class_docstr: Perform Affinity Propagation Clustering of data.
  
  Read more in the :ref:`User Guide <affinity_propagation>`.
  
  Parameters
  ----------
  damping : float, optional, default: 0.5
      Damping factor between 0.5 and 1.
  
  convergence_iter : int, optional, default: 15
      Number of iterations with no change in the number
      of estimated clusters that stops the convergence.
  
  max_iter : int, optional, default: 200
      Maximum number of iterations.
  
  copy : boolean, optional, default: True
      Make a copy of input data.
  
  preference : array-like, shape (n_samples,) or float, optional
      Preferences for each point - points with larger values of
      preferences are more likely to be chosen as exemplars. The number
      of exemplars, ie of clusters, is influenced by the input
      preferences value. If the preferences are not passed as arguments,
      they will be set to the median of the input similarities.
  
  affinity : string, optional, default=``euclidean``
      Which affinity to use. At the moment ``precomputed`` and
      ``euclidean`` are supported. ``euclidean`` uses the
      negative squared euclidean distance between points.
  
  verbose : boolean, optional, default: False
      Whether to be verbose.
  
  
  Attributes
  ----------
  cluster_centers_indices_ : array, shape (n_clusters,)
      Indices of cluster centers
  
  cluster_centers_ : array, shape (n_clusters, n_features)
      Cluster centers (if affinity != ``precomputed``).
  
  labels_ : array, shape (n_samples,)
      Labels of each point
  
  affinity_matrix_ : array, shape (n_samples, n_samples)
      Stores the affinity matrix used in ``fit``.
  
  n_iter_ : int
      Number of iterations taken to converge.
  
  Notes
  -----
  See examples/cluster/plot_affinity_propagation.py for an example.
  
  The algorithmic complexity of affinity propagation is quadratic
  in the number of points.
  
  References
  ----------
  
  Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
  Between Data Points", Science Feb. 2007
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code: def __init__(
      self,
      damping=0.5,
      max_iter=200,
      convergence_iter=15,
      copy=True,
      preference=None,
      affinity="euclidean",
      verbose=False,
  ):
      self.damping = damping
      self.max_iter = max_iter
      self.convergence_iter = convergence_iter
      self.copy = copy
      self.verbose = verbose
      self.preference = preference
      self.affinity = affinity
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/cluster/affinity_propagation_.py
imports: import numpy
  from ..base import BaseEstimator, ClusterMixin
  from ..utils import as_float_array, check_array
  from ..utils.validation import check_is_fitted
  from ..metrics import euclidean_distances
  from ..metrics import pairwise_distances_argmin
label_desc: Use image node_id 1 to create a new AffinityPropagation object from inherited base classes: BaseEstimator, ClusterMixin with example: obj = AffinityPropagation(damping, max_iter, convergence_iter, copy, preference, affinity, verbose)