1 files changed, 65 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/netlab3.3/rbfgrad.m b/sourcecodes/bnt-master/netlab3.3/rbfgrad.m
new file mode 100644
index 00000000..af6767ac
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/rbfgrad.m
@@ -0,0 +1,65 @@
+function [g, gdata, gprior] = rbfgrad(net, x, t)
+%RBFGRAD Evaluate gradient of error function for RBF network.
+%
+%	Description
+%	G = RBFGRAD(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 gradient G of the error function with respect to
+%	the network weights (i.e. including the hidden unit parameters). The
+%	error function is sum of squares. Each row of X corresponds to one
+%	input vector and each row of T contains the corresponding target
+%	vector. If the output function is 'NEUROSCALE' then the gradient is
+%	only computed for the output layer weights and biases.
+%
+%	[G, GDATA, GPRIOR] = RBFGRAD(NET, X, T) also returns separately  the
+%	data and prior contributions to the gradient. In the case of multiple
+%	groups in the prior, GPRIOR is a matrix with a row for each group and
+%	a column for each weight parameter.
+%
+%	See also
+%	RBF, RBFFWD, RBFERR, RBFPAK, RBFUNPAK, RBFBKP
+%
+
+%	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
+
+ndata = size(x, 1);
+
+[y, z, n2] = rbffwd(net, x);
+
+switch net.outfn
+case 'linear'
+
+   % Sum squared error at output units
+   delout = y - t;
+
+   gdata = rbfbkp(net, x, z, n2, delout);
+   [g, gdata, gprior] = gbayes(net, gdata);
+
+case 'neuroscale'
+   % Compute the error gradient with respect to outputs
+   y_dist = sqrt(dist2(y, y));
+   D = (t - y_dist)./(y_dist+diag(ones(ndata, 1)));
+   temp = y';
+   gradient = 2.*sum(kron(D, ones(1, net.nout)) .* ...
+      (repmat(y, 1, ndata) - repmat((temp(:))', ndata, 1)), 1);
+   gradient = (reshape(gradient, net.nout, ndata))';
+   % Compute the error gradient
+   gdata = rbfbkp(net, x, z, n2, gradient);
+   [g, gdata, gprior] = gbayes(net, gdata);
+otherwise
+   error(['Unknown output function ', net.outfn]);
+end
+