WIP: Game of Life in Avalonia, MVU / Elmish style

A couple of days ago, I came across a toot from Khalid Abuhakmeh, showcasing a C# + MVVM implementation of the Game of Life on Avalonia. I have been experimenting with Avalonia funcUI recently, and thought a conversion would be both a fun week-end exercise, and an interesting way to take a look at performance.

Long story short, I took a look at his repository as a starting point, and proceeded to rewrite it in an Elmish style, shamelessly lifting the core from his code. The good news is, it did not take a lot of time to get it running, the less good news is, my version has clear performance issues.

gif: game of life running

In this post, I will go over how I approached it so far, and where I think the performance issues might be coming from. In a later post, I’ll try to see if I can fix these. As the French saying goes, “A chaque jour suffit sa peine”.

You can find the full code here on GitHub


First look at Avalonia with Elmish: wrapping OxyPlot charts

In the recent weeks, I came across a use case which sounded like a good fit for a desktop application, which got me curious about the state of affairs for .NET desktop clients these days. And, as I was looking into this, I quickly came across Avalonia, and specifically Avalonia.FuncUI. Cross platform XAML apps, using F# and the Elmish loop? My curiosity was piqued, and I figured it was worth giving it a try.

In this post, I will go over my first steps trying the library out. My ambitions are limited: first, how hard is it to get something running? Then, how hard is it to take an existing Avalonia library (in this case, the charting library OxyPlot), and bolt it into an Elmish style Avalonia app?

You can find the full code here on GitHub


Quipu, a simple Nelder Mead solver in F#

Some time back, I wrote a small post digging into the mechanics behind the Nelder Mead solver. As it turns out, I had a use for it recently, and after copy-pasting my own code a few times, I figured it would make my life easier to turn that into a NuGet package, Quipu.

So what does it do, and why might you care?

A code example might be the quickest explanation here. Suppose that, for whatever reason, you were interested in the function f(x) = x ^ 2, and wanted to know for what value of x this function reaches its minimum.

That is easy to solve with the Quipu Nelder-Mead solver:

open Quipu
open Quipu.NelderMead

let f x = x ** 2.0
let solution =
        (Objective.from f) [ 100.0 ]
printfn $"{solution}"

… which produces the following result:

Optimal (0.0001556843433, [|0.01247735322|])

The function f reaches a minimum of 0.0001, for x = 0.0124.


Study notes: function minimization with DiffSharp

This post is intended primarily as a note to myself, keeping track as my findings as I dig into automatic differentiation with DiffSharp. Warning: as a result, I won’t make a particular effort at pedagogy – hopefully you’ll still find something of interest in here!

The main question I am interested in here is, how can I use DiffSharp to find the minimum of a function? I will take a look first at basic gradient descent, to get us warmed up. In a future installment I plan to explore using the built-in SGD and Adam optimizers for that same task.

The full code is here on GitHub, available as a .NET interactive notebook.

Test function

The function we will be using in our exploration is the following:

$f(x,y)=0.26 \times (x^2+y^2) - 0.48 \times (x \times y)$

This function, which I lifted this function from this blog post, translates into this F# code:

let f (x: float, y: float) =
    0.26 * (pown x 2 + pown y 2) - 0.48 * x * y

Graphically, this is how the function looks like:

2D surface of the function f

This function has a global minimum for (x = 0.0, y = 0.0), and is unimodal, that is, it has a single peak (or valley in this case). This makes it a good test candidate for function minimization using gradient descent.


Simulating the Wrapinator 5000

It is that time of the year again! The holidays are approaching, and the F# Advent calendar is in full swing. My contribution this year might not be for the broadest audience, sorry about that :) But if you are into F#, probability theory, and numeric optimization, this post is for you - hope you enjoy it! And big shout out to Sergey Tihon for making this happen once again.

You can find the full code for this post here on GitHub.

With the Holidays approaching, Santa Claus, CEO of the Santa Corp, was worried. In preparation for the season’s spike in activity, Santa had invested in the top-of-the-line gift wrapping machine for the Elves factory, the Wrapinator 5000. This beast of a machine has two separate feeders, one delivering Paper, the other Ribbon, allowing the elves to wrap gifts at a cadence never achieved before.

So why worry? Mister Claus, being no fool, had also invested in monitoring, and the logs for the Wrapinator 5000 showed quite a few failures. Would the gift wrapping production lines hold up during the Merry Season?

Mister Claus fiddled anxiously with his luscious beard, alone in his office. And then, being a Man of Science, he did what any self-respecting CEO would do, and decided it was time to build a simulation model of his Wrapinator 5000. With a simulation model in hand, he could analyze his Elves factory, evaluate potential alternative operating policies, and most importantly, get that peace of mind he so desperately longed for.