Notes on codes, projects and everything
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.
I was trying to learn scala and clojure to find one that I may want to use in my postgraduate project. After trying to learn scala for a couple of days, I gave up because I really don’t like the syntax (too OO for my liking). Then I continued with clojure and found myself liking the syntax better.
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.
After the last post, I found that it may be fun to write a wrapper for YUI in order to make it behave like jQuery. Therefore, the code below is clearly mainly for self-amusement and is not intended to be used in production projects. However, through coding this, I found that although the difference in design, but YUI is obviously capable to do what jQuery offers (if not more). I will not continue working on this so whoever interested may just copy and paste the code to further developing it.
Often times one would have to write code to evaluate logical statements. For example, given statement p and q, what is p implies q? As there’s no operator for implication in PHP, one would have to rewrite the statement that consists only in NOT (
!), AND (
&&) and OR (
||) operators. When there are a huge load of these statements, code can be difficult to read.
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.(more…)