A Python library for large-scale exact nearest neighbor search using Buffer k-d Trees (bufferkdtree).
The bufferkdtree library is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and many-core devices (e.g., GPUs) via the OpenCL framework.
The buffer k-d tree technique can be seen as an intermediate version between a standard parallel k-d tree traversal and massively-parallel brute-force implementations for nearest neigbhor search. The implementation is well-suited for data sets with a large reference set (e.g., 1,000,000 points) and a huge query set (e.g., 10,000,000 points) with a moderate-sized feature space (e.g., from d=5 to d=25).
See the documentation for details and examples.
The package can be installed via pip via:
pip install bufferkdtree
To install the package from the sources, get the current version via:
git clone https://github.com/gieseke/bufferkdtree.git
To install the package locally on a Linux system, use:
python setup.py install --user
On Debian/Ubuntu systems, the package can be installed globally for all users via:
python setup.py build sudo python setup.py install
To run the tests, type nosetests -v bufferkdtree from outside the source directory.
The bufferkdtree package is tested under Python 2.6 and Python 2.7. The required Python dependencies are:
- NumPy >= 1.6.1
The source code is published under the GNU General Public License (GPLv2). The authors are not responsible for any implications that stem from the use of this software.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size bufferkdtree-1.0.1.tar.gz (98.2 kB)||File type Source||Python version None||Upload date||Hashes View|