pyts.image.GramianAngularField

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

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

[R972e7a1f5b03-1]

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__(self[, image_size, sample_range, …])	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)	Transform each time series into a GAF image.

__init__(self, image_size=1.0, sample_range=(-1, 1), method='summation', overlapping=False, flatten=False)

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

fit(self, X=None, y=None)

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.

Parameters:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.

transform(self, X)

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

Gramian Angular Field