Notes on codes, projects and everything
I am currently preparing myself in applying a postgrad programme and is looking for a research topic. At first I wanted to do something that is related to cloud computing but after some discussion with people around me, they suggest me to do something on semantic web. While posting my notes here, I realized that I had posted something on semantic network that looks like the base of semantic web here (Post still “Under construction” as of writing, will post the diagrams later tonight).
Just a quick update to the previous post, the virtuoso storage engine works with redland provided the required packages are properly installed (yes, yes, yes, I know I haven’t release my PHP OO wrapper for Redland). Now that the package is installed, we need to do some configuration so that Redland can use it.
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.
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.
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.