sparse-matrix

问题 I can't find a definitive answer on whether keras supports scipy sparse matrices as training / testing vectors. I've read this link, which seems to imply that it doesn't https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/forums/t/18154/training-neural-network-with-scipy-sparse-matrix. This PR seems to imply that it does, though https://github.com/fchollet/keras/pull/1886. The docs don't state explicitly -- could someone shed some light? Thanks 来源： https://stackoverflow.com

问题 I wonder is there any good way to extract blocks/ROIs from Eigen::SparseMatrix? More precisely, what I want to extract is inner vectors . What I want to do is like: typedef Eigen::SparseMatrix<double,Eigen::RowMajor> SpMat; // Prepare some sparse matrix SpMat spmat; // Extract lines from it const SpMat& row_i = spmat.innerVector(i); const SpMat& row_j = spmat.innerVector(j); // Some calculation with row_i and row_j... As I tested, the data of row_i and row_j is copied (!!) from spmat .

问题 I have encountered a difference in how slicing a scipy sparse matrix works in 0.10.0 and 0.10.1. Consider the following piece of code: from numpy import array, ravel from scipy.sparse import csr_matrix mat = csr_matrix(array([[1, 0, 0], [0,1,0], [1,0,0]])) desired_cols = ravel(mat.sum(0)) > 0 print mat[:, desired_cols].A In scipy 0.10.0, I get what I expect to get: [[1 0] [0 1] [1 0]] In 0.10.1 and 0.12.0, I get [[0 0 1] [1 1 0] [0 0 1]] I am not sure if this is a bug or I am doing something

问题 I am computing the solution of a linear system Ax=b with A a large (typically 200,000 lines and columns for the associated dense matrix) sparse matrix and b a sparse matrix of about 100 columns. When I run my code on Windows systems (Python 2.7 , scipy 0.14.0 ), the following command from scipy.sparse.linalg import spsolve ... Temp = spsolve(A.tocsc(),b.tocsc()) runs smoothly and requires about 7 GB of ram memory. Running the exact same code with the exact same matrices on Linux systems (same

问题 I am working with large sparse binary matrices. I have condensed them using Scipy sparse matrix implementation. The calculation of Jaccard distance from scipy.spatial.distance does not support direct operation on sparse matrices, so either: convert the entire sparse matrix to dense and then operate on each row as vectors which is memory hungry or Loop through the sparse, grab each row using getrow() and operate. or Write our own implementation to work on sparse matrices. To put things to

问题 I have large but sparse arrays and I want to rearrange them by swapping rows an columns. What is a good way to do this in scipy.sparse ? Some issues I don't think that permutation matrices are well suited for this task, as they like randomly change the sparsity structure. And a manipulation will always 'multiply' all columns or rows, even if there are only a few swaps necessary. What is the best sparse matrix representation in scipy.sparse for this task? Suggestions for implementation are

问题 How to extract a block from a Eigen::SparseMatrix<double> . It seems there aren't the methods I used for the dense ones. ‘class Eigen::SparseMatrix<double>’ has no member named ‘topLeftCorner’ ‘class Eigen::SparseMatrix<double>’ has no member named ‘block’ There is a way to extract a block as a Eigen::SparseMatrix<double> ? 回答1: I made this function to extract blocks from a Eigen::SparseMatrix<double,ColMaior> typedef Triplet<double> Tri; SparseMatrix<double> sparseBlock(SparseMatrix<double

问题 I'm the maintainer of the seqMeta package and looking for ideas on how to speed up the bottleneck of splitting a large matrix into smaller pieces a large number of times. Background The seqMeta package is used to analyze genetic data. So you have a group of subjects (n_subject) and a number of genetic markers (n_snps). This leads to a n_subject x n_snp matrix (Z). There is also a data frame that tells you which snps get grouped together for analysis (typically which snps comprise a given gene

问题 I am dealing with quite a big sparse matrix, its size is 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 the code below: tic H = []; for i = 1: size(A,2) [a,b,c] = find(A(i,:)); % extract the rows add = diff([0 c(2:end) 0]); % the replacing rule aa = i*ones(1,size(a,2)); % return back the old position of rows G0 = [aa' b' add']; % put it back the old position with replaced values H = [H; G0]; end H1 = H(:,1);

问题 I have for loop like this, trying to implement the solution here, with dummy vars such that aaa <- DFM %*% t(DFM) #DFM is Quanteda dfm-sparse-matrix for(i in 1:nrow(aaa)) aaa[i,] <- aaa[i,][order(aaa[i,], decreasing = TRUE)] but now for(i in 1:nrow(mmm)) mmm[i,] <- aaa[i,][order(aaa[i,], decreasing = TRUE)] where mmm does not exist yet, the goal is to do the same thing as mmm <- t(apply(a, 1, sort, decreasing = TRUE)) . But now before the for loop I need to initialise the mmm otherwise Error: