semvec/pypi-packages huggingface dataset:
dftlib 0.9.0
dftlib - Python library for dynamic fault tree analysis
Requires Python: >=3.9

dftlib - Python library for dynamic fault tree analysis
=======================================================

[![Build Status](https://github.com/volkm/dftlib/workflows/Build%20Test/badge.svg)](https://github.com/volkm/dftlib/actions)

### Main author:
- Matthias Volk

The development of dftlib received significant contributions from:
- Dustin Jungen
knnn 0.0.9
An implementation of KNNN algorithm
Requires Python: >=3.7
Keywords: knnn, knn, embedding

# knnn
K-Nearest Neighbors of Neighbors - for Anomaly Detection 
```bash 
pip install knnn
```
<div style="display: flex; justify-content: center; align-items: center; gap: 20px;">

  <div style="text-align: center;">
    <img src="knn_teaser.png" alt="k-NN" title="k-NN" width="200">
    <p>k-NN</p>
  </div>

  <div style="text-align: center;">
    <img src="knnn_teaser.png" alt="k-NNN" title="k-NNN" width="200">
    <p>k-NNN</p>
  </div>

</div>

# 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
``` python
from knnn import KNNN
import numpy as np

# Random data
x_normal = np.random.rand(100, 5)
x_test = np.random.rand(20, 5) + 1

# Create a KNNN object
knnn = KNNN(number_of_neighbors=3, number_of_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:
```bash 
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)
```bash
git clone https:\\github.com\knnn
cd knnn
python3 -m pip install -e . 
```
If you want to build the package from source, run:
```bash
python3 -m build
``` 
and to install the built package, run:
```bash
python3 -m pip install --force-reinstall dist/*.whl
```
To run the tests, run:
```bash
pytest
```

# Cite
```bib
@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}
}
```