Editing User:Ozzy

== A: An introduction ==
* Please describe yourself in three sentences, one of them regarding your current studies.
''I am doing my PhD in computer science. I am also a bit concerned about my privacy here and would like not to disclose too much personal information here.''
** Which languages do you speak?
I prefer to communicate in English
** What's your overall background?
''computer science''
* Why do you want to participate in the Google Summer of Code? What do you hope to gain by doing so?
''I want to add some functionality to Octave. I believe it is a good occasion to get to know Octave's internals and get some extra money from Google ''
** Please also describe your previous experience with the GSoC, if any. 
''This is my first time''
* Why are you choosing Octave?
''If there is possibility to improve the tool I'm using almost every day why should I choose anything else?''

== C: Contact ==
* Please state the (unique and identical where possible) nick you use on IRC and any other communication channel related to Octave. 
''I am using '''ozzy''' ''
* Which time zone do you live in? Will that change over GSoC duration? 
''UTC+2''
* Please state the timeframe (in UTC+0) when you feel most comfortable working during GSoC. Where are your time buffers? 
''I usually start at 7:00 and try not to spend more than 8 hours in front of the computer. I can also sometimes shift it into the eventing''

== E: Coding experience ==
This part is one of the more important ones in your application. You are allowed to be as verbose as you want, as long as you stay on topic ;-)
* Please describe your experience with C++, Octave or Matlab m-scripts, OpenGL and Qt.
''I do a lot of scripting in Octave for my research and sometimes I have to port them to Matlab. I have quite good experience in C++. I know basics of Qt and have some minor experience in OpenGL.''
* Please describe your experience with other programming languages.
''I often use Python for scripting. I also believe I have good experience in developing algorithms for GPGPU.''
* Please describe your experience with being in a development team. 
''I am a leader of a medium-sized simulation software which I do with my colleagues for our research work. It it written in C++ and heavily depends on templates. Simulation results are further processed in Octave. The development of the project progresses slowly. We have rather friendly atmosphere in our group. If there are any differences between us, we usually resolve them on a meetings which tend to be very long sometimes''
* Please describe the biggest project you have written code for and what you learned by doing so. Also describe your role in that project over time.
''The biggest project I have written code for was an medical image processing platform for a big company. I was one of the developers who worked on the actual data processing being done inside the application. This is were I've really learned how to use basic development tools like version control, unit testing, debugger and how to document the code.''
* Please state the commits and patches you already contributed to Octave.
''I didn't commit anything to Octave so far''

== F: Feeling fine ==
* Please describe (in short) your experience with the following tools: ''We only use this question to determine where you need guidance, not for rating!  We by no means expect you to be familiar with all of these and you'll won't necessarily need them while working with us.''
** IRC and mailing lists
I don't have any problem using them
** Mercurial or other source code management systems
I have some experience with SVN and GIT, I believe Hg is not much different
** Mediawiki or other wiki software
They do not seem to be difficult either.
** make, gcc, gdb or other development tools
I'm fine with them
* What will make you actively stay in our community after this GSoC is over?

== O: Only out of interest ==
* Did you ever hear about Octave before? 
''Yes I did. I started using it on my second year of studies where it was a recommended tool for signal processing on my university''
** If so, when and where? How far have you been involved already?
''I am using Octave in daily basis for my research work and I actually prefer it over Matlab due to relaxed syntax, easy access to additional packages and lower price ;)''
* What was the first question concerning Octave you could not find an answer to rather quickly?
''Unfortunately there was a lot of them and they mostly relate to poorly documented packages''

== P: Prerequisites ==
* Please state the operating system you work with.
** If you have access to more than one, please state them and the conditions under which you are granted this access.
''I have one computer with Debian Linux (it's in some state between Wheezy and Jesse) and Windows 7 -- as an alternative boot configuration. I also have an access the OSX 10.10 on my other computer''
* Please estimate an average time per day you will be able to ''(if separated)'' access
** an internet connection
''16 hours -- I need to sleep sometimes''
** a computer
''8 hours -- I' trying to not spend more time in front of a computer than that''
** a computer with your progressing work on
''8 hours''
* Please describe the degree up to which you can install new software on computers you have access to.
''I can install anything as long as I know what it does''

== S: Self-assessment ==
* Please describe how useful criticism looks from your point of view as committing student.
* How autonomous are you when developing?
** Do you like to discuss changes intensively and not start coding until you know what you want to do?
''It is usually enough if we agree on the "boundary conditions" from that point I can take care of it''
** Do you like to code a proof of concept to 'see how it turns out', modifying that and taking the risk of having work thrown away if it doesn't match what the project or original proponent had in mind?
''When writing octave scripts playing around with ideas is quite easy. I usually try out the ideas in command line and if the subsequent steeps seem to work I append them to the script''

== Y: Your task ==
* Did you select a task from our list of proposals and ideas?
** If yes, what task did you choose? Please describe what part of it you especially want to focus on if you can already provide this information. ''Please also wiki-link the page for your elaborated proposal here.''
** If you apply for a task you have added yourself instead, please describe this task, its scope and people you already talked to concerning it. What field of tasks did you miss on the list?



I would like to implement a general algorithm for maximum entropy reconstruction. This is an algorithm for estimating distributions which has applications in various inverse and/or ill-posed problems. It is used for deblurring/deconvolution of images, power spectrum estimation, smoothing, measurement data processing in biology, physics and more. The ground for the implementation would be the work of Skilling and Bryan [http://articles.adsabs.harvard.edu/full/1984MNRAS.211..111S]

The algorithm would find its place in one of the existing packages (where ''optim'' or ''signal'' sound appropriate) or as a separate package. I plan to prepare two versions of the general algorithm, (temporal name {{codeline|maxent}})
* a version for problems defined by matrix. The function's declaration should be something like this
{{Code|Matrix problem declaration|<syntaxhighlight lang="octave" style="font-size:13px">function [x,info,...]=maxent(y,D,sigma,alpha=0.95, model=1, optset)

</syntaxhighlight>}}


where {{codeline|y} is the data vector, and {{codeline|D}} is the transformation matrix. {{codeline|sigma}} should be a vector or scalar which describes standard deviation of values of {{codeline|y}}. The optional parameter {{codeline|alpha}} and {{codeline|model}} describe confidence and a priori distribution of {{codeline|x}} (defaults to flat) respectively. The last parameter {{codeline|optset}} would allow to pass additional parameters to function, similar to the ones in {{codeline|optim}} package.

The returned value {{codeline|x}} is such that

<math> y \approx Dx</math>

where each of the coordinates of {{codeline|y}} lies within {{codeline|alpha}} confidence interval (normal distributed error assumed). Out of all possible {{codeline|x}} the one with the highest entropy is chosen. {{codeline|info}} describes the convergence of the algorithm. The other returned parameters will describe final gradients, Hessians and Lagrange's coefficient.

* the second calling form would be defined in a similar way, but with the transformation defined in the means of function provided by the user. The declaration would be:
{{Code|Functional problem declaration|<syntaxhighlight lang="octave" style="font-size:13px">function [x,info,...]=maxent(y,tfun,sigma,alpha=0.95, model=1, optset)

</syntaxhighlight>}}

All the parameters have the similar meaning here, and the new parameter {{codeline|tfun}} is the handle to a function which accepts vector argument, which describes the problem to be inverted. This time the returned value should obey 

<math> y \approx \mbox{tfun}(x)</math>

It is convenient to have this version of the algorithm for problem where obtaining the transformation matrix is difficult to compute or affects performance (think fft). The algorithm is expected to give good results for linear functions. For not-too-complicated non-linear cases the chances are still there.

* the third version would be the most general one. Here, the chi-squared criterium used as the objective function can be substituted with an arbitrary function provided by the user (it should be a convex function to guarantee the uniqueness of the solution). The calling form would be
{{Code|Functional problem declaration|<syntaxhighlight lang="octave" style="font-size:13px">function [x,info,...]=maxent(objfun,aim, model=1, optset)

</syntaxhighlight>}}

The arguments {{codeline|objfun}} and {{codeline|aim}} are the objective function and a value of the objective function the algorithm should try to attain. The algorithm will try find {{codeline|x}} such that 
<math> \mbox{aim} \approx \mbox{objfun}(x)  </math> and the entropy is the highest out of all the solutions with this property.


Additional  work will be put to provide some wrapper functions to allow the user quickly use MEM in specific problems. This includes function for 1D and image deconvolutions, time series components analysis, power spectral estimation and other applications I will be able to find in Matlab or other computational software.

Another sub-task is to analyze the speed and numerical precision of the implemented algorithms.

* Please provide a rough estimated timeline for your work on the task. ''This should include the GSoC midterms and personal commitments like exams or vacation ("non-coding time"). Optionally include two or three milestones you expect.''

** Start of GSoC
***''development of the algorithm in matrix version with future extension in mind''
** Midterm evaluation
***''upgrading the algorithm to accept the arbitrary objective function''
***''stability tests''
***''identification of the remaining issues''
***''adding wrappers for easy use''

** Final evaluation

Timeline (brief):
* Getting involved -- two weeks
* Cleaning Pytave -- four weeks
* Octave module for Python -- two weeks
* Symbolics -- two weeks
(since in June-August period there are 13 weeks there still is some reserve for holidays and possible delays) 

[[Category: Summer of Code]]