Search
Code Directory
 ASP
 ASP.NET
 C/C++
 CFML
 CGI/PERL
 Delphi
 Development
 Flash
 HTML
 Java
 JavaScript
 Pascal
 PHP
 Python
 SQL
 Tools
 Visual Basic & VB.NET
 XML
New Code
AnyGantt JS Gantt Charts 8.1.0
PHP Scripts Mall Pvt Ltd 1.0.2
Bytescout PDF To HTML SDK 8.7.0.2980
JavaScript Framework Shield UI 1.7.34
Fortune Car Rental Script 3.4
Fortune Stackoverflow Clone 3.4
VintaSoft Imaging .NET SDK 8.6
wolfSSL 3.12.2
Bytescout BarCode Generator SDK 4.62.0.964
ThomasNet Clone Script 2.0
Easy Button & Menu Maker 5.0
Entrepreneur News Portal 1.6
Fortune Quibids Clone 3.4
Database Workbench Pro 5.3.4
Extensibility Studio 2.0
Top Code
Output Messenger - company chat software 1.7.6
Indiegogo Clone 3.0
PHP Image Resize Script 1.0
Jango Clone Script 1.0
Best Spotify Clone 1.0
Get Random Record Based on Weight 1.0.0
Travel Portal Script 9.29
Magento Product Designer 1.0
OFOS - Just Eat Clone Script 1.0
PrestaShop Upload Images Module 1.2.1
Trading Software 1.2.4
Readymade MLM Products 2.01
ADO.NET Provider for ExactTarget 1.0
Solid File System OS edition 5.1
Classified Ad Lister 1.0
Report About FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION 1.0
- required fields

Please enter text on the image
  



In this file you can find a simple but very effective algorithm for Nearest Neighbour Search which I megalomaniacly called the GLTree.

You want more? go to the Professional version of GLTree

It has been designed for uniformly random data, where is the fastest I ever used, but works fine even on sparse ones. If points are too sparse, for example logspace data, search is still performed correctly but speed can degenerate to a brute search algorithm. In these cases a different data structure is needed but for lack of time I havend-deOaot still coded. If query points are close to reference it is very efficient on sparse dataset too.

The tree can be build without running any search. The pointer passed to workspace can be used for the above routines. The tree costruction has linear time complexity and it is very fast, so it becomes advantageus against brute search even for a small number of points. Id-deOaod like to point that in GL-Tree searching has linear complexity (on uniform dataset) instead of n*log(n) like in kd-tree.
It is possible to choose if return the distances.

<!--dp_youtube_begin:http://www.youtube.com/watch?v=mJ2ztIDGVNU&feature=player_embedded--><object width="425" height="344"><param name="movie" value="http://www.youtube.com/v/mJ2ztIDGVNU&hl=ru&fs=1"></param><param name="wmode" value="transparent" /><param name="allowFullScreen" value="true"></param><param name="allowscriptaccess" value="always"></param><embed src="http://www.youtube.com/v/mJ2ztIDGVNU&hl=ru&fs=1" type="application/x-shockwave-flash" allowscriptaccess="always" allowfullscreen="true" wmode="transparent" width="425" height="344"></embed></object><!--dp_youtube_end-->

Back