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sklearn.neighbors.radius_neighbors_graph(X, radius, mode='connectivity')

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 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, in ‘distance’ the edges are Euclidean distance between points.

Returns :

A : sparse matrix in CSR format, shape = [n_samples, n_samples]

A[i, j] is assigned the weight of edge that connects i to j.

See also



>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import radius_neighbors_graph
>>> A = radius_neighbors_graph(X, 1.5)
>>> A.todense()
matrix([[ 1.,  0.,  1.],
        [ 0.,  1.,  0.],
        [ 1.,  0.,  1.]])