(self, X, y=None, sample_weight=None)
return self.labels_
Fit to the data, and return the found clusters.

        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.

        sample_weight : array-like or None, optional, default: ``None``
            The weights for each observation in the full data. If ``None``,
            all observations are assigned equal weight. Otherwise, it has
            shape (n_samples,).

        Returns
        -------
        labels : ndarray of shape (n_samples,)
            See :attr:`labels_`.
Fit to the data, and return the found clusters.
Fit to the data and return the found clusters .
def fit_predict(self, X, y=None, sample_weight=None):
        """Fit to the data, and return the found clusters.

        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.

        sample_weight : array-like or None, optional, default: ``None``
            The weights for each observation in the full data. If ``None``,
            all observations are assigned equal weight. Otherwise, it has
            shape (n_samples,).

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

        """
        self.fit(X, sample_weight=sample_weight)
        return self.labels_
def fit_predict ( self , X , y = None , sample_weight = None ) : self . fit ( X , sample_weight = sample_weight ) return self . labels_