SVMCrossVal.git

1) %% subject loop
2) function decode = xsvm_subject_loop(header,subjectdata,svmopts)

3) global NODALYZE_LIBSVM;
4) addpath(fullfile(getTbxPath,NODALYZE_LIBSVM));

5) 

6) nSubjects = numel(subjectdata);

7) if(nSubjects < 2) 
8)     error('SVMCrossVal:xsvmSubjectLoop:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');
9) end

10) 

11) RANDOMIZE_DATAPOINTS = header.svmrnd;
12) NAN_AS_ZERO = header.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) 

74)         svmmodel = svmtrain(svm_train_label,svm_train_data,svmopts);
75)         
76)         [plabel accuracy dvalue] = svmpredict(svm_validation_label,svm_validation_data,svmmodel,'');

77)         cross_value = [cross_value accuracy(1)];

78)         

79)     end

80)     decode.decodePerformance = [decode.decodePerformance; cross_value];

81)     

82) end

83) 
84) disp('decode done');