original_docstring: Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
  
          Special implementation optimised for Gaussian.
          
docstring: Returns the kernel smoothing estimate for point x based on x-values
  xs and y-values ys.
  Not expected to be called by the user.
  
  Special implementation optimised for Gaussian.
docstring_tokens: Returns the kernel smoothing estimate for point x based on x - values xs and y - values ys . Not expected to be called by the user . Special implementation optimised for Gaussian .
code: def smooth(self, xs, ys, x):
          """Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
  
          Special implementation optimised for Gaussian.
          """
          w = np.sum(exp(multiply(square(divide(subtract(xs, x),
                                                self.h)),-0.5)))
          v = np.sum(multiply(ys, exp(multiply(square(divide(subtract(xs, x),
                                                            self.h)), -0.5))))
          return v/w
code_tokens: def smooth ( self , xs , ys , x ) : w = np . sum ( exp ( multiply ( square ( divide ( subtract ( xs , x ) , self . h ) ) , - 0.5 ) ) ) v = np . sum ( multiply ( ys , exp ( multiply ( square ( divide ( subtract ( xs , x ) , self . h ) ) , - 0.5 ) ) ) ) return v / w
short_docstring: Returns the kernel smoothing estimate for point x based on x-values
  xs and y-values ys.
short_docstring_tokens: Returns the kernel smoothing estimate for point x based on x - values xs and y - values ys .
comment: """Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
  
          Special implementation optimised for Gaussian.
          """