class_docstr: Standard GMM.
  
  this class contains the following members
  k (int): the number of components in the mixture
  dim (int): is the dimension of the data
  prec_type = 'full' (string) is the parameterization
            of the precisions/covariance matrices:
            either 'full' or 'diagonal'.
  means: array of shape (k,dim):
        all the means (mean parameters) of the components
  precisions: array of shape (k,dim,dim):
             the precisions (inverse covariance matrix) of the components
  weights: array of shape(k): weights of the mixture
  
  fixme
  -----
  no copy method
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docstr: return the MAP labelling of x
  
  Parameters
  ----------
  x array of shape (n_samples,dim)
    the data under study
  like=None array of shape(n_samples,self.k)
         component-wise likelihood
         if like==None, it is recomputed
  
  Returns
  -------
  z: array of shape(n_samples): the resulting MAP labelling
     of the rows of x
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code: def map_label(self, x, like=None):
      """return the MAP labelling of x
  
      Parameters
      ----------
      x array of shape (n_samples,dim)
        the data under study
      like=None array of shape(n_samples,self.k)
             component-wise likelihood
             if like==None, it is recomputed
  
      Returns
      -------
      z: array of shape(n_samples): the resulting MAP labelling
         of the rows of x
      """
      if like is None:
          like = self.likelihood(x)
      z = np.argmax(like, 1)
      return z
  
imports: import numpy
  from scipy.linalg import eigvalsh
label_desc: Use image node_id 18 for calling the GMM obj's underlying member method code with example usage: obj.map_label(x, like) and returns: z