Notes on codes, projects and everything
I just failed a programming assessment test miserably yesterday and thought I should at least document it down. However, the problem with this is that the questions are copyrighted, so I guess I would write it from another point of view. So the main reason I failed was because I chose the wrong strategy to the problem, thinking it should be solution but as I put in time to that I ended up creating more problems.
Recently I am involved in developing some small modules for a enterprise class website using CodeIgniter (CI). There was no restriction given on which framework should I use for the development and I chose CI as I learned a bit on it (when I was considering whether to shift my personal development project). Of course there are other reasons why I chose to learn CI, for example the superior documentation and screencasts available.
One of my recent tasks involving crawling a lot of geo-tagged data from a given service. The most recent one is crawling files containing a point cloud for a given location. So I began by observing the behavior in the browser. After exporting the list of HTTP requests involved in loading the application, I noticed there are a lot of requests fetching resources with a common rXXX
pattern.
After a year and half, a lot of things changed, and annoy also changed the splitting strategy too. However, I always wanted to do a proper follow up to the original post, where I compared boosting to Annoy. I still remember the reason I started that (flawed) experiment was because I found boosting easy.
(more…)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.
(more…)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.