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).
Back then in college, we were given a lot of programming practices. These questions usually shows a desired output format, and we were required to write a program to print out the exact thing. Usually it involves printing a matrix of numbers, or symbols etc. For these problems, usually a loop structure or two should solve the problem.
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…)Just survived a job interview, so I should probably celebrate this despite the outcome. Well, considering I was off the job market for a couple of years, I probably has all the reason to be nervous. Anyway, like most geeky serious job interview, there are a test given by the company to the attendees.
I haven’t got much time lately, so didn’t write about this new phone that I recently imported. For some reason, this new phone of mine do not act as mass storage device like its predecessors (to certain extend). Thankfully I can still ssh in the phone and this makes it possible to mount it as a sshfs volume.
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).