sparse-matrix

问题 I'm having trouble efficiently loading data into a sparse matrix format in R. Here is an (incomplete) example of my current strategy: library(Matrix) a1=Matrix(0,5000,100000,sparse=T) for(i in 1:5000) a1[i,idxOfCols]=x Where x is usually around length 20. This is not efficient and eventually slows to a crawl. I know there is a better way but wasn't sure how. Suggestions? 回答1: You can populate the matrix all at once: library(Matrix) n <- 5000 m <- 1e5 k <- 20 idxOfCols <- sample(1:m, k) x <-

问题 I am using Eigen Sparse Matrices for the first time, and now I would like to know how to get the indices of the nonzero elements. I constructed my Sparse Matrix as follows: Eigen::SparseMatrix<Eigen::ColMajor> Am(3,3); and I can see some indices in VS by looking into the m_indices variable. But I can't access them. Can anyone please help me? For a Matrix like ( 1 0 1 0 1 1 0 0 0 ) I would like the indices to be like (0,0), (0,2), (1,1), (1,2) . Is there any way to do it? P.S. My matrices are

问题 Suppose I have a high dimensional vector v which is dense and another high dimensional vector x which is sparse and I want to do an operation which looks like v = v + x Ideally since one needs to update only a few entries in v this operation should be fast but it is still taking a good amount of time even when I have declared x to be sparse. I have tried with v being in full as well as v being in sparse form and both are fairly slow. I have also tried to extract the indices from the sparse

问题 I need a library to solve Ax=b systems, where A is a non-symmetric sparse matrix, with 8 entry per row (and it might be quite big). I think a library that implements biconjugate gradient should be fine, but i can't find one that works (i've tried iml++, but there are some headers missing in the iml++/sparselib++ packages). Any tips? 回答1: There are standard ways of treating overdetermined systems. For example Wikipedia says this: A set of linear simultaneous equations can be written in matrix

问题 I need a library to solve Ax=b systems, where A is a non-symmetric sparse matrix, with 8 entry per row (and it might be quite big). I think a library that implements biconjugate gradient should be fine, but i can't find one that works (i've tried iml++, but there are some headers missing in the iml++/sparselib++ packages). Any tips? 回答1: There are standard ways of treating overdetermined systems. For example Wikipedia says this: A set of linear simultaneous equations can be written in matrix

问题 I need a library to solve Ax=b systems, where A is a non-symmetric sparse matrix, with 8 entry per row (and it might be quite big). I think a library that implements biconjugate gradient should be fine, but i can't find one that works (i've tried iml++, but there are some headers missing in the iml++/sparselib++ packages). Any tips? 回答1: There are standard ways of treating overdetermined systems. For example Wikipedia says this: A set of linear simultaneous equations can be written in matrix

问题 I want to do the element-wise outer product of three (or four) large 2D arrays in python (values are float32 rounded to 2 decimals). They all have the same number of rows "n", but different number of columns "i", "j", "k". The resulting array should be of shape (n, i*j*k). Then, I want to sum each column of the result to end up with a 1D array of shape (i*j*k). np.shape(a) = (75466, 10) np.shape(b) = (75466, 28) np.shape(c) = (75466, 66) np.shape(intermediate_result) = (75466, 18480) np.shape

问题 I'm trying to figure out how to iterate through a scipy sparse matrix by column. I'm trying to compute the sum of each column, then weight the members of that column by that sum. What I want to do is basically: for i=0 to #columns for j=0 to #rows sum=sum+matrix[i,j] for j=0to #rows matrix[i,j]=matrix[i,j]/sum All of the iterators I've seen in examples iterate over the entire matrix at once instead of doing it per column. Is there a way to do what I'm trying to do? 回答1: Scipy sparse matrices

问题 I am attempting to use the Matrix package to bind two sparse matrices of different size together. The binding is on rows, using the column names for matching. Table A: ID | AAAA | BBBB | ------ | ------ | ------ | XXXX | 1 | 2 | Table B: ID | BBBB | CCCC | ------ | ------ | ------ | YYYY | 3 | 4 | Binding table A and B : ID | AAAA | BBBB | CCCC | ------ | ------ | ------ | ------ | XXXX | 1 | 2 | | YYYY | | 3 | 4 | The intention is to insert a large number of small matrices into a single

问题 I've found a quite good sparse matrix implementation for c# over http://www.blackbeltcoder.com/Articles/algorithms/creating-a-sparse-matrix-in-net. But as i work in 3d coordinate-system, i need a sparse-matrix implementation that i can use to map the 3d-coordinate system. Details: I'm storing large amounts of primitive shapes data in memory like cubes. I do have large amounts of them (around 30 million) and i've lots of null (zero) entries around. Given that my each entry costs 1-bytes of