annoviko / pyclustering

        self.__centers.pop(indexes[1]);    # remove merged center.

    def __merge_by_complete_link(self):

        """!

        @brief Merges the most similar clusters in line with complete link type.

        """

        minimum_complete_distance = float('Inf');

        indexes = None;

        for index_cluster1 in range(0, len(self.__clusters)):

            for index_cluster2 in range(index_cluster1 + 1, len(self.__clusters)):

                candidate_maximum_distance = self.__calculate_farthest_distance(index_cluster1, index_cluster2);

                if (candidate_maximum_distance < minimum_complete_distance):

                    minimum_complete_distance = candidate_maximum_distance;

                    indexes = [index_cluster1, index_cluster2];

        self.__clusters[indexes[0]] += self.__clusters[indexes[1]];  

        self.__clusters.pop(indexes[1]);   # remove merged cluster.        

    def __calculate_farthest_distance(self, index_cluster1, index_cluster2):

        minimum_average_distance = float('Inf');

        for index_cluster1 in range(0, len(self.__clusters)):

            for index_cluster2 in range(index_cluster1 + 1, len(self.__clusters)):

                # Find farthest objects

                candidate_average_distance = 0.0;

                for index_object1 in self.__clusters[index_cluster1]:

                    for index_object2 in self.__clusters[index_cluster2]:

                        candidate_average_distance += euclidean_distance_sqrt(self.__pointer_data[index_object1], self.__pointer_data[index_object2]);

                candidate_average_distance /= (len(self.__clusters[index_cluster1]) + len(self.__clusters[index_cluster2]));

                if (candidate_average_distance < minimum_average_distance):

                    minimum_average_distance = candidate_average_distance;

                    indexes = [index_cluster1, index_cluster2];

        self.__clusters[indexes[0]] += self.__clusters[indexes[1]];  

        self.__clusters.pop(indexes[1]);   # remove merged cluster.  

    def __merge_by_centroid_link(self):

        """!

        @brief Merges the most similar clusters in line with centroid link type.