BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 59 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/fevbayes.m b/sourcecodes/bnt-master/netlab3.3/fevbayes.m
new file mode 100644
index 00000000..d5681596
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/fevbayes.m
@@ -0,0 +1,59 @@
+function [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess)
+%FEVBAYES Evaluate Bayesian regularisation for network forward propagation.
+%
+%	Description
+%	EXTRA = FEVBAYES(NET, Y, A, X, T, X_TEST) takes a network data
+%	structure  NET together with a set of hidden unit activations A from
+%	test inputs X_TEST, training data inputs X and T and outputs a matrix
+%	of extra information EXTRA that consists of error bars (variance) for
+%	a regression problem or moderated outputs for a classification
+%	problem. The optional argument (and return value)  INVHESS is the
+%	inverse of the network Hessian computed on the training data inputs
+%	and targets.  Passing it in avoids recomputing it, which can be a
+%	significant saving for large training sets.
+%
+%	This is called by network-specific functions such as MLPEVFWD which
+%	are needed since the return values (predictions and hidden unit
+%	activations) for different network types are in different orders (for
+%	good reasons).
+%
+%	See also
+%	MLPEVFWD, RBFEVFWD, GLMEVFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+w = netpak(net);
+g = netderiv(w, net, x_test);
+if nargin < 7
+  % Need to compute inverse hessian
+  hess = nethess(w, net, x, t);
+  invhess = inv(hess);
+end
+
+ntest = size(x_test, 1);
+var = zeros(ntest, 1);
+for idx = 1:1:net.nout,
+  for n = 1:1:ntest,
+    grad = squeeze(g(n,:,idx));
+    var(n,idx) = grad*invhess*grad';  
+  end
+end
+
+switch net.outfn
+    case 'linear'
+	% extra is variance
+	extra = ones(size(var))./net.beta + var;
+    case 'logistic'
+	% extra is moderated output
+	kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8));
+	extra = 1./(1 + exp(-kappa.*a));
+    case 'softmax'
+	% Use extended Mackay formula; beware that this may not
+	% be very accurate
+	kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8));
+	temp = exp(kappa.*a);
+	extra = temp./(sum(temp, 2)*ones(1, net.nout));
+    otherwise
+	error(['Unknown activation function ', net.outfn]);
+end