content: """
  The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
  cluster analysis results. There are two forms of evaluation:
  
  - supervised, which uses a ground truth class values for each sample.
  - unsupervised, which does not and measures the 'quality' of the model itself.
  """
  from .supervised import adjusted_mutual_info_score
  from .supervised import normalized_mutual_info_score
  from .supervised import adjusted_rand_score
  from .supervised import completeness_score
  from .supervised import contingency_matrix
  from .supervised import expected_mutual_information
  from .supervised import homogeneity_completeness_v_measure
  from .supervised import homogeneity_score
  from .supervised import mutual_info_score
  from .supervised import v_measure_score
  from .supervised import entropy
  from .unsupervised import silhouette_samples
  from .unsupervised import silhouette_score
  from .bicluster import consensus_score
  
  __all__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score",
             "adjusted_rand_score", "completeness_score", "contingency_matrix",
             "expected_mutual_information", "homogeneity_completeness_v_measure",
             "homogeneity_score", "mutual_info_score", "v_measure_score",
             "entropy", "silhouette_samples", "silhouette_score",
             "consensus_score"]
  