def fit(self, 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
"""
# if n_clusters is given and no r, estimate n_clusters
if self.radius is None:
from htmd.clustering.kcenters import KCenter
estClust = KCenter(n_clusters=self.n_clusters)
estClust.fit(data)
self.radius = estClust.distance.max()
logger.info("Estimated radius = {}".format(self.radius))
from pyemma.coordinates.clustering.regspace import RegularSpaceClustering
self._reg = RegularSpaceClustering(dmin=self.radius)
self.labels_ = self._reg.fit_transform(data).flatten()
def setUp(self):
self.dmin = 0.3
self.clustering = RegularSpaceClustering(dmin=self.dmin)
self.src = RandomDataSource()
def setUp(self):
self.dmin = 0.3
self.clustering = RegularSpaceClustering(dmin=self.dmin)
self.clustering.data_producer = RandomDataSource()
