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])
              sqresid = square( subtract(ys, fittedvals) )
              w = np.sum(self((xs-x)/self.h))
              v = np.sum([rr*self((xx-x)/self.h) for xx, rr in zip(xs, sqresid)])
              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 ] ) sqresid = square ( subtract ( ys , fittedvals ) ) w = np . sum ( self ( ( xs - x ) / self . h ) ) v = np . sum ( [ rr * self ( ( xx - x ) / self . h ) for xx , rr in zip ( xs , sqresid ) ] ) 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.
          """