如何迭代SciPy稀疏矩阵中的一行？

Question

I have a sparse matrix random matrix created as follows:

import numpy as np
from scipy.sparse import rand
foo = rand(100, 100, density=0.1, format='csr')

I want to iterate over the cells in a particular row and perform two calculations:

row1 = foo.getrow(bar1)
row2 = foo.getrow(bar2)

"""
Like the following:
sum1 = 0
sum2 = 0
for each cell x in row1:
    sum1 += x
    if the corresponding cell (in the same column) in row2 y is non-zero:
        sum2 += x*y
"""

Answer 1

Here's an approach -

# Get first row summation by simply using sum method of sparse matrix
sum1 = row1.sum()

# Get the non-zero indices of first row
idx1 = row1.indices
data1 = row1.data  # Or get sum1 here with : `data1.sum()`.

# Get the non-zero indices of second row and corresponding data
idx2 = row2.indices
data2 = row2.data

# Get mask of overlap from row1 nonzeros on row2 nonzeros. 
# Select those from data2 and sum those up for the second summation o/p.
sum2 = data1[np.in1d(idx1,idx2)].dot(data2[np.in1d(idx2,idx1)])

Alternatively, as suggested in comments by @user2357112 , we can simply use matrix-multiplication to get the second summation -

sum2 = sum((row1*row2.T).data)