I'm looking for a tool to convert a SBML model into a Matlab function. I've tried SBMLTranslate() function from libSBML but this returns a Matlab struct, not a function. Does anybody know if such tool exists? Thanks
There are at least three efforts in this direction:
Frank Bergmann offers an online service for SBML translation where you can upload an SBML file and it will generate a MATLAB file. The comments at the top of the generated MATLAB file explain how to use the results. The C++ source code is available on SourceForge.
Bergmann's code referenced above was used by Stanley Gu to create sbml2matlab, a Windows standalone program. Off-hand, I don't know whether Gu's version changed or enhanced the algorithm used by the Bergmann version, but it seems likely. (Note: Gu now works at Google and does not maintain this code anymore, as far as I know.)
The Systems Biology Format Converter (SBFC) is a framework written principally by Nicolas Rodriguez; it includes a collection of converters, one of which is an SBML-to-MATLAB converter. This converter is written in Java.
I have not compared the results of the translators myself yet, so cannot speak to the differences or quality of output. If you try them and have any feedback to relate, please let the authors know. Knowing what has or hasn't worked for real users will help improve things in the future.
A final caveat is that all of these have been research projects, so make sure to set your expectations accordingly. (This is not a criticism of the authors; the authors are very good – I know most of them personally – but the reality of academic development work is that we all lack the time and resources to make these systems comprehensive, hardened, polished, and documented to the degree that we wish we could.)
Related
I have been exploring Open Modelica for a week for system Dynamics. All though I learned few basic things(like function and class syntax etc..) from Spoken Tutorial, I need to go a long way. I am not to find a proper source mentioning the syntaxes for various aspects. For example, I am not able to find syntax for writing derivatives(dy/dx) apart from the time derivative[ der(x)]. Is there someplace where I can look into for further proceeding?
P.s. I am new to Softwares like MATLAB, Octave etc.. and OpenModelica is my first one.
OpenModelica is a Modelica tool. All Modelica related resources, e.g. language specification, can be found here: https://www.modelica.org/
I'm looking for a function in Julia to estimate coefficients for an ARMA process.
For example using the Prediction Error Model as pem and armax in Matlab (part of system identification toolbox) do. pem documentation and armax documentation.
I've looked at the following packages, but can't see that they do what I'm looking for:
TimeSeries.jl
TimeModels.jl
One solution is of course to use Matlab.jl and use the Matlab functions, but I was hoping to do it all in Julia.
If there isn't anything right now, does anyone know of if there are any good Julia functions for multidimensional numerical minimisation (like Newton-Raphson), that can be used for implementing a PEM function?
UPDATE: I've just pushed a module to github called RARIMA.jl. This module can be used to estimate, forecast, and simulate ARIMA models (of which ARMA is a special case). Some of the functions are implemented in Julia, others (particularly estimation) call equivalent R functions using the RCall package which you will need to install and verify it works prior to using RARIMA. The package isn't officially registered (yet), so Pkg.add("RARIMA") won't work for now. If you want to use RARIMA, instead try Pkg.clone("https://github.com/colintbowers/RARIMA.jl"). If this fails, you can file an issue on the repository github page, but be sure to check RCall is installed and working before doing this. Cheers, I'll come back and update here if/when the package is officially registered.
ORIGINAL ANSWER: I just had a glance at the source, and TimeModels does not appear to have any functionality for estimating ARIMA models, although does have one function for simulating them. Given time though, I suspect this will be the package that deals with ARIMA modelling. The TimeSeries package is more about building the object type TimeSeries rather than implementing time series models, so I would be surprised if ARIMA modelling is ever merged into that package.
As near as I can tell, at this point if you want a fully functioning ARIMA package you'll need to use Matlab or R. The R one is very good (see the forecast package written by Rob Hyndman - it is very nice) and is probably easier to interface with from Julia than the Matlab option. Of course, the other option is to start it yourself and merge the code with the TimeModels package :-)
In terms of optimization procedures, Julia has a fair few that are written in Julia, and can be found under the JuliaOpt umbrella. The Optim package in particular is quite popular and well developed. However, most of the people I know who are really into this stuff use NLOpt which is a free open source library callable from many languages (including Julia). I have heard nothing but good things about this library from people who tend to work with this stuff 24/7.
I am using Eclipse (version: Kepler Service Release 1) with Prolog Development Tool (PDT) plug-in for Prolog development in Eclipse. Used these installation instructions: http://sewiki.iai.uni-bonn.de/research/pdt/docs/v0.x/download.
I am working with Multi-Agent IndiGolog (MIndiGolog) 0 (the preliminary prolog version of MIndiGolog). Downloaded from here: http://www.rfk.id.au/ramblings/research/thesis/. I want to use MIndiGolog because it represents time and duration of actions very nicely (I want to do temporal planning), and it supports planning for multiple agents (including concurrency).
MIndiGolog is a high-level programming language based on situation calculus. Everything in the language is exactly according to situation calculus. This however does not fit with the project I'm working on.
This other high-level programming language, Incremental Deterministic (Con)Golog (IndiGolog) (Download from here: http://sourceforge.net/p/indigolog/code/ci/master/tree/) (also made with Prolog), is also (loosly) based on situation calculus, but uses fluents in a very different way. It makes use of causes_val-predicates to denote which action changes which fluent in what way, and it does not include the situation in the fluent!
However, this is what the rest of the team actually wants. I need to rewrite MIndiGolog so that it is still an offline planner, with the nice representation of time and duration of actions, but with the causes_val predicate of IndiGolog to change the values of the fluents.
I find this extremely hard to do, as my knowledge in Prolog and of situation calculus only covers the basics, but they see me as the expert. I feel like I'm in over my head and could use all the help and/or advice I can get.
I already removed the situations from my fluents, made a planning domain with causes_val predicates, and tried to add IndiGolog code into MIndiGolog. But with no luck. Running the planner just returns "false." And I can make little sense of the trace, even when I use the GUI-tracer version of the SWI-Prolog debugger or when I try to place spy points as strategically as possible.
Thanks in advance,
Best, PJ
If you are still interested (sounds like you might not be): this isn't actually very hard.
If you look at Reiter's book, you will find that causes_vals are just effect axioms, while the fluents that mention the situation are usually successor-state-axioms. There is a deterministic way to convert from the former to the latter, and the correct interpretation of the causes_vals is done in the implementation of regression. This is always the same, and you can just copy that part of Prolog code from indiGolog to your flavor.
A friend of mine needs to implement some statistical calculations in hardware.
She wants it to be accomplished using VHDL.
(cross my heart, I haven't written a line of code in VHDL and know nothing about its subtleties)
In particular, she needs a direct analogue of MATLAB's betainc function.
Is there a good package around for doing this?
Any hints on the implementation are also highly appreciated.
If it's not a good idea at all, please tell me about it as well.
Thanks a lot!
There isn't a core available that performs an incomplete beta function in the Xilinx toolset. I can't speak for the other toolsets available, although I would doubt that there is such a thing.
What Xilinx does offer is a set of signal processing blocks, like multipliers, adders and RAM Blocks (amongst other things, filters, FFTs), that can be used together to implement various custom signal transforms.
In order for this to be done, there needs to be a complete understanding of the inner workings of the transform to be applied.
A good first step is to implement the function "manually" in matlab as a proof of concept:
Instead of using the built-in function in matlab, your friend can try to implement the function just using fundamental operators like multipliers and adders.
The results can be compared with those produced by the built-in function for verification.
The concept can then be moved to VHDL using the building blocks that are provided.
Doing this for the incomplete beta function isn't something for the faint-hearted, but it can be done.
As far as I know there is no tool which allow interface of VHDL and matlab.
But interface of VHDL and C is fairly easy, so if you can implement your code(MATLAB's betainc function) in C then it can be done easily with FLI(foreign language interface).
If you are using modelsim below link can be helpful.
link
First of all a word of warning, if you haven't done any VHDL/FPGA work before, this is probably not the best place to start. With VHDL (and other HDL languages) you are basically describing hardware, rather than a sequential line of commands to execute on a processor (as you are with C/C++, etc.). You thus need a completely different skill- and mind-set when doing FPGA-development. Just because something can be written in VHDL, it doesn't mean that it actually can work in an FPGA chip (that it is synthesizable).
With that said, Xilinx (one of the major manufacturers of FPGA chips and development tools) does provide the System Generator package, which interfaces with Matlab and can automatically generate code for FPGA chips from this. I haven't used it myself, so I'm not at all sure if it's usable in your friend's case - but it's probably a good place to start.
The System Generator User guide (link is on the previously linked page) also provides a short introduction to FPGA chips in general, and in the context of using it with Matlab.
You COULD write it yourself. However, the incomplete beta function is an integral. For many values of the parameters (as long as both are greater than 1) it is fairly well behaved. However, when either parameter is less than 1, a singularity arises at an endpoint, making the problem a bit nasty. The point is, don't write it yourself unless you have a solid background in numerical analysis.
Anyway, there are surely many versions in C available. Netlib must have something, or look in Numerical Recipes. Or compile it from MATLAB. Then link it in as nav_jan suggests.
As an alternative to VHDL, you could use MyHDL to write and test your beta function - that can produce synthesisable (ie. can go into an FPGA chip) VHDL (or Verilog as you wish) out of the back end.
MyHDL is an extra set of modules on top of Python which allow hardware to be modelled, verified and generated. Python will be a much more familiar environment to write validation code in than VHDL (which is missing many of the abstract data types you might take for granted in a programming language).
The code under test will still have to be written with a "hardware mindset", but that is usually a smaller piece of code than the test environment, so in some ways less hassle than figuring out how to work around the verification limitations of VHDL.
I'm doing a research project on random forest algorithm. I have found numerous implementations of the algorithm but the main part of the code is often written in Fortran while I'm completely naive in it.
I have to edit the code, change the main parameters (like tree depth, num of feature variables, ...) and trace the algorithm's performance during each run.
Currently I'm using "Windows-Precompiled-RF_MexStandalone-v0.02-". The train and predict functions are matlab mex files and can not be opened or edited. Can anyone give me a piece of advice on what to do or is there a valid and completely matlab-based version of random forests.
I've read the randomforest-matlab carefully. The main training part unfortunately is a dll file. Through reading more, most of my wonders is now resolved. My question mainly was how to run several trees simultaneously.
Have you taken a look at these libraries?
Stochastic Bosque
randomforest-matlab
If you're doing a research project on it, the best thing is probably to implement the individual tree training yourself in C and then write Mex wrappers. I'd start with an ID3 tree (before attempting C4.5 for instance.) Then write the random forest code itself, which, once you write the tree code, isn't all that hard.
You'll:
learn a lot
be able to modify them as much as you like
eventually move on to exploring new areas with them
I've implemented them myself from scratch so I can help once you post some of your own code. But I don't think anybody on this site will write the code for you.
Will it take effort? Yes. Will you come out of it with more knowledge and ability than you had going in? Undoubtably.
There is a nice library in R called randomForest. It is based on the original implementation of Breiman in Fortran but it is now mainly recoded in C.
http://cran.r-project.org/web/packages/randomForest/index.html
The main parameters you talk about (tree depth, number of features to be tested, ...) are directly available.
Another library I would recommend is Weka. It is java based and lucid.Performance is slightly off though compared to R. The source code can be downloaded from http://www.cs.waikato.ac.nz/ml/weka/