f@2: function [results] = perform_classification(experiment, db, condition)
f@2: 
f@2:   db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
f@4:   
f@2: 
f@2:   optt.kernel_type = 'gaussian';
f@2:   optt.C = 2.^[0:4:8];
f@2:   optt.gamma = 2.^[-16:4:-8];
f@2:   optt.search_depth = 3;
f@4: 
f@4:   % This causes the accuracy to be lower than it could!
f@4:   switch(experiment)
f@4:     case('time_scat_l3')
f@4:       optt.full_test_kernel = 0;
f@4:     otherwise 
f@4:       optt.full_test_kernel = 1;
f@4:   end  
f@4: 
f@4:   %
f@2: 
f@2:   if nargin < 3
f@2:     condition = cellstr(['none '; 'fault']);
f@4:   else
f@4:     condition = cellstr(condition);
f@2:   end
f@2: 
f@2:   for ii=1:length(condition)
f@2: 
f@2:     [train_set,test_set] = createFolds(condition{ii});
f@2:     train_set = find(train_set)';
f@2:     test_set = find(test_set)';
f@2: 
f@2:     % Only consider excerpts that are in src struct
f@2: 
f@2:     elems = length(db.src.objects);
f@2:     ids = zeros(elems, 1);
f@2:     for jj=1:elems
f@2:       ids(jj) = db.src.objects(jj).ind;
f@2:     end
f@2: 
f@2:     train_set = intersect(train_set, ids);
f@2:     test_set = intersect(test_set, ids);
f@2: 
f@2:     %
f@2: 
f@2:     [dev_err_grid,C_grid,gamma_grid] = ...
f@2:       svm_adaptive_param_search(db,train_set,[],optt);
f@2: 
f@2:     [dev_err,ind] = min(mean(dev_err_grid{end},2));
f@2:     C = C_grid{end}(ind);
f@2:     gamma = gamma_grid{end}(ind);
f@2: 
f@2:     optt1 = optt;
f@2:     optt1.C = C;
f@2:     optt1.gamma = gamma;
f@2: 
f@2:     model = svm_train(db,train_set,optt1);
f@2:     labels = svm_test(db,model,test_set);
f@2:     err = classif_err(labels,test_set,db.src);
f@2: 
f@2:     % dummy renaming of variables
f@2:     id = test_set;
f@2:     true_label = kron((1:10), ones(1, 100));
f@2:     true_label = true_label(test_set)';
f@2:     pred_label = labels';
f@2: 
f@2:     % saving results in struct
f@2:     results.(condition{ii}).err = err;
f@2:     results.(condition{ii}).labels = labels;
f@2:     results.(condition{ii}).dev_err = dev_err;
f@2:     results.(condition{ii}).C = C;
f@2:     results.(condition{ii}).gamma = gamma;
f@2:     results.(condition{ii}).tab = table(id, true_label, pred_label);
f@2: 
f@2:   end
f@2: 
f@2: end