Optimizing Fourier Series Fitting Function Matlab

Question

I am trying to iterate through a set of samples that seems to show periodic changes. I need continuously apply the fit function to get the fourier series coefficients, the regression has to be n samples in the past (in my case, around 30). The problem is, my code is extremely slow! It will take like 1 hour to do this for a set of 50,000 samples. Is there any way to optimize this? What am I doing wrong?

Here's my code:

function[coefnames,coef] = fourier_regression(vect_waves,n)        

    j = 1;
    coef = zeros(length(vect_waves)-n,10);

    for i=n+1:length(vect_waves)

        take_fourier = vect_waves(i-n+1:i);
        x = 1:n;
        f = fit(x,take_fourier,'fourier4');
        current_coef = coeffvalues(f);
        coef(j,1:length(current_coef)) = current_coef;
        j = j + 1;



    end
    coefnames = coeffnames(f);

end

When I call [coefnames,coef] = fourier_regression(VECTOR,30); This takes forever to compute. Is there any way to fix it? What's wrong with my code?

Note: I have a intel i7 5500 U cpu, 16GB RAM, and using Matlab 2015a.

Answer 1

As I am not familiar with your application, I am not sure whether it is possible to vectorize the code to improve performance. However, I have a couple of other tips.

One thing you should consider is preallocation of arrays . In this case, you should preallocate at least the array coef since I believe you do know its size before starting the loop.

Another thing I suggest is to profile your code . This will provide information on what parts of your code are consuming the most time, helping you focus your effort on improving those parts' performance.