code: def aitchison_aitken(h, Xi, x, num_levels=None):
      r"""
      The Aitchison-Aitken kernel, used for unordered discrete random variables.
  
      Parameters
      ----------
      h : 1-D ndarray, shape (K,)
          The bandwidths used to estimate the value of the kernel function.
      Xi : 2-D ndarray of ints, shape (nobs, K)
          The value of the training set.
      x : 1-D ndarray, shape (K,)
          The value at which the kernel density is being estimated.
      num_levels : bool, optional
          Gives the user the option to specify the number of levels for the
          random variable.  If False, the number of levels is calculated from
          the data.
  
      Returns
      -------
      kernel_value : ndarray, shape (nobs, K)
          The value of the kernel function at each training point for each var.
  
      Notes
      -----
      See p.18 of [2]_ for details.  The value of the kernel L if :math:`X_{i}=x`
      is :math:`1-\lambda`, otherwise it is :math:`\frac{\lambda}{c-1}`.
      Here :math:`c` is the number of levels plus one of the RV.
  
      References
      ----------
      .. [*] J. Aitchison and C.G.G. Aitken, "Multivariate binary discrimination
             by the kernel method", Biometrika, vol. 63, pp. 413-420, 1976.
      .. [*] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation
             and Trends in Econometrics: Vol 3: No 1, pp1-88., 2008.
      """
      Xi = Xi.reshape(Xi.size)  # seems needed in case Xi is scalar
      if num_levels is None:
          num_levels = np.asarray(np.unique(Xi).size)
  
      kernel_value = np.ones(Xi.size) * h / (num_levels - 1)
      idx = Xi == x
      kernel_value[idx] = (idx * (1 - h))[idx]
      return kernel_value
  
file_path: statsmodels/statsmodels/nonparametric/kernels.py
imports: import numpy
  from scipy.special import erf
label_desc: Use image node_id 1 for calling a global function with example usage: aitchison_aitken(h, Xi, x, num_levels) and returns: kernel_value