Row-normalizing large sparse matrix
6 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
I have a large (n x n), sparse matrix with N entries per row, named W. I would like to normalize this matrix so that each row sums to 1, but I'm running into some numerical issues.
I store the nonzero values in the following matrix:
vals_W(i,j) = % jth nonzero entry in ith row of W.
I then calculate the matrix W, which I want to row-normalize, and normalize it as follows:
% idx and jdx reflect the row and column indices respectively.
W = sparse(idx, jdx, reshape(vals_W,1,NN*n)); % Matrix we wish to row-normlaize
sum_vals = sum(W,2); % sum of rows in W
W_normalized = sparse(idx, jdx, reshape(vals_W./sum_vals,1,NN*n)); % W, but row-normalized.
However, the following two yield very different values:
sum1 = sum(vals_W./sum_vals,2);
sum2 = sum(W_normalized,2));
They seem like they should be theoretically equivalent. Is there something about the sparsity that causes this issue? Or am I just coding this incorrectly? What's the best way to get around this problem?
Thank you.
Réponse acceptée
Matt J
le 20 Fév 2021
Modifié(e) : Matt J
le 20 Fév 2021
The attempt you've posted will only work if vals_W is the same size as sum_vals, which can only occur when there is exactly one non-zero element per row in W. What you really want is,
s=sum(W,2);
s(~s)=1; %avoid division by 0
W_normalized=W./s;
0 commentaires
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Introduction to Installation and Licensing dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!