sklearn.preprocessing.normalize
-
sklearn.preprocessing.normalize(X, norm=’l2’, axis=1, copy=True, return_norm=False)
[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). -
return_norm : boolean, default False -
whether to return the computed norms |
Returns: |
-
X : {array-like, sparse matrix}, shape [n_samples, n_features] -
Normalized input X. -
norms : array, shape [n_samples] if axis=1 else [n_features] -
An array of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm ‘l1’ or ‘l2’. |
Notes
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.