SVMCrossVal.git

@@ -1,100 +0,0 @@

-function classify(varargin)

-

-

-

-switch nargin

-    case 1

-        paramModel = varargin{1};

-        % PROJECT_BASE_PATH = 'D:\Analyze\Stimolos';

-        PROJECT_BASE_PATH = 'D:\Analyze\Choice\24pilot';

-        PROJECT_RESULT_PATH = 'results\SPM.mat';

-    otherwise

-        error('spmtoolbox:SVMCrossVal:arginError','Please Specify action and parameter model');

-end

-

-        

-        % common params

-        calculateParams  = struct;

-        calculateParams.smoothed        = getDouble(paramModel.txtSmoothed);

-

-        calculateParams.frameShiftStart = getDouble(paramModel.txtFrameShiftStart);  % -20;

-        calculateParams.frameShiftEnd   = getDouble(paramModel.txtFrameShiftEnd); %15;

-        calculateParams.decodeDuration  = getDouble(paramModel.txtFrameShiftDur);

-        calculateParams.psthStart       = getDouble(paramModel.txtPSTHStart); % -25;

-        calculateParams.psthEnd         = getDouble(paramModel.txtPSTHEnd); % 20;

-        calculateParams.baselineStart   = getDouble(paramModel.txtBaselineStart); % -22;

-        calculateParams.baselineEnd     = getDouble(paramModel.txtBaselineEnd); % -20;

-

-        calculateParams.svmargs         = get(paramModel.txtSVMopts,'String');

-        calculateParams.sessionList     = 1:3;

-

-        classStruct = parseClassDef(paramModel);

-        

-        

-        calculateParams.labelMap        = LabelMap(classStruct.labelCells , classStruct.conditionCells, 'auto'); % LabelMap({'<','>','<+<','>+>','<+>','>+<'},{-2,-1,1,2,3,4}); 0 is autolabel

-        calculateParams.classList       = getClasses(calculateParams.labelMap);

-        calculateParams.eventList       = classStruct.eventMatrix; %[9,11,13; 10,12,14];

-%         calculateParams.eventList       = getPSTEventMatrix(calculateParams.labelMap);

-        

-        subjectSelection = getSubjectIDString(paramModel);

-        decode = struct;

-        decode.decodePerformance = [];

-        decode.rawTimeCourse     = [];

-        

-        for subjectCell = subjectSelection

-            SubjectID = cell2mat(subjectCell);

-            namehelper = strcat('s',SubjectID); %Vars can not start with numbers.

-

-            display('loading SPM.mat ...');

-            spm = load(fullfile(PROJECT_BASE_PATH,SubjectID,PROJECT_RESULT_PATH));

-            display('... done.');

-

-            %% calculate

-            calculateParams.(namehelper).des             = spm.SPM;

-            calculateParams.(namehelper).voxelList       = parseVoxelList(paramModel,SubjectID);

-            assignin('base','calculateParams',calculateParams);

-

-            display(sprintf('calculating cross-validation performance time-shift for Subject %s. Please Wait. ...',SubjectID));

-            display('switching off all warnings');

-            warning_state               = warning('off','all');

-            display('calculating ...');

-            decode.(namehelper)         = calculateDecodePerformance(calculateParams,SubjectID);

-            display('... done');

-            display('restoring warnings');

-            warning(warning_state);

-            

-            decode.decodePerformance    = [decode.decodePerformance decode.(namehelper).decodePerformance];

-            decode.rawTimeCourse        = [decode.rawTimeCourse decode.(namehelper).rawTimeCourse];

-

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

-        end

-

-        display('Finished calculations.');

-        display('Plotting...');

-

-        plotParams                  = struct;

-        plotParams.psthStart        = calculateParams.psthStart;

-        plotParams.psthEnd   =  calculateParams.psthEnd;

-        plotParams.nClasses  = length(calculateParams.classList);

-        

-        plotParams.frameShiftStart   = calculateParams.frameShiftStart;

-        plotParams.frameShiftEnd     = calculateParams.frameShiftEnd;

-        plotParams.decodePerformance = decode.decodePerformance;

-        plotParams.rawTimeCourse     = decode.rawTimeCourse;

-        

-        if numel(subjectSelection) == 1

-          plotParams.SubjectID         = SubjectID;

-        else

-          plotParams.SubjectID         = 'Multiple';

-        end

-

-        plotParams.smoothed          = boolToYesNoString(calculateParams.smoothed);

-         

-

-        assignin('base','plotParams',plotParams);

-%         plotDecodePerformance(params.psthStart,params.psthEnd,params.nClasses,decode.decodeTable,params.frameShiftStart,params.frameShiftEnd,decode.rawTimeCourse);

-        plotDecodePerformance(plotParams);

-            

-        display('all done.');

-

-    end

\ No newline at end of file

@@ -1,103 +0,0 @@

-function plotDecodePerformance(varargin)

-% plotDecodePerformance(timeline,decodePerformance,nClasses,rawData)

-

-PSTH_AXIS_MIN = -1;

-PSTH_AXIS_MAX = 1;

-

-switch nargin

-    

-    case 1

-        inputStruct       = cell2mat(varargin(1));

-

-        psthStart         = inputStruct.psthStart;

-        psthEnd           = inputStruct.psthEnd;

-        nClasses          = inputStruct.nClasses;

-        decodePerformance = inputStruct.decodePerformance;

-        frameStart        = inputStruct.frameShiftStart;

-        frameEnd          = inputStruct.frameShiftEnd;

-        psth              = inputStruct.rawTimeCourse;

-        SubjectID         = inputStruct.SubjectID;

-        smoothed          = inputStruct.smoothed;

-

-    otherwise

-        error('spmtoolbox:SVMCrossVal:plotDecodePerformance:WrongArgument','Wrong Arguments');

-end

-

-    f = figure;

-    subplot(2,1,1);

-    hold on;

-      for voxel = 1:size(psth,2)

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

-              psthData = [];

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

-                  psthData = nanmean(psth{voxel}{label});

-              end

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

-          end

-      end

-    axis([psthStart psthEnd PSTH_AXIS_MIN PSTH_AXIS_MAX])

-    hold off

-    

-    subplot(2,1,2)    

-    hold on;

-    

-    chanceLevel = 100/nClasses;

-    goodPredictionLevel = chanceLevel*1.5;

-    plot([psthStart psthEnd],[chanceLevel chanceLevel],'r');

-    plot([psthStart psthEnd],[goodPredictionLevel goodPredictionLevel],'g');

-    axis([psthStart psthEnd 0 100])

-    

-    plot(frameStart:frameEnd, mean(decodePerformance,2) ,'b');

-    PLOT_STD_ERR = 1;

-    PLOT_CLASS_PERFORMANCE = 1;

-    if PLOT_STD_ERR 

-        se = myStdErr(decodePerformance,2);

-        plot(frameStart:frameEnd, mean(decodePerformance,2)+se ,'b:');

-        plot(frameStart:frameEnd, mean(decodePerformance,2)-se ,'b:');

-    end

-    if PLOT_CLASS_PERFORMANCE

-        for c = 1:nClasses

-            plot(frameStart:frameEnd, decodePerformance() ,[colorChooser(c+2) '-']);

-        end

-    end

-    

-    

-    hold off;

-

-    title = sprintf('Subject %s, over %g voxel, smoothed %s',SubjectID,size(psth,2),smoothed);

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

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

-

-

-

-end

-

-

-function color = colorChooser(n)

-    switch (mod(n,8))

-    case 0

-        color = 'y';

-    case 1

-        color = 'r';

-    case 2

-        color = 'b';

-    case 3

-        color = 'g';

-    otherwise

-        color = 'k';

-    end

-end

-

-function style = lineStyleChooser(n)

-switch(mod(n,4))

-    case 0

-      style = '--';

-    case 1

-        style = '-';

-    case 2 

-        style = ':';

-    case 3

-        style = '-.';

-end

-end

-

@@ -3,9 +3,10 @@ function outputStruct = calculateDecodePerformance(inputStruct,SubjectID)

 addpath 'libsvm-mat-2.88-1';

-METHOD = 'single subject SVM';

-% METHOD = 'cross subject SVM';

-% METHOD = 'SOM';

+METHOD_DEF = inputStruct.CROSSVAL_METHOD_DEF;

+METHOD     = inputStruct.CROSSVAL_METHOD;

+

+RANDOMIZE_DATAPOINTS = inputStruct.RANDOMIZE;

 outputStruct = struct;

@@ -49,7 +50,7 @@ maxPerformance = -inf;

         decodePerformance = [];

         for index = 1:timeLineEnd-timeLineStart+1

-            RANDOMIZE_DATAPOINTS = 0;

+

             svmdata      = timePointMatrix{index}(:,2:size(timePointMatrix{index},2));

             svmlabel     = timePointMatrix{index}(:,1);

@@ -59,16 +60,18 @@ maxPerformance = -inf;

                 svmlabel  = svmlabel(rndindex);

             end

-            SVM_METHOD = 'som training'

-            switch SVM_METHOD;

-                case 'libsvm crossval'

+            switch METHOD;

+                case CROSSVAL_METHOD_DEF.svmcrossval

+                    

                     performance  = svmtrain(svmlabel, svmdata, svmargs);

                     minPerformance = min(minPerformance,performance);

                     maxPerformance = max(maxPerformance,performance);

                     decodePerformance = [decodePerformance; performance];

-                case 'class performance'

+                    

+                case CROSSVAL_METHOD_DEF.classPerformance

+                    

                     newsvmopt = killCrossvalOpt(svmargs);

                     model = svmtrain(svmlabel,svmdata,newsvmopt);

@@ -84,7 +87,8 @@ maxPerformance = -inf;

                     end

                     decodePerformance = [decodePerformance; classperformance];

-                case 'som training'

+                case CROSSVAL_METHOD_DEF.somTraining

+                    

                     display('SOM TRAINING');

                     addpath 'somtoolbox2';

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

@@ -93,7 +97,7 @@ maxPerformance = -inf;

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

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

-                    

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

             end

         end

@@ -1,5 +1,10 @@

 function classify(varargin)

+CROSSVAL_METHOD_DEF.svmcrossval       = 'svm crossval';

+CROSSVAL_METHOD_DEF.classPerformance  = 'svm class performance';

+CROSSVAL_METHOD_DEF.crossSubject      = 'svm cross subject testing';

+CROSSVAL_METHOD_DEF.somTraining       = 'som Training';

+

 switch nargin

     case 1

         paramModel = varargin{1};

@@ -12,6 +17,12 @@ end

         % common params

         calculateParams  = struct;

+        

+        calculateParams.CROSSVAL_METHOD_DEF = CROSSVAL_METHOD_DEF;

+        calculateParams.CROSSVAL_METHOD     = CROSSVAL_METHOD_DEF.svmcrossval;

+        

+        calculateParams.RANDOMIZE       = 0;

+        

         calculateParams.smoothed        = getChkValue(paramModel.chkSmoothed);

         calculateParams.frameShiftStart = getDouble(paramModel.txtFrameShiftStart);  % -20;

@@ -46,7 +57,7 @@ end

             spm = load(fullfile(PROJECT_BASE_PATH,SubjectID,PROJECT_RESULT_PATH));

             display('... done.');

-            %% calculate

+            % calculate

             calculateParams.(namehelper).des             = spm.SPM;

             calculateParams.(namehelper).voxelList       = parseVoxelList(paramModel,SubjectID);

             assignin('base','calculateParams',calculateParams);