MarkovTransitionField

class pyts.image.MarkovTransitionField(image_size: int | float = 1.0, n_bins: int = 8, strategy: str = 'quantile', overlapping: bool = False, flatten: bool = False)

Markov Transition Field.

Parameters:

image_sizeint 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.

n_binsint (default = 5)

Number of bins (also known as the size of the alphabet)

strategy‘uniform’, ‘quantile’ or ‘normal’ (default = ‘quantile’)

Strategy used to define the widths of the bins:

• ‘uniform’: All bins in each sample have identical widths

• ‘quantile’: All bins in each sample have the same number of points

• ‘normal’: Bin edges are quantiles from a standard normal distribution

overlappingbool (default = False)

If False, reducing the image with the blurring kernel will be applied on non-overlapping rectangles. If True, it will be applied on possibly overlapping squares.

flattenbool (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 MarkovTransitionField
>>> X, _, _, _ = load_gunpoint(return_X_y=True)
>>> transformer = MarkovTransitionField()
>>> X_new = transformer.transform(X)
>>> X_new.shape
(50, 150, 150)

Methods

__init__([image_size, n_bins, strategy, ...])
fit([X, y])	Pass.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
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 MTF image.

__init__(image_size: int | float = 1.0, n_bins: int = 8, strategy: str = 'quantile', overlapping: bool = False, flatten: bool = False) → None

fit(X: ArrayLike | None = None, y: ArrayLike | None = None) → Self

Pass.

Parameters:

X

Ignored

y

Ignored

Returns:

selfobject

transform(X: ArrayLike) → NDArray[float64]

Transform each time series into a MTF image.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps)

Input data

Returns:

X_newarray-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.MarkovTransitionField

Data set of Markov transition fields

Data set of Markov transition fields

Single Markov transition field

Single Markov transition field