original_docstring: Returns the kernel smoothing estimate with confidence 1sigma bounds
          
docstring: Returns the kernel smoothing estimate with confidence 1sigma bounds
docstring_tokens: Returns the kernel smoothing estimate with confidence 1sigma bounds
code: def smoothconf(self, xs, ys, x):
          """Returns the kernel smoothing estimate with confidence 1sigma bounds
          """
          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)])
              var = v / w
              sd = np.sqrt(var)
              K = self.L2Norm
              yhat = self.smooth(xs, ys, x)
              err = sd * K / np.sqrt(w * self.h * self.norm_const)
              return (yhat - err, yhat, yhat + err)
          else:
              return (np.nan, np.nan, np.nan)
code_tokens: def smoothconf ( 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 ) ] ) var = v / w sd = np . sqrt ( var ) K = self . L2Norm yhat = self . smooth ( xs , ys , x ) err = sd * K / np . sqrt ( w * self . h * self . norm_const ) return ( yhat - err , yhat , yhat + err ) else : return ( np . nan , np . nan , np . nan )
short_docstring: Returns the kernel smoothing estimate with confidence 1sigma bounds
short_docstring_tokens: Returns the kernel smoothing estimate with confidence 1sigma bounds
comment: """Returns the kernel smoothing estimate with confidence 1sigma bounds
          """