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    Metadata details:
    filename: demev2.m
    path of file: ./repos/pcm/matlab/lib/netlab3_3
    
    the code of the file until where you have to start completion: %DEMEV2	Demonstrate Bayesian classification for the MLP.
%
%	Description
%	A synthetic two class two-dimensional dataset X is sampled  from a
%	mixture of four Gaussians.  Each class is associated with two of the
%	Gaussians so that the optimal decision boundary is non-linear. A 2-
%	layer network with logistic outputs is trained by minimizing the
%	cross-entropy error function with isotroipc Gaussian regularizer (one
%	hyperparameter for each of the four stan
    
function represented by the trained networks.')
disp('We show the decision boundaries (output = 0.5) and the optimal')
disp('decision boundary given by applying Bayes'' theorem to the true')
disp('data model.')
disp(' ')
disp('Press any key to add the boundaries to the plot.')
pause;

% Evaluate predictions.
[yg, ymodg] = mlpevfwd(net, data, target, [X(:) Y(:)]);
yg = reshape(yg(:,1),size(X));
ymodg = reshape(ymodg(:,1),size(X));

% Bayesian decision boundary
[cB, hB] = contour(xrange,yrange,p1_x,[0.5 0.5],'b-');
[cNb, hNb] = contour(xrange,yrange,yg,[0.5 0.5],'r-');
[cN, hN] = contour(xrange,yrange,y2g,[0.5 0.5],'g-');
set(hB, 'LineWidth', 2);
set(hNb, 'LineWidth', 2);
set(hN, 'LineWidth', 2);
Chandles = [hB(1) hNb(1) hN(1)];
legend