% run_classifier: This function takes a single record from the
% challenge training or test set, and outputs a vector of
% probabilities per sample. You will need to edit this file to suit
% your algorithm.
%
% Written by Mohammad Ghassemi and Benjamin Moody, 2018
function predictions = run_classifier(header_file_name)
% Read record info from the header file
data = parse_header(header_file_name);
X_te = [];
% collect a list of all the trained models
files = dir(); files = {files.name};
models = find(contains(files,'_model'));
% load all the the data associated with this subject
signals = load(data.signal_location); signals = signals.val;
fs = str2num(data.fs);
n_samples = str2num(data.n_samples);
sid = data.subject_id;
signal_names = data.signal_names;
% select the window sie and step size we want to use to
% compute features
window_size = 300 * fs;
window_step = 300 * fs;
% find the index of the SaO2 signal.
sao2_ind = find(contains(signal_names,'SaO2'));
% For each 'window', extract the variance of the SaO2
ind = 1;
for j = 1:window_step:n_samples-window_step
X_te(ind) = var(signals(sao2_ind,j:j+window_step));
ind = ind + 1;
end
% generate the probability vectors
display('Generating Scores')
for k = 1:length(models)
% loading model
load(files{models(k)});
% generate the probability vectors
pred_short = glmval(coeff,X_te,'logit');
pred = mean(pred_short)*ones(n_samples,1);
for j = 1:length(pred_short)
paste_in = (j-1)*window_step+1 : j*window_step;
pred(paste_in) = pred_short(j)*ones(window_step,1);
end
% Compute average of the predictions.
if k > 1
avg_pred = avg_pred + (pred - avg_pred) / (j+1);
else
avg_pred = pred;
end
end
predictions = avg_pred;