原地CSR矩阵行L1范数归一化

sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(X)

原地按其L1范数归一化CSR矩阵或数组的行。

参数：

Xscipy.sparse.csr_matrix 和 scipy.sparse.csr_array, 形状=(n_samples, n_features)

需要就地修改的输入矩阵或数组。

示例

>>> 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.        ]])