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1 function [] = classify(db, optt, preffix)
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2
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3 cond = cellstr(['none '; 'fault']);
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4
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5 for ii=1:length(cond)
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6
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7 [train_set,test_set] = createFolds(cond{ii});
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8 train_set = find(train_set)';
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9 test_set = find(test_set)';
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10
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11 [dev_err_grid,C_grid,gamma_grid] = ...
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12 svm_adaptive_param_search(db,train_set,[],optt);
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13
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14 [dev_err,ind] = min(mean(dev_err_grid{end},2));
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15 C = C_grid{end}(ind);
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16 gamma = gamma_grid{end}(ind);
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17
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18 optt1 = optt;
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19 optt1.C = C;
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20 optt1.gamma = gamma;
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21
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22 model = svm_train(db,train_set,optt1);
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23 labels = svm_test(db,model,test_set);
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24 err = classif_err(labels,test_set,db.src);
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25
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26 fprintf('dev err = %f, test err = %f\n\n',dev_err,err);
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27
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28 % dummy renaming of variables
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29 id = test_set';
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30 true_label = kron((1:10), ones(1, 100));
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31 true_label = true_label(test_set)';
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32 pred_label = labels';
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33
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34 % saving results in table
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35 results = table(id, true_label, pred_label)
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36
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37 run_name = [preffix, cond{ii}];
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38
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39 global results_dir;
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40 [s, mess, messid] = mkdir(results_dir);
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41 if(s ~= 1) exit; end;
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42
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43 save([results_dir, run_name, '.mat'],'dev_err','err','C','gamma', 'labels', 'results');
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44 writetable(results, [results_dir, run_name, '_results.csv'])
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45
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46 end;
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47
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