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",")"]
code: def __init__(self, h=1.0):
      CustomKernel.__init__(
          self,
          shape=lambda x: 0.3989422804014327 * np.exp(-(x**2) / 2.0),
          h=h,
          domain=None,
          norm=1.0,
      )
      self._L2Norm = 1.0 / (2.0 * np.sqrt(np.pi))
      self._kernel_var = 1.0
      self._order = 2
  
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 1 to create a new Gaussian object from inherited base classes: CustomKernel with example: obj = Gaussian(h)