Notes on codes, projects and everything
Everyone knows folksonomy is (or was) cool and useful, however, when it is applied in real life, then problem arises. The idea of blogging this came while I am struggling to get my literature review report done (been doing it for months, I am being so ridiculous, I know). As a matter of fact, as I am dying to get it done, there are a couple of things that I found to be blog-worthy. So, I will be publishing a couple of brief overview to some of the topics involved in the coming days in a really casual (read: lazy, and full of personal speculations) way to this very humble little blog of mine.
Just recently I volunteered to do a pre-101 kinda workshop for people wanting to learn programming. I had done this a few times in the past, but in different settings and goals in mind. The whole structure predates the sessions but I can’t remember when I first created them.
(more…)Often times one would have to write code to evaluate logical statements. For example, given statement p and q, what is p implies q? As there’s no operator for implication in PHP, one would have to rewrite the statement that consists only in NOT (!
), AND (&&
) and OR (||
) operators. When there are a huge load of these statements, code can be difficult to read.
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.
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.
While working on a text classification task, I spent quite some time preparing the training set for a given document collection. The project is supposed to be a pure golang implementation, so after some quick searching I found some libraries that are either a wrapper to libsvm, or a re-implementation. So I happily started to prepare my training set in the libsvm format.