sktime.datasets.base
.load_gunpoint¶
-
sktime.datasets.base.
load_gunpoint
(split=None, return_X_y=False)[source]¶ Loads the GunPoint time series classification problem and returns X and y :param split: Whether to load the train or test partition of the problem. By
default it loads both.
- Parameters
return_X_y (bool, optional (default=False)) – If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.
- Returns
X (pandas DataFrame with m rows and c columns) – The time series data for the problem with m cases and c dimensions
y (numpy array) – The class labels for each case in X
Details
——-
Dimensionality (univariate)
Series length (150)
Train cases (50)
Test cases (150)
Number of classes (2)
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.
Dataset details (http://timeseriesclassification.com/description.php)
?Dataset=GunPoint