Mercurial > hg > camir-aes2014
view core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM_singletraining.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function [out, stats] = write_mat_results_ISMIR13RBM_singletraining(dirin,fileout) % combine the test results from the directories supplied, % group them according to dataset parameter values % combine the test results from the directories supplied, % group them according to dataset parameter values features = []; show = 1; if nargin == 0 dirin{1} = './'; end global comparison; global comparison_ids; newout = []; thisdir = pwd; % loop through al lthe result directories and for diri = 1:numel(dirin) % --- % go to directory and locate file % --- cd(dirin{diri}); u = dir(); u = {u.name}; [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); if numel(idx) < 1 error 'This directory contains no valid test data'; end % just one or more tests in this folder? if exist('file','var') && isnumeric(file) cprint(1, 'loading one result file'); file = u{idx(file)}; data = load(file); sappend(out,data.out); else for filei = 1:numel(idx) cprint(1, 'loading result file %i of %i',filei, numel(idx)); file = u{idx(filei)}; data = load(file); newout = sappend(newout,data.out); end end % reset act directory cd(thisdir); end % --- % filter according to training parameter C % % NOTE :if we don't filter by C, we get strong overfitting with training % success > 96 % and test set performance aorund 65 % % --- cs = zeros(numel(newout),1); for i=1:numel(newout) cs(i) = newout(i).trainparams.C; end cvals = unique(cs); for ci=1:numel(cvals) valididx = find(cs == cvals(ci)); filteredout = newout(valididx); % --- % get parameter statistics % --- stats = test_generic_display_param_influence(filteredout, show); % get maximal values for each test set bin % --- % trainparams.dataset contains sets which have each only one bin of the % ismir testsets % --- max_idx = [stats.trainparams.dataset.mean_ok_train.max_idx]; ok_test = zeros(2, numel(max_idx)); ok_train = zeros(2, numel(max_idx)); ok_config = []; % cycle over all test sets and save best result for i=1:numel(max_idx) ok_test(:,i) = filteredout(max_idx(i)).mean_ok_test; ok_train(:,i) = filteredout(max_idx(i)).mean_ok_train; ok_config = sappend(ok_config,struct('trainparams',filteredout(max_idx(i)).trainparams, ... 'fparams',filteredout(max_idx(i)).fparams)); end % save the stuff out(ci).mean_ok_test = mean(ok_test,2); out(ci).var_ok_test = var(ok_test,0,2); out(ci).mean_ok_train = mean(ok_train,2); out(ci).var_ok_train = var(ok_train,0,2); out(ci).trainparams.C = cvals(ci); out(ci).ok_config = ok_config; out(ci).ok_test = ok_test; out(ci).ok_train = ok_train; end % --- % show results for different C % --- if numel([out.mean_ok_test]) > 1 && show % plot means % plot std = sqrt(var) % plot training results figure; boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]); title (sprintf('Performance for all configs')); end % --- % write max. test success % --- mean_ok_test = [out.mean_ok_test]; [val, idx] = max(mean_ok_test(1,:)); if show fprintf(' --- Maximal test set success: nr. %d, %3.2f percent. --- \n', idx, val * 100) end % save save([hash(strcat(dirin{:}),'md5') '_summary'], 'out'); end function boxplot(mean, std, train); bar([train; mean]', 1.5); hold on; errorbar(1:size(mean,2), mean(1,:), std(1,:),'.'); % plot(train,'rO'); colormap(spring); axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]); end