class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, outlier_label=None, metric_params=None, n_jobs=None, **kwargs)
[source]
Classifier implementing a vote among neighbors within a given radius
Read more in the User Guide.
Parameters: 


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
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import RadiusNeighborsClassifier >>> neigh = RadiusNeighborsClassifier(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsClassifier(...) >>> print(neigh.predict([[1.5]])) [0]
fit (X, y)  Fit the model using X as training data and y as target values 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict the class labels for the provided data 
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 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
set_params (**params)  Set the parameters of this estimator. 
__init__(radius=1.0, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, outlier_label=None, metric_params=None, n_jobs=None, **kwargs)
[source]
fit(X, y)
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Fit the model using X as training data and y as target values
Parameters: 


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


Returns: 

predict(X)
[source]
Predict the class labels for the provided data
Parameters: 


Returns: 

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.]])
score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: 


Returns: 

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.RadiusNeighborsClassifier.html