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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');
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