def __init__(self,
estimators=default_estimators,
warm_start=False,
metric='accuracy',
random_state=None,
verbose=0):
TimeSeriesClassifier.__init__(self,
warm_start=warm_start,
metric=metric,
random_state=random_state,
verbose=verbose)
for e in estimators:
check_estimator(e)
self.n = len(estimators)
self.base_estimators = estimators
self.set_warm_start(warm_start)
self.set_metric(metric)
self.set_verbose(verbose)
self.set_random_state(random_state)
def __init__(self,
layer_1=TimeSeriesEnsemble(),
layer_2=TimeSeriesEnsemble(),
warm_start=False,
metric='accuracy',
random_state=None,
verbose=0):
TimeSeriesClassifier.__init__(self,
warm_start=warm_start,
metric=metric,
random_state=random_state,
verbose=verbose)
if not issubclass(type(layer_1), TimeSeriesClassifier):
raise ValueError("Layer 1 must be a TimeSeriesClassifier")
if not issubclass(type(layer_2), TimeSeriesClassifier):
raise ValueError("Layer 2 must be a TimeSeriesClassifier")
self.layer_1 = layer_1
self.layer_2 = layer_2
self.set_warm_start(warm_start)
self.set_metric(metric)
self.set_verbose(verbose)
self.set_random_state(random_state)