Eliminate terrible for loop, code optimisation, data attached
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The problem:
I have 3 (very large) grids of data.... x y z, where x and y are variables that determine 'performance' z.
There are multiple sets of x y z..... so, x1 y1 z1, x2 y2 z2, x3 y3 z3....
y values are set by the user.... The x value combinations are calculated from other code. They are all possible combinations that can make a user-specified total (xtotal)
My goal is to find z values for known x and y... and repeat for different sets of x values. Added a picture to illustrate what I'm aiming to do - get the values from the red elements:
I have managed to create a for loop that achieves this - however it is SLOW. I am sure there is a more efficient way to do this using grids but have struggled.
Working code and data is attached, the time-consuming (10+ seconds!) loop in there is:
Outputs=zeros(size(xvals),length(xvals))
for i=1:size(indz)
x1loc=find(x1(1,:)==xvals(i,1))
Outputs(i,1)=z1(y1loc,x1loc)
x2loc=find(x2(1,:)==xvals(i,2))
Outputs(i,2)=z2(y2loc,x2loc)
x3loc=find(x3(1,:)==xvals(i,3))
Outputs(i,3)=z3(y3loc,x3loc)
end
Fairly sure there will be some way to optimise this using grids, or some other matlab trick I'm less familiar with... Stuck!
回答（2 个）
No need to loop at all (nor to use the FIND command). Just do
x1loc = ( x1(1,:)==xvals(:,1).' );
Outputs(:,1)=z1(y1loc,x1loc);
and similarly with x2loc and x3loc.
How about
F1=griddedInterpolant(x1,y1,z1,'nearest');
Output(:,1)=F1({xvals,y1loc});
It might also be worth conserving memory by making x1,y1 into grid vectors instead of a Full Grid, as explained in the documentation for griddedInterpolant .
2 个评论
Mat
2014-12-10
I had a quick try and this doesn't work either (Undefined function or method 'griddedInterpolant' for input arguments of type 'double'.)
You seem to have a pretty old version of MATLAB. You could use interp2 instead. It is supported by the Matlab Coder.
In the script, I modify the inputs so that they fit the nearest value that already exist on the existing grid, so not sure how interpolation would help here.
That's the same as nearest-neighbor interpolation.
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