parameters: (theta, d)
return_statement: return r
docstring: Spatial independence correlation model (pure nugget).
      (Useful when one wants to solve an ordinary least squares problem!)::
  
                                             n
          theta, d --> r(theta, d) = 1 if   sum |d_i| == 0
                                           i = 1
                                     0 otherwise
  
      Parameters
      ----------
      theta : array_like
          None.
  
      d : array_like
          An array with shape (n_eval, n_features) giving the componentwise
          distances between locations x and x' at which the correlation model
          should be evaluated.
  
      Returns
      -------
      r : array_like
          An array with shape (n_eval, ) with the values of the autocorrelation
          model.
docstring_summary: Spatial independence correlation model (pure nugget).
      (Useful when one wants to solve an ordinary least squares problem!)::
docstring_tokens: Spatial independence correlation model ( pure nugget ) . ( Useful when one wants to solve an ordinary least squares problem! ) ::
function: def pure_nugget(theta, d):
      """
      Spatial independence correlation model (pure nugget).
      (Useful when one wants to solve an ordinary least squares problem!)::
  
                                             n
          theta, d --> r(theta, d) = 1 if   sum |d_i| == 0
                                           i = 1
                                     0 otherwise
  
      Parameters
      ----------
      theta : array_like
          None.
  
      d : array_like
          An array with shape (n_eval, n_features) giving the componentwise
          distances between locations x and x' at which the correlation model
          should be evaluated.
  
      Returns
      -------
      r : array_like
          An array with shape (n_eval, ) with the values of the autocorrelation
          model.
      """
  
      theta = np.asarray(theta, dtype=np.float64)
      d = np.asarray(d, dtype=np.float64)
  
      n_eval = d.shape[0]
      r = np.zeros(n_eval)
      r[np.all(d == 0., axis=1)] = 1.
  
      return r
function_tokens: def pure_nugget ( theta , d ) : theta = np . asarray ( theta , dtype = np . float64 ) d = np . asarray ( d , dtype = np . float64 ) n_eval = d . shape [ 0 ] r = np . zeros ( n_eval ) r [ np . all ( d == 0. , axis = 1 ) ] = 1. return r