c2cs
sysidentpy
| c2cs | sysidentpy | |
|---|---|---|
| 2 | 7 | |
| 218 | 312 | |
| 3.2% | - | |
| 6.7 | 7.8 | |
| 16 days ago | 22 days ago | |
| C# | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
c2cs
-
[NOOB-ish] Code works when target is x86 but not x64
Using LayoutKind.Sequential for a "C union" is not correct. If you want I can fix up my project https://github.com/bottlenoselabs/c2cs later this week to add support for generating the P/Invoke C# bindings for a target Windows API. It's how I got the information.
- I'm giving out microgrants to open source projects for the third year in a row! Brag about your projects here so I can see them, big or small!
sysidentpy
-
Contribuição em biblioteca open source
Link para o site da documentação: SysIdentPy - SysIdentPy
- sysidentpy: A Python Package For System Identification Using NARMAX Models
-
I'm giving out microgrants to open source projects for the third year in a row! Brag about your projects here so I can see them, big or small!
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.
-
A Comparison of Time Series Model Forecasting
benchmark codes: Welcome to SysIdentPy’s documentation! — NARMAX models
-
Criei uma biblioteca open source para previsão de séries temporais
github: wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models (github.com)
-
Help with time series analysis
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
What are some alternatives?
OpenRGB
pysindy - A package for the sparse identification of nonlinear dynamical systems from data
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
neural_prophet - NeuralProphet: A simple forecasting package
keepassxc - KeePassXC is a cross-platform community-driven port of the Windows application “Keepass Password Safe”.
sktime - A unified framework for machine learning with time series
rav1e - The fastest and safest AV1 encoder.
forge - Lua scriptable build tool
space-nerds-in-space - Multi-player spaceship bridge simulator. Captain your starship through adventures with your friends. See https://smcameron.github.io/space-nerds-in-space
rpi-rgb-led-matrix - Controlling up to three chains of 64x64, 32x32, 16x32 or similar RGB LED displays using Raspberry Pi GPIO
Ichor - C++20 Microservice Bootstrapping Framework
ConverterNOW - The Unit Converter app: easy, immediate and multi-platform