def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medoids()
"""
if (self.__ccore is True):
self.__clusters = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance);
self.__medoids, self.__medoid_indexes = self.__update_medoids();
else:
changes = float('inf');
stop_condition = self.__tolerance * self.__tolerance; # Fast solution
#stop_condition = self.__tolerance; # Slow solution
while (changes > stop_condition):
self.__clusters = self.__update_clusters();
updated_medoids, update_medoid_indexes = self.__update_medoids(); # changes should be calculated before asignment
changes = max([euclidean_distance_square(self.__medoids[index], updated_medoids[index]) for index in range(len(updated_medoids))]); # Fast solution
self.__medoids = updated_medoids;
self.__medoid_indexes = update_medoid_indexes;
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
@return (kmedoids) Returns itself (K-Medoids instance).
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medoids()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters, self.__medoid_indexes = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, self.__itermax, ccore_metric.get_pointer(), self.__data_type)
else:
changes = float('inf')
iterations = 0
while changes > self.__tolerance and iterations < self.__itermax:
self.__clusters = self.__update_clusters()
update_medoid_indexes = self.__update_medoids()
changes = max([self.__distance_calculator(self.__medoid_indexes[index], update_medoid_indexes[index]) for index in range(len(update_medoid_indexes))])
self.__medoid_indexes = update_medoid_indexes
iterations += 1
return self
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medoids()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, ccore_metric.get_pointer(), self.__data_type)
self.__medoid_indexes = self.__update_medoids()
else:
changes = float('inf')
stop_condition = self.__tolerance
while changes > stop_condition:
self.__clusters = self.__update_clusters()
update_medoid_indexes = self.__update_medoids()
changes = max([self.__distance_calculator(self.__medoid_indexes[index], update_medoid_indexes[index]) for index in range(len(update_medoid_indexes))])
self.__medoid_indexes = update_medoid_indexes
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
@return (kmedoids) Returns itself (K-Medoids instance).
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medoids()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters, self.__medoid_indexes = wrapper.kmedoids(
self.__pointer_data, self.__medoid_indexes, self.__tolerance,
self.__itermax, ccore_metric.get_pointer(), self.__data_type)
else:
changes = float('inf')
previous_deviation, current_deviation = float('inf'), float('inf')
iterations = 0
if self.__itermax > 0:
current_deviation = self.__update_clusters()
while (changes > self.__tolerance) and (iterations <
self.__itermax):
swap_cost = self.__swap_medoids()
if swap_cost != float('inf'):
previous_deviation = current_deviation
current_deviation = self.__update_clusters()
changes = previous_deviation - current_deviation
else:
break
iterations += 1
self.__erase_empty_clusters()
return self
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
@return (kmedoids) Returns itself (K-Medoids instance).
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medoids()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters, self.__medoid_indexes, self.__iterations, self.__total_deviation = kmedoids_wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, self.__itermax, ccore_metric.get_pointer(), self.__data_type)
# TODO: calculate it on C++ side as well
for index_cluster in range(len(self.__clusters)):
for index_point in self.__clusters[index_cluster]:
self.__labels[index_point] = index_cluster
else:
changes = float('inf')
previous_deviation, self.__total_deviation = float('inf'), 0
self.__iterations = 0
if self.__itermax > 0:
self.__total_deviation = self.__update_clusters()
while (changes > self.__tolerance) and (self.__iterations < self.__itermax):
self.__iterations += 1
swap_cost = self.__swap_medoids()
if swap_cost != float('inf'):
previous_deviation = self.__total_deviation
self.__total_deviation = self.__update_clusters()
changes = previous_deviation - self.__total_deviation
else:
break
self.__erase_empty_clusters()
return self
