RSpec style guide
Kalman-and-Bayesian-Filters-in-Python
RSpec style guide | Kalman-and-Bayesian-Filters-in-Python | |
---|---|---|
5 | 32 | |
3,448 | 15,817 | |
0.1% | - | |
1.0 | 0.0 | |
12 months ago | 3 months ago | |
HTML | Jupyter Notebook | |
- | GNU General Public License v3.0 or later |
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.
RSpec style guide
-
The RSpec Book worth reading in 2022?
betterspecs.org is a good resource.
- Learning RSpec
- Best course to learn for 2022?
-
Understanding Rspec Best Practices
Both sites advocate for factories over fixtures (though there is a not clear consensus). With fixtures, test objects are all defined in fixture files with predefined data. Fixtures can be used across tests but modifying an existing fixture can break tests that depend on that fixture. As a codebase grows managing fixtures for all the various states of your object can be difficult. In comparison, factories let you build and configure new objects per test.
-
Free 500+ books and learning resources for every programmer.
Better Specs (RSpec Guidelines with Ruby)
Kalman-and-Bayesian-Filters-in-Python
-
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.
-
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!
-
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
-
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
-
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.
-
want to learn kalman filter
Try this book
-
kalman filter & c++
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general
-
Do you use particle/Kalman filters at work?
- Kalman and Bayesian Filters in Python
What are some alternatives?
Rails style guide - A community-driven Ruby on Rails style guide
30-days-of-elixir - A walk through the Elixir language in 30 exercises.
Ruby style guide - A community-driven Ruby coding style guide
clojure-style-guide - A community coding style guide for the Clojure programming language
Fundamental Ruby - :books: Fundamental programming with ruby examples and references. It covers threads, SOLID principles, design patterns, data structures, algorithms. Books for reading. Repo for website https://github.com/khusnetdinov/betterdocs
git-internals-pdf - PDF on Git Internals
Best-Ruby - Ruby Tricks, Idiomatic Ruby, Refactoring and Best Practices
kalmanpy - Implementation of Kalman Filter in Python
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
react-bits - ✨ React patterns, techniques, tips and tricks ✨
contracts.ruby - Contracts for Ruby.
elm-architecture-tutorial - How to create modular Elm code that scales nicely with your app