SVMCrossVal.git

@@ -20,12 +20,15 @@ timeline = header.timeline;

         smoothed          = 'yes';

         PLOT_METHOD       = SVMCROSSVAL_CROSSVAL_METHOD_DEF.svmcrossval;

+        PLOT_METHOD       = SVMCROSSVAL_CROSSVAL_METHOD_DEF.classPerformance;

 %         CROSSVAL_METHOD_DEF = inputStruct.CROSSVAL_METHOD_DEF;

     f = figure;

     subplot(2,1,1);

     hold on;

+    size(psth)

+    if (size(psth) > 0)

       for voxel = 1:size(psth,2)

           for label = 1:size(psth{voxel},2)

               psthData = [];

@@ -35,6 +38,7 @@ timeline = header.timeline;

               plot(psthStart:psthEnd,psthData,[colorChooser(voxel), lineStyleChooser(label)]);

           end

       end

+    end

     axis([psthStart psthEnd PSTH_AXIS_MIN PSTH_AXIS_MAX])

     xlabel('time [sec]');

     ylabel('fMRI-signal change [%]');

@@ -7,9 +7,7 @@ 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

-

+disp(sprintf('computinig additional datastructs for %u subjects',nSubjects));

 timeline = header.timeline;

@@ -27,13 +25,16 @@ timeLineEnd     = timeline.frameShiftEnd;

 addpath 'libsvm-mat-2.88-1';

+display(sprintf('%u -fold cross validation for %u timeslices.\n',nSubjects,size(1:timeLineEnd-timeLineStart+1,2)));

+disp(sprintf('Press ANY-Key to continue.\n Use Retrun if your Keyboard lacks the ANY-Key.'));

+pause

 for timeIndex = 1:timeLineEnd-timeLineStart+1

+    cross_value = [];

+    for validationSubjectID = 1:nSubjects

         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

@@ -45,11 +46,17 @@ for timeIndex = 1:timeLineEnd-timeLineStart+1

             end

         end

+%         display(sprintf('Time %u: validation subject: %u, validation set size %g, training set size %g with %u subjects',...

+%             timeIndex, validationSubjectID, numel(svm_validation_label), numel(svm_train_label),nSubjects-1));

+        

         svmmodel = svmtrain(svm_train_label,svm_train_data,svmopts);

         [plabel accuracy dvalue] = svmpredict(svm_validation_label,svm_validation_data,svmmodel,'');

+        cross_value = [cross_value accuracy(1)];

-        accuracy(1)

+    end

+    decode.decodePerformance = [decode.decodePerformance mean(cross_value)];

+%     decode.rawTimeCourse = [decode.rawTimeCourse cross_value];

 %         decode.(namehelper)         = calculateDecodePerformance(header,currentSubject,svmopts);

 % 

@@ -68,6 +75,6 @@ for timeIndex = 1:timeLineEnd-timeLineStart+1

         %         svmlabel  = svmlabel(rndindex);

         %         end

-    end

+    

 end

 end