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 datanet.nin = dim_data;net.dim_latent = dim_latent;% Default is no regularisationif nargin == 5   prior = 0.0;end% Only allow scalar priorif isstruct(prior) | size(prior) ~= [1 1]   error('Prior must be a scalar');end% Create RBF networknet.rbfnet = rbf(dim_latent, ncentres, dim_data, rbfunc, ...   'linear', prior);% Mask all but output weightsnet.rbfnet.mask = rbfprior(rbfunc, dim_latent, ncentres, dim_data);% Create field for GMM output modelnet.gmmnet = gmm(dim_data, nlatent, 'spherical');% Create empty latent data samplenet.X = [];