htmd.clustering.regular module¶

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

Bases: sklearn.base.BaseEstimator, sklearn.base.ClusterMixin, sklearn.base.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