Gaming

Rise of Civilizations: Holiday Events

Rise of Civilizations holiday events come around every few months. The last one involved collecting ornaments for the Christmas tree, and the current Valentine's Day event requires collection of roses. Using a rose gives you points, and you need a certain number of points to complete the event. We know how many points are needed in total to complete the event (2888 in the case of Valentine's Day), but the problem is that each rose gives you a varying number of points due to critical hits. For example, using one rose could give you 1 point or it could give…

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February 15, 2019

Gaming

Rise of Civilizations: Commander Pairings

Rise of Civilizations (iOS, Android) has many possible primary/secondary commander pairings, some of which are amazing, while others are absolutely terrible. Tier lists for commander pairings have been discussed by many people on YouTube, but I wanted to find a way to condense the information in those lengthy videos into a more digestible form. The goal was to create a quick and easy-to-use resource that could answer the question: "which commander works best with ____?" I ended up creating D3.js force-directed graphs to show viable pairings between primary and secondary commanders, along with the rank for each pairing. One neat thing about…

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January 4, 2019

Data Collection

Qualtrics: Conditional display of images

Let's say you want to create a Qualtrics survey where you assign respondents to groups, and you want to display certain images depending on their group assignment. For example, if they're assigned to Group 1 you want a question item to display one set of images, while a different set will be shown to Group 2. Qualtrics doesn't offer a built-in way to do this. However, it does provide the ability to use custom Javascript and CSS, which we can use to display images conditionally. You can see this in action here. The steps are fairly straightforward: In the question…

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September 14, 2018

Gaming

Overwatch Data Visualization

In a previous post I talked about an Overwatch dataset I've been collecting from my ranked games. Before running any statistical analysis on the Overwatch data, it is usually a good idea to explore and visualize the dataset. This helps us get a general sense of data patterns, which can help generate hypotheses that can then be tested with more formal statistical models. My own personal philosophy about data visualization/exploration is to approach it with targeted questions of interest. It is all too easy to fall down the rabbit hole of plotting absolutely everything, without a sense of what the…

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June 1, 2018

Gaming

Overwatch Ranked Data

I started playing Overwatch towards the end of Season 2 and I thought it would be interesting to start collecting data from my own ranked games. The goal was to maintain a dataset of Overwatch ranked data that I could analyze to better understand how skill rating (SR) changes as a function of, for example, win/loss streaks and medals. I have made the dataset public in case anyone wants to run their own statistics or visualizations. However, I only have a few seasons worth of data as I took a break from the game after Season 4, and picked it up again…

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April 18, 2018

Machine Learning

Colour image classification (CIFAR-10) using a CNN

As I mentioned in a previous post, a convolutional neural network (CNN) can be used to classify colour images in much the same way as grey scale classification. The way to achieve this is by utilizing the depth dimension of our input tensors and kernels. In this example I'll be using the CIFAR-10 dataset, which consists of 32x32 colour images belonging to 10 different classes. You can see a few examples of each class in the following image from the CIFAR-10 website: Although previously I've talked about the Lasagne and nolearn packages (here and here), extending those to colour images…

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April 4, 2018

Machine Learning

Visualizing Convolutional Neural Networks using nolearn

We previously talked about Convolutional Neural Networks (CNN) and how use them to recognize handwritten digits using Lasagne. While we can manually extract kernel parameters to visualize weights and activation maps (as discussed in the previous post), the nolearn package offers an easy way to visualize different elements of CNNs. nolearn is a wrapper around Lasagne (which itself is a wrapper around Theano), and offers some nice visualization options such as plotting occlusion maps to help diagnose the performance of a CNN model. Additionally, nolearn offers a very high level API that makes model training even simpler than with Lasagne. In…

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March 18, 2018

Machine Learning

Handwritten digit recognition with a CNN using Lasagne

Following my overview of Convolutional Neural Networks (CNN) in a previous post, now lets build a CNN model to 1) classify images of handwritten digits, and 2) see what is learned by this type of model. Handwritten digit recognition is the 'Hello World' example of the CNN world. I'll be using the MNIST database of handwritten digits, which you can find here. The MNIST database contains grey scale images of size 28x28 (pixels), each containing a handwritten number from 0-9 (inclusive). The goal: given a single image, how do we build a model that can accurately recognize the number that…

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March 2, 2018

Programming

Automatic Python documentation with Sphinx autodoc and ReadTheDocs

Generating Python documentation for packages/modules can be quite time consuming, but there's a way to generate it automatically from docstrings. This post is mostly a summary of the fantastic guide by Sam Nicholls found here, but with one important addition (see the section on mocking). We'll be using the following: Sphinx - Python package for generating documentation Sphinx autodoc - Sphinx extension to generate documentation from docstrings ReadTheDocs - build and host documentation online Before you start, make sure you've written docstrings for your modules/functions/methods. This is the most time consuming part, but you should be commenting and documenting your…

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May 13, 2017

Machine Learning

Overview of Convolutional Neural Networks (CNN)

Regular feed-forward artificial neural networks (ANN), like the type featured below, allow us to learn higher order non-linear features, which typically results in improved prediction accuracy over smaller models like logistic regression. However, artificial neural networks have a number of problems that make them less ideal for certain types of problems. For example, imagine a case where we wanted to classify images of handwritten digits. An image is just a 2D array of pixel intensity values, so a small 28x28 pixel image has a total of 784 pixels. If we wanted to classify this using an ANN, we would flatten…

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April 18, 2017