Algoritmo das Projeções Sucessivas Para Seleção de ... - PPGQ

Anexos
Programas Auxiliares do SPA-LDA
• multilda.m
• mahal2.m
1) multilda.m
function [class,errors] = multilda(sample,training,group,Group_Test)
%MULTILDA Multi-Class Linear discriminant analysis.
% [class,errors] = multilda(sample,training,group,Group_Test) classifies each row
% of the data in SAMPLE into one of the values of the vector
% GROUP. GROUP contains integers from one to the number of
% groups in the training set, which is the matrix, TRAINING.
%
% SAMPLE and TRAINING must have the same number of columns.
% TRAINING and GROUP must have the same number of rows.
% CLASS is a vector with the same number of rows as SAMPLE.
%
% The biased Maximum-Likelihood estimate for the covariance matrix is employed: cov(X,1)
%
% B.A. Jones 2-05-95
% Copyright 1993-2000 The MathWorks, Inc.
% $Revision: 2.9 $ $Date: 2000/05/26 17:28:36 $
[gr,gc] = size(group);
if min(gr,gc) ~= 1
error('Requires the third argument to be a vector.');
end
if gc ~= 1,
group = group(:);
gr = gc;
end
if any(group - round(group)) | any(group < 1)
error('The third input argument must be positive integers.');
end
maxg = max(group);
[tr,tc] = size(training);
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