class_docstr: This is the basic one dimensional Gaussian-Gamma Mixture estimation class
  Note that it can work with positive or negative values,
  as long as there is at least one positive value.
  NB : The gamma distribution is defined only on positive values.
  
  5 scalar members
  - mean: gaussian mean
  - var: gaussian variance (non-negative)
  - shape: gamma shape (non-negative)
  - scale: gamma scale (non-negative)
  - mixt: mixture parameter (non-negative, weight of the gamma)
class_docstr_tok: ["This","is","the","basic","one","dimensional","Gaussian-Gamma","Mixture","estimation","class","Note","that","it","can","work","with","positive","or","negative","values",",","as","long","as","there","is","at","least","one","positive","value",".","NB",":","The","gamma","distribution","is","defined","only","on","positive","values",".","5","scalar","members","-","mean",":","gaussian","mean","-","var",":","gaussian","variance","(","non-negative",")","-","shape",":","gamma","shape","(","non-negative",")","-","scale",":","gamma","scale","(","non-negative",")","-","mixt",":","mixture","parameter","(","non-negative",",","weight","of","the","gamma",")"]
docstr: print the parameters of self
          
docstr_tok: ["print","the","parameters","of","self"]
code: def parameters(self):
      """print the parameters of self"""
      print("Gaussian: mean: ", self.mean, "variance: ", self.var)
      print("Gamma: shape: ", self.shape, "scale: ", self.scale)
      print("Mixture gamma: ", self.mixt, "Gaussian: ", 1 - self.mixt)
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 2 for calling the GGM obj's underlying member method code with example usage: obj.parameters() without return types