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annotate extra/NaN/test/test_xval.m @ 12583:21f4d96c4959 octave-forge

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[nan] modify tests
author schloegl
date Fri, 03 Apr 2015 19:24:06 +0000
parents 41f92a4ada86
children
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1 % test_classifier;
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2
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3 % $Id$
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4 % Copyright (C) 2010 by Alois Schloegl <alois.schloegl@gmail.com>
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5 % This function is part of the NaN-toolbox
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6 % http://pub.ist.ac.at/~schloegl/matlab/NaN/
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7
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8 % This program is free software; you can redistribute it and/or
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9 % modify it under the terms of the GNU General Public License
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10 % as published by the Free Software Foundation; either version 3
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11 % of the License, or (at your option) any later version.
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12 %
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13 % This program is distributed in the hope that it will be useful,
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14 % but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 % GNU General Public License for more details.
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17 %
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18 % You should have received a copy of the GNU General Public License
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19 % along with this program; if not, write to the Free Software
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20 % Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA 02110-1301, USA.
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21
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22
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23 if 1,
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24 clear
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25 N=100; % number of samples
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26 M=10; % number of features
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27 classifier= {'SVM:LIB','REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','RDA','GDBC','SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'};
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28 %classifier= {'SVM:RBF'};
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29
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30 x = randn(N,M); % data
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31 c = ([1:N]'>(N/2))+1; % classlabel
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32 %w = [ones(1,N/2)/5,ones(1,N/10),zeros(1,4*N/10)];
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33 w = []; % no weightening
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34
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35 x = randn(N,M);
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36 x = x+c*ones(1,M);
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37
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38 if 1,
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39 %x(2:2:N/2,2) = NaN;
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40 x(2:2:N,2) = NaN;
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41 x(3,2:2:end) = NaN;
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42 end;
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43 end;
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44
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45 for k = 1:length(classifier);
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46 try,
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47 [R{k},CC{k}] = xval(x, {c,w}, classifier{k});
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48 fprintf(1,'%8s\t%i\t%5.2f\t%5.2f+-%5.2f\n',classifier{k},sum(R{k}.data(:)),R{k}.ACC*100,R{k}.kappa,R{k}.kappa_se);
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49 save -v6 debug.mat
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50 catch,
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51 R{k} = [];
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52 end;
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53 end;
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54
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55 for k = 1:length(R)
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56 if isempty(R{k})
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57 fprintf(1,'%8s \t failed\n',classifier{k});
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58 else
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59 fprintf(1,'%8s\t%i\t%5.2f\t%5.2f+-%5.2f\n',classifier{k},sum(R{k}.data(:)),R{k}.ACC*100,R{k}.kappa,R{k}.kappa_se);
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60 end;
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61 end
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62