semvec/pypi-packages huggingface dataset:
fvhd 0.2.0
Fast visualization of high-dimensional data
License: MIT
Requires Python: <3.15,>=3.14.0

# FVHD: Fast Visualization of High-Dimensional Data

FVHD is a Python library for efficient visualization of high-dimensional data using force-directed graph layout algorithms. It provides an implementation of high-dimensional data visualization with neighbor-based force calculations.

## Features

- Fast neighbor search using scikit-learn's NearestNeighbors
- Force-directed graph layout optimization
- Support for both optimizer-based and force-directed methods
- Automatic parameter adaptation
- Efficient binary graph storage format

## Installation

FVHD requires Python 3.12+. We recommend using [uv](https://docs.astral.sh/uv/) for dependency management:

1.  **Install uv** (if missing):
    ```bash
    curl -LsSf https://astral.sh/uv/install.sh | sh
    ```

2.  **Sync dependencies**:
    ```bash
    uv sync
    ```

3.  **Run scripts**:
    ```bash
    uv run python your_script.py
    ```

## Quick Start

```python
import torch
import pandas as pd
from fvhd import FVHD
from knn import Graph, NeighborConfig, NeighborGenerator

X = torch.rand(1000, 784)
df = pd.DataFrame(X.numpy())

config = NeighborConfig(metric="euclidean")
generator = NeighborGenerator(df=df, config=config)
graph_knn, graph_mutual = generator.run(nn=5)

fvhd = FVHD(
    n_components=2,
    nn=5,
    rn=2,
    c=0.1,
    eta=0.2,
    epochs=3000,
    device="cuda" if torch.cuda.is_available() else "cpu",
    velocity_limit=True,
    autoadapt=True
)

embeddings = fvhd.fit_transform(X, graph=graph_knn)
```

## Example with MNIST

```python
from fvhd import FVHD

fvhd = FVHD(
    n_components=2,
    nn=5,
    rn=2,
    c=0.005,
    eta=0.003,
    epochs=3000,
    device="cuda"
)

embeddings = fvhd.fit_transform(X)
```

## Parameters

- `n_components`: Output dimensionality (default: 2)
- `nn`: Number of nearest neighbors (default: 2)
- `rn`: Number of random neighbors (default: 1)
- `c`: Repulsion strength coefficient (default: 0.1)
- `eta`: Learning rate (default: 0.1)
- `epochs`: Number of optimization epochs (default: 200)
- `device`: Computation device ("cpu", "cuda", "mps")
- `autoadapt`: Enable automatic learning rate adaptation
- `velocity_limit`: Enable velocity limiting for stability

## Citation

If you use this software in your research, please cite:

```
@misc{fvhd2025,
  author = {Minch, Bartosz, Ręka, Filip and Dzwinel, Witold},
  title = {FVHD: Fast Visualization of High-Dimensional Data},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/langMonk/fvhd}
}
```

## License

This project is licensed under the MIT License - see the LICENSE.md file for details.
mcp-vods 0.1.9
MCP Server for Binge-watch / MoonTV / LunaTV. 一个用于追剧/追番的MCP服务器，为AI提供搜索影视播放地址的能力，并支持在小米等安卓电视/投影仪上直接播放
License: MIT
Requires Python: >=3.10
Keywords: binge, llm, mcp, media, movie, tv, video

# 📺 MCP Server for Binge-watch

<!-- mcp-name: io.github.aahl/mcp-vods -->

一个用于追剧/追番的MCP服务器，为AI提供搜索影视播放地址的能力，并支持在小米电视上直接播放。


## 📲 安装

### 方式1: uvx
```yaml
{
  "mcpServers": {
    "vods": {
      "command": "uvx",
      "args": ["mcp-vods"],
      "env": {
        "MITV_LIST_CFG": "客厅电视:192.168.1.11"
      }
    }
  }
}
```

### 方式2: Docker
```bash
mkdir /opt/mcp-vods
cd /opt/mcp-vods
wget https://raw.githubusercontent.com/aahl/mcp-vods/refs/heads/main/docker-compose.yml
docker-compose up -d
```
```yaml
{
  "mcpServers": {
    "vods": {
      "url": "http://0.0.0.0:8821/mcp" # Streamable HTTP
    }
  }
}
```

### 方式3: Home Assistant OS [Add-on](https://gitee.com/hasscc/addons/tree/main/mcp-vods)
[![添加加载项仓库](https://my.home-assistant.io/badges/supervisor_add_addon_repository.svg)](https://my.home-assistant.io/redirect/supervisor_add_addon_repository/?repository_url=https%3A%2F%2Fgitee.com%2Fhasscc%2Faddons)


### 快速开始
- 添加到 Claude Code, 执行命令:
  - `claude mcp add vods -- uvx mcp-vods`
  - `claude mcp add vods --env MITV_LIST_CFG=客厅电视:192.168.1.11 -- uvx mcp-vods`
- 添加到 OpenAI CodeX, 执行命令: `codex mcp add vods -- uvx mcp-vods`
- 添加到 Cursor [![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/zh/install-mcp?name=vods&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJtY3Atdm9kcyJdfQ==)
- 添加到 VS Code [![Install MCP Server](https://img.shields.io/badge/VS_Code-+?label=Add+MCP+Server&color=0098FF)](https://insiders.vscode.dev/redirect?url=vscode:mcp/install%3F%7B%22name%22%3A%22vods%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-vods%22%5D%7D)
- 添加到 Cherry Studio [![Install MCP Server](https://img.shields.io/badge/Cherry_Studio-+?label=Add+MCP+Server&color=FF5F5F)](https://gitee.com/link?target=cherrystudio%3A%2F%2Fmcp%2Finstall%3Fservers%3DeyJtY3BTZXJ2ZXJzIjp7InZvZHMiOnsiY29tbWFuZCI6InV2eCIsImFyZ3MiOlsibWNwLXZvZHMiXX19fQ%3D%3D)


### ⚙️ 环境变量

#### 免配置开箱即用
- `VOD_CONFIG_URL`: LunaTV/MoonTV订阅源[远程配置文件](https://github.com/hafrey1/LunaTV-config)URL，可选(默认已内置)
- `SEARCH_CACHE_TTL`: 搜索缓存TTL，可选(默认5分钟)
- `MAX_SEARCH_SITES`: 单次最多搜索站点数，可选(默认10)

#### 使用已部署的LunaTV/MoonTV
- `MOON_BASE_URL`: 已部署的MoonTV服务地址，可选，如: `http://0.0.0.0:3000`
- `LUNA_BASE_URL`: 已部署的LunaTV服务地址，可选
- `LUNA_USERNAME`: LunaTV 登录账号，可选
- `LUNA_PASSWORD`: LunaTV 登录密码，可选

#### 小米电视/投影/机顶盒
> 如需在小米电视上播放视频，要至少配置`MITV_LOCAL_IP`或`MITV_LIST_CFG`之一

- `MITV_LOCAL_IP`: 单台小米电视本地IP，可选
- `MITV_LIST_CFG`: 多台小米电视配置，可选，如: `客厅电视:192.168.1.11;主卧电视:192.168.1.12`

#### 其他安卓电视/投影/机顶盒
> 如需在非小米安卓电视上播放视频，需要安装[TvBox](https://github.com/o0HalfLife0o/TVBoxOSC/releases)，并至少配置`TVBOX_LOCAL_IP`或`TVBOX_LIST_CFG`之一

- `TVBOX_LOCAL_IP`: 单台电视本地IP，可选
- `TVBOX_LIST_CFG`: 多台电视配置，可选，如: `客厅电视:192.168.1.11;主卧电视:192.168.1.12`


## 🔗 Links
- https://zread.ai/aahl/mcp-vods
- [LunaTV](https://github.com/MoonTechLab/LunaTV)
- [MoonTV](https://github.com/aahl/MoonTV)