%DEMMLP1 Demonstrate simple regression using a multi-layer perceptron%%	Description%	The problem consists of one input variable X and one target variable%	T with data generated by sampling X at equal intervals and then%	generating target data 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 using the scaled conjugate gradient%	optimizer.%%	See also%	MLP, MLPERR, MLPGRAD, SCG%%	Copyright (c) Ian T Nabney (1996-2001)% Generate the matrix of inputs x and targets t.ndata = 20;			% Number of data points.noise = 0.2;			% Standard deviation of noise distribution.x = [0:1/(ndata - 1):1]';randn('state', 1);t = sin(2*pi*x) + noise*randn(ndata, 1);clcdisp('This demonstration illustrates the use of a Multi-Layer Perceptron')disp('network for regression problems.  The data is generated from a noisy')disp('sine function.')disp(' ')disp('Press any key to continue.')pause% Set up network parameters.nin = 1;			% Number of inputs.nhidden = 3;			% Number of hidden units.nout = 1;			% Number of outputs.alpha = 0.01;			% Coefficient of weight-decay prior. % Create and initialize network weight vector.net = mlp(nin, nhidden, nout, 'linear', alpha);% Set up vector of options for the optimiser.options = zeros(1,18);options(1) = 1;			% This provides display of error values.options(14) = 100;		% Number of training cycles. clcdisp(['The network has ', num2str(nhidden), ' hidden units and a weight decay'])disp(['coefficient of ', num2str(alpha), '.'])disp(' ')disp('After initializing the network, we train it use the scaled conjugate')disp('gradients algorithm for 100 cycles.')disp(' ')disp('Press any key to continue')pause% Train using scaled conjugate gradients.[net, options] = netopt(net, options, x, t, 'scg');disp(' ')disp('Now we plot the data, underlying function, and network outputs')disp('on a single graph to compare the results.')disp(' ')disp('Press any key to continue.')pause% Plot the data, the original function, and the trained network function.plotvals = [0:0.01:1]';y = mlpfwd(net, plotvals);fh1 = figure;plot(x, t, 'ob')hold onxlabel('Input')ylabel('Target')axis([0 1 -1.5 1.5])[fx, fy] = fplot('sin(2*pi*x)', [0 1]);plot(fx, fy, '-r', 'LineWidth', 2)plot(plotvals, y, '-k', 'LineWidth', 2)legend('data', 'function', 'network');disp(' ')disp('Press any key to end.')pauseclose(fh1);clear all;