def __insert_for_leaf_node(self, entry, search_node):
        """!
        @brief Recursive insert entry from leaf node to the tree.
        
        @param[in] entry (cfentry): Clustering feature.
        @param[in] search_node (cfnode): None-leaf node from that insertion should be started.
        
        @return (bool) True if number of nodes at the below level is changed, otherwise False.
        
        """
        
        node_amount_updation = False;
        
        # Try to absorb by the entity
        index_nearest_entry = search_node.get_nearest_index_entry(entry, self.__type_measurement);
        merged_entry = search_node.entries[index_nearest_entry] + entry;
        
        # Otherwise try to add new entry
        if (merged_entry.get_diameter() > self.__threshold):
            # If it's not exceeded append entity and update feature of the leaf node.
            search_node.insert_entry(entry);
            
            # Otherwise current node should be splitted
            if (len(search_node.entries) > self.__max_entries):
                self.__split_procedure(search_node);
                node_amount_updation = True;
            
            # Update statistics
            self.__amount_entries += 1;
            
        else:
            search_node.entries[index_nearest_entry] = merged_entry;
            search_node.feature += entry;
        
        return node_amount_updation;
def __insert_for_leaf_node(self, entry, search_node):
        
        
        node_amount_updation = False;
        
        
        index_nearest_entry = search_node.get_nearest_index_entry(entry, self.__type_measurement);
        merged_entry = search_node.entries[index_nearest_entry] + entry;
        
        
        if (merged_entry.get_diameter() > self.__threshold):
            
            search_node.insert_entry(entry);
            
            
            if (len(search_node.entries) > self.__max_entries):
                self.__split_procedure(search_node);
                node_amount_updation = True;
            
            
            self.__amount_entries += 1;
            
        else:
            search_node.entries[index_nearest_entry] = merged_entry;
            search_node.feature += entry;
        
        return node_amount_updation;
def __insert_for_leaf_node ( self , entry , search_node ) : node_amount_updation = False # Try to absorb by the entity
 index_nearest_entry = search_node . get_nearest_index_entry ( entry , self . __type_measurement ) merged_entry = search_node . entries [ index_nearest_entry ] + entry # Otherwise try to add new entry
 if ( merged_entry . get_diameter ( ) > self . __threshold ) : # If it's not exceeded append entity and update feature of the leaf node.
 search_node . insert_entry ( entry ) # Otherwise current node should be splitted
 if ( len ( search_node . entries ) > self . __max_entries ) : self . __split_procedure ( search_node ) node_amount_updation = True # Update statistics
 self . __amount_entries += 1 else : search_node . entries [ index_nearest_entry ] = merged_entry search_node . feature += entry return node_amount_updation
!
        @brief Recursive insert entry from leaf node to the tree.
        
        @param[in] entry (cfentry): Clustering feature.
        @param[in] search_node (cfnode): None-leaf node from that insertion should be started.
        
        @return (bool) True if number of nodes at the below level is changed, otherwise False.
!