SVMCrossVal.git

@@ -5,6 +5,8 @@ switch task

         preprocess(model,subtask);

     case 'decode'

         decode(model,subtask);

+    case 'plot'

+        decode_plot(model,subtask);

 end

 end

 % disp('all warnings OFF')

@@ -101,7 +103,11 @@ end

 % warning('on',warn);

 end

-function decode_plot()

+function decode_plot(model,type)

+switch type

+    case 'SIMPLE'

+        

+end

 end

@@ -8,8 +8,7 @@ function subjectData = runCoordTable(args)

     nSubjects = numel(subjects);

     sessionlist = args.sessionList;

-    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('batch processing %g subjects.',nSubjects));

     for s = 1:nSubjects

         subjectStruct{s}.dir = fullfile(args.basedir,cell2mat(subjects(s)));

@@ -33,12 +33,12 @@ DEFAULT.wd  = fullfile('d:','Analyze','Choice','24pilot');

     task = struct;

-    model = struct;

-    model.baseDir = DEFAULT.wd;

+    model1 = struct;

+    model1.baseDir = DEFAULT.wd;

-    model.txtBaseDir = createLabel(frame,[0 0.97 1 0.03],model.baseDir);

-    set(model.txtBaseDir,'BackgroundColor','w');

-    set(model.txtBaseDir,'ForegroundColor','b');

+    model1.txtBaseDir = createLabel(frame,[0 0.97 1 0.03],model1.baseDir);

+    set(model1.txtBaseDir,'BackgroundColor','w');

+    set(model1.txtBaseDir,'ForegroundColor','b');

     TASK_HEIGHT = 1-0.13;

@@ -47,19 +47,24 @@ DEFAULT.wd  = fullfile('d:','Analyze','Choice','24pilot');

     set(task.preprocessing,'BackgroundColor','w');

     set(task.preprocessing,'Units','normalized');

-    model.selectedSubject = DEFAULT.selectedSubject;

-    model = createFirstStepPanel(model,task.preprocessing,DEFAULT);

+    model1.selectedSubject = DEFAULT.selectedSubject;

+    model1 = createFirstStepPanel(model1,task.preprocessing,DEFAULT);

+    % fill with data

+    model1 = scanDirs(model1);

     % CLASSIFICATION

+    model2 = struct;

+    

     task.classification = uipanel(frame,'Title','Classification','Position',[0 0.0 1 TASK_HEIGHT]);

     set(task.classification,'BackgroundColor','w');

-    model = createSecondStepPanel(model,task.classification,DEFAULT);

+    model2 = createSecondStepPanel(model2,task.classification,DEFAULT);

     % PLOT

+    model3 = struct;

     task.plot = uipanel(frame,'Title','Plot','Position',[0 0.0 1 TASK_HEIGHT]);

     set(task.plot,'BackgroundColor','w');

-    model = createVisualStepPanel(model,task.plot,DEFAULT);

+    model3 = createVisualStepPanel(model3,task.plot,DEFAULT);

     % TASK

     task.taskSwitch = uipanel(frame,'Position',[0 1-0.13 1 0.10]);

@@ -85,15 +90,18 @@ DEFAULT.wd  = fullfile('d:','Analyze','Choice','24pilot');

     % menu

     savemenu = uimenu(frame,'Label','Save/Load');

-        uimenu(savemenu,'Label','Save Preprocessing Parameter','Callback',{@mcb_save,model});

-        uimenu(savemenu,'Label','Load Preprocessing Parameter','Callback',{@mcb_load,model});

+        uimenu(savemenu,'Label','Save Preprocessing Parameter','Callback',{@mcb_save,model1});

+        uimenu(savemenu,'Label','Load Preprocessing Parameter','Callback',{@mcb_load,model1});

+        uimenu(savemenu,'Label','Save Decode Parameter','Callback',{@mcb_save,model2},'Enable','off');

+        uimenu(savemenu,'Label','Load Decode Parameter','Callback',{@mcb_load,model2},'Enable','off');

+        uimenu(savemenu,'Label','Save All','Callback',{@mcb_save,model1},'Enable','off');

+        uimenu(savemenu,'Label','Load All','Callback',{@mcb_load,model1},'Enable','off');

-    uimenu(frame,'Label','change Study','Callback',{@mcb_cd,model},'Enable','off');

+    uimenu(frame,'Label','change Study','Callback',{@mcb_cd,model1},'Enable','off');

     cbSwitchTask(0,0,'PRE',task);

-    % fill with data

-    model = scanDirs(model);

+

     set(frame,'Visible','on');

 %     assignin('base','model',model);

@@ -4,6 +4,9 @@ function decode = xsvm_subject_loop(header,subjectdata,svmopts)

 addpath 'libsvm-mat-2.88-1';

 nSubjects = numel(subjectdata);

+if(nSubjects < 2) 

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

+end

 RANDOMIZE_DATAPOINTS = 0;