sparse-matrix

I have a generator that generates single dimension numpy.array s of the same length. I would like to have a sparse matrix containing that data. Rows are generated in the same order I'd like to have them in the final matrix. csr matrix is preferable over lil matrix, but I assume the latter will be easier to build in the scenario I'm describing. Assuming row_gen is a generator yielding numpy.array rows, the following code works as expected. def row_gen(): yield numpy.array([1, 2, 3]) yield numpy.array([1, 0, 1]) yield numpy.array([1, 0, 0]) matrix = scipy.sparse.lil_matrix(list(row_gen()))

I have data reporting sales by every month and by every customer. When I count the values, the zero-values are not reported because of the sparsa data format. Suppose customer 1-4. Suppose only customers 1-2 have recordings. Straight table has customerIDs on rows and months on the columns such that |CustomerID|MonthID|Value| -------------------------| | 1 |201101 | 10 | | 2 |201101 | 100 | and then they are reported in Crosstab format such that |CustomerID|201101|201102|2011103|...|201501| --------------------------------------------- | 1 | 10 | 0 | 0 |...| 0 | | 2 | 100 | 0 | 0 |...| 0 | | 3

I'm having trouble resizing a matrix - the set_shape function seems to have no effect: >>> M <14x3562 sparse matrix of type '<type 'numpy.float32'>' with 6136 stored elements in LInked List format> >>> new_shape = (15,3562) >>> M.set_shape(new_shape) >>> M <14x3562 sparse matrix of type '<type 'numpy.float32'>' with 6136 stored elements in LInked List format> Anyone else come across this? I also tried doing this by hand, i.e. >>> M._shape = new_shape >>> M.data = np.concatenate(M.data, np.empty((0,0), dtype=np.float32)) but that throws up an error: *** TypeError: only length-1 arrays can be

For example I have a coo_matrix A : import scipy.sparse as sp A = sp.coo_matrix([3,0,3,0], [0,0,2,0], [2,5,1,0], [0,0,0,0]) How can I get result [0,0,0,1], which indicates that first 3 columns contain non-zero values, only the 4th column is all zeros. PS : cannot convert A to other type. PS2 : I tried using np.nonzeros but it seems that my implementation is not very elegant. Approach #1 We could do something like this - # Get the columns indices of the input sparse matrix C = sp.find(A)[1] # Use np.in1d to create a mask of non-zero columns. # So, we invert it and convert to int dtype for

NOTE: I allready asked this question, but it was closed because of "too broad" without much explanation. I can't see how this question could be more specific (it deals with a specific class of a specific library for a specific usage...), so I assume that it was something like a "moderator's mistake" and ask it again... I would like to perfom sparse matrix/matrix multiplication using Eigen on sparse matrices. These matrices are already defined in the code I am working on in standard 3-arrays compressed row/column strorage. Then I would like to use the Eigen::SparseMatrix class as a wrapper on

I have two files: A and B A has 400,000 lines each having 50 float values B has 40,000 lines having 50 float values. For every line in B, I need to find corresponding lines in A which have >90% similarity (cosine). For linear search and computation, the code takes ginormous computing time. (40-50 hours) Reaching out to the community for suggestions on how to fasten the process (link of blogs/resources such as AWS/Cloud to be used to achieve it). Have been stuck with this for quite a while! [There were mentions of rpud/rpudplus to do it, but can't seem to perform them on cloud resources] N.B.