# skmultiflow.transform.WindowedMinmaxScaler¶

class skmultiflow.transform.WindowedMinmaxScaler(window_size=200)[source]

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.

Parameters
window_size: int (Default: 200)

Defines the window size to compute min and max values.

Methods

 get_info(self) Collects and returns the information about the configuration of the estimator get_params(self[, deep]) 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. reset(self) Resets the estimator to its initial state. set_params(self, **params) Set the parameters of this estimator. transform(self, X) Does the transformation process in the samples in X.
get_info(self)[source]

Collects and returns the information about the configuration of the estimator

Returns
string

Configuration of the estimator.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y=None)[source]

Partial fits the model.

Parameters
X: numpy.ndarray of shape (n_samples, n_features)

The sample or set of samples that should be transformed.

y: numpy.ndarray (optional, default=None)

The target values.

Returns
MinmaxScaler

self

partial_fit_transform(self, X, y=None)[source]

Partially fits the model and then apply the transform to the data.

Parameters
X: numpy.ndarray of shape (n_samples, n_features)

The sample or set of samples that should be transformed.

y: numpy.ndarray (optional, default=None)

The target values.

Returns
numpy.ndarray of shape (n_samples, n_features)

The transformed data.

reset(self)[source]

Resets the estimator to its initial state.

Returns
self
set_params(self, **params)[source]

Set the parameters of this estimator.

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.

Returns
self
transform(self, X)[source]

Does the transformation process in the samples in X.

Parameters
X: numpy.ndarray of shape (n_samples, n_features)

The sample or set of samples that should be transformed.