annoviko / pyclustering

        return candidate_maximum_distance;

    def __merge_by_signle_link(self):

        """!

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

        """

        minimum_single_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_minimum_distance = self.__calculate_nearest_distance(index_cluster1, index_cluster2);

                if (candidate_minimum_distance < minimum_single_distance):

                    minimum_single_distance = candidate_minimum_distance;

                    indexes = [index_cluster1, index_cluster2];

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

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

    def __calculate_nearest_distance(self, index_cluster1, index_cluster2):

        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):