function Classify_Scalars_LASSO_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

    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;

    trainsize = Size_Per_Set-2*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;
    % get shuffled trials for test set
    Test2Bool = zeros(1,Size_Per_Set); if rep==28, Test2Bool(1)=1; else Test2Bool(rep+1)=1; end
    % same for standards and targets
    TrainBool = [TrainBool TrainBool];
    Test1Bool = [Test1Bool Test1Bool];
    Test2Bool = [Test2Bool Test2Bool];
    
    
    % 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);
    
    % test set
    xtstset2 = AllData(Test2Bool == 1,:);
    % normalize to train set
    xtstset2_norm = (xtstset2-repmat(x_mean,size(xtstset2,1),1));
    xtstset2_norm = xtstset2_norm./ repmat(x_std,size(xtstset2,1),1); % normalize to trn data
    y_tstset2 = GROUPS(Test2Bool==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;
    x_test2 = xtstset2_norm;
    
    % ############# LASSO #############
    [B,stats] = lasso(x_train, y_train');    
    
    % predict train
    predictor_train = logical((x_train * B >0) );
    IsTarget_train = repmat(y_train',1,size(B,2));
    
    % predict val
    predictor_tstset1 = logical((x_test1 * B >0) );
    IsTarget_tstset1 = repmat(y_tstset1',1,size(B,2));
    
    % predict test
    predictor_tstset2 = logical((x_test2 * B >0) );
    IsTarget_tstset2 = repmat(y_tstset2',1,size(B,2));
    
    % select regularization param that gets best generalization on
    % validation set. If many, go for sparsity.
    generalize = mean(predictor_tstset1==IsTarget_tstset1);
    [Test1Acc,tochoose] = max(generalize);
    tochoose = tochoose(1);
    
    % Get Training accuracy for that param
    TrainAcc = mean(IsTarget_train(:,tochoose)==predictor_train(:,tochoose));
    % Get test set accuracy for that param, unbiased
    Test2Acc = mean(IsTarget_tstset2(:,tochoose)==predictor_tstset2(:,tochoose));
    
    % Get separately for the two conditions
    targets = find(y_train == 1);
    TrainAcc1 = mean(IsTarget_train(targets,tochoose)==predictor_train(targets,tochoose));
    targets = find(y_tstset1 == 1);
    Test1Acc1 = mean(IsTarget_tstset1(targets,tochoose)==predictor_tstset1(targets,tochoose));
    targets = find(y_tstset2 == 1);
    Test2Acc1 = mean(IsTarget_tstset2(targets,tochoose)==predictor_tstset2(targets,tochoose));
    
    targets = find(y_train == 0);
    TrainAcc0 = mean(IsTarget_train(targets,tochoose)==predictor_train(targets,tochoose));
    targets = find(y_tstset1 == 0);
    Test1Acc0 = mean(IsTarget_tstset1(targets,tochoose)==predictor_tstset1(targets,tochoose));
    targets = find(y_tstset2 == 0);
    Test2Acc0 = mean(IsTarget_tstset2(targets,tochoose)==predictor_tstset2(targets,tochoose));
    
    % store
    LASSO_Probability_Train(rep,:) = [TrainAcc TrainAcc1 TrainAcc0];
    LASSO_Probability_Tst1(rep,:) = [Test1Acc Test1Acc1 Test1Acc0];
    LASSO_Probability_Tst2(rep,:) = [Test2Acc Test2Acc1 Test2Acc0];
    LASSO_Betas(rep,:)=B(:,tochoose)';
    % ############# SVM #############
    
    clearvars -except norm LASSO_Probability_Train LASSO_Probability_Tst1 LASSO_Probability_Tst2 LASSO_Betas rep Big_A Big_B InData Xval TITLE iterations
    
end

% Save
save(['LASSO_',TITLE,'_',Xval,'_iter28.mat'],'LASSO_Probability_Train','LASSO_Probability_Tst1','LASSO_Probability_Tst2','LASSO_Betas');