htmd.clustering.regular module#

class htmd.clustering.regular.RegCluster(radius=None, n_clusters=None)#

Bases: BaseEstimator, ClusterMixin, TransformerMixin

Class to perform regular clustering of a given data set

RegCluster can be passed a radius or an approximate number of clusters. If a number of clusters is passed, KCenter clustering is used to estimate the necessary radius. RegCluster randomly chooses a point and assigns all points within the radius of this point to the same cluster. Then it proceeds with the nearest point, which is not yet assigned to a cluster and puts all unassigned points within the radius of this point in the next cluster and so on.

Parameters:

  • radius (float) – radius of clusters

  • n_clusters (int) – desired number of clusters

Examples

>>> cluster = RegCluster(radius=5.1)
>>> 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

property clusterSize#
property cluster_centers_#
fit(data)#

performs clustering of data

Parameters:

  • data (np.ndarray) – array of data points to cluster

  • merge (int) – minimal number of frames within each cluster. Smaller clusters are merged into next big one

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

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

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