sklearn.neighbors
.radius_neighbors_graph¶

sklearn.neighbors.
radius_neighbors_graph
(X, radius, *, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=None)[source]¶ Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than radius.
Read more in the User Guide.
 Parameters
 Xarraylike of shape (n_samples, n_features) or BallTree
Sample data, in the form of a numpy array or a precomputed
BallTree
. radiusfloat
Radius of neighborhoods.
 mode{‘connectivity’, ‘distance’}, default=’connectivity’
Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, and ‘distance’ will return the distances between neighbors according to the given metric.
 metricstr, default=’minkowski’
The distance metric used to calculate the neighbors within a given radius for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is ‘euclidean’ (‘minkowski’ metric with the param equal to 2.)
 pint, default=2
Power parameter for the Minkowski metric. 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_paramsdict, default=None
additional keyword arguments for the metric function.
 include_selfbool or ‘auto’, default=False
Whether or not to mark each sample as the first nearest neighbor to itself. If ‘auto’, then True is used for mode=’connectivity’ and False for mode=’distance’.
 n_jobsint, default=None
The number of parallel jobs to run for neighbors search.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
 Returns
 Asparse matrix of shape (n_samples, n_samples)
Graph where A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format.
See also
Examples
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import radius_neighbors_graph >>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', ... include_self=True) >>> A.toarray() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]])