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)