(self, config_ids, losses)
return(ranks < max(self.min_samples_advance, self.num_configs[self.stage] * (1-self.resampling_rate)) )
SuccessiveHalving simply continues the best based on the current loss.
SuccessiveHalving simply continues the best based on the current loss.
SuccessiveHalving simply continues the best based on the current loss .
def _advance_to_next_stage(self, config_ids, losses):
		"""
			SuccessiveHalving simply continues the best based on the current loss.
		"""
		
		ranks = np.argsort(np.argsort(losses))
		return(ranks < max(self.min_samples_advance, self.num_configs[self.stage] * (1-self.resampling_rate)) )
def _advance_to_next_stage ( self , config_ids , losses ) : ranks = np . argsort ( np . argsort ( losses ) ) return ( ranks < max ( self . min_samples_advance , self . num_configs [ self . stage ] * ( 1 - self . resampling_rate ) ) )