original_docstring: 
          Returns the kernel smoothing estimate of the variance at point x.
          
docstring: Returns the kernel smoothing estimate of the variance at point x.
docstring_tokens: Returns the kernel smoothing estimate of the variance at point x .
code: def smoothvar(self, xs, ys, x):
          """
          Returns the kernel smoothing estimate of the variance at point x.
          """
          xs, ys = self.inDomain(xs, ys, x)
  
          if len(xs) > 0:
              fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
              rs = square(subtract(ys, fittedvals))
              w = np.sum(square(subtract(1.0, square(divide(subtract(xs, x),
                                                          self.h)))))
              v = np.sum(multiply(rs, square(subtract(1, square(divide(
                                                  subtract(xs, x), self.h))))))
              return v / w
          else:
              return np.nan
code_tokens: def smoothvar ( self , xs , ys , x ) : xs , ys = self . inDomain ( xs , ys , x ) if len ( xs ) > 0 : fittedvals = np . array ( [ self . smooth ( xs , ys , xx ) for xx in xs ] ) rs = square ( subtract ( ys , fittedvals ) ) w = np . sum ( square ( subtract ( 1.0 , square ( divide ( subtract ( xs , x ) , self . h ) ) ) ) ) v = np . sum ( multiply ( rs , square ( subtract ( 1 , square ( divide ( subtract ( xs , x ) , self . h ) ) ) ) ) ) return v / w else : return np . nan
short_docstring: Returns the kernel smoothing estimate of the variance at point x.
short_docstring_tokens: Returns the kernel smoothing estimate of the variance at point x .
comment: """
          Returns the kernel smoothing estimate of the variance at point x.
          """