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| − | + | <source lang="matlab"> | |
// function to compute the multivariate normal distribution | // function to compute the multivariate normal distribution | ||
//note that it asks for the sigma inverse, as well as the Sigma's determinant | //note that it asks for the sigma inverse, as well as the Sigma's determinant | ||
function [g] = MultivariateNormalDensity(x,mu, sigma_inv, sigma_det) | function [g] = MultivariateNormalDensity(x,mu, sigma_inv, sigma_det) | ||
| − | d=length(x); | + | d=length(x); |
| − | r2 = (x-mu)'*sigma_inv*(x-mu); | + | r2 = (x-mu)'*sigma_inv*(x-mu); |
| − | factor = 1/sqrt(((2*%pi)^d)*sigma_det); | + | factor = 1/sqrt(((2*%pi)^d)*sigma_det); |
| − | g = factor * exp (-(1/2)*r2); | + | g = factor * exp (-(1/2)*r2); |
endfunction | endfunction | ||
| + | </source> | ||
Revision as of 09:53, 20 March 2008
Here you can find relevant information on how to implement Pattern Recognition projects using Scilab.
Brief Introduction to Scilab
Scilab <http://www.scilab.org> is a open-source Matlab-like tool developed at INRIA. It can be downloaded for several platforms from the link:
http://www.scilab.org/download/
Tutorials describing how to use Scilab can be found here:
- `The official documentation <http://www.scilab.org/doc/intro/node1.html>`_
- `A hands-on tutorial <http://128.220.138.60:8080/download/attachments/1343559/Scilab+Tutorial+Annigeri.pdf?version=1>`_
- `ANU Scilab Tutorial <http://comptlsci.anu.edu.au/Numerical-Methods/tutorial-all.pdf>`_
- `Une introduction a Scilab <http://www.iecn.u-nancy.fr/~pincon/scilab/docletter.pdf>`_ , 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:
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:
!`MultivariateNormalDensity.sci`__
__ MultivariateNormalDensity.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 <http://www.scilab.org/contrib/index_contrib.php?page=download&category=MANUALS>`_
- `Scilab Toolbox especialized on Pattern Recognition -- Presto-Box <http://www.scilab.org/contrib/index_contrib.php?page=displayContribution&fileID=194>`_
- `Manual for Presto-Box -- Scilab <http://lmb.informatik.uni-freiburg.de/lmbsoft/presto-box/presto-box-docu.pdf>`_
- `ANN - Neural Networks Tool Box <http://dir.filewatcher.com/d/Mandrake/10.2/src/Sciences/Mathematics/scilab-toolbox-ANN-0.4.2-4mdk.src.rpm.27699.html>`_
- `SCIsvm <http://www.informatik.uni-freiburg.de/~fehr/scisvm.html>`_ , a plugin for the libsvm C++ library.
Scilab Code
All the relevant code for the EE662 course written in Scilab is posted below:
!`MultivariateNormalDensity.sci`__ - Implementation of a function to compute the multivariate normal density
__ MultivariateNormalDensity.sci
