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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/netlab3.3/glmunpak.m
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
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
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+function net = glmunpak(net, w)
+%GLMUNPAK Separates weights vector into weight and bias matrices. 
+%
+%	Description
+%	NET = GLMUNPAK(NET, W) takes a glm network data structure NET and  a
+%	weight vector W, and returns a network data structure identical to
+%	the input network, except that the first-layer weight matrix W1 and
+%	the first-layer bias vector B1 have been set to the corresponding
+%	elements of W.
+%
+%	See also
+%	GLM, GLMPAK, GLMFWD, GLMERR, GLMGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm');
+if ~errstring
+  error(errstring);
+end
+
+if net.nwts ~= length(w)
+  error('Invalid weight vector length')
+end
+
+nin = net.nin;
+nout = net.nout;
+net.w1 = reshape(w(1:nin*nout), nin, nout);
+net.b1 = reshape(w(nin*nout + 1: (nin + 1)*nout), 1, nout);