"""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.

    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:`scipy.spatial.cKDtree`
        - '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 cKDTree.  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.

    Examples
    --------
      >>> from sklearn.neighbors import NearestNeighbors
      >>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]

      >>> neigh = NearestNeighbors(2, 0.4)
      >>> neigh.fit(samples)
      NearestNeighbors(algorithm='auto', leaf_size=30, n_neighbors=2, radius=0.4)

      >>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
      array([[2, 0]])

      >>> neigh.radius_neighbors([0, 0, 1.3], 0.4, return_distance=False)
      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``.

    References
    ----------
    http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
    """

    def __init__(self, n_neighbors=5, radius=1.0,
                 algorithm='auto', leaf_size=30):
        self._init_params(n_neighbors=n_neighbors,
                          radius=radius,
                          algorithm=algorithm,
                          leaf_size=leaf_size)
