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