class_docstr: The basic one dimensional Gamma-Gaussian-Gamma Mixture estimation
  class, where the first gamma has a negative sign, while the second
  one has a positive sign.
  
  7 parameters are used:
  - shape_n: negative gamma shape
  - scale_n: negative gamma scale
  - mean: gaussian mean
  - var: gaussian variance
  - shape_p: positive gamma shape
  - scale_p: positive gamma scale
  - mixt: array of mixture parameter
  (weights of the n-gamma,gaussian and p-gamma)
class_docstr_tok: ["The","basic","one","dimensional","Gamma-Gaussian-Gamma","Mixture","estimation","class",",","where","the","first","gamma","has","a","negative","sign",",","while","the","second","one","has","a","positive","sign",".","7","parameters","are","used",":","-","shape_n",":","negative","gamma","shape","-","scale_n",":","negative","gamma","scale","-","mean",":","gaussian","mean","-","var",":","gaussian","variance","-","shape_p",":","positive","gamma","shape","-","scale_p",":","positive","gamma","scale","-","mixt",":","array","of","mixture","parameter","(","weights","of","the","n-gamma",",","gaussian","and","p-gamma",")"]
docstr: Print the parameters
          
docstr_tok: ["Print","the","parameters"]
code: def parameters(self):
      """Print the parameters"""
      print(
          "Negative Gamma: shape: ",
          self.shape_n,
          "scale: ",
          self.scale_n,
      )
      print("Gaussian: mean: ", self.mean, "variance: ", self.var)
      print(
          "Positive Gamma: shape: ",
          self.shape_p,
          "scale: ",
          self.scale_p,
      )
      mixt = self.mixt
      print(
          "Mixture neg. gamma: ",
          mixt[0],
          "Gaussian: ",
          mixt[1],
          "pos. gamma: ",
          mixt[2],
      )
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 2 for calling the GGGM obj's underlying member method code with example usage: obj.parameters() without return types