(runs)
return([lc,])
function to get the hyperband learning curves

	This is an example function showing the interface to use the
	HB_result.get_learning_curves method.

	Parameters
	----------

	runs: list of HB_result.run objects
		the performed runs for an unspecified config

	Returns
	-------

	list of learning curves: list of lists of tuples
		An individual learning curve is a list of (t, x_t) tuples.
		This function must return a list of these. One could think
		of cases where one could extract multiple learning curves
		from these runs, e.g. if each run is an independent training
		run of a neural network on the data.
function to get the hyperband learning curves
function to get the hyperband learning curves
def extract_HBS_learning_curves(runs):
	"""
	function to get the hyperband learning curves

	This is an example function showing the interface to use the
	HB_result.get_learning_curves method.

	Parameters
	----------

	runs: list of HB_result.run objects
		the performed runs for an unspecified config

	Returns
	-------

	list of learning curves: list of lists of tuples
		An individual learning curve is a list of (t, x_t) tuples.
		This function must return a list of these. One could think
		of cases where one could extract multiple learning curves
		from these runs, e.g. if each run is an independent training
		run of a neural network on the data.
		
	"""
	sr = sorted(runs, key=lambda r: r.budget)
	lc = list(filter(lambda t: not t[1] is None, [(r.budget, r.loss) for r in sr]))
	return([lc,])
def extract_HBS_learning_curves ( runs ) : sr = sorted ( runs , key = lambda r : r . budget ) lc = list ( filter ( lambda t : not t [ 1 ] is None , [ ( r . budget , r . loss ) for r in sr ] ) ) return ( [ lc , ] )