`pyts.approximation`.PiecewiseAggregateApproximation¶

class pyts.approximation.PiecewiseAggregateApproximation(window_size=1, output_size=None, overlapping=True)[source]¶

Piecewise Aggregate Approximation.

Parameters:

window_size : int, float or None (default = 1): Length of the sliding window. If float, it represents a percentage of the size of each time series and must be between 0 and 1.
output_size : int, float or None (default = None): Size of the returned time series. If float, it represents a percentage of the size of each time series and must be between 0. and 1. Ignored if window_size is not None. It can’t be None if window_size is None. If you want to use output_size over window_size, you must set window_size=None.
overlapping : bool (default = True): When window_size=None, output_size is used to derive the window size; the window size is fixed if overlapping=True and may vary if overlapping=False. Ignored if window_size is specified.

References

[R5f91eb1fc1e8-1]

E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, “Dimensionality reduction for fast similarity search in large time series databases”. Knowledge and information Systems, 3(3), 263-286 (2001).

Examples

>>> from pyts.approximation import PiecewiseAggregateApproximation
>>> X = [[0, 4, 2, 1, 7, 6, 3, 5],
...      [2, 5, 4, 5, 3, 4, 2, 3]]
>>> transformer = PiecewiseAggregateApproximation(window_size=2)
>>> transformer.transform(X)
array([[2. , 1.5, 6.5, 4. ],
       [3.5, 4.5, 3.5, 2.5]])

Methods

`__init__`(self[, window_size, output_size, …])	Initialize self.
`fit`(self[, X, y])	Pass.
`fit_transform`(self, X[, y])	Fit to data, then transform it.
`get_params`(self[, deep])	Get parameters for this estimator.
`set_params`(self, \\params)	Set the parameters of this estimator.
`transform`(self, X)	Reduce the dimensionality of each time series.

__init__(self, window_size=1, output_size=None, overlapping=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X=None, y=None)[source]¶

Pass.

Parameters:	X Ignored y Ignored
Returns:	self object

fit_transform(self, X, y=None, **fit_params)¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. **fit_params : dict Additional fit parameters.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(self, deep=True)¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

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.