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]#

Compute the (weighted) graph of k-Neighbors for points in X.

Read more in theUser Guide.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Sample data.

n_neighborsint

Number of neighbors for each sample.

mode{‘connectivity’, ‘distance’}, default=’connectivity’

Type of returned matrix: ‘connectivity’ will return the connectivitymatrix with ones and zeros, and ‘distance’ will return the distancesbetween neighbors according to the given metric.

metricstr, default=’minkowski’

Metric to use for distance computation. Default is “minkowski”, whichresults in the standard Euclidean distance when p = 2. See thedocumentation ofscipy.spatial.distance andthe metrics listed indistance_metrics for valid metricvalues.

pfloat, default=2

Power parameter for the Minkowski metric. When p = 1, this is equivalentto using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.For arbitrary p, minkowski_distance (l_p) is used. This parameter is expectedto be positive.

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 toitself. If ‘auto’, then True is used for mode=’connectivity’ and Falsefor 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. SeeGlossaryfor more details.

Returns:

Asparse matrix of shape (n_samples, n_samples)

Graph where A[i, j] is assigned the weight of edge thatconnects i to j. The matrix is of CSR format.

See also

radius_neighbors_graph

Compute the (weighted) graph of Neighbors for points in X.

Examples

>>>X=[[0],[3],[1]]>>>fromsklearn.neighborsimportkneighbors_graph>>>A=kneighbors_graph(X,2,mode='connectivity',include_self=True)>>>A.toarray()array([[1., 0., 1.],       [0., 1., 1.],       [1., 0., 1.]])

Gallery examples#

Agglomerative clustering with and without structure

Agglomerative clustering with and without structure

Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

Hierarchical clustering: structured vs unstructured ward

Hierarchical clustering: structured vs unstructured ward