https://github.com/aaronjs99/tk2nn
tk2NN: TKinter-based K-Nearest Neighbors
https://github.com/aaronjs99/tk2nn
data-science data-visualization feature-extraction gui interactive k-nearest-neighbours knn pattern-recognition python tkinter
Last synced: about 2 months ago
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tk2NN: TKinter-based K-Nearest Neighbors
- Host: GitHub
- URL: https://github.com/aaronjs99/tk2nn
- Owner: aaronjs99
- License: mit
- Created: 2020-05-31T12:26:17.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2025-06-25T09:33:21.000Z (12 months ago)
- Last Synced: 2026-03-06T00:39:31.951Z (4 months ago)
- Topics: data-science, data-visualization, feature-extraction, gui, interactive, k-nearest-neighbours, knn, pattern-recognition, python, tkinter
- Language: Python
- Homepage:
- Size: 724 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TKinter-based K-Nearest Neighbors (tk2NN)
**tk2NN** is an interactive visualization of the **k-nearest neighbors (k-NN)** algorithm built using **Tkinter**. It lets you tweak the number of labels, points, and the value of *k*, and watch how the classification map changes in real time. You can toggle between **Euclidean** and **Manhattan** distance metrics, and colors for each label are randomized every time the graph is redrawn - even with the same points. Don’t like the current color scheme? Just re-select the same distance type. Boom. Fresh new look.
## Features
- Switch between Euclidean and Manhattan distances
- Rerun classification instantly with updated parameters
- Random color assignments for each label every time
- Clear all points with a right-click
- Visualizes decision boundaries pixel-by-pixel
- Lightweight Tkinter GUI — no heavy dependencies
- Fully customizable via `config.yaml` — canvas size, colors, default parameters
## Getting Started
### Requirements
- Python **3.8+** (recommended)
- `colour`
- `Pillow`
Install the required packages:
```bash
pip install -r requirements.txt
```
### Run the App
```bash
python run.py
```
> If you're using Linux and want to install Python 3.8 explicitly:
```bash
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt-get install python3.8
python3.8 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python run.py
```
## Project Structure
```
tk2nn/
├── app/ # Core modules
│ ├── gui.py # Main Tkinter app
│ ├── knn_canvas.py # k-NN algorithm and classification logic
│ └── utils.py # Random point generation, color logic, multimode fallback
│
├── assets/ # Images and visual resources
│ ├── bg.png
│ ├── demo.png
│ └── multidemo.png
│
├── config.yaml # Centralized configuration
├── run.py # Entry point to launch the app
├── requirements.txt
├── README.md
└── .gitignore
```
## Notes
- The color scheme is randomized **every time** you update or re-select a distance metric.
- This project was inspired by another GUI project [TKayTint](https://github.com/aaronjohnsabu1999/TKayTint) (same developer — different beast).
- It’s a simple educational tool that can be extended to explore classification boundaries, real datasets, or even point dragging and manual labeling.
## Future Ideas
- [ ] Add click-to-label custom point placement
- [ ] Export the canvas as a PNG snapshot
- [ ] Add more distance metrics (Chebyshev? Cosine?)
- [ ] Toggle persistent vs. random color palettes
- [ ] Performance boost with NumPy or shaders (if you’re crazy)
## Contributing
Got ideas? Bugs? Feature requests? Feel free to:
- Fork the repo
- Submit a pull request
- Or just open an issue and yell into the void
## License
This project is licensed under the MIT License. See the `LICENSE` file for more info.