/mvptree

A fork of D Grant Starkweather's multiple vantage point tree library

Primary LanguageC

MVPTree C library

  • Version: 1.0.0
  • Date: 2010/12/17
  • Creator: D. Grant Starkweather
  • License: GPLv3
  • Contact: starkd88@gmail.com

Build Status

Background

The MVP tree is a distance-based data structure for the storage and retrieval of n-dimensional data points. It relies on the relative distances from selected vantage points to index the points into a tree-like hierarchy. It thus cuts the search space into distinct 'hyper-spheres' around each vantage point.

libmvptree.a is a generic implementation of the MVP tree. It allows the user to define a distance function, the type of data and array length (e.g. its bit width for each data element - 1,2,4 and 8), as well as experiment with various tree shapes (e.g. branch factor, leaf capacity, and a path length variable to save the distances between each point and all all the vantage points).

Platforms

This release should work fine on all Linux/Unix platforms. Successful compilation and testing has been achieved on Windows using Cygwin. However, msys/mingw is still a problem due to the memory mapping functions in Windows.

Installation

  1. Type make all to build the libmvptree.a library and test programs. Run ./testmvp to do a basic test of the library. More involved tests can be done with ./testmvp2 to test it with various number of randomly simulated data points. Run it without arguments to see what options are available.

    Note: For the testing, a specified number of uniformly random data points are generated and added to the tree. Then a cluster of data points around another randomly chosen point is generated and added to a tree; each element in these data points is a Poisson distributed random variable to serve as a difference from the central cluster point's respective element. The point that serves as the center of the cluster is then used to retrieve k nearest neighbors (in this case, the number in the cluster) from the tree. For the test to be successful, all data points must be retrieved.

  2. Type make imget to build the imget image indexing program.

  3. make install to install in the target directory. You might want to edit the Makefile to change the DESTDIR variable from /usr/local/lib.

  4. Run ./testmvp to run the test program.

API

A demo of API use exists in the testmvp.c file.

References

Bozkaya, Tolga; Ozsoyoglu, Meral 1999."Indexing Large Metric Spaces for Similarity Search Queries". ACM Transactions in Database Systems, Vol. 24, No. 3, September 1999, pg. 361-404.