Daniel@0: function write_mat_resutls_ismir12(filein, subrun, fileout)
Daniel@0: % write_mat_resutls_ismir12(filein, subrun, fileout)
Daniel@0: %
Daniel@0: % write results from result file into ismir12 generic format.
Daniel@0: % if subrun is a vector, the results are averaged over the 
Daniel@0: % runs specified
Daniel@0: 
Daniel@0: 
Daniel@0: [out, stats, features, individual] = test_generic_display_results(filein);
Daniel@0: 
Daniel@0: % ---
Daniel@0: % we get the single individual values and 
Daniel@0: % ---
Daniel@0: for i = 1:numel(subrun)
Daniel@0:     individual = individual(subrun);
Daniel@0:     
Daniel@0:     % number of inctrain cycles
Daniel@0:     n_inctrain = numel(individual.diag.inctrain);
Daniel@0: 
Daniel@0:     % ---
Daniel@0:     % ok_train_unused
Daniel@0:     % ---
Daniel@0:     values_ok_train_unused{i} = reshape([individual.diag.inctrain.ok_notin_train], [],n_inctrain);
Daniel@0:     values_ok_train_unused{i} = values_ok_train_unused(1:2:end,:).*100;
Daniel@0: 
Daniel@0:     mean_ok_train_unused{i} = mean(values_ok_train_unused, 1);
Daniel@0:     var_ok_train_unused{i} = var(values_ok_train_unused,[], 1);
Daniel@0: 
Daniel@0:     % ---
Daniel@0:     % ok_train
Daniel@0:     % ---
Daniel@0:     values_ok_train{i} = reshape([individual.diag.inctrain.ok_train], [],n_inctrain);
Daniel@0:     values_ok_train{i} = values_ok_train(1:2:end,:).*100;
Daniel@0: 
Daniel@0:     mean_ok_train{i} = mean(values_ok_train, 1);
Daniel@0:     var_ok_train{i} = var(values_ok_train,[], 1);
Daniel@0: 
Daniel@0:     % ---
Daniel@0:     % ok_test
Daniel@0:     % ---
Daniel@0:     values_ok_test{i} = reshape([individual.diag.inctrain.ok_test], [],n_inctrain);
Daniel@0:     values_ok_test{i} = values_ok_test(1:2:end,:).*100;
Daniel@0: 
Daniel@0:     mean_ok_test{i} = mean(values_ok_test, 1);
Daniel@0:     var_ok_test{i} = var(values_ok_test,[], 1);
Daniel@0: end
Daniel@0: 
Daniel@0: 
Daniel@0: 
Daniel@0: clear ('out', 'stats', 'features', 'individual');
Daniel@0: save(fileout)