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.
So I first heard about Panda probably a year ago when I was in my previous job. It looked nice, but I didn’t really get the chance to use it. So practically it is a library that makes data looks like a mix of relational database table and excel sheet. It is easy to do query with it, and provides a way to process it fast if you know how to do it properly (no, I don’t, so I cheated).
A friend of mine recently posted a screenshot containing a code snippet for a fairly straight forward problem. So after reading the solution I suddenly had the itch to propose another solution that I initially thought would be better (SPOILER: Turns out it isn’t). Then mysteriously I stuck myself to my seat and started coding an alternative solution to it instead of playing Diablo 3 just now.
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
.
As the name implies, Resource Definition Framework, or RDF in short, is a language to represent information about resources in world wide web. Information that can be represented is mostly metadata like title (assuming the resource is a web-page), author, last modified date etc. Besides representing resource that is network-accessible, it can be used to represent things that cannot be accessed through the network, as long as it can be identified using a URI.
Had a discussion with my secondary supervisor and it turned out pretty bad because I wasn’t fully prepared and he was rushing to somewhere else for a meeting. So I am jotting down a brief summary (read: highly based on personal/subjective feelings/opinions) of my readings here to help organize things before the followup meeting that is taking place next week.
Recently I switched my search code to Annoy because the input dataset is huge (7.5mil records with 20k dictionary count). It wasn’t without issues though, however I would probably talk about it next time. In order to figure out what each parameters meant, I spent some time watching through the talk given by the author @fulhack.