How to adjust TrainingSet,Testset and GroupTrain to use for multiclass svm?

First of all my project consists of 4 classes of many samples total.Each of those samples has a features vector of 50 elements normalized using zscore. Trying to understand the following example

%% SVM Multiclass Example 
% SVM is inherently one vs one classification. 
% This is an example of how to implement multiclassification using the 
% one vs all approach. 
TrainingSet=[ 1 10;2 20;3 30;4 40;5 50;6 66;3 30;4.1 42]; 
TestSet=[3 34; 1 14; 2.2 25; 6.2 63]; 
GroupTrain=[1;1;2;2;3;3;2;2]; 
results = multisvm(TrainingSet, GroupTrain, TestSet); 
disp('multi class problem'); 
disp(results);

If i have a total image number of 100,80 for training and 20 for testing,that's 20 each for training and 5 each for testing,and the number of image for class one are 1 to 25,class 2 26 to 50 class 3 51 to 75 class 4 76 to 100 would the following version be right:

TrainingSet=[v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20;v26 v27 v28 v29 v30 v31 v32 v33 v34 v35 v36 v37 v38 v39 v40 v41 v42 v43 v44 v45;V51 v52 v53 v54 v55 v56 v57 v58 v59 v60 v61 v62 v63 v64 v65 v66 v67 v68 v69 v70; v76 v77 v78 v79 v80 v81 v82 v83 v84 v85 v86 v87 v88 v89 v90 v91 v92 v93 v94 v95];
Testset=[v21 v22 v23 v24 v25;v46 v47 v48 v49 v50;v71 v72 v73 v74 v75;v96 v97 v98 v99 v100];
Group train%no clue didn't understand it

The initial code submitted for the first example is:

u=unique(GroupTrain);
numClasses=length(u);
result = zeros(length(TestSet(:,1)),1);
%build models
for k=1:numClasses
    %Vectorized statement that binarizes Group
    %where 1 is the current class and 0 is all other classes
    G1vAll=(GroupTrain==u(k));
    models(k) = svmtrain(TrainingSet,G1vAll);
end
%classify test cases
for j=1:size(TestSet,1)
    for k=1:numClasses
        if(svmclassify(models(k),TestSet(j,:))) 
            break;
        end
    end
    result(j) = k;
end