emotion_classify_compare.asv

    clear
    %READ FROM FILES
    cd Emo_features
    features_sv_a=dlmread('emo_a.dat');
    features_sv_f=dlmread('emo_f.dat');
    features_sv_n=dlmread('emo_n.dat');
    features_sv_w=dlmread('emo_w.dat');
    cd ..
    
    %STORE LENGTHS OF EACH TYPE OF FEATURE
    len_fea(1)=size(features_sv_a,1);
    len_fea(2)=size(features_sv_f,1);
    len_fea(3)=size(features_sv_n,1);
    len_fea(4)=size(features_sv_w,1);
    
    n=4;
    max_len=max(len_fea);
    emo_features=zeros(max_len,60,n);
    
    %ICORPORATE ALL FEATURES IN A 3-D ARRAY
    emo_features(1:len_fea(1),:,1)=features_sv_a;
    emo_features(1:len_fea(2),:,2)=features_sv_f;
    emo_features(1:len_fea(3),:,3)=features_sv_n;
    emo_features(1:len_fea(4),:,4)=features_sv_w;
    
    
    
    group=ones(max_len,n);
    for i=[1:n]
        group(:,i)=group(:,i)*i;
    end
    
    perc=zeros(5,n+1);
    total=zeros(5,n);
    incorrect=zeros(5,n);
            
    
    for x=[1:5]
            %-----------------------------------------------------------------%
            %SEPERATE TEST AND TRAIN FILES

            train=logical(zeros(max_len,n));
            test=logical(zeros(max_len,n));

            for i=[1:n]
                [train_n, test_n] = crossvalind('holdOut', group(1:len_fea(i),i) ,0.3);


                train(1:len_fea(i),i)=train_n;
                test(1:len_fea(i),i)=test_n;
            end

             %-----------------------------------------------------------------%
            %CREATE SVM STRUCTS AFTER TRAINING


            k=1;
            for i=[1:n-1]
                for j=[i+1:n]
                    svm(k)=svmtrain(  [emo_features( train(:,i),:,i);emo_features(train(:,j),:,j)]  , [group(train(:,i),i);group(train(:,j),j)]  );
                    k=k+1;
                end
            end
            k=k-1;

             %-----------------------------------------------------------------%
            %CLASSIFY TEST DATA
            label=zeros( max_len , k ,n);
            for i=[1:n]
                for j=[1:k]
                    label_new=svmclassify( svm(j),emo_features( test(:,i),:,i) );
                    len_label(i)=length(label_new);
                    label(1:len_label(i),j,i)=label_new;
                end
            end

             %-----------------------------------------------------------------%
            %COMPUTE FINAL EMOTION AND RESULTS
            len1=floor(max_len*0.3);
            emo=zeros(len1,n);
            
            for i=[1:n]
                for j=[1: (len_fea(i)*0.3)]
                    emo(j,i)=mode( label(j,:,i));
                    total(x,i)=total(x,i)+1;
                    if not(emo(j,i)==i)
                        incorrect(x,i)=incorrect(x,i)+1;
                    end
                end
                perc(x,i)=double(incorrect(x,i)/total(x,i));
            end
            perc(x,n+1)=double(sum(incorrect(x,:))/sum(total(x,:)));
            %-------------------------------------------------------------%
            
           %ADABOOST

    
    
    
    end