uvisbox.Core.BandDepths.functional_banddepth
Module Contents
- uvisbox.Core.BandDepths.functional_banddepth.functional_banddepth(data, dtype=np.float64)[source]
Compute the functional band depth of the input data. The band is assumed to be formed by 2-subsets This implements Sun, Y., Genton, M.G. and Nychka, D.W. (2012), Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked?. Stat, 1: 68-74. https://doi.org/10.1002/sta4.8 Data is a 2D array of shape (N, D)
- Parameters:
data (np.ndarray) – 2D array of shape (N, D) where N is the number of samples and D is the number of features
dtype (data-type, optional) – Desired data-type for the computation (default is np.float64)
- Returns:
band_depths – 1D array of band depths of shape (N,)
- Return type:
np.ndarray
- uvisbox.Core.BandDepths.functional_banddepth.modified_functional_banddepth(data, dtype=np.float64)[source]
Compute the modified functional band depth of the input data The band is assumed to be formed by 2-subsets This implements Sun, Y., Genton, M.G. and Nychka, D.W. (2012), Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked?. Stat, 1: 68-74. https://doi.org/10.1002/sta4.8 Data is a 2D array of shape (N, D) :param data: 2D array of shape (N, D) where N is the number of samples and D is the number of features :type data: np.ndarray :param dtype: Desired data-type for the computation (default is np.float64) :type dtype: data-type, optional
- Returns:
band_depths – 1D array of band depths of shape (N,)
- Return type:
np.ndarray
- uvisbox.Core.BandDepths.functional_banddepth.fdb
- uvisbox.Core.BandDepths.functional_banddepth.mdb