SVMCrossVal.git

1) %% subject loop
2) function decode = som_xsubject_performance(header,subjectdata,somOpts)
3) 

4) addpath(fullfile(getTbxPath,'somtoolbox2'));

5) 
6) nSubjects = numel(subjectdata);
7) if(nSubjects < 2) 
8)     error('SVMCrossVal:somXSubjectPerformance:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');
9) end
10) 

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

13) 
14) decode = struct;
15) decode.decodePerformance = [];
16) decode.rawTimeCourse     = [];
17) 
18) disp(sprintf('computinig additional datastructs for %u subjects',nSubjects));
19) 
20) timeline = header.timeline;
21) timeline.frameShiftStart = header.frameShift.frameShiftStart;
22) timeline.frameShiftEnd   = header.frameShift.frameShiftEnd;
23) timeline.decodeDuration  = header.frameShift.decodeDuration;
24) 
25) % TimePointMatrix
26) for subjectDataID = 1:nSubjects
27)     currentSubject = subjectdata{subjectDataID};
28)     timePointArgs.pst           = currentSubject.pst;
29)     timePointArgs.labelMap      = LabelMap(header.classDef.labelCells,header.classDef.conditionCells);
30)     timePointArgs.eventList     = header.classDef.eventMatrix;
31) 
32)     timePointMatrix{subjectDataID} = buildTimePointMatrix(timeline,timePointArgs);
33)     
34)     decode.rawTimeCourse = [decode.rawTimeCourse currentSubject.pst];
35) end
36) 
37) % timeframe x-subject validation
38) timeLineStart   = timeline.frameShiftStart;
39) timeLineEnd     = timeline.frameShiftEnd;
40) 
41) display(sprintf('%u -fold cross validation for %u timeslices.\n',nSubjects,size(1:timeLineEnd-timeLineStart+1,2)));
42) % disp(sprintf('Press ANY-Key to continue.\n Use Retrun if your Keyboard lacks the ANY-Key.'));
43) % pause
44) 
45) for timeIndex = 1:timeLineEnd-timeLineStart+1
46)     cross_value = [];
47)     for validationSubjectID = 1:nSubjects
48)         svm_train_label = [];
49)         svm_train_data  = [];
50)         svm_validation_label = [];
51)         svm_validation_data  = [];
52)         for subjectDataID = 1:nSubjects
53)             svmstruct = calculateSVMTables(timePointMatrix{subjectDataID},timeIndex);
54)             if subjectDataID == validationSubjectID
55)                 svm_validation_label = svmstruct.svmlabel;
56)                 svm_validation_data  = svmstruct.svmdata;
57)             else
58)                 svm_train_label = [svm_train_label; svmstruct.svmlabel];
59)                 svm_train_data  = [svm_train_data;  svmstruct.svmdata];
60)             end
61)         end
62)         
63)         if RANDOMIZE_DATAPOINTS
64)             rndindex  = randperm(length(svm_train_label));
65)             svm_train_data   = svm_train_data(rndindex,:);
66)             svm_train_label  = svm_train_label(rndindex);
67)         end
68) 

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

77) 

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