A fork of D Grant Starkweather's multiple vantage point tree library
Pull request Compare This branch is even with michaelmior:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.


MVPTree c library 
version: 1.0.0
date: 2010/12/17
creator: D. Grant Starkweather 
license: GPLv3
contact: starkd88@gmail.com


The MVP tree is a distance-based data structure for the storage and retrieval of
n-dimensional datapoints.  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 implemention of the mvp tree. It allows the user to define
the 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).



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.  (Any hints at mmap emulation on windows would be greatly 



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 datapoints are generated and
   added to the tree.  Then a cluster of datapoints 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 knearest neighbors 
   - in this case, the number in the cluster - from the tree. For the test to be successful, 
   all data points are 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.



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



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.