Notes on codes, projects and everything
So apparently Annoy is now splitting points by using the centroids of 2 means clustering. It is claimed that it provides better results for ANN search, however, how does this impact regression? Purely out of curiosity, I plugged a new point splitting function and generated a new set of points.
(more…)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…)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.
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.
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.
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.
Should have done this earlier, I was just being lazy to go through all the steps to publish it properly. So here it is, the full source is published to bitbucket. Feel free to fork the project if you are interested. I have not attach a licence to it but it will most probably be BSD licence. I have also uploaded the latest 0.0.2 release to bitbucket and would update the download link posted previously soon.
I have been following this excellent guide written by Benjamin Thomas to set up my virtual machine for development purpose. However, when I am starting to configure a Ubuntu Quantal alpha machine, parts of the guide became inapplicable. Hence, this post is written as a small revision to the previously mentioned guide.
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.
Semantic Web always sounds like some magic power stuff that a group of people keep yelling about. Chances are, if one is into web development, he/she would have heard of it somehow or other. However, despite the supposedly wide awareness about it, are we using it? Or rather, am I publishing enough data to Semantic Web? OK, I don’t, but why?