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

first hg version after svn

+function [e, edata, eprior, y, a] = glmerr(net, x, t)
+%GLMERR	Evaluate error function for generalized linear model.
+%
+%	Description
+%	 E = GLMERR(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 error function E. The choice
+%	of error function corresponds to the output unit activation function.
+%	Each row of X corresponds to one input vector and each row of T
+%	corresponds to one target vector.
+%
+%	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
+%	and prior components of the total error.
+%
+%	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
+%	giving the outputs of the models and a matrix A  giving the summed
+%	inputs to each output unit, where each row corresponds to one
+%	pattern.
+%
+%	See also
+%	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+[y, a] = glmfwd(net, x);
+
+switch net.outfn
+
+  case 'linear'  	% Linear outputs
+    edata = 0.5*sum(sum((y - t).^2));
+
+  case 'logistic'  	% Logistic outputs
+    edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
+
+  case 'softmax'   	% Softmax outputs
+    edata = - sum(sum(t.*log(y)));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);
+end
+
+[e, edata, eprior] = errbayes(net, edata);