Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012 1.0.0

function [outclass, prob] = svmclassify_ML(svmStruct,sample)
%SVMCLASSIFY classifies data using a support vector machine
%
%   GROUP = SVMCLASSIFY(SVMStruct,SAMPLE) classifies each row of the data
%   in SAMPLE using the information in a support vector machine classifier
%   structure SVMStruct created using SVMTRAIN. SAMPLE must have the same
%   number of columns as the data used to train the classifier in SVMTRAIN.
%   GROUP indicates the group to which each row of SAMPLE is assigned.



% check inputs
bioinfochecknargin(nargin,2,mfilename);

% deal with struct input case
if ~isstruct(svmStruct)
    error('Bioinfo:svmclassify:TwoInputsNoStruct',...
        'The first input should be a struct generated by SVMTRAIN.');
end

groupnames = svmStruct.GroupNames;

% check group is a vector -- though char input is special...
if ~isvector(groupnames) && ~ischar(groupnames)
    error('Bioinfo:svmclassify:GroupNotVector',...
        'Group must be a vector.');
end

% grp2idx sorts a numeric grouping var ascending, and a string grouping
% var by order of first occurrence
[g,groupString] = grp2idx(groupnames);  

% do the classification
if ~isempty(sample)
    % shift and scale the data if necessary:
    if ~isempty(svmStruct.ScaleData)
        for i = 1:size(sample,1)
            sample(i,:) = sample(i,:)./svmStruct.ScaleData;
        end
    end
 
    try
        [outclass, prob] = svmdecision_ML(sample,svmStruct);
    catch theException
        error('Bioinfo:svmclassify:ClassifyFailed',...
            'An error was encountered during classification.\n%s',theException.message);
    end
  
else
    outclass = [];
    prob = [];
end