A while ago I wrote about my school project that involves generating pretty trees and concluded the post with the idea that I now have to implement some way for the branch lengths to be a bit random to add more variability.

There is an easy and an awesome way to doing this. The easy way involves doing everything as usual and then simply adding a small random buzz, fuzz or whatever you could call it to the length of a branch. Obviously this approach would work and the branches wouldn't be of uniform length anymore ... but there's just no fun in that. The results it produces also aren't quite that awesome.

So I chose a different way.

First a little background on how branch lengths were calculated originally. Every time a branch is needed the basic brench length is multiplied with a factor chosen based on how deep inside the tree we are. So for example, if the trunk is of length 5 and we are on the third level of the tree the length would be 5*0.25, or a quarter of the trunk's length.

I wanted to expand on that by randomly selecting a multiplier from the list for every branch I'm looking at. As you can probably expect, this produced rather funny looking trees.

What's needed is a way to randomly choose the length of a branch, but probabilistically making sure that branches on deeper levels of the tree are shorter than the ones before them. This leads us to the idea of needing a weighed random that has a slightly higher probability of returning a certain length over others.

After a bunch of googling on how such a thing might be achieved I settled on a pretty simple solution. Basically produce a list of possible indexes, tweaked so there's more of those that need a higher probability, and then simply picking a random one. Something like so:

(nth choices(rand-nth (0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 4))

This, however, isn't very elegant or even pretty to look at. Changing it so the maximum of the probability distribution moves down the choices depending on how deep inside the tree we are would be painful.

My next step was changing the weighed random choice so it would generate such a horrible list on its own and use a provided function to calculated the weights for every specific index. This gives us a way to neatly define the probability distribution we want every time we make a random choice from a list.

Here's what the final function looks like

(defn weighed-random-choice [choices weight](defn indexes [](flatten (map #(replicate (weight %1) %1)(take (count choices)(iterate inc 0)))))(nth choices(rand-nth (indexes))))

To be honest I still haven't quite figured out a good probability distribution to get perfectly looking trees, but here's my current weight function. I like to think of it as mathematically somewhat elegant, but it might be a bit slow to calculate at times ... seems to have quite an impact on generating trees when the maximum depth is big-ish. And I need it to have a bigger drop off on the left side where the longer branches are.

(defn weight [x pivot]; cos(x + sin(x)*0.9)*0.5+0.5(let [x (* x (/ Math/PI (count lengths)))pivot (* pivot (/ Math/PI (count lengths)))](int (Math/floor (* 10(+ 0.5 (* 0.5(Math/cos (+ (- x pivot)(* 0.9 (Math/sin (- x pivot))))))))))))

Pivot is where the highest probability density needs to be and usually denotes the current depth we are at. If you see anything wrong with my approach go ahead and tell me :)

###### Related articles

- Exploiting Randomness (ignoranceanduncertainty.wordpress.com)
- How to transform random variables from a non-normal distribution to a normal distribution? (ask.metafilter.com)

## Learned something new?

Want to become a high value JavaScript expert?

Here's how it works 👇

Leave your email and I'll send you an **Interactive Modern JavaScript Cheatsheet** 📖right away. After that you'll get thoughtfully written emails every week about **React**, **JavaScript**, and **your career**. Lessons learned over my 20 years in the industry working with companies ranging from tiny startups to Fortune5 behemoths.

### Start with an interactive cheatsheet 📖

Then get thoughtful
letters 💌 on **mindsets, tactics, and technical skills** for your
career.

"Man, love your simple writing! Yours is the only email I open from marketers and only blog that I give a fuck to read & scroll till the end. And wow always take away lessons with me. Inspiring! And very relatable. 👌"

**Have a burning question that you think I can answer?** I don't have all of the answers, but I have some! Hit me up on twitter or book a 30min ama for in-depth help.

**Ready to Stop copy pasting D3 examples and create data visualizations of your own?**
Learn how to build scalable dataviz components your whole team can understand
with React for Data Visualization

**Curious about Serverless and the modern backend?** Check out
Serverless Handbook, modern backend for the frontend engineer.

**Ready to learn how it all fits together and build a modern webapp from scratch?**
Learn how to launch a webapp and make your first 💰 on the side with ServerlessReact.Dev

**Want to brush up on your modern JavaScript syntax?** Check out my interactive cheatsheet: es6cheatsheet.com

**By the way, just in case no one has told you it yet today: I love and appreciate you for who you are ❤️**