I am a computer science student who is in their second year at Hull University. I started out teaching myself about various web technologies, and then I managed to get a place at University, where I am now, though I'm currently on an industrial placement. I currently know C# + Monogame / XNA (+ WPF), HTML5, CSS3, Javascript (ES6 + Node.js), PHP and a bit of Python. Oh yeah, and I can use XSLT too.

I love to experiment and learn about new things on a regular basis. You can find some of the things that I've done in the labs and code sections of this website, or on GitHub. My current projects are Pepperminty Wiki, an entire wiki engine in a single file (the source code is spread across multiple files - don't worry!), and a Prolog Visualisation Tool, although the latter is in its very early stages.

I can also be found in a number of other different places around the web. I've compiled a list of the places that I can remember below.

I can be contacted at the email address webmaster at starbeamrainbowlabs dot com. Suggestions, bug reports and constructive criticism are always welcome.


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Let's build a weighted random number generator!

Ever wondered how random loot in a dungeon is generated? Or how the rooms in a procedurally generated castle might be picked? Perhaps you need to skew the number of times an apple is picked by your game engine over a banana. If you've considered any of these things, then you want a weighted random number generator. In this post, I'll be showing you how I built one, and how you can build one too.

If you're interested in trying to build one for yourself first though, then look away now! Come back when you're done (or stuck) to see my solution.

To start with, let's consider what a weighted random number generator actually is. Let's say we've got 3 rewards for a treasure chest: a cool-looking shield, a health potion, and a fancy ring. We want to give the player 1 of the 3 when they option the chest, making sure that the health potion is more common than the others. We can represent that as a ratio: $3 : 4 : 3$.

The introduction image that explains my weighted random number generator.

(Above: The ratio between the different items. See below for the explanation of the math!).

In order to pick one of the 3 items using the ratio, we need to normalise the ratio so that it's between $0$ and $1$. That's rather easy, as far as maths goes: All we have to do is convert each part of the ratio into a fraction, and that into a decimal. Let's calculate the denominator of the fraction first. That's easy-peasy too - we just add up all the parts of the ratio, as we want to represent each part as a fraction of a whole: $3 + 4 + 3 = 10$. With our denominator sorted, we can convert each part into a fraction:

$$ \frac{3}{10} + \frac{4}{10} + \frac{3}{10} = 1 $$

Fractions are nice, but it's be better to have that as a decimal:

$$ 0.3 + 0.4 + 0.3 = 10 $$

That's much better. Now, with the initial theory out of the way, let's start writing a class for it.

using System;
using System.Collections.Generic;
using System.Linq;

namespace SBRL.Algorithms
    public class WeightedRandom<ItemType>
        protected Random rand = new Random();

        protected Dictionary<double, ItemType> weights = new Dictionary<double, ItemType>();

        /// <summary>
        /// Creates a new weighted random number generator.
        /// </summary>
        /// <param name="items">The dictionary of weights and their corresponding items.</param>
        public WeightedRandom(IDictionary<double, ItemType> items)
            if(items.Count == 0)
                throw new ArgumentException("Error: The items dictionary provided is empty!");

            double totalWeight = items.Keys.Aggregate((double a, double b) => a + b);
            foreach(KeyValuePair<double, ItemType> itemData in items)
                weights.Add(itemData.Key / totalWeight, itemData.Value);

I've created a template class here, to allow the caller to provide us with any type of item (so long as they are all the same). That's what the <ItemType> bit is on the end of the class name - it's the same syntax behind the List class:

List<TreasureReward> rewards = new List<TreasureReward>() {

Next, let's go through that constructor bit by bit. First, we make sure that we actually have some weights in the first place:

if(items.Count == 0)
    throw new ArgumentException("Error: The items dictionary provided is empty!");

Then, it's more Linq to the rescue in calculating the total of the weights we've been provided with:

double totalWeight = items.Keys.Aggregate((double a, double b) => a + b);

Finally, we loop over each of the items in the provided dictionary, dividing them by the sum of the weights and adding them to our internal dictionary of normalised weights.

foreach(KeyValuePair<double, ItemType> itemData in items)
    weights.Add(itemData.Key / totalWeight, itemData.Value);

Now that we've got our items loaded and the weights normalised, we can start picking things from our dictionary. For this part, I devised a sort of 'sliding window' algorithm to work out which item to pick. It's best explained through a series of whiteboard images:

Basically, I have 2 variables: lower and higher. When I loop over each of the weights, I do the following things:

  1. Add the current normalised weight to higher
  2. Check if the target is between lower and higher a. If it is, then return the current item b. If not, then keep going
  3. Bring lower up to the same value as higher
  4. Loop around again until we find the weight in which the target lies.

With that in mind, here's the code I cooked up:

/// <summary>
/// Picks a new random item from the list provided at initialisation, based
/// on the weights assigned to them.
/// </summary>
/// <returns>A random item, picked according to the assigned weights.</returns>
public ItemType Next()
    double target = rand.NextDouble();

    double lower = 0;
    double higher = 0;
    foreach(KeyValuePair<double, ItemType> weightData in weights)
        higher += weightData.Key;
        if(target >= lower && target <= higher)
            return weightData.Value;
        lower += weightData.Key;

    throw new Exception($"Error: Unable to find the weight that matches {target}");

That pretty much completes the class. While it seems daunting at first, it's actually quite easy once you get your head around it. Personally, I find whiteboards very useful in that regard! Here's the completed class:

(License: MPL-2.0)

Found this interesting? Got stuck? Have a suggestion for another cool algorithm I could implement? Comment below!

By on


A night sky full of pretty twinkling stars.
Starry Sky
A small gem I found in my archives. From 2013.
Archives: Colour Picker
A bicycle riding through some procedural scrolling parallax hills.
Parallax Bicycle
A procedural castle generator I wrote for /r/proceduralgeneration
Procedural Castles
A pen I created as a demo whilst writing a class to draw regular shapes.
Rotating Shapes
A pen I created as a demo whilst writing a class to draw smooth lines.
Smooth Lines
A small experiment to get my head around how fractals work.
Fractal Shapes
An example of context.ellipse in action, written for a blog post.
Get all the fun of the fair without the noise and the cold.
Big Wheel
Some treasure is hidden on your screen. Can you find it using only your ears?
Audio Treasure Hunter
A Voronoi Diagram Generator
Voronoi Diagrams
A random snowflake generator
Snowflake Generator
A fully functional wiki in a box.
Pepperminty Wiki
Some clouds drifting across the screen, drawn via the HTML5 Canvas.
HTML5 Canvas Clouds
A turtle based drawing program for your first forays into simple programming.
A client side online tool for stitching strings of still images into an animated gif.
Gif Renderer
A set of parallax scrolling stars using the HTML5 Canvas
Parallax Scrolling Stars
An (almost) pure CSS spotlight demo.
(Almost) Pure CSS Spotlight
A Javascript Bookmarklet to fade the unimportant parts of a page. Also features HTML5 fullscreen API integration.
A small script to trianglify (draw triangles on) an image.
Image Trianglifier


The one and only C♯ class generator. Tired of typing the same old scaffolding out all the time? Give this tool a try.
A class modelled on StreamWriter that makes it easy to generate CSV files.
A web based tool that generates diagrams based on Prolog traces.
Prolog Visualisation Tool
A command line tool to generate random noise, written in C#
A (hopefully) better traceroute utility written in C#
An easier way to generate XML.
Simple XML Writer
A PHP based Atom feed generator.
PHP Atom Generator
A simple CodeMirror based javascript bookmarklet editor.
Bookmarklet Playground


I find useful tools on the internet occasionally. I will list them here.

I'm Only Resting
Art by Mythdael