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A Python library for large-scale exact nearest neighbor search using Buffer k-d Trees (bufferkdtree).

Project description

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).

Documentation

See the documentation for details and examples.

Quickstart

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.

Dependencies

The bufferkdtree package is tested under Python 2.6 and Python 2.7. The required Python dependencies are:

  • NumPy >= 1.6.1

and a working C/C++ compiler. Further, Swig and OpenCL need to be installed. See the documentation for more details.

Disclaimer

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.

Project details


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