sklearn.neighbors.kneighbors_graph — scikit-learn 0.20.4 documentation (original) (raw)
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 k-Neighbors for points in X
Read more in the User Guide.
| Parameters: | X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputedBallTree. 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 k-Neighbors 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 Glossaryfor more details. |
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| 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.]])