Cellfun for mean squared error

I have a 450x2 cell data structure called data, with each cell containing 10000 x 1 Doubles.

I want to calculate the mean squared error (via the immse function) between for each row in the cell, i.e. immse(data{i,1},data{i,2}) with i indicating one of the 450 rows. Then I want to add the calculated mean squared error to a variable called data_sum (My overall goal is to calculate the average mean squared error).

So far, I am solving this by looping over data:

data_sum = 0;
for p = 1:size(data,1)
    data_sum = data_sum + immse(data{p,1},data{p,2});
end 
data_avg = data_sum / size(data,1);

I want to optimize my code and use cellfun instead. So far I have tried the following:

C = cellfun(@immse, data{:,1}, data{:,2}, 'UniformOutput', false);

However, this gives me the error:

Error using cellfun
Input #2 expected to be a cell array, was double instead.

I read online that I should convert double to cell to solve this problem. Since I am using cellfun because, well, I am applying it to a cell, it makes me assume that I am indexing data in a wrong way, because otherwise I wouldn't have to convert it to a cell first. Or should I be using a different function than cellfun?

[Matt J]

cellfun will not make the code run faster. It will just reduce the number of lines of code.

[Phoebe Daphne]

I want to reduce my lines of code.

Answer 1

[Matt J]

Seems like you could do the whole thing in one line with,

data_avg = immse( cell2mat(data(:,1)) ,  cell2mat(data(:,2))    );

[Phoebe Daphne]

Thank you!