\
        Compute common error metrics (Quantization err. and Topographic err.)
        for this data.

        :param data: sparse input matrix (ideal dtype is `numpy.float32`)
        :type data: :class:`scipy.sparse.csr_matrix`
        :returns: the BMUs, the QE and the TE
        :rtype: tuple

def bmus_with_errors(self, data):
        """\
        Compute common error metrics (Quantization err. and Topographic err.)
        for this data.

        :param data: sparse input matrix (ideal dtype is `numpy.float32`)
        :type data: :class:`scipy.sparse.csr_matrix`
        :returns: the BMUs, the QE and the TE
        :rtype: tuple
        """
        bmus, seconds, mdst = self._som._bmus_and_seconds(data)
        quant_error = np.sqrt(mdst, out=mdst).mean()
        topog_error = self._som._topographic_error(bmus, seconds, data.shape[0])
        return self._som._to_bmus(bmus), quant_error, topog_error