function [risk,prediction]=physionet2012(time,param,value)



I=1;
i=1;

[ALL_CATEGORIES,time_series_names,descriptors]=get_param_names();
num_params=length(ALL_CATEGORIES);
num_ts_params=length(time_series_names);
num_descriptors=length(descriptors);

MEAN_DATA_24=zeros(I,num_ts_params) + NaN;
MEAN_DATA_48=zeros(I,num_ts_params) + NaN;

DESCRIPTORS=zeros(I,num_descriptors) + NaN;




[times,values,names]=extract_param_series(time,param,value);

[ts_times,ts_values,ts_names]=get_param_subset(time_series_names,times,values,names);

[des_times,des_values,des_names]=get_param_subset(descriptors,times,values,names);

DESCRIPTORS(i,:)=cell2mat(des_values(:))';

means24=calculate_mean(ts_times,ts_values,ts_names,time_series_names,[0 24*60]);
means48=calculate_mean(ts_times,ts_values,ts_names,time_series_names,[24*60 48*60]);
MEAN_DATA_24(i,:)=means24;
MEAN_DATA_48(i,:)=means48;


    [cont_times,cont_values,cont_names,d,dd,md_max,cont_values2,vari]=resample_params(time_series_names,ts_times,ts_values,ts_names,des_values,des_names);
    MD_DATA_D(i,1:length(d))=d';
    MD_DATA_DD(i,1:length(d))=dd';
    MD_VARI(i,:)=vari';
    MD_DATA(i)=md_max;
    DYN_DATA(:,:,i)=cont_values;

    
    R=~isnan(MEAN_DATA_24);
    
%SAPS_SCORES(i)=saps_score(time,param,value,1,[0 24]);
%SAPS_SCORES_48(i)=saps_score(time,param,value,1,[24 48]);


[risk1,prediction1]=lin_reg_classify(time_series_names,MD_DATA_D,MD_DATA_DD,MD_DATA,DESCRIPTORS,des_names,R,MD_VARI,MEAN_DATA_24,MEAN_DATA_48);

[risk2,prediction2]=lin_reg_classify2(time_series_names,MD_DATA_D,MD_DATA_DD,MD_DATA,DESCRIPTORS,des_names,R,MD_VARI,MEAN_DATA_24,MEAN_DATA_48);

%[risk3,prediction3]=lda_classify(DYN_DATA);

prediction=prediction1> 0.39 | prediction2> 0.35;
risk=mean([risk1 risk2 ]);        

% [PHAT,TH]=PCA_classify(MEAN_DATA_24,MEAN_DATA_48,DESCRIPTORS,time_series_names,des_names);

% if isnan(PHAT(:,2))
%     X_trunc_saps=[SAPS_SCORES-SAPS_SCORES_48 SAPS_SCORES ];
%     PHAT_saps = mnrval(B_saps,X_trunc_saps);
% end

% if isnan(PHAT(2))
%     % [risk,prediction]=physionet2012_SAPS(time,param,value);
%          risk=0.5;
%          prediction=risk>TH;
% elseif PHAT(2)<0.01
%     risk=0.01;
%     prediction=risk>TH;
% elseif PHAT(2)>0.99
%     risk=0.99;
%     prediction=risk>TH;
% else
%     risk=PHAT(2);
%     prediction=risk>TH;
% end


function [risk,prediction]=lin_reg_classify(time_series_names,MD_DATA_D,MD_DATA_DD,MD_DATA,DESCRIPTORS,des_names,R,MD_VARI,MEAN_DATA_24,MEAN_DATA_48);


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

%X_new_rec=zeros(size(X));

 % savefile='gender0.mat'; 
  savefile='gender0_60.mat';
  %  savefile='gender0_60_ppca';

%  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
      load(savefile,'N2','best_th','A','Mu','V', 'CV','N','mu','sigma');
%save('gender0_60_karsittu.mat','N2','best_th','A','Mu','V', 'CV','N','mu','sigma')
     % S_new=calc_S_new_data(X',A,V,Mu,N,CV);
   % X_new_rec =( repmat(Mu,1,size(S,2)) + (A*S))';

         S_new=calc_S_new_data(X',A,V,Mu,N,CV);
            X_new_rec =( repmat(Mu,1,1) + A*S_new)';
   

      sigma0 = sigma;
    sigma0(sigma0==0) = 1;
    z = bsxfun(@minus,X_new_rec', mu');
    z = bsxfun(@rdivide, z, sigma0');
    z(isnan(z))=0;
           %  x_in=(pinv(pc1)*z)';
  % X(Gender_a==0,:)=x_in(Gender_a==0,:);
  z=z';
    X(Gender_a==0,:)=z(Gender_a==0,:);
   
 elseif Gender_a==1 || Gender_a==-1
%     savefile='gender1.mat'; 
  savefile='gender1_60.mat';
 %   savefile='gender1_60_ppca';

  %load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
   load(savefile,'N2','best_th','A','Mu','V', 'CV','N','mu','sigma');
%save('gender1_60_karsittu.mat','N2','best_th','A','Mu','V', 'CV','N','mu','sigma')
 % 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)';
      
   S_new=calc_S_new_data(X',A,V,Mu,N,CV);
   X_new_rec =( repmat(Mu,1,1) + A*S_new)';
    
 
      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,:);
 z=z';
  z(isnan(z))=0;
 X(Gender_a==1,:)=z(Gender_a==1,:);
  X(Gender_a==-1,:)=z(Gender_a==-1,:);

 end

 
%X=[ MD_DATA_D MD_DATA_DD MD_DATA double(R) zscore(MD_VARI) X];
X=[Gender_a double(R) MD_VARI X];



load('w1.mat','w1')

[ResTrain,labelsEstimTrain,probsTrain,P] = predict_LRoma(w1,X,1);
%[ResTrain,labelsEstimTrain,probsTrain,P2] = predict_LRoma(w2,X,1);

 PHAT = P';
 PHAT2 = P';   
 
 
 class_th=0.32;
 
   % IHD_=IHD;
    
%     max_score1=0;
%     max_score2=100000;
%     best_th=0;
%     best_th2=0;
    
  %  for class_th=.1:.01:0.5
        
        
        
        DATA(:,1)=str2double('0000');
        
        DATA(:,2)=PHAT2(:,1);
        
        DATA(:,3)=PHAT(:,1)> class_th;
        
        DATA(DATA(:,2)<0.01,2)=0.01;
        DATA(DATA(:,2)>0.99,2)=0.99;
        
        
        risk=DATA(:,2);
        prediction=DATA(:,3);
        



end


function [risk,prediction]=lin_reg_classify2(time_series_names,MD_DATA_D,MD_DATA_DD,MD_DATA,DESCRIPTORS,des_names,R,MD_VARI,MEAN_DATA_24,MEAN_DATA_48);


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=[Gender_a (AGE_a) (ICUTYPE_a) double(R) MD_DATA_D,MD_DATA_DD,MD_DATA,MD_VARI];



load('w2.mat','w2')

[ResTrain,labelsEstimTrain,probsTrain,P] = predict_LRoma(w2,X,1);
%[ResTrain,labelsEstimTrain,probsTrain,P2] = predict_LRoma(w2,X,1);

 PHAT = P';
 PHAT2 = P';   
 
 
 class_th=0.32;
 
   % IHD_=IHD;
    
%     max_score1=0;
%     max_score2=100000;
%     best_th=0;
%     best_th2=0;
    
  %  for class_th=.1:.01:0.5
        
        
        
        DATA(:,1)=str2double('0000');
        
        DATA(:,2)=PHAT2(:,1);
        
        DATA(:,3)=PHAT(:,1)> class_th;
        
        DATA(DATA(:,2)<0.01,2)=0.01;
        DATA(DATA(:,2)>0.99,2)=0.99;
        
        
        risk=DATA(:,2);
        prediction=DATA(:,3);
        



end



function [risk,prediction]=lda_classify(DYN_DATA)

    
    matrixData=DYN_DATA;
 load('weightsLDA.mat','weightsLDA')
 featureVectors = featureExtractionLDA2(matrixData,weightsLDA);
 X=featureVectors;

load('w3.mat','w3')

[ResTrain,labelsEstimTrain,probsTrain,P] = predict_LRoma(w3,X,1);
%[ResTrain,labelsEstimTrain,probsTrain,P2] = predict_LRoma(w2,X,1);

 PHAT = P';
 PHAT2 = P';   
 
 
 class_th=0.32;
 
   % IHD_=IHD;
    
%     max_score1=0;
%     max_score2=100000;
%     best_th=0;
%     best_th2=0;
    
  %  for class_th=.1:.01:0.5
        
        
        
        DATA(:,1)=str2double('0000');
        
        DATA(:,2)=PHAT2(:,1);
        
        DATA(:,3)=PHAT(:,1)> class_th;
        
        DATA(DATA(:,2)<0.01,2)=0.01;
        DATA(DATA(:,2)>0.99,2)=0.99;
        
        
        risk=DATA(:,2);
        prediction=DATA(:,3);
        



end






end