code: def fit(self, verbose=True, factr=1e5, pgtol=1e-7):
          r
          self._verbose = verbose
          self._maximize(verbose=verbose, factr=factr, pgtol=pgtol)
          self._verbose = False
docs: r"""Maximise the marginal likelihood.
  
          Parameters
          ----------
          verbose : bool
              ``True`` for progress output; ``False`` otherwise.
              Defaults to ``True``.
          factr : float, optional
              The iteration stops when
              ``(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps``, where ``eps`` is
              the machine precision.
          pgtol : float, optional
              The iteration will stop when ``max{|proj g_i | i = 1, ..., n} <= pgtol``
              where ``pg_i`` is the i-th component of the projected gradient.
  
          Notes
          -----
          Please, refer to :func:`scipy.optimize.fmin_l_bfgs_b` for further information
          about ``factr`` and ``pgtol``.