Fit the data, then transform in one step. Parameters ---------- X : array-like The data to fit. Can be, for example a list, or an array of at least 2d. The first dimension must be of length n_samples, where samples are the independent samp
(self, X, y=None)
| 355 | return X.reshape(len(X), -1) |
| 356 | |
| 357 | def fit_transform(self, X, y=None): |
| 358 | """Fit the data, then transform in one step. |
| 359 | |
| 360 | Parameters |
| 361 | ---------- |
| 362 | X : array-like |
| 363 | The data to fit. Can be, for example a list, or an array of at |
| 364 | least 2d. The first dimension must be of length n_samples, where |
| 365 | samples are the independent samples used by the estimator |
| 366 | (e.g. n_epochs for epoched data). |
| 367 | y : None | array, shape (n_samples,) |
| 368 | Used for scikit-learn compatibility. |
| 369 | |
| 370 | Returns |
| 371 | ------- |
| 372 | X : array, shape (n_samples, -1) |
| 373 | The transformed data. |
| 374 | """ |
| 375 | return self.fit(X).transform(X) |
| 376 | |
| 377 | def inverse_transform(self, X): |
| 378 | """Transform 2D data back to its original feature shape. |