I want to add a value to each non-zero element in my sparse matrix. Can someone give me a method to do that.
y=sparse.csc_matrix((df[column_name].values,(df['user_id'].values, df['anime_id'].values)),shape=(rows, cols))
x=np.random.laplace(0,scale)
y=y+x
The above code is giving me an error.
Offered without comment:
In [166]: from scipy import sparse
In [167]: M = sparse.random(5,5,.2,'csc')
In [168]: M
Out[168]:
<5x5 sparse matrix of type '<class 'numpy.float64'>'
with 5 stored elements in Compressed Sparse Column format>
In [169]: M.A
Out[169]:
array([[0.24975586, 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0.6863175 , 0. ],
[0.43488131, 0.19245474, 0.26190903, 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ]])
In [171]: x=np.random.laplace(0,10)
In [172]: x
Out[172]: 0.4773577605565098
In [173]: M+x
Traceback (most recent call last):
Input In [173] in <cell line: 1>
M+x
File /usr/local/lib/python3.8/dist-packages/scipy/sparse/_base.py:464 in __add__
raise NotImplementedError('adding a nonzero scalar to a '
NotImplementedError: adding a nonzero scalar to a sparse matrix is not supported
This is the error message you should have shown initially.
In [174]: M.data += x
In [175]: M.A
Out[175]:
array([[0.72711362, 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 1.16367526, 0. ],
[0.91223907, 0.6698125 , 0.73926679, 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ]])