load_japanese_vowels#
- load_japanese_vowels(split=None, return_X_y=True, return_type='np-list')[source]#
Load the JapaneseVowels time series classification problem.
Example of a multivariate problem with unequal length series.
- Parameters:
- split: None or one of “TRAIN”, “TEST”, optional (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, optional (default=True)
If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.
- return_type: string, default=”np-list”
Data structure to use for time series, should be “nested_univ” or “np-list”.
- Returns:
- X: np.Pandas dataframe with 12 columns and a pd.Series in each cell
- y: 1D numpy array of length n, only returned if return_X_y if True
The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.
Notes
Dimensionality: 12 Series length: variable (7-29) Train cases: 270 Test cases: 370 Number of classes: 9 Details: http://timeseriesclassification.com/description.php?Dataset=JapaneseVowels
Examples
>>> from aeon.datasets import load_japanese_vowels >>> X, y = load_japanese_vowels()