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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
See also
-
Normalizer
- Performs normalization using the
Transformer
API (e.g. as part of a preprocessingsklearn.pipeline.Pipeline
).
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sklearn.preprocessing.normalize()
2017-01-15 04:26:53
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