Use the trained classifier to predict the class label for each example in **X**. Parameters ---------- X: :py:class:`ndarray ` of shape `(N, M)` A dataset of `N` examples, each of dimension `M` Returns -------
(self, X)
| 126 | return self |
| 127 | |
| 128 | def predict(self, X): |
| 129 | """ |
| 130 | Use the trained classifier to predict the class label for each example |
| 131 | in **X**. |
| 132 | |
| 133 | Parameters |
| 134 | ---------- |
| 135 | X: :py:class:`ndarray <numpy.ndarray>` of shape `(N, M)` |
| 136 | A dataset of `N` examples, each of dimension `M` |
| 137 | |
| 138 | Returns |
| 139 | ------- |
| 140 | labels : :py:class:`ndarray <numpy.ndarray>` of shape `(N)` |
| 141 | The predicted class labels for each example in `X` |
| 142 | """ |
| 143 | return self.labels[self._log_posterior(X).argmax(axis=1)] |
| 144 | |
| 145 | def _log_posterior(self, X): |
| 146 | r""" |