diff core/magnatagatune/tests_evals/test_generic_display_results_absviolated.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/core/magnatagatune/tests_evals/test_generic_display_results_absviolated.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,99 @@
+function out = test_generic_display_results_absviolated(file)
+% out = test_generic_display_results_absviolated(file) 
+%
+% displays the finalresults mat file and enables 
+% further analysis and duiagnostics of the individual runs
+
+global db_MTTAudioFeatureSlaney08;
+global db_magnamixedfeat_genrebasicsm;
+global db_MTTMixedFeatureGenreBasicSmPCA;
+
+
+global comparison;
+global comparison_ids;
+        
+if nargin < 1 || isempty(file) || isnumeric(file)
+    u = dir();
+    u = {u.name};
+    [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1);
+    
+    if exist('file','var') && isnumeric(file)
+        file = u{idx(file)};
+    else
+        file = u{idx(1)};
+    end
+end
+
+load(file);
+
+% ---
+% % get statistics for feature parameters
+% Visualise the accuracy and variance
+% ---
+if isfield(out, 'inctrain')
+    for i = 1:numel(out)
+        
+        % ---
+        % get training and test sample sizes
+        % ---
+        nData = [];
+        n_train_data = zeros(1,numel(out(i).inctrain.dataPartition));
+        n_test_data = zeros(1,numel(out(i).inctrain.dataPartition));
+        for j = 1:numel(out(i).inctrain.dataPartition)
+            n_train_data(j) = mean(out(i).inctrain.dataPartition(j).TrainSize);
+            n_test_data(j) = mean(out(i).inctrain.dataPartition(j).TestSize);
+        end
+        
+        % ---
+        % get lost percentages
+        % ---
+        mean_lost_test = 1 - [out(i).inctrain.mean_ok_test];
+        mean_lost_test = mean_lost_test(1,:).* n_test_data;
+        
+        var_ok_test = sqrt([out(i).inctrain.var_ok_test]);
+        
+        mean_lost_train = 1 - [out(i).inctrain.mean_ok_train];
+        mean_lost_train = mean_lost_train(1,:).* n_train_data;
+        
+        % plot test results
+        figure;
+        subplot(2,1,1)
+        plot(n_train_data, mean_lost_test,'r');
+        hold;
+        % plot training results
+        plot(n_train_data, mean_lost_train,'m');
+        
+        xlabel ('# training constraints');
+        ylabel ('# constraints violated');
+        legend ('test','training');
+        
+        plot(n_train_data, mean_lost_test + var_ok_test(1,:).* n_test_data,'r:');
+        plot(n_train_data, mean_lost_test - (var_ok_test(1,:).* n_test_data),'r:');
+        
+        % ---
+        % get percentage of unknown data examples learnt
+        % ---
+        lost_test_not_in_train = mean_lost_test - mean_lost_train;
+        ntest_not_in_train = n_test_data - n_train_data;
+        
+        lost_test_not_in_train = lost_test_not_in_train ./ ntest_not_in_train;
+    
+        lost_test_not_in_train(isnan(lost_test_not_in_train)) = 0;
+        
+        subplot(2,1,2)
+        plot(n_train_data, lost_test_not_in_train);
+        
+        xlabel ('# training constraints');
+        ylabel ('% unknown constraints violated');
+    end
+end
+
+   
+% --- 
+% write max. training success
+% ---
+mean_ok_test = [out.mean_ok_test];
+[val, idx] = max(mean_ok_test(1,:));
+fprintf(' --- Maximal training success: nr. %d, %3.2f percent. --- \n', idx, val * 100)
+
+end