## (note (code cslai))

Notes on codes, projects and everything

# Machine Learning

• ### 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.

• ### 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.

• ### 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.

• ### 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

• ### Building Dynamic Form the hard way with React-Redux

Javascript is getting so foreign to me these days, but mostly towards a better direction. So I recently got myself to learn react through work and the JSX extension makes web development bearable again. On the other hand, I picked up a little bit on Vue.js but really hated all the magic involved (No I don’t enjoy putting in code into quotes).

• ### Proof of Concept: YQuery

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.

• ### Evaluating logical statements

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.

• ### Semantic Network

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.

• ### Overdoing a task part 2: csv-to-json

This is the second part of the golang learning rant log. Previously on (note (code cslai)) I managed to make each line in the CSV into a hash map. So today I am going to make it into JSON Lines.