Calculate Similarity of Sparse Matrix
classification, numpy, python, scipy, sparse-matrix
Solution
If you choose the sparse `coo_matrix` you can create it passing the indices like:
from scipy.sparse import coo_matrix
import scipy
nrows = 100000
ncols = 100000
row = scipy.array([1,3,5,7,9])
col = scipy.array([2,4,6,8,10])
values = scipy.ones(col.size)
m = coo_matrix((values, (row,col)), shape=(nrows, ncols), dtype=float)
Problem
I am using Python with numpy, scipy and scikit-learn module. I'd like to classify the arrays in very big sparse matrix. (100,000 * 100,000) The values in the matrix are equal to 0 or 1. The only thing I have is the index of value = 1. ``` a = [1,3,5,7,9] b = [2,4,6,8,10] ``` which means ``` a = [0,1,0,1,0,1,0,1,0,1,0] b = [0,0,1,0,1,0,1,0,1,0,1] ``` How can I change the index array to the sparse array in scipy ? How can I classify those array quickly ? Thank you very much.