wolffd@0: function [mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)
wolffd@0: %RBFPRIOR Create Gaussian prior and output layer mask for RBF.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	[MASK, PRIOR] = RBFPRIOR(RBFUNC, NIN, NHIDDEN, NOUT, AW2, AB2)
wolffd@0: %	generates a vector MASK  that selects only the output layer weights.
wolffd@0: %	This is because most uses of RBF networks in a Bayesian context have
wolffd@0: %	fixed basis functions with the output layer as the only adjustable
wolffd@0: %	parameters.  In particular, the Neuroscale output error function is
wolffd@0: %	designed to work only with this mask.
wolffd@0: %
wolffd@0: %	The return value PRIOR is a data structure,  with fields PRIOR.ALPHA
wolffd@0: %	and PRIOR.INDEX, which specifies a Gaussian prior distribution for
wolffd@0: %	the network weights in an RBF network. The parameters AW2 and AB2 are
wolffd@0: %	all scalars and represent the regularization coefficients for two
wolffd@0: %	groups of parameters in the network corresponding to  second-layer
wolffd@0: %	weights, and second-layer biases respectively. Then PRIOR.ALPHA
wolffd@0: %	represents a column vector of length 2 containing the parameters, and
wolffd@0: %	PRIOR.INDEX is a matrix specifying which weights belong in each
wolffd@0: %	group. Each column has one element for each weight in the matrix,
wolffd@0: %	using the standard ordering as defined in RBFPAK, and each element is
wolffd@0: %	1 or 0 according to whether the weight is a member of the
wolffd@0: %	corresponding group or not.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	RBF, RBFERR, RBFGRAD, EVIDENCE
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: nwts_layer2 = nout + (nhidden *nout);
wolffd@0: switch rbfunc
wolffd@0: case 'gaussian'
wolffd@0:    nwts_layer1 = nin*nhidden + nhidden;
wolffd@0: case {'tps', 'r4logr'}
wolffd@0:    nwts_layer1 = nin*nhidden;
wolffd@0: otherwise
wolffd@0:    error('Undefined activation function');
wolffd@0: end  
wolffd@0: nwts = nwts_layer1 + nwts_layer2;
wolffd@0: 
wolffd@0: % Make a mask only for output layer
wolffd@0: mask = [zeros(nwts_layer1, 1); ones(nwts_layer2, 1)];
wolffd@0: 
wolffd@0: if nargout > 1
wolffd@0:   % Construct prior
wolffd@0:   indx = zeros(nwts, 2);
wolffd@0:   mark2 = nwts_layer1 + (nhidden * nout);
wolffd@0:   indx(nwts_layer1 + 1:mark2, 1) = ones(nhidden * nout, 1);
wolffd@0:   indx(mark2 + 1:nwts, 2) = ones(nout, 1);
wolffd@0: 
wolffd@0:   prior.index = indx;
wolffd@0:   prior.alpha = [aw2, ab2]';
wolffd@0: end