ten_fold_cross_validation_parameter_estimation.m

function[results] = ten_fold_cross_validation_parameter_estimation(x, y)
 
     results = [];
     
     % Train and pick the best tree using 10-fold cross validation.
     %[x2, y2] = ANNdata(x, y);
     for hidden_neurons_1 = 10:25
         hidden_neurons_1
         for hidden_neurons_2 = 10:25
             hidden_neurons_2
             for lr = 0.5:0.05:0.6
                 lr
                 cum_sum_confusion_matrix = zeros(2);         
                 for i = 1 : 10 
                    [train_idx, validation_idx] = partition(i, x);
                    net = create_traingd(x, y, [hidden_neurons_1, hidden_neurons_2], 100, ...
                        train_idx, validation_idx, [], lr);    
                    predicted = testANN(net, x(:, validation_idx), @NNout2labels);
                    cum_sum_confusion_matrix = cum_sum_confusion_matrix ...
                          + confusion_matrix(2, predicted, y(validation_idx)');
                 end  
                 avg_confusion_matrix = cum_sum_confusion_matrix / 10
                 results = [results; mean(f1_measure(avg_confusion_matrix))]
             end
         end
     end
end