def get_config(self, budget):
		"""
			Function to sample a new configuration

			This function is called inside Hyperband to query a new configuration


			Parameters:
			-----------
			budget: float
				the budget for which this configuration is scheduled

			returns: config
				should return a valid configuration

		"""
		# No observations available for this budget sample from the prior
		if len(self.kde_models.keys()) == 0:
			return self.configspace.sample_configuration().get_dictionary()
		# If we haven't seen anything with this budget, we sample from the kde trained on the highest budget
		if budget not in self.kde_models.keys():
			budget = sorted(self.kde_models.keys())[-1]
		# TODO: This only works in continuous space and with gaussian kernels
		kde = self.kde_models[budget]
		idx = np.random.randint(0, len(self.kde_models[budget].data))

		vector = [sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw) for m,bw in zip(self.kde_models[budget].data[idx], kde.bw)]
		
		if np.any(np.array(vector)>1) or np.any(np.array(vector)<0):
			raise RuntimeError("truncated normal sampling problems!")
		
		sample = ConfigSpace.Configuration(self.configspace, vector=vector)
		return sample.get_dictionary(), {}
Function to sample a new configuration

			This function is called inside Hyperband to query a new configuration


			Parameters:
			-----------
			budget: float
				the budget for which this configuration is scheduled

			returns: config
				should return a valid configuration