Notes on codes, projects and everything
Implementing a Information Retrieval system is a fun thing to do. However, doing it efficiently is not (at least to me). So my first few attempts didn’t really end well (mostly uses just Go/golang with some bash tricks here and there, with or without a database). Then I jumped back to Python, which I am more familiar with and was very surprised with all the options available. So I started with Pandas and Scikit-learn combo.
While following through the Statistical Learning course, I came across this part on doing regression with boosting. Then reading through the material, and going through it makes me wonder, the same method may be adapted to Erik Bernhardsson‘s annoy algorithm.
With most of my stuff more or less set, I guess it is time to start documenting the steps before I forget. So I heard a lot of good things about docker for quite some time, but haven’t really have the time to do it due to laziness (plus my relatively n00b-ness in the field of dev-ops). Just a few months ago, I decided to finally migrate away from webfaction (thanks for all the superb support) to a VPS so I can run more things on it.
After publishing the previous note on setting up my development environment, I find myself spending more time in the CLI (usually via SSH from host). Then I find myself not needing all the GUI apps in a standard Ubuntu desktop environment so I went ahead and set up a new environment based on Ubuntu Quantal server edition beta-1. For some reason my network stopped working and didn’t really want to spend time finding out the cause, so I reinstalled everything again today using the final installer, as well as the updated Virtualbox 4.2.6.
data() method to store everything (from scalar values to functions and callbacks). Recently, after knowing (briefly) how a jQuery plugin should be written, it does greatly simplify my code.
After being frustrated of not getting consistent and accurate result via standard DOM methods especially
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