Adds the new data and label to the known data, but does not retrain the model. Args: X: The new samples for which the labels are supplied by the expert. y: Labels corresponding to the new instances in X. Note: If the classifier has been
(self, X: modALinput, y: modALinput)
| 86 | self._fit_to_known(bootstrap=bootstrap_init, **fit_kwargs) |
| 87 | |
| 88 | def _add_training_data(self, X: modALinput, y: modALinput) -> None: |
| 89 | """ |
| 90 | Adds the new data and label to the known data, but does not retrain the model. |
| 91 | |
| 92 | Args: |
| 93 | X: The new samples for which the labels are supplied by the expert. |
| 94 | y: Labels corresponding to the new instances in X. |
| 95 | |
| 96 | Note: |
| 97 | If the classifier has been fitted, the features in X have to agree with the training samples which the |
| 98 | classifier has seen. |
| 99 | """ |
| 100 | check_X_y(X, y, accept_sparse=True, ensure_2d=False, allow_nd=True, multi_output=True, dtype=None, |
| 101 | force_all_finite=self.force_all_finite) |
| 102 | |
| 103 | if self.X_training is None: |
| 104 | self.X_training = X |
| 105 | self.y_training = y |
| 106 | else: |
| 107 | try: |
| 108 | self.X_training = data_vstack((self.X_training, X)) |
| 109 | self.y_training = data_vstack((self.y_training, y)) |
| 110 | except ValueError: |
| 111 | raise ValueError('the dimensions of the new training data and label must' |
| 112 | 'agree with the training data and labels provided so far') |
| 113 | |
| 114 | def _fit_to_known(self, bootstrap: bool = False, **fit_kwargs) -> 'BaseLearner': |
| 115 | """ |