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Hi, I would like to ask how I could sum over a n dimensional matrix except one dimension, so the output is a vector. The ndims are not known in forehand. The summation is giving always a vector. (In my case a marginal pdf in statistics). Something like sum(K(:) except i-th dimension)

The best in a cyclus ( ndims not known).

For example having matrix K, the sums would be [6 22], [12 16], [10 18]

K(:,:,1) =

0 1

2 3

K(:,:,2) =

4 5

6 7

Stephen Cobeldick
on 2 Nov 2017

Edited: Stephen Cobeldick
on 2 Nov 2017

Here is one very simple way:

>> d = 3; % dimension

>> v = 1:ndims(K);

>> v([1,d]) = v([d,1]);

>> s = sum(reshape(permute(K,v),size(K,d),[]),2)

s =

6

22

It works by swapping the first and the desired dimension, then reshaping so that all trailing dimensions are reduced to one. The sum is then trivially along each row.

Note that the method I show here also has the significant advantage that it does not create any variables with copies of the data from the input array: this will be important for scaling to larger input arrays. Also note that slow and complex arrayfun or cellfun are not required for this task!

Stephen Cobeldick
on 2 Nov 2017

@Rafael Schwarzenegger: the dimensions of the input array are automatically measured using ndims(K).

Cedric Wannaz
on 2 Nov 2017

Here is one way, but there is probably a simpler approach:

buffer = arrayfun(@(k) permute(KL, circshift(1:ndims(KL), k-1)), 1:ndims(KL), 'UniformOutput', false) ;

s = cellfun(@(M) sum(reshape(M, [], size(M,2))), buffer, 'UniformOutput', false) ;

where s is a cell array of sum vectors.

EDIT : I won't have time, but maybe you can see how dimensions work with SHIFTDIM and understand how to have the outputs ordered as needed:

buffer = arrayfun(@(k) shiftdim(KL, k-1), 1:ndims(KL), 'UniformOutput', false) ;

s = cellfun(@(M) sum(reshape(M, [], size(M,2))), buffer, 'UniformOutput', false) ;

PS : you'll have to test whether it really does what you need. The approach is based on the fact that MATLAB reads memory column first

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