function DATA=PCA_classificator(IHD,time_series_names,MEAN_DATA_24,MEAN_DATA_48,DESCRIPTORS,des_names)




X=[MEAN_DATA_24 MEAN_DATA_48 ];

%X=[X1];

%X=[X];
AGE_a=DESCRIPTORS(:,strcmp(des_names,'Age'));
Gender_a=DESCRIPTORS(:,strcmp(des_names,'Gender'));
ICUTYPE_a=[DESCRIPTORS(:,strcmp(des_names,'ICUType'))];

ICUTYPE_t=ICUTYPE_a;

ICUTYPE_a(ICUTYPE_t==1)=2;
ICUTYPE_a(ICUTYPE_t==2)=4;
ICUTYPE_a(ICUTYPE_t==3)=1;
ICUTYPE_a(ICUTYPE_t==4)=3;

X=[AGE_a ICUTYPE_a X];





%if des{:,strcmp(des_names,'Gender')}==0

%X_new_rec=zeros(size(X));

  savefile='gender0.mat'; 
  savefile='gender0_60.mat';
  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
   

     % S_new=calc_S_new_data(X',A,V,Mu,N,CV);
    X_new_rec =( repmat(Mu,1,size(S,2)) + (A*S))';
  %  mu1=Mu;
      sigma0 = sigma;
    sigma0(sigma0==0) = 1;
    z = bsxfun(@minus,X_new_rec', mu');
    z = bsxfun(@rdivide, z, sigma0');
             x_in=(pinv(pc1)*z)';
   X(Gender_a==0,:)=x_in(Gender_a==0,:);
%  X(Gender_a==0,:)=x_in;
   N1=N2;
   
   %X_reconstructed=zeros(size(X));
 %  X_reconstructed(Gender_a==0,:)=X_new_rec(Gender_a==0,:);
 % X_reconstructed(Gender_a==0,:)=X_new_rec;
              %  x_in=x_in(1:N2);
  
%elseif des{:,strcmp(des_names,'Gender')}==1
  savefile='gender1.mat'; 
  savefile='gender1_60.mat';
  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
      %   mu2=Mu;
%   plot(mu1)
%   hold on
%   plot(mu2)
 % S_new=calc_S_new_data(X(Gender_a==1,:),A,V,Mu,N,CV);
    X_new_rec =( repmat(Mu,1,size(S,2)) + A*S)';
      sigma0 = sigma;
    sigma0(sigma0==0) = 1;
    z = bsxfun(@minus,X_new_rec', mu');
    z = bsxfun(@rdivide, z, sigma0');
             x_in=(pinv(pc1)*z)';
   X(Gender_a==1,:)=x_in(Gender_a==1,:);
 %  X(Gender_a==1,:)=x_in;
   
   
     
   % X_reconstructed(Gender_a==1,:)=X_new_rec(Gender_a==1,:);
%X_reconstructed(Gender_a==1,:)=X_new_rec;
    


%     data1=X_reconstructed(Gender_a==0,3:39);
% data2=X_reconstructed(Gender_a==0,40:76);
% S0=analyze_params(data1,data2,IHD(Gender_a==0),time_series_names);
% 
% X_classify=zeros(size(data1,1),[]);
% add_idx=1;
% 
% for param_idx=1:size(S0,1)
%     
%     if S0(param_idx,1)==1
%         X_classify(:,add_idx)=data1(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S0(param_idx,2)==1
%         X_classify(:,add_idx)=data2(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S0(param_idx,3)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     elseif S0(param_idx,4)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) abs(data2(:,param_idx))];
%         add_idx=add_idx+2;
%     elseif S0(param_idx,5)==1
%         X_classify(:,add_idx)=(data1(:,param_idx)+data2(:,param_idx))./2;
%         add_idx=add_idx+1;
%     else
%         % pad parameter
%     end
% end
% 
% for param_idx=1:size(S0,1)
%     
%     if max(S0(param_idx,:))==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     else
%         % pad parameter
%     end
% end
% 
%     X_classify=[AGE_a(Gender_a==0) ICUTYPE_a(Gender_a==0) X_classify];
% [best_th1,max_score_1,D0] = opt_th(X_classify,IHD(Gender_a==0));
%     
%     
% data1=X_reconstructed(Gender_a==1,3:39);
% data2=X_reconstructed(Gender_a==1,40:76);
% S1=analyze_params(data1,data2,IHD(Gender_a==1),time_series_names);
% 
% X_classify=zeros(size(data1,1),[]);
% add_idx=1;
% 
% for param_idx=1:size(S1,1)
%     
%     if S1(param_idx,1)==1
%         X_classify(:,add_idx)=data1(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S1(param_idx,2)==1
%         X_classify(:,add_idx)=data2(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S1(param_idx,3)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     elseif S1(param_idx,4)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) abs(data2(:,param_idx))];
%         add_idx=add_idx+2;
%     elseif S1(param_idx,5)==1
%         X_classify(:,add_idx)=(data1(:,param_idx)+data2(:,param_idx))./2;
%         add_idx=add_idx+1;
%     else
%         % pad parameter
%     end
% end
% 
% for param_idx=1:size(S0,1)
%     
%     if max(S1(param_idx,:))==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     else
%         % pad parameter
%     end
% end
% 
%     X_classify=[AGE_a(Gender_a==1) ICUTYPE_a(Gender_a==1) X_classify];
% [best_th1,max_score_1,D0] = opt_th(X_classify,IHD(Gender_a==1));
%     
%     
% data1=X_reconstructed(:,3:39);
% data2=X_reconstructed(:,40:76);
% S=analyze_params(data1,data2,IHD,time_series_names);
% X_classify=zeros(4000,[]);
% add_idx=1;
% 
% for param_idx=1:size(S,1)
%     
%     if S(param_idx,1)==1
%         X_classify(:,add_idx)=data1(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S(param_idx,2)==1
%         X_classify(:,add_idx)=data2(:,param_idx);
%         add_idx=add_idx+1;
%     elseif S(param_idx,3)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     elseif S(param_idx,4)==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) abs(data2(:,param_idx))];
%         add_idx=add_idx+2;
%     elseif S(param_idx,5)==1
%         X_classify(:,add_idx)=(data1(:,param_idx)+data2(:,param_idx))./2;
%         add_idx=add_idx+1;
%     else
%         % pad parameter
%     end
% end
% 
% for param_idx=1:size(S,1)
%     
%     if max(S(param_idx,:))==1
%         X_classify(:,add_idx:add_idx+1)=[data1(:,param_idx) data2(:,param_idx)];
%         add_idx=add_idx+2;
%     else
%         % pad parameter
%     end
% end
% 
%     X_classify=[AGE_a ICUTYPE_a X_classify];
% % plot(data1(100,:),'r');hold on;plot(data2(100,:),'g')
% [best_th1,max_score_1,D0] = opt_th(X_classify(Gender_a==0,:),IHD(Gender_a==0));
% [best_th2,max_score_2,D1] = opt_th(X_classify(Gender_a==1,:),IHD(Gender_a==1));
% 
% DATA(:,1)=0;
% DATA(Gender_a==0,2)=D0(:,2);
% DATA(Gender_a==1,2)=D1(:,2);
% DATA(Gender_a==0,3)=D0(:,3);
% DATA(Gender_a==1,3)=D1(:,3);
% return
% % [best_th0,max_score_0,D] = opt_th(X_classify,IHD);
% % pause(.1)
% 
% 












    %0.2708
    %X=[R(:,~strcmp(time_series_names,'MechVent'))];   
    
    %0.277
%     X=[R(:,strcmp(time_series_names,'TroponinT'))];    
%     X=[R(:,strcmp(time_series_names,'TroponinI')) X];
%     X=[R(:,strcmp(time_series_names,'RespRate')) X];
%     X=[R(:,strcmp(time_series_names,'Mg')) X];
%     X=[R(:,strcmp(time_series_names,'Lactate')) X];
%     X=[R(:,strcmp(time_series_names,'K')) X];
%     X=[R(:,strcmp(time_series_names,'FiO2')) X];
%     X=[R(:,strcmp(time_series_names,'Bilirubin')) X];
%     X=[R(:,strcmp(time_series_names,'AST')) X];
%     X=[R(:,strcmp(time_series_names,'ALT')) X];
%     X=[R(:,strcmp(time_series_names,'ALP')) X];
%     X=[R(:,strcmp(time_series_names,'Albumin')) X];
%     

% % % max_N=min(N1,N2);
% % % X=X(:,1:max_N);
% % % 
% % % X_orig=X;
% % % 
% % % scores=zeros(max_N,3);
% % % 
% % % for idx=10:max_N
% % % 
% % %     X=X_orig(:,1:idx);
% % % %[best_th0,max_score_0,D] = opt_th(X,IHD);
% % % % 
% % % % [best_th1,max_score_1,D0] = opt_th(X(Gender_a==0,:),IHD(Gender_a==0));
% % % % [best_th2,max_score_2,D1] = opt_th(X(Gender_a==1,:),IHD(Gender_a==1));
% % % 
% % % [best_th0,max_score_0,D] = opt_th_valid(X,IHD);
% % % [best_th1,max_score_1,D0] = opt_th_valid(X(Gender_a==0,:),IHD(Gender_a==0));
% % % [best_th2,max_score_2,D1] = opt_th_valid(X(Gender_a==1,:),IHD(Gender_a==1));
% % % 
% % % scores(idx,:)=[max_score_1 max_score_2 max_score_0];
% % % end
% % % 
% % % figure(1)
% % % plot(scores(:,1),'g')
% % % hold on
% % % plot(scores(:,2),'r')
% % % plot(scores(:,3),'b')
% % % pause(.1)


max_N=min(N1,N2);
X=X(:,1:20);

[best_th1,max_score_1,D0] = opt_th(X(Gender_a==0,:),IHD(Gender_a==0));
[best_th2,max_score_2,D1] = opt_th(X(Gender_a==1,:),IHD(Gender_a==1));




DATA(:,1)=0;
DATA(Gender_a==0,2)=D0(:,2);
DATA(Gender_a==1,2)=D1(:,2);
DATA(Gender_a==0,3)=D0(:,3);
DATA(Gender_a==1,3)=D1(:,3);

function [best_th,max_score1,BEST_DATA]=opt_th(X_,IHD_)
        
  %  num_params=size(time_series_names,1);
    DATA_=zeros(size(IHD_,1),3);
    B = mnrfit(X_,IHD_+1);
    
    PHAT = mnrval(B,X_);
    
    BEST_DATA=[];
    
    
    max_score1=0;
    max_score2=0;
    best_th=0;
    
    for class_th=.1:.01:0.5
        
        
        
        DATA_(:,1)=str2double('0000');
        
        DATA_(:,2)=PHAT(:,2);
        
        DATA_(:,3)=PHAT(:,2)> class_th;
        
        DATA_(DATA_(:,2)<0.01,2)=0.01;
        DATA_(DATA_(:,2)>0.99,2)=0.99;
        
        
     %   if(~isempty(results))
            
            % Calculate sensitivity (Se) and positive predictivity (PPV)
            TP=sum(DATA_(IHD_==1,3));
            FN=sum(~DATA_(IHD_==1,3));
            FP=sum(DATA_(IHD_==0,3));
            Se=TP/(TP+FN);
            PPV=TP/(TP+FP);
            
            show=0; % if show is 1, the decile graph will be displayed by lemeshow()
         %   H=lemeshow([IHD_ DATA_(:,2)],show);
            
            % Use the title of figure to display the results
           % title(['H= ' num2str(H) ' Se= ' num2str(Se) ' PPV= ' num2str(PPV) '. ' num2str(class_th) ])
            
            % The event 1 score is the smaller of Se and PPV.
            score1 = min(Se, PPV);
            if score1>max_score1
                max_score1=score1;
                best_th=class_th;
               % max_score2=H;
                BEST_DATA=DATA_;
                %  display(['Unofficial Event 1 score: ' num2str(score1)]);
            end
            
       % end
        
    end
    
%    survivals1=sum(X==1 & IHD==0);
%    deaths1=sum(X==1 & IHD==1);
%    survivals2=sum(X==0 & IHD==0);
%    deaths2=sum(X==0 & IHD==1);
%    
%    disp([' non NaNs:' num2str(deaths1/(deaths1+survivals1)) ])
%    disp([' NaNs:' num2str(deaths2/(deaths2+survivals2)) ])
%    
   
   max_score1
   
end


function [best_th,max_score1,BEST_DATA]=opt_th_valid(X_,IHD_)
        
  %  num_params=size(time_series_names,1);
    
    
    n=size(X_,1);
    s=zeros(n,1);
    s(1:round(n/2))=1;
    s=boolean(s);
    B = mnrfit(X_(s,:),IHD_(s)+1);
    
    PHAT = mnrval(B,X_(~s,:));
    
    BEST_DATA=[];
    DATA_=zeros(size(PHAT,1),3);
    
    max_score1=0;
    max_score2=0;
    best_th=0;
    
    for class_th=.1:.01:0.5
        
        
        
        DATA_(:,1)=str2double('0000');
        
        DATA_(:,2)=PHAT(:,2);
        
        DATA_(:,3)=PHAT(:,2)> class_th;
        
        DATA_(DATA_(:,2)<0.01,2)=0.01;
        DATA_(DATA_(:,2)>0.99,2)=0.99;
        
        
     %   if(~isempty(results))
            
            % Calculate sensitivity (Se) and positive predictivity (PPV)
            TP=sum(DATA_(IHD_(~s)==1,3));
            FN=sum(~DATA_(IHD_(~s)==1,3));
            FP=sum(DATA_(IHD_(~s)==0,3));
            Se=TP/(TP+FN);
            PPV=TP/(TP+FP);
            
          %  show=0; % if show is 1, the decile graph will be displayed by lemeshow()
         %   H=lemeshow([IHD_ DATA_(:,2)],show);
            
            % Use the title of figure to display the results
           % title(['H= ' num2str(H) ' Se= ' num2str(Se) ' PPV= ' num2str(PPV) '. ' num2str(class_th) ])
            
            % The event 1 score is the smaller of Se and PPV.
            score1 = min(Se, PPV);
            if score1>max_score1
                max_score1=score1;
                best_th=class_th;
               % max_score2=H;
                BEST_DATA=DATA_;
                %  display(['Unofficial Event 1 score: ' num2str(score1)]);
            end
            
       % end
        
    end
    
%    survivals1=sum(X==1 & IHD==0);
%    deaths1=sum(X==1 & IHD==1);
%    survivals2=sum(X==0 & IHD==0);
%    deaths2=sum(X==0 & IHD==1);
%    
%    disp([' non NaNs:' num2str(deaths1/(deaths1+survivals1)) ])
%    disp([' NaNs:' num2str(deaths2/(deaths2+survivals2)) ])
%    
%    
%    max_score1
   
end



end