https://github.com/justinethier/node-kdtree

A node.js add-on for performing efficient Nearest Neighbor searches using libkdtree.
https://github.com/justinethier/node-kdtree

c kd-tree nodejs

Last synced: 2 months ago
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A node.js add-on for performing efficient Nearest Neighbor searches using libkdtree.

• Host: GitHub
• URL: https://github.com/justinethier/node-kdtree
• Owner: justinethier
• License: mit
• Created: 2011-03-13T18:27:51.000Z (almost 14 years ago)
• Default Branch: master
• Last Pushed: 2018-07-17T09:41:02.000Z (over 6 years ago)
• Last Synced: 2024-11-05T14:19:53.042Z (3 months ago)
• Topics: c, kd-tree, nodejs
• Language: C
• Homepage: http://justinethier.github.com/node-kdtree
• Size: 113 KB
• Stars: 58
• Watchers: 5
• Forks: 60
• Open Issues: 3
• Metadata Files:
  • Readme: README.markdown
  • License: LICENSE

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README

        
[](https://github.com/justinethier/node-kdtree) 

#node-kdtree

node-kdtree is a node.js addon that defines a wrapper to libkdtree, allowing one to work with KD trees directly in node. A [KD tree](http://en.wikipedia.org/wiki/Kd-tree) is a data structure that organizes points in a multi-dimensional space, and in particular is useful for performing efficient nearest neighbor searches.

## Dependencies

The [kdtree](https://github.com/jtsiomb/kdtree) C library is required. In order to install, get the latest version from [here](https://github.com/jtsiomb/kdtree/releases) and run the following commands:

    ./configure

    make

    sudo make install PREFIX=/usr 

## Installation

The easiest way to install node-kdtree is to use the [npm](https://github.com/isaacs/npm) package manager:

    npm install kdtree

## Usage

###Creating a tree

You may create a tree by instantiating a new `KDTree` object:

    var kd = require('kdtree');

    var tree = new kd.KDTree(3); // A new tree for 3-dimensional points

When creating a new tree we can specify the dimensions of the data. For example, a three-dimensional tree will contain points of the form (x, y, z). If a dimension is not specified, the tree defaults to three dimensions.

###Adding data to a tree

Data may be added to the tree using the `insert` method:

    tree.insert(1, 2, 3);

    tree.insert(10, 20, 30);

There must be one argument for each dimension of the data - for example, a three dimensional tree would have three arguments to `insert`. An optional data parameter may also be specified to store a data value alongside the point data:

    tree.insert(39.285785, -76.610262, "USS Constellation");

###Nearest neighbor searches

The `nearest` method is used to find the point in the tree that is closest to a target point. For example:

    > tree.nearest(39.273889, -76.738056);

    [39.272051, -76.731917, "Bill's Music, Inc."]

`nearest` will return an array containing closest point, or an empty array if no points were found. As shown above, if the point contains a data value, that value will also be returned at the end of the array.

A `nearestRange` method is also provided, which allows us to find all of the points within a given range. For example:

    > tree.nearestRange(0, 0, 3);

    [ [ 1, 1 ],

      [ 0, 2 ],

      [ 2, 0 ],

      [ 1, 0 ],

      [ 0, 1 ],

      [ 0, 0 ] ]

The first arguments to `nearestRange` are the components of the point to begin searching at. The last argument is the search range.

##API

[API documentation](https://github.com/justinethier/node-kdtree/blob/master/doc/API.markdown)

##Credits

node-kdtree is developed by [Justin Ethier](http://github.com/justinethier).

Thanks to John Tsiombikas for developing libkdtree!

Patches are welcome; please send via pull request on github.