transformer.py 文件源码

def fit(self, epochs_data, y):
        """Standardizes data across channels

        Parameters
        ----------
        epochs_data : array, shape (n_epochs, n_channels, n_times)
            The data to concatenate channels.
        y : array, shape (n_epochs,)
            The label for each epoch.

        Returns
        -------
        self : instance of Scaler
            Returns the modified instance.
        """
        if not isinstance(epochs_data, np.ndarray):
            raise ValueError("epochs_data should be of type ndarray (got %s)."
                             % type(epochs_data))

        X = np.atleast_3d(epochs_data)

        picks_list = dict()
        picks_list['mag'] = pick_types(self.info, meg='mag', ref_meg=False,
                                       exclude='bads')
        picks_list['grad'] = pick_types(self.info, meg='grad', ref_meg=False,
                                        exclude='bads')
        picks_list['eeg'] = pick_types(self.info, eeg=True, ref_meg=False,
                                       meg=False, exclude='bads')

        self.picks_list_ = picks_list

        for key, this_pick in picks_list.items():
            if self.with_mean:
                ch_mean = X[:, this_pick, :].mean(axis=1)[:, None, :]
                self.ch_mean_[key] = ch_mean  # TODO rename attribute
            if self.with_std:
                ch_std = X[:, this_pick, :].mean(axis=1)[:, None, :]
                self.std_[key] = ch_std  # TODO rename attribute

        return self