function [risk,prediction]=physionet2012(time,param,value)
% [risk,prediction]=physionet2012(time,param,value)
%
% Sample Submission for the PhysioNet 2012 Challenge. Variables are:
%
% time - (Nx1 Cell Array) Cell array containing time of measurement
% param - (Nx1 Cell Array) Cell array containing type (category) of
% measurement
% value - (Nx1 Cell Array) Cell array containing value of measurement
%
%
% risk - (Scalar) estimate of the risk of the patient dying in hospital
% prediction - (Logical)Binary classification if the patient is going to die
% in the hospital (1 - Died, 0 - Survived)
%
% Example:
% [risk,prediction]=physionet2012(time,param,value)
load('helpers.mat');
% threshold
% h
% train_dvs
% train_labels
% w
%
% subtract
% divide
%
% all_bin_cuts
% motifs
% load CCF features
f = create_CCF(param, value);
% convert data into a grid
M = create_grid(time, param, value);
% count the motifs
m = count_motifs_newdata( M, 1, 3, all_bin_cuts, motifs );
% scale features
x = [f m];
x = x - subtract;
x = x ./ divide;
% append for bias term
x = [x 1];
%x = [1 x];
% apply classifier
dv = w*x';
% calibrate results
risk = kernel_regress(dv, train_dvs, train_labels, h);
prediction = (dv >= threshold);
end