scripts/classification_scripts/Classify_Scalars_SVM_MatchSubjs.m

function Classify_Scalars_SVM_MatchSubjs(InData,TITLE,MATCHTOEEG,vars,Xval)
% Nomenclature is that 'A' TARGETS are classified (1's) from 'B' STANDARDS (0's)

Big_A=InData(InData(:,1)==1,vars) ;  % PD
Big_B=InData(InData(:,1)~=1,vars) ;  % CTL
% Align them so each CTL matches each PD
Big_B=Big_B(MATCHTOEEG',:);

for rep = 1:28
    if rem(rep,100)==0
        ['SVM   '  Xval '   '   TITLE  '   '  num2str(rep)]
    end

    AllData = cat(1,Big_A,Big_B);
    GROUPS = [ones(1,size(Big_A,1)),zeros(1,size(Big_B,1))];
    Size_Per_Set = size(AllData,1) .* .5;
    
    % Cross Validate
    testsize = 1;
    trainsize = Size_Per_Set-testsize;
    % get shuffled trials for train set
    TrainBool = ones(1,Size_Per_Set); TrainBool(rep)=0;
    % get shuffled trials for validate set
    Test1Bool = zeros(1,Size_Per_Set); Test1Bool(rep)=1;
    % same for standards and targets
    TrainBool = [TrainBool TrainBool];
    Test1Bool = [Test1Bool Test1Bool];
    
    % Classify ****** Targets ******  

    % training set
    x_train = AllData(TrainBool==1,:);
    % normalize!
    x_mean = mean(x_train); x_std = std(x_train);
    x_train_norm = (x_train - repmat(x_mean,size(x_train,1),1));  % mean normalize
    x_train_norm = x_train_norm./repmat(x_std,size(x_train,1),1);
    y_train = GROUPS(TrainBool==1);
    
    % validate set
    xtstset1 = AllData(Test1Bool == 1,:);
    % normalize to train set
    xtstset1_norm = (xtstset1-repmat(x_mean,size(xtstset1,1),1));
    xtstset1_norm = xtstset1_norm./ repmat(x_std,size(xtstset1,1),1); % normalize to trn data
    y_tstset1 = GROUPS(Test1Bool==1);
    
    % store normalizing constants. Important to normalize to that when applying
    % weights to a different data set later.
    norm{1}(rep,:) = x_mean;
    norm{2}(rep,:) = x_std;
   
    x_train = x_train_norm;
    x_test1 = xtstset1_norm;
    
    % ############# SVM #############
    SVMModel = fitcsvm(x_train,y_train','KernelFunction','linear');
    [label,score] = predict(SVMModel,x_test1);
    
    acc=label==y_tstset1';
    Aset_acc(rep,:)=acc(1:length(acc)/2);
    Bset_acc(rep,:)=acc((length(acc)/2)+1:end);
    
    Aset_score(rep,:)=score(1,1);
    Bset_score(rep,:)=score(2,1);
    % ############# SVM #############
    
    clearvars -except Aset_acc Bset_acc Aset_score Bset_score rep Big_A Big_B InData Xval TITLE iterations MATCHTOEEG
    
end

% Save
save(['SVM_',TITLE,'_',Xval,'_iter28','.mat'],'Aset_acc','Bset_acc','Aset_score','Bset_score');