semvec/pypi-packages huggingface dataset:
SE21003UNO 1.0.0
Librería creada para resolver sistemas de ecuaciones lineales y no lineales PARA LA MATERIA DE Cálculo Numérico para Desarrollo de Aplicaciones
Requires Python: >=3.7

Libreria creada ARA LA MATERIA DE CÃ¡lculo NumÃ©rico para Desarrollo de Aplicaciones, esta libreria busca resolver de manera breve y de forma facil sistemas de ecuaciones lineales y no lineales utilizando los siguientes metodos de resoluciÃ³n:


    EliminaciÃ³n de Gauss
    Gauss-Jordan
    Crammer
    DescomposiciÃ³n LU
    Jacobi
    Gauss-Seidel
    BisecciÃ³n
SC19013UNO 1.0.0
Librería de métodos numéricos para resolver sistemas de ecuaciones lineales y no lineales
Requires Python: >=3.6

# SC19013UNO

**SC19013UNO** es una librería en Python que proporciona métodos numéricos para resolver:

- **Sistemas de ecuaciones lineales** (como el método de Gauss, Gauss-Jordan, Jacobi, etc.).
- **Ecuaciones no lineales** (como el método de Bisección).

---

## Estructura del Proyecto
SC19013UNO/ 
├── SC19013UNO/
│ ├── init.py # Inicialización del paquete 
│ ├── lineales.py # Métodos para sistemas lineales 
│ └── no_lineales.py # Métodos para ecuaciones no lineales 
├── tests/ # Archivos de prueba 
│ ├── test_lineales.py # Pruebas para métodos lineales 
│ └── test_no_lineales.py # Pruebas para métodos no lineales 
├── setup.py # Configuración para instalación 
└── LICENSE # Licencia del proyecto



---

## Instalación

### Instalación usando `pip`:

1. Si tienes `pip` instalado, puedes instalar la librería directamente desde PyPI:

   ```bash
   pip install SC19013UNO

## Ejemplo: Resolver un Sistema Lineal
from SC19013UNO.lineales import SolucionadorLineales

A = [[4, 1, 2],
     [3, 5, 1],
     [1, 1, 3]]
b = [4, 7, 3]

solucion = SolucionadorLineales.jacobi(A, b)
print(solucion)


## Ejemplo: Resolver una Ecuación No Lineal (Método de Bisección)

import math
from SC19013UNO.no_lineales import SolucionadorNoLineales

## Definir la función
f = lambda x: math.cos(x) - x

### Encontrar la raíz en el intervalo [0, 1]
raiz = SolucionadorNoLineales.biseccion(f, 0, 1)
print(raiz)


## Métodos Disponibles
### Para Sistemas Lineales (Clase SolucionadorLineales):
gauss(A, b)

gauss_jordan(A, b)

cramer(A, b)

lu(A, b)

jacobi(A, b, x0, tol, max_iter)

gauss_seidel(A, b, x0, tol, max_iter)

### Para Ecuaciones No Lineales (Clase SolucionadorNoLineales):
biseccion(f, a, b, tol, max_iter)
voice-emotion-detector 0.1.0
A containerized voice emotion detection application.
Requires Python: >=3.10

![Lint-free](https://github.com/software-students-spring2025/4-containers-emmmm/actions/workflows/lint.yml/badge.svg)
![ML Client CI](https://github.com/software-students-spring2025/4-containers-emmmm/actions/workflows/ml-client.yml/badge.svg)
![Web App CI](https://github.com/software-students-spring2025/4-containers-emmmm/actions/workflows/web-app.yml/badge.svg)


# Voice Emotion Detector
---

## Team Members
- **Eli Sun**: [IDislikeName](https://github.com/IDislikeName)
- **Jasmine Zhang**: [Jasminezhang666666](https://github.com/Jasminezhang666666)
- **Yifan Zhang**: [YifanZZZZZZ](https://github.com/YifanZZZZZZ)
- **Shuyuan Yang**: [shuyuanyyy](https://github.com/shuyuanyyy)

---

## Project Overview

Voice Emotion Detector is a containerized web application that allows users to record audio in the browser, and detects the emotional content analyzing by machine learning models.

---

## Setup Instructions

### 1. Clone the Repository

```bash
git clone https://github.com/software-students-spring2025/4-containers-emmmm.git
cd 4-containers-emmmm
```


### 2. Start the System with Docker

```bash
docker-compose build --no-cache
docker-compose up  
```

### 3. Visit the Web through Browser

```bash
http://127.0.0.1:6000
```

---

## References
The pre-trained model in machine learning client part is from Hugging Face https://huggingface.co/speechbrain/emotion-recognition-wav2vec2-IEMOCAP, which is an audio emotion recognition model.
sbyb 0.1.1
A comprehensive ML library that unifies the entire ML pipeline
Requires Python: >=3.8

# SBYB - Step-By-Your-Byte

A comprehensive machine learning library that unifies the entire ML pipeline.

## Overview

SBYB (Step-By-Your-Byte) is a Python library designed to provide a unified, offline-capable machine learning toolkit that outperforms existing solutions like TensorFlow and Keras. It integrates the entire ML pipeline from data preprocessing to model deployment in a single, cohesive package.

## Key Features

- **Unified Data Preprocessing**: Automatic handling of missing values, outliers, encoding, and scaling
- **Task Type & Data Type Auto-detection**: Intelligent identification of ML tasks and data characteristics
- **AutoML Engine**: Automated model selection, hyperparameter tuning, and ensemble creation
- **Evaluation & Explainability**: Comprehensive metrics and model interpretation tools
- **Deployment & Serving**: Easy model export and deployment options
- **Zero-code UI Generation**: Automatic creation of user interfaces for models
- **Project Scaffolding**: Quick setup of new ML projects with best practices
- **EDA Tools**: Powerful data profiling and visualization capabilities
- **Plugin System**: Extensible architecture for custom components
- **Local Experiment Tracking**: Track, compare, and visualize ML experiments
- **CLI & Programmatic API**: Multiple interfaces for different workflows

## Installation

```bash
pip install sbyb
```

## Quick Start

### Using the CLI

```bash
# Create a new project
sbyb project create --name my_project --template classification

# Run AutoML on a dataset
sbyb automl run --data data.csv --target target_column

# Generate a UI for a model
sbyb ui generate --model model.pkl --output ui_app
```

### Using the API

```python
from sbyb.api import SBYB

# Initialize SBYB
sbyb = SBYB()

# Preprocess data
import pandas as pd
data = pd.read_csv("data.csv")
preprocessed_data = sbyb.preprocess_data(data)

# Run AutoML
result = sbyb.run_automl(
    data=preprocessed_data,
    target="target_column",
    output_dir="output"
)

# Generate UI
sbyb.generate_ui(
    model=result.model,
    output_dir="ui_app",
    ui_type="dashboard",
    framework="streamlit"
)
```

## Documentation

For detailed documentation, visit [https://sbyb.readthedocs.io/](https://sbyb.readthedocs.io/)

## License

MIT License