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.
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.
When one start writting Javascript in patterns like the module pattern, then sooner or later he would want to maintain the state when an event handler is called. The reason I am still using YUI to handle my event handling code is because I like how state can be maintained.
Back then when I was attending a job interview, I was asked to write a Fizz Buzz program to prove that my coding ability. There was only a pen and a piece of paper, so basically means there’s no way I can refer to the documentation for the API syntax. Fortunately I somehow managed to remember and not screw up.
I finally put in some time and effort learning myself a bit of Rust. Though I am still struggling with ownership and lifetimes (which is essentially everything about the language, to be honest), I find it more interesting compared to Golang, which is relatively boring, though being functional (no pun intended). While learning the language, the one thing I came across often is the Option
enum, then I remembered that I read something about Monad.
Recently the term “Semantic Web” becomes extremely popular that Sitepoint blogs keep posting articles on this topic (1, 2). In my college days, I learned about Semantic Network and I wonder if there is some relationship between them. I’m not sure whether I get the concept correctly but in this article I would like to revise a bit on semantic network before going to semantic web. Please correct me if I’m wrong.