CODEWITHSUNDEEP
matlabcorrelation
Frank Meulenaar
Frank Meulenaar

Reputation: 1209

Optimizing a kind-of-correlation computation

I have some code that currently reads:

data = repmat(1:10, 1, 2);
N = 6;
period = 10;

result = NaN * zeros(1, period);
for i=1:period
    range_indices = i:i+N;
    temp_data = data(range_indices);
    result(i) = sum( temp_data .* fliplr(temp_data));
end

I'm trying to make this faster (for larger datasets, e.g. period = 2000 and N = 1600), but I'm unable to get this into a form where it's a matrix operation (e.g. by using conv or xcorr).

Upvotes: 1

Views: 40

Answers (1)

MrAzzaman
MrAzzaman

Reputation: 4768

You should be able to completely linearise this. Firstly, consider the range_indices. These have the form:

1 -> N
2 -> N+1
...
P -> N+P

where P is the period. We can set up a matrix of these values like so:

range_indices = bsxfun(@plus,1:N,(1:period)'-1);

We can use these to grab the data directly, like so:

temp_data = data(range_indices);

It is then fairly simply to complete the function:

result = sum(temp_data.*fliplr(temp_data));

Finally, this isn't really related to the question, but just something I thought I'd point out - in future if you need to generate a matrix of NaN values, you should use nan(1,period) instead.

Upvotes: 1

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