(self, featureset)
| 49 | return list(set(labels)) |
| 50 | |
| 51 | def classify(self, featureset): |
| 52 | # Decision leaf: |
| 53 | if self._fname is None: |
| 54 | return self._label |
| 55 | |
| 56 | # Decision tree: |
| 57 | fval = featureset.get(self._fname) |
| 58 | if fval in self._decisions: |
| 59 | return self._decisions[fval].classify(featureset) |
| 60 | elif self._default is not None: |
| 61 | return self._default.classify(featureset) |
| 62 | else: |
| 63 | return self._label |
| 64 | |
| 65 | def error(self, labeled_featuresets): |
| 66 | errors = 0 |