load_gunpoint#

load_gunpoint(split=None, return_X_y=True, return_type='numpy3d')[source]#

Load the GunPoint univariate time series classification problem.

Parameters:

split: None or one of “TRAIN”, “TEST”, default=None: Whether to load the train or test instances of the problem. By default it loads both train and test instances into a single array.
return_X_y: bool, default=True: If True, returns (features, target) separately instead of as single data structure.
return_type: string, optional (default=”numpy3d”): Data structure to use for time series, should be either “numpy2d” or “numpy3d”.

Returns:

X: np.ndarray: shape (n_cases, 1, 150) (return_type=”numpy3d”) or shape (n_cases, 150) (return_type=”numpy2d”), where n_cases is either 150 (split=”train” or “test”) or 300.
y: np.ndarray: 1D array of length 150 or 300, only returned if return_X_y is True The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.

Raises:

ValueError is raised if the data cannot be stored in the requested return_type.

Notes

Dimensionality: univariate Series length: 150 Train cases: 50 Test cases: 150 Number of classes: 2 Details: http://timeseriesclassification.com/description.ph?Dataset=GunPoint

Examples

>>> from aeon.datasets import load_gunpoint
>>> X, y = load_gunpoint()