How can I get the prediction matrix to use in voting between two classifiers?

function [result] = multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1)

%Models a given training set with a corresponding group vector and 
%classifies a given test set using an SVM classifier according to a 
%one vs. all relation. 
%
%This code was written by Cody Neuburger [e-mail]
%Florida Atlantic University, Florida USA...



%This code was adapted and cleaned from Anand Mishra's multisvm function
%found at http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/

u=unique(Group_Train1);
numClasses=length(u);

result = categorical.empty();

%build models
models = cell(numClasses,1);
for k=1:numClasses
    %Vectorized statement that binarizes Group
    %where 1 is the current class and 0 is all other classes
    G1vAll=(Group_Train1==u(k));
    models{k} = fitcsvm(TrainingSet,G1vAll,'KernelFunction','polynomial','polynomialorder',3,'Solver','ISDA','Verbose',0,'Standardize',true);

    if ~models{k}.ConvergenceInfo.Converged
        fprintf('Training did not converge for class "%s"\n', string(u(k)));
        
    end
    
    
end

%classify test cases
for t=1:size(TestSet,1)
   matched = false;
   for k = numClasses:-1:1
    % for k =1: numClasses
    
      if(predict(models{k},TestSet(t,: ))) 

         matched = true;
           break;
end

    end
   if matched
   result(t,1) = u(k);
   %result(t) = u(k);

   else
   result(t,1) = 'No Match';
    
%--------------------------------
   end
end
%Accuracy = mean(Group_Test1==result) * 100;
%fprintf('Accuracy = %.2f\n', Accuracy);
%fprintf('error rate = %.2f\n ', mean(result ~= Group_Test1 ) * 100);

end