Matlab_GPMultiClass/multiclass_gp_1D_example.m

function multiclass_gp_1D_example()
% plot GP model outputs for 1D artificial data
% play around with param settings to become familiar with GP models
%
% Copyright (c) by Alexander Freytag, 2013-11-13.

  % get some artificial 1D data
  train_data = [1;2.5;4.5]';
  train_labels = [1; 2; 4]';
  test_data = (0.1:0.01:5);

  % some default settings of hyperparameters
  loghyper = [-1 0];
  gpnoise = 0.01;

  
  % learn multi-class GP model
  K = feval('covSEiso', loghyper, train_data');
  model = learn_multiclass_gp(K, train_labels, gpnoise);

  % evaluate model on test data
  Ks = feval('covSEiso', loghyper, train_data', test_data');
  Kss = feval('covSEiso', loghyper, test_data', 'diag');  
  [mu, pred_labels, variance] = test_multiclass_gp(model, Ks, Kss);

  % visualize everything nicely
  hfigMean = figure(1);
  plot(train_data(1), 1, 'bx');
  title('Mean curve of each binary task');

  hold on
  plot(train_data(2), 1, 'gx');
  plot(train_data(3), 1, 'rx');

  plot(test_data, mu(1,:), 'b');
  plot(test_data, mu(2,:), 'g');
  plot(test_data, mu(3,:), 'r');
  hold off

  hfigPred = figure(2);
  plot(test_data, pred_labels, 'kx');
  title('Predicted multi-class labels');

  hfigPosterior = figure(3);
  title('Multi-class posterior mean and variance');
  colors = {'b', 'g', 'r'};
  hold on
  max_mean = max(mu,[],1);
  lower = max_mean-sqrt(variance);
  upper = max_mean+sqrt(variance);
  
  px = [test_data, flipdim(test_data,2)];
  py = [lower, flipdim(upper,2)];
  fill(px, py, 'y');
  
  for k=1:length(model.unique_labels)

    tmp_ID = pred_labels == model.unique_labels(k);
    plot( test_data(tmp_ID), ...
          mu(k,tmp_ID),...
          colors{k}, ...
          'LineWidth', 2 ...
         );

  end
  hold off
  
  
  
  %% update
  
  disp( 'Now add one example...' )
  
  pause
  
  
  train_data_new = [2.0];
  train_labels_new = [1];
  
  Ks = feval('covSEiso', loghyper, train_data', train_data_new');
  Kss = feval('covSEiso', loghyper, train_data_new', 'diag');    
  model = update_multiclass_gp(model, Kss, Ks, train_labels_new);
  
  
  train_data = [train_data, train_data_new];
  
  % evaluate model on test data
  Ks = feval('covSEiso', loghyper, train_data', test_data');
  Kss = feval('covSEiso', loghyper, test_data', 'diag');  
  [mu, pred_labels, variance] = test_multiclass_gp(model, Ks, Kss);

  % visualize everything nicely
  hfigMeanUpd = figure(4);
  hold on
  plot(train_data(1), 1, 'bx');
  plot(train_data(4), 1, 'bx');
  title('Mean curve of each binary task');

  
  plot(train_data(2), 1, 'gx');
  plot(train_data(3), 1, 'rx');

  plot(test_data, mu(1,:), 'b');
  plot(test_data, mu(2,:), 'g');
  plot(test_data, mu(3,:), 'r');
  hold off

  hfigPredUpd = figure(5);
  plot(test_data, pred_labels, 'kx');
  title('Predicted multi-class labels');

  hfigPosteriorUpd = figure(6);
  title('Multi-class posterior mean and variance');
  colors = {'b', 'g', 'r'};
  hold on
  max_mean = max(mu,[],1);
  lower = max_mean-sqrt(variance);
  upper = max_mean+sqrt(variance);
  
  px = [test_data, flipdim(test_data,2)];
  py = [lower, flipdim(upper,2)];
  fill(px, py, 'y');
  
  for k=1:length(model.unique_labels)

    tmp_ID = pred_labels == model.unique_labels(k);
    plot( test_data(tmp_ID), ...
          mu(k,tmp_ID), ...
          colors{k}, ...
          'LineWidth', 2 ...
        );

  end
  hold off  
  
  
  pause
  
  close ( hfigMean );
  close ( hfigPred );
  close ( hfigPosterior );
  %
  close ( hfigMeanUpd );
  close ( hfigPredUpd );
  close ( hfigPosteriorUpd );

end