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)
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docstr: Compute the likelihood of the data x under
  the three components negative gamma, gaussina, positive gaussian
  
  Parameters
  -----------
  x: array of shape (nbitem,)
      the data under evaluation
  
  Returns
  --------
  ng,y,pg: three arrays of shape(nbitem)
      The likelihood of the data under the 3 components
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code: def component_likelihood(self, x):
      """
      Compute the likelihood of the data x under
      the three components negative gamma, gaussina, positive gaussian
  
      Parameters
      -----------
      x: array of shape (nbitem,)
          the data under evaluation
  
      Returns
      --------
      ng,y,pg: three arrays of shape(nbitem)
          The likelihood of the data under the 3 components
      """
      ng = _gam_dens(self.shape_n, self.scale_n, -x)
      y = _gaus_dens(self.mean, self.var, x)
      pg = _gam_dens(self.shape_p, self.scale_p, x)
  
      return ng, y, pg
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 9 for calling the GGGM obj's underlying member method code with example usage: obj.component_likelihood(x) and returns: ng,  y,  pg