Reputation: 69
I have a sparse matrix in which I want to increment all the values of non-zero elements by one. However, I cannot figure it out. Is there a way to do it using standard packages in python? Any help will be appreciated.
Upvotes: 2
Views: 7391
Reputation: 626
I cannot comment on it's performance but you can do (Scipy 1.1.0);
>>> from scipy.sparse import csr_matrix
>>> a = csr_matrix([[0, 2, 0], [1, 0, 0]])
>>> print(a)
(0, 1) 2
(1, 0) 1
>>> a[a.nonzero()] = a[a.nonzero()] + 1
>>> print(a)
(0, 1) 3
(1, 0) 2
Upvotes: 4
Reputation: 9
When you have a scipy
sparse matrix (scipy.sparse
) is:
import scipy.sparse as sp
my_matrix = [[2,0,0,0,7],[0,0,0,4,0]]
my_matrix = sp.csc_matrix(my_matrix)
my_matrix.data += 1
my_matrix.todense()
Returns:
[[3, 0, 0, 0, 8], [0, 0, 0, 5, 0]]
Upvotes: 0
Reputation: 133
You can use the package numpy
which has efficient functions for dealing with n-dimensional arrays. What you need is:
array[array>0] += 1
where array
is the numpy array of your matrix. Example here:
`
import numpy as np
my_matrix = [[2,0,0,0,7],[0,0,0,4,0]]
array = np.array(my_matrix);
print("Matrix before incrementing values: \n", array)
array[array>0] += 1
print("Matrix after incrementing values: \n", array)`
Outputs:
Matrix before incrementing values:
[[2 0 0 0 7]
[0 0 0 4 0]]
Matrix after incrementing values:
[[3 0 0 0 8]
[0 0 0 5 0]]
Hope this helps!
Upvotes: 0
Reputation: 2522
If your matrix have 2 dimensions, you can do the following:
sparse_matrix = [[element if element==0 else element+1 for element in row ]for row in sparse_matrix]
It will iterate over every element of your matrix and return the element without any change if it is equals to zero, else it add 1 to the element and return it.
More about conditionals in list comprehension in the answer for this question.
Upvotes: 1