In the following sparse matrix:
A=[1 1 1 3];
C = sparse(A',1:length(A),ones(length(A),1),4,4);
C =
(1,1) 1
(1,2) 1
(1,3) 1
(3,4) 1
>>full(C)
ans =
1 1 1 0
0 0 0 0
0 0 0 1
0 0 0 0
How could I compute the mean value of non-zero elements in each row? I couldn't use the built-in mean function of matlab on these sparse matrices. I found this similar question and I can apply it to my problem
[row, ~, v] = find(C);
K>> rowmean = accumarray(row, v, [], @mean);
K>> rowmean
rowmean =
1
0
1
However, I would like to get zero value for the last row instead of this row being removed from the answer.
You can specify the output size as the third input ofaccumarray
:
[row, ~, v] = find(C);
rowmean = accumarray(row, v, [size(C,1), 1], @mean);
If desired, you can use the sixth input of accumarray
to obtain sparse
output:
rowmean = accumarray(row, v, [size(C,1), 1], @mean, 0, true);
You can do something simpler like this. The result is sparse
:
rowmean = sum(C, 2) ./ sum(C~=0, 2); % mean of nonzeros in each row, manually
rowmean(isnan(rowmean)) = 0; % replace NaN (resulting from 0/0) by 0
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.