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: Returns the log-likelihood of the mixture for x
  
  Parameters
  ----------
  x array of shape (n_samples,self.dim)
    the data used in the estimation process
  
  Returns
  -------
  ll: array of shape(n_samples)
      the log-likelihood of the rows of x
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code: def test(self, x, tiny=1.0e-15):
      """Returns the log-likelihood of the mixture for x
  
      Parameters
      ----------
      x array of shape (n_samples,self.dim)
        the data used in the estimation process
  
      Returns
      -------
      ll: array of shape(n_samples)
          the log-likelihood of the rows of x
      """
      return np.log(np.maximum(self.mixture_likelihood(x), tiny))
  
imports: import numpy
  from scipy.linalg import eigvalsh
label_desc: Use image node_id 22 for calling the GMM obj's underlying member method code with example usage: obj.test(x, tiny) and returns: np