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))