Transform 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.
For the training set we consider a window of a given length.
Defines the window size to compute min and max values.
Collects and returns the information about the configuration of the estimator
Get parameters for this estimator.
partial_fit(self, X[, y])
Partial fits the model.
partial_fit_transform(self, X[, y])
Partially fits the model and then apply the transform to the data.
Resets the estimator to its initial state.
Set the parameters of this estimator.
Does the transformation process in the samples in X.
Configuration of the estimator.
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Parameter names mapped to their values.
The sample or set of samples that should be transformed.
The target values.
The transformed data.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter> so that it’s possible to update each
component of a nested object.