class_docstr: GaussianMixture Bayes Classifier
  
  This is a generalization to the Naive Bayes classifier: rather than
  modeling the distribution of each class with axis-aligned gaussians,
  GMMBayes models the distribution of each class with mixtures of
  gaussians.  This can lead to better classification in some cases.
  
  Parameters
  ----------
  n_components : int or list
      number of components to use in the GaussianMixture. If specified as
      a list, it must match the number of class labels. Default is 1.
  **kwargs : dict, optional
      other keywords are passed directly to GaussianMixture
code: def _joint_log_likelihood(self, X):
      X = np.asarray(np.atleast_2d(X))
      logprobs = np.array([g.score_samples(X) for g in self.gmms_]).T
      return logprobs + np.log(self.class_prior_)
  
file_path: astroML/astroML/classification/gmm_bayes.py
imports: import warnings
  import numpy
  from sklearn.mixture import GaussianMixture
  from sklearn.utils import check_array
label_desc: Use image node_id 3 for calling the GMMBayes obj's underlying member method code with example usage: obj._joint_log_likelihood(X) and returns: unknown