diff toolboxes/FullBNT-1.0.7/netlab3.3/rbfhess.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/netlab3.3/rbfhess.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,91 @@
+function [h, hdata] = rbfhess(net, x, t, hdata)
+%RBFHESS Evaluate the Hessian matrix for RBF network.
+%
+%	Description
+%	H = RBFHESS(NET, X, T) takes an RBF 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.  Currently, the
+%	implementation only computes the Hessian for the output layer
+%	weights.
+%
+%	[H, HDATA] = RBFHESS(NET, X, T) returns both the Hessian matrix H and
+%	the contribution HDATA arising from the data dependent term in the
+%	Hessian.
+%
+%	H = RBFHESS(NET, X, T, HDATA) takes a network data structure NET, a
+%	matrix X of input values, and a matrix T of  target values, together
+%	with the contribution HDATA arising from the data dependent term in
+%	the Hessian, and returns the full Hessian matrix H corresponding to
+%	the second derivatives of the negative log posterior distribution.
+%	This version saves computation time if HDATA has already been
+%	evaluated for the current weight and bias values.
+%
+%	See also
+%	MLPHESS, HESSCHEK, EVIDENCE
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'rbf', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if nargin == 3
+  % Data term in Hessian needs to be computed
+  [a, z] = rbffwd(net, x); 
+  hdata = datahess(net, z, t);
+end
+
+% Add in effect of regularisation
+[h, hdata] = hbayes(net, hdata);
+
+% Sub-function to compute data part of Hessian
+function hdata = datahess(net, z, t)
+
+% Only works for output layer Hessian currently
+if (isfield(net, 'mask') & ~any(net.mask(...
+      1:(net.nwts - net.nout*(net.nhidden+1)))))
+  hdata = zeros(net.nwts);
+  ndata = size(z, 1);
+  out_hess = [z ones(ndata, 1)]'*[z ones(ndata, 1)];
+  for j = 1:net.nout
+    hdata = rearrange_hess(net, j, out_hess, hdata);
+  end
+else
+  error('Output layer Hessian only.');
+end
+return
+
+% Sub-function to rearrange Hessian matrix
+function hdata = rearrange_hess(net, j, out_hess, hdata)
+
+% Because all the biases come after all the input weights,
+% we have to rearrange the blocks that make up the network Hessian.
+% This function assumes that we are on the jth output and that all outputs
+% are independent.
+
+% Start of bias weights block
+bb_start = net.nwts - net.nout + 1;
+% Start of weight block for jth output
+ob_start = net.nwts - net.nout*(net.nhidden+1) + (j-1)*net.nhidden...
+   + 1; 
+% End of weight block for jth output
+ob_end = ob_start + net.nhidden - 1; 
+% Index of bias weight
+b_index = bb_start+(j-1);   
+% Put input weight block in right place
+hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nhidden, ...
+   1:net.nhidden);
+% Put second derivative of bias weight in right place
+hdata(b_index, b_index) = out_hess(net.nhidden+1, net.nhidden+1);
+% Put cross terms (input weight v bias weight) in right place
+hdata(b_index, ob_start:ob_end) = out_hess(net.nhidden+1, ...
+   1:net.nhidden);
+hdata(ob_start:ob_end, b_index) = out_hess(1:net.nhidden, ...
+   net.nhidden+1);
+
+return 
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