diff toolboxes/FullBNT-1.0.7/netlab3.3/rbfsetbf.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/rbfsetbf.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,39 @@
+function net = rbfsetbf(net, options, x)
+%RBFSETBF Set basis functions of RBF from data.
+%
+%	Description
+%	NET = RBFSETBF(NET, OPTIONS, X) sets the basis functions of the RBF
+%	network NET so that they model the unconditional density of the
+%	dataset X.  This is done by training a GMM with spherical covariances
+%	using GMMEM.  The OPTIONS vector is passed to GMMEM. The widths of
+%	the functions are set by a call to RBFSETFW.
+%
+%	See also
+%	RBFTRAIN, RBFSETFW, GMMEM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'rbf', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+% Create a spherical Gaussian mixture model
+mix = gmm(net.nin, net.nhidden, 'spherical');
+
+% Initialise the parameters from the input data
+% Just use a small number of k means iterations
+kmoptions = zeros(1, 18);
+kmoptions(1) = -1;	% Turn off warnings
+kmoptions(14) = 5;  % Just 5 iterations to get centres roughly right
+mix = gmminit(mix, x, kmoptions);
+
+% Train mixture model using EM algorithm
+[mix, options] = gmmem(mix, x, options);
+
+% Now set the centres of the RBF from the centres of the mixture model
+net.c = mix.centres;
+
+% options(7) gives scale of function widths
+net = rbfsetfw(net, options(7));
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