semvec/pypi-packages huggingface dataset:
BezierInterpolate 0.0.2
A small package to fill missing values using Bezier Interpolation
Requires Python: >=3.9

A small package to fill missing values using Bezier Interpolation.

Installation:

```
pip install BezierInterpolate
```

Usage:

```python
from BezierInterpolate.interpolate import bezier

imputed = bezier(data=data, degree=3) #data is pandas DataFrame or pandas Series
```
asuswrtspeedtest 0.2.1
Programatically run internet speedtests on your AsusWRT-powered routers
Requires Python: >=3.8

# python-asuswrt-speedtest

Programatically run internet speedtests on your AsusWRT-powered routers.

It uses the [pyasuswrt](https://github.com/ollo69/pyasuswrt) package to make http(s) requests to the router.

Confirmed to be working with AsusWRTMerlin.  It should work for other AsusWRT-powered routers with the Internet Spedtest function available, but this has not been tested.  Use at your own risk.

## Usage

### Config

Configuration is stored in the config/config.ini file.

        [asus_router]
        host = 
        port = 8443
        use_https = true
        username = 
        password = 

        [speedtest]
        timeout = 120
        poll_frequency = 15
        history_limit = 10

### Script

The following will run a speedtest on your router.  The results will be available on the Internet Speed page

1. Clone the repository
2. Copy config/config.example.ini to config/config.ini
3. Update the config/config.ini file with your router credentials and desired settings
4. Run ```python3 ./run_speedtest.py```

### Package

The latest package release is available on PyPI

    pip install asuswrtspeedtest

Example of how to use this package in your own project:

    import asyncio
    import configparser
    from asuswrtspeedtest import SpeedtestClient


    config = configparser.ConfigParser()
    config.read('config/config.ini')


    async def run_speedtest():
        async with SpeedtestClient(config) as speedtest_client:
            await speedtest_client.run()

    asyncio.run(run_speedtest())
InnoML 0.1.0
A collection of machine learning models implemented from scratch
Requires Python: >=3.6

# InnoML

## Overview

This package is a custom-built implementation of several foundational machine learning algorithms developed entirely from scratch, without reliance on libraries like scikit-learn. It provides an educational and practical resource for understanding and applying machine learning techniques at a granular level.

### Implemented Algorithms
1. **Linear Regression**
2. **Multiple Linear Regression**
3. **Logistic Regression**
4. **K-Nearest Neighbors (KNN)**
5. **Decision Tree**
6. **Random Forest**

---

## Features

- **Custom Implementation**: All algorithms are implemented from first principles, ensuring transparency and understanding of the underlying mechanics.
- **Versatile**: Supports both regression and classification tasks.
- **No External Dependencies**: Built without using pre-existing ML libraries, providing full control over model behavior and implementation details.
- **Educational Resource**: Ideal for those who want to learn the inner workings of machine learning algorithms.

---

## Installation

Clone the repository to your local machine:

```bash
git clone https://github.com/SriramR123/InnoML.git
cd InnoML
```

No additional libraries are required beyond Python's standard libraries.

---

## Usage

### Example: Linear Regression

```python
from InnoML.linear_regression import LinearRegression

# Example data
X = [[1], [2], [3], [4], [5]]
y = [2, 4, 6, 8, 10]

# Initialize and fit the model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict([[6], [7]])
print(predictions)  # Output: [12, 14]
```

### Available Modules
Each algorithm is implemented in a separate module for clarity and modularity:

- `linear_regression.py`: Implementation of simple and multiple linear regression.
- `logistic_regression.py`: Logistic regression for binary classification.
- `knn.py`: K-Nearest Neighbors for classification and regression.
- `decision_tree.py`: Decision tree for classification and regression.
- `random_forest.py`: Ensemble learning using Random Forest.

---

## Contributing

Contributions are welcome! If you find a bug or have a feature request, feel free to open an issue or submit a pull request.

### Steps to Contribute:
1. Fork the repository.
2. Create a new branch: `git checkout -b feature-name`.
3. Commit your changes: `git commit -m 'Add feature name'`.
4. Push to the branch: `git push origin feature-name`.
5. Open a pull request.

---

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---

## Contact

For questions or feedback, feel free to reach out:

- **Email**: sriramachandranram@gmail.com
- **GitHub**: [SriramR123](https://github.com/SriramR123)

---

## Acknowledgements

This project is inspired by the desire to understand and implement machine learning algorithms at a foundational level. Special thanks to the open-source community for their resources and support.
mbox2eml 0.1.2
Convert Mbox to EML Files Easily
Requires Python: <4.0,>=3.11

# mbox2eml: Convert Mbox to EML Files Easily

`mbox2eml` is a Python utility for converting `.mbox` files (used by many email clients to store messages) into individual `.eml` files. This tool is perfect for anyone needing to export or process emails from `.mbox` archives.

## Features

- Converts `.mbox` files into multiple `.eml` files.
- Automatically creates the output directory if it doesn't exist.
- Handles single-part and multi-part email messages.
- Validates `.mbox` file format before conversion.
- Provides detailed error handling for corrupted or inaccessible files.
- Supports Unicode and special characters in email headers and content.

## Installation

```sh
pip install mbox2eml
```

## Usage

The utility provides a command-line interface (CLI) for easy usage.

### Command-line Arguments

- `--file, -f`: Path to the input `.mbox` file (required).
- `--output_dir, -o`: Path to the output directory where `.eml` files will be saved (required).

### Example

Convert an `.mbox` file to `.eml` files:

```sh
mbox2eml --file path/to/input.mbox --output_dir path/to/output/
```

After running the command, all emails in the `.mbox` file will be converted to `.eml` files in the specified output directory.

## Code Example (Programmatic Use)

You can also use the utility programmatically in Python:

```py
from mbox2eml import convert_mbox_to_eml

mbox_file = "path/to/input.mbox"
output_dir = "path/to/output"

success, fail = convert_mbox_to_eml(mbox_file, output_dir)
print(f"Conversion completed: {success} succeeded, {fail} failed")
```

## Error Handling

The tool gracefully handles various edge cases:

- **File Not Found**: Raises `FileNotFoundError` if the `.mbox` file doesn't exist.
- **Permission Issues**: Raises `PermissionError` if the `.mbox` file or output directory is not accessible.
- **Invalid Output Path**: Raises `IsADirectoryError` if the output directory path is a file.
- **Empty Mbox**: Outputs a message if the `.mbox` file is empty but does not treat it as an error.
- **Corrupted Mbox**: Skips invalid messages while continuing to process others.

## Development

Clone the repository to contribute or run the utility locally:

```sh
git clone https://github.com/yangeok/mbox2eml.git
cd mbox2eml
```

Install dependencies and run the script:

```sh
python -m pip install -r requirements.txt
python mbox2eml.py --file path/to/input.mbox --output_dir path/to/output/
```

## Tests

The package includes a comprehensive test suite to ensure stability and handle edge cases.

Run the test suite with `pytest`:

Run the tests in watch mode:

```sh
poetry run test-watch
```

Run the tests normally:

```sh
poetry run test
```

Run tests in debug mode:

```sh
poetry run test-debug
```

## License

This project is licensed under the MIT License. See the `LICENSE` file for details.

## Contributing

Contributions are welcome! Please submit a pull request or open an issue to report bugs or request features.

## Author

Developed by **Yangwook Ian Jeong**. For inquiries, please contact `yangwoookee@gmail.com`.
shangshan 0.1.1
一个用于从GitLab下载和处理模型的Python SDK
Requires Python: >=3.6

# Shangshan SDK

一个用于下载和处理数据的Python SDK。

## 安装

```bash
pip install shangshan
```

## 使用

```python
from shangshan import download

download('testuser/model_llm')
```

## 下载模型

```python
from shangshan import snapshot_download
model_dir = snapshot_download('testuser/model_llm')
print(model_dir)
```

## 功能特性

- 支持文件下载
- 自动重试机制
- 进度显示

## 许可证

MIT License
khamster 0.1.0
A Python package called khamster
Requires Python: >=3.6

# Khamster

khamster는 Robomation의 `roboid` 패키지의 `Hamster` 클래스를 한글로 접근할 수 있도록 만든 패키지입니다.

이를 통해 영어가 아닌 한글과 한국어로 햄스터 코딩 로봇을 움직여볼 수 있습니다.

## 설치

```bash
pip install khamster
```

## 사용법

기본적인 사용 예시:

```python
from khamster import 햄스터, 로봇찾기, 대기, 준비될때까지_대기

# 로봇 찾기 및 초기화
로봇들 = 로봇찾기()
준비될때까지_대기()

# 로봇 생성
로봇 = 햄스터()

# 기본 동작
로봇.앞으로(50)  # 50% 속도로 전진
대기(2)          # 2초 대기
로봇.정지()      # 정지
```

## 주요 기능

### 이동 제어
```python
로봇.앞으로(속도=50)      # 앞으로 이동
로봇.뒤로(속도=50)       # 뒤로 이동
로봇.왼쪽으로(속도=50)    # 왼쪽으로 회전
로봇.오른쪽으로(속도=50)   # 오른쪽으로 회전
로봇.정지()             # 정지
로봇.바퀴설정(왼쪽속도, 오른쪽속도)  # 개별 바퀴 제어
```

### LED 제어
```python
# LED 색상 상수
로봇.LED_끄기   # 0
로봇.LED_빨강   # 1
로봇.LED_노랑   # 2
로봇.LED_초록   # 3
로봇.LED_청록   # 4
로봇.LED_파랑   # 5
로봇.LED_보라   # 6
로봇.LED_하양   # 7

# LED 제어
로봇.LED_설정(왼쪽=LED_빨강, 오른쪽=LED_파랑)
로봇.LED_끄기()
```

### 센서 읽기
```python
# 거리 및 바닥 센서
거리 = 로봇.거리센서()
왼쪽, 오른쪽 = 로봇.바닥센서()

# 가속도 및 기울기
x, y, z = 로봇.가속도()
기울기_x, 기울기_y, 기울기_z = 로봇.기울기()

# 기타 센서
충격 = 로봇.진동감지()
배터리 = 로봇.배터리()
신호 = 로봇.신호세기()
```

### 음악 및 소리
```python
# 음계 상수
로봇.도      # 4
로봇.레      # 5
로봇.미      # 6
로봇.파      # 7
로봇.솔      # 8
로봇.라      # 9
로봇.시      # 10
로봇.높은도   # 11

# 소리 내기
로봇.소리내기(로봇.도, 박자=0.5)  # 도를 0.5초 동안 연주

# 멜로디 연주
음계들 = [로봇.도, 로봇.레, 로봇.미, 로봇.파, 로봇.솔]
박자들 = [0.5, 0.5, 0.5, 0.5, 1.0]
로봇.멜로디(음계들, 박자들)

# 버저 소리
로봇.삐소리(길이=0.2)
```

### 외부 포트 제어
```python
# 포트 모드 설정 및 입출력
로봇.IO모드_설정(1, 'INPUT')   # 포트 1을 입력 모드로 설정
값 = 로봇.포트값_읽기(1)       # 포트 1의 값 읽기
로봇.포트값_쓰기(2, 1)         # 포트 2에 1 출력
```

## 라이선스

이 프로젝트는 MIT 라이선스를 따릅니다.

## 참고 자료

- [Robomation](https://www.robomation.co.kr/)
- [햄스터 스쿨](https://hamster.school/)
gbtoolbox 0.0.5
Tools for neural network generalization bounds
Requires Python: >=3.7

# Generalization_Bound_Toolbox

Tools related to computing generlization error bounds for machine-learning applications. Note that standard use depends on the domain of the target functions to be $x \in (-1,1)^d$ where $d$ is the dimension of the feature vectors. If your feature vectors are not in this domain, than they can be rescaled. Additionally, best results are if there is small correlation between any two components of the feature vector.

For directions on use, check out

    tests/test_bound.py

    tests/TestProductSinesCompression.ipynb

    tests/TestProductSines.ipynb.

# Installation

A release version is availalbe on PyPI. Currently requires Python version less than 3.12 and greater than 3.7.

    pip install gbtoolbox

    pip install gbtoolbox[GPU11]

    pip install gbtoolbox[GPU12]    

The following should be performed to manually install. See pyproject.toml for dependencies.

Install the build package

    pip install build
    
First, from the same directory as this README, build the sdist and wheel using the following command.

    python -m build

Then install the wheel (*** indicates text that is version and user specific)

    pip install dist/gbtoolbox-***.whl

Build and install the c files

    cd src/gbtoolbox
    make && make install

Update your ld_library_path environment variable

    export LD_LIBRARY_PATH=/usr/local/lib/gbtoolbox/

You may want to add the previous export statement to your ~/.bashrc file, otherwise the change is only for the currently open session. 

# CUDA
There is a legacy CUDA version of nu_dft that runs much faster than the C version, but that runs slower than the cupy version. The following should be helpful for getting set up to run the legacy CUDA version

    conda install pytorch torchvision torchaudio pytorch-cuda=11.6 cuda-toolkit=11.6 numba python-build scipy -c pytorch -c nvidia

    conda install pytorch torchvision torchaudio pytorch-cuda cuda-toolkit numba python-build scipy -c pytorch-nightly -c nvidia

Information about pytorch is available at https://pytorch.org/.

# Reference

This toolbox was developed by a collaboration between Euler Scientific ( www.euler-sci.com ) and Fermilab ( www.fnal.gov ). Papers are in progress. Initial developmenet was made possible by the National Geospatial-Intelligence Agency (NGA) under Contract No. HM047622C0003.

The central theory behind this was initially developed by Barron and then extended by E et al. Details in

https://arxiv.org/abs/1810.06397 

https://arxiv.org/abs/2009.10713

https://arxiv.org/abs/1607.01434

http://www.stat.yale.edu/~arb4/publications_files/UniversalApproximationBoundsForSuperpositionsOfASigmoidalFunction.pdf
mcp-server-docs 0.1.6
An MCP Server that makes it easy to load documentation files from locally available repositories
Requires Python: >=3.11

# mcp-server-docs

A Model Context Protocol server that provides access to documentation across multiple repositories. This server allows LLMs to search and retrieve documentation content from markdown files.

## Features

- Browse documentation across multiple repositories
- Full markdown/MDX support with frontmatter parsing
- Automatic title and description extraction
- Documentation content exposed as MCP resources
- Search and fetch tools for documentation retrieval

## Installation

### Using uv

When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed. We will
use [`uvx`](https://docs.astral.sh/uv/guides/tools/) to directly run _mcp-server-docs_.

### Using PIP

Install via pip:

```bash
pip install mcp-server-docs
```

## Usage

The server needs to know which repositories to crawl for documentation. Provide these as repository mappings:

```bash
# Using uvx
uvx mcp-server-docs "mcp=/path/to/mcp/docs,anthropic=/path/to/anthropic/docs"

# Using pip installation
python -m mcp_server_docs "mcp=/path/to/mcp/docs,anthropic=/path/to/anthropic/docs"
```

### Configure for Claude Desktop

Add to your Claude Desktop config:

```json
"mcpServers": {
  "docs": {
    "command": "uvx",
    "args": [
      "mcp-server-docs",
      "mcp=/path/to/mcp/docs,anthropic=/path/to/anthropic/docs"
    ]
  }
}
```

## Available Tools

- `fetch-documents`: Retrieve documentation content by repository and path
  - Search through available documentation files
  - Get documentation content with preserved structure
  - Support for fetching multiple related documents

## Resources

All documentation files are exposed as MCP resources with:

- URI format: `file://{repository}/{path}`
- Rich metadata including titles and descriptions
- Full markdown content

## Contributing

For examples of other MCP servers and implementation patterns, see:
https://github.com/modelcontextprotocol/example-servers/

Pull requests welcome!