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

first hg version after svn

+function [g, gdata, gprior] = gbayes(net, gdata)
+%GBAYES	Evaluate gradient of Bayesian error function for network.
+%
+%	Description
+%	G = GBAYES(NET, GDATA) takes a network data structure NET together
+%	the data contribution to the error gradient for a set of inputs and
+%	targets. It returns the regularised error gradient using any zero
+%	mean Gaussian priors on the weights defined in NET.  In addition, if
+%	a MASK is defined in NET, then the entries in G that correspond to
+%	weights with a 0 in the mask are removed.
+%
+%	[G, GDATA, GPRIOR] = GBAYES(NET, GDATA) additionally returns the data
+%	and prior components of the error.
+%
+%	See also
+%	ERRBAYES, GLMGRAD, MLPGRAD, RBFGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Evaluate the data contribution to the gradient.
+if (isfield(net, 'mask'))
+   gdata = gdata(logical(net.mask));
+end
+if isfield(net, 'beta')
+  g1 = gdata*net.beta;
+else
+  g1 = gdata;
+end
+
+% Evaluate the prior contribution to the gradient.
+if isfield(net, 'alpha')
+   w = netpak(net);
+   if size(net.alpha) == [1 1]
+      gprior = w;
+      g2 = net.alpha*gprior;
+   else
+      if (isfield(net, 'mask'))
+         nindx_cols = size(net.index, 2);
+         nmask_rows = size(find(net.mask), 1);
+         index = reshape(net.index(logical(repmat(net.mask, ...
+            1, nindx_cols))), nmask_rows, nindx_cols);
+      else
+         index = net.index;
+      end
+      
+      ngroups = size(net.alpha, 1);
+      gprior = index'.*(ones(ngroups, 1)*w);
+      g2 = net.alpha'*gprior;
+   end
+else
+  gprior = 0;
+  g2 = 0;
+end
+
+g = g1 + g2;