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: demev1.m
    path of file: ./repos/bnt/netlab3.3
    
    the code of the file until where you have to start completion: %DEMEV1	Demonstrate Bayesian regression for the MLP.
%
%	Description
%	The problem consists an input variable X which sampled from a
%	Gaussian distribution, and a target variable T generated by computing
%	SIN(2*PI*X) and adding Gaussian noise. A 2-layer network with linear
%	outputs is trained by minimizing a sum-of-squares error function with
%	isotropic Gaussian regularizer, using the scaled conjugate gradient
%	optimizer. The hyperparameters ALPHA and BETA are re-estimated using
%	the function EVIDENCE. A graph  is plotted of the original function,
%	the training data, the trained network function, and the error bars.
%
%	See also
%	EVIDENCE, MLP, SCG, DEMARD, DEMMLP1
%

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

clc;
disp('This demonstration illustrates the application of Bayesian')
di
    
function with
%	isotropic Gaussian regularizer, using the scaled conjugate gradient
%	optimizer. The hyperparameters ALPHA and BETA are re-estimated using
%	the function EVIDENCE. A graph  is plotted of the original function,
%	the training data, the trained network function, and the error bars.
%
%	See also
%	EVIDENCE, MLP, SCG, DEMARD, DEMMLP1
%

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

clc;
disp('This demonstration illustrates the application of Bayesian')
disp('re-estimation to determine the hyperparameters in a simple regression')
disp('problem. It is based on a local quadratic approximation to a mode of')
disp('the posterior distribution and the evidence maximization framework of')
disp('MacKay.')
disp(' ')
disp('First, we generate a synthetic data set consisting of a single input')
disp('variable x sampled from a Gaussian distribution, and a target variable')
disp('t obtained by evaluating sin(2*pi*x) and adding Gaussian noise.')
disp(' ')
disp('Press any key to see a plot of the data together with the sine function.')
pause;

% Generate the matrix of inputs x and targets t.

ndata = 16;			% Number of data points.
noise = 0.1;			% Standard deviation of noise distribution.
randn('state', 0);
x = 0.25 + 0.07*randn(ndata, 1);
t = sin(2*pi*x) + noise*randn(size(x));

% Plot the data and the original sine function.
h = figure;
nplot = 200;
plotvals = linspace(0, 1, nplot)';
plot(x, t, 'ok')
xlabel('Input')
ylabel('Target')
hold on
axis([0 1 -1.5 1.5])
fplot('sin(2*pi*x)', [0 1], '-g')
legend