class_docstr: Gaussian (Normal) Kernel
  
  K(u) = 1 / (sqrt(2*pi)) exp(-0.5 u**2)
class_docstr_tok: ["Gaussian","(","Normal",")","Kernel","K","(","u",")","=","1","\/","(","sqrt","(","2","*","pi",")",")","exp","(","-0.5","u","*","*","2",")"]
docstr: 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 optimized 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 optimized 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
  
imports: from statsmodels.compat.python import lzip, lfilter
  import numpy
  import scipy.integrate
  from scipy.special import factorial
  from numpy import exp, multiply, square, divide, subtract, inf
label_desc: Use image node_id 2 for calling the Gaussian obj's underlying member method code with example usage: obj.smooth(xs, ys, x) and returns: unknown