clojure-style-guide VS Kalman-and-Bayesian-Filters-in-Python

Fixed link to that style guide entry: https://guide.clojure.style/#opt-commas-in-map-literals

Per that style guide, the above map would be formatted like this (on HN, just indent by two spaces):

  {:a 1

As for the broader question of Clojure style, there are style guides like https://github.com/bbatsov/clojure-style-guide and tools like clj-kondo to help learn and reinforce important practices.

Basically, you learn the expected places to put whitespace, make sure to edit your code accordingly and all of the parens will be automatically closed and adjusted. Using parinfer—which you can also combine with the more traditional paredit—makes writing Clojure code a lot like writing Python.

Also, there are guidelines, the styleguide, clj-kondo, kibit etc. And if you don't review your interns/juniors code to teach them good practices - you're doing it wrong (well, this one is true for any practical PL out there).

Another good resource is https://guide.clojure.style/ -- the (unofficial) community style guide for Clojure.

It's not an answer to your question but i can refer you to https://github.com/bbatsov/clojure-style-guide

The same could be said about the "Clojure Style Guide" from the Cider guy. As a matter of fact, there was an issue about it that was quickly declined https://github.com/bbatsov/clojure-style-guide/issues/232

Formatting is not that great, see https://github.com/bbatsov/clojure-style-guide btw

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.

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!

This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

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.

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...

Try this book

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general

- Kalman and Bayesian Filters in Python