semvec/pypi-packages huggingface dataset:
arcmg 0.0.2
arcmg: Attracting Regions Classifier by Morse Graph

# arcmg
Attracting Regions Classifier by Morse Graph

## Installation
```
git clone https://github.com/Ewerton-Vieira/arcmg.git
cd arcmg
pip install -e .
```

## Usage
The `examples` directory contains an example of how to use arcmg.
usl-embedding 0.1.2
Implements the USL Technique for embeddings of any data, be it text, image, etc.
Requires Python: >=3.6


# Unsupervised Learning for Embedding (USL)

This project implements the USL and USL-t methods for unsupervised learning with embeddings, allowing the use of embeddings to form clusters and potentially improve learning outcomes in unsupervised settings.

## Installation

Clone the repository and install the required packages:

```bash
git clone [URL_to_this_repo]
cd [repo_name]
pip install -r requirements.txt
```

## Usage

The `usl_for_embedding` function is designed to process embedding arrays for clustering. It can be used directly by importing from the `selective_labeling.py`:

```python
from selective_labeling import usl_for_embedding

# Generate 10 embeddings, each with 10 random floating-point numbers
data = {'embedding': [list(np.random.rand(10)) for _ in range(10)]}

# method is 'usl' or 'usl-t'
# n_clusters is the number of samples that should be selected for labeling
selected_indices = usl_for_embedding(data['embedding'],method='usl',n_clusters=2,
                            learning_rate=0.001,batch_size=64,n_init=10,m_reg=0.9,k=10,lambda_=0.5,
                            epsilon=1e-5,alpha=0.75,num_epochs_cluster=100,num_heads=3)

print(selected_indices)
```

### Parameters
- **embeddings (array-like)**: Input data, embeddings to be clustered.
- **method (str)**: The clustering method to use ('usl' or 'usl-t').
- **n_clusters (int)**: Number of clusters to form.
- Additional parameters include learning rate, batch size, initialization runs, regularization term, and more described in `main.py`.

## Project Structure

```
project_root/
│
├── methods/                # Module for clustering algorithms
│   ├── usl.py              # Contains the density regulation based USL
│   └── usl_t.py            # Contains the transformer based USL-t
│
├── models/                 # Models used in USL-t
│   └── ssl_t_models/
│       └── clustering_model.py  # Clustering model for USL-t
│
└── main.py                 # Main script to use USL methods
```

## Main Files and Their Roles

- **main.py**: Contains the primary functions used to interface with the USL methods and demonstrates an example usage.
- **usl.py** and **usl_t.py**: Implementation of the USL and USL-t methods, respectively.
- **clustering_model.py**: Defines the neural network models used in the USL-t method.

## Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request with your features or fixes.
This code is used parts of the paper and codebase from repository: 
https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling

## License

This project is licensed under the MIT License - see the LICENSE file for details.
a2dl 0.1.3b1
Generate draw.io icon libraries from AsciiDoc-based descriptions.
Requires Python: >=3



## a2dl | (A)sciidoc(2D)rawio(L)ibrary

Generate Draw.io / Diagrams.net libraries out of AsciiDoc-based descriptions and images.  

- AsciiDoc-based descriptions get integrated into HTML tooltips of draw.io icons.
- The icons are bundled into a draw.io / diagrams.net library.
- It allows to update a diagram if a library that it used has been updated.
libdescriptor 0.2.3
Fully differentiable descriptors for atomistic systems

Libdescriptor 
==============
<img src="https://libdescriptor.readthedocs.io/en/latest/_static/libdescriptor_logo.png" width=200>

[![Documentation Status](https://readthedocs.org/projects/libdescriptor/badge/?version=latest)](https://libdescriptor.readthedocs.io/en/latest/?badge=latest)

Supported Python versions: 3.8, 3.9, 3.10

Libdescriptor is a high performance descriptor library for providing access to fully differentiable descriptor functions.
While `libdescriptor` is a general purpose descriptor library, it's API compatible with KIM models and associated projects.
This will also provide uniform access to various selected descriptors for KLIFF using Pybind11 ports.
For gradient calculations, Libdescriptor relies on [Enzyme AD](https://github.com/EnzymeAD/Enzyme), which provides it with capability to trivially generate near analytical performance gradient functions.
Use of Enzyme AD enables Libdescriptor to not only provide gradients against coordinates, but against hyperparameters as well, thus opening way for better optimized descriptors.
This should enable rapid development, extension and deployment of various descriptors.

<img src="https://libdescriptor.readthedocs.io/en/latest/_images/libdescriptor.svg" width="800">

## Installation
For AMD/Intel based Linux systems, we provide a precompiled binary package for libdescriptor. It can be installed using
```shell
pip install libdescriptor
```

Github Source: [libdescriptor](https://github.com/ipcamit/libdescriptor)