sparse-matrix

For example, A = [ -1 0 -2 0 0 2 8 0 1 0 0 0 3 0 -2 0 -3 2 0 0 1 2 0 0 -4]; how can I get a vector of the first nonzero elements of each row? You can use max : >> [sel, c] = max( A ~=0, [], 2 ); Rows for which sel equalse zero - are all zeros and the corresponding column in c should be ignored. Result: >> [sel c]= max( A~=0, [], 2 ) sel = 1 1 1 1 1 c = 1 1 3 2 1 In order to find the first non-zero row index (for each column) you just need to apply max on the first dimension: >> [sel r] = max( A~=0, [], 1 ); Here is a solution based on accumarray that will work even if a row is all zeros. A = [

When I execute the following code I get a spares matrix: import numpy as np from scipy.sparse import csr_matrix row = np.array([0, 0, 1, 2, 2, 2]) col = np.array([0, 2, 2, 0, 1, 2]) data = np.array([1, 2, 3, 4, 5, 6]) sp = csr_matrix((data, (row, col)), shape=(3, 3)) print(sp) (0, 0) 1 (0, 2) 2 (1, 2) 3 (2, 0) 4 (2, 1) 5 (2, 2) 6 I want to add another column to this sparse matrix so the output is: (0, 0) 1 (0, 2) 2 (0, 3) 7 (1, 2) 3 (1, 3) 7 (2, 0) 4 (2, 1) 5 (2, 2) 6 (2, 3) 6 Basically I want to add another column that has the values 7,7,7. The sparse.hstack used in @Paul Panzer's link is the

Are there any storage optimized Sparse Matrix implementations in C#? Amirshk There is Math.NET . It has some Spare Matrix implementations . (link is to the old Math.NET site. There is no longer an online version of the documentation). If you are looking for high performance sparse matrix implementation check out NMath from CenterSpace software. Here's a partial list of functionality cut from here on CenterSpace's website. Full-featured structured sparse matrix classes, including triangular, symmetric, Hermitian, banded, tridiagonal, symmetric banded, and Hermitian banded. Functions for

This is an extension to an existing question: Convert table into matrix by column names I am using the final answer: https://stackoverflow.com/a/2133898/1287275 The original CSV file matrix has about 1.5M rows with three columns ... row index, column index, and a value. All numbers are long integers. The underlying matrix is a sparse matrix about 220K x 220K in size with an average of about 7 values per row. The original read.table works just fine. x <- read.table("/users/wallace/Hadoop_Local/reference/DiscoveryData6Mo.csv", header=TRUE); My problem comes when I do the reshape command. reshape

I have a question about Eigen library in C++. Actually, I want to calculate inverse matrix of sparse matrix. When I used Dense matrix in Eigen, I can use .inverse() operation to calculate inverse of dense matrix. But in Sparse matrix, I cannot find inverse operation anywhere. Does anyone who know to calculate inverse of sparse matrix? help me. You cannot do it directly, but you can always calculate it, using one of the sparse solvers. The idea is to solve A*X=I , where I is the identity matrix. If there is a solution, X will be your inverse matrix. The eigen documentation has a page about

I am working with data from neuroimaging and because of the large amount of data, I would like to use sparse matrices for my code (scipy.sparse.lil_matrix or csr_matrix). In particular, I will need to compute the pseudo-inverse of my matrix to solve a least-square problem. I have found the method sparse.lsqr, but it is not very efficient. Is there a method to compute the pseudo-inverse of Moore-Penrose (correspondent to pinv for normal matrices). The size of my matrix A is about 600'000x2000 and in every row of the matrix I'll have from 0 up to 4 non zero values. The matrix A size is given by