Kalman-and-Bayesian-Filters-in-Python
CppCoreGuidelines
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32 | 307 | |
15,859 | 41,650 | |
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0.0 | 7.6 | |
3 months ago | 27 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Kalman-and-Bayesian-Filters-in-Python
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The Kalman Filter
A fantastic interactive introduction to Kalman filters can be found on the following repo:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.
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Kalman Filter Explained Simply
No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.
So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(
I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.
Somebody help me out, here!
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Recommendations for undergrad to learn optimal state estimation
This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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A Non-Mathematical Introduction to Kalman Filters for Programmers
If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
Interactive examples programmed in Jupyter notebooks.
- Looking for a study partner to learn kalman filter
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Kalman Filter for Beginners
Thank you, very good resource! Timely too, as I am revising this topic.
My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
- Starting out with Kalman Filter.
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want to learn kalman filter
Try this book
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kalman filter & c++
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general
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Do you use particle/Kalman filters at work?
- Kalman and Bayesian Filters in Python
CppCoreGuidelines
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Are We Modules Yet?
If you aren't aware of the c++ core guidelines[1] - it should be on your radar.
Also, it might not be a popular opinion, but I think Bjarne's books are just fine.
A Tour of C++ (3rd edition) [2]
Principles and Practice Using C++ (3rd Edition) was just published in april 2023 [3]
[1] https://github.com/isocpp/CppCoreGuidelines/blob/master/CppC...
- Learn Modern C++
- C++ Core Guidelines
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Modern C++ Programming Course
You need to talk to Bjarne and Herb...
"C++ Core Guidelines" - https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines
- CLion Nova Explodes onto the C and C++ Development Scene
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Toward a TypeScript for C++"
In addition to the other comments -
TypeScript deliberately takes a "good enough" approach to improving JavaScript, instead of designing an ideal but incompatible approach. For example, its handling of [function parameter bivariance](https://www.typescriptlang.org/docs/handbook/type-compatibil...) is unsound but works much better with the existing JavaScript ecosystem. By contrast, a more academic functional programming language would guarantee a sound type system but would be a huge shift from JavaScript.
By analogy, Herb Sutter is arguing that something like the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines), with tooling help in this new Cpp2 syntax, can bring real improvements to safety. Something like Rust's borrow checker would bring much stricter guarantees, backed by academic research and careful design, but would be incompatible and a huge adjustment.
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MechE student here. Is there benefit to learning C in addition to C++, or can one do everything with C++ that can be done with C?
https://www.youtube.com/watch?v=2olsGf6JIkU
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C++ is everywhere, but noone really talks about it. What are people's thoughts?
Take a look at Effective Modern c++ by Scott Meyers and the ISO c++ core guidelines. These resources are great for learning how to write better, more modern C++. I don't think it would be hard to grasp if you're already familiar with the language, just make sure to actually write some code which makes use of this stuff, otherwise it's easy to forget.
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What are some C++ specific antipatterns that might be missed by C#/Java devs?
Look to the C++ Core Guidelines. It's not perfect, it has some flaws, including some sabotaging advice apparently adopted for political reasons. But at least it has some C++ authorities (Bjarne and Herb) as authors.
What are some alternatives?
30-days-of-elixir - A walk through the Elixir language in 30 exercises.
Crafting Interpreters - Repository for the book "Crafting Interpreters"
clojure-style-guide - A community coding style guide for the Clojure programming language
github-cheat-sheet - A list of cool features of Git and GitHub.
git-internals-pdf - PDF on Git Internals
LearnOpenGL - Code repository of all OpenGL chapters from the book and its accompanying website https://learnopengl.com
kalmanpy - Implementation of Kalman Filter in Python
react-bits - ✨ React patterns, techniques, tips and tricks ✨
Power-Fx - Power Fx low-code programming language
elm-architecture-tutorial - How to create modular Elm code that scales nicely with your app