diff toolboxes/FullBNT-1.0.7/netlab3.3/demmlp1.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demmlp1.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,88 @@
+%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);
+
+clc
+disp('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. 
+
+clc
+disp(['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 on
+xlabel('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.')
+pause
+close(fh1);
+clear all;