comparison core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM_windows.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [out, stats] = write_mat_results_ISMIR13RBM_windows(dirin,fileout)
+% combine the test results from the directories supplied,
+% and recombine the bins to a coherent dataset
+
+% folders_win1 = {'130507_svm_disttest_subsconv_rbm_largegrid_21_r2385','130507_svm_disttest_subsconv_rbm_largegrid_22_r2385','130507_svm_disttest_subsconv_rbm_largegrid_23_r2385','130507_svm_disttest_subsconv_rbm_largegrid_24_r2385','130508_svm_disttest_subsconv_rbm_largegrid_25_r2391'};
+% folders_win2 = {'130511_svm_disttest_subsconv_rbm_largegrid_21_r2391','130511_svm_disttest_subsconv_rbm_largegrid_22_r2391','130511_svm_disttest_subsconv_rbm_largegrid_23_r2391','130511_svm_disttest_subsconv_rbm_largegrid_24_r2391','130513_svm_disttest_subsconv_rbm_largegrid_25_r2391'};
+%folders = {folders_win1{:}, folders_win2{:}};
+
+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 C values!
+% ---
+allcs = zeros(numel(newout),1);
+for i=1:numel(newout)
+    allcs(i) = newout(i).trainparams.C;
+end
+valididx = find(allcs == 1); % select C!
+newout = newout(valididx);
+
+% ---
+% filter according to training parameter deltafun_params(windows)
+% ---
+wind = cell(numel(newout),1);
+for i=1:numel(newout)
+    wind{i} = cell2str(newout(i).trainparams.deltafun_params);
+end
+windu = unique(wind);
+
+% save the combined out
+newout2 = newout;
+
+% do the loop as in write_mat_results_ISMIR13RBM
+for wi=1:numel(windu)
+    newout = newout2(strcellfind(wind, windu{wi},1));
+    
+    % bundle all datasets
+    fout = sameparamsubset(newout, 'dataset','');
+    out = [];
+    for ci=1:numel(fout)
+        filteredout = fout{ci};
+
+        ok_test = zeros(2, numel(filteredout));
+        ok_train =  zeros(2, numel(filteredout));
+        ok_config = [];
+
+
+        tmpout = filteredout(1);
+        % cycle over all test sets and get new means
+        for i=1:numel(filteredout)
+            ok_test(:,i) = filteredout(i).mean_ok_test;
+            ok_train(:,i) = filteredout(i).mean_ok_train;
+        end
+
+        % save the stuff
+        tmpout.mean_ok_test = mean(ok_test,2);
+        tmpout.var_ok_test = var(ok_test,0,2);
+        tmpout.mean_ok_train = mean(ok_train,2);
+        tmpout.var_ok_train = var(ok_train,0,2);
+
+        tmpout.ok_test = ok_test;
+        tmpout.ok_train = ok_train;
+
+        % put it in output structure
+        out = sappend(out,tmpout);
+    end
+
+%     % ---
+%     % show results
+%     % ---
+%     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/train success
+    % ---
+    mean_ok_train = [out.mean_ok_train];
+    [val, idx] = max(mean_ok_train(1,:));
+    if show
+        fprintf(' --- Maximal train set success for %s: nr. %d, %3.2f percent, test result %3.2f percent. --- \n', windu{wi}, idx, val * 100,out(idx).mean_ok_test(1,:)*100)
+    end
+
+end
+
+end
+
+function [out, param_hash,idx] = sameparamsubset(in, ignoret,ignoref)
+    % ---
+    % build index of all existing configurations
+    % ---
+    param_hash = cell(numel(in),1);
+    for i=1:numel(in)
+        params = struct('trainparams',in(i).trainparams, ...
+                        'fparams',in(i).fparams);
+
+        % remove the dataset param
+        if ~isempty(ignoret)
+            params.trainparams = rmfield(params.trainparams,ignoret);
+        end
+        if ~isempty(ignoref)
+            params.fparams = rmfield(params.fparams,ignoref);
+        end
+
+        phash = hash(xml_format(params),'md5');          
+        param_hash{i} = phash;
+    end
+
+    % ---
+    %  recombine the data for different datasets!
+    % ---
+    cvals = unique(param_hash);
+
+    out = {};
+    for ci=1:numel(cvals)
+        idx{ci} = strcellfind(param_hash,cvals(ci),1);
+        out{ci} = in(idx{ci});
+    end
+
+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