class_docstr: 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 parameters are used:
  - mean: gaussian mean
  - var: gaussian variance
  - shape: gamma shape
  - scale: gamma scale
  - mixt: mixture parameter (weight of the gamma)
class_docstr_tok: ["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","parameters","are","used",":","-","mean",":","gaussian","mean","-","var",":","gaussian","variance","-","shape",":","gamma","shape","-","scale",":","gamma","scale","-","mixt",":","mixture","parameter","(","weight","of","the","gamma",")"]
code: def check(self, x):
      if x.min() < 0:
          raise ValueError("negative values in input")
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 3 for calling the Gamma obj's underlying member method code with example usage: obj.check(x) without return types