inplace_csr_row_normalize_l2

sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2(X)

Normalize inplace the rows of a CSR matrix or array by their L2 norm.

Parameters:

Xscipy.sparse.csr_matrix, shape=(n_samples, n_features)

The input matrix or array to be modified inplace.

Examples

>>> from scipy.sparse import csr_matrix
>>> from sklearn.utils.sparsefuncs_fast import inplace_csr_row_normalize_l2
>>> import numpy as np
>>> indptr = np.array([0, 2, 3, 4])
>>> indices = np.array([0, 1, 2, 3])
>>> data = np.array([1.0, 2.0, 3.0, 4.0])
>>> X = csr_matrix((data, indices, indptr), shape=(3, 4))
>>> X.toarray()
array([[1., 2., 0., 0.],
       [0., 0., 3., 0.],
       [0., 0., 0., 4.]])
>>> inplace_csr_row_normalize_l2(X)
>>> X.toarray()
array([[0.44...   , 0.89...   , 0.        , 0.        ],
       [0.        , 0.        , 1.        , 0.        ],
       [0.        , 0.        , 0.        , 1.        ]])