comparison toolboxes/FullBNT-1.0.7/netlab3.3/rbferr.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [e, edata, eprior] = rbferr(net, x, t)
+%RBFERR	Evaluate error function for RBF network.
+%
+%	Description
+%	E = RBFERR(NET, X, T) takes a network data structure NET together
+%	with a matrix X of input vectors and a matrix T of target vectors,
+%	and evaluates the appropriate error function E depending on
+%	NET.OUTFN.  Each row of X corresponds to one input vector and each
+%	row of T contains the corresponding target vector.
+%
+%	[E, EDATA, EPRIOR] = RBFERR(NET, X, T) additionally returns the data
+%	and prior components of the error, assuming a zero mean Gaussian
+%	prior on the weights with inverse variance parameters ALPHA and BETA
+%	taken from the network data structure NET.
+%
+%	See also
+%	RBF, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+switch net.outfn
+case 'linear'
+   errstring = consist(net, 'rbf', x, t);
+case 'neuroscale'
+   errstring = consist(net, 'rbf', x);
+otherwise
+   error(['Unknown output function ', net.outfn]);
+end
+if ~isempty(errstring);
+  error(errstring);
+end
+
+switch net.outfn
+case 'linear'
+   y = rbffwd(net, x);
+   edata = 0.5*sum(sum((y - t).^2));
+case 'neuroscale'
+   y = rbffwd(net, x);
+   y_dist = sqrt(dist2(y, y));
+   % Take t as target distance matrix
+   edata = 0.5.*(sum(sum((t-y_dist).^2)));
+otherwise
+   error(['Unknown output function ', net.outfn]);
+end
+
+% Compute Bayesian regularised error
+[e, edata, eprior] = errbayes(net, edata);
+