semvec/pypi-packages huggingface dataset:
lmure 0.0.0
LLM-Based Neural Network Generator
Requires Python: >=3.10

# <img src='https://abrain.one/img/lemur-nn-icon-64x64.png' width='32px'/> LLM-Based Neural Network Engineer

<img src='https://abrain.one/img/lemur-nn-gen-whit.jpg' width='25%'/>

<h3>Overview 📖</h3>

This Python-based project leverages large language models to automate the creation of neural network architectures, streamlining the design process for machine learning practitioners.

## Update of NN Dataset
Remove old version of the LEMUR Dataset and its database:
```bash
source .venv/bin/activate
pip uninstall nn-dataset -y
rm -rf db
```
Installing the stable version:
```bash
source .venv/bin/activate
pip install nn-dataset --upgrade --extra-index-url https://download.pytorch.org/whl/cu124
```
Installing from GitHub to get the most recent code and statistics updates:
```bash
source .venv/bin/activate
pip install git+https://github.com/ABrain-One/nn-dataset --upgrade --force --extra-index-url https://download.pytorch.org/whl/cu124
```
Adding functionality to export data to Excel files and generate plots for <a href='https://github.com/ABrain-One/nn-stat'>analyzing neural network performance</a>:
```bash
source .venv/bin/activate
pip install nn-stat --upgrade --extra-index-url https://download.pytorch.org/whl/cu124
```
and export/generate:
```bash
source .venv/bin/activate
python -m ab.stat.export
```

## Environment for NN Gen Developers
### Pip package manager
Create a virtual environment, activate it, and run the following command to install all the project dependencies:
```bash
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu124
```

### Docker
All versions of this project are compatible with <a href='https://hub.docker.com/r/abrainone/ai-linux' target='_blank'>AI Linux</a> and can be run inside a Docker image:
```bash
docker run -v /a/mm:. abrainone/ai-linux bash -c "PYTHONPATH=/a/mm python -m ab.gen.train_n_eval"
```

## Citation

The original version of this project was created at the Computer Vision Laboratory of the University of Würzburg by the authors mentioned below. If you find this project to be useful for your research, please consider citing:
```bibtex
@misc{ABrain-One.NN-Gen,
  author       = {... and Goodarzi, Arash Torabi and ... and Ignatov, Dmitry and Timofte, Radu},
  title        = {LLM-Based Neural Network Generator},
  howpublished = {\url{https://github.com/ABrain-One/nn-gen}},
  year         = {2024},
}
```

## Licenses

This project is distributed under the following licensing terms:
<ul><li>for neural network models adopted from other projects
  <ul>
    <li> Python code under the legacy ... license</li>
    <li> models with pretrained weights under the legacy ... license</li>
  </ul></li>
<li> all neural network models and their weights not covered by the above licenses, as well as all other files and assets in this project, are subject to the <a href="LICENSE">MIT license</a></li> 
</ul>

#### The idea of Dr. Dmitry Ignatov
nn-eng 0.0.0
LLM-Based Neural Network Generator
Requires Python: >=3.10

# <img src='https://abrain.one/img/lemur-nn-icon-64x64.png' width='32px'/> LLM-Based Neural Network Engineer

<img src='https://abrain.one/img/lemur-nn-gen-whit.jpg' width='25%'/>

<h3>Overview 📖</h3>

This Python-based project leverages large language models to automate the creation of neural network architectures, streamlining the design process for machine learning practitioners.

## Update of NN Dataset
Remove old version of the LEMUR Dataset and its database:
```bash
source .venv/bin/activate
pip uninstall nn-dataset -y
rm -rf db
```
Installing the stable version:
```bash
source .venv/bin/activate
pip install nn-dataset --upgrade --extra-index-url https://download.pytorch.org/whl/cu124
```
Installing from GitHub to get the most recent code and statistics updates:
```bash
source .venv/bin/activate
pip install git+https://github.com/ABrain-One/nn-dataset --upgrade --force --extra-index-url https://download.pytorch.org/whl/cu124
```
Adding functionality to export data to Excel files and generate plots for <a href='https://github.com/ABrain-One/nn-stat'>analyzing neural network performance</a>:
```bash
source .venv/bin/activate
pip install nn-stat --upgrade --extra-index-url https://download.pytorch.org/whl/cu124
```
and export/generate:
```bash
source .venv/bin/activate
python -m ab.stat.export
```

## Environment for NN Gen Developers
### Pip package manager
Create a virtual environment, activate it, and run the following command to install all the project dependencies:
```bash
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu124
```

### Docker
All versions of this project are compatible with <a href='https://hub.docker.com/r/abrainone/ai-linux' target='_blank'>AI Linux</a> and can be run inside a Docker image:
```bash
docker run -v /a/mm:. abrainone/ai-linux bash -c "PYTHONPATH=/a/mm python -m ab.gen.train_n_eval"
```

## Citation

The original version of this project was created at the Computer Vision Laboratory of the University of Würzburg by the authors mentioned below. If you find this project to be useful for your research, please consider citing:
```bibtex
@misc{ABrain-One.NN-Gen,
  author       = {... and Goodarzi, Arash Torabi and ... and Ignatov, Dmitry and Timofte, Radu},
  title        = {LLM-Based Neural Network Generator},
  howpublished = {\url{https://github.com/ABrain-One/nn-gen}},
  year         = {2024},
}
```

## Licenses

This project is distributed under the following licensing terms:
<ul><li>for neural network models adopted from other projects
  <ul>
    <li> Python code under the legacy ... license</li>
    <li> models with pretrained weights under the legacy ... license</li>
  </ul></li>
<li> all neural network models and their weights not covered by the above licenses, as well as all other files and assets in this project, are subject to the <a href="LICENSE">MIT license</a></li> 
</ul>

#### The idea of Dr. Dmitry Ignatov
evaluation-lumo 0.1.10
evaluation_lumo is a package for evaluating the LUMO damage detection system.
Requires Python: >=3.9.0

# LUMO Damage Detection Evaluation Package

This package provides a standardized framework for evaluating damage detection and localization strategies using the LUMO dataset. Users can input timestamps alongside their corresponding anomaly indices, and the package computes various performance scores for each damage case, promoting consistency in damage detection evaluation.

## Features

- **Standardized Evaluation Metrics**: Calculates TPR and FPR at a threshold set such as FPR for training data is 1%.
The training dataset should be only the first month of data.
- **Damage Case Analysis**: Provides detailed performance evaluations for each specific damage scenario within the LUMO dataset.

## Installation

To install the package, run:

```bash
pip install evaluation_lumo
```

## Usage

To use the package, import the `evaluation_lumo.evaluation` module and call the `compute_tr_by_events` function, `compute_mean_variation` function, or `compute_mad` function.

```python
from evaluation_lumo.evaluation import compute_tr_by_events, compute_mean_variation, compute_mad

# Example usage

date_index = pd.date_range(start='2021-08-01', ends="2022-08-01", freq='10T')
associated_damage_index = np.random.random(len(date_index))
compute_tr_by_events(date_index, associated_damage_index)
compute_median_variation(date_index, associated_damage_index)
compute_mad(date_index, associated_damage_index)
```

## Contributing

pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
pre-commit is used to ensure code quality. Please install it before making any changes.
run the following command to install pre-commit:

```bash
pre-commit run --all-files
```
then push your changes.
best-hashtags 0.1.1
A tool to find the best hashtags for a given word
Requires Python: >=3.6

# 📈 Best Hashtags

**Best Hashtags** is a Python tool that helps you find the most popular hashtags for a given word. It’s perfect for content creators, marketers, and social media enthusiasts looking to boost their reach with relevant hashtags.

## 🚀 Features

- **Retrieve Top Hashtags**: Instantly find the most popular hashtags for any keyword.
- **Customizable Results**: Get a specific number of hashtags based on your needs.
- **Randomized Option**: Shuffle the hashtags for more variety.

## ⚙️ Installation

You can install **Best Hashtags** in two ways:

### 1️⃣ Install via PyPI (Recommended)

The easiest way to get started is by installing the package from [PyPI]

```pip install best-hashtags```

## 💡 Usage

You can use **Best Hashtags** directly in your Python code.

### Example 1: Using from the Command Line

```python src/best_hashtags.py music```

This will display the top 30 hashtags related to **"music"**.

### Example 2: Using in Python Scripts

```bash

from best_hashtags import BestHashtags

# Initialize the BestHashtags class
hashtag_finder = BestHashtags()

# Search for hashtags related to "music"
hashtags = hashtag_finder.get_hashtags('music', quantity=30, ordered=True)

# Display the results
print(hashtags)

```

## 📄 License

This project is licensed under the [MIT License](LICENSE).

---

💡 *Contributions and feedback are always welcome!*