Can I use PDEModelica for calculations in 2D domains. How many domains PDEModelica supports, or DomainLineSegment1D only?
I use OpenModelica.
Seems not.
https://openmodelica.org/doc/OpenModelicaUsersGuide/latest/pdemodelica.html
PDEModelica1 is nonstandardised experimental Modelica language extension for 1-dimensional partial differential extensions (PDE).
Related
I am currently trying to use Simscape to design and simulate the electrical circuit and COMSOL Multiphysics to simulate the electromagnetic interaction between coils. What I'm not certain of is whether or not we can successfully link the two software packages nicely via MATLAB or not. They both have MATLAB support though.
I am also researching whether ANSYS suite. There may be some software we can use that would take the place of one or both of the previous software.
Several years ago I had to make a connection between COMSOL and Matlab as well. This is called a Matlab Comsol LiveLink.
However, I think (not sure), that the electrical circuit should be modeled in COMSOL. Using the LiveLink you can set parameters in COMSOL using Matlab and extract the updated calculation. To my knowledge you cannot input a model, do calculations, and then extract the results.
You can look in the users guide, maybe it says something about this
Some other links which may be useful:
LiveLink™ for MATLAB®
Integrate COMSOL Multiphysics® with
MATLAB® Scripting
The selection methods I am looking for are the ones based on subset evaluation (i.e. do not simply rank individual features). I prefer implementations in Matlab or based on WEKA, but implementations in any other language will still be useful.
I am aware of the existence of CsfSubsetEval and ConsistencySubsetEval in WEKA, but they did not lead to good classification performance, probably because they suffer from the following limitation:
CsfSubsetEval is biased toward small feature subsets, which may prevent locally predictive features from being included in the selected subset, as noted in [1].
ConsistencySubsetEval use min-features bias [2] which, similarly to CsfSubsetEval, result in the selection of too few features.
I know it is "too few" because I have built classification models with larger subsets and their classification performance were relatively much better.
[1] M. A. Hall, Correlation-based Feature Subset Selection for Machine Learning, 1999.
[2] Liu, Huan, and Lei Yu, Toward integrating feature selection algorithms for classification and clustering, 2005.
Check out python scikit learn simple and efficient tools for data mining and data analysis. There are various implemented methods for feature selection, classification, evaluation and a lot of documentations and tutorials.
My search has led me to the following implementations:
FEAST toolbox: it is an interesting toolbox, developed by the University of Manchester, and provide implementations of Shannon's Information Theory functions. The implementations can be downloaded from THIS webpage, and they can be used to evaluate individual features as well as subset of features.
I have also found THIS matlab code, which is an implementation for a selection algorithm based on Interaction Information.
PY_FS: A Python Package for Feature Selection
I came across this package [1] which was just released (2021) and contains many methods with reference to their original papers.
Is there a module for the Perl Data Language that is similar to the Matlab signal processing toolbox? I'm aware of PDL::FFT(W), but can't find any functions for filter construction or estimation of statistical properties.
Alas, no. While it is likely that you can accomplish the same ends as Matlab's signal processing toolbox with your own combination of convolutions, cross products, etc, nobody has written a comprehensive signal processing toolkit with PDL. PDL started as a competitor to IDL, so its strongest bindings focus on image manipulation.
If you are considering making your own toolbox (which would be FANTASTIC!), I would suggest PDL::TimeSeries.
There is now at least https://metacpan.org/pod/PDL::DSP::Fir for finite impulse response filter kernels.
PDL::Audio has filters (including FIR filters, aka convolution). I'm sure it's nowhere near as full-featured as the Matlab toolbox, and it has lots of stuff you're probably not interested in (it's designed for making sound, after all), but it will do at least some of what you want.
If I were making this module, I'd call it PDL::DSP.
I'm looking for a good Math library in objective-c that does your usual Matrix Vector and Quaternion tasks for use in conjunction with Core Animation. The library needs to be free for commercial use.
Thanks!
Current versions of iOS include the Accelerate Framework, which includes a large set of accelerated matrix math routines (BLAS, LAPACK, etc.)
Added: You can also use any C math libraries/routines you might have, as Objective C is a compatible superset of ANSI C.
Eigen is a lightweight C++ linear algebra package which provides could matrix, vector and quaternion support. While I have not used it in anger, it seems to have a much nicer interface than BLAS/LAPACK and supports a bunch of things those libraries don't
I don't think you find ObjectiveC math library. I use vfpmathlibrary
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