Approximation¶

The documentation of the approximation module.

The pyts.approximation module includes approximation algorithms.

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

Piecewise Aggregate Approximation.

Parameters:	window_size : int or None (default = None) Length of the sliding window. output_size : int or None (default = None) Size of the returned time series. overlapping : bool (default = True) When `output_size` is specified, the window size is fixed if `overlapping=True` and may vary if `overlapping=False`. Ignored if `window_size` is specified.

Methods

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

fit(X=None, y=None)[source]¶

Pass.

Parameters:	X ignored y Ignored

transform(X)[source]¶

Reduce the dimensionality of each time series.

Parameters:	X : array-like, shape = [n_samples, n_features]
Returns:	X_new : array-like, shape = [n_samples, n_features] Transformed data.

class pyts.approximation.DFT(n_coefs=None, anova=False, norm_mean=True, norm_std=True)[source]¶

Discrete Fourier Transform.

Parameters:

n_coefs : None or int (default = None): The number of Fourier coefficients to keep. If n_coefs=None, all Fourier coefficients are returned. If n_coefs is an integer, the n_coefs most significant Fourier coefficients are returned if anova=True, otherwise the first n_coefs Fourier coefficients are returned. A even number is required (for real and imaginary values) if anova=False.
anova : bool (default = False): If True, the Fourier coefficients selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected.
norm_mean : bool (default = True): If True, center the data before scaling. If norm_mean=True and anova=False, the first Fourier coefficient will be dropped.
norm_std : bool (default = True): If True, scale the data to unit variance.

Attributes:

coefs_ : array-like, shape [n_coefs]: Indices of the Fourier coefficients that are kept.

Methods

`fit`(X[, y])	Fit the model according to the given training data.
`fit_transform`(X[, y])	Fit the model than transform the given training data.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform the provided data.

fit(X, y=None)[source]¶

Fit the model according to the given training data.

Parameters:	X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : None or array-like, shape = [n_samples] (default = None) Class labels for each data sample.
Returns:	self : object

fit_transform(X, y=None)[source]¶

Fit the model than transform the given training data.

Parameters:	X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : None or array-like, shape = [n_samples] (default = None) Class labels for each data sample.
Returns:	X_new : array-like, shape [n_samples, n_coefs] The selected Fourier coefficients for each sample.

transform(X)[source]¶

Transform the provided data.

Parameters:	X : array-like, shape [n_samples, n_features] The data used to scale along the features axis.
Returns:	X_new : array-like, shape [n_samples, n_coefs] The selected Fourier coefficients for each sample.