annoviko / pyclustering

        """

        for index_point in range(0, len(self.__pointer_data)):

            point = self.__pointer_data[index_point];

            self.__tree.insert_cluster( [ point ] );

            if (self.__tree.amount_entries > self.__entry_size_limit):

                self.__tree = self.__rebuild_tree(index_point);

        #self.__tree.show_feature_destibution(self.__pointer_data);

    def __rebuild_tree(self, index_point):

        """!

        @brief Rebuilt tree in case of maxumum number of entries is exceeded.

        @param[in] index_point (uint): Index of point that is used as end point of re-building.

        @return (cftree) Rebuilt tree with encoded points till specified point from input data space.

        """

        rebuild_result = False;

        increased_diameter = self.__tree.threshold * self.__diameter_multiplier;

        tree = None;

        while(rebuild_result is False):

            # increase diameter and rebuild tree

            if (increased_diameter == 0.0):

                    farthest_distance = candidate_distance;

                    farthest_node1 = candidate1;

                    farthest_node2 = candidate2;        

                    return [farthest_node1, farthest_node2];

    def get_nearest_successors(self, type_measurement):

        """!

        @brief Find pair of nearest successors of the node in line with measurement type.

        @param[in] type_measurement (measurement_type): Measurement type that is used for obtaining nearest successors.

        @return (list) Pair of nearest successors represented by list.

        """

        nearest_node1 = None;

        nearest_node2 = None;

        nearest_distance = float("Inf");

        for i in range(0, len(self.successors)):

            candidate1 = self.successors[i];

            for j in range(i + 1, len(self.successors)):

                candidate2 = self.successors[j];

                candidate_distance = candidate1.get_distance(candidate2, type_measurement);

                if (candidate_distance < nearest_distance):

                    nearest_distance = candidate_distance;

                    nearest_node1 = candidate1;

                    nearest_node2 = candidate2;