sklearn.neighbors.radius_neighbors_graph()

X : array-like or BallTree, shape = [n_samples, n_features]

Sample data, in the form of a numpy array or a precomputed BallTree.

radius : float

Radius of neighborhoods.

mode : {?connectivity?, ?distance?}, optional

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.

metric : string, 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.)

include_self: bool, default=False :

Whether or not to mark each sample as the first nearest neighbor to itself. If None, then True is used for mode=?connectivity? and False for mode=?distance? as this will preserve backwards compatibilty.

p : int, 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_params: dict, optional :

additional keyword arguments for the metric function.

n_jobs : int, optional (default = 1)

The number of parallel jobs to run for neighbors search. If -1, then the number of jobs is set to the number of CPU cores.