comparison toolboxes/FullBNT-1.0.7/netlabKPM/mlpgrad_weighted.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [g, gdata, gprior] = mlpgrad_weighted(net, x, t, eso_w)
+%MLPGRAD Evaluate gradient of error function for 2-layer network.
+%
+%	Description
+%	G = MLPGRAD(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. The error funcion corresponds to the choice of
+%	output unit activation function. Each row of X corresponds to one
+%	input vector and each row of T corresponds to one target vector.
+%
+%	[G, GDATA, GPRIOR] = MLPGRAD(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
+%	MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+[y, z] = mlpfwd(net, x);
+temp = y - t;
+ndata = size(x, 1);
+for m=1:ndata,
+      delout(m,:)=eso_w(m,1)*temp(m,:);
+end
+clear temp;
+gdata = mlpbkp(net, x, z, delout);
+
+[g, gdata, gprior] = gbayes(net, gdata);