mars.learn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False)[source]#

Scale input vectors individually to unit norm (vector length).

  • 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 tensor and if axis is 1).

  • return_norm (boolean, default False) – whether to return the computed norms


  • X ({array-like, sparse matrix}, shape [n_samples, n_features]) – Normalized input X.

  • norms (Tensor, shape [n_samples] if axis=1 else [n_features]) – A tensor of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm ‘l1’ or ‘l2’.

See also


Performs normalization using the Transformer API (e.g. as part of a preprocessing mars.learn.pipeline.Pipeline).