onion_clustering.onion_uni.OnionUni¶
- class onion_clustering.onion_uni.OnionUni(bins='auto', number_of_sigmas=2.0)[source]¶
Performs onion clustering from data array.
- Parameters:
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_sigma * state.sigms times from state.mean.
- labels_¶
Cluster labels for each point. Unclassified points are given the label -1.
- Type:
ndarray of shape (n_particles * n_windows,)
Methods
Performs onion clustering from data array.
Computes clusters from a data matrix and predict labels.
get_params
set_params
- fit(X, y=None)[source]¶
Performs onion clustering from data array.
- Parameters:
X (ndarray of shape (n_particles * n_windows, tau_window)) – The raw data.
- Returns:
self – A fitted instance of self.
- Return type:
- fit_predict(X, y=None)[source]¶
Computes clusters from a data matrix and predict labels.
- Parameters:
X (ndarray of shape (n_particles * n_windows, tau_window)) – The raw data.
- Returns:
labels_ – Cluster labels for each point. Unclassified points are given the label -1.
- Return type:
ndarray of shape (n_particles * n_windows,)