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…)Traversing a tree structure often involves writing a recursive function. However, Python isn’t the best language for this purpose. Therefore I started flattening the tree into a key-value dictonary structure. Logically it is still a tree, but it is physically stored as a dictionary. Therefore it is now easier to write a simple loop to traverse 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.
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.
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).
This is basically a small incremental update to my script published here. For some reason, the previous version of the script didn’t really work, so this release should fix the problem. Besides fixing the problem where the daemon did not actually launched at start up, I have added a settings applet for this script as well.
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.
Just happened to see this post a few months ago, and the author created another cloud that uses almost the same technique to ‘visualize’ a list of countries. The author uses PHP to generate the cloud originally and I thought I may be able to do in javascript. After some quick coding I managed to produce something similar to the first example, source code after the jump.
The Sports Tracker app for my awesome Nokia N9 is not receiving any updates and doesn’t look like things are going to change any time soon. Recently the development team at Sports Tracker published a status update post and sadly there’s no mention of N9 port at all. It’s really sad considering how incomplete the N9 port is at the moment (horrible GPS positioning, no pedometer to name a few).
It is very much expected that there will be endless stream of new (and often times better) tools introduced to solve the same set of problems. While I am slowly resuming my programming work, and in the process of reviving my very much dead postgrad project, I found some alternative to the tools I had used in the past. I suppose I shall just jot them down here so that there’s a reference for later use.