Mercurial > hg > camir-aes2014
view core/magnatagatune/tests_evals/test_generic_display_results_absviolated.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line source
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