tropea_clustering.onion_multi¶
- tropea_clustering.onion_multi(X, ndims=2, bins='auto', number_of_sigmas=2.0)[source]¶
Performs onion clustering on the data array ‘X’.
- Parameters:
X (ndarray of shape (n_particles * n_windows, tau_window * n_features)) – The raw data. Notice that each signal window is considered as a single data point.
bins (int, default="auto") – The number of bins used for the construction of the histograms. Can be an integer value, or “auto”. If “auto”, the default of numpy.histogram_bin_edges is used (see https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges).
number_of_sigmas (float, default=2.0) – Sets the thresholds for classifing a signal window inside a state: the window is contained in the state if it is entirely contained inside number_of_sigmas * state.sigmas times from state.mean.
ndims (int)
- Returns:
states_list (List[StateMulti]) – The list of the identified states.Refer to the documentation of StateMulti for accessing the information on the states.
labels (ndarray of shape (n_particles * n_windows,)) – Cluster labels for each signal window. Unclassified points are given the label -1.