diff toolboxes/FullBNT-1.0.7/netlab3.3/gpcovarf.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/gpcovarf.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,44 @@
+function covf = gpcovarf(net, x1, x2)
+%GPCOVARF Calculate the covariance function for a Gaussian Process.
+%
+%	Description
+%
+%	COVF = GPCOVARF(NET, X1, X2) takes  a Gaussian Process data structure
+%	NET together with two matrices X1 and X2 of input vectors,  and
+%	computes the matrix of the covariance function values COVF.
+%
+%	See also
+%	GP, GPCOVAR, GPCOVARP, GPERR, GPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', x1);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if size(x1, 2) ~= size(x2, 2)
+  error('Number of variables in x1 and x2 must be the same');
+end
+
+n1 = size(x1, 1);
+n2 = size(x2, 1);
+beta = diag(exp(net.inweights));
+
+% Compute the weighted squared distances between x1 and x2
+z = (x1.*x1)*beta*ones(net.nin, n2) - 2*x1*beta*x2' ... 
+  + ones(n1, net.nin)*beta*(x2.*x2)';
+
+switch net.covar_fn
+
+  case 'sqexp'		% Squared exponential
+    covf = exp(net.fpar(1) - 0.5*z);
+
+  case 'ratquad'	% Rational quadratic
+    nu = exp(net.fpar(2));
+    covf = exp(net.fpar(1))*((ones(size(z)) + z).^(-nu));
+
+  otherwise
+    error(['Unknown covariance function ', net.covar_fn]);  
+end
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