sparse-matrix

问题 I have quite a big sparse matrix, about 150,000*150,000. I need to access into its rows, extract the non-zero elements and replace these values following the rule as as the code below: H = []; for i = 1: size(A,2) [a,b,c] = find(A(i,:)); % extract the rows if size(c,2)==1 % only 2 add = 0; elseif size(c,2) > 1 && any(c<2)== 0 % many 2s add = c; add(1) = -2; add(end) = 2; add(2:end-1) = 0; elseif size(c,2) > 1 && any(c<2)~= 0 % 2 and 1 k = find(diff(c)==-1); % find right 2 position add = c;

问题 Consider a graph object G in python-igraph 0.7. If I want the adjacency matrix A of G, I have to write A=G.get_adjacency() , but there are two problems: Even if G is sparse with 3000 nodes, A is generated in a long time on my commercial laptop. Is it possible that the creation of the adjacency matrix is so expensive? The output A is a Matrix object, so if I want to operate with the numpy module on A, I have to convert it first in a list and then in a numpy.matrix. Moreover if A is sparse I

问题 Consider a graph object G in python-igraph 0.7. If I want the adjacency matrix A of G, I have to write A=G.get_adjacency() , but there are two problems: Even if G is sparse with 3000 nodes, A is generated in a long time on my commercial laptop. Is it possible that the creation of the adjacency matrix is so expensive? The output A is a Matrix object, so if I want to operate with the numpy module on A, I have to convert it first in a list and then in a numpy.matrix. Moreover if A is sparse I

问题 I am working with Raman spectra, which often have a baseline superimposed with the actual information I am interested in. I therefore would like to estimate the baseline contribution. For this purpose, I implemented a solution from this question. I do like the solution described there, and the code given works fine on my data. A typical result for calculated data looks like this with the red and orange line being the baseline estimates: Typical result of baseline estimation with calculated

问题 I am creating a co-occurring matrix, which is of size 1M by 1M integer numbers. After the matrix is created, the only operation I will do on it is to get top N values per each row (or column. as it is a symmetric matrix). I have to create matrix as sparse to be able to fit it in memory. I read input data from a big file, and update co-occurance of two indexes (row, col) incrementally. The sample code for Sparse dok_matrix specifies that I should declare the size of matrix before hand. I know

问题 Foreword In order to store banded matrices whose full counterparts can have both rows and columns indexed from indices other than 1 , I defined a derived data type as TYPE CDS REAL, DIMENSION(:,:), ALLOCATABLE :: matrix INTEGER, DIMENSION(2) :: lb, ub INTEGER :: ld, ud END TYPE CDS where CDS stands for compressed diagonal storage. Given the declaration TYPE(CDS) :: A , The rank-2 component matrix is supposed to contain, as columns, the diagonals of the actual full matrix (like here, except

问题 I am using scipy to generate a sparse finite difference matrix, constructing it initially from block matrices and then editing the diagonal to account for boundary conditions. The resulting sparse matrix is of the BSR type. I have found that if I convert the matrix to a dense matrix and then back to a sparse matrix using the scipy.sparse.BSR_matrix function, I am left with a sparser matrix than before. Here is the code I use to generate the matrix: size = (4,4) xDiff = np.zeros((size[0]+1

问题 I have two sparse matrices in Eigen, and I would like to join them vertically into one. As an example the target of the code would be: SparseMatrix<double> matrix1; matrix1.resize(10, 10); SparseMatrix<double> matrix2; matrix2.resize(5, 10); SparseMatrix<double> MATRIX_JOIN; MATRIX_JOIN.resize(15, 10); MATRIX_JOIN << matrix1, matrix2; I found some solutions on a forum however, I wasn't able to implement it. What's the proper way to join the matrices vertically? Edit My implementation:

问题 I am attempting to perform fastclust on a very large set of distances, but running into a problem. I have a very large csv file (about 91 million rows so a for loop takes too long in R) of similarities between keywords (about 50,000 unique keywords) that when I read into a data.frame looks like: > df kwd1 kwd2 similarity a b 1 b a 1 c a 2 a c 2 It is a sparse list and I can convert it into a sparse matrix using sparseMatrix(): > myMatrix a b c a . . . b 1 . . c 2 . . However, when I attempt

问题 I was calculating a Cosine Similarity Matrix for sparse vectors, and the elements expected to be float numbers appeared to be 'nan'. 'visits' is a sparse matrix showing how many times each user has visited each website. This matrix used to have a shape 1 500 000 x 1500, but I converted it into sparse matrix, using coo_matrix().tocsc(). The task is to find out, how similar the websites are, so I decided to calculate the cosine metric between each two sites. Here is my code: cosine_distance