pyts.datasets
.load_gunpoint¶
-
pyts.datasets.
load_gunpoint
(return_X_y=False)[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_y : bool (default = False)
If True, return
(data_train, data_test, target_train, target_test)
instead of a Bunch object.
Returns: - data : Bunch
Dictionary-like object, with attributes:
- data_train : array of floats
The time series in the training set.
- data_test : array of floats
The time series in the test set.
- target_train : array of integers
The classification labels in the training set.
- target_test : array of integers
The classification labels in the test set.
- DESCR : str
The full description of the dataset.
- url : str
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)
Examples using pyts.datasets.load_gunpoint
¶

Symbolic Aggregate approXimation in Vector Space Model (SAX-VSM)

Word ExtrAction for time SEries cLassification (WEASEL)