(self, config_ids, losses)
return(ranks < self.num_configs[self.stage])
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 < self.num_configs[self.stage])
def _advance_to_next_stage ( self , config_ids , losses ) : ranks = np . argsort ( np . argsort ( losses ) ) return ( ranks < self . num_configs [ self . stage ] )