Binarize A Sparse Matrix In Python In A Different Way

Assume I have a matrix like: 4 0 3 5 0 2 6 0 7 0 1 0 I want it binarized as: 0 0 0 0 0 1 0 0 0 0 1 0 That is set threshold equal to 2, any element greater than the threshold is s

Solution 1:

When dealing with a sparse matrix, s, avoid inequalities that include zero since a sparse matrix (if you're using it appropriately) should have a great many zeros and forming an array of all the locations which are zero would be huge. So avoid s <= 2 for example. Use inequalities that select away from zero instead.

import numpy as np
from scipy import sparse

s = sparse.csr_matrix(np.array([[4, 0, 3, 5],
         [0, 2, 6, 0],
         [7, 0, 1, 0]]))

print(s)
# <3x4 sparse matrix of type'<type 'numpy.int64'>'
#   with 7 stored elements in Compressed Sparse Row format>

s[s > 2] = 0
s[s != 0] = 1print(s.todense())

yields

matrix([[0, 0, 0, 0],
        [0, 1, 0, 0],
        [0, 0, 1, 0]])

Solution 2:

You can use numpy.where for this:

>>>import numpy as np>>>import scipy.sparse>>>mat = scipy.sparse.csr_matrix(np.array([[4, 0, 3, 5],
         [0, 2, 6, 0],
         [7, 0, 1, 0]])).todense()
>>>np.where(np.logical_and(mat <= 2, mat !=0), 1, 0)
matrix([[0, 0, 0, 0],
        [0, 1, 0, 0],
        [0, 0, 1, 0]])

Solution 3:

There might be very efficient way to do this but it can be achieved using simple function and list operations as below

def binarized(matrix, threshold):
    forrowin matrix:
        foreachinrange(len(matrix)+1):
            if row[each] > threshold:
                row[each] =0
            elif row[each] !=0:
                row[each] =1return matrix


matrix = [[4, 0, 3, 5],
          [0, 2, 6, 0],
          [7, 0, 1, 0]]

print binarized(matrix, 2)

Yeilds :

[[0, 0, 0, 0],
 [0, 1, 0, 0],
 [0, 0, 1, 0]]

Solution 4:

import numpy as np                                                                                            

x = np.array([[4, 0, 3, 5],                                                                                   
              [0, 2, 6, 0],                                                                                   
              [7, 0, 1, 0]])                                                                                  

threshold = 2                                                                                                  
x[x<=0]=threshold+1                                                                                            
x[x<=threshold]=1                                                                                              
x[x>threshold]=0print x

output:

[[0 0 0 0]
 [0 1 0 0]
 [0 0 1 0]]