Big data is all over the place. Another wonderful source of a large dataset is StackOverflow. As of this moment, the site has more than 10 million questions and around 1.3 million users. Stackoverflow provides data about all answers, questions and voting information through their creative commons data dump. This dataset consists of a number of xml files and I consider this a very rich and interesting dataset.

I wanted to build a suggestion engine on top of this dataset. The idea is to suggest other geeks in the StackOverflow community who have technical interests and technical expertise similar to the given user (assuming that the user has a stackoverflow account). My features are entirely based on the tags in which a particular user has participated (questions/comments/answers). While I was on my way building the tool, I thought that it might be fun to just post the top contributors for each tag.

I decided to consider the measure of contribution as the number of instances of participation. Answering, commenting and asking a question are considered as participation. I just heard you, I do have double counting in cases where there are multiple participation from the same person on a given question.

Below is the list of the top 100 tags and contributors:

You can also Download the full set of 30K  tags and contributors to review it offline

Tags	Display Name	Contribution Count	Gravatar
c#	Jon Skeet	8542
android	CommonsWare	5103
jquery	Nick Craver	4614
java	BalusC	4558
python	Alex Martelli	3639
.net	Jon Skeet	3106
php	Pekka	2935
asp.net-mvc	Darin Dimitrov	2844
sql	OMG Ponies	2263
sql-server	gbn	2232
jsf	BalusC	2081
c++	Jerry Coffin	2067
javascript	Nick Craver	2043
asp.net	Darin Dimitrov	1833
jsp	BalusC	1790
django	Daniel Roseman	1770
asp.net-mvc-3	Darin Dimitrov	1593
cocoa	Peter Hosey	1565
xslt	Dimitre Novatchev	1564
git	VonC	1561
silverlight	AnthonyWJones	1478
c	R..	1422
wcf	marc_s	1400
linq	Jon Skeet	1391
google-app-engine	Nick Johnson	1347
html	Quentin	1331
mysql	Quassnoi	1294
winforms	Hans Passant	1277
maven-2	Pascal Thivent	1236
css	thirtydot	1234
servlets	BalusC	1233
entity-framework	Ladislav Mrnka	1212
swing	camickr	1187
eclipse	VonC	1185
iphone	TechZen	1178
jqgrid	Oleg	1172
bash	Dennis Williamson	1132
hibernate	Pascal Thivent	1125
scala	Daniel C. Sobral	1105
asp.net-mvc-2	Darin Dimitrov	1104
objective-c	Dave DeLong	1079
wpf	H.B.	1057
flex	www.Flextras.com	1020
xml	Dimitre Novatchev	1003
windows-phone-7	Matt Lacey	993
regex	Tim Pietzcker	989
tsql	gbn	971
matlab	gnovice	964
perl	Sinan Ünür	956
oracle	Gary Myers	945
delphi	Mason Wheeler	872
ms-access	Remou	859
r	Dirk Eddelbuettel	830
sql-server-2005	gbn	829
nhibernate	Diego Mijelshon	811
mod-rewrite	Gumbo	800
spring	skaffman	800
core-data	TechZen	793
xpath	Dimitre Novatchev	784
jsf-2.0	BalusC	761
ruby	the Tin Man	757
f#	Tomas Petricek	750
jpa	Pascal Thivent	747
orm	Pascal Thivent	713
sql-server-2008	gbn	701
security	Rook	669
xhtml	Jitendra Vyas	658
drupal	googletorp	648
ruby-on-rails	apneadiving	646
powershell	Keith Hill	630
vb.net	Hans Passant	624
mercurial	Ry4an	615
entity-framework-4	Ladislav Mrnka	611
.htaccess	Gumbo	611
generics	Jon Skeet	609
windows-mobile	ctacke	608
visual-studio	JaredPar	603
rest	Darrel Miller	591
haskell	Don Stewart	589
shell	Dennis Williamson	585
compact-framework	ctacke	554
web-services	John Saunders	550
ruby-on-rails-3	AnApprentice	543
multithreading	Jon Skeet	540
linux	Ignacio Vazquez-Abrams	539
emacs	Trey Jackson	537
magento	clockworkgeek	510
jaxb	Blaise Doughan	499
performance	Mike Dunlavey	494
winapi	Hans Passant	493
windows	Hans Passant	486
apache	Gumbo	479
version-control	VonC	464
entity-framework-4.1	Ladislav Mrnka	457
cakephp	deceze	452
database	HLGEM	450
postgresql	Frank Heikens	449
wordpress	songdogtech	447
ajax	Darin Dimitrov	445

MLSP, the machine learning and signal processing conference typically hosts a contest along with paper submissions and porter sessions. This year, it has partnered with Amazon to produce a contest that is tailored to building a classifier. The deadline is May 17th. For more information visit – http://mlsp2012.conwiz.dk/index.php?id=43

Machine learning research and just about many fields of engineering utilize matlab for prototyping and development. Matlab costs quite a good amount of money and when your applications needs production quality, you are more likely to move away from matlab.

The core functionality of matlab that makes it a good, easy and quick prototyping platform is its easy to use matrix and linear algebraic operations. Beyond prototyping, speed of execution and production standards require your code to be implemented in C,C++,C#, java or one of the numerous new languages that pop up each morning. In this post we will see the implementation of matrix operations in C++ (the ones you thought was only possible in matlab) with the least programming effort.

BLAS:

BLAS (Basic Linear Algebra Subprograms) is a set of highly optimized library for vector and matrix operations. BLAS forms the backbone for numerous libraries that are built to facilitate ‘Matlab like ‘ vector operations. BLAS is written in fortran.

LAPACK:

While BLAS contains optimized routines for most fundamental operations, more sophisticated functions like matrix decompositions, factorizations etc are grouped into a library called LAPACK (Linear Algebra PACKage). The fundamental operations in LAPACK routines are optimized since they use the BLAS package. LAPACK is fortran aswell.

ATLAS:

ATLAS is a package that helps to optimize build variables so as to optimize LAPACK further. Apart from the mentioned packages, there are numerous variants of such libraries that claim to outperform one another.

The question that naturally arises is the usability of these packages. The good news is that you can integrate these libraries in C++ using a package called Armadillo. Armadillo by itself has most of the basic vector and matrix operations. Armadillo is a C++ linear algebra library which offers most operations on vectors and matrix in a stand alone manner. In order to access the more complicated operations like matrix factorizations that are described well in LAPACK, Armadillo offers nicer interface to call those functions from your C++ code.

To give a quick overview:

Just for the sake of code comparison between matlab and C++ using armadillo (offcourse including LAPACK and BLAS installed on a machine) we will write a small matrix multiplication code here. We will create a random matrix of dimensions 5×5 and multiply with itself and print the results.

MATLAB:

A = rand(5,5);

A = A*A;

A

C++ code:

mat A = rand(5, 5);

A = A*A;

A.print(“A = “);

Now that does not sound all that different , does it? Armadillo thus provides a nice set of classes that can perform most of the function that are a part of the core MATLAB software. I am sure MATLAB is a great tool when it comes to quick prototyping and visualization but I am sure these opensource packages are giving the companies a run for their money. We will continue the art of converting your Matlab code to C++ and the details about using Armadillo, LAPACK and BLAS for that purpose in future on this blog.