A basic but super fast JavaScript implementation of the k-dimensional tree data structure.
As of version 1.01, the library is defined as an UMD module (based on https://github.com/umdjs/umd/blob/master/commonjsStrict.js).
In computer science, a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. k-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches). k-d trees are a special case of binary space partitioning trees.
- Spiders - animated multiple nearest neighbour search
- Google Map - show nearest 20 out of 3000 markers on mouse move
- Colors - search color names based on color space distance
- Mutable - dynamically add and remove nodes
When you include the kd-tree script via HTML, the global variables kdTree and BinaryHeap will be exported.
// Create a new tree from a list of points, a distance function, and a
// list of dimensions.
var tree = new kdTree(points, distance, dimensions);
// Query the nearest *count* neighbours to a point, with an optional
// maximal search distance.
// Result is an array with *count* elements.
// Each element is an array with two components: the searched point and
// the distance to it.
tree.nearest(point, count, [maxDistance]);
// Insert a new point into the tree. Must be consistent with previous
// contents.
tree.insert(point);
// Remove a point from the tree by reference.
tree.remove(point);
// Get an approximation of how unbalanced the tree is.
// The higher this number, the worse query performance will be.
// It indicates how many times worse it is than the optimal tree.
// Minimum is 1. Unreliable for small trees.
tree.balanceFactor();requirejs(['path/to/kdTree.js'], function (ubilabs) {
// Create a new tree from a list of points, a distance function, and a
// list of dimensions.
var tree = new ubilabs.kdTree(points, distance, dimensions);
// Query the nearest *count* neighbours to a point, with an optional
// maximal search distance.
// Result is an array with *count* elements.
// Each element is an array with two components: the searched point and
// the distance to it.
tree.nearest(point, count, [maxDistance]);
// Insert a new point into the tree. Must be consistent with previous
// contents.
tree.insert(point);
// Remove a point from the tree by reference.
tree.remove(point);
// Get an approximation of how unbalanced the tree is.
// The higher this number, the worse query performance will be.
// It indicates how many times worse it is than the optimal tree.
// Minimum is 1. Unreliable for small trees.
tree.balanceFactor();
});var points = [
{x: 1, y: 2},
{x: 3, y: 4},
{x: 5, y: 6},
{x: 7, y: 8}
];
var distance = function(a, b){
return Math.pow(a.x - b.x, 2) + Math.pow(a.y - b.y, 2);
}
var tree = new kdTree(points, distance, ["x", "y"]);
var nearest = tree.nearest({ x: 5, y: 5 }, 2);
console.log(nearest);Developed at Ubilabs. Released under the MIT Licence.
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