gamultisvm.m

function [result] = multisvm(TrainingSet,GroupTrain,TestSet,Testlabels)
%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 cneuburg@fau.edu
%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(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
 load GASVM.mat
 Testlabels=mattestlab ;result=result;
 confMat=myconfusionmat(Testlabels,result);
disp('confusion matrix:')
disp(confMat)
conf=sum(result==Testlabels)/length(result);
disp(['accuracy = ',num2str(conf*100),'%'])
% confMat=myconfusionmat(Testlabels,result);
% disp('confusion matrix:')
% disp(confMat)
% conf=sum(result==Testlabels)/length(result);
% disp(['accuracy = ',num2str(conf*100),'%'])