diff toolboxes/FullBNT-1.0.7/netlab3.3/demgp.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/demgp.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,160 @@
+%DEMGP	Demonstrate simple regression using a Gaussian Process.
+%
+%	Description
+%	The problem consists of one input variable X and one target variable
+%	T. The values in X are chosen in two separated clusters and the
+%	target data is generated by computing SIN(2*PI*X) and adding Gaussian
+%	noise. Two Gaussian Processes, each with different covariance
+%	functions are trained by optimising the hyperparameters  using the
+%	scaled conjugate gradient algorithm.  The final predictions are
+%	plotted together with 2 standard deviation error bars.
+%
+%	See also
+%	GP, GPERR, GPFWD, GPGRAD, GPINIT, SCG
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Find out if flops is available (i.e. pre-version 6 Matlab)
+v = version;
+if (str2num(strtok(v, '.')) >= 6)
+    flops_works = logical(0);
+else
+    flops_works = logical(1);
+end
+
+randn('state', 42);
+x = [0.1 0.15 0.2 0.25  0.65 0.7 0.75 0.8 0.85 0.9]';
+ndata = length(x);
+t = sin(2*pi*x) + 0.05*randn(ndata, 1);
+
+xtest = linspace(0, 1, 50)';
+
+clc
+disp('This demonstration illustrates the use of a Gaussian Process')
+disp('model for regression problems.  The data is generated from a noisy')
+disp('sine function.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+flops(0);
+% Initialise the parameters.
+net = gp(1, 'sqexp');
+prior.pr_mean = 0;
+prior.pr_var = 1;
+net = gpinit(net, x, t, prior);
+
+clc
+disp('The first GP uses the squared exponential covariance function.')
+disp('The hyperparameters are initialised by sampling from a Gaussian with a')
+disp(['mean of ', num2str(prior.pr_mean), ' and variance ', ...
+    num2str(prior.pr_var), '.'])
+disp('After initializing the network, we train it using the scaled conjugate')
+disp('gradients algorithm for 20 cycles.')
+disp(' ')
+disp('Press any key to continue')
+pause
+
+% Now train to find the hyperparameters.
+options = foptions;
+options(1) = 1;    % Display training error values
+options(14) = 20;
+flops(0)
+[net, options] = netopt(net, options, x, t, 'scg');
+if flops_works
+    sflops = flops;
+end
+
+disp('The second GP uses the rational quadratic covariance function.')
+disp('The hyperparameters are initialised by sampling from a Gaussian with a')
+disp(['mean of ', num2str(prior.pr_mean), ' and variance ', num2str(prior.pr_var)])
+disp('After initializing the network, we train it using the scaled conjugate')
+disp('gradients algorithm for 20 cycles.')
+disp(' ')
+disp('Press any key to continue')
+pause
+flops(0)
+net2 = gp(1, 'ratquad');
+net2 = gpinit(net2, x, t, prior);
+flops(0)
+[net2, options] = netopt(net2, options, x, t, 'scg');
+if flops_works
+    rflops = flops;
+end
+
+disp(' ')
+disp('Press any key to continue')
+disp(' ')
+pause
+clc
+
+fprintf(1, 'For squared exponential covariance function,');
+if flops_works    
+    fprintf(1, 'flops = %d', sflops);
+end
+fprintf(1, '\nfinal hyperparameters:\n')
+format_string = strcat('  bias:\t\t\t%10.6f\n  noise:\t\t%10.6f\n', ...
+  '  inverse lengthscale:\t%10.6f\n  vertical scale:\t%10.6f\n');
+fprintf(1, format_string, ...
+    exp(net.bias), exp(net.noise), exp(net.inweights(1)), exp(net.fpar(1)));
+fprintf(1, '\n\nFor rational quadratic covariance function,');
+if flops_works
+    fprintf(1, 'flops = %d', rflops);
+end
+fprintf(1, '\nfinal hyperparameters:\n')
+format_string = [format_string '  cov decay order:\t%10.6f\n'];
+fprintf(1, format_string, ...
+      exp(net2.bias), exp(net2.noise), exp(net2.inweights(1)), ...
+      exp(net2.fpar(1)), exp(net2.fpar(2)));
+disp(' ')
+disp('Press any key to continue')
+pause
+
+disp(' ')
+disp('Now we plot the data, underlying function, model outputs and two')
+disp('standard deviation error bars on a single graph to compare the results.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+cn = gpcovar(net, x); 
+cninv = inv(cn);
+[ytest, sigsq] = gpfwd(net, xtest, cninv);
+sig = sqrt(sigsq);
+
+fh1 = figure;
+hold on
+plot(x, t, 'ok');
+xlabel('Input')
+ylabel('Target')
+fplot('sin(2*pi*x)', [0 1], '--m');
+plot(xtest, ytest, '-k');
+plot(xtest, ytest+(2*sig), '-b', xtest, ytest-(2*sig), '-b');
+axis([0 1 -1.5 1.5]);
+title('Squared exponential covariance function')
+legend('data', 'function', 'GP', 'error bars');
+hold off
+
+cninv2 = inv(gpcovar(net2, x));
+[ytest2, sigsq2] = gpfwd(net2, xtest, cninv2);
+sig2 = sqrt(sigsq2);
+fh2 = figure;
+hold on
+plot(x, t, 'ok');
+xlabel('Input')
+ylabel('Target')
+fplot('sin(2*pi*x)', [0 1], '--m');
+plot(xtest, ytest2, '-k');
+plot(xtest, ytest2+(2*sig2), '-b', xtest, ytest2-(2*sig2), '-b');
+axis([0 1 -1.5 1.5]);
+title('Rational quadratic covariance function')
+legend('data', 'function', 'GP', 'error bars');
+hold off
+
+disp(' ')
+disp('Press any key to end.')
+pause
+close(fh1);
+close(fh2);
+clear all;
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