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benchmarking anonymous functions with timeit

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seth patterson
seth patterson 2022-11-24
评论： Jan 2022-11-25
I was reading this Measure the Performance of Your Code and was wondering about a more complicated example using timeit with nested anonymous functions?
Suppose that you want to compare the speed of different ways to flatten a matrix into a vector.
% make an image
widthPixels = 640;
heightPixels = 480;
raw_data = @(width,height)(randn(width,height));
nrmi = @(x)(x/(max(x(:))));
image = @(counts)(single( uint8(255*counts)).^0.5);
% different ways to flatten
alg1 = @(x)(x(:));
alg2 = @(x)(reshape(x,1,[]));
alg3 = @(x)(reshape(x,[],1)'));
% basic way
nIterations = 100;
totalTime = 0;
for i = 1:nIterations
tic;
y = alg1(x);
totalTime = totalTime + toc;
end
avgTime = totalTime / nIterations;
But I really want to use timeit but not quite sure how it works. Here's the basic idea of what I'd want to do.
timeit(alg1(image(raw_data(heightPixels,widthPixels)))
Or just define multiple anonymous functions on one line. Maybe something like this? I'm not sure how you would chain all of them together...
image = @()(single( uint8(255*@(x)(x/(max(x(:)))).^0.5);
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Stephen23
Stephen23 2022-11-25
编辑：Stephen23 2022-11-25
"And you wanted the timeit function to call the pipeline of functions?"
As I wrote in my last comment, you need to call them, unlike your attempts to create function handles out of things that are already function handles:
thisisafunctionhandle = @(x) dowhateverwith(x); % this defines a function handle.
@thisisafunctionhandle % <- ERROR! What you are doing, trying to create a function handle out of a function handle
thisisafunctionhandle(invalue) % <- What you should be doing, calling the function!
You should revise how function handles are created and called:
https://www.mathworks.com/help/matlab/function-handles.html
https://www.mathworks.com/help/matlab/matlab_prog/creating-a-function-handle.html
w = 3;
h = 4;
timeit(@() alg1(image(nrmi(raw_data(w,h)))))
Jan
Jan 2022-11-25
Ran in:
@seth patterson: Just a note: sqrt is faster than .^0.5 :
x = rand(640, 480);
timeit(@() sqrt(x))
ans = 3.1235e-04
timeit(@() x.^0.5)
ans = 6.9270e-04

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