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| − | Here you can find relevant information on how to implement Pattern Recognition projects using Scilab. | + | Here you can find relevant information on how to implement Pattern Recognition projects using Scilab. |
| + | ==Brief Introduction to Scilab== | ||
| − | + | [http://www.scilab.org| Scilab] is a open-source Matlab-like tool developed at INRIA. It can be [http://www.scilab.org/download/| downloaded] for several platforms. | |
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| − | http://www.scilab.org/download/ | + | |
Tutorials describing how to use Scilab can be found here: | Tutorials describing how to use Scilab can be found here: | ||
| + | * [http://www.scilab.org/doc/intro/node1.html The official documentation] | ||
| + | * [http://128.220.138.60:8080/download/attachments/1343559/Scilab+Tutorial+Annigeri.pdf?version=1 A hands-on tutorial] | ||
| + | * [http://comptlsci.anu.edu.au/Numerical-Methods/tutorial-all.pdf ANU Scilab Tutorial] | ||
| + | * [http://www.iecn.u-nancy.fr/~pincon/scilab/docletter.pdf Une introduction a Scilab], if you want to have some fun reading a Scilab tutorial in French. | ||
| − | + | ==Homework #1 related functionality== | |
| − | + | ===Random Number Generator=== | |
| + | ''grand'' is the function used to generate random numbers. In order to generate a multivariate normally distributed sequence of ''n'' vectors with mean ''mu'' and covariance ''cov'', ''grand'' should be called as: | ||
| − | + | <source lang="matlab"> | |
| + | numbers = grand(n, 'mn',mu, cov); | ||
| + | </source> | ||
| − | + | ===Function declaration=== | |
| + | example that computes the multivariate normal probability density: | ||
| + | <source lang="matlab"> | ||
| + | // function to compute the multivariate normal distribution | ||
| + | //note that it asks for the sigma inverse, as well as the Sigma's determinant | ||
| + | function [g] = MultivariateNormalDensity(x,mu, sigma_inv, sigma_det) | ||
| + | d=length(x); | ||
| + | r2 = (x-mu)'*sigma_inv*(x-mu); | ||
| + | factor = 1/sqrt(((2*%pi)^d)*sigma_det); | ||
| + | g = factor * exp (-(1/2)*r2); | ||
| + | endfunction | ||
| + | </source> | ||
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The file with the code above can be downloaded from the link below: | The file with the code above can be downloaded from the link below: | ||
| + | [[Media:MultivariateNormalDensity_OldKiwi.sci]] | ||
| − | + | ==Tool Boxes== | |
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| + | There are several ''tool boxes'' of functions written by people all over the world adding extra functionality to Scilab. Here are some useful links: | ||
| − | + | * [http://www.scilab.org/contrib/index_contrib.php?page=download&category=MANUALS Toolboxes for Scilab and Their Manuals] | |
| + | * [http://www.scilab.org/contrib/index_contrib.php?page=displayContribution&fileID=194 Scilab Toolbox especialized on Pattern Recognition -- Presto-Box] | ||
| + | * [http://lmb.informatik.uni-freiburg.de/lmbsoft/presto-box/presto-box-docu.pdf Manual for Presto-Box -- Scilab] | ||
| + | * [http://dir.filewatcher.com/d/Mandrake/10.2/src/Sciences/Mathematics/scilab-toolbox-ANN-0.4.2-4mdk.src.rpm.27699.html ANN - Neural Networks Tool Box] | ||
| + | * [http://www.informatik.uni-freiburg.de/~fehr/scisvm.html SCIsvm], a plugin for the libsvm C++ library. | ||
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| − | + | ==Scilab Code== | |
| − | + | All the relevant code for the EE662 course written in Scilab is posted [[Media:MultivariateNormalDensity_OldKiwi.sci| here]]. | |
Latest revision as of 10:20, 20 March 2008
Here you can find relevant information on how to implement Pattern Recognition projects using Scilab.
Contents
Brief Introduction to Scilab
Scilab is a open-source Matlab-like tool developed at INRIA. It can be downloaded for several platforms.
Tutorials describing how to use Scilab can be found here:
- The official documentation
- A hands-on tutorial
- ANU Scilab Tutorial
- Une introduction a Scilab, if you want to have some fun reading a Scilab tutorial in French.
Random Number Generator
grand is the function used to generate random numbers. In order to generate a multivariate normally distributed sequence of n vectors with mean mu and covariance cov, grand should be called as:
numbers = grand(n, 'mn',mu, cov);
Function declaration
example that computes the multivariate normal probability density:
// function to compute the multivariate normal distribution //note that it asks for the sigma inverse, as well as the Sigma's determinant function [g] = MultivariateNormalDensity(x,mu, sigma_inv, sigma_det) d=length(x); r2 = (x-mu)'*sigma_inv*(x-mu); factor = 1/sqrt(((2*%pi)^d)*sigma_det); g = factor * exp (-(1/2)*r2); endfunction
The file with the code above can be downloaded from the link below:
Media:MultivariateNormalDensity_OldKiwi.sci
Tool Boxes
There are several tool boxes of functions written by people all over the world adding extra functionality to Scilab. Here are some useful links:
- Toolboxes for Scilab and Their Manuals
- Scilab Toolbox especialized on Pattern Recognition -- Presto-Box
- Manual for Presto-Box -- Scilab
- ANN - Neural Networks Tool Box
- SCIsvm, a plugin for the libsvm C++ library.
Scilab Code
All the relevant code for the EE662 course written in Scilab is posted here.
