code: def prior_neighbor(C, alpha=0.001):
      r
  
      if isdense(C):
          B = sparse.prior.prior_neighbor(csr_matrix(C), alpha=alpha)
          return B.toarray()
      else:
          return sparse.prior.prior_neighbor(C, alpha=alpha)
docs: r"""Neighbor prior for the given count matrix.
  
      Parameters
      ----------
      C : (M, M) ndarray or scipy.sparse matrix
          Count matrix
      alpha : float (optional)
          Value of prior counts
  
      Returns
      -------
      B : (M, M) ndarray or scipy.sparse matrix
          Prior count matrix
  
      Notes
      ------
      The neighbor prior :math:`b_{ij}` is defined as
  
      .. math:: b_{ij}=\left \{ \begin{array}{rl}
                       \alpha & c_{ij}+c_{ji}>0 \\
                       0      & \text{else}
                       \end{array} \right .
  
      Examples
      --------
  
      >>> import numpy as np
      >>> from msmtools.estimation import prior_neighbor
  
      >>> C = np.array([[10, 1, 0], [2, 0, 3], [0, 1, 4]])
      >>> B = prior_neighbor(C)
      >>> B
      array([[ 0.001,  0.001,  0.   ],
             [ 0.001,  0.   ,  0.001],
             [ 0.   ,  0.001,  0.001]])