pyclustering/cluster/dbscan.py
def process(self):
        """!
        @brief Performs cluster analysis in line with rules of DBSCAN algorithm.
        
        @see get_clusters()
        @see get_noise()
        
        """
        
        if self.__ccore is True:
            (self.__clusters, self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps, self.__neighbors, self.__data_type)
            
        else:
            if self.__data_type == 'points':
                self.__kdtree = kdtree(self.__pointer_data, range(len(self.__pointer_data)))

            for i in range(0, len(self.__pointer_data)):
                if self.__visited[i] is False:
                    cluster = self.__expand_cluster(i)
                    if cluster is not None:
                        self.__clusters.append(cluster)

            for i in range(0, len(self.__pointer_data)):
                if self.__belong[i] is False:
                    self.__noise.append(i)
def process(self):
        """!
        @brief Performs cluster analysis in line with rules of DBSCAN algorithm.
        
        @see get_clusters()
        @see get_noise()
        
        """
        
        if self.__ccore is True:
            (self.__clusters, self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps, self.__neighbors, self.__data_type)
            
        else:
            if self.__data_type == 'points':
                self.__kdtree = kdtree(self.__pointer_data, range(len(self.__pointer_data)))

            for i in range(0, len(self.__pointer_data)):
                if self.__visited[i] is False:
                    cluster = self.__expand_cluster(i)
                    if cluster is not None:
                        self.__clusters.append(cluster)

            for i in range(0, len(self.__pointer_data)):
                if self.__belong[i] is False:
                    self.__noise.append(i)
def process ( self ) : if self . __ccore is True : ( self . __clusters , self . __noise ) = wrapper . dbscan ( self . __pointer_data , self . __eps , self . __neighbors , self . __data_type ) else : if self . __data_type == 'points' : self . __kdtree = kdtree ( self . __pointer_data , range ( len ( self . __pointer_data ) ) ) for i in range ( 0 , len ( self . __pointer_data ) ) : if self . __visited [ i ] is False : cluster = self . __expand_cluster ( i ) if cluster is not None : self . __clusters . append ( cluster ) for i in range ( 0 , len ( self . __pointer_data ) ) : if self . __belong [ i ] is False : self . __noise . append ( i )
!
        @brief Performs cluster analysis in line with rules of DBSCAN algorithm.
        
        @see get_clusters()
        @see get_noise()
!