2018 July 24

• Reinventing wheels with cheats using Rustlang

  I finally put in some time and effort learning myself a bit of Rust. Though I am still struggling with ownership and lifetimes (which is essentially everything about the language, to be honest), I find it more interesting compared to Golang, which is relatively boring, though being functional (no pun intended). While learning the language, the one thing I came across often is the Option enum, then I remembered that I read something about Monad.

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Random Posts

• Using YUI for DOM Manipulation

  After being frustrated of not getting consistent and accurate result via standard DOM methods especially html_element.getAttribute('key'); and html_element.setAttribute('key', 'value');, I came across some YUI library components that provides abstractions to various DOM methods. Some interesting DOM-related tools covered in this post are YAHOO.util.Element, YAHOO.util.DOM and YAHOO.util.Selector.

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• Dragon Curve

  I really don’t know how to start explaining what is a Dragon Curve. However, I find it is interesting enough after finding out that there’s actually a fixed pattern of occurrence. Therefore I spent some time writing a series of scripts to plot the generated fractal into a graph. What I didn’t expect is, the series get really complicated after a while.

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• Building a simple ASGI Websocket Application

  Getting comfortable to asyncio takes a bit of practice, so I revisited a practice project I did when I was working for my previous company. Suppose I want to build a very simple websocket application, without use of any web application library/framework. In order to keep it simple, I also opt to just build the frontend with minimal setup (just plain ES6 without webpack/vite).

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• Fuzzy C-Means and Content Recommendation

  This post is purely based on my own speculation as there’s no experiment on real-life data to actually back the arguments. I am currently trying to document down a plan for my experiment(s) on recommender system (this reminds me that I have not release the Flickr data collection tool :/) and my supervisor advised to write a paragraph or two on some of the key things. Since he is not going to read it, so I might as well just post it here as a note.

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• Approximate Neighbour Search in Multiple Dimensions

  In the previous post, I re-implemented Annoy in 2D with some linear algebra maths. Then I spent some time going through some tutorial on vectors, and expanded the script to handle data in 3D and more. So instead of finding gradient, the perpendicular line in the middle of two points, I construct a plane, and find the distance between it and points to construct the tree.

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