def process(self):
"""!
@brief Performs cluster analysis in line with rules of X-Means algorithm.
@remark Results of clustering can be obtained using corresponding gets methods.
@see get_clusters()
@see get_centers()
"""
if (self.__ccore is True):
self.__clusters, self.__centers = wrapper.xmeans(self.__pointer_data, self.__centers, self.__kmax, self.__tolerance, self.__criterion);
else:
self.__clusters = [];
while ( len(self.__centers) <= self.__kmax ):
current_cluster_number = len(self.__centers);
self.__clusters, self.__centers = self.__improve_parameters(self.__centers);
allocated_centers = self.__improve_structure(self.__clusters, self.__centers);
if (current_cluster_number == len(allocated_centers)):
#if ( (current_cluster_number == len(allocated_centers)) or (len(allocated_centers) > self.__kmax) ):
break;
else:
self.__centers = allocated_centers;
self.__clusters, self.__centers = self.__improve_parameters(self.__centers);
def process(self):
"""!
@brief Performs cluster analysis in line with rules of X-Means algorithm.
@remark Results of clustering can be obtained using corresponding gets methods.
@see get_clusters()
@see get_centers()
"""
if self.__ccore is True:
result = wrapper.xmeans(self.__pointer_data, self.__centers, self.__kmax, self.__tolerance, self.__criterion)
self.__clusters = result[0]
self.__centers = result[1]
self.__total_wce = result[2][0]
else:
self.__clusters = []
while len(self.__centers) <= self.__kmax:
current_cluster_number = len(self.__centers)
self.__clusters, self.__centers, _ = self.__improve_parameters(self.__centers)
allocated_centers = self.__improve_structure(self.__clusters, self.__centers)
if current_cluster_number == len(allocated_centers):
break
else:
self.__centers = allocated_centers
self.__clusters, self.__centers, self.__total_wce = self.__improve_parameters(self.__centers)
def __process_by_ccore(self):
"""!
@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
"""
result = wrapper.xmeans(self.__pointer_data, self.__centers, self.__kmax, self.__tolerance, self.__criterion, self.__repeat)
self.__clusters = result[0]
self.__centers = result[1]
self.__total_wce = result[2][0]
def __process_by_ccore(self):
"""!
@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
"""
ccore_metric = metric_wrapper.create_instance(self.__metric)
result = wrapper.xmeans(self.__pointer_data, self.__centers, self.__kmax, self.__tolerance, self.__criterion,
self.__alpha, self.__beta, self.__repeat, self.__random_state,
ccore_metric.get_pointer())
self.__clusters = result[0]
self.__centers = result[1]
self.__total_wce = result[2][0]
