sklearn.neighbors.kneighbors_graph

sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode=’connectivity’, metric=’minkowski’, p=2, metric_params=None, include_self=False, n_jobs=None)
[source]

Computes the (weighted) graph of kNeighbors for points in X
Read more in the User Guide.
Parameters: 

X : arraylike 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, and ‘distance’ will return the distances between neighbors according to the given metric. 
metric : string, default ‘minkowski’ 
The distance metric used to calculate the kNeighbors for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is ‘euclidean’ (‘minkowski’ metric with the p param equal to 2.) 
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. 
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 compatibility. 
n_jobs : int or None, optional (default=None) 
The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. 1 means using all processors. See Glossary for more details. 
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. 
Examples
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import kneighbors_graph
>>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True)
>>> A.toarray()
array([[1., 0., 1.],
[0., 1., 1.],
[1., 0., 1.]])
Examples using sklearn.neighbors.kneighbors_graph