semvec/pypi-packages huggingface dataset:
estatistica-simples 0.1.0
Um pacote Python simples para cálculos estatísticos básicos.
Requires Python: >=3.6

# proj-pct-estatistico
Projeto em Python: Pacote Estatístico Simples

# estatistica_simples

Um pacote Python simples para realizar cálculos estatísticos básicos como média, mediana, desvio padrão e variância.

## Instalação

```bash
pip install estatistica_simples


# estrutura do pacote

estatistica_simples/
    ├── estatistica_simples/
    │   ├── __init__.py
    │   ├── medidas_tendencia_central.py
    │   ├── medidas_dispersao.py
    │   └── utilidades.py
    ├── setup.py
    └── README.md


# exemplo de uso

import estatistica_simples as est

dados = [1, 2, 3, 4, 5]

media = est.media(dados)
mediana = est.mediana(dados)
desvio_padrao = est.desvio_padrao(dados)

print(f"Média: {media}")
print(f"Mediana: {mediana}")
print(f"Desvio Padrão: {desvio_padrao}")
hkopenai.hk-transportation-mcp-server 0.2.8
Hong Kong transportation MCP Server providing transportation data tools
Requires Python: >=3.7

# Hong Kong transportation Data MCP Server

[![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/hkopenai/hk-transportation-mcp-server)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This is an MCP server that provides access to transportation related data in Hong Kong through a FastMCP interface.

## Features

### Passenger Traffic Statistics
- Get daily passenger traffic statistics at Hong Kong control points. Filter data by date ranges. Breakdown statistics by visitor types (Hong Kong Residents, Mainland Visitors, Other Visitors)

### Real time Arrival Data of Kowloon Motor Bus and Long Win Bus Services
- Get all bus routes of Kowloon Motor Bus (KMB) and Long Win Bus Services. Filter by language (English, Traditional Chinese, Simplified Chinese)

### Land Boundary Control Points Waiting Times
- Fetch current waiting times at land boundary control points in Hong Kong. Filter by language (English, Traditional Chinese, Simplified Chinese)

## Data Source

- Passenger traffic data from Hong Kong Immigration Department
- Bus route data from Kowloon Motor Bus and Long Win Bus Services

## Examples

* How many hong kong arrival yesterday through high speed railway.
* Get all KMB/LWB bus routes in Traditional Chinese.
* What are the current waiting times at Hong Kong land boundary control points?

## Setup

1. Clone this repository
2. Install Python dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Run the server:
   ```bash
   python app.py
   ```

### Running Options

- Default stdio mode: `python app.py`
- SSE mode (port 8000): `python app.py --sse`

## Cline Integration

To connect this MCP server to Cline using stdio:

1. Add this configuration to your Cline MCP settings (cline_mcp_settings.json):
```json
{
  "hk-transportation": {
    "disabled": false,
    "timeout": 3,
    "type": "stdio",
    "command": "uvx",
    "args": [
      "hkopenai.hk-transportation-mcp-server"
    ]
  }
}
```

## Testing

Tests are available in the `tests/` directory. Run with:
```bash
pytest
```
model-wrapper 1.1.3
Model wrapper for Pytorch, which can training, predict, evaluate, etc.
Requires Python: >=3.6
Keywords: Pytorch, Training, AI, Machine learning, Deep learning, torch

Usage Sample
''''''''''''

.. code:: python

        from model_wrapper import SplitClassifyModelWrapper

        classes = ['class1', 'class2', 'class3'...]
        X = [[...], [...],]
        y = [0, 0, 1, 2, 1...]

        model = ...
        wrapper = SplitClassifyModelWrapper(model, classes=classes)
        wrapper.train(X, y, val_size=0.2)

        X_test = [[...], [...],]
        y_test = [0, 1, 1, 2, 1...]
        result = wrapper.evaluate(X_test, y_test)
        # 0.953125

        result = wrapper.predict(X_test)
        # [0, 1]

        result = wrapper.predict_classes(X_test)
        # ['class1', 'class2']

        result = wrapper.predict_proba(X_test)
        # ([0, 1], array([0.99439645, 0.99190724], dtype=float32))

        result = wrapper.predict_classes_proba(X_test)
        # (['class1', 'class2'], array([0.99439645, 0.99190724], dtype=float32))