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

first hg version after svn

+function g = rbfbkp(net, x, z, n2, deltas)
+%RBFBKP	Backpropagate gradient of error function for RBF network.
+%
+%	Description
+%	G = RBFBKP(NET, X, Z, N2, DELTAS) takes a network data structure NET
+%	together with a matrix X of input vectors, a matrix  Z of hidden unit
+%	activations, a matrix N2 of the squared distances between centres and
+%	inputs, and a matrix DELTAS of the  gradient of the error function
+%	with respect to the values of the output units (i.e. the summed
+%	inputs to the output units, before the activation function is
+%	applied). The return value is the gradient G of the error function
+%	with respect to the network weights. Each row of X corresponds to one
+%	input vector.
+%
+%	This function is provided so that the common backpropagation
+%	algorithm can be used by RBF network models to compute gradients for
+%	the output values (in RBFDERIV) as well as standard error functions.
+%
+%	See also
+%	RBF, RBFGRAD, RBFDERIV
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Evaluate second-layer gradients.
+gw2 = z'*deltas;
+gb2 = sum(deltas);
+
+% Evaluate hidden unit gradients
+delhid = deltas*net.w2';
+
+gc = zeros(net.nhidden, net.nin);
+ndata = size(x, 1);
+t1 = ones(ndata, 1);
+t2 = ones(1, net.nin);
+% Switch on activation function type
+switch net.actfn
+      
+case 'gaussian' % Gaussian
+   delhid = (delhid.*z);
+   % A loop seems essential, so do it with the shortest index vector
+   if (net.nin < net.nhidden)
+      for i = 1:net.nin
+         gc(:,i) = (sum(((x(:,i)*ones(1, net.nhidden)) - ...
+            (ones(ndata, 1)*(net.c(:,i)'))).*delhid, 1)./net.wi)';
+      end
+   else
+      for i = 1:net.nhidden
+         gc(i,:) = sum((x - (t1*(net.c(i,:)))./net.wi(i)).*(delhid(:,i)*t2), 1);
+      end
+   end
+   gwi = sum((n2.*delhid)./(2.*(ones(ndata, 1)*(net.wi.^2))), 1);
+   
+case 'tps'	% Thin plate spline activation function
+   delhid = delhid.*(1+log(n2+(n2==0)));
+   for i = 1:net.nhidden
+      gc(i,:) = sum(2.*((t1*(net.c(i,:)) - x)).*(delhid(:,i)*t2), 1);
+   end
+   % widths are not adjustable in this model
+   gwi = [];
+case 'r4logr' % r^4 log r activation function
+   delhid = delhid.*(n2.*(1+2.*log(n2+(n2==0))));
+   for i = 1:net.nhidden
+      gc(i,:) = sum(2.*((t1*(net.c(i,:)) - x)).*(delhid(:,i)*t2), 1);
+   end
+   % widths are not adjustable in this model
+   gwi = [];
+otherwise
+   error('Unknown activation function in rbfgrad')
+end
+   
+g = [gc(:)', gwi, gw2(:)', gb2];