 2D array at once. I would have to think more about whether there would be a practical way to transform a least-squared model to have one more dimension; it is not obvious to me that it is possible in general. However, if we had the code for your PLmodel and we could see what it is you are fitting, we just might be able to think of a different approach.
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Walter Roberson
Walter Roberson 2016-10-15
Vectorize that function to improve performance.
function [fx] = PLmodel(parm,X)
C=1;
BAT=1;
TTP = parm(1);
slop1 = parm(2);
slop2 = parm(3);
fx = zeros(size(X));
mask = X < BAT;
fx(mask) = C;
mask = BAT <= X & X < TTP;
fx(mask) = C + slop1 * (X(mask) - BAT);
mask = X >= TTP;
fx(mask) = C + slop1 * (TTP-BAT) + slop2 * (X(mask) - TTP);

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