content: """Unsupervised nearest neighbors learner"""
  
  from .base import NeighborsBase
  from .base import KNeighborsMixin
  from .base import RadiusNeighborsMixin
  from .base import UnsupervisedMixin
  
  
  class NearestNeighbors(NeighborsBase, KNeighborsMixin,
                         RadiusNeighborsMixin, UnsupervisedMixin):
      """Unsupervised learner for implementing neighbor searches.
  
      Read more in the :ref:`User Guide <unsupervised_neighbors>`.
  
      Parameters
      ----------
      n_neighbors : int, optional (default = 5)
          Number of neighbors to use by default for :meth:`k_neighbors` queries.
  
      radius : float, optional (default = 1.0)
          Range of parameter space to use by default for :meth`radius_neighbors`
          queries.
  
      algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
          Algorithm used to compute the nearest neighbors:
  
          - 'ball_tree' will use :class:`BallTree`
          - 'kd_tree' will use :class:`KDtree`
          - 'brute' will use a brute-force search.
          - 'auto' will attempt to decide the most appropriate algorithm
            based on the values passed to :meth:`fit` method.
  
          Note: fitting on sparse input will override the setting of
          this parameter, using brute force.
  
      leaf_size : int, optional (default = 30)
          Leaf size passed to BallTree or KDTree.  This can affect the
          speed of the construction and query, as well as the memory
          required to store the tree.  The optimal value depends on the
          nature of the problem.
  
      p: integer, optional (default = 2)
          Parameter for the Minkowski metric from
          sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
          equivalent to using manhattan_distance (l1), and euclidean_distance
          (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
  
      metric : string or callable, default 'minkowski'
          metric to use for distance computation. Any metric from scikit-learn
          or scipy.spatial.distance can be used.
  
          If metric is a callable function, it is called on each
          pair of instances (rows) and the resulting value recorded. The callable
          should take two arrays as input and return one value indicating the
          distance between them. This works for Scipy's metrics, but is less
          efficient than passing the metric name as a string.
  
          Distance matrices are not supported.
  
          Valid values for metric are:
  
          - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
            'manhattan']
  
          - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
            'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
            'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
            'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
            'sqeuclidean', 'yule']
  
          See the documentation for scipy.spatial.distance for details on these
          metrics.
  
      metric_params: dict, optional (default = None)
          additional keyword arguments for the metric function.
  
      Examples
      --------
        >>> import numpy as np
        >>> from sklearn.neighbors import NearestNeighbors
        >>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]
  
        >>> neigh = NearestNeighbors(2, 0.4)
        >>> neigh.fit(samples)  #doctest: +ELLIPSIS
        NearestNeighbors(...)
  
        >>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
        ... #doctest: +ELLIPSIS
        array([[2, 0]]...)
  
        >>> rng = neigh.radius_neighbors([0, 0, 1.3], 0.4, return_distance=False)
        >>> np.asarray(rng[0][0])
        array(2)
  
      See also
      --------
      KNeighborsClassifier
      RadiusNeighborsClassifier
      KNeighborsRegressor
      RadiusNeighborsRegressor
      BallTree
  
      Notes
      -----
      See :ref:`Nearest Neighbors <neighbors>` in the online documentation
      for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
      http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
      """
  
      def __init__(self, n_neighbors=5, radius=1.0,
                   algorithm='auto', leaf_size=30, metric='minkowski',
                   p=2, metric_params=None, **kwargs):
          self._init_params(n_neighbors=n_neighbors,
                            radius=radius,
                            algorithm=algorithm,
                            leaf_size=leaf_size, metric=metric, p=p,
                            metric_params=metric_params, **kwargs)
  