pyts.approximation.DiscreteFourierTransform

class pyts.approximation.DiscreteFourierTransform(n_coefs=None, drop_sum=False, anova=False, norm_mean=False, norm_std=False)[source]

Discrete Fourier Transform.

Parameters:
n_coefs : None, int or float (default = None)

The number of Fourier coefficients to keep. If None, all the Fourier coeeficients are kept. If an integer, the n_coefs most significant Fourier coefficients are returned if anova=True, otherwise the first n_coefs Fourier coefficients are returned. If a float, it represents a percentage of the size of each time series and must be between 0 and 1. The number of coefficients will be computed as ceil(n_coefs * (n_timestamps - 1)) if drop_sum=True and ceil(n_coefs * n_timestamps) if drop_sum=False.

drop_sum : bool (default = False)

If True, the first Fourier coefficient (i.e. the sum of the subseries) is dropped. Otherwise, it is kept.

anova : bool (default = False)

If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected.

norm_mean : bool (default = False)

If True, center each time series before scaling.

norm_std : bool (default = False)

If True, scale each time series to unit variance.

References

[1]P. Schäfer, and M. Högqvist, “SFA: A Symbolic Fourier Approximation and Index for Similarity Search in High Dimensional Datasets”, International Conference on Extending Database Technology, 15, 516-527 (2012).

Examples

>>> from pyts.approximation import DiscreteFourierTransform
>>> from pyts.datasets import load_gunpoint
>>> X, _, _, _ = load_gunpoint(return_X_y=True)
>>> transformer = DiscreteFourierTransform(n_coefs=4)
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(50, 4)
Attributes:
support_ : array, shape = (n_coefs,)

Indices of the kept Fourier coefficients.

Methods

__init__([n_coefs, drop_sum, anova, …]) Initialize self.
fit(X[, y]) Learn indices of the Fourier coefficients to keep.
fit_transform(X[, y]) Learn and return the Fourier coeeficients to keep.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Return the selected Fourier coefficients for each sample.
__init__(n_coefs=None, drop_sum=False, anova=False, norm_mean=False, norm_std=False)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]

Learn indices of the Fourier coefficients to keep.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Training vector.

y : None or array-like, shape = (n_samples,) (default = None)

Class labels for each data sample. Only used if anova=True.
Returns:
self : object
fit_transform(X, y=None)[source]

Learn and return the Fourier coeeficients to keep.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

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, shape (n_samples, n_coefs)

The selected Fourier coefficients for each sample.
get_params(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 : dict

Parameter names mapped to their values.
set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**params : dict

Estimator parameters.
Returns:
self : estimator instance

Estimator instance.
transform(X)[source]

Return the selected Fourier coefficients for each sample.

Parameters:
X : array-like, shape (n_samples, n_timestamps)

Input data.
Returns:
X_new : array, shape (n_samples, n_coefs)

The selected Fourier coefficients for each sample.

Examples using pyts.approximation.DiscreteFourierTransform

Discrete Fourier Transform

Discrete Fourier Transform

Discrete Fourier Transform