function net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta)%RBF	Creates an RBF network with specified architecture%%	Description%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC) constructs and initialises a%	radial basis function network returning a data structure NET. The%	weights are all initialised with a zero mean, unit variance normal%	distribution, with the exception of the variances, which are set to%	one. This makes use of the Matlab function RANDN and so the seed for%	the random weight initialization can be  set using RANDN('STATE', S)%	where S is the seed value. The activation functions are defined in%	terms of the distance between the data point and the corresponding%	centre.  Note that the functions are computed to a convenient%	constant multiple: for example, the Gaussian is not normalised.%	(Normalisation is not needed as the function outputs are linearly%	combined in the next layer.)%%	The fields in NET are%	  type = 'rbf'%	  nin = number of inputs%	  nhidden = number of hidden units%	  nout = number of outputs%	  nwts = total number of weights and biases%	  actfn = string defining hidden unit activation function:%	    'gaussian' for a radially symmetric Gaussian function.%	    'tps' for r^2 log r, the thin plate spline function.%	    'r4logr' for r^4 log r.%	  outfn = string defining output error function:%	    'linear' for linear outputs (default) and SoS error.%	    'neuroscale' for Sammon stress measure.%	  c = centres%	  wi = squared widths (null for rlogr and tps)%	  w2 = second layer weight matrix%	  b2 = second layer bias vector%%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUND, OUTFUNC) allows the user to%	specify the type of error function to be used.  The field OUTFN is%	set to the value of this string.  Linear outputs (for regression%	problems) and Neuroscale outputs (for topographic mappings) are%	supported.%%	NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC, OUTFUNC, PRIOR, BETA), in which%	PRIOR is a scalar, allows the field NET.ALPHA in the data structure%	NET to be set, corresponding to a zero-mean isotropic Gaussian prior%	with inverse variance with value PRIOR. Alternatively, PRIOR can%	consist of a data structure with fields ALPHA and INDEX, allowing%	individual Gaussian priors to be set over groups of weights in the%	network. Here ALPHA is a column vector in which each element%	corresponds to a separate group of weights, which need not be%	mutually exclusive.  The membership of the groups is defined by the%	matrix INDX in which the columns correspond to the elements of ALPHA.%	Each column has one element for each weight in the matrix, in the%	order defined by the function RBFPAK, and each element is 1 or 0%	according to whether the weight is a member of the corresponding%	group or not. A utility function RBFPRIOR is provided to help in%	setting up the PRIOR data structure.%%	NET = RBF(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the%	additional field NET.BETA in the data structure NET, where beta%	corresponds to the inverse noise variance.%%	See also%	RBFERR, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK%%	Copyright (c) Ian T Nabney (1996-2001)net.type = 'rbf';net.nin = nin;net.nhidden = nhidden;net.nout = nout;% Check that function is an allowed typeactfns = {'gaussian', 'tps', 'r4logr'};outfns = {'linear', 'neuroscale'};if (strcmp(rbfunc, actfns)) == 0  error('Undefined activation function.')else  net.actfn = rbfunc;endif nargin <= 4   net.outfn = outfns{1};elseif (strcmp(outfunc, outfns) == 0)   error('Undefined output function.')else   net.outfn = outfunc; end% Assume each function has a centre and a single width parameter, and that% hidden layer to output weights include a bias.  Only the Gaussian function% requires a widthnet.nwts = nin*nhidden + (nhidden + 1)*nout;if strcmp(rbfunc, 'gaussian')  % Extra weights for width parameters  net.nwts = net.nwts + nhidden;endif nargin > 5  if isstruct(prior)    net.alpha = prior.alpha;    net.index = prior.index;  elseif size(prior) == [1 1]    net.alpha = prior;  else    error('prior must be a scalar or a structure');  end    if nargin > 6    net.beta = beta;  endendw = randn(1, net.nwts);net = rbfunpak(net, w);% Make widths equal to oneif strcmp(rbfunc, 'gaussian')  net.wi = ones(1, nhidden);endif strcmp(net.outfn, 'neuroscale')  net.mask = rbfprior(rbfunc, nin, nhidden, nout);end