def predict_single(self, input_array):
"""Makes a prediction with a single network
"""
if self.network['trees'] is not None:
input_array = pp.tree_transform(input_array, self.network['trees'])
return self.to_prediction(self.model_predict(input_array,
self.network))
def predict_list(self, input_array):
if self.network['trees'] is not None:
input_array_trees = pp.tree_transform(input_array,
self.network['trees'])
youts = []
for model in self.networks:
if model['trees']:
youts.append(self.model_predict(input_array_trees, model))
else:
youts.append(self.model_predict(input_array, model))
return self.to_prediction(net.sum_and_normalize(youts,
self.regression))
