annoviko / pyclustering

            for node in self.__tree.leafes:

                if (self.__outlier_detector < node.feature.number_points):

                    self.__features.append(node.feature);

                else:

                    self.__outlier_features.append(node.feature);

    def __decode_data(self):

        """!

        @brief Decodes data from CF-tree features.

        """

        self.__clusters = [ [] for _ in range(self.__number_clusters) ];

        self.__noise = [];

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

            (cluster_distance, cluster_index) = self.__get_nearest_feature(self.__pointer_data[index_point], self.__features);

            (outlier_distance, _) = self.__get_nearest_feature(self.__pointer_data[index_point], self.__outlier_features);

            if (cluster_distance < outlier_distance):

                self.__clusters[cluster_index].append(index_point);

            else:

                self.__noise.append(index_point);

    def __insert_data(self):

        """!

        @brief Inserts input data to the tree.

        @remark If number of maximum number of entries is exceeded than diameter is increased and tree is rebuilt.

        farthest_node2 = None;

        farthest_distance = 0;

        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 > farthest_distance):

                    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)):