Onr / knnn

KNNN algorithm implementation

knnn

K-Nearest Neighbors of Neighbors

pip install knnn

Description

This package provides a simple implementation of the K-Nearest Neighbors of Neighbors algorithm. The algorithm is a simple extension of the K-Nearest Neighbors algorithm, which is used for anomaly detection. The algorithm is based on the idea that the neighbors of the neighbors of a point gives more information than its neighbors. The algorithm can be used to improve the accuracy of the KNN algorithm.

Usage

from knnn import KNNN
import numpy as np

# Random data
x_normal = np.random.rand(100, 2)
x_test = np.random.rand(20, 2) + 1

# Create a KNNN object
knnn = KNNN(num_neighbors=3, num_neighbors_of_neighbors=25)
# Fit the model
knnn.fit(x_normal)
# Predict the labels of the test data
y_pred = knnn.predict(x_test)

Installation

The simplest way to install the package is to run:

pip install knnn

If you want to install the latest version from the master branch:

(-e option will allow you to change the code without reinstalling the package)

git clone https:\\github.com\knnn
cd knnn
python3 -m pip install -e . 

If you want to build the package from source, run:

python3 -m build

and to install the built package, run:

python3 -m pip install --force-reinstall dist/*.whl

To run the tests, run:

pytest

Cite

@inproceedings{nizan2024k,
  title={k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection},
  author={Nizan, Ori and Tal, Ayellet},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1005--1014},
  year={2024}
}