How to normalize all the matrices in a loop so that each row sums up to 1

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N = 4
n = 2
A = cell(1,N);
for i = 1:N
A{i} = rand(n,n)
end
celldisp(A)
From above command I will get 4 matrices.How to normalize all the matrices( ie all 4 matrices) so that each row sums up to 1.
Thanks

Accepted Answer

harsha001
harsha001 on 20 Mar 2019
Edited: harsha001 on 20 Mar 2019
There are two parts to your question - (a) how to normalise each row of a matrix at once, and (b) how to do it independently for each matrix in a cell array.
(a)
use the dot notation to divide each row by the sum of that row
So for a matrix M,
M = M./sum(M,2); % sum acros the 2nd dimension (column) and do a row-wise division
will normalise each row to sum to 1.
If instead you want to normalise each column, simply:
M = M./sum(M,1);
(b) You can either use a for loop to do the same for each matrix A{jj} of the cell array
for jj=1:N
A{jj} = A{jj}./sum(A{jj},2);
end
OR use a neat arrayfun to do the same:
A = arrayfun( @(jj) A{jj}./sum(A{jj},2), 1:N , 'UniformOutput', false );
where i use the array fun to loop over 1 to N, setting uniform output to false so my result is also a cell-array. Imagine it like:
output = arrayfun( @jj, func(something), loop over 1 to N, 'UniformOutput', false)

More Answers (2)

Steven Lord
Steven Lord on 20 Mar 2019
The sum of the absolute values of the elements of a vector is the 1-norm. You can use the normalize function introduced in release R2018a to normalize each row of a matrix by the 1-norm.
A = rand(6);
B = normalize(A, 2, 'norm', 1);
shouldBeCloseTo1 = sum(B, 2)
You can use a for loop or arrayfun to apply normalize to each matrix in the cell array.

Moritz Hesse
Moritz Hesse on 20 Mar 2019
Edited: Moritz Hesse on 20 Mar 2019
If you have the deep learning toolbox installed, you can use normr to normalise matrix rows. You can access cell contents with curly brace notation
N = 4
n = 2
A = cell(1,N);
for i = 1:N
A{i} = rand(n,n)
end
celldisp(A)
% Loop through cells and normalise matrix rows
for i = 1:N
A{i} = normr(A{i})
end

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