class_docstr: Student Skew t Distribution
  ----
  This class contains methods relating to the Student Skew t distribution for time series.
docstr: GAS Skew t Regression Update term using gradient only - native Python function
  
  Parameters
  ----------
  X : float
      datapoint for the right hand side variable
  
  y : float
      datapoint for the time series
  
  mean : float
      location parameter for the Skew t distribution
  
  scale : float
      scale parameter for the Skew t distribution
  
  shape : float
      tail thickness parameter for the Skew t distribution
  
  skewness : float
      skewness parameter for the Skew t distribution
  
  Returns
  ----------
  - Score of the Skew t family
docstr_tok: ["GAS","Skew","t","Regression","Update","term","using","gradient","only","-","native","Python","function","Parameters","--","--","--","--","--","X",":","float","datapoint","for","the","right","hand","side","variable","y",":","float","datapoint","for","the","time","series","mean",":","float","location","parameter","for","the","Skew","t","distribution","scale",":","float","scale","parameter","for","the","Skew","t","distribution","shape",":","float","tail","thickness","parameter","for","the","Skew","t","distribution","skewness",":","float","skewness","parameter","for","the","Skew","t","distribution","Returns","--","--","--","--","--","-","Score","of","the","Skew","t","family"]
code: def reg_score_function(X, y, mean, scale, shape, skewness):
      """GAS Skew t Regression Update term using gradient only - native Python function
  
      Parameters
      ----------
      X : float
          datapoint for the right hand side variable
  
      y : float
          datapoint for the time series
  
      mean : float
          location parameter for the Skew t distribution
  
      scale : float
          scale parameter for the Skew t distribution
  
      shape : float
          tail thickness parameter for the Skew t distribution
  
      skewness : float
          skewness parameter for the Skew t distribution
  
      Returns
      ----------
      - Score of the Skew t family
      """
      m1 = (np.sqrt(shape) * sp.gamma((shape - 1.0) / 2.0)) / (
          np.sqrt(np.pi) * sp.gamma(shape / 2.0)
      )
      mean = mean + (skewness - (1.0 / skewness)) * scale * m1
      if (y - mean) >= 0:
          return (
              ((shape + 1) / shape)
              * ((y - mean) * X)
              / (
                  np.power(skewness * scale, 2)
                  + (np.power(y - mean, 2) / shape)
              )
          )
      else:
          return (
              ((shape + 1) / shape)
              * ((y - mean) * X)
              / (
                  np.power(scale, 2)
                  + (np.power(skewness * (y - mean), 2) / shape)
              )
          )
  
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
  from .flat import Flat
  from .normal import Normal
  from .gas_recursions import gas_recursion_skewt_orderone, gas_recursion_skewt_ordertwo
  from .gas_recursions import gasx_recursion_skewt_orderone, gasx_recursion_skewt_ordertwo
  from .gas_recursions import gas_llev_recursion_skewt_orderone, gas_llev_recursion_skewt_ordertwo
  from .gas_recursions import gas_llt_recursion_skewt_orderone, gas_llt_recursion_skewt_ordertwo
  from .gas_recursions import gas_reg_recursion_skewt_orderone, gas_reg_recursion_skewt_ordertwo
label_desc: Use image node_id 16 for calling the Skewt obj's underlying member method code with example usage: obj.reg_score_function(X, y, mean, scale, shape, skewness) and returns: unknown, unknown