Notes on codes, projects and everything
As the name implies, Resource Definition Framework, or RDF in short, is a language to represent information about resources in world wide web. Information that can be represented is mostly metadata like title (assuming the resource is a web-page), author, last modified date etc. Besides representing resource that is network-accessible, it can be used to represent things that cannot be accessed through the network, as long as it can be identified using a URI.
After coded enough Javascript few months back, I found that there are a couple of functions that I kept re-using in different projects. Therefore I took some time to refactor them and re-arrange them into a single file. The common code that I keep reusing even today consists of functions that does prototypical inheritance, scope maintenance, some jquery stuff, google maps api stuff and some general ajax application related code.
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.
(more…)Sometimes, letting a piece of code evolving by itself without much planning does not usually end well. However I was quite pleased with a by-product of it and I am currently formalizing it. So the by-product is some sort of DSL for a rule engine that I implemented to process records. It started as some lambda functions in Python but eventually becomes something else.
Getting comfortable to asyncio takes a bit of practice, so I revisited a practice project I did when I was working for my previous company. Suppose I want to build a very simple websocket application, without use of any web application library/framework. In order to keep it simple, I also opt to just build the frontend with minimal setup (just plain ES6 without webpack/vite).
(more…)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.