#NEWLINE# def get_parameters( self ) : #NEWLINE# #INDENT# '\n        This function creates some parameters to be used with DBScan.\n        @return: A tuple with the generated parameters and an empty list corresponding to the clusterings.\n        ' #NEWLINE# run_parameters = [ ] #NEWLINE# if ( 'max' in self . parameters [ 'clustering' ] [ 'algorithms' ] [ 'dbscan' ] ) : #NEWLINE# #INDENT# max_eps_tries = self . parameters [ 'clustering' ] [ 'algorithms' ] [ 'dbscan' ] [ 'max' ] #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# max_eps_tries = 10 #NEWLINE# #UNINDENT# num_elements = self . distance_matrix . row_length #NEWLINE# klist = k_scale_gen ( math . log ( num_elements ) ) #NEWLINE# buffer = numpy . empty ( num_elements ) #NEWLINE# kdist_matrix = k_dist ( klist , buffer , self . distance_matrix ) #NEWLINE# dbscan_param_pairs = ( dbscanTools . dbscan_param_space_search ( self . parameters [ 'clustering' ] [ 'evaluation' ] [ 'maximum_noise' ] , max_eps_tries , num_elements , klist , kdist_matrix ) + zhou_adaptative_determination ( num_elements , self . distance_matrix ) ) #NEWLINE# for ( minpts , eps ) in dbscan_param_pairs : #NEWLINE# #INDENT# run_parameter = ParametersGenerator . get_base_parameters ( ) #NEWLINE# run_parameter [ 'minpts' ] = minpts #NEWLINE# run_parameter [ 'eps' ] = eps #NEWLINE# run_parameters . append ( run_parameter ) #NEWLINE# #UNINDENT# return ( run_parameters , [ ] )