Class MaxAbsScalerScikitsLearnNode

Scale each feature by its maximum absolute value.

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

This estimator scales and translates each feature individually such
that the maximal absolute value of each feature in the
training set will be 1.0. It does not shift/center the data, and
thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

.. versionadded:: 0.17

**Parameters**

copy : boolean, optional, default is True
    Set to False to perform inplace scaling and avoid a copy (if the input
    is already a numpy array).

**Attributes**

``scale_`` : ndarray, shape (n_features,)
    Per feature relative scaling of the data.

    .. versionadded:: 0.17
       *scale_* attribute.

``max_abs_`` : ndarray, shape (n_features,)
    Per feature maximum absolute value.

``n_samples_seen_`` : int
    The number of samples processed by the estimator. Will be reset on
    new calls to fit, but increments across ``partial_fit`` calls.

Overrides: object.__init__

Scale the data

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

Parameters

X : {array-like, sparse matrix}

The data that should be scaled.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Compute the maximum absolute value to be used for later scaling.

This node has been automatically generated by wrapping the sklearn.preprocessing.data.MaxAbsScaler 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 used to compute the per-feature minimum and maximum used for later scaling along the features axis.

Overrides: Node.stop_training