How to normalize all the matrices in a loop so that each row sums up to 1
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    parag gupta
 am 20 Mär. 2019
  
    
    
    
    
    Bearbeitet: Moritz Hesse
 am 20 Mär. 2019
            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
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Akzeptierte Antwort
  harsha001
      
 am 20 Mär. 2019
        
      Bearbeitet: harsha001
      
 am 20 Mär. 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
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) 
2 Kommentare
Weitere Antworten (2)
  Steven Lord
    
      
 am 20 Mär. 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.
0 Kommentare
  Moritz Hesse
 am 20 Mär. 2019
        
      Bearbeitet: Moritz Hesse
 am 20 Mär. 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
2 Kommentare
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