Game of Life in Avalonia, MVU / Elmish style, take 2

This is a follow-up to my recent post trying to implement the classic Conway Game of Life in an MVU style with Avalonia.FuncUI. While I managed to get a version going pretty easily, the performance was not great. The visualization ran OK until around 100 x 100 cells, but started to degrade severely beyond that.

After a bit of work, I am pleased to present an updated version, which runs through a 200 x 200 cells visualization pretty smoothly:

As a side note, I wanted to point out that the size change is significative. Increasing the grid size from 100 to 200 means that for every frame, the number of elements we need to refresh grows from 10,000 to 40,000.

In this post, I will go over what changed between the two versions.

You can find the full code here on GitHub

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.

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 =
    NelderMead.solve 
        Configuration.defaultValue
        (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:

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.