%% subject loop function decode = xsvm_subject_loop(header,subjectdata,svmopts) nSubjects = numel(subjectdata); decode = struct; decode.decodePerformance = []; decode.rawTimeCourse = []; disp(sprintf('we have %g subjects. Press ANY-Key to continue.\n Use Retrun if your Keyboard lacks the ANY-Key.',nSubjects)); pause timeline = header.timeline; for subjectDataID = 1:nSubjects currentSubject = subjectdata{subjectDataID}; timePointArgs.pst = currentSubject.pst; timePointArgs.labelMap = LabelMap(header.classDef.labelCells,header.classDef.conditionCells); timePointArgs.eventList = header.classDef.eventMatrix; timePointMatrix{subjectDataID} = buildTimePointMatrix(timeline,timePointArgs); end timeLineStart = timeline.frameShiftStart; timeLineEnd = timeline.frameShiftEnd; addpath 'libsvm-mat-2.88-1'; for timeIndex = 1:timeLineEnd-timeLineStart+1 svm_train_label = []; svm_train_data = []; svm_validation_label = []; svm_validation_data = []; for validationSubjectID = 1:nSubjects for subjectDataID = 1:nSubjects svmstruct = calculateSVMTables(timePointMatrix{subjectDataID},timeIndex); if subjectDataID == validationSubjectID svm_validation_label = svmstruct.svmlabel; svm_validation_data = svmstruct.svmdata; else svm_train_label = [svm_train_label; svmstruct.svmlabel]; svm_train_data = [svm_train_data; svmstruct.svmdata]; end end svmmodel = svmtrain(svm_train_label,svm_train_data,svmopts); [plabel accuracy dvalue] = svmpredict(svm_validation_label,svm_validation_data,svmmodel,''); accuracy(1) % decode.(namehelper) = calculateDecodePerformance(header,currentSubject,svmopts); % % display('... done'); % display('restoring warnings'); % warning(warning_state); % % decode.decodePerformance = [decode.decodePerformance decode.(namehelper).decodePerformance]; % decode.rawTimeCourse = [decode.rawTimeCourse decode.(namehelper).rawTimeCourse]; % assignin('base','decode',decode); % if RANDOMIZE_DATAPOINTS % rndindex = randperm(length(svmlabel)); % svmdata = svmdata(rndindex,:); % svmlabel = svmlabel(rndindex); % end end end end