buddy_alloc VS sysidentpy

If you need a sub-allocator with predictable performance feel free to give it a try. The code is here and it is licensed under the 0BSD license, making it as lax and as close to public domain as possible. Comments, issues and PRs are always welcomed and appreciated. Thanks!

I maintain a project that's not technically MISRA compliant (due to being a memory allocator and MISRA disallowing the very idea) and I keep it at 100% test coverage with support for multiple compilers and operating systems. Over time I had a few users reporting back - since it's working for them I count that as success. Is it wildly popular ? Of course not, but it doesn't have to be.

You are right about the tests - they are written with 64-bit in mind. I ought to rework them to switch sizes based on arch but that will take a weekend. I've filed https://github.com/spaskalev/buddy_alloc/issues/19 to track this.

I also maintain a application-based malloc (that doesn't do obtaining and releasing memory through the OS, just managing sub-diving a larger memory block into smaller requests) at https://github.com/spaskalev/buddy_alloc - feel free to ping me with any questions about it.

I'm the author of https://github.com/spaskalev/buddy_alloc - a custom memory allocator for C (modern C11, works with C++ as well) designed for predictable and repeatable performance. It is suitable for use in embedded, games and any other system with soft or hard real-time demands. It has 100% line and branch test coverage and uses a fixed amount of space on the call stack when called. Recently the project had its first external contribution as well. Cheers!

I made a memory allocator that turned out rather neat - https://github.com/spaskalev/buddy_alloc

Link para o site da documentação: SysIdentPy - SysIdentPy

I'm the only maintainer, but I keep including new features (some exclusives, like the algorithm I've developed in my thesis to create NARMAX models), improving the code and documentation, and fixing bugs.

benchmark codes: Welcome to SysIdentPy’s documentation! — NARMAX models

github: wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models (github.com)

I built a package where you can build polynomial NARMAX models using the most used method for model selection of this class of models in the beckend. In addition, you can built NARX Neural Networks for forecasting problems (this is built on top of Pytorch) and you can use any model that have a fit/predict method (Catboost, any estimator from sklearn) in a NARX configuration to perform infinity-steps-ahead prediction. Maybe its worth a try. Here is the link of the package: GitHub - wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models