%% Classify Scalars
clear all; clc

% Set the random seed
RandStream.setGlobalStream(RandStream('mt19937ar','seed',sum(100*clock)));

% Data are here
cd('Y:\EEG_Data\CLASSIFY\Classify for Arun\');

% Load up IDs, match with CTL IDs, get correlates
[EEG_IDs,~,~]=xlsread('All_POWER_ROI_INFO.xlsx','Subjects_IDs'); % Col 1 = Pd, Col 2 = CTL
[VAR_DATA,VAR_HDR,VAR_BOTH]=xlsread('PD_CONFLICT_VARS.xlsx');
% Match up
PD_ID=VAR_DATA(:,1);
MATCH_ID=VAR_DATA(:,5);
YrsDx=VAR_DATA(:,9);
if sum( EEG_IDs(:,1)==PD_ID )==28  % If PD are numerically aligned in both data sets
    for matchi=1:28
        MATCHTOEEG(matchi)=find( EEG_IDs(:,2)==MATCH_ID(matchi) ); % Find the ctl subj that matches with this patient
    end
end

% Load EEG Data
SHEET={'Cue_locked_Conf','Response_locked_Conf','Response-locked_CorrectError','PostCorrectError','Alpha','RelativeAlpha'};
for sheeti=1:6
    [DATA{sheeti},HDR{sheeti},BOTH{sheeti}]=xlsread('All_POWER_ROI_INFO.xlsx',SHEET{sheeti});
end

% ********* Setup Data *********   Do you want control only, or control-benign condi diffs?
for sheeti=1:4
    CTL(:,sheeti)=DATA{sheeti}(:,3) % -DATA{sheeti}(:,2);
    ON(:,sheeti)=DATA{sheeti}(:,5) % -DATA{sheeti}(:,4);
    OFF(:,sheeti)=DATA{sheeti}(:,7) % -DATA{sheeti}(:,6);
end
% Alpha
for sheeti=5:6
    CTL(:,sheeti)=DATA{sheeti}(:,1)  ;
    ON(:,sheeti)=DATA{sheeti}(:,2)  ;
    OFF(:,sheeti)=DATA{sheeti}(:,3) ;
end


% ********* Setup Contrasts *********  InData should have Col 1 = group (1=patient, 0=Ctl) and Cols 2-N are data
InData=[  [ones(28,1),CTL] ; [zeros(28,1),ON]  ] ;
TITLE='CTL_ON_NoDiff';
iterations=500;
vars=[2,3,4,5,6]  % [2,3,4,5,7];  % Column 1 is group, 2-N are variables to select any number of here
% ********* ***** *********

% Classify
for Xvali=1:3
    if     Xvali==1, Xval='5X';
    elseif Xvali==2, Xval='10X';
    elseif Xvali==3, Xval='LOO';
    end
    Classify_Scalars_SVM(InData,TITLE,iterations,vars,Xval);
    Classify_Scalars_LASSO(InData,TITLE,iterations,vars,Xval);  % not fully validated, just playing with this for now.
end
% Now each paired with their best match CTL
Classify_Scalars_SVM_MatchSubjs(InData,TITLE,MATCHTOEEG,vars,'Match');
Classify_Scalars_LASSO_MatchSubjs(InData,TITLE,MATCHTOEEG,vars,'Match');

%% Aggregate Different Predictors / SVM

% ########################
Xval='LOO';
iterations=500;  % If 'Match', iterations needs to = 28
Classifier='SVM';  % 'SVM'  'LASSO'
% TITLE={'CTL_ON_Cue','CTL_ON_Resp','CTL_ON_Err','CTL_ON_PE','CTL_ON_RelAlpha','CTL_ON'};   
 TITLE={'CTL_OFF_Cue','CTL_OFF_Resp','CTL_OFF_Err','CTL_OFF_PE','CTL_OFF_RelAlpha','CTL_OFF'}; 
% TITLE={'CTL_ON_NoDiff_Cue','CTL_ON_NoDiff_Resp','CTL_ON_NoDiff_Err','CTL_ON_NoDiff_PE','CTL_ON_NoDiff_Alpha','CTL_ON_NoDiff'};  % not C-I diff 
% ########################

for vari=1:6
        
    if strmatch(Classifier,'SVM')
        load(['SVM_',TITLE{vari},'_',Xval,'_iter',num2str(iterations),'.mat']);
        OUTPUTS(vari,1)=mean(mean(Aset_acc));
        OUTPUTS(vari,2)=mean(mean(Bset_acc));
        if strmatch(Xval,'Match')
            SCORES(vari,1,:)=Aset_score;
            SCORES(vari,2,:)=Bset_score;
        else
            SCORES(vari,1,:)=nanmean(Aset_score');
            SCORES(vari,2,:)=nanmean(Bset_score');            
        end
        clear A* B*;  classifier=1;
        
    elseif strmatch(Classifier,'LASSO')
        load(['LASSO_',TITLE{vari},'_',Xval,'_iter',num2str(iterations),'.mat']);
        OUTPUTS(vari,1)=mean(LASSO_Probability_Tst2(:,2));
        OUTPUTS(vari,2)=mean(LASSO_Probability_Tst2(:,3));
        if strmatch(Xval,'Match')
            if vari==6
                SCORES(vari,1,:)=LASSO_Betas(LASSO_Betas==max(abs(LASSO_Betas)')');
                SCORES(vari,2,:)=LASSO_Betas(LASSO_Betas==max(abs(LASSO_Betas)')');
            else
                SCORES(vari,1,:)=LASSO_Betas;
                SCORES(vari,2,:)=LASSO_Betas;
            end
        end
        clear A* B*; classifier=2;
    end
    
end


% Plot
COL={'m','y'};
SHAPE={'d','o','s','p','h','^'};

figure;
subplot(1,3,1:2);
hold on
for vari=1:6
    plot(1-OUTPUTS(vari,2),OUTPUTS(vari,1),...
        SHAPE{vari},'MarkerFaceColor','k','MarkerEdgeColor',COL{classifier},'MarkerSize',10)
end
set(gca,'ylim',[0 1],'ytick',[0:.1:1],'xlim',[0 1],'xtick',[0:.1:1]);
legend(TITLE,'location','southeast', 'Interpreter', 'none');
ylabel('Sensitivity'); xlabel('1-Specificity');
plot([0 1],[0 1],'k');
title(['Classification of ',TITLE{6}], 'Interpreter', 'none')

subplot(1,3,3);
hold on
for vari=1:6
    bar(vari,mean(OUTPUTS(vari,:),2),.4,COL{classifier})
end
set(gca,'ylim',[.4 1],'ytick',[.4:.1:1],'xlim',[0 7],'xtick',[1:1:6],'xticklabels',TITLE);
xtickangle(90)
title('Average')

if strmatch(Classifier,'SVM')
    figure;
    for paneli=1:6
        subplot(2,3,paneli); hold on
        scatter(YrsDx,abs(squeeze(SCORES(paneli,1,:)))); lsline
        [rho,p]=corr(abs(squeeze(SCORES(paneli,1,:))),YrsDx,'type','Spearman');
        text(.1,.1,['rho=',num2str(rho),' p=',num2str(p)],'sc');
        xlabel('YrsDx'); ylabel('confidence');
        title(TITLE{paneli}, 'Interpreter', 'none')
    end
end

% Aggregate all X-Vals and Algorithms
ToAgg=6; iterations=500;

for Xvali=1:3
    if     Xvali==1, Xval='5X';
    elseif Xvali==2, Xval='10X';
    elseif Xvali==3, Xval='LOO';
    end
    load(['SVM_',TITLE{ToAgg},'_',Xval,'_iter',num2str(iterations),'.mat']);
    OUTPUT_AGG(1,Xvali,1)=mean(mean(Aset_acc));
    OUTPUT_AGG(1,Xvali,2)=mean(mean(Bset_acc));
    clear A* B*;
    
    load(['LASSO_',TITLE{ToAgg},'_',Xval,'_iter',num2str(iterations),'.mat']);
    OUTPUT_AGG(2,Xvali,1)=mean(LASSO_Probability_Tst2(:,2));
    OUTPUT_AGG(2,Xvali,2)=mean(LASSO_Probability_Tst2(:,3));
    clear LASSO*;
end

load(['SVM_',TITLE{ToAgg},'_Match_iter28.mat']);
OUTPUT_AGG(1,4,1)=mean(mean(Aset_acc));
OUTPUT_AGG(1,4,2)=mean(mean(Bset_acc));
clear A* B*;

load(['LASSO_',TITLE{ToAgg},'_Match_iter28.mat']);
OUTPUT_AGG(2,4,1)=mean(LASSO_Probability_Tst2(:,2));
OUTPUT_AGG(2,4,2)=mean(LASSO_Probability_Tst2(:,3));
clear LASSO*;

% Plot
COL={'m','y'};
SHAPE={'d','o','s','p'};
SHIFT=[-.2,-.1,.1,.2];

figure;
subplot(1,3,1:2);
hold on
for classifier=1:2
    for Xvali=1:4
        plot(1-OUTPUT_AGG(classifier,Xvali,2),OUTPUT_AGG(classifier,Xvali,1),...
            SHAPE{Xvali},'MarkerFaceColor','k','MarkerEdgeColor',COL{classifier},'MarkerSize',10)
    end
end
set(gca,'ylim',[0 1],'ytick',[0:.1:1],'xlim',[0 1],'xtick',[0:.1:1]);
legend({'SVM 5X','SVM 10X','SVM LOO','SVM Match','LASSO 5X','LASSO 10X','LASSO LOO','LASSO Match'},'location','southeast');
ylabel('Sensitivity'); xlabel('1-Specificity');
plot([0 1],[0 1],'k');
title(['Classification of ',TITLE{ToAgg}], 'Interpreter', 'none')

subplot(1,3,3);
hold on
for classifier=1:2
    for Xvali=1:4
        bar(classifier+SHIFT(Xvali),mean(OUTPUT_AGG(classifier,Xvali,:),3),.4,COL{classifier})
    end
end
set(gca,'ylim',[.5 1],'ytick',[.5:.1:1],'xlim',[0 3],'xtick',[1:1:2],'xticklabels',{'SVM','LASSO'});
title('Average')


%%