Notes on codes, projects and everything
I like how Kohana 3 organizes the classes, and I thought the same thing may be applied to my Zend Framework experimental project. Basically what this means is that I can name the controller class according to PEAR naming convention, and deduce the location of the file by just parsing the class name.
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).
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).
It is useful to have the terminal around whenever I code. However, while real screen estate is finite, having a terminal further limiting the amount of information that can be displayed at the same time. The problem with the terminal is that I don’t really need it all the time, so I usually find it buried under a group of windows.
Previously, I started practising recursions by implementing a type check on lat (list of atoms), and ismember
(whether an atom is a member of a given lat). Then in the third chapter, named “Cons the Magnificent”, more list manipulation methods are being introduced.
This is basically a small incremental update to my script published here. For some reason, the previous version of the script didn’t really work, so this release should fix the problem. Besides fixing the problem where the daemon did not actually launched at start up, I have added a settings applet for this script as well.
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.
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.