semvec/pypi-packages huggingface dataset:
gemini-agario 1
This package contains implementation of Agar.io game with Google Gemini AI integrated into it.
License: MIT

# gemini_agario

**Agar.io** game with Google Gemini AI integrated into it.

# Source Code

The source code of the game **Gemini Agar.io** is available in [Source Code](https://github.com/SoftwareApkDev/gemini_agario/blob/master/gemini_agario/gemini_agario.py).

# Installation

```
pip install gemini_agario
```

# How to Run the Game?

Pre-requisites:

1. [Python](https://www.python.org/downloads/) installed in your device.
2. .env file in the same directory as <GEMINI_AGARIO_DIRECTORY> and has the value of GEMINI_API_KEY.

```
cd <GEMINI_AGARIO_DIRECTORY>
python gemini_agario.py
```

**Note:** Replace <GEMINI_AGARIO_DIRECTORY> with the path to the directory of the game **Gemini Agar.io**.

Then, the game will start like as follows.

![Game Start](images/Game_Start.png)

To move the player, move the mouse to the direction you want the player to move to.
env-config-lib 0.1.1
Удобная работа с переменными окружения через схему
License: MIT
Requires Python: >=3.8

# env-config-lib

Удобная работа с переменными окружения через валидацию по схеме.

## Установка

1. python -m venv .venv
2. Активация вертуального окружения 
3. pip install env_config_lib

---

## Пример использования

```python
from env_config_lib import EnvConfig

env = EnvConfig(schema={"TEST": str})
print(env.get("TEST"))

```
pk-regression 0.0.2
Pure Kernel Regression: noise-free, explainable tabular regression via high-purity feature windows
License: MIT
Requires Python: >=3.8

# PKR‑Regression

Ultra‑light, transparent and noise‑proof regression library

---
## 🚀 Why PKR‑Regression?
| Pain Point | Classic ML | **PKR** |
|------------|-----------|---------|
| Hyper‑parameters | ❌ Yes | ✅ No (3‑4 flags) |
| Black‑box outputs | ❌ Yes | ✅ Rule‑based explanation |
| Sensitive to noise | ❌ | ✅ Middle zone muted by median |
| Training time | Long / tuning | Seconds |

In short: *“Keep the crystal‑clear signal, ignore the rest.”*

---
## 🔍 4‑Step Method
1. **Pole Labeling**  
   Sort target *y*.  
   * Bottom 33 % → label `0`  
   * Top 33 %   → label `1`  
2. **Cell Scanning**  
   Build tiny cells for every 1‑, 2‑, 3‑feature combo (fixed width / exact category).  
3. **Valuable Kernel Selection**  
   * ≥ 90 % same label  
   * ≥ 10 rows  
   ⇒ kernel value = mean of that cell.  
4. **Prediction**  
   * Test row hits ≥1 valuable kernel → average(kernel_values)  
   * Hits none → global median.

> Optional flags: `--bins 5` (five quantiles), `--max-dim 1/2/3`, `--gpu` for fast parallel scan.

---
## ⚡ Quick Start
```bash
pip install pkr-regression

pkr \
  --train train.csv \
  --test  test.csv \
  --target Listening_Time_minutes \
  --output submission.csv
```
Outputs: `submission.csv` (Kaggle‑ready) + `kernels.txt` (selected cells)

---
## 📊 Kernel Example
| Kernel Rule | Prediction |
|-------------|-----------|
| Podcast = *Comedy Corner* & Length 0‑0.5 | **6.4 min** |
| Podcast = *Innovators* & Length 0.5‑1.0 | **91.7 min** |

A test row intersecting the first rule returns **6.4**; the second rule → **91.7**.

---
## 💡 FAQ
**Q:** Overfitting risk?  
**A:** No – noisy cells (< 90 % majority or < 10 rows) are discarded automatically.

**Q:** Big data?  
**A:** 1M+ rows train in minutes on one core; `--gpu` accelerates further.

---
Make your model **explainable** with PKR‑Regression — try it & share feedback!
autocorrect-py 2.14.0
A linter and formatter for help you improve copywriting, to correct spaces, words, punctuations between CJK (Chinese, Japanese, Korean).
License: MIT

# AutoCorrect for Python

The Python version of [AutoCorrect](https://github.com/huacnlee/autocorrect).

- Rust - [autocorrect](https://github.com/huacnlee/autocorrect)
- Ruby - [autocorrect-rb](https://github.com/huacnlee/autocorrect/tree/main/autocorrect-rb)
- Go - [autocorrect-go](https://github.com/longbridgeapp/autocorrect)
- Python - [autocorrect-py](https://github.com/huacnlee/autocorrect/tree/main/autocorrect-py)
- Node.js - [autocorrect-node](https://github.com/huacnlee/autocorrect/tree/main/autocorrect-node)
- JavaScript (Browser) - [autocorrect-wasm](https://github.com/huacnlee/autocorrect/tree/main/autocorrect-wasm)
- Java - [autocorrect-java](https://github.com/huacnlee/autocorrect/tree/main/autocorrect-java)

## Installation

```bash
$ pip install autocorrect-py
```

## Usage

```py
import autocorrect_py as autocorrect

autocorrect.format("Hello你好.")
# => "Hello 你好。"

autocorrect.format_for("let title = 'Hello你好。'", "js")
# => "let title = 'Hello 你好。'"

result = autocorrect.lint_for("<h1>这是 Heading标题</h1>", "html")
print(result)
# => LintResult(filepath='html', lines=[LineResult { line: 1, col: 5, new: "这是 Heading 标题", old: "这是 Heading标题", severity: Error }], enable=true)

# Load config
autocorrect.load_config('{ textRules: { "你好hello": 0 } }')
autocorrect.format("Hello你好.")
# => "Hello 你好。"
autocorrect.format("你好hello.")
# => "你好hello."

# Ignorer, if /path/to/workdir contains .autocorrectignore or .gitignore
ignorer = autocorrect.Ignorer('/path/to/workdir');
ignorer.is_ignored('README.md');
```