inplace_csr_row_normalize_l1 (original) (raw)

sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(X)#

Normalize inplace the rows of a CSR matrix or array by their L1 norm.

Parameters:

Xscipy.sparse.csr_matrix and scipy.sparse.csr_array, 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_l1 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_l1(X) X.toarray() array([[0.33... , 0.66... , 0. , 0. ], [0. , 0. , 1. , 0. ], [0. , 0. , 0. , 1. ]])