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Diffstat (limited to 'sourcecodes/bnt-master/netlab3.3/rbfsetbf.m')
| -rw-r--r-- | sourcecodes/bnt-master/netlab3.3/rbfsetbf.m | 39 |
1 files changed, 39 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/netlab3.3/rbfsetbf.m b/sourcecodes/bnt-master/netlab3.3/rbfsetbf.m new file mode 100644 index 00000000..9e8c3ca4 --- /dev/null +++ b/sourcecodes/bnt-master/netlab3.3/rbfsetbf.m @@ -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)); \ No newline at end of file |