8.21.7. sklearn.neighbors.kneighbors_graph¶
- sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode='connectivity')¶
Computes the (weighted) graph of k-Neighbors for points in X
Parameters : X : array-like or BallTree, shape = [n_samples, n_features]
Sample data, in the form of a numpy array or a precomputed BallTree.
n_neighbors : int
Number of neighbors for each sample.
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
Examples
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import kneighbors_graph >>> A = kneighbors_graph(X, 2) >>> A.todense() matrix([[ 1., 0., 1.], [ 0., 1., 1.], [ 1., 0., 1.]])