Question

I’m trying to normalize a csr_matrix:

<5400x6845 sparse matrix of type '<type 'numpy.float64'> with 91833 stored elements in Compressed Sparse Row format>

What I tried was this:

import numpy as np
from scipy import sparse

# ve is my csr_matrix
ve_sum = ve.sum(axis=1)
ve_sums = sparse.csr_matrix(np.tile(ve_sum, (1, ve.shape[1]))) # <-- here I get MemoryError
n_ve = ve/ve_sums 

This is obviously not the correct way of doing this kind of easy normalization.

What is the correct way?

Answer 1

# Normalize the rows of ve.
row_sums = np.array(ve.sum(axis=1))[:,0]
row_indices, col_indices = ve.nonzero()
ve.data /= row_sums[row_indices]

A quick google search reveals this also.