Class MinMaxScalerScikitsLearnNode

Transforms features by scaling each feature to a given range.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.MinMaxScaler`` 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 it is in the given range on the training set, i.e. between
zero and one.

The transformation is given by::


    X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
    X_scaled = X_std * (max - min) + min

where min, max = feature_range.

This transformation is often used as an alternative to zero mean,
unit variance scaling.

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

**Parameters**

feature_range: tuple (min, max), default=(0, 1)
    Desired range of transformed data.

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).

**Attributes**

``min_`` : ndarray, shape (n_features,)
    Per feature adjustment for minimum.

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

    .. versionadded:: 0.17
       *scale_* attribute.

``data_min_`` : ndarray, shape (n_features,)
    Per feature minimum seen in the data

    .. versionadded:: 0.17
       *data_min_* instead of deprecated *data_min*.

``data_max_`` : ndarray, shape (n_features,)
    Per feature maximum seen in the data

    .. versionadded:: 0.17
       *data_max_* instead of deprecated *data_max*.

``data_range_`` : ndarray, shape (n_features,)
    Per feature range ``(data_max_ - data_min_)`` seen in the data

    .. versionadded:: 0.17
       *data_range_* instead of deprecated *data_range*.

Overrides: object.__init__

Scaling features of X according to feature_range.

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

Parameters

X : array-like, shape [n_samples, n_features]

Input data that will be transformed.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Compute the minimum and maximum to be used for later scaling.

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

Parameters

X : array-like, 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