package sklearn

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(nbrs00) 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

Fit the model using X as training data

Parameters ---------- X : array-like, sparse matrix, BallTree, KDTree Training data. If array or matrix, shape n_samples, n_features, or n_samples, n_samples if metric='precomputed'.

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

Parameters ---------- X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

n_neighbors : int Number of neighbors to get (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True. If False, distances will not be returned

Returns ------- neigh_dist : array, shape (n_queries, n_neighbors) Array representing the lengths to points, only present if return_distance=True

neigh_ind : array, shape (n_queries, n_neighbors) Indices of the nearest points in the population matrix.

Examples -------- In the following example, we construct a NearestNeighbors class from an array representing our data set and ask who's the closest point to 1,1,1

>>> samples = [0., 0., 0.], [0., .5, 0.], [1., 1., .5] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) NearestNeighbors(n_neighbors=1) >>> print(neigh.kneighbors([1., 1., 1.])) (array([0.5]), array([2]))

As you can see, it returns [0.5], and [2], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points:

>>> X = [0., 1., 0.], [1., 0., 1.] >>> neigh.kneighbors(X, return_distance=False) array([1], [2]...)

Computes the (weighted) graph of k-Neighbors for points in X

Parameters ---------- X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

n_neighbors : int Number of neighbors for each sample. (default is value passed to the constructor).

mode : 'connectivity', 'distance', optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points.

Returns ------- A : sparse graph in CSR format, shape = n_queries, n_samples_fit n_samples_fit is the number of samples in the fitted data Ai, j is assigned the weight of edge that connects i to j.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) NearestNeighbors(n_neighbors=2) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([1., 0., 1.], [0., 1., 1.], [1., 0., 1.])

See also -------- NearestNeighbors.radius_neighbors_graph

Finds the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size ``radius`` around the points of the query array. Points lying on the boundary are included in the results.

The result points are *not* necessarily sorted by distance to their query point.

Parameters ---------- X : array-like, (n_samples, n_features), optional The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Limiting distance of neighbors to return. (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True. If False, distances will not be returned.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. If return_distance == False, setting sort_results = True will result in an error.

.. versionadded:: 0.22

Returns ------- neigh_dist : array, shape (n_samples,) of arrays Array representing the distances to each point, only present if return_distance=True. The distance values are computed according to the ``metric`` constructor parameter.

neigh_ind : array, shape (n_samples,) of arrays An array of arrays of indices of the approximate nearest points from the population matrix that lie within a ball of size ``radius`` around the query points.

Examples -------- In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who's the closest point to 1, 1, 1:

>>> import numpy as np >>> samples = [0., 0., 0.], [0., .5, 0.], [1., 1., .5] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) NearestNeighbors(radius=1.6) >>> rng = neigh.radius_neighbors([1., 1., 1.]) >>> print(np.asarray(rng00)) 1.5 0.5 >>> print(np.asarray(rng10)) 1 2

The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time.

Notes ----- Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, `radius_neighbors` returns arrays of objects, where each object is a 1D array of indices or distances.

Computes the (weighted) graph of Neighbors for points in X

Neighborhoods are restricted the points at a distance lower than radius.

Parameters ---------- X : array-like of shape (n_samples, n_features), default=None The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Radius of neighborhoods. (default is the value passed to the constructor).

mode : 'connectivity', 'distance', optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. Only used with mode='distance'.

.. versionadded:: 0.22

Returns ------- A : sparse graph in CSR format, shape = n_queries, n_samples_fit n_samples_fit is the number of samples in the fitted data Ai, j is assigned the weight of edge that connects i to j.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(radius=1.5) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([1., 0., 1.], [0., 1., 0.], [1., 0., 1.])

See also -------- kneighbors_graph

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute effective_metric_: get value or raise Not_found if None.

Attribute effective_metric_: get value as an option.

Attribute effective_metric_params_: get value or raise Not_found if None.

Attribute effective_metric_params_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.