Efficient way to normalize a Scipy Sparse Matrix

Question

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

d         


        

Answer 1

here is my solution.

• transpose A
• calculate sum of each col
• format diagonal matrix B with reciprocal of sum
• A*B equals normalization

• transpose C

  import scipy.sparse as sp
  import numpy as np
  import math
  
  minf = 0.0001
  
  A = sp.lil_matrix((5,5))
  b = np.arange(0,5)
  A.setdiag(b[:-1], k=1)
  A.setdiag(b)
  print A.todense()
  A = A.T
  print A.todense()
  
  sum_of_col = A.sum(0).tolist()
  print sum_of_col
  c = []
  for i in sum_of_col:
      for j in i:
          if math.fabs(j)