pyts.image.GramianAngularField

class pyts.image.GramianAngularField(image_size=1.0, sample_range=(-1, 1), method='summation', overlapping=False, flatten=False)[source]

Gramian Angular Field.

Parameters:
image_size : int or float (default = 1.)

Shape of the output images. If float, it represents a percentage of the size of each time series and must be between 0 and 1. Output images are square, thus providing the size of one dimension is enough.

sample_range : None or tuple (min, max) (default = (-1, 1))

Desired range of transformed data. If None, no scaling is performed and all the values of the input data must be between -1 and 1. If tuple, each sample is scaled between min and max; min must be greater than or equal to -1 and max must be lower than or equal to 1.

method : ‘summation’ or ‘difference’ (default = ‘summation’)

Type of Gramian Angular Field. ‘s’ can be used for ‘summation’ and ‘d’ for ‘difference’.

overlapping : bool (default = False)

If True, reduce the size of each time series using PAA with possible overlapping windows.

flatten : bool (default = False)

If True, images are flattened to be one-dimensional.

References

[1]Z. Wang and T. Oates, “Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks.” AAAI Workshop (2015).

Examples

>>> from pyts.datasets import load_gunpoint
>>> from pyts.image import GramianAngularField
>>> X, _, _, _ = load_gunpoint(return_X_y=True)
>>> transformer = GramianAngularField()
>>> X_new = transformer.transform(X)
>>> X_new.shape
(50, 150, 150)

Methods

__init__([image_size, sample_range, method, …]) Initialize self.
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) Transform each time series into a GAF image.
__init__(image_size=1.0, sample_range=(-1, 1), method='summation', overlapping=False, flatten=False)[source]

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

fit(X=None, y=None)[source]

Pass.

Parameters:
X

Ignored

y

Ignored

Returns:
self : object
fit_transform(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 : array-like, shape = (n_samples, n_timestamps)

Univariate time series.

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

Target values (None for unsupervised transformations).

**fit_params : dict

Additional fit parameters.

Returns:
X_new : array

Transformed array.

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]

Transform each time series into a GAF image.

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

Transformed data. If flatten=True, the shape is (n_samples, image_size * image_size).

Examples using pyts.image.GramianAngularField