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 likelihood of each data for each component
  the values are not weighted by the component weights
  
  Parameters
  ----------
  x: array of shape (n_samples,self.dim)
     the data used in the estimation process
  
  Returns
  -------
  like, array of shape(n_samples,self.k)
    unweighted component-wise likelihood
  
  Notes
  -----
  Hopefully faster
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code: def unweighted_likelihood(self, x):
      """
      return the likelihood of each data for each component
      the values are not weighted by the component weights
  
      Parameters
      ----------
      x: array of shape (n_samples,self.dim)
         the data used in the estimation process
  
      Returns
      -------
      like, array of shape(n_samples,self.k)
        unweighted component-wise likelihood
  
      Notes
      -----
      Hopefully faster
      """
      xt = x.T.copy()
      n = x.shape[0]
      like = np.zeros((n, self.k))
  
      for k in range(self.k):
          # compute the data-independent factor first
          w = -np.log(2 * np.pi) * self.dim
          m = np.reshape(self.means[k], (self.dim, 1))
          b = self.precisions[k]
          if self.prec_type == "full":
              w += np.log(eigvalsh(b)).sum()
              dx = xt - m
              sqx = dx * np.dot(b, dx)
              q = np.zeros(n)
              for d in range(self.dim):
                  q += sqx[d]
          else:
              w += np.sum(np.log(b))
              q = np.dot(b, (m - xt) ** 2)
          w -= q
          w /= 2
          like[:, k] = np.exp(w)
      return like
  
imports: import numpy
  from scipy.linalg import eigvalsh
label_desc: Use image node_id 10 for calling the GMM obj's underlying member method code with example usage: obj.unweighted_likelihood(x) and returns: like