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function Classify_Scalars_SVM(InData,TITLE,iterations,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
Aset_score=NaN(28,iterations);
Bset_score=NaN(28,iterations);
for rep = 1:iterations
if rem(rep,100)==0
['SVM ' Xval ' ' TITLE ' ' num2str(rep)]
end
% X-Validation: Equate epochs
size_targs=size(Big_A,1); % -------- "TARGETS" are Patient
size_stds=size(Big_B,1); % -------- "STANDARDS" are CTL
if size_targs<size_stds
temp=shuffle(1:size_stds);
TARGETS=Big_A;
STANDARDS=Big_B(temp(1:size_targs),:); clear temp size_stds size_targs;
elseif size_stds<size_targs
temp=shuffle(1:size_targs);
TARGETS=Big_A(:,:,temp(1:size_stds)); clear temp size_stds size_targs;
STANDARDS=Big_B;
else
TARGETS=Big_A;
STANDARDS=Big_B;
end
AllData = cat(1,TARGETS,STANDARDS);
GROUPS = [ones(1,size(TARGETS,1)),zeros(1,size(STANDARDS,1))];
Size_Per_Set = size(AllData,1) .* .5;
% Cross Validate
if strmatch(Xval,'5X'); testsize = floor(.2*Size_Per_Set);
elseif strmatch(Xval,'10X'); testsize = floor(.1*Size_Per_Set);
elseif strmatch(Xval,'LOO'); testsize = 1;
end
trainsize = Size_Per_Set-testsize;
% For TARGETS
ForRand = shuffle(1:Size_Per_Set);
TrainBool_T = zeros(1,Size_Per_Set); TrainBool_T(ForRand(1:trainsize))=1;
Test1Bool_T = zeros(1,Size_Per_Set); Test1Bool_T(ForRand(trainsize+1:trainsize+testsize))=1;
% For STANDARDS
ForRand = shuffle(1:Size_Per_Set);
TrainBool_S = zeros(1,Size_Per_Set); TrainBool_S(ForRand(1:trainsize))=1;
Test1Bool_S = zeros(1,Size_Per_Set); Test1Bool_S(ForRand(trainsize+1:trainsize+testsize))=1;
% same for standards and targets
TrainBool = [TrainBool_T TrainBool_S];
Test1Bool = [Test1Bool_T Test1Bool_S];
% 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(find(Test1Bool_T==1),rep)=score(1:length(acc)/2,1);
Bset_score(find(Test1Bool_S==1),rep)=score((length(acc)/2)+1:length(acc),1);
% ############# SVM #############
clearvars -except Aset_acc Bset_acc Aset_score Bset_score rep Big_A Big_B InData Xval TITLE iterations
end
% Save
save(['SVM_',TITLE,'_',Xval,'_iter',num2str(iterations),'.mat'],'Aset_acc','Bset_acc','Aset_score','Bset_score');
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