Notes on codes, projects and everything
Machine Learning
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Regression with 2-means clustering Annoy (non-scientific)
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.
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Regression with Annoy (cont’d)
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.
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Quick experiment, boosting vs annoy
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.
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Further Hack on the Multi-Dimensional Approximate Neighbour Search
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.
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Approximate Neighbour Search in Multiple Dimensions
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.
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Re-implementing Approximate Nearest Neighbour Search
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.
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Random notes on Pandas and Scikit-learn
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).
Random Posts
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Re-organizing my MacVim configuration
It is very difficult to like the way vim handle plugins by default, so I was really thrilled to find out about pathogen when a geek I followed tweeted about it. It took me some time to actually re-organize my current configuration to this new format. Then I thought why not reorganize my .vimrc as well, as my current version looks a bit cryptic after a while.
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Visualization of my profile, in (not quite) RDF way
After a miserable trip back to academic world, I finally re-gained the courage to get back to job-market. For the time spent in university, I spent quite some time reading about Semantic Web and RDF. Then I thought, I should have published more in this format in future. However, that didn’t really happen, mostly because I am too lazy.
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Revisiting Fizz Buzz in PHP
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.
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Deploying docker containers with HAProxy on a VPS
With most of my stuff more or less set, I guess it is time to start documenting the steps before I forget. So I heard a lot of good things about docker for quite some time, but haven’t really have the time to do it due to laziness (plus my relatively n00b-ness in the field of dev-ops). Just a few months ago, I decided to finally migrate away from webfaction (thanks for all the superb support) to a VPS so I can run more things on it.
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Re-implementing Approximate Nearest Neighbour Search
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.
