semvec/pypi-packages huggingface dataset:
gcomvkm 0.1.0
Globally Collaborative Multi-View k-Means Clustering Algorithm
Requires Python: >=3.7

# G-CoMVKM

## Globally Collaborative Multi-View k-Means Clustering

[![PyPI version](https://badge.fury.io/py/gcomvkm.svg)](https://badge.fury.io/py/gcomvkm)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview

G-CoMVKM is a Python implementation of the Globally Collaborative Multi-View k-Means clustering algorithm, originally developed by Kristina P. Sinaga. The algorithm integrates a collaborative transfer learning framework with entropy-regularized feature-view reduction, enabling dynamic elimination of uninformative components. This method achieves clustering by balancing local view importance and global consensus.

## Installation

You can install G-CoMVKM directly from PyPI:

```bash
pip install gcomvkm
```

### Requirements
- Python 3.7+
- NumPy
- SciPy
- Matplotlib
- scikit-learn
- seaborn

## Usage

```python
from gcomvkm import GCoMVKM
from gcomvkm.utils import load_synthetic_data
from gcomvkm.evaluation import nmi, rand_index, adjusted_rand_index

# Load the dataset
X, label = load_synthetic_data()

# Create and fit the model
model = GCoMVKM(
    n_clusters=2,
    gamma=5.0,
    theta=4.0,
    max_iter=100,
    tol=1e-4,
    verbose=True,
    random_state=42
)

model.fit(X)

# Get the cluster assignments
predicted_labels = model.labels_

# Evaluate the clustering performance
nmi_score = nmi(label, predicted_labels)
ri_score = rand_index(label, predicted_labels)
ari_score = adjusted_rand_index(label, predicted_labels)

print(f"NMI: {nmi_score:.4f}")
print(f"RI: {ri_score:.4f}")
print(f"ARI: {ari_score:.4f}")
```

## Algorithm Parameters

- `n_clusters`: Number of clusters to form
- `gamma`: Exponent parameter to control the weights of V (typically in range [0,1])
- `theta`: Coefficient parameter to control the weights of W (typically > 0)
- `max_iter`: Maximum number of iterations
- `tol`: Convergence tolerance
- `verbose`: Whether to print progress information
- `random_state`: Random seed for reproducibility

## Features

- Multi-view clustering with feature and view weighting
- Automatic dimensionality reduction through entropy regularization
- Balanced local and global consensus clustering
- Comprehensive evaluation metrics

## Citation

If you use this package in your research, please cite the original paper:

```
@article{sinaga2025gcomvkm,
  title={A Globally Collaborative Multi-View k-Means Clustering},
  author={Sinaga, Kristina P.},
  journal={MDPI Electronics},
  year={2025}
}
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
6. Performance metrics across different initializations
7. Convergence plot

## References
This implementation is based on the MATLAB code by Kristina P. Sinaga. For more details about the algorithm, please refer to the original paper.
lkeb-progress 0.2.0
Progressbar based on Rich
Requires Python: >=3.9
Keywords: Rich

## Progress display based on Rich

Progress display with the following columns:

⠇ Processing... ━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 48/50 Elapsed: 0:00:10 Remaining: 0:00:00 Completion: Wed 10:00:22
jabutiles 0.1.2
A procedural tileset interface for games.
Requires Python: >=3.11
Keywords: arcade, autotiler, brazil, gamedev, graphics, procedural, tiling

# Jabutiles

A procedural tileset interface for games.

![Jabutiles Logo](https://github.com/ArthurBohn95/jabutiles/blob/main/examples/logo/imgs/logo.png?raw=true)

<br>

Provides support for orthogonal, isometric and hexagonal tiles.

Most of the backend is `pillow` (a PIL fork), the rest is `numpy`.

<br>

Install with: `pip install jabutiles`

[PyPI release](https://pypi.org/project/jabutiles/) 

[Documentation](https://github.com/ArthurBohn95/jabutiles/blob/main/docs/)

<br>

Trivia: the name comes from merging the words "jabuti" and "tiles":
- "jabuti" meaning "tortoise" in Portuguese;
- "tiles" coming from the stacked scutes on the back of their shells;