semvec/pypi-packages huggingface dataset:
FCKLN 1.0.1
Flexible Conditional Modeling for Structured Dependencies
Requires Python: >=3.8

### Overview
We propose a flexible conditional modeling framework that learns structured dependencies between variables by blending linear and nonlinear transformations. Our method dynamically adjusts to data characteristics via a learnable mixture parameter, allowing the model to preserve simplicity where appropriate while capturing complex behaviors when necessary. We validate the approach on synthetic nonlinear datasets, achieving significant improvements over traditional linear models. This framework opens the door for scalable integration into large models, including language model retraining where structured paths in latent space matter.

## Features
- Dynamic alpha-weighted mixture of linear and nonlinear paths
- Experiments on:
  - Simple synthetic functions
  - Spiral curve datasets
  - OpenMathReasoning (real-world text embeddings)
- Full paper included

### Usage
``` python
from kln import FlexibleConditional

model = FlexibleConditional(input_dim=2, hidden_dim=64, output_dim=1)
```

### Acknowledgements
OpenAI's ChatGPT provided substantial assistance with the research, writing, and development of this work. The authors gratefully acknowledge its contributions while assuming full responsibility for the final content.

We thank the contributors to open-source geometry libraries and acknowledge the support of interdisciplinary visualization research.

We would also like to include [this poem](https://github.com/michalkrupa/kln/blob/main/FlexibleConditionalPaper/poem.md), written by ChatGPT o4
#### REFERENCES
[1] J. A. Reeds and L. A. Shepp. 1990. Optimal paths for a car that goes both forwards and backwards. Pacific J. Math. 145, 2 (1990), 367–393.

[2] Michal Krupa. 2025. Geometrically-Constrained Pathfinding: An Arc-Based Algorithm for Navigation in Restricted Domains. In . ACM, New York, NY, USA, 1 page.
pseudonymizer-uk 0.1.3
A Python library for pseudonymizing Ukrainian text using Presidio analyzer with Ukrainian NER model
Requires Python: <3.10,>=3.9

# Ukrainian Text Pseudonymizer

A Python library for pseudonymizing Ukrainian text using the Presidio analyzer framework with a Ukrainian NER model. This tool can identify and anonymize various types of entities in Ukrainian text, including:

- Person names
- Job titles
- Locations
- Organizations
- Date/Time expressions
- Email addresses
- Credit card numbers
- URLs
- Phone numbers

## Requirements

- Python 3.8
- Git LFS (for model download)

## Installation

1. Install the package using `uv`:
```bash
uv pip install pseudonymizer-uk
```

2. Install Git LFS (required for model download):
```bash
git lfs install
```

3. Download the Ukrainian NER model:
```bash
git clone https://huggingface.co/dchaplinsky/uk_ner_web_trf_13class
```

## Usage

```python
from pseudonymizer_uk import UkPseudonymizer

# Initialize the pseudonymizer with the path to the downloaded model
pseudonymizer = UkPseudonymizer(path_to_model="./uk_ner_web_trf_13class")

# Pseudonymize text
text = "Іван Франко народився в селі Нагуєвичі"
anonymized_text = pseudonymizer.pseudonymize(text)
```

## Supported Entity Types

By default, the pseudonymizer recognizes the following entity types:
- PERSON
- JOB
- LOCATION
- ORGANIZATION
- DATE_TIME

You can customize which entities to recognize by passing the `entities` parameter:

```python
pseudonymizer = UkPseudonymizer(
    path_to_model="uk_ner_web_trf_13class",
    entities=['PERSON', 'LOCATION']  # Only recognize persons and locations
)
```

## Custom Recognizers and Operators

You can extend the functionality by adding custom recognizers and operators:

```python
from presidio_analyzer import EntityRecognizer
from presidio_anonymizer import OperatorConfig

# Add custom recognizer
pseudonymizer.add_custom_recognizer(your_custom_recognizer)

# Add custom operator
pseudonymizer.add_custom_operator(
    "CUSTOM_ENTITY",
    OperatorConfig("custom", {"param": "value"})
)
```

## Development

To set up the development environment:

1. Clone the repository:
```bash
git clone https://github.com/fox-rudie/pseudonymizer-uk.git
cd pseudonymizer-uk
```

2. Create a virtual environment and install dependencies using `uv`:
```bash
uv venv
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows

uv pip install -e ".[dev]"
```

3. Install pre-commit hooks (optional):
```bash
uv pip install pre-commit
pre-commit install
```

## Publishing

To publish a new version to PyPI:

1. Update version in `pyproject.toml` and `__init__.py`

2. Build the package:
```bash
uv pip install build
python -m build
```

3. Upload to PyPI:
```bash
uv pip install twine
python -m twine upload dist/*
```

## License

MIT License
epic-awesome-gamer 0.4.1
🍷 Gracefully claim weekly free games from Epic Store.
Requires Python: >=3.12
Keywords: epic, epic-awesome-gamer, epic-games-store

<div align="center">
    <h1> EPIC 免费人</h1>
    <p>🍷 Gracefully claim weekly free games from Epic Store.</p>
    <img src="https://img.shields.io/static/v1?message=reference&color=blue&style=for-the-badge&logo=micropython&label=python">
    <img src="https://img.shields.io/github/license/QIN2DIM/epic-awesome-gamer?style=for-the-badge">
    <a href="https://hub.docker.com/r/ech0sec/awesome-epic"><img src="https://img.shields.io/docker/pulls/ech0sec/awesome-epic?color=green&style=for-the-badge"></a>
	<a href=""><img src="https://img.shields.io/github/actions/workflow/status/QIN2DIM/epic-awesome-gamer/ci_docker_fish.yaml?style=for-the-badge"></a>
	<br>
    	<a href="https://discord.gg/KA66wZBQ"><img alt="Discord" src="https://img.shields.io/discord/978108215499816980?style=social&logo=discord&label=echosec"></a>
	<a href="https://t.me/+Wdtxnn1yxU5jMmY5"><img src="https://img.shields.io/static/v1?style=social&logo=telegram&label=chat&message=studio" ></a>
	<br>
	<br>
</div>


![scaffold-get-demo-output-small](https://github.com/QIN2DIM/img_pool/blob/main/img/scaffold-get-demo-output-small.gif)

## Introduction 👋

[Epic 免费人](https://github.com/QIN2DIM/epic-awesome-gamer) 帮助玩家优雅地领取免费游戏。内置 [hcaptcha-challenger](https://github.com/QIN2DIM/hcaptcha-challenger) AI 模块，直面人机挑战。

## Guides

[ [`简体中文`](https://echosec.notion.site/Epic-7c74f1e29117420dbac5551e4b031f82?pvs=4) ] [ [`English`](https://echosec.notion.site/Epic-Awesome-Gamer-ba870cdf64c149e69f417448b1eb83c5?pvs=4) ]

## Features

| Component | Support |
| :------------------- | :------ |
| hCaptcha Solver      | ✅       |
| Docker development | ✅       |
| Persistent context @multi-user |   ✅      |
| Rolling Upgrade      | 🚧   |
| Epicgames DLC | 🚧 |
| 2FA OTP support | 🚧 |
dickerbotclient 1.0.4
Python client library for communicating with DickerBot robots.
Requires Python: >=3.7

# DickerBotClient

DickerBotClient is a Python client library designed for seamless communication with DickerBot robots. It provides a simple interface to interact with DickerBot robots, enabling control over sensors and motors via WebSocket communication.

## Features

- **WebSocket Communication**: Connects to a WebSocket server to send and receive sensor and control data.
- **Sensor Data**: Retrieves and processes sensor information such as accelerometer, gyroscope, and distance sensors.
- **Image Data**: Receives image data from the robot and converts it into a usable format.
- **Control Data**: Sends motor control data (speed and direction) to the robot.

## Installation

You can install the latest release of DickerBotClient from PyPI using `pip`:

```bash
pip install dickerbotclient
```

## Usage in Python

### Importing the library in your python script
```python
import dickerbotclient as dbc
```

### Creating an instance of the class
```python
bot = dbc.DickerBotClient()
```

### Connecting to the host socket
Note: Get `ip_address` and `port` from the Host UI
```python
bot.connect(ip_address, port)
```

### Polling sensor data
```python
sensor_data = bot.get_sensor_data()
```
Format: `[acceleration_x, acceleration_y, acceleration_z, angular_velocity_x, angular_velocity_y, angular_velocity_z, temperature, distance_left, distance_front, distance_right, distance_back]`

Value `999` anywhere indicates error

### Polling image data
```python
image = bot.get_image_data()
```

Format: `96x96 grayscale`

### Sending control data
```python
bot.set_control_data(left_wheel_speed, left_wheel_direction, right_wheel_speed, right_wheel_direction)
```
| **Parameter** | **Description** |
|---------------|-----------------|
| speed | `0`-`255`; `999` = error |
| direction | `0` = neutral; `1` = forward; `2` = backward; `999` = error |

### Disconnecting from host socket
```python
bot.disconnect()
```

## Example Teleop
```python
# TODO: Create a program to do basic keyboard teleop using w,a,s,d while showing realtime video stream and IMU/proximity data.
```

## DickerBot Project

You can find information about the DickerBot on the [GitHub page](https://github.com/keshavshankar08/DickerBot/tree/main).