(self,
                 configspace,
                 max_budget,
                 n_points=2000,
                 delta=1.0,
                 n_candidates=1024,
                 **kwargs)
Parameters:
        -----------

        directory: string
            where the results are logged
        logger: hpbandster.utils.result_logger_v??
            the logger to store the data, defaults to v1
        overwrite: bool
            whether or not existing data will be overwritten
Parameters:
        -----------
Parameters : -----------
def __init__(self,
                 configspace,
                 max_budget,
                 n_points=2000,
                 delta=1.0,
                 n_candidates=1024,
                 **kwargs):
        """
        Parameters:
        -----------

        directory: string
            where the results are logged
        logger: hpbandster.utils.result_logger_v??
            the logger to store the data, defaults to v1
        overwrite: bool
            whether or not existing data will be overwritten

        """

        super(LCNetWrapper, self).__init__(**kwargs)

        self.n_candidates = n_candidates
        self.model = LCNet(sampling_method="sghmc",
                           l_rate=np.sqrt(1e-4),
                           mdecay=.05,
                           n_nets=100,
                           burn_in=500,
                           n_iters=3000,
                           get_net=get_lc_net,
                           precondition=True)

        self.config_space = configspace
        self.max_budget = max_budget
        self.train = None
        self.train_targets = None
        self.n_points = n_points
        self.is_trained = False
        self.counter = 0
        self.delta = delta
        self.lock = threading.Lock()
def __init__ ( self , configspace , max_budget , n_points = 2000 , delta = 1.0 , n_candidates = 1024 , * * kwargs ) : super ( LCNetWrapper , self ) . __init__ ( * * kwargs ) self . n_candidates = n_candidates self . model = LCNet ( sampling_method = "sghmc" , l_rate = np . sqrt ( 1e-4 ) , mdecay = .05 , n_nets = 100 , burn_in = 500 , n_iters = 3000 , get_net = get_lc_net , precondition = True ) self . config_space = configspace self . max_budget = max_budget self . train = None self . train_targets = None self . n_points = n_points self . is_trained = False self . counter = 0 self . delta = delta self . lock = threading . Lock ( )