def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medians algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medians()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters, self.__medians = wrapper.kmedians(self.__pointer_data, self.__medians, self.__tolerance, ccore_metric.get_pointer())
else:
changes = float('inf')
# Check for dimension
if len(self.__pointer_data[0]) != len(self.__medians[0]):
raise NameError('Dimension of the input data and dimension of the initial medians must be equal.')
while changes > self.__tolerance:
self.__clusters = self.__update_clusters()
updated_centers = self.__update_medians()
changes = max([self.__metric(self.__medians[index], updated_centers[index]) for index in range(len(updated_centers))])
self.__medians = updated_centers
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medians algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medians()
"""
if (self.__ccore is True):
self.__clusters = wrapper.kmedians(self.__pointer_data, self.__medians, self.__tolerance);
self.__medians = self.__update_medians();
else:
changes = float('inf');
stop_condition = self.__tolerance * self.__tolerance; # Fast solution
#stop_condition = self.__tolerance; # Slow solution
# Check for dimension
if (len(self.__pointer_data[0]) != len(self.__medians[0])):
raise NameError('Dimension of the input data and dimension of the initial cluster medians must be equal.');
while (changes > stop_condition):
self.__clusters = self.__update_clusters();
updated_centers = self.__update_medians(); # changes should be calculated before asignment
changes = max([euclidean_distance_square(self.__medians[index], updated_centers[index]) for index in range(len(updated_centers))]); # Fast solution
self.__medians = updated_centers;
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medians algorithm.
@return (kmedians) Returns itself (K-Medians instance).
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_medians()
"""
if self.__ccore is True:
ccore_metric = metric_wrapper.create_instance(self.__metric)
self.__clusters, self.__medians = wrapper.kmedians(self.__pointer_data, self.__medians, self.__tolerance, self.__itermax, ccore_metric.get_pointer())
else:
changes = float('inf')
# Check for dimension
if len(self.__pointer_data[0]) != len(self.__medians[0]):
raise NameError('Dimension of the input data and dimension of the initial medians must be equal.')
iterations = 0
while changes > self.__tolerance and iterations < self.__itermax:
self.__clusters = self.__update_clusters()
updated_centers = self.__update_medians()
changes = max([self.__metric(self.__medians[index], updated_centers[index]) for index in range(len(updated_centers))])
self.__medians = updated_centers
iterations += 1
return self
