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

first hg version after svn

+function [e, edata, eprior, y, a] = glmerr_weighted(net, x, t, eso_w)
+%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 (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+[y, a] = glmfwd(net, x);
+
+%switch net.actfn
+  switch net.outfn
+
+  case 'softmax'   	% Softmax outputs
+  
+    nout = size(a,2);
+    % Ensure that sum(exp(a), 2) does not overflow
+    maxcut = log(realmax) - log(nout);
+    % Ensure that exp(a) > 0
+    mincut = log(realmin);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    temp = exp(a);
+    y = temp./(sum(temp, 2)*ones(1,nout));
+    % Ensure that log(y) is computable
+    y(y<realmin) = realmin;
+    e_app=sum(t.*log(y),2);
+    edata = - eso_w'*e_app;
+    
+  otherwise
+    error(['Unknown activation function ', net.actfn]);
+end
+
+[e, edata, eprior] = errbayes(net, edata);