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)
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docstr: Posterior probability of observing the data x for each component
  
  Parameters
  ----------
  x: array of shape (nbitems,)
      the data to be processed
  
  Returns
  -------
  y, pg : arrays of shape (nbitem)
      the posterior probability
docstr_tok: ["Posterior","probability","of","observing","the","data","x","for","each","component","Parameters","--","--","--","--","--","x",":","array","of","shape","(","nbitems",",",")","the","data","to","be","processed","Returns","--","--","--","-","y",",","pg",":","arrays","of","shape","(","nbitem",")","the","posterior","probability"]
code: def posterior(self, x):
      """Posterior probability of observing the data x for each component
  
      Parameters
      ----------
      x: array of shape (nbitems,)
          the data to be processed
  
      Returns
      -------
      y, pg : arrays of shape (nbitem)
          the posterior probability
      """
      p = self.mixt
      pg = p * _gam_dens(self.shape, self.scale, x)
      y = (1 - p) * _gaus_dens(self.mean, self.var, x)
      return y / (y + pg), pg / (y + pg)
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 7 for calling the GGM obj's underlying member method code with example usage: obj.posterior(x) and returns: unknown,  unknown