-itBSrSSKJr SSKJrJrJr "SS\\\5rg)z&Unsupervised nearest neighbors learner) _fit_context)KNeighborsMixin NeighborsBaseRadiusNeighborsMixinc ^^\rSrSrSrSSSSSSS S S .U4S jjr\"S S 9SSj5rSrU=r $)NearestNeighbors a Unsupervised learner for implementing neighbor searches. Read more in the :ref:`User Guide `. .. 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' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. 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. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. p : float (positive), 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 ` 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. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_samples_fit_ : int Number of samples in the fitted data. See Also -------- KNeighborsClassifier : Classifier implementing the k-nearest neighbors vote. RadiusNeighborsClassifier : Classifier implementing a vote among neighbors within a given radius. KNeighborsRegressor : Regression based on k-nearest neighbors. RadiusNeighborsRegressor : Regression based on neighbors within a fixed radius. BallTree : Space partitioning data structure for organizing points in a multi-dimensional space, used for nearest neighbor search. Notes ----- See :ref:`Nearest Neighbors ` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``. https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm 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) g?auto minkowskirN n_neighborsradius algorithm leaf_sizemetricp metric_paramsn_jobsc .>[T U]UUUUUUUUS9 g)Nr)super__init__) selfrrrrrrrr __class__s R/var/www/html/venv/lib/python3.13/site-packages/sklearn/neighbors/_unsupervised.pyrNearestNeighbors.__init__s/ #'  F)prefer_skip_nested_validationc$URU5$)aFit the nearest neighbors estimator from the training dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric='precomputed' Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : NearestNeighbors The fitted nearest neighbors estimator. )_fit)rXys rfitNearestNeighbors.fits*yy|r)N) __name__ __module__ __qualname____firstlineno____doc__rrr%__static_attributes__ __classcell__)rs@rr r sJz~   .&+ rr N)r,baser_baserrrr r'rrr1s&,  GGi(