Notes on codes, projects and everything
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…)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.
(more…)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…)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.
I have just re-started to find myself a job as my work in mybloggercon almost come to an end (after helping them to set up an April Fool Prank). I have sent some enquiry letters to apply for a job in web-development field mostly involves PHP. I prefer PHP over ASP.NET because I can have greater flexibilities in developing in PHP as what I experienced when I was developing my final year project.
Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users [4, 5]. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications [1, 5]. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive [3]. Both content hierarchy and folksonomy can be used together to better content classification.
I am currently preparing myself in applying a postgrad programme and is looking for a research topic. At first I wanted to do something that is related to cloud computing but after some discussion with people around me, they suggest me to do something on semantic web. While posting my notes here, I realized that I had posted something on semantic network that looks like the base of semantic web here (Post still “Under construction” as of writing, will post the diagrams later tonight).
In the last part, I implemented a couple of primitive functions so that they can be applied in the following chapters. The second chapter of the book, is titled “Do it again, and again, and again…”. The title already hints that readers will deal with repetitions throughout the chapter.
Back then, when I was still working on my postgraduate degree research, I used RDF, which was the preferred format in the world of Semantic Web to represent data. I eventually dropped the degree, and stopped following the development of the related technology and standards. Until I volunteered to update the import script for popit when I was looking for the next job/project.
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