Matlab_GPMultiClass/test_multiclass_gp.m

function [pred_mean, pred_labels, pred_var] = test_multiclass_gp(model, Ks, Kss)
% Prediction with multi-class GP models, outputs predictive mean and variance
% 
% function [pred_mean, pred_labels, pred_var] = test_multiclass_gp(model, Ks, Kss)
%
% INPUT: model -- model structure obtained from function "learn_multiclass_gp"
%        Ks -- n x m matrix of kernel values between n training and m test samples
%        Kss -- m x 1 vector of self-similarities of test samples
%
% OUTPUT: pred_mean -- m x c matrix of predicitive mean values for each of the m test samples and each of the c binary tasks 
%         pred_labels -- m x 1 vector containing predicted multi-class label for each test sample
%         pred_var -- m x 1 vector containing predicted variance for each test sample
%
% NOTE: to get the maximum mean value for each test sample out of the c binary tasks, use: max_mean = max(pred_mean,[],2);
% Copyright (c) by Alexander Freytag, 2013-11-13.

    if ( nargin < 2)
        Kss = [];
    end

  pred_mean = zeros( size(Ks,2),length(model.unique_labels) );

  % loop over classes and thus over binary one-vs-all tasks
  for k=1:length(model.unique_labels)

    pred_mean(:,k) = Ks'*model.alpha{k};
  
  end

  % obtain predicted labels using max-pooling
  [~, maxID] = max(pred_mean,[],2);
  pred_labels = model.unique_labels(maxID);

  % compute predictive variances if necessary
  if nargout > 2
    
    v = model.L\Ks;
    
    if ( isempty(Kss) ) 
        disp('No Kss given - break!');
        return;
    end
    
    pred_var = Kss' - sum(v .* v)' + model.noise;

  end

end