(self, X, y=None, **fit_params)
return Xt
Alias for :meth:`fit_predict`.

        Allows for this class to be used as an intermediate step in a
        scikit-learn pipeline.

        Parameters
        ----------
        X : list-like of form ``[X_tot, masks]``
            Input data as a list of length 2. ``X_tot`` is an ndarray of shape
            (n_samples, n_features) or (n_samples, n_samples) specifying the
            full data. ``masks`` is a boolean ndarray of shape
            (n_samples, n_portions) whose columns are boolean masks
            on ``X_tot``, specifying the portions of ``X_tot`` to be
            independently clustered.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        Xt : ndarray of shape (n_samples,)
            See :attr:`labels_`.
Alias for :meth:`fit_predict`.
Alias for : meth : fit_predict .
def fit_transform(self, X, y=None, **fit_params):
        """Alias for :meth:`fit_predict`.

        Allows for this class to be used as an intermediate step in a
        scikit-learn pipeline.

        Parameters
        ----------
        X : list-like of form ``[X_tot, masks]``
            Input data as a list of length 2. ``X_tot`` is an ndarray of shape
            (n_samples, n_features) or (n_samples, n_samples) specifying the
            full data. ``masks`` is a boolean ndarray of shape
            (n_samples, n_portions) whose columns are boolean masks
            on ``X_tot``, specifying the portions of ``X_tot`` to be
            independently clustered.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        Xt : ndarray of shape (n_samples,)
            See :attr:`labels_`.

        """
        Xt = self.fit_predict(X, y, **fit_params)
        return Xt
def fit_transform ( self , X , y = None , * * fit_params ) : Xt = self . fit_predict ( X , y , * * fit_params ) return Xt