Notes on codes, projects and everything
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.
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.
Sometimes I really doubt about the advantage of recycling old stuff to fund for new units beyond goodwill. Sure you get to convince yourself that you are saving the environment by doing so, and it also saves money in the long run. However, I didn’t realize how much it generates it may be after trying to work out an answer for a fictional IQ question.
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).
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.
While working on a text classification task, I spent quite some time preparing the training set for a given document collection. The project is supposed to be a pure golang implementation, so after some quick searching I found some libraries that are either a wrapper to libsvm, or a re-implementation. So I happily started to prepare my training set in the libsvm format.
I was invited to try Go (the programming language, not that board game) a few months ago, however I didn’t complete back then. The main reason was because it felt raw, compared to other languages that I know a fair bit better (for example Ruby). There was no much syntatic sugar around, and getting some work done with it feels “dirty”.
This update took me quite a bit more time than I initially expected. Anyway, I have done some refactoring work to the original code, and thought it would be nice to document the changes. Overall, most of the changes involved the refactoring of function names. I am not sure if this would stick, but I am quite satisfied for now.
I really don’t know how to start explaining what is a Dragon Curve. However, I find it is interesting enough after finding out that there’s actually a fixed pattern of occurrence. Therefore I spent some time writing a series of scripts to plot the generated fractal into a graph. What I didn’t expect is, the series get really complicated after a while.
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.
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.