sklearn.preprocessing.normalize¶
- sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True)[source]¶
Scale input vectors individually to unit norm (vector length).
Read more in the User Guide.
Parameters: X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.
norm : ‘l1’, ‘l2’, or ‘max’, optional (‘l2’ by default)
The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).
axis : 0 or 1, optional (1 by default)
axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.
copy : boolean, optional, default True
set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix and if axis is 1).
See also
sklearn.preprocessing.Normalizer, using, sklearn.pipeline.Pipeline