\
        Classify data according to previous calibration.

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

def predict(self, data, unkown=None):
        """\
        Classify data according to previous calibration.

        :param data: sparse input matrix (ideal dtype is `numpy.float32`)
        :type data: :class:`scipy.sparse.csr_matrix`
        :param unkown: the label to attribute if no label is known
        :returns: the labels guessed for data
        :rtype: `numpy.array`
        """
        assert self.classifier is not None, 'not calibrated'
        bmus = self._som.bmus(data)
        return self._predict_from_bmus(bmus, unkown)