Introducing Luna: My Rust-based NNUE Chess Engine

[Spunc595]

​Hello everyone,
​I am thrilled to have been accepted into this community! I would like to take this opportunity to introduce my chess engine, Luna.
​I have been developing Luna from scratch using Rust, focusing on creating a robust and efficient architecture. One of the core components I am particularly proud of is the NNUE evaluation; I have trained the network personally using a custom pipeline written in Python.
​The engine currently supports the standard UCI protocol, and I have implemented features like a bitwise-masked transposition table to enhance search performance. As I am still actively working on optimizations and refining the search algorithms, I would truly appreciate any feedback, constructive criticism, or advice from the experienced developers here.
​You can find the source code and the current status of the project on my GitHub repository:
https://github.com/Spunc595/Luna-Chess-Engine
​I look forward to learning from this community and contributing where I can. Thank you all for your time!
​Best regards,
​Daniele

[Sylwy]

​Hello everyone,
​I am thrilled to have been accepted into this community! I would like to take this opportunity to introduce my chess engine, Luna.
​I have been developing Luna from scratch using Rust, focusing on creating a robust and efficient architecture. One of the core components I am particularly proud of is the NNUE evaluation; I have trained the network personally using a custom pipeline written in Python.
​The engine currently supports the standard UCI protocol, and I have implemented features like a bitwise-masked transposition table to enhance search performance. As I am still actively working on optimizations and refining the search algorithms, I would truly appreciate any feedback, constructive criticism, or advice from the experienced developers here.
​You can find the source code and the current status of the project on my GitHub repository:
https://github.com/Spunc595/Luna-Chess-Engine
​I look forward to learning from this community and contributing where I can. Thank you all for your time!
​Best regards,
​Daniele

viewtopic.php?start=820&t=81223&sid=40b ... 715beeaf45

1.- let static_eval = crate::evaluation::evaluate(board); ...or,

let static_eval = if let Some(n) = nnue {
n.evaluate(board)
} else {
crate::evaluation::evaluate(board)
};

2.- let stand_pat = crate::evaluation::evaluate(board); ...or,

let stand_pat = if let Some(n) = nnue {
n.evaluate(board)
} else {
crate::evaluation::evaluate(board)
};

???

[Spunc595]

Hi.

I am actually using the second approach. Since the NNUE is passed as an Option, I implemented a fallback to the classical evaluation function in both the main search (for static_eval) and the quiescence search (for stand_pat) in case the network file is missing or fails to load.

This allows me to test and compare the search behavior with and without the neural network weights.