(self, configspace, top_n_percent=10, update_after_n_points=50,
				 min_points_in_model = None,
				 *kwargs)
Fits for each given budget a kernel density estimator on the best N percent of the
			evaluated configurations on this budget.


			Parameters:
			-----------
			configspace: ConfigSpace
				Configuration space object
			top_n_percent: int
				Determines the percentile of configurations that will be used as training data
				for the kernel density estimator, e.g if set to 10 the 10% best configurations will be considered
				for training.
			update_after_n_points: int
				Specifies after how many new observed points the kernel density will be retrained.
			min_points_in_model: int
				minimum number of datapoints needed to fit a model
Fits for each given budget a kernel density estimator on the best N percent of the
			evaluated configurations on this budget.
Fits for each given budget a kernel density estimator on the best N percent of the evaluated configurations on this budget .
def __init__(self, configspace, top_n_percent=10, update_after_n_points=50,
				 min_points_in_model = None,
				 *kwargs):
		"""
			Fits for each given budget a kernel density estimator on the best N percent of the
			evaluated configurations on this budget.


			Parameters:
			-----------
			configspace: ConfigSpace
				Configuration space object
			top_n_percent: int
				Determines the percentile of configurations that will be used as training data
				for the kernel density estimator, e.g if set to 10 the 10% best configurations will be considered
				for training.
			update_after_n_points: int
				Specifies after how many new observed points the kernel density will be retrained.
			min_points_in_model: int
				minimum number of datapoints needed to fit a model

		"""
		super(KernelDensityEstimator, self).__init__(**kwargs)

		self.top_n_percent = top_n_percent
		self.update_after_n_points = update_after_n_points
		self.configspace = configspace
		
		self.min_points_in_model = min_points_in_model
		if min_points_in_model is None:
			self.min_points_in_model = len(self.configspace.get_hyperparameters())+1


		# TODO: so far we only consider continuous configuration spaces
		self.var_type = "c" * len(self.configspace.get_hyperparameters())
		self.configs = dict()
		self.losses = dict()
		self.kde_models = dict()
def __init__ ( self , configspace , top_n_percent = 10 , update_after_n_points = 50 , min_points_in_model = None , * kwargs ) : super ( KernelDensityEstimator , self ) . __init__ ( * * kwargs ) self . top_n_percent = top_n_percent self . update_after_n_points = update_after_n_points self . configspace = configspace self . min_points_in_model = min_points_in_model if min_points_in_model is None : self . min_points_in_model = len ( self . configspace . get_hyperparameters ( ) ) + 1 # TODO: so far we only consider continuous configuration spaces self . var_type = "c" * len ( self . configspace . get_hyperparameters ( ) ) self . configs = dict ( ) self . losses = dict ( ) self . kde_models = dict ( )