(Brief Introduction to Scilab)
(Brief Introduction to Scilab)
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`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:
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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/
 
http://www.scilab.org/download/
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Tutorials describing how to use Scilab can be found here:
 
Tutorials describing how to use Scilab can be found here:
  
* `The official documentation <http://www.scilab.org/doc/intro/node1.html>`_
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* 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>`_
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* 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>`_
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* `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.
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* `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 ==
 
== Homework #1 related functionality ==

Revision as of 23:00, 11 March 2008

Here you can find relevant information on how to implement Pattern Recognition projects using Scilab.


Brief Introduction to Scilab

Scilab [1] 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[2]
  • A hands-on tutorial [3]
  • `ANU Scilab Tutorial [4]
  • `Une introduction a Scilab [5] , 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

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

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:


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

Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood