I would like to know if there is any available Gaussian hypergeometric function (2F1 function) code for Matlab.
I perfectly know that in the newest Matlab releases there is the hypergeom function, but it works really slow.
Therefore I was wondering about the existance of any mex function or whatever similar code performing what hypergeom does.
I thank you all in advance for support.
Best regards,
Francesco
The GNU Scientific Library implements hypergeometric functions including 2F1. You shouldn't have too much trouble wrapping that inside a mex-file.
I expect you'll find other sources knocking around on the Internet too.
Do report back and let us know if it does work faster than the intrinsic function.
After googleing a bit in the Internet, I came up with this tool provided within the Mathworks File Exchange:
http://www.mathworks.com/matlabcentral/fileexchange/35008-generation-of-random-variates/content/pfq.m
It consists of 1900 distributions, and among them the Gaussian hypergeometric function 2F1.
Furthermore, it has better performances than the standard hypergeom function.
Related
The Matlab function interpn does n-grid interpolation. According to the documentation page:
In a future release, interpn will not accept mixed combinations of row and column vectors for the sample and query grids.
This page provides a bit more information but is still kind of cryptic.
My question is this: Why is this modification being implemented? In particular, are there any pitfalls to using interpn?
I am writing a program in fortran that is supposed to produce similar results to a Matlab program that uses interpn as a crucial component. I'm wondering if the Matlab program might have a problem that is related to this modification.
No, I don't think this indicates that there is any sort of problem with using interpn, or any of the other MATLAB interpolation functions.
Over the last few releases MathWorks has been introducing some new/better functionality for interpolation (for example the griddedInterpolant, scatteredInterpolant and delaunayTriangulation classes). This has been going on in small steps since R2009a, when they replaced the underlying QHULL libraries for computational geometry with CGAL.
It seems likely to me that interpn has for a long time supported an unusual form of input arguments (i.e. mixed row and column vectors to define the sample grid) that is probably a bit confusing for people, hardly ever used, and a bit of a pain for MathWorks to support. So as they move forward with the newer functionality, they're just taking the opportunity to simplify some of the syntaxes supported: it doesn't mean that there is any problem with interpn.
I've been searching around for some way to easily interface opencl code with MATLAB code, however most of what I've found is several years old and I'm wondering if it's still applicable.
Does anyone know of a way to do this?
Have you tried the OpenCL Toolbox?
Also the MATLAB Parallel Computing Toolbox may be a useful alternative if you have the license for it.
Also see Jacket.
The last two may require you to rewrite your code in the matlab language, if that is an acceptable price.
I am now using fminbnd in Matlab, and I find it relatively slow (I am using it inside a nested loop). The function itself, its interface and the values it returns are great, but when looking into the .m file I see it is not optimized. As a matter of fact, I was hoping for something like that to be written as a mex.
Anyone knows of an alternative to fminbnd that works much faster and does not have as much overhead?
It's written like that because it has to evaluate (feval) your user-defined function(s) on every iteration. Matlab's ODE solvers work in the same way. In current Matlab it's costly for a C/C++ code to call a user-defined Matlab function and read in its return values iteratively.
Make sure you're using the options correctly, that fminbnd is the correct tool (maybe a simpler scheme would be better or, since this in a loop, maybe a multi-dimensional method like fminsearch would be more appropriate), and have optimized your objective function. The next easiest thing would be to try compiling your Matlab code to C or C++ (see codgen). You'll likely need to compile in your objective function, and all of the options, as well in order to avoid the slowdown issues mentioned above. I've not tried this for fminbnd, but I did see mention of it working online. If your objective function itself is complicated, you could try just converting it to a mex function.
fminbnd is based on Brent's method. You can find C, C++, and FORTRAN code for that here. The GSL also has a version: gsl_min_fminimizer_brent.
Is it possible to use a MATLAB code on Scilab? Is that what is meant when saying that Scilab is a "clone" from MATLAB?
There is a tool to automatically convert Matlab source to Scilab source, it's called M2SCI. A script parses the Matlab source code and replaces Matlab-specific functions by Scilab ones. See the documentation of the mfile2sci function.
Yes you can use MATLAB code on scilab. See these links for more information:
http://help.scilab.org/docs/5.4.0/fr_FR/section_36184e52ee88ad558380be4e92d3de21.html
http://help.scilab.org/docs/5.4.0/en_US/index.html
I would not bet on it. But if your code is simple enough chances are good.
Problems are:
There is encrypted p-code in Matlab that Scilab will not be able to open.
Matlab usually comes with a number of toolboxes that might not be available to you (i think especially Simulink)
last but not least (i don't know about scilab) there usually are minute differences in how functions are implemented.
There are a number of projects out there trying to replicate/replace MATLAB:
Julia language: which has a relatively similar syntax to MATLAB and offers great performance, but still lacks a lot of toolboxes/libraries, as well as not having a GUI like MATLAB. I think this has the brightest future among all MATLAB alternatives.
Python language and its libraries NumPy and matplotlib: which is the most used alternative. I think at this moment the community is a couple of orders of magnitude even bigger than MATLAB. Python is the de facto standard in machine learning and data science at the moment. But still, the syntax and memory concept is a bit far from what people are used to in the MATLAB ecosystem. There are also no equivalent to SIMULINK, although Spyder and Jupyter projects have come a long way in terms of the development environment.
Octave: is basically a clone of MATLAB to a point they consider any incompatibility as a bug. If you have a long MATLAB code that you don't want to touch, this is the safest bet. But again no alternative for SIMULINK.
SciLab and it's fork ScicoLab are the best alternatives in terms of GUI, having a SIMULINK replica xcos / scicos and a graphical user interface development features. However the community is not as big as Octave and the syntax is not completely compatible. Sadly the Scilab development team has gone through a devastating family crisis leading to the software falling behind.
Honorary mention of Modelica language implementations OpenModelica and jModelica for being a superior alternative to SIMULINK-SimScape. You should know that you can load Modelica scrips also in xcos and scicos. If you want to kno wmore about JModelica you may see this post.
you may check the MATLAB's Alternativeto page to see more Free and Open source alternatives.
Anyone knows where I cand find an .m (matlab ) file with the Levenberg-Marquardt moditication to the Newton's method to optimize a function?
Thanks
I always start with a search on file exchange. Found a LMF nonlinear solution. It also seems that there is a lsqnonlin function in The optimization toolbox. Of course that costs a small fortune and limits the portability of your code (one of many reasons I use Python these days).
You can try using the MATLAB MEX version of CMPFIT, if that fits your purpose.
Try it here: http://people.cas.uab.edu/~mosya/cl/MATLABcircle.html
This is a web-page from proffesor Chernov, who published some papers and a book on the matter. There are also c and matlab sources.