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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Processing JSON with dask.bag
Often times, I am dealing with JSONL files, though panda’s DataFrame is great (and blaze to certain extend), however it is offering too much for the job. Most of the received data is in the form of structured text and I do all sorts of work with them. For example checking for consistency, doing replace based on values of other columns, stripping whitespace etc.
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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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Semantic Web
I am currently preparing myself in applying a postgrad programme and is looking for a research topic. At first I wanted to do something that is related to cloud computing but after some discussion with people around me, they suggest me to do something on semantic web. While posting my notes here, I realized that I had posted something on semantic network that looks like the base of semantic web here (Post still “Under construction” as of writing, will post the diagrams later tonight).
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Selecting Node with XPath
To do node selection for DOM operations, one typically uses CSS selectors as (probably) popularized by jQuery. However, there is another alternative that is as powerful if not better known as XPath. XPath may be able to do a lot more than just selecting node (which I have no time to find out for now) but I will just focus on how to do node selection in this blog post.
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MVC: Kohana vs. Zend Framework
After comparing my own implementation of MVC with CodeIgniter’s, now I’m comparing Kohana’s and Zend’s. I have just shifted from CodeIgniter to Kohana recently in work and is currently learning on how to use Zend Framework to build my web-app. As everybody knows, Zend Framework is more like a collection of library classes than a framework a la Ruby on Rails, using MVC in Zend Framework would require one to begin from bootstrapping stage. However, in Kohana, just like other frameworks, bootstrapping is done by the framework itself so the developer will get an installation that almost just works (after a little bit of configuration).
