def predict(self, x, significance=None):
		is_regression =\
			self.predictor.__class__.get_problem_type() == 'regression'

		n_examples = x.shape[0]

		if is_regression and significance is None:
			signs = np.arange(0.01, 1.0, 0.01)
			pred = np.zeros((n_examples, 2, signs.size))
			for i, s in enumerate(signs):
				predictions = np.dstack([p.predict(x, s)
				                         for p in self.predictors])
				predictions = self.agg_func(predictions)
				pred[:, :, i] = predictions
			return pred
		else:
			def f(p, x):
				return p.predict(x, significance if is_regression else None)
			predictions = np.dstack([f(p, x) for p in self.predictors])
			predictions = self.agg_func(predictions)

			if significance and not is_regression:
				return predictions >= significance
			else:
				return predictions
Predict the output values for a set of input patterns.
dataset/ETHPy150Open donlnz/nonconformist/nonconformist/acp.py/AggregatedCp.predict