sparse-matrix

Is it possible to determine the byte size of a scipy.sparse matrix? In NumPy you can determine the size of an array by doing the following: import numpy as np print(np.zeros((100, 100, 100).nbytes) 8000000 A sparse matrix is constructed from regular numpy arrays, so you can get the byte count for any of these just as you would a regular array. If you just want the number of bytes of the array elements: >>> from scipy.sparse import csr_matrix >>> a = csr_matrix(np.arange(12).reshape((4,3))) >>> a.data.nbytes 88 If you want the byte counts of all arrays required to build the sparse matrix, then

I normally use matrix[:, i:] It seems not work as fast as I expected. Saullo G. P. Castro If you want to obtain a sparse matrix as output the fastest way to do row slicing is to have a csr type, and for columns slicing csc , as detailed here . In both cases you just have to do what you are currently doing: matrix[l1:l2,c1:c2] If you want another type as output there maybe faster ways. In this other answer it is explained many methods for slicing a matrix and their different timings compared. For example, if you want a ndarray as output the fastest slicing is: matrix.A[l1:l2,c1:c2] or: matrix

Is there support for sparse matrices in python? Possibly in numpy or in scipy? Steve Tjoa Yes. SciPi provides scipy.sparse , a "2-D sparse matrix package for numeric data". There are seven available sparse matrix types: csc_matrix: Compressed Sparse Column format csr_matrix: Compressed Sparse Row format bsr_matrix: Block Sparse Row format lil_matrix: List of Lists format dok_matrix: Dictionary of Keys format coo_matrix: COOrdinate format (aka IJV, triplet format) dia_matrix: DIAgonal format CVXOPT - Sparse matrices Pysparse 来源： https://stackoverflow.com/questions/4623800/is-there-support-for

I'd like to write a function that normalizes the rows of a large sparse matrix (such that they sum to one). from pylab import * import scipy.sparse as sp def normalize(W): z = W.sum(0) z[z < 1e-6] = 1e-6 return W / z[None,:] w = (rand(10,10)<0.1)*rand(10,10) w = sp.csr_matrix(w) w = normalize(w) However this gives the following exception: File "/usr/lib/python2.6/dist-packages/scipy/sparse/base.py", line 325, in __div__ return self.__truediv__(other) File "/usr/lib/python2.6/dist-packages/scipy/sparse/compressed.py", line 230, in __truediv__ raise NotImplementedError Are there any reasonably

Does anyone know how to perform svd operation on a sparse matrix in python? It seems that there is no such functionality provided in scipy.sparse.linalg. You can use the Divisi library to accomplish this; from the home page: It is a library written in Python, using a C library (SVDLIBC) to perform the sparse SVD operation using the Lanczos algorithm. Other mathematical computations are performed by NumPy. Sounds like sparsesvd is what you're looking for! SVDLIBC efficiently wrapped in Python (no extra data copies made in RAM). Simply run "easy_install sparsesvd" to install. You can try scipy

Is there some easy and fast way to convert a sparse matrix to a dense matrix of doubles? Because my SparseMatrix is not sparse any more, but became dense after some matrix products. Another question I have: The Eigen library has excellent performance, how is this possible? I don't understand why, because there are only header files, no compiled source. ggael Let's declare two matrices: SparseMatrix<double> spMat; MatrixXd dMat; Sparse to dense: dMat = MatrixXd(spMat); Dense to sparse: spMat = dMat.sparseView(); 来源： https://stackoverflow.com/questions/15484622/how-to-convert-sparse-matrix-to

I coded my model in gurobipy and I want to get the matrix of constraints and vector of cost. Is there any way to access those? From the python API, there's no single function to get the matrix coefficients from a Gurobi model, but it's not to hard to write one yourself. It is convenient to have lists of your variables and constraints. If you have a gurobi model in variable m dvars = m.getVars() constrs = m.getConstrs() will give you the list of variables and constraints. You can then use m.getAttr to retrieve attributes related to the variables. To obtain the objective function coefficients,