k-nearest neighbors search for RBush. Implements a simple depth-first kNN search algorithm using a priority queue.
import RBush from 'rbush';
import knn from 'rbush-knn';
const tree = new RBush(); // create RBush tree
tree.load(data); // bulk insert
const neighbors = knn(tree, 40, 40, 10); // return 10 nearest items around point [40, 40]
You can optionally pass a filter function to find k neighbors that satisfy a certain condition:
const neighbors = knn(tree, 40, 40, 10, function (item) {
return item.foo === 'bar';
});
knn(tree, x, y, [k, filterFn, maxDistance])
tree
: an RBush treex
,y
: query coordinatesk
: number of neighbors to search for (Infinity
by default)filterFn
: optional filter function;k
nearest items wherefilterFn(item) === true
will be returned.maxDistance
(optional): maximum distance between neighbors and the query coordinates (Infinity
by default)