sparse-matrix

According to MKL BLAS documentation "All matrix-matrix operations (level 3) are threaded for both dense and sparse BLAS." http://software.intel.com/en-us/articles/parallelism-in-the-intel-math-kernel-library I have built Scipy with MKL BLAS. Using the test code below, I see the expected multithreaded speedup for dense, but not sparse, matrix multiplication. Are there any changes to Scipy to enable multithreaded sparse operations? # test dense matrix multiplication from numpy import * import time x = random.random((10000,10000)) t1 = time.time() foo = dot(x.T, x) print time.time() - t1 # test

I have this example of matrix by matrix multiplication using numpy arrays: import numpy as np m = np.array([[1,2,3],[4,5,6],[7,8,9]]) c = np.array([0,1,2]) m * c array([[ 0, 2, 6], [ 0, 5, 12], [ 0, 8, 18]]) How can i do the same thing if m is scipy sparse CSR matrix? This gives dimension mismatch: sp.sparse.csr_matrix(m)*sp.sparse.csr_matrix(c) You can call the multiply method of csr_matrix to do pointwise multiplication. sparse.csr_matrix(m).multiply(sparse.csr_matrix(c)).todense() # matrix([[ 0, 2, 6], # [ 0, 5, 12], # [ 0, 8, 18]], dtype=int64) When m and c are numpy arrays, then m * c is

I have a very large absorbing Markov chain (scales to problem size -- from 10 states to millions) that is very sparse (most states can react to only 4 or 5 other states). I need to calculate one row of the fundamental matrix of this chain (the average frequency of each state given one starting state). Normally, I'd do this by calculating (I - Q)^(-1) , but I haven't been able to find a good library that implements a sparse matrix inverse algorithm! I've seen a few papers on it, most of them P.h.D. level work. Most of my Google results point me to posts talking about how one shouldn't use a

Does anyone know how to compute a correlation matrix from a very large sparse matrix in python? Basically, I am looking for something like numpy.corrcoef that will work on a scipy sparse matrix. You can compute the correlation coefficients fairly straightforwardly from the covariance matrix like this: import numpy as np from scipy import sparse def sparse_corrcoef(A, B=None): if B is not None: A = sparse.vstack((A, B), format='csr') A = A.astype(np.float64) n = A.shape[1] # Compute the covariance matrix rowsum = A.sum(1) centering = rowsum.dot(rowsum.T.conjugate()) / n C = (A.dot(A.T.conjugate

I have a point cloud C, where each point has an associated value. Lets say the points are in 2-d space, so each point can be represented with the triplet (x, y, v). I'd like to find the subset of points which are local maxima. That is, for some radius R, I would like to find the subset of points S in C such that for any point Pi (with value vi) in S, there is no point Pj in C within R distance of Pi whose value vj is greater that vi. I see how I could do this in O(N^2) time, but that seems wasteful. Is there an efficient way to do this? Side Notes: The source of this problem is that I'm trying

When using pytables , there's no support (as far as I can tell) for the scipy.sparse matrix formats, so to store a matrix I have to do some conversion, e.g. def store_sparse_matrix(self): grp1 = self.getFileHandle().createGroup(self.getGroup(), 'M') self.getFileHandle().createArray(grp1, 'data', M.tocsr().data) self.getFileHandle().createArray(grp1, 'indptr', M.tocsr().indptr) self.getFileHandle().createArray(grp1, 'indices', M.tocsr().indices) def get_sparse_matrix(self): return sparse.csr_matrix((self.getGroup().M.data, self.getGroup().M.indices, self.getGroup().M.indptr)) The trouble is