comparison core/tools/write_mat_results_ismir12.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function write_mat_results_ismir12(filein, subrun, fileout)
+% write_mat_resutls_ismir12(filein, subrun, fileout)
+%
+% write results from result file into ismir12 generic format.
+% if subrun is a vector, the results are averaged over the 
+% runs specified
+
+
+[out, stats, features, individual] = test_generic_display_results(filein, 0);
+
+% ---
+% we get the single individual values and 
+% ---
+for i = 1:numel(subrun)
+    individual = individual(subrun);
+    
+    % number of inctrain cycles
+    n_inctrain = numel(individual.diag.inctrain);
+
+    % ---
+    % ok_train_unused
+    % ---
+    values_ok_train_unused{i} = reshape([individual.diag.inctrain.ok_notin_train], [],n_inctrain);
+    values_ok_train_unused{i} = values_ok_train_unused(1:2:end,:).*100;
+
+    mean_ok_train_unused{i} = mean(values_ok_train_unused, 1);
+    var_ok_train_unused{i} = var(values_ok_train_unused,[], 1);
+
+    % ---
+    % ok_train
+    % ---
+    values_ok_train{i} = reshape([individual.diag.inctrain.ok_train], [],n_inctrain);
+    values_ok_train{i} = values_ok_train(1:2:end,:).*100;
+
+    mean_ok_train{i} = mean(values_ok_train, 1);
+    var_ok_train{i} = var(values_ok_train,[], 1);
+
+    % ---
+    % ok_test
+    % ---
+    values_ok_test{i} = reshape([individual.diag.inctrain.ok_test], [],n_inctrain);
+    values_ok_test{i} = values_ok_test(1:2:end,:).*100;
+
+    mean_ok_test{i} = mean(values_ok_test, 1);
+    var_ok_test{i} = var(values_ok_test,[], 1);
+end
+
+
+
+clear ('out', 'stats', 'features', 'individual');
+save(fileout)