(self, X, y=None)
| 38 | self.loss_grad = None |
| 39 | |
| 40 | def fit(self, X, y=None): |
| 41 | self._setup_input(X, y) |
| 42 | # bias |
| 43 | self.wo = 0.0 |
| 44 | # Feature weights |
| 45 | self.w = np.zeros(self.n_features) |
| 46 | # Factor weights |
| 47 | self.v = np.random.normal( |
| 48 | scale=self.init_stdev, size=(self.n_features, self.n_components) |
| 49 | ) |
| 50 | self._train() |
| 51 | |
| 52 | def _train(self): |
| 53 | for epoch in range(self.max_iter): |
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