I tried to find the source code for some integrated MATLAB functions. Is there any way to find MATLAB's integrated functions? For example I can't find hypot source code.
Most MATLAB functions are supplied as MATLAB source code, and you can view the source by typing edit <functionname>. Some of the lower level functions, however, are implemented in C for better performance and are built-in - you can't see the source code. hypot is one of these.
However the algorithms are not typically that secret - you can read an article about hypot (including the algorithm) on Cleve Moler's blog.
Related
I'm specifically looking at something like Matlab's firpm function (I know there are C implementations of Remez available online in several sources).
The firpm page on the MATLAB documentation, under "Extended Capabilities", says the function is compatible with C/C++ code generation with the MATLAB Coder.
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.
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.
How can I get the source code of the Canny algorithm as used by MATLAB, it is fast and accurate. I want the source code because I want to implement it on hardware.
About the implementing it on hardware part, I would strongly advice you consult with a lawyer first. That being said, invoking
edit edge.m
from MATLAB gets you the source code.
You can't always get the source code for MATLAB functions (some you can, some you can't). But a quick Google search for the Canny algorithm in MATLAB returns a number of implementations. For example, there is one on MathWorks File Exchange site. Is this what you need?
I am trying to do some text classification with SVMs in MATLAB and really would to know if MATLAB has any methods for feature selection(Chi Sq.,MI,....), For the reason that I wan to try various methods and keeping the best method, I don't have time to implement all of them. That's why I am looking for such methods in MATLAB.Does any one know?
svmtrain
MATLAB has other utilities for classification like cluster analysis, random forests, etc.
If you don't have the required toolbox for svmtrain, I recommend LIBSVM. It's free and I've used it a lot with good results.
The Statistics Toolbox has sequentialfs. See also the documentation on feature selection.
A similar approach is dimensionality reduction. In MATLAB you can easily perform PCA or Factor analysis.
Alternatively you can take a wrapper approach to feature selection. You would search through the space of features by taking a subset of features each time, and evaluating that subset using any classification algorithm you decide (LDA, Decision tree, SVM, ..). You can do this as an exhaustively or using some kind of heuristic to guide the search (greedy, GA, SA, ..)
If you have access to the Bioinformatics Toolbox, it has a randfeatures function that does a similar thing. There's even a couple of cool demos of actual use cases.
May be this might help:
There are two ways of selecting the features in the classification:
Using fselect.py from libsvm tool directory (http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#feature_selection_tool)
Using sequentialfs from statistics toolbox.
I would recommend using fselect.py as it provides more options - like automatic grid search for optimum parameters (using grid.py). It also provides an F-score based on the discrimination ability of the features (see http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf for details of F-score).
Since fselect.py is written in python, either you can use python interface or as I prefer, use matlab to perform a system call to python:
system('python fselect.py <training file name>')
Its important that you have python installed, libsvm compiled (and you are in the tools directory of libsvm which has grid.py and other files).
It is necessary to have the training file in libsvm format (sparse format). You can do that by using sparse function in matlab and then libsvmwrite.
xtrain_sparse = sparse(xtrain)
libsvmwrite('filename.txt',ytrain,xtrain_sparse)
Hope this helps.
For sequentialfs with libsvm, you can see this post:
Features selection with sequentialfs with libsvm