skmultiflow.transform.WindowedStandardScaler

class skmultiflow.transform.WindowedStandardScaler(window_size=200)

Standardize features by removing the mean and scaling to unit variance. Mean and stdev are computed in given window frame.

Parameters

window_size: int (Default: 200)

Defines the window size to compute mean and standard deviation.

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)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_params(self, deep=True)

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)

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

StandardScaler

self

partial_fit_transform(self, X, y=None)

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)

Resets the estimator to its initial state.

Returns

self

set_params(self, **params)

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)

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.