(self, X, top_k_list)
| 46 | return results |
| 47 | |
| 48 | def predict(self, X, top_k_list): |
| 49 | X_ = numpy.asarray([self.embeddings[x] for x in X]) |
| 50 | Y = self.clf.predict(X_, top_k_list=top_k_list) |
| 51 | return Y |
| 52 | |
| 53 | def split_train_evaluate(self, X, Y, train_precent, seed=0): |
| 54 | state = numpy.random.get_state() |