Let's say I have some data TxN. The first column (N_1) of the data changes every time, while the rest stays fixed. Therefore, I want to create a multidimensional array TxNxM, where M takes care of the different N_1s.
Let me take an example to make it clearer:
% original data where x changes every time while z is always the same
x = randn(100,10);
z = ones(100, 5);
% this is just one of the 10 datasets
y = [x(:,1),z];
% store results
Y = nan(size(y,1), size(y,2),size(x,2));
% Create 10 datasets where the first column changes every time (x(:,i))
% while the rest stays fixed
for i = size(x,2)
Y(:,:,i) = [x(:,i), z];
end
The issue with this is that I only get data for the last column of x while all the rest is NaN.
What am I doing wrong?
Can anyone help me?

 Akzeptierte Antwort

Matt J
Matt J am 4 Jul. 2021
Bearbeitet: Matt J am 4 Jul. 2021

0 Stimmen

You could fix it by changing the opening of your for-loop to,
for i = 1:size(x,2)
but below is a more efficient loop-free method.
Y = nan(size(y,1), size(y,2),size(x,2));
Y(:,1,:)=x;
Y(:,2:end,:)=repmat(z,1,1,size(x,2));

3 Kommentare

Armando MAROZZI
Armando MAROZZI am 4 Jul. 2021
thanks a lot!
Matt J
Matt J am 4 Jul. 2021
I would like to note, though, that it seems a bit wasteful to put so much duplicate data in Y. I don't see why working with x and z as separate arrays wouldn't be preferred.
Armando MAROZZI
Armando MAROZZI am 4 Jul. 2021
I see your points. However, I need to apply a function to different datasets (of the same type you saw above) and that's the quickest solution I came up with

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