Adrian Lara-Quintanilla

Thanks, I appreciate your feedback. I have solved that just yielding the closest value of those in the vector.

Anyway, this is a very simple function. I am sure it can be optimized a lot, but it works very well for my purposes and I decided to share it with the people. I do not know how the "experts" in Matlab deals with this, but I need to use this function in almost everything I do... :)

Again, thanks for helping me to improve :)

Thanks, I appreciate your feedback. I have solved that just yielding the closest value of those in the vector.

Anyway, this is a very simple function. I am sure it can be optimized a lot, but it works very well for my purposes and I decided to share it with the people. I do not know how the "experts" in Matlab deals with this, but I need to use this function in almost everything I do... :)

Again, thanks for helping me to improve :)

If you want to know what's fast, use this:

http://www.mathworks.de/matlabcentral/fileexchange/12276-movingaverage-v3-1-mar-2008

A moving average does not require to use a for loop at all, and the runtime can be almost independent from window size.

Looping over the elements of the vector is very slow for large vectors. Then looping over the elements of the window to be averaged is much faster:
function Y = movemean(X, N)
% X is filtered along 1st dimension
% Window size is 2*N+1
[d1, d2] = size(X);
M = X;
divV = ones(d1, 1);
for i = 1:N
i2 = i + i; % Slightly faster
Z = zeros(i, d2);
M = M + [Z; X(1:d1 - i2, :) + X(1 + i2:d1, :); Z];
divV(i + 1:d1 - i, :) = divV(i + 1:d1 - i, :) + 2;
end
Y = bsxfun(@rdivide, M, divV);