SVMCrossVal.git

1) function decode = som_xsubject_performance(header,subjectdata,somOpts)

2) global NODALYZE_SOMTOOLBOX;
3) addpath(fullfile(getTbxPath,NODALYZE_SOMTOOLBOX));

4) 
5) nSubjects = numel(subjectdata);
6) if(nSubjects < 2) 
7)     error('SVMCrossVal:somXSubjectPerformance:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');
8) end
9) 

10) RANDOMIZE_DATAPOINTS = somOpts.rnd;
11) NAN_AS_ZERO = somOpts.nantozero;

12) 
13) decode = struct;
14) decode.decodePerformance = [];
15) decode.rawTimeCourse     = [];
16) 
17) disp(sprintf('computinig additional datastructs for %u subjects',nSubjects));
18) 
19) timeline = header.timeline;
20) timeline.frameShiftStart = header.frameShift.frameShiftStart;
21) timeline.frameShiftEnd   = header.frameShift.frameShiftEnd;
22) timeline.decodeDuration  = header.frameShift.decodeDuration;
23) 
24) % TimePointMatrix
25) for subjectDataID = 1:nSubjects
26)     currentSubject = subjectdata{subjectDataID};
27)     timePointArgs.pst           = currentSubject.pst;
28)     timePointArgs.labelMap      = LabelMap(header.classDef.labelCells,header.classDef.conditionCells);
29)     timePointArgs.eventList     = header.classDef.eventMatrix;
30) 
31)     timePointMatrix{subjectDataID} = buildTimePointMatrix(timeline,timePointArgs);
32)     
33)     decode.rawTimeCourse = [decode.rawTimeCourse currentSubject.pst];
34) end
35) 
36) % timeframe x-subject validation
37) timeLineStart   = timeline.frameShiftStart;
38) timeLineEnd     = timeline.frameShiftEnd;
39) 
40) display(sprintf('%u -fold cross validation for %u timeslices.\n',nSubjects,size(1:timeLineEnd-timeLineStart+1,2)));
41) % disp(sprintf('Press ANY-Key to continue.\n Use Retrun if your Keyboard lacks the ANY-Key.'));
42) % pause
43) 
44) for timeIndex = 1:timeLineEnd-timeLineStart+1
45)     cross_value = [];
46)     for validationSubjectID = 1:nSubjects
47)         svm_train_label = [];
48)         svm_train_data  = [];
49)         svm_validation_label = [];
50)         svm_validation_data  = [];
51)         for subjectDataID = 1:nSubjects
52)             svmstruct = calculateSVMTables(timePointMatrix{subjectDataID},timeIndex);
53)             if subjectDataID == validationSubjectID
54)                 svm_validation_label = svmstruct.svmlabel;
55)                 svm_validation_data  = svmstruct.svmdata;
56)             else
57)                 svm_train_label = [svm_train_label; svmstruct.svmlabel];
58)                 svm_train_data  = [svm_train_data;  svmstruct.svmdata];
59)             end
60)         end
61)         
62)         if RANDOMIZE_DATAPOINTS
63)             rndindex  = randperm(length(svm_train_label));
64)             svm_train_data   = svm_train_data(rndindex,:);
65)             svm_train_label  = svm_train_label(rndindex);
66)         end
67) 

68)         if NAN_AS_ZERO
69)             svm_train_data(isnan(svm_train_data))=0;
70)         end
71)         
72)         if isempty(svm_train_data)
73)             performance = 0;
74)         else
75)             [sD sM] = som_train(svm_train_label, svm_train_data, somOpts);

76) 

77)             performance = som_decode(sM, svm_validation_data,svm_validation_label);
78)         end