class sklearn.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm=’auto’, leaf_size=30, metric=’minkowski’, p=2, metric_params=None, n_jobs=None, **kwargs)
[source]
Unsupervised learner for implementing neighbor searches.
Read more in the User Guide.
Parameters: 


See also
KNeighborsClassifier
, RadiusNeighborsClassifier
, KNeighborsRegressor
, RadiusNeighborsRegressor
, BallTree
See Nearest Neighbors in the online documentation for a discussion of the choice of algorithm
and leaf_size
.
https://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
>>> 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) 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)
fit (X[, y])  Fit the model using X as training data 
get_params ([deep])  Get parameters for this estimator. 
kneighbors ([X, n_neighbors, return_distance])  Finds the Kneighbors of a point. 
kneighbors_graph ([X, n_neighbors, mode])  Computes the (weighted) graph of kNeighbors for points in X 
radius_neighbors ([X, radius, return_distance])  Finds the neighbors within a given radius of a point or points. 
radius_neighbors_graph ([X, radius, mode])  Computes the (weighted) graph of Neighbors for points in X 
set_params (**params)  Set the parameters of this estimator. 
__init__(n_neighbors=5, radius=1.0, algorithm=’auto’, leaf_size=30, metric=’minkowski’, p=2, metric_params=None, n_jobs=None, **kwargs)
[source]
fit(X, y=None)
[source]
Fit the model using X as training data
Parameters: 


get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

kneighbors(X=None, n_neighbors=None, return_distance=True)
[source]
Finds the Kneighbors of a point. Returns indices of and distances to the neighbors of each point.
Parameters: 


Returns: 

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]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> 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]]...)
kneighbors_graph(X=None, n_neighbors=None, mode=’connectivity’)
[source]
Computes the (weighted) graph of kNeighbors for points in X
Parameters: 


Returns: 

>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]])
radius_neighbors(X=None, radius=None, return_distance=True)
[source]
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: 


Returns: 

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.
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(algorithm='auto', leaf_size=30, ...) >>> rng = neigh.radius_neighbors([[1., 1., 1.]]) >>> print(np.asarray(rng[0][0])) [1.5 0.5] >>> print(np.asarray(rng[1][0])) [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.
radius_neighbors_graph(X=None, radius=None, mode=’connectivity’)
[source]
Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than radius.
Parameters: 


Returns: 

See also
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]])
set_params(**params)
[source]
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.
Returns: 


© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html