load_basic_motions#

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

Load and return the Basic Motions dataset.

The data was generated as part of a student project where four students performed four activities whilst wearing a smart watch. The watch collects 3D accelerometer and a 3D gyroscope It consists of four classes, which are walking, resting, running and badminton. Participants were required to record motion a total of five times, and the data is sampled once every tenth of a second, for a ten second period.

Training samples

40

Test samples

40

Dimensionality

6

Timestamps

100

Classes

4
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

Examples

>>> from pyts.datasets import load_basic_motions
>>> bunch = load_basic_motions()
>>> bunch.data_train.shape
(40, 6, 100)
>>> X_train, X_test, y_train, y_test = load_basic_motions(return_X_y=True)
>>> X_train.shape
(40, 6, 100)

Examples using pyts.datasets.load_basic_motions#

Joint Recurrence Plot

Joint Recurrence Plot

WEASEL+MUSE

WEASEL+MUSE