| Name | chess_features JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | Transform chess positions to various encodings |
| upload_time | 2024-11-17 21:42:31 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | None |
| keywords |
chess
encoding
features
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Chess Features
This Python package provides a convenient way to convert chess board representations from the Python Chess library into formats suitable for machine learning algorithms. It offers various representations that can be directly utilized as input for machine learning tasks.
## Features
- Convert Python Chess board representations into machine learning-friendly formats.
- Supports several common representations used in machine learning tasks.
- Easy-to-use interface for seamless integration into your projects.
## Installation
You can install the package using pip:
```bash
pip install chess_features
```
## Usage
Here's a basic example demonstrating how to use the package:
```python
from chess import Board
from chess_features import ChessFeatures
# Create a Chess board using the Python Chess library
board = Board()
# Initialize the ChessFeatures
converter = ChessFeatures()
# Convert the board representation into a machine learning-friendly format
# Example: Convert to a feature vector
feature_vector = converter.to_stockfish_feature_vector(board)
# Example: Convert to a bitmap
bitmap = converter.to_bitmap(board)
```
## Available Representations
- Feature Vector: A flattened vector representation of the board.
- Bitmap: A bitmap representation of the board.
## Acknowledgements
- This package utilizes the [Python Chess library](https://python-chess.readthedocs.io/en/latest/).
- The information for Stockfish features are taken from [Stockfish Evaluation Guide](https://hxim.github.io/Stockfish-Evaluation-Guide/).
## ChatGPT
Apart from this readme no ChatGPT was used.
Raw data
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"description": "# Chess Features\n\nThis Python package provides a convenient way to convert chess board representations from the Python Chess library into formats suitable for machine learning algorithms. It offers various representations that can be directly utilized as input for machine learning tasks.\n\n## Features\n\n- Convert Python Chess board representations into machine learning-friendly formats.\n- Supports several common representations used in machine learning tasks.\n- Easy-to-use interface for seamless integration into your projects.\n\n## Installation\n\nYou can install the package using pip:\n\n```bash\npip install chess_features\n```\n\n## Usage\n\nHere's a basic example demonstrating how to use the package:\n\n```python\nfrom chess import Board\nfrom chess_features import ChessFeatures\n\n# Create a Chess board using the Python Chess library\nboard = Board()\n\n# Initialize the ChessFeatures\nconverter = ChessFeatures()\n\n# Convert the board representation into a machine learning-friendly format\n# Example: Convert to a feature vector\nfeature_vector = converter.to_stockfish_feature_vector(board)\n\n# Example: Convert to a bitmap\nbitmap = converter.to_bitmap(board)\n\n```\n\n## Available Representations\n\n- Feature Vector: A flattened vector representation of the board.\n- Bitmap: A bitmap representation of the board.\n\n## Acknowledgements\n\n- This package utilizes the [Python Chess library](https://python-chess.readthedocs.io/en/latest/).\n- The information for Stockfish features are taken from [Stockfish Evaluation Guide](https://hxim.github.io/Stockfish-Evaluation-Guide/).\n\n## ChatGPT\n\nApart from this readme no ChatGPT was used.\n",
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