Your issue is line 228 (as of MATLAB R2017b) of the in-built
mle function, which happens just before the custom function is called:
data = data(:);
The input variable
data is converted to a column array without warning. This is typically done to ensure that all further calculations are robust to the orientation of the input vector.
However, this is causing you issues, because your custom function assumes
data is a row vector, specifically this line:
out = sum(log(1+exp(lambda))-data.*lambda);
Due to implicit expansion, when the row vector
lambda and the column vector
data interact, you get a huge square matrix per your error message.
Adding these two lines to make it explicit that both are column vectors resolves the issue, avoids implicit expansion, and applies the calculation element-wise as you intended.
lambda = lambda(:); data = data(:);
So your function becomes
function out = loglike(data, params, T_1, T_2, npast) N = length(data); if npast == 0 past = 0; else past = zeros(1,N); past(npast+1:end) = movmean(data(npast:end-1),[npast-1, 0]); % Average number of events in the previous n years end lambda = params(1) + ... (params(2)*cos(2*pi*(1:N)/T_1)) + ... (params(3)*sin(2*pi*(1:N)/T_1)) + ... (params(4)*cos(2*pi*(1:N)/T_2)) + ... (params(5)*sin(2*pi*(1:N)/T_2)) + ... params(6)*past; lambda = lambda(:); data = data(:); out = sum(log(1+exp(lambda))-data.*lambda); end
An alternative would be to re-write your function so that it uses column vectors, but you create new row vectors with the
(1:N) steps and the concatenation within the
movmean. The suggested approach is arguably “lazier”, but also robust to row or column inputs.
Note also I’ve changed your variable name from
size is an in-built function which you should avoid shadowing.
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