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https://github.com/ubilabs/kd-tree-javascript

JavaScript k-d Tree Implementation
https://github.com/ubilabs/kd-tree-javascript

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JavaScript k-d Tree Implementation

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README 

        
# k-d Tree JavaScript Library



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](http://en.wikipedia.org/wiki/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.



### Demos



* [Spiders](http://ubilabs.github.com/kd-tree-javascript/examples/basic/) - animated multiple nearest neighbour search

* [Google Map](http://ubilabs.github.com/kd-tree-javascript/examples/map/) - show nearest 20 out of 3000 markers on mouse move

* [Colors](http://ubilabs.github.com/kd-tree-javascript/examples/colors/) - search color names based on color space distance

* [Mutable](http://ubilabs.github.com/kd-tree-javascript/examples/mutable/) - dynamically add and remove nodes



### Usage



#### Using global exports

When you include the kd-tree script via HTML, the global variables *kdTree* and *BinaryHeap* will be exported.



```js

// 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();

```



#### Using RequireJS

```js

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();

});

```



### Example



```js

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);

```



## About



Developed at [Ubilabs](http://ubilabs.net).

Released under the MIT Licence.