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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 = [];
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