(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose)
| 506 | self.model = self.model.to(self.device) |
| 507 | |
| 508 | def generate(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sample_count, verbose): |
| 509 | |
| 510 | x_tensor = torch.from_numpy(np.array(x).astype(np.float32)).to(self.device) |
| 511 | x_stamp_tensor = torch.from_numpy(np.array(x_stamp).astype(np.float32)).to(self.device) |
| 512 | y_stamp_tensor = torch.from_numpy(np.array(y_stamp).astype(np.float32)).to(self.device) |
| 513 | |
| 514 | preds = auto_regressive_inference(self.tokenizer, self.model, x_tensor, x_stamp_tensor, y_stamp_tensor, self.max_context, pred_len, |
| 515 | self.clip, T, top_k, top_p, sample_count, verbose) |
| 516 | preds = preds[:, -pred_len:, :] |
| 517 | return preds |
| 518 | |
| 519 | def predict(self, df, x_timestamp, y_timestamp, pred_len, T=1.0, top_k=0, top_p=0.9, sample_count=1, verbose=True): |
| 520 |
no test coverage detected