How to make multiple output from SVMStruct = [ svmtrain(d​ata(train,​:),Y(train​),'Kernel_​Function',​'linear','​BoxConstra​int',1,'sh​owplot',tr​ue); ] for example i need more than 1 input after the boxconstraint

clear all, close all, clc

   %%LOAD SALIVA DATA (ROI_nobase)
  myfile=uigetfile('*.mat','MAT-files (*.mat)');
  my_data=load(myfile);
  mixture = struct2cell (my_data);
  mixture = cell2mat (mixture);

%%LOAD MIXTURE DATA (ROI_nobase1)
myfile=uigetfile('*.mat','MAT-files (*.mat)');
my_data=load(myfile); 
saliva = struct2cell (my_data);
saliva = cell2mat (saliva);
spot = input('insert no of PC=')
N=size(saliva,1);
N1=size(mixture,1);

data=[saliva(:,1:spot);mixture(:,1:spot)];

%  data=[saliva;mixture];
 Ndata=size(data,1);

   labelsaliva={'saliva'};labelmix={'mixture'};

   Y=nominal([repmat(labelsaliva,N,1);repmat(labelmix,N1,1)]);

   K = input('insert K-fold=')
   indices = crossvalind('Kfold',Y,K);

       for i = 1:K
           figure
           foldNo=i
           test = (indices == i);
           train = ~test;

           SVMStruct = svmtrain(data(train,:),Y(train),'Kernel_Function','linear','BoxConstraint',1,'showplot',true);
Group = svmclassify(SVMStruct,data(test,:),'showplot', true);
hold off;

grp1=Y(test);
testset=data(test,:);

hold on
mydiff = (Group == grp1(:,1)); % classified correctly
for j = mydiff
plot ( testset (j ,1) , testset (j ,2) ,'ro ', 'MarkerSize' ,15)
end

for j = not( mydiff ) % check plot black circles misclass
plot ( testset (j ,1) , testset (j ,2) ,'bo ', 'MarkerSize' ,15)
end
hold off;
cp = classperf(grp1);
classperf(cp,Group)
cp.CorrectRate
hold off;
conMat = confusionmat(grp1,Group)
hold on;

% Group = double(Group);
% [X1,Y1,T,AUC] = perfcurve(Y(test),Group,'saliva');

% plot(X1,Y1)
% xlabel('False positive rate'); ylabel('True positive rate')
% title('ROC for classification by SVM')
% Group = nominal(Group)
     end

%

and the answer that i need will be more than 1 (KFOLD) like this

insert no of PC=2

spot =

       2

insert K-fold=3

K =

       3

foldNo =

       1

                          Label: ''
                    Description: ''
                    ClassLabels: {2x1 cell}
                    GroundTruth: [42x1 double]
           NumberOfObservations: 42
                 ControlClasses: 2
                  TargetClasses: 1
              ValidationCounter: 1
             SampleDistribution: [42x1 double]
              ErrorDistribution: [42x1 double]
      SampleDistributionByClass: [2x1 double]
       ErrorDistributionByClass: [2x1 double]
                 CountingMatrix: [3x2 double]
                    CorrectRate: 0.9762
                      ErrorRate: 0.0238
                LastCorrectRate: 0.9762
                  LastErrorRate: 0.0238
               InconclusiveRate: 0
                 ClassifiedRate: 1
                    Sensitivity: 1
                    Specificity: 0.9524
        PositivePredictiveValue: 0.9545
        NegativePredictiveValue: 1
             PositiveLikelihood: 21
             NegativeLikelihood: 0
                     Prevalence: 0.5000
                DiagnosticTable: [2x2 double]

ans =

      0.9762

conMat =

      20     1
       0    21

foldNo =

       2

                          Label: ''
                    Description: ''
                    ClassLabels: {2x1 cell}
                    GroundTruth: [43x1 double]
           NumberOfObservations: 43
                 ControlClasses: 2
                  TargetClasses: 1
              ValidationCounter: 1
             SampleDistribution: [43x1 double]
              ErrorDistribution: [43x1 double]
      SampleDistributionByClass: [2x1 double]
       ErrorDistributionByClass: [2x1 double]
                 CountingMatrix: [3x2 double]
                    CorrectRate: 0.9302
                      ErrorRate: 0.0698
                LastCorrectRate: 0.9302
                  LastErrorRate: 0.0698
               InconclusiveRate: 0
                 ClassifiedRate: 1
                    Sensitivity: 1
                    Specificity: 0.8636
        PositivePredictiveValue: 0.8750
        NegativePredictiveValue: 1
             PositiveLikelihood: 7.3333
             NegativeLikelihood: 0
                     Prevalence: 0.4884
                DiagnosticTable: [2x2 double]

ans =

      0.9302

conMat =

      19     3
       0    21

foldNo =

       3

                          Label: ''
                    Description: ''
                    ClassLabels: {2x1 cell}
                    GroundTruth: [43x1 double]
           NumberOfObservations: 43
                 ControlClasses: 2
                  TargetClasses: 1
              ValidationCounter: 1
             SampleDistribution: [43x1 double]
              ErrorDistribution: [43x1 double]
      SampleDistributionByClass: [2x1 double]
       ErrorDistributionByClass: [2x1 double]
                 CountingMatrix: [3x2 double]
                    CorrectRate: 0.9535
                      ErrorRate: 0.0465
                LastCorrectRate: 0.9535
                  LastErrorRate: 0.0465
               InconclusiveRate: 0
                 ClassifiedRate: 1
                    Sensitivity: 0.9545
                    Specificity: 0.9524
        PositivePredictiveValue: 0.9545
        NegativePredictiveValue: 0.9524
             PositiveLikelihood: 20.0455
             NegativeLikelihood: 0.0477
                     Prevalence: 0.5116
                DiagnosticTable: [2x2 double]

ans =

      0.9535

conMat =

      20     1
       1    21

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