\
        Fit and classify data efficiently.

        :param data: sparse input matrix (ideal dtype is `numpy.float32`)
        :type data: :class:`scipy.sparse.csr_matrix`
        :param labels: the labels associated with data
        :type labels: iterable
        :param unkown: the label to attribute if no label is known
        :returns: the labels guessed for data
        :rtype: `numpy.array`

def fit_predict(self, data, labels, unkown=None):
        """\
        Fit and classify data efficiently.

        :param data: sparse input matrix (ideal dtype is `numpy.float32`)
        :type data: :class:`scipy.sparse.csr_matrix`
        :param labels: the labels associated with data
        :type labels: iterable
        :param unkown: the label to attribute if no label is known
        :returns: the labels guessed for data
        :rtype: `numpy.array`
        """
        self.fit(data, labels)
        return self._predict_from_bmus(self._bmus, unkown)