def process(self):
"""!
@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
@see get_clusters()
@see get_noise()
"""
if self.__ccore is True:
(self.__clusters,
self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps,
self.__neighbors, self.__data_type)
else:
if self.__data_type == 'points':
self.__kdtree = kdtree(self.__pointer_data,
range(len(self.__pointer_data)))
for i in range(0, len(self.__pointer_data)):
if self.__visited[i] is False:
cluster = self.__expand_cluster(i)
if cluster is not None:
self.__clusters.append(cluster)
else:
self.__noise.append(i)
self.__belong[i] = True
def process(self):
"""!
@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
@see get_clusters()
@see get_noise()
"""
if (self.__ccore is True):
(self.__clusters,
self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps,
self.__neighbors, True)
else:
for i in range(0, len(self.__pointer_data)):
if (self.__visited[i] == False):
cluster = self.__expand_cluster(i)
# Fast mode
if (cluster != None):
self.__clusters.append(cluster)
else:
self.__noise.append(i)
self.__belong[i] = True
def process(self):
"""!
@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
@see get_clusters()
@see get_noise()
"""
if self.__ccore is True:
(self.__clusters, self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps, self.__neighbors, self.__data_type)
else:
if self.__data_type == 'points':
self.__kdtree = kdtree(self.__pointer_data, range(len(self.__pointer_data)))
for i in range(0, len(self.__pointer_data)):
if self.__visited[i] is False:
cluster = self.__expand_cluster(i)
if cluster is not None:
self.__clusters.append(cluster)
for i in range(0, len(self.__pointer_data)):
if self.__belong[i] is False:
self.__noise.append(i)
def process(self):
"""!
@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
@return (dbscan) Returns itself (DBSCAN instance).
@see get_clusters()
@see get_noise()
"""
if self.__ccore is True:
(self.__clusters,
self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps,
self.__neighbors, self.__data_type)
else:
if self.__data_type == 'points':
self.__kdtree = kdtree_balanced(
self.__pointer_data, range(len(self.__pointer_data)))
self.__visited = [False] * len(self.__pointer_data)
self.__belong = [False] * len(self.__pointer_data)
self.__neighbor_searcher = self.__create_neighbor_searcher(
self.__data_type)
for i in range(0, len(self.__pointer_data)):
if self.__visited[i] is False:
cluster = self.__expand_cluster(i)
if cluster is not None:
self.__clusters.append(cluster)
for i in range(0, len(self.__pointer_data)):
if self.__belong[i] is False:
self.__noise.append(i)
return self