class_docstr: 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.
  
  n_jobs : int, optional (default = 1)
      The number of parallel jobs to run for neighbors search.
      If ``-1``, then the number of jobs is set to the number of CPU cores.
      Affects only :meth:`k_neighbors` and :meth:`kneighbors_graph` methods.
  
  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]]...)
  
    >>> nbrs = neigh.radius_neighbors([[0, 0, 1.3]], 0.4, return_distance=False)
    >>> np.asarray(nbrs[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``.
  
  https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
code: def __init__(
      self,
      n_neighbors=5,
      radius=1.0,
      algorithm="auto",
      leaf_size=30,
      metric="minkowski",
      p=2,
      metric_params=None,
      n_jobs=1,
      **kwargs,
  ):
      self._init_params(
          n_neighbors=n_neighbors,
          radius=radius,
          algorithm=algorithm,
          leaf_size=leaf_size,
          metric=metric,
          p=p,
          metric_params=metric_params,
          n_jobs=n_jobs,
          **kwargs,
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neighbors/unsupervised.py
imports: from .base import NeighborsBase
  from .base import KNeighborsMixin
  from .base import RadiusNeighborsMixin
  from .base import UnsupervisedMixin
label_desc: Use image node_id 1 to create a new NearestNeighbors object from inherited base classes: NeighborsBase, KNeighborsMixin, RadiusNeighborsMixin, UnsupervisedMixin with example: obj = NearestNeighbors(n_neighbors, radius, algorithm, leaf_size, metric, p, metric_params, n_jobs)