comparison toolboxes/FullBNT-1.0.7/netlab3.3/gtm.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function net = gtm(dim_latent, nlatent, dim_data, ncentres, rbfunc, ...
+   prior)
+%GTM	Create a Generative Topographic Map.
+%
+%	Description
+%
+%	NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC), takes the
+%	dimension of the latent space DIMLATENT, the number of data points
+%	sampled in the latent space NLATENT, the dimension of the data space
+%	DIMDATA, the number of centres in the RBF model NCENTRES, the
+%	activation function for the RBF RBFUNC and returns a data structure
+%	NET. The parameters in the RBF and GMM sub-models are set by calls to
+%	the corresponding creation routines RBF and GMM.
+%
+%	The fields in NET are
+%	  type = 'gtm'
+%	  nin = dimension of data space
+%	  dimlatent = dimension of latent space
+%	  rbfnet = RBF network data structure
+%	  gmmnet = GMM data structure
+%	  X = sample of latent points
+%
+%	NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC, PRIOR),
+%	sets a Gaussian zero mean prior on the parameters of the RBF model.
+%	PRIOR must be a scalar and represents the inverse variance of the
+%	prior distribution.  This gives rise to a weight decay term in the
+%	error function.
+%
+%	See also
+%	GTMFWD, GTMPOST, RBF, GMM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'gtm';
+% Input to functions is data
+net.nin = dim_data;
+net.dim_latent = dim_latent;
+
+% Default is no regularisation
+if nargin == 5
+   prior = 0.0;
+end
+
+% Only allow scalar prior
+if isstruct(prior) | size(prior) ~= [1 1]
+   error('Prior must be a scalar');
+end
+
+% Create RBF network
+net.rbfnet = rbf(dim_latent, ncentres, dim_data, rbfunc, ...
+   'linear', prior);
+
+% Mask all but output weights
+net.rbfnet.mask = rbfprior(rbfunc, dim_latent, ncentres, dim_data);
+
+% Create field for GMM output model
+net.gmmnet = gmm(dim_data, nlatent, 'spherical');
+
+% Create empty latent data sample
+net.X = [];