@inproceedings{ram2019revisiting,
title={Revisiting kd-tree for nearest neighbor search},
author={Ram, Parikshit and Sinha, Kaushik},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1378--1388},
year={2019}
}
This code has been tested on Centos 7 (RHEL 7) and Ubuntu 18.04. The code is designed for python2.7 because of the Fast Fast Walsh-Hadamard Transform library we use for Fast Walsh-Hadamard Transform. We assume the presence of the following:
- "Development Tools" (using sudo yum groupinstall "Development Tools" or equivalent)
- epel-release
- python python-devel
- python-pip
- wget
Once we have those, we require the following python2.7 libraries:
- numpy
- scipy
- pandas
- matplotlib
- scikit-learn
- tqdm
- h5py
In addition, as mentioned earlier, we use the FFHT library for the Fast Walsh-Hadamard Transform. This can be done:
wget https://github.com/FALCONN-LIB/FFHT/archive/master.zip
unzip master.zip
cd FFHT-master
pip install . --user
OR
git clone [email protected]:FALCONN-LIB/FFHT.git
cd FFHT
pip install . --user
If everything is installed successfully, you can test the working of the code as well as its correctness by executing the following from the root of the library:
cd tests
tar -xzvf USPS.tar.gz
cd ..
python2.7 tests/test_all_trees.py