function [prob,died]=physionet2012_F(tm,category,val)
% [prob,died]=physionet2012(tm,category,val) 
%   Submission A for the PhysioNet 2012 Challenge.
%
%	Inputs:
% tm      - (Nx1 Cell Array) Cell array containing time of measurement
% category- (Nx1 Cell Array) Cell array containing type (category)
%           measurement
% value   - (Nx1 Cell Array) Cell array containing value of measurement
%
%	Outputs:
% prob    - (Scalar) Probability value of the patient dying in the hospital
% died    - (Logical) Binary classification if the patient is going to die
%           on the hospital (1 - Died, 0 - Survived)
%
%	Example
%       [prob,died]=physionet2012(tm,category,val)

%	Copyright 2012 Alistair Johnson

%	$LastChangedBy: alistair $
%	$LastChangedDate: 2012-04-25 01:27:43 +0100 (Wed, 25 Apr 2012) $
%	$Revision: 345 $
%	Originally written on GLNXA64 by Alistair Johnson, 24-Apr-2012 14:02:25
%	Contact: alistairewj@gmail.com

%=== Miscellanious default values

%=======================%
%=== PREPROCESS DATA ===%
%=======================%
%=== Put into expected format for function
% Convert time from string to numeric minutes
tm = cellfun(@(x) str2double(x(1:2)), tm)*60 + cellfun(@(x) str2double(x(4:5)),tm);
data = [{1},{tm},{category},{val}];
[ data ] = pnPreprocess(data);

%=====================%
%=== LOAD FEATURES ===%
%=====================%
load datahi;
[ X ] = pniExtractFeaturesF(data);

idxTraining = true(4000,1);
idxTraining(4:4:end) = false;
idxTest = ~idxTraining;

medianVals = nanmedian(X(idxTraining,:),1);
for k=1:size(X,2)
    X(isnan(X(:,k)),k) = medianVals(k);
end
lr = glmfit(X(idxTraining,:),data_target(idxTraining),'binomial');

[pred] = glmval(lr,X,'logit');
prob = pred;
stats = stat_calc_struct(prob(idxTest),data_target(idxTest));
%===================%
%=== CLASSIFY ======%
%===================%

%=== Load trained rf
mdl=load('ModelF.mat');
[~,pred] = predict(mdl.rf,X); 
prob = pred(:,2);
prob = glmval(mdl.betas,prob,'logit');

%===================%
%=== SET OUTPUTS ===%
%===================%
died = prob >= mdl.T; % Thresholded probability to maximize PPV/Sens


end