groups=xlsread('ground_truth.xls'); %Returns ' 1' or '-1'

[trainIdx testIdx] = crossvalind('HoldOut', groups,0.75); % Total groups allocated for testing
%trainIdx and testIdx returns 1 or 0

%Scaling variables, to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges
 for (ii=1:size(featuresMatrix,2))
         featuresMatrix(:,ii)=featuresMatrix(:,ii)./max(featuresMatrix(:,ii));
 end

groups = cellstr(num2str(groups));
 cp = classperf(groups);

%Train
[SVMModel] = svmtrain(featuresMatrix(trainIdx,:), groups(trainIdx),'kernel_function','rbf','boxconstraint',1,'rbf_sigma',1,'tolkkt',3e-6);

%Predict
[predicted_label] = svmclassify(SVMModel, featuresMatrix(testIdx,:));
classperf(cp,predicted_label,testIdx);