Notes on codes, projects and everything
I came across a video on Youtube on Pi day. Coincidently it was about estimating the value of Pi produced by Matt Parker aka standupmaths. While I am not quite interested in knowing the best way to estimate Pi, I am quite interested in the algorithm he showed in the video however. Specifically, I am interested to find out how easy it is to implement in Python.
Just managed to migrate all my blog sites to one centralized multi-site, so no more
half-baked solution and hopefully this brings better plugin compatibility. I have not check with other related services (like Google Webmaster Tools) whether this cause any breakage though. Well, the main purpose of this blog post is actually a draft of what I did for the past two months for my postgraduate programme. Yea, I should have posted more stuff to this blog (just realized that my last post here is already like half a year ago).
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.
After reading through the documentation, I find that the role based ACL and work flow can be more tightly integrated. Therefore I made all the transaction into many FSMs and my work flow component now consists of one work flow library and one work flow management model. As I am going a more normalized design (I use denormalized design in work as it deals with a lot of documents, however for a small project like mine, a denormalized design should do well).
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.