sparse-matrix

I have a large matrix that I would like to convert to sparse CSR format. When I do: import scipy as sp Ks = sp.sparse.csr_matrix(A) print Ks Where A is dense, I get (0, 0) -2116689024.0 (0, 1) 394620032.0 (0, 2) -588142656.0 (0, 12) 1567432448.0 (0, 14) -36273164.0 (0, 24) 233332608.0 (0, 25) 23677192.0 (0, 26) -315783392.0 (0, 45) 157961968.0 (0, 46) 173632816.0 etc... I can get vectors of row index, column index, and value using: Knz = Ks.nonzero() sparserows = Knz[0] sparsecols = Knz[1] #The Non-Zero Value of K at each (Row,Col) vals = np.empty(sparserows.shape).astype(np.float) for i in

I am doing a text classification task with R, and I obtain a document-term matrix with size 22490 by 120,000 (only 4 million non-zero entries, less than 1% entries). Now I want to reduce the dimensionality by utilizing PCA (Principal Component Analysis). Unfortunately, R cannot handle this huge matrix, so I store this sparse matrix in a file in the "Matrix Market Format", hoping to use some other techniques to do PCA. So could anyone give me some hints for useful libraries (whatever the programming language), which could do PCA with this large-scale matrix with ease, or do a longhand PCA by

I have a Markov chain given as a large sparse scipy matrix A . (I've constructed the matrix in scipy.sparse.dok_matrix format, but converting to other ones or constructing it as csc_matrix are fine.) I'd like to know any stationary distribution p of this matrix, which is an eigenvector to the eigenvalue 1 . All entries in this eigenvector should be positive and add up to 1, in order to represent a probability distribution. This means I want any solution for the system (A-I) p = 0 , p.sum()=1 (where I=scipy.sparse.eye(*A.shape) is the idententy matrix), but (A-I) will not be of full rank, and

I'm using the Anaconda distribution of Python, together with Numba, and I've written the following Python function that multiplies a sparse matrix A (stored in a CSR format) by a dense vector x : @jit def csrMult( x, Adata, Aindices, Aindptr, Ashape ): numRowsA = Ashape[0] Ax = numpy.zeros( numRowsA ) for i in range( numRowsA ): Ax_i = 0.0 for dataIdx in range( Aindptr[i], Aindptr[i+1] ): j = Aindices[dataIdx] Ax_i += Adata[dataIdx] * x[j] Ax[i] = Ax_i return Ax Here A is a large scipy sparse matrix, >>> A.shape ( 56469, 39279 ) # having ~ 142,258,302 nonzero entries (so about 6.4% ) >>> type(

I need to pass a scipy.sparse CSR matrix to a cython function. How do I specify the type, as one would for a numpy array? Here is an example about how to quickly access the data from a coo_matrix using the properties row , col and data . The purpose of the example is just to show how to declare the data types and create the buffers (also adding the compiler directives that will usually give you a considerable boost)... #cython: boundscheck=False #cython: wraparound=False #cython: cdivision=True #cython: nonecheck=False import numpy as np from scipy.sparse import coo_matrix cimport numpy as np

Is there any sparse matrix library that can do these: solve linear algebraic equations support operations like matrix-matrix/number multiplication/addition/subtraction,matrix transposition, get a row/column of a matrix,and so on matrix size could be 40k*40k or bigger,like 250k*250k fast can be used in Windows Can someone recommend some libraries for me? If you recommend, please tell me the advantages and disadvantages of it, and the reason why you recommend it. By the way,I have searched many sparse matrix libraries on the internet and tested some of them. I found that each of them only