python sklearn.preprocessing.MinMaxScaler 的transform和fit_transform方法_python的minmaxscaler.transform

MinMaxScaler.fit_transform()

Init signature: MinMaxScaler(feature_range=(0, 1), copy=True)
Docstring:     
Transforms features by scaling each feature to a given range.

This estimator scales and translates each feature individually such
that it is in the given range on the training set, e.g. 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.

The transformation is calculated as::

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

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. Equivalent to
    ``min - X.min(axis=0) * self.scale_``

scale_ : ndarray, shape (n_features,)
    Per feature relative scaling of the data. Equivalent to
    ``(max - min) / (X.max(axis=0) - X.min(axis=0))``

    .. versionadded:: 0.17
       *scale_* attribute.

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

    .. versionadded:: 0.17
       *data_min_*

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

    .. versionadded:: 0.17
       *data_max_*

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

    .. versionadded:: 0.17
       *data_range_*

Examples
--------
>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler(copy=True, feature_range=(0, 1))
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

See also
--------
minmax_scale: Equivalent function without the estimator API.

Notes
-----
NaNs are treated as missing values: disregarded in fit, and maintained in
transform.

For a comparison of the different scalers, transformers, and normalizers,
see :ref:`examples/preprocessing/plot_all_scaling.py
<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
File:           c:\users\huawei\appdata\local\programs\python\python36\lib\site-packages\sklearn\preprocessing\data.py
Type:           type
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参考文章: 有关StandardScaler的transform和fit_transform方法
https://www.jianshu.com/p/2a635d9e894d