Class NormalizerScikitsLearnNode

Normalize samples individually to unit norm.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.Normalizer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Each sample (i.e. each row of the data matrix) with at least one
non zero component is rescaled independently of other samples so
that its norm (l1 or l2) equals one.

This transformer is able to work both with dense numpy arrays and
scipy.sparse matrix (use CSR format if you want to avoid the burden of
a copy / conversion).

Scaling inputs to unit norms is a common operation for text
classification or clustering for instance. For instance the dot
product of two l2-normalized TF-IDF vectors is the cosine similarity
of the vectors and is the base similarity metric for the Vector
Space Model commonly used by the Information Retrieval community.

Read more in the :ref:`User Guide <preprocessing_normalization>`.

**Parameters**

norm : 'l1', 'l2', or 'max', optional ('l2' by default)
    The norm to use to normalize each non zero sample.

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).

**Notes**

This estimator is stateless (besides constructor parameters), the
fit method does nothing but is useful when used in a pipeline.

See also

:func:`sklearn.preprocessing.normalize` equivalent function
without the object oriented API

Overrides: object.__init__

Scale each non zero row of X to unit norm

This node has been automatically generated by wrapping the sklearn.preprocessing.data.Normalizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape [n_samples, n_features]

The data to normalize, row by row. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Do nothing and return the estimator unchanged

This node has been automatically generated by wrapping the sklearn.preprocessing.data.Normalizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

This method is just there to implement the usual API and hence work in pipelines.

Overrides: Node.stop_training