SVMCrossVal.git

@@ -133,14 +133,17 @@ switch task

         assignin('base','decode',decode);

     case 'SOM'

         display('SOM');

-        somopts = '';

-        decode = som_combined_subject_batch(header,data,somopts);

+        disp('not implemented')

+

+    case 'X-SOM'

+        display('X-SOM');

+        somopts.size = [3 3];

+        somopts.lattice = 'rect';

+        decode = som_xsubject_performance(header,data,somopts);

         decode.header = header;

         assignin('base','decode',decode);

-    case 'X-SOM'

-        disp('not implemented')

     case 'FBS'

-        disp('this method is under high development.')

+        display('FBS');

         fbsargs.svmopts = getSvmArgs(model,1);

         header.svmrnd   = getSvmRnd(model);

         fbsargs.radius  = getSearchlightRadius(model);

@@ -1,7 +1,7 @@

 function output =  runFBSImageMaskMode(header,subjectdata,fbsargs)

 addpath('NIFTI_20090325');

-savePath = fullfile('output',sprintf('%s',date))

+savePath = fullfile('output',datestr(now,30));

 mkdir(savePath);

 args = header.args;

@@ -172,6 +172,7 @@ for s = 1:nSubjects

     end %for each timeslice

     display('rastering done');

+    display(sprintf('result images saved to %s',savePath));

     elapsed{s}.decode = toc;

     assignin('base','timing',elapsed);

@@ -1,12 +1,15 @@

 %% subject loop

 function decode = som_combined_subject_batch(header,subjectdata,somOpts)

+somOpts.size = [3 3];

+somOpts.lattice = 'rect';

+

 addpath 'somtoolbox2';

-nSubjects = numel(subjectdata);

-if(nSubjects < 2) 

-    error('SVMCrossVal:xsvmSubjectLoop:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');

-end

+% nSubjects = numel(subjectdata);

+% if(nSubjects < 2) 

+%     error('SVMCrossVal:xsvmSubjectLoop:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');

+% end

 RANDOMIZE_DATAPOINTS = 1;

@@ -42,17 +45,22 @@ display(sprintf('%u -fold cross validation for %u timeslices.\n',nSubjects,size(

 % 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 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

         if RANDOMIZE_DATAPOINTS

             rndindex  = randperm(length(svm_train_label));

@@ -60,15 +68,13 @@ for timeIndex = 1:timeLineEnd-timeLineStart+1

             svm_train_label  = svm_train_label(rndindex);

         end

-        [sD sM] = train_som(svmlabel, svmdata, somOpts)

+        [sD sM] = train_som(svm_train_label, svm_train_data, somOpts);

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

-        cross_value = [cross_value accuracy(1)];

+        performance = som_decode(sD, sM, svm_validation_data,svm_validation_label);

-    end

+        cross_value = [cross_value performance];

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

-    

 end

-

-disp('decode done');

+   display('decode done'); 

 end

+

@@ -0,0 +1,6 @@

+function decode = som_decode(sD, sM, svm_validation_data,svm_validation_label)

+% the number of hits in each map-unit.

+

+sValD = som_data_struct(svm_validation_data,'label',num2str(svm_validation_label));

+h   = som_hits(sM,sValD);

+bmu = som_bmus(sM, sValD, 'best');

\ No newline at end of file

@@ -0,0 +1,17 @@

+function [sD sM] = som_train(svmlabel, svmdata, somOptions)

+

+som_size = somOptions.size;

+som_lattice = somOptions.lattice;

+

+display('SOM TRAINING');

+addpath 'somtoolbox2';

+sD = som_data_struct(svmdata,'label',num2str(svmlabel));

+assignin('base','sD',sD);

+sM = som_make(sD,'msize', som_size,'lattice', som_lattice);

+

+sM = som_autolabel(sM,sD,'vote');

+

+assignin('base','sD',sD);

+assignin('base','sM',sM);

+display('type ''figure'' before visualisation');

+end

\ No newline at end of file

@@ -0,0 +1,79 @@

+%% subject loop

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

+

+addpath 'somtoolbox2';

+

+nSubjects = numel(subjectdata);

+if(nSubjects < 2) 

+    error('SVMCrossVal:somXSubjectPerformance:tooFewSubjects','You need at least 2 Subjects in this Across-Subject analysis!');

+end

+

+RANDOMIZE_DATAPOINTS = 1;

+

+decode = struct;

+decode.decodePerformance = [];

+decode.rawTimeCourse     = [];

+

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

+

+timeline = header.timeline;

+timeline.frameShiftStart = header.frameShift.frameShiftStart;

+timeline.frameShiftEnd   = header.frameShift.frameShiftEnd;

+timeline.decodeDuration  = header.frameShift.decodeDuration;

+

+% TimePointMatrix

+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);

+    

+    decode.rawTimeCourse = [decode.rawTimeCourse currentSubject.pst];

+end

+

+% timeframe x-subject validation

+timeLineStart   = timeline.frameShiftStart;

+timeLineEnd     = timeline.frameShiftEnd;

+

+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 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

+        

+        if RANDOMIZE_DATAPOINTS

+            rndindex  = randperm(length(svm_train_label));

+            svm_train_data   = svm_train_data(rndindex,:);

+            svm_train_label  = svm_train_label(rndindex);

+        end

+

+        [sD sM] = som_train(svm_train_label, svm_train_data, somOpts);

+

+        performance = som_decode(sD, sM, svm_validation_data,svm_validation_label);

+        

+        cross_value = [cross_value performance];

+    end

+        

+    decode.decodePerformance = [decode.decodePerformance; cross_value];

+end

+   display('decode done'); 

+end

+

@@ -1,10 +1,15 @@

 function [sD sM] = train_som(svmlabel, svmdata, somOptions)

+som_size = somOptions.size;

+som_lattice = somOptions.lattice;

+

 display('SOM TRAINING');

 addpath 'somtoolbox2';

 sD = som_data_struct(svmdata,'label',num2str(svmlabel));

 assignin('base','sD',sD);

-sM = som_make(sD,'msize', [3 3],'lattice', 'hexa');

+sM = som_make(sD,'msize', som_size,'lattice', som_lattice);

+

+sM = som_autolabel(sM,sD,'vote');

 assignin('base','sD',sD);

 assignin('base','sM',sM);

@@ -335,7 +335,7 @@ pSOM = uipanel(parent,'Units','normalized','Position',[0.5 0.4 0.5 0.4]);

     set(pSOM,'Title','SOM Classification');

     set(pSOM,'BackgroundColor',basecolor);

-    model.txtSOMopts = createTextField(pSOM,[0 0.75 1 0.25],'4x3 rect');

+    model.txtSOMopts = createTextField(pSOM,[0 0.75 1 0.25],'3x3 rect');

     set(model.txtSOMopts,'HorizontalAlignment','left');

     set(model.txtSOMopts,'Enable','off');

@@ -351,7 +351,7 @@ pSOM = uipanel(parent,'Units','normalized','Position',[0.5 0.4 0.5 0.4]);

     btnRunXSOM = uicontrol(pSOM,'String','run SOM X-Subject validation',...

         'Units','normalized',...

         'Position',[0.0 0.0 1 0.25]);

-    set(btnRunXSOM,'Enable','off');

+    set(btnRunXSOM,'Enable','on');

 pSearchlight = uipanel(parent,'Units','normalized','Position',[0.0 0.1 1 0.3]);

     set(pSearchlight,'Title','Spatiotemporal FB classification');