I am currently working in matlab R2011a. These are the tools that I am using
neural-network toolbox
curve-fitting toolbox
I want to use forecast and kalman filter functions which are unavailable in R2011a . Hence I am considering to update to R2013a . But, I am concerned that all of my functions would give the exact same output or not.
Can anyone post some reference or any experience in this matter , so that I can be sure.
Reference or experience to any issues in upgrading the version is appreciated.
I would suggest going through the release notes for each of those 3 products to check for known compatibility considerations:
http://www.mathworks.co.uk/help/nnet/release-notes.html
http://www.mathworks.co.uk/help/curvefit/release-notes.html
(I don't know what the time series toolbox is, it doesn't seem to be a MathWorks product).
Related
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.
Will code from Matlab R2012b work in Matlab R2013a environment?
I am not sure if I should switch from Matlab R2012b to Matlab R2013a?
I heard there are nice new features, but my major concern is whether my
code from 2012b will work with 2013a.
Please let me know if you have some major warnings about this.
Thanks
It is very rare for a new version to be incompatible with code of previous versions (unless you upgrade from dinosaur versions).
There's a brief list of changes here. This includes several set theory functions whose behavior has changed, and a few less commonly used things that have been removed.
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 have been recently trying to use svm for feature classification. While i was doing so, a question came to my mind.
Which would be a better method to use, LIBSVM or svmclassify? What I mean by svmclassify is to use in-built functions in MATLAB such as svmtrain and svmclassify. In that sense, I was interested to know which method would be more accurate and which would be easier to use.
Since MATLAB has already the Bioinformatics toolbox already, why would you use LIBSVM? Aren't the functions like svmtrain and svmclassify already built in.. what additional benefits does LIBSVM bring about?
I would like to hear some of your opinions. Please Pardon me if the question is stupid..
I expect you would get very similar result using each library.
They are both very easy to use. The only big difference is that one comes with the MATLAB Bioinformatics toolbox and the other one you need to obtain from the authors web site and install by hand. If to you this is an issue I would recommend you stick to what is already installed in your computer. If not consider using LIBSVM, as it is a very well tested and well regarded library.
Also, from personal experience on playing with both, libSVM is much faster than MATLAB svm routines for obvious reasons. Last but not the least, libSVM has MATLAB plugins which can be called from MATLAB if you are more comfortable within a MATLAB environment.
I have also the same question, but I think that Libsvm is very useful and very easy in the case of multi-classes classification , but the matlab toolbox is designed for only two classes classification.
In my experience the libsvm performed giving cross validaion results as 45% where matlab code did 90%. So I looked up the explanation of matlab function for svm where they had such options related with perceptrones, I wonder if they are using pure svm or not but will write again in my case matlab was much better. (multiclass svm)
Could you provide an example of ICA Independent Component Analysis IN MATLAB?
I know PCA is implemented in matlab but ICA, what about RCA?
Have a look at the FastICA implementation. I've used their R version before, I assume the matlab implementation does the same thing... On that page you get a description of the algorithm and pointers to more info.
Dr G was right.
Now, you are able to find a complete and a very useful Matlab Package (works also with 2013a version):
FastICA
Also you can find a another ICA and PCA Matlab implementation package there: ICA/PCA. But I have no experience with it.
The topic is quite old, but it is worth mentioning that in 2017a, matlab introduced reconstruction independent component analysis (RICA), which may come in handy for someone searching for ICA.