tropea_clustering.OnionUni¶

class tropea_clustering.OnionUni(bins='auto', number_of_sigmas=2.0)[source]¶

Performs onion clustering on a data array.

Returns an array of integer labels, one for each signal sequence. Unclassified sequences are labelled “-1”.

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 sequence inside a state: the sequence is contained in the state if it is entirely contained inside number_of_sigmas * state.sigmas times from state.mean.

state_list_¶

List of the identified states. Refer to the documentation of StateUni for accessing the information on the states.

Type:: List[StateUni]

labels_¶

Cluster labels for signal sequence. Unclassified points are given the label “-1”.

Type:: ndarray of shape (n_particles * n_seq,)

Example

import numpy as np
from tropea_clustering import OnionUni, helpers

# Select time resolution
delta_t = 5

# Create random input data
np.random.seed(1234)
n_particles = 5
n_steps = 1000

input_data = np.random.rand(n_particles, n_steps)

# Create input array with the correct shape
reshaped_input_data = helpers.reshape_from_nt(
    input_data, delta_t,
)

# Run Onion Clustering
clusterer = OnionUni()
clust_params = {"bins": 100, "number_of_sigmas": 2.0}
clusterer.set_params(**clust_params)
clusterer.fit(reshaped_input_data)

Methods

`fit`	Performs onion clustering on the data array 'X'.
`fit_predict`	Computes clusters on the data array 'X' and returns labels.
`get_params`	Get parameters for this estimator.
`set_params`	Set the parameters of this estimator.

fit(X, y=None)[source]¶

Performs onion clustering on the data array ‘X’.

Parameters:: X (ndarray of shape (n_particles * n_seq, delta_t)) – The data to cluster. Each signal sequence is considered as a single data point.
Returns:: self – A fitted instance of self.
Return type:: object

fit_predict(X, y=None)[source]¶

Computes clusters on the data array ‘X’ and returns labels.

Parameters:: X (ndarray of shape (n_particles * n_seq, delta_t)) – The data to cluster. Each signal sequence is considered as a single data point.
Returns:: labels_ – Cluster labels for signal sequence. Unclassified points are given the label “-1”.
Return type:: ndarray of shape (n_particles * n_seq,)

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance