%% plot performance curves of individual cpc groups

clear

time_step = 6;

time_range = 12:time_step:96;

neuron_num_short = 30:10:100;

neuron_num_long = 30:10:40;

load(['RF_clinical_regardless_time']);

prob_pts_clinical = prob_pts;

a = mean(prob_pts_clinical');

prob_pts_clinical = repmat(a',1,length(time_range));

load(['\long_limitSeq_8_bilstm_two_neurons_',num2str(neuron_num_long(2)),'_epoch_30.mat']);

prob_pts_long = prob_pts;

load(['\short_bilstm_two_neurons_',num2str(neuron_num_short(3)),'_epoch_15.mat']);

prob_pts_short = prob_pts;

prob_pts = (prob_pts_clinical+prob_pts_long+prob_pts_short)/3;

pred_prob_all = nan(size(prob_pts));

for i = 1:size(pred_prob_all,1)

for j = 1:size(pred_prob_all,2)

    if ~isnan(prob_pts(i,j))

        pred_prob_all(i,j) = nanmean(prob_pts(i,1:j));

    end

end

end

labels = nanmean(label_pts');

num_pts = length(labels);

index_tmp = find(labels==2);

feature_good = pred_prob_all(labels==2,:);

feature_mean = nanmean(feature_good');

[a,index_good] = sort(feature_mean);

index_good = index_tmp(index_good);

feature_good = pred_prob_all(index_good,:);

unique_names_good = unique_names(index_good);

index_tmp = find(labels==1);

feature_bad = pred_prob_all(labels==1,:);

feature_mean = nanmean(feature_bad');

[a,index_bad]=sort(feature_mean);

index_bad = index_tmp(index_bad);

feature_bad = pred_prob_all(index_bad,:);

unique_names_bad = unique_names(index_bad);

unique_names1 = [unique_names_good;unique_names_bad];

feature_all1 = [feature_good;feature_bad];

figure,

% mn = prctile(feature_all1(:),0); mx = prctile(feature_all1(:),100);% 90

imagesc(feature_all1(:,1:11));

colormap(flipud(cold));

% set(gca, 'YTick', 371, 'YTickLabel', 'poor')

xlabel('Hours After Cardiac Arrest');

title('Outcome Prediction Probability over Time');

colorbar;

set(gca, 'Xtick',1:11,'Xticklabel',num2cell(time_range(1:11)))

% set(gca, 'YTick', (1/num_pts:1/num_pts:1)*num_pts, 'YTickLabel', unique_names1)

load('\all_features.mat','cpc_scores','unique_names');

cpc_labels = zeros(size(labels));

for i = 1:length(labels)

    indx = ~cellfun(@isempty, strfind(unique_names,unique_names1{i}));

    indx = find(indx==1);

    indx = indx(1);

    cpc_labels(i) = cpc_scores(indx);

end

figure,

tmp = feature_all1(cpc_labels==1,1:11);

[h1,p] = boundedline(1:11, nanmean(tmp), nanstd(tmp)./sum(~isnan(tmp)),'-ro','alpha');% --b* --ro --g+ --cs

% h1 = outlinebounds(l,p);

hold on,

tmp = feature_all1(cpc_labels==2,1:11);

[h2,p] = boundedline(1:11, nanmean(tmp), nanstd(tmp)./sum(~isnan(tmp)),'-b*','alpha');% --b* --ro --g+ --cs

% h2 = outlinebounds(l,p);

hold on,

tmp = feature_all1(cpc_labels==3,1:11);

[h3,p] = boundedline(1:11, nanmean(tmp), nanstd(tmp)./sum(~isnan(tmp)),'-g+','alpha');% --b* --ro --g+ --cs

% h3 = outlinebounds(l,p);

hold on,

tmp = feature_all1(cpc_labels==4,1:11);

if ~isempty(find(sum(~isnan(tmp))==1))

    tmp(:,find(sum(~isnan(tmp))==1)) = nan;

end

[h4,p] = boundedline(1:11, nanmean(tmp), nanstd(tmp)./sum(~isnan(tmp)),'-cs','alpha');% --b* --ro --g+ --cs --md

% h4 = outlinebounds(l,p);

hold on,

tmp = feature_all1(cpc_labels==5,1:11);

[h5,p] = boundedline(1:11, nanmean(tmp), nanstd(tmp)./sum(~isnan(tmp)),'-k^','alpha');% --b* --ro --g+ --cs

% h5 = outlinebounds(l,p);

set(gca, 'XTick', 1:length(time_range(1:11)), 'XTickLabel', strsplit(num2str(time_range(1:11))));

xlabel('Hours After Cardiac Arrest');

ylabel('Mean Outcome Prediction Probability');

xlim([0 12]);

title('Mean Outcome Prediction Probability at Different Time Intervals')

legend([h5,h4,h3,h2,h1],'CPC =5','CPC = 4','CPC = 3','CPC = 2','CPC = 1');

legend boxoff