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.