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venv/Lib/site-packages/sklearn/neighbors/_unsupervised.py

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+"""Unsupervised nearest neighbors learner"""
+from ._base import NeighborsBase
+from ._base import KNeighborsMixin
+from ._base import RadiusNeighborsMixin
+from ._base import UnsupervisedMixin
+from ..utils.validation import _deprecate_positional_args
+
+
+class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin,
+                       UnsupervisedMixin, NeighborsBase):
+    """Unsupervised learner for implementing neighbor searches.
+
+    Read more in the :ref:`User Guide <unsupervised_neighbors>`.
+
+    .. versionadded:: 0.9
+
+    Parameters
+    ----------
+    n_neighbors : int, default=5
+        Number of neighbors to use by default for :meth:`kneighbors` queries.
+
+    radius : float, default=1.0
+        Range of parameter space to use by default for :meth:`radius_neighbors`
+        queries.
+
+    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
+        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, 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.
+
+    metric : str or callable, default='minkowski'
+        the distance metric to use for the tree.  The default metric is
+        minkowski, and with p=2 is equivalent to the standard Euclidean
+        metric. See the documentation of :class:`DistanceMetric` for a
+        list of available metrics.
+        If metric is "precomputed", X is assumed to be a distance matrix and
+        must be square during fit. X may be a :term:`sparse graph`,
+        in which case only "nonzero" elements may be considered neighbors.
+
+    p : int, 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_params : dict, default=None
+        Additional keyword arguments for the metric function.
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run for neighbors search.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    Attributes
+    ----------
+    effective_metric_ : str
+        Metric used to compute distances to neighbors.
+
+    effective_metric_params_ : dict
+        Parameters for the metric used to compute distances to neighbors.
+
+    Examples
+    --------
+      >>> import numpy as np
+      >>> from sklearn.neighbors import NearestNeighbors
+      >>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]
+
+      >>> neigh = NearestNeighbors(n_neighbors=2, radius=0.4)
+      >>> neigh.fit(samples)
+      NearestNeighbors(...)
+
+      >>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
+      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
+    """
+
+    @_deprecate_positional_args
+    def __init__(self, *, n_neighbors=5, radius=1.0,
+                 algorithm='auto', leaf_size=30, metric='minkowski',
+                 p=2, metric_params=None, n_jobs=None):
+        super().__init__(
+              n_neighbors=n_neighbors,
+              radius=radius,
+              algorithm=algorithm,
+              leaf_size=leaf_size, metric=metric, p=p,
+              metric_params=metric_params, n_jobs=n_jobs)