load_gunpoint#

pyts.datasets.load_gunpoint(return_X_y: bool = False) → Bunch | tuple[NDArray[float64], NDArray[float64], NDArray[Any], NDArray[Any]][source]#

Load and return the GunPoint dataset.

This dataset involves one female actor and one male actor making a motion with their hand. The two classes are: Gun-Draw and Point: For Gun-Draw the actors have their hands by their sides. They draw a replicate gun from a hip-mounted holster, point it at a target for approximately one second, then return the gun to the holster, and their hands to their sides. For Point the actors have their gun by their sides. They point with their index fingers to a target for approximately one second, and then return their hands to their sides. For both classes, we tracked the centroid of the actor’s right hands in both X- and Y-axes, which appear to be highly correlated. The data in the archive is just the X-axis.

Training samples	50
Test samples	150
Timestamps	150
Classes	2

Parameters:

return_X_ybool (default = False): If True, return (data_train, data_test, target_train, target_test) instead of a Bunch object.

Returns:

dataBunch

Dictionary-like object, with attributes:

data_trainarray of floats: The time series in the training set.
data_testarray of floats: The time series in the test set.
target_trainarray of integers: The classification labels in the training set.
target_testarray of integers: The classification labels in the test set.
DESCRstr: The full description of the dataset.
urlstr: The url of the dataset.

(data_train, data_test, target_train, target_test)tuple if return_X_y is True

References

[1]

UCR archive entry for the PigCVP dataset

Examples

>>> from pyts.datasets import load_gunpoint
>>> bunch = load_gunpoint()
>>> bunch.data_train.shape
(50, 150)
>>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
>>> X_train.shape
(50, 150)