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, 45 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/netinit.m b/sourcecodes/bnt-master/netlab3.3/netinit.m
new file mode 100644
index 00000000..f94e30b3
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/netinit.m
@@ -0,0 +1,45 @@
+function net = netinit(net, prior)
+%NETINIT Initialise the weights in a network.
+%
+%	Description
+%
+%	NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
+%	the weights and biases by sampling from a Gaussian distribution. If
+%	PRIOR is a scalar, then all of the parameters (weights and biases)
+%	are sampled from a single isotropic Gaussian with inverse variance
+%	equal to PRIOR. If PRIOR is a data structure of the kind generated by
+%	MLPPRIOR, then the parameters are sampled from multiple Gaussians
+%	according to their groupings (defined by the INDEX field) with
+%	corresponding variances (defined by the ALPHA field).
+%
+%	See also
+%	MLPPRIOR, NETUNPAK, RBFPRIOR
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if isstruct(prior)
+    if (isfield(net, 'mask'))
+	if find(sum(prior.index, 2)) ~= find(net.mask)
+	    error('Index does not match mask');
+	end
+	sig = sqrt(prior.index*prior.alpha);
+	% Weights corresponding to zeros in mask will not be used anyway
+	% Set their priors to one to avoid division by zero
+	sig = sig + (sig == 0);  
+	sig = 1./sqrt(sig);
+    else
+	sig = 1./sqrt(prior.index*prior.alpha);
+    end
+    w = sig'.*randn(1, net.nwts); 
+elseif size(prior) == [1 1]
+  w = randn(1, net.nwts).*sqrt(1/prior);
+else
+  error('prior must be a scalar or a structure');
+end  
+
+if (isfield(net, 'mask'))
+    w = w(logical(net.mask));
+end
+net = netunpak(net, w);
+