Use the trained model to generate predictions on a new collection of data points. Parameters ---------- X : :py:class:`ndarray ` of shape `(Z, M)` A dataset consisting of `Z` new examples, each of dimension `M`. Returns
(self, X)
| 84 | return self |
| 85 | |
| 86 | def predict(self, X): |
| 87 | """ |
| 88 | Use the trained model to generate predictions on a new collection of |
| 89 | data points. |
| 90 | |
| 91 | Parameters |
| 92 | ---------- |
| 93 | X : :py:class:`ndarray <numpy.ndarray>` of shape `(Z, M)` |
| 94 | A dataset consisting of `Z` new examples, each of dimension `M`. |
| 95 | |
| 96 | Returns |
| 97 | ------- |
| 98 | y_pred : :py:class:`ndarray <numpy.ndarray>` of shape `(Z, K)` |
| 99 | The model predictions for the items in `X`. |
| 100 | """ |
| 101 | # convert X to a design matrix if we're fitting an intercept |
| 102 | if self.fit_intercept: |
| 103 | X = np.c_[np.ones(X.shape[0]), X] |
| 104 | return np.dot(X, self.beta) |