Estimate distances of each data slice to the hyperplanes. Parameters ---------- X : array, shape (n_samples, nd_features, n_tasks) The input samples. For each data slice, the corresponding estimator outputs the distance to the hyperplane, e.g.:
(self, X)
| 264 | return self._transform(X, "predict_proba") |
| 265 | |
| 266 | def decision_function(self, X): |
| 267 | """Estimate distances of each data slice to the hyperplanes. |
| 268 | |
| 269 | Parameters |
| 270 | ---------- |
| 271 | X : array, shape (n_samples, nd_features, n_tasks) |
| 272 | The input samples. For each data slice, the corresponding estimator |
| 273 | outputs the distance to the hyperplane, e.g.: |
| 274 | ``[estimators[ii].decision_function(X[..., ii]) for ii in range(n_estimators)]``. |
| 275 | The feature dimension can be multidimensional e.g. |
| 276 | X.shape = (n_samples, n_features_1, n_features_2, n_estimators). |
| 277 | |
| 278 | Returns |
| 279 | ------- |
| 280 | y_pred : array, shape (n_samples, n_estimators, n_classes * (n_classes-1) // 2) |
| 281 | Predicted distances for each estimator/data slice. |
| 282 | |
| 283 | Notes |
| 284 | ----- |
| 285 | This requires base_estimator to have a ``decision_function`` method. |
| 286 | """ # noqa: E501 |
| 287 | return self._transform(X, "decision_function") |
| 288 | |
| 289 | def _check_Xy(self, X, y=None, fit=False): |
| 290 | """Aux. function to check input data.""" |