You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: demev2.m
    path of file: ./repos/bnt/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 standard weight groups), using
%	the scaled conjugate gradient optimizer. The hyperparameter vectors
%	ALPHA and BETA are re-estimated using the function EVIDENCE. A graph
%	is plotted of the optimal, regularised, and unregularised decision
%	boundaries.  A further plot of the moderated versus unmoderated
%	contours is generated.
%
%	See also
%	EVIDENCE, MLP, SCG, DEMARD, DEMMLP2
%

%	Copyright (c) Ian T Nabney (1996-2001)


clc;

disp('This program demonstrates the use of the evidence procedure on')
disp('a two-class problem.  It also shows the improved generalisation')
disp('performance that can be achieved with moderated outputs; that is')
disp('predictions where an approximate integration over the true')
disp('posterior distribution is carried out.')
disp(' ')
disp('First we generate a synthetic dataset with two-dimensional input')
disp('sampled from a mixture of four Gaussians.  Each class is')
disp('associated with two of the Gaussians so that the optimal decision')
disp('boundary is non-linear.')
disp(' ')
disp('Press any key to see a plot of the data.')
pause;

% Generate the matrix of inputs x and targets t.

rand('state', 423);
randn('state', 423);

ClassSymbol1 = 'r.';
ClassSymbol2 = 'y.';
PointSize = 12;
titleSize = 10;

fh1 = figure;
set(fh1, 'Name', 'True Data Distribution');
whitebg(fh1, 'k');

% 
% Generate the data
% 
n=200;

% Set up mixture model: 2d data with four centres
% Class 1 is first two centres, class 2 from the other two
mix = gmm(2, 4, 'full');
mix.priors = [0.25 0.25 0.25 0.25];
mix.centres = [0 -0.1; 1.5 0; 1 1; 1 -1];
mix.covars(:,:,1) = [0.625 -0.2165; -0.2165 0.875];
mix.covars(:,:,2) = [0.25 0; 0 0.25];
mix.covars(:,:,3) = [0.2241 -0.1368; -0.1368 0.9759];
mix.covars(:,:,4) = [0.2375 0.1516; 0.1516 0.4125];

[data, label] = gmmsamp(mix, n);

% 
% Calculate some useful axis limits
% 
x0 = min(data(:,1));
x1 = max(data(:,1));
y0 = min(data(:,2));
y1 = max(data(:,2));
dx = x1-x0;
dy = y1-y0;
expand = 5/100;			% Add on 5 percent each way
x0 = x0 - dx*expand;
x1 = x1 + dx*expand;
y0 = y0 - dy*expand;
y1 = y1 + dy*expand;
resolution = 100;
step = dx/resolution;
xrange = [x0:step:x1];
yrange = [y0:step:y1];
% 					
% Generate the grid
% 
[X Y]=meshgrid([x0:step:x1],[y0:step:y1]);
% 
% Calculate the class conditional densities, the unconditional densities and
% the posterior probabilities
% 
px_j = gmmactiv(mix, [X(:) Y(:)]);
px = reshape(px_j*(mix.priors)',size(X));
post = gmmpost(mix, [X(:) Y(:)]);
p1_x = reshape(post(:, 1) + post(:, 2), size(X));
p2_x = reshape(post(:, 3) + post(:, 4), size(X));

plot(data((label<=2),1),data(label<=2,2),ClassSymbol1, 'MarkerSize', ...
PointSize)
hold on
axis([x0 x1 y0 y1])
plot(data((label>2),1),data(label>2,2),ClassSymbol2, 'MarkerSize', ...
    PointSize)

% Convert targets to 0-1 encoding
target=[label<=2];
disp(' ')
disp('Press any key to continue')
pause; clc;

disp('Next we create a two-layer MLP network with 6 hidden units and')
disp('one logistic output.  We use a separate inverse variance')
disp('hyperparameter for each group of weights (inputs, input bias,')
disp('outputs, output bias) and the weights are optimised with the')
disp('scaled conjugate gradient algorithm.  After each 100 iterations')
disp('the hyperparameters are re-estimated twice.  There are eight')
disp('cycles of the whole algorithm.')
disp(' ')
disp('Press any key to train the network and determine the hyperparameters.')
pause;

% Set up network parameters.
nin = 2;		% Number of inputs
    
function with isotroipc Gaussian regularizer (one
%	hyperparameter for each of the four standard weight groups), using
%	the scaled conjugate gradient optimizer. The hyperparameter vectors
%	ALPHA and BETA are re-estimated using the function EVIDENCE. A graph
%	is plotted of the optimal, regularised, and unregularised decision
%	boundaries.  A further plot of the moderated versus unmoderated
%	contours is generated.
%
%	See also
%	EVIDENCE, MLP, SCG, DEMARD, DEMMLP2
%

%	Copyright (c) Ian T Nabney (1996-2001)


clc;

disp('This program demonstrates the use of the evidence procedure on')
disp('a two-class problem.  It also shows the improved generalisation')
disp('performance that can be achieved with moderated outputs; that is')
disp('predictions where an approximate integration over the true')
disp('posterior distribution is carried out.')
disp(' ')
disp('First we generate a synthetic dataset with two-dimensional input')
disp('sampled from a mixture of four Gaussians.  Each class is')
disp('associated with two of the Gaussians so that the optimal decision')
disp('boundary is non-linear.')
disp(' ')
disp('Press any key to see a plot of the data.')
pause;

% Generate the matrix of inputs x and targets t.

rand('state', 423);
randn('state', 423);

ClassSymbol1 = 'r.';
ClassSymbol2 = 'y.';
PointSize = 12;
titleSize = 10;

fh1 = figure;
set(fh1, 'Name', 'True Data Distribution');
whitebg(fh1, 'k');

% 
% Generate the data
% 
n=200;

% Set up mixture model: 2d data with four centres
% Class 1 is first two centres, class 2 from the other two
mix = gmm(2, 4, 'full');
mix.priors = [0.25 0.25 0.25 0.25];
mix.centres = [0 -0.1; 1.5 0; 1 1; 1 -1];
mix.covars(:,:,1) = [0.625 -0.2165; -0.2165 0.875];
mix.covars(:,:,2) = [0.25 0; 0 0.25];
mix.covars(:,:,3) = [0.2241 -0.1368; -0.1368 0.9759];
mix.covars(:,:,4) = [0.2375 0.1516; 0.1516 0.4125];

[data, label] = gmmsamp(mix, n);

% 
% Calculate some useful axis limits
% 
x0 = min(data(:,1));
x1 = max(data(:,1));
y0 = min(data(:,2));
y1 = max(data(:,2));
dx = x1-x0;
dy = y1-y0;
expand = 5/100;			% Add on 5 percent each way
x0 = x0 - dx*expand;
x1 = x1 + dx*expand;
y0 = y0 - dy*expand;
y1 = y1 + dy*expand;
resolution = 100;
step = dx/resolution;
xrange = [x0:step:x1];
yrange = [y0:step:y1];
% 					
% Generate the grid
% 
[X Y]=meshgrid([x0:step:x1],[y0:step:y1]);
% 
% Calculate the class conditional densities, the unconditional densities and
% the posterior probabilities
% 
px_j = gmmactiv(mix, [X(:) Y(:)]);
px = reshape(px_j*(mix.priors)',size(X));
post = gmmpost(mix, [X(:) Y(:)]);
p1_x = reshape(post(:, 1) + post(:, 2), size(X));
p2_x = reshape(post(:, 3) + post(:, 4), size(X));

plot(data((label<=2),1),data(label<=2,2),ClassSymbol1, 'MarkerSize', ...
PointSize)
hold on
axis([x0 x1 y0 y1])
plot(data((label>2),1),data(label>2,2),ClassSymbol2, 'MarkerSize', ...
    PointSize)

% Convert targets to 0-1 encoding
target=[label<=2];
disp(' ')
disp('Press any key to continue')
pause; clc;

disp('Next we create a two-layer MLP network with 6 hidden units and')
disp('one logistic output.  We use a separate inverse variance')
disp('hyperparameter for each group of weights (inputs, input bias,')
disp('outputs, output bias) and the weights are optimised with the')
disp('scaled conjugate gradient algorithm.  After each 100 iterations')
disp('the hyperparameters are re-estimated twice.  There are eight')
disp('cycles of the whole algorithm.')
disp(' ')
disp('Press any key to train the network and determine the hyperparameters.')
pause;

% Set up network parameters.
nin = 2;		% Number of inputs.
nhidden = 6;		% Number of hidden units.
nout = 1;		% Number of outputs.
alpha = 0.01;		% Initial prior hyperparameter.
aw1 = 0.01;
ab1 = 0.01;
aw2 = 0.01;
ab2 = 0.01;

% Create and initialize network weight vector.
prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2);
net = mlp(nin, nhidden, nout, 'logistic', prior);

% Set up vector of options for the optimiser.
nouter = 8;			% Number of outer loops.
ninner = 2;			% Number of innter loops.
options = foptions;		% Default options vector.
options(1) = 1;			% This provides display of error values.
options(2) = 1.0e-5;		% Absolute precision for weights.
options(3) = 1.0e-5;		% Precision for objective function.
options(14) = 100;		% Number of training cycles in inner loop. 

% Train using scaled conjugate gradients, re-estimating alpha and beta.
for k = 1:nouter
  net = netopt(net, options, data, target, 'scg');
  [net, gamma] = evidence(net, data, target, ninner);
  fprintf(1, '\nRe-estimation cycle %d:\n', k);
  disp(['  alpha = ', num2str(net.alpha')]);
  fprintf(1, '  gamma =  %8.5f\n\n', gamma);
  disp(' ')
  disp('Press any key to continue.')
  pause;
end