def process(self):
"""!
@brief Performs cluster analysis in line with rules of agglomerative algorithm and similarity.
@return (agglomerative) Returns itself (Agglomerative instance).
@see get_clusters()
"""
if self.__ccore is True:
self.__clusters = wrapper.agglomerative_algorithm(
self.__pointer_data, self.__number_clusters, self.__similarity)
else:
self.__clusters = [[index]
for index in range(0, len(self.__pointer_data))]
current_number_clusters = len(self.__clusters)
while current_number_clusters > self.__number_clusters:
self.__merge_similar_clusters()
current_number_clusters = len(self.__clusters)
return self
def process(self):
"""!
@brief Performs cluster analysis in line with rules of agglomerative algorithm and similarity.
@see get_clusters()
"""
if (self.__ccore is True):
self.__clusters = wrapper.agglomerative_algorithm(self.__pointer_data, self.__number_clusters, self.__similarity);
else:
self.__clusters = [[index] for index in range(0, len(self.__pointer_data))];
current_number_clusters = len(self.__clusters);
while (current_number_clusters > self.__number_clusters):
self.__merge_similar_clusters();
current_number_clusters = len(self.__clusters);
def process(self):
"""!
@brief Performs cluster analysis in line with rules of agglomerative algorithm and similarity.
@see get_clusters()
"""
if (self.__ccore is True):
self.__clusters = wrapper.agglomerative_algorithm(self.__pointer_data, self.__number_clusters, self.__similarity);
else:
self.__clusters = [[index] for index in range(0, len(self.__pointer_data))];
current_number_clusters = len(self.__clusters);
while (current_number_clusters > self.__number_clusters):
self.__merge_similar_clusters();
current_number_clusters = len(self.__clusters);