Fit to data, then transform it. Fits transformer to epochs_data and y and returns a transformed version of epochs_data. Parameters ---------- epochs_data : array, shape (n_epochs, n_channels, n_times) The data. y : None | array, shape (n_
(self, epochs_data, y=None)
| 236 | return _sklearn_reshape_apply(self.scaler_.transform, True, epochs_data) |
| 237 | |
| 238 | def fit_transform(self, epochs_data, y=None): |
| 239 | """Fit to data, then transform it. |
| 240 | |
| 241 | Fits transformer to epochs_data and y and returns a transformed version |
| 242 | of epochs_data. |
| 243 | |
| 244 | Parameters |
| 245 | ---------- |
| 246 | epochs_data : array, shape (n_epochs, n_channels, n_times) |
| 247 | The data. |
| 248 | y : None | array, shape (n_epochs,) |
| 249 | The label for each epoch. |
| 250 | Defaults to None. |
| 251 | |
| 252 | Returns |
| 253 | ------- |
| 254 | X : array, shape (n_epochs, n_channels, n_times) |
| 255 | The data concatenated over channels. |
| 256 | |
| 257 | Notes |
| 258 | ----- |
| 259 | This function makes a copy of the data before the operations and the |
| 260 | memory usage may be large with big data. |
| 261 | """ |
| 262 | return self.fit(epochs_data, y).transform(epochs_data) |
| 263 | |
| 264 | def inverse_transform(self, epochs_data): |
| 265 | """Invert standardization of data across channels. |