diff toolboxes/FullBNT-1.0.7/netlabKPM/glmhess_weighted.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlabKPM/glmhess_weighted.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,57 @@
+function [h, hdata] = glmhess_weighted(net, x, t, eso_w, hdata)
+%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
+%
+%	Description
+%	H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a
+%	matrix X of input values, and a matrix T of target values and returns
+%	the full Hessian matrix H corresponding to the second derivatives of
+%	the negative log posterior distribution, evaluated for the current
+%	weight and bias values as defined by NET. Note that the target data
+%	is not required in the calculation, but is included to make the
+%	interface uniform with NETHESS.  For linear and logistic outputs, the
+%	computation is very simple and is  done (in effect) in one line in
+%	GLMTRAIN.
+%
+%	See also
+%	GLM, GLMTRAIN, HESSCHEK, NETHESS
+%
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+nparams = net.nwts;
+nout = net.nout;
+p = glmfwd(net, x);
+inputs = [x ones(ndata, 1)];
+
+if nargin == 4
+   hdata = zeros(nparams);	% Full Hessian matrix
+   % Calculate data component of Hessian
+   switch net.outfn
+    
+   case 'softmax'
+    bb_start = nparams - nout + 1;	% Start of bias weights block
+    ex_hess = zeros(nparams);	% Contribution to Hessian from single example
+    for m = 1:ndata
+      X = x(m,:)'*x(m,:);
+      a = diag(p(m,:))-((p(m,:)')*p(m,:)); 
+      a=eso_w(m,1)*a;
+      ex_hess(1:nparams-nout,1:nparams-nout) = kron(a, X);
+      ex_hess(bb_start:nparams, bb_start:nparams) = a.*ones(net.nout, net.nout);
+      temp = kron(a, x(m,:));
+      ex_hess(bb_start:nparams, 1:nparams-nout) = temp;
+      ex_hess(1:nparams-nout, bb_start:nparams) = temp';
+      hdata = hdata + ex_hess;
+    end
+    
+    otherwise
+      error(['Unknown activation function ', net.actfn]);
+    end
+end
+
+[h, hdata] = hbayes(net, hdata);