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

first hg version after svn

+function [g, gdata, gprior] = glmgrad(net, x, t, eso_w)
+%GLMGRAD Evaluate gradient of error function for generalized linear model.
+%
+%	Description
+%	G = GLMGRAD(NET, X, T) takes a generalized linear model 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 function
+%	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] = GLMGRAD(NET, X, T) also returns separately  the
+%	data and prior contributions to the gradient.
+%
+%	See also
+%	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+y = glmfwd(net, x);
+temp = y - t;
+ndata = size(x, 1);
+for m=1:ndata,
+      delout(m,:)=eso_w(m,1)*temp(m,:);
+end
+gw1 = x'*delout;
+gb1 = sum(delout, 1);
+
+gdata = [gw1(:)', gb1];
+
+[g, gdata, gprior] = gbayes(net, gdata);