htmd.clustering.kcenters module

class htmd.clustering.kcenters.KCenter(n_clusters)

Bases: BaseEstimator, ClusterMixin, TransformerMixin

Class to perform KCenter clustering of a given data set

KCenter randomly picks one point from the data, which is now the center of the first cluster. All points are put into the first cluster. In general the furthest point from its center is chosen to be the new center. All points, which are closer to the new center than the old one are assigned to the new cluster. This goes on, until K clusters have been created.

Parameters:

n_clusters (int) – desired number of clusters

Examples

>>> cluster = KCenter(n_cluster=200)
>>> cluster.fit(data)

cluster_centers

list with the points, which are the centers of the clusters

Type:

list

centerFrames

list of indices of center points in data array

Type:

list

labels_

list with number of cluster of each frame

Type:

list

clusterSize_

list with number of frames in each cluster

Type:

list

distance

list with the distance of each frame from the nearest center

Type:

list

fit(data)

Compute the centroids of data.

Parameters:

data (np.ndarray) – A 2D array of data. Columns are features and rows are data examples.

set_fit_request(*, data: bool | None | str = '$UNCHANGED$') → KCenter

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

data (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for data parameter in fit.

Returns:

self – The updated object.

Return type:

object