code: def predict(self, data, unkown=None):
          
          assert self.classifier is not None, 'not calibrated'
          bmus = self._som.bmus(data)
          return self._predict_from_bmus(bmus, unkown)
docs: \
          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`