SVMCrossVal.git

@@ -18,11 +18,7 @@ nSubjects         = size(SubjectID,2);

     f = figure;

-    subplot(2,1,1); 

-        plotPSTH(psth,psthStart,psthEnd);

-    % plot performance timeline

-    subplot(2,1,2)    

     hold on;

     chanceLevel = 100/nClasses;

     goodPredictionLevel = chanceLevel*1.5;

@@ -33,6 +29,8 @@ nSubjects         = size(SubjectID,2);

     ylabel('decode performance [%]');

     switch type

+        case 'psth'

+            plotPSTH(psth,psthStart,psthEnd);  

         case 'simple'

             plotDecodePerformanceWithSE(frameStart,frameEnd,decodePerformance)

         case 'class performance'

@@ -49,28 +47,6 @@ nSubjects         = size(SubjectID,2);

 end

-function setTitle(f,header,decode,subjectData)

-

-    nVoxelPerSubject = size(psth,2)/size(SubjectID,2);

-    

-    if strcmp(smoothed,'yes')

-        smoothedString = 'using smoothed data';

-    else

-        smoothedString = 'using unsmoothed data';

-    end

-    

-    if nSubjects == 1

-        subjectName = SubjectID{1}.name;

-        title = sprintf('Subject %s, over %g voxel, %s',subjectName,nVoxelPerSubject,smoothedString);

-    else

-        title = sprintf('%g Subjects, %g Voxel per Subject, %s',nSubjects,nVoxelPerSubject,smoothedString);

-    end

-    

-

-    set(f,'Name',title);

-    display(sprintf('%s',title));

-end

-

 function plotClassPerformance(frameStart,frameEnd,decodePerformance,nClasses)

 for c = 1:size(decodePerformance,2)

     plot(frameStart:frameEnd, decodePerformance(:,c) ,[colorChooser(mod(c,nClasses)+3) '-']);

@@ -109,6 +109,8 @@ for s = 1:nSubjects

     labelMap        = timePointArgs.labelMap;

     eventList       = pstopts.eventList;

+    res             = timeline.tr*timeline.trFactor;

+    

     % tmp = spm_imatrix(V(kImage).mat); %

     % vdim = tmp(7:9); % Voxel-Size

@@ -123,10 +125,11 @@ for s = 1:nSubjects

     display(sprintf('starting timesliceing'));

-    for timeShift = 1:timeLineEnd-timeLineStart+1

+    nSamplePoints = ((timeLineEnd-timeLineStart)/res) +1;

+    for timeShiftIdx = 1:nSamplePoints

     % center timepoint && relative shift

-    frameStart  = floor(-globalStart+1+timeShift - 0.5*decodeDuration);

-    frameEnd    = min(ceil(frameStart+decodeDuration + 0.5*decodeDuration),-globalStart+globalEnd);

+    frameStartIdx  = floor(-globalStart+1+timeShiftIdx - 0.5*decodeDuration);

+    frameEndIdx    = min(ceil(frameStart+decodeDuration + 0.5*decodeDuration),-globalStart+globalEnd);

         img3D = zeros(size(mask_image)); %output image prepare

@@ -143,7 +146,7 @@ for s = 1:nSubjects

                     svmlabel = [svmlabel; lm_getSVMLabel(labelMap,eventList(pstConditionGroup,1))];

                     row = [];

                     for voxel = 1:size(sphere,2)

-                        row = [row, pst{1,voxel}{1,pstConditionGroup}(dp,frameStart:frameEnd)]; % label,[value,value,...],[value,value,...]...

+                        row = [row, pst{1,voxel}{1,pstConditionGroup}(dp,frameStartIdx:frameEndIdx)]; % label,[value,value,...],[value,value,...]...

                     end

                     svmdata  = [svmdata; row];

                 end

@@ -168,8 +170,7 @@ for s = 1:nSubjects

         end  %for each voxel

         nii = make_nii(img3D,vdim,mask_image.hdr.hist.originator(1:3),16,sprintf('decode performance, time relative to onset: %g to %g sec',frameStart,frameEnd));

-        save_nii(nii,fullfile(savePath,sprintf('%s-%03g',subjects{s}.name,timeShift)));

-    

+        save_nii(nii,fullfile(savePath,sprintf('%s-%03g',subjects{s}.name,timeShiftIdx)));

     end %for each timeslice

     display('rastering done');

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

@@ -378,27 +378,38 @@ pSearchlight = uipanel(parent,'Units','normalized','Position',[0.0 0.1 1 0.3]);

 end

 function model = createVisualStepPanel(model,parent,DEFAULT)

+

+    grid_h = 0.25;

+    grid_w = 0.5;

+    plot_grid = cell([3 3]);

+    plot_grid{1,1} = [0.0 0.63 grid_w grid_h];

+    plot_grid{1,2} = [0.0 0.33 grid_w grid_h];

+    plot_grid{1,3} = [0.0 0.03 grid_w grid_h];

+    plot_grid{2,1} = [0.5 0.63 grid_w grid_h];

+    plot_grid{2,2} = [0.5 0.33 grid_w grid_h];

+    plot_grid{2,3} = [0.5 0.03 grid_w grid_h];

+

     pButtonPane = uipanel(parent,'Units','normalized','Position',[0 0.5 1 0.5]);

 %     set(pButtonPane,'Title','Plot');

     set(pButtonPane,'BackgroundColor','w');

     btnPlot01 = uicontrol(pButtonPane,'String','plot performance and SE',...

         'Units','normalized',...

-        'Position',[0.0 0.0 0.5 0.25]);

+        'Position',cell2mat(plot_grid(1,3)));

     set(btnPlot01,'Callback',{@cbPlot,model,'simple'});

     set(btnPlot01,'Enable','on');

     btnPlot02 = uicontrol(pButtonPane,'String','plot subject performance and mean with SE',...

         'Units','normalized',...

-        'Position',[0.5 0.0 0.5 0.25]);

+        'Position',cell2mat(plot_grid(2,3)));

     set(btnPlot02,'Callback',{@cbPlot,model,'x-subject-val'});

     set(btnPlot02,'Enable','on');

-%     btnPlot03 = uicontrol(pButtonPane,'String','plot class performance and mean',...

-%         'Units','normalized',...

-%         'Position',[0.0 0.5 0.5 0.25]);

-%     set(btnPlot03,'Callback',{@cbPlot,model,'class performance'}); 

-%     set(btnPlot03,'Enable','on');

+    btnPlot03 = uicontrol(pButtonPane,'String','plot PSTH',...

+        'Units','normalized',...

+        'Position',cell2mat(plot_grid(1,2)));

+    set(btnPlot03,'Callback',{@cbPlot,model,'psth'}); 

+    set(btnPlot03,'Enable','on');

 end