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A JavaScript implementation of the Vantage-Point Tree nearest neighbor search algorithm.

The VP tree is particularly useful in dividing data in a non-standard metric space into a BSP tree. Tree construction executes in O(n log(n)) time, and search is under certain circumstances and in the limit, O(log(n)) expected time. This makes it suitable when distance computations are expensive.

Simple Example

// A whole lot of strings
var stringList = [
    'empathy'     // and on and on...
// building the tree
vptree = VPTreeFactory.build(stringList, levenshteinDistance);
nearest = vptree.search('democratic'); // [{"i":1,"d":3}]
index = nearest[0].i; // index of nearest element is 1
distance = nearest[0].d; // distance of nearest element is 3
alert( stringList[index] ); // alerts 'democracy'


The API exposes the VPTreeFactory object.

Building the tree

VPTreeFactory.build(S, distance[, bucketSize])

Builds a fresh VPTree instance.

  • S (array) the set of elements
  • distance a function that computes the distance between two elements of S
  • bucketSize (optional) to save space, tree leaves can be collapsed into buckets. This parameter gives the maximum number of leaves to collapse in each bucket.

VPTreeFactory.select(list, k, comp)

An implementation of the quick select algorithm like the nth_element function of the Standard C++ Library.

You will probably never use this function. However, it is used internally, and exposed as a bonus. Could be useful. Who knows.

  • list an array of objects or values
  • k the index of the nth_element to select, between 0 and list.length-1
  • comp comparator, a boolean function with two parameters a and b, and returning true if a < b and false if a ≥ b.

Searching the tree

vptree.search(element[, n])

Searches the n nearest neighbors of element in S.

  • element an object to search in S
  • n the number of closest elements to retrieve. Defaults to 1.

This function returns the list of the n nearest elements found, ordered from the closest to the furthest. Each item in the list is an object with 2 properties :

  • i the index of the element in S
  • d its distance to the query element

Precomputing the tree

Typical usage of this library involves large datasets or expensive distance computations. You will probably want to precompute the vp-tree structure, so that your final application does just the searching.


Returns a stringified JavaScript object literal of the vp-tree structure. Like JSON.stringify but without nulls and quotes to save space. It is valid JavaScript, but not valid JSON, so JSON.parse() will complain.

The stringified object is not the whole VPTree instance : it does not contain the initial dataset, nor the distance function. Its only purpose is to be pasted in the code of your final app, where it will have to be turned back into a searchable VPTree instance with the load() function.

VPTreeFactory.load(S, distance, tree)

Reuses a precomputed stringified vp-tree, and returns a searchable VPTree instance.

  • S the array that was used to pre-build the vp-tree.
  • distance the distance function that was used to pre-build the vp-tree.
  • tree the vp-tree structure object. Must be a plain object.

About the distance function

The vp-tree algorithm needs a real metric. In particular, the squared euclidean distance won't do the job because it does not satisfy the triangle inequality : if you want to use the standard euclidean distance, don't forget the square root.

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