sunfish VS NumPy

The NNUE version does but the non-NNUE version doesn't use numpy. That numpy import is for a neural network based evaluation functions. The base engine still plays at a strong human level and doesn't use numpy to do any of the heavy lifting. It does rely on itertools and collections though.

I'm currently working on a chess bot in C++. The literature and common sense tells you that faster is better, which was my main reason for choosing C++. Then I discovered Sunfish on GitHub that in around 400 lines of Python (without any low level libraries like ctypes) and it still beat me somewhat convincingly. It was definitely fast enough.

Just one file: Sunfish.py

sunfish – A Python Chess Engine in 111 lines of code

https://github.com/thomasahle/sunfish sunfish is a pretty simple one that uses piece square tables for evaluating positions. Wouldn’t be too difficult to change or even tune

It's from 1990. My father passed it down to me. It works... Uhmm.. idk. There's a proprietary chess engine built in. It says it's around 1700 elo points strong. I am a big noob when it comes to chess engines but if you're interested in learning you can check out sunfish , but (Python) programming skills are required.

Incomprehensible scheiße code. I looked around and I like this one because it has "meta-level" definition of movements and liitle bit of strategy. You could implement context-free chess games with varying rules for us congenitally lazy and dull-witted. https://github.com/thomasahle/sunfish/blob/master/sunfish.py

sunfish https://github.com/thomasahle/sunfish (sunfish is the most compact and still understandable code in my opinion)

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]