function [ pred ] = pniClassifyC(data, mdl, D1var)
%PNILOUIS Louis's initial entry - severity of illness score
% [ pred ] = pniLouis(data) calculates a mortality prediction for each
% each row (observation/subject) in data
%
% The score uses the following variables:
% urine, platelets, BUN, creatinine, PaFi ratio, PaO2, PaCO2, pH,
% heart_rate, temperature, BP, and age.
%
% Inputs:
% data - Cell array of data.
% Column 1 - Subject IDs
% Column 2 - Time stamp vectors for each subject
% Column 3 - Feature name vectors for each subject
% Column 4 - Data value vectors for each subject
%
% Outputs:
% pred - Column vector of predictions
%
% Example
% %=== Load data in
% load('data_processed_cell.mat');
%
% %=== Calculate score
% [ score ] = pniAndrew(data);
%
% See also PNMAIN PNPREPROCESSDATA
% References:
% Physionet Challenge 2012
% http://physionet.org/challenge/2012/
%
% Copyright 2012 Alistair Johnson
% $LastChangedBy: alistair $
% $LastChangedDate: 2012-04-25 01:26:50 +0100 (Wed, 25 Apr 2012) $
% $Revision: 344 $
% Originally written on GLNXA64 by Alistair Johnson, 15-Apr-2012 14:40:13
% Contact: alistairewj@gmail.com
D2var = find(D1var==0);
%=== Hard-coded model
b_surv=mdl.b_surv;
b_Nsurv=mdl.b_Nsurv;
test_pred_surv=zeros(size(data,1),sum(D1var));
test_pred_Nsurv=zeros(size(data,1),sum(D1var));
for v = 1:length(D2var)
test_pred_surv(:,v) = [ones(size(data,1),1) data(:,D1var)]*b_surv(:,v) ;
test_pred_Nsurv(:,v) = [ones(size(data,1),1) data(:,D1var)]*b_Nsurv(:,v) ;
end
% Look at distance between actual Day-2 data and the predicted D2-data
Diff_surv = sum(abs(test_pred_surv - data(:,D2var)),2);
Diff_Nsurv = sum(abs(test_pred_Nsurv - data(:,D2var)),2);
% Prediction is a ration of distances
pred = Diff_Nsurv ./ (Diff_surv + Diff_Nsurv) ;
end