How to run the following code for n number of rows and m number of columns.

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Prabha Kumaresan 2018-1-7

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/375939-how-to-run-the-following-code-for-n-number-of-rows-and-m-number-of-columns

评论： Prabha Kumaresan 2018-1-19

A=[ 1 2 3 4 5;
    11 12 13 14 15;
    6 7 8 9 10;
    21 22 23 24 25;
    26 27 28 29 30];
B=[0 0 41 0 0;
    45 0 0 0 0;
    0 43 0 0 0;
    0 0 0 42 0;
    0 0 0 0 44];
  C_after_step_1=cluster_rows(A,B,[1 4])
%  C_after_step_1=cluster_rows(B_part)
 C=cluster_rows(A,C_after_step_1,[2 3 5])
% C=cluster_rows(A,C_after_step_1)
calling function
function C=cluster_rows(A,B,rows)
%extract the parts of the matrices
A_part=A(rows,:);
B_part=B(rows,:);
%sum along the the vertical axis to get indices for the non-0 columns
non_0=sum(B_part);
%Repeat the vector back to the same size as the partial matrix. It will be
%converted to a logical later on.
non_0=repmat(non_0,length(rows),1);
%create a copy of B, as that is the basis for C
C=B;C_part=B_part;
%for all non-zero columns, replace the 0 positions with the values from A
C_part(non_0 & C_part==0)=A_part(non_0 & C_part==0);
%paste the edited partial matrix back
C(rows,:)=C_part;
end

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jaah navi 2018-1-10

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I would like to check a small clarification with respect to the coding

A=[ 1 2 3 4 5;
    11 12 13 14 15;
    6 7 8 9 10;
    21 22 23 24 25;
    26 27 28 29 30];
B=[0 0 41 0 0;
    45 0 0 0 0;
    0 43 0 0 0;
    0 0 0 42 0;
    0 0 0 0 44];
  C_after_step_1=cluster_rows(A,B,[1 4])
%  C_after_step_1=cluster_rows(B_part)
 C=cluster_rows(A,C_after_step_1,[2 3 5])
% C=cluster_rows(A,C_after_step_1)
calling function
function C=cluster_rows(A,B,rows)
%extract the parts of the matrices
A_part=A(rows,:);
B_part=B(rows,:);
%sum along the the vertical axis to get indices for the non-0 columns
non_0=sum(B_part);
%Repeat the vector back to the same size as the partial matrix. It will be
%converted to a logical later on.
non_0=repmat(non_0,length(rows),1);
%create a copy of B, as that is the basis for C
C=B;C_part=B_part;
%for all non-zero columns, replace the 0 positions with the values from A
C_part(non_0 & C_part==0)=A_part(non_0 & C_part==0);
%paste the edited partial matrix back
C(rows,:)=C_part;
end

In this coding with respect to the command C(rows,:)=C_part,how can i view the C_part in the workspace.Could you please help me to view the C_part in the workspace.

jaah navi 2018-1-17

could you please tell me how to store all the C_part in to one cell array

Stephen23 2018-1-17

编辑：Stephen23 2018-1-17

@jaah navi: please do not ask the same question in multiple locations:

https://www.mathworks.com/matlabcentral/answers/377558-could-anyone-tell-me-how-to-view-all-three-separate-u-matrix-together-in-a-single-u-matrix

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采纳的回答

Rik 2018-1-7

2
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/375939-how-to-run-the-following-code-for-n-number-of-rows-and-m-number-of-columns#answer_299026

在 MATLAB Online 中打开

%generate random data for A and B (replace this with your real data)
m=50;n=2*m;
A=randi(m*n,m,n);
%B=randi(m*n,m,n).*(rand(m,n)>0.9);
B=[diag(1:n),diag(n+1:m)];
unused_rows=1:m;C=B;
while ~isempty(unused_rows)
    %This method of random selection will have a bias to start off with
    %large groups and end with small ones. If this is a problem, you will
    %have to devise a method yourself that results in a more stable
    %distribution. Taking a square root and rounding the result already
    %helps a bit to equalize group size.
    n_rows=ceil(sqrt(randi(numel(unused_rows))));
    rows=unused_rows(randsample(length(unused_rows),n_rows));
    [~,idx]=find(ismember(unused_rows,rows));
    unused_rows(idx)=[];
    %If you want to see the rows that were grouped, uncomment the next two
    %lines of code. (e.g. if you want to see the group size)
    %fprintf('%02d ',rows)
    %fprintf('\n')
    %apply the row clustering
    C=cluster_rows(A,C,rows);
end

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jaah navi 2018-1-10

在 MATLAB Online 中打开

I would like to check with the following code

%generate random data for A and B (replace this with your real data)
m=50;n=2*m;
A=randi(m*n,m,n);
%B=randi(m*n,m,n).*(rand(m,n)>0.9);
B=[diag(1:m),diag(m+1:n)];
unused_rows=1:m;C=B;
while ~isempty(unused_rows)
    %This method of random selection will have a bias to start off with
    %large groups and end with small ones. If this is a problem, you will
    %have to devise a method yourself that results in a more stable
    %distribution. Taking a square root and rounding the result already
    %helps a bit to equalize group size.
    n_rows=ceil(sqrt(randi(numel(unused_rows))));
    rows=unused_rows(randsample(length(unused_rows),n_rows));
    [~,idx]=find(ismember(unused_rows,rows));
    unused_rows(idx)=[];
    %If you want to see the rows that were grouped, uncomment the next two
    %lines of code. (e.g. if you want to see the group size)
    %fprintf('%02d ',rows)
    %fprintf('\n')
%apply the row clustering
   [D,D_part]=cluster_rows(A,C,rows);
end
calling function
function [D,D_part]=cluster_rows(A,C,rows)
%extract the parts of the matrices
A_part=A(rows,:)
C_part=C(rows,:)
%sum along the the vertical axis to get indices for the non-0 columns
non_0=sum(C_part)
%Repeat the vector back to the same size as the partial matrix. It will be
%converted to a logical later on.
non_0=repmat(non_0,length(rows),1)
%create a copy of B, as that is the basis for C
 D=C
 D_part=C_part
%for all non-zero columns, replace the 0 positions with the values from A
   D_part(non_0 & D_part==0)=A_part(non_0 & D_part==0)
  %paste the edited partial matrix back
  D(rows,:)=D_part
  end

while i run the code it executes and it displays the last result of D_part in the workspace.How to view the D_part in the workspace for each time when the function is being called.Could you please help me on this.

Rik 2018-1-10

在 MATLAB Online 中打开

You are changing variable names left and right, without checking if what you are doing is correct. You changed C to D only in some places, which now means the code you pasted doesn't work anymore (although it does run without errors). Below I have fixed the code again by reverting most changes. You can see the C_part matrix as a cell array containing each step. It is overwritten in your code, which is why you can only see the last result.

m=50;n=2*m;
A=randi(m*n,m,n);
%B=randi(m*n,m,n).*(rand(m,n)>0.9);
B=[diag(1:m),diag(m+1:n)];
unused_rows=1:m;C=B;
C_part=cell(1);
while ~isempty(unused_rows)
    %This method of random selection will have a bias to start off with
    %large groups and end with small ones. If this is a problem, you will
    %have to devise a method yourself that results in a more stable
    %distribution. Taking a square root and rounding the result already
    %helps a bit to equalize group size.
    n_rows=ceil(sqrt(randi(numel(unused_rows))));
    rows=unused_rows(randsample(length(unused_rows),n_rows));
    [~,idx]=find(ismember(unused_rows,rows));
    unused_rows(idx)=[];
    %If you want to see the rows that were grouped, uncomment the next two
    %lines of code. (e.g. if you want to see the group size)
    %fprintf('%02d ',rows)
    %fprintf('\n')
    %apply the row clustering
    [C,C_part{k}]=cluster_rows(A,C,rows);
end
calling function
function [C,C_part]=cluster_rows(A,B,rows)
%extract the parts of the matrices
A_part=A(rows,:);
B_part=B(rows,:);
%sum along the the vertical axis to get indices for the non-0 columns
non_0=sum(B_part);
%Repeat the vector back to the same size as the partial matrix. It will
%be converted to a logical later on.
non_0=repmat(non_0,length(rows),1);
%create a copy of B, as that is the basis for C
C=B;
C_part=B_part;
%for all non-zero columns, replace the 0 positions with the values from A
C_part(non_0 & C_part==0)=A_part(non_0 & C_part==0);
%paste the edited partial matrix back
C(rows,:)=C_part;
end

Rik 2018-1-11

Sure. Start off by thinking in a structure way what should happen when. In other words, write psuedocode. Then convert that pseudocode into real Matlab. The first step you should be able to do on your own, the second you can ask follow up questions for on this forum.

tip: Write down explicitly what rules n_rows must obey.

Prabha Kumaresan 2018-1-19

在 MATLAB Online 中打开

I would like to check if

m=[10 20 30 40 50] and 
n=[60 70 80 90 100]
how it can be done with respect to the below section
A=randi(m*n,m,n);
%B=randi(m*n,m,n).*(rand(m,n)>0.9);
B=[diag(1:n),diag(n+1:m)];
unused_rows=1:m;C=B;
while ~isempty(unused_rows)
  %This method of random selection will have a bias to start off with
  %large groups and end with small ones. If this is a problem, you will
  %have to devise a method yourself that results in a more stable
  %distribution. Taking a square root and rounding the result already
  %helps a bit to equalize group size.
  n_rows=ceil(sqrt(randi(numel(unused_rows))));
  rows=unused_rows(randsample(length(unused_rows),n_rows));
  [~,idx]=find(ismember(unused_rows,rows));
  unused_rows(idx)=[];
  %If you want to see the rows that were grouped, uncomment the next two
  %lines of code. (e.g. if you want to see the group size)
  %fprintf('%02d ',rows)
  %fprintf('\n')
  %apply the row clustering
  C=cluster_rows(A,C,rows);
end