This is a classic example of how badly formatted code makes buggy code. When the code is formatted using MATLAB's default formatting rules (select all, ctrl+i), then the cause is much easier to spot:
P_pdf = [];
%Inices with NaN
idxnan = find(isnan(P_b));
for i = 1:size(idxnan, 1) - 1
%Indices of numeric values
idxlow = idxnan(i) + 1;
idxup = idxnan(i + 1) - 1;
%Group P_b Matrices according to NaN values
P_mat = P_b(idxlow:idxup);
%
%Reject empty matrices and treat singular values
if size(P_mat) == [1, 1];
avg = P_mat;
elseif size(P_mat) == [0, 0];
avg = NaN;
%Create distribution fit
pdf = fitdist(P_mat, 'Normal');
avg = pdf.mu;
end
P_pdf = [P_pdf; avg];
end
Now it is clear that there is an if and an elseif, but if neither of these conditions have been fulfilled then there is no else and so avg never gets defined. The error is due to testing the matrix size like this:
size(P_mat) == [0, 0]
which is not every going to be true when P_mat is created by indexing like this:
P_mat = P_b(idxlow:idxup);
Try it yourself at home:
>> V = 1:3;
>> size(V(2:1))
ans =
1 0
So that test ==[0, 0] will always fail. The logic is bad anyway: surely you want to test for non-empty vectors and apply the fit to them?
Here is a slightly more robust version of your loop:
P_b = [2;3;4;5;6;NaN;3;4;5;6;NaN;3;4;2;NaN;3;NaN];
idn = isnan(P_b);
idd = diff(idn);
idb = find([~idn(1);idd<0])
ide = find([idd>0;~idn(end)])
out = NaN(size(idb));
for k = 1:numel(idb)
tmp = P_b(idb(k):ide(k));
pdf = fitdist(tmp,'Normal'); % untested, I don't have fitdist
out(k) = pdf.mu; % untested
end
Personally I would not write all of that code: I would simply split the input vector using accumarray, and then use cellfun to do whatever processing:
P_b = [2;3;4;5;6;NaN;3;4;5;6;NaN;3;4;2;NaN;3;NaN];
idx = isnan(P_b);
idy = cumsum([1;diff(idx)>0]);
C = accumarray(idy(~idx),P_b(~idx),[],@(n){n});
D = cellfun(@(v)fitdist(v,'Normal'),C); % untested: I don't have fitdist
P_pdf = arrayfun(@(s)s.mu,D) % untested
It might be required to get cellfun to return a cell array:
D = cellfun(@(v)fitdist(v,'Normal'),C,'Uni',0); % untested
P_pdf = cellfun(@(s)s.mu,D) % untested
