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function fh=hme_reg_plot(net, nodes_info, train_data, test_data)
%
% Use this function ONLY when the input dimension is 1
% and the problem is a regression one.
% We assume that each row of 'train_data' & 'test_data' is an example.
%
% ----------------------------------------------------------------------------------------------------
% -> pierpaolo_b@hotmail.com or -> pampo@interfree.it
% ----------------------------------------------------------------------------------------------------
fh=figure('Name','HME based regression', 'MenuBar', 'none', 'NumberTitle', 'off');
mn_x_train = round(min(train_data(:,1)));
mx_x_train = round(max(train_data(:,1)));
x_train = mn_x_train(1):0.01:mx_x_train(1);
Z_train=fhme(net, nodes_info, x_train',size(x_train,2)); % forward propagation trougth the HME
if nargin==4,
subplot(2,1,1);
mn_x_test = round(min(test_data(:,1)));
mx_x_test = round(max(test_data(:,1)));
x_test = mn_x_test(1):0.01:mx_x_test(1);
Z_test=fhme(net, nodes_info, x_test',size(x_test,2)); % forward propagation trougth the HME
end
hold on;
set(gca, 'Box', 'on');
plot(x_train', Z_train, 'r');
plot(train_data(:,1),train_data(:,2),'+k');
title('Training set and prediction');
hold off
if nargin==4,
subplot(2,1,2);
hold on;
set(gca, 'Box', 'on');
plot(x_train', Z_train, 'r');
if size(test_data,2)==2,
plot(test_data(:,1),test_data(:,2),'+k');
end
title('Test set and prediction');
hold off
end
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