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 __init__(self, n_components=1, **kwargs):
      self.n_components = np.atleast_1d(n_components)
      self.kwargs = kwargs
  
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 1 to create a new GMMBayes object from inherited base classes: _BaseNB with example: obj = GMMBayes(n_components)