pyclustering/cluster/optics.py
def __initialize(self, sample):
        """!
        @brief Initializes internal states and resets clustering results in line with input sample.
        
        """
        
        self.__processed = [False] * len(sample)
        self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))]      # List of OPTICS objects that corresponds to objects from input sample.
        self.__ordered_database = []        # List of OPTICS objects in traverse order.
        
        self.__clusters = None      # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
        self.__noise = None
def __initialize(self, sample):
        """!
        @brief Initializes internal states and resets clustering results in line with input sample.
        
        """
        
        self.__processed = [False] * len(sample)
        self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))]      # List of OPTICS objects that corresponds to objects from input sample.
        self.__ordered_database = []        # List of OPTICS objects in traverse order.
        
        self.__clusters = None      # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
        self.__noise = None
def __initialize ( self , sample ) : self . __processed = [ False ] * len ( sample ) self . __optics_objects = [ optics_descriptor ( i ) for i in range ( len ( sample ) ) ] # List of OPTICS objects that corresponds to objects from input sample.
 self . __ordered_database = [ ] # List of OPTICS objects in traverse order.
 self . __clusters = None # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
 self . __noise = None
!
        @brief Initializes internal states and resets clustering results in line with input sample.
!