wolffd@0: function [a, z, n2] = rbffwd(net, x)
wolffd@0: %RBFFWD	Forward propagation through RBF network with linear outputs.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	A = RBFFWD(NET, X) takes a network data structure NET and a matrix X
wolffd@0: %	of input vectors and forward propagates the inputs through the
wolffd@0: %	network to generate a matrix A of output vectors. Each row of X
wolffd@0: %	corresponds to one input vector and each row of A contains the
wolffd@0: %	corresponding output vector. The activation function that is used is
wolffd@0: %	determined by NET.ACTFN.
wolffd@0: %
wolffd@0: %	[A, Z, N2] = RBFFWD(NET, X) also generates a matrix Z of the hidden
wolffd@0: %	unit activations where each row corresponds to one pattern. These
wolffd@0: %	hidden unit activations represent the design matrix for the RBF.  The
wolffd@0: %	matrix N2 is the squared distances between each basis function centre
wolffd@0: %	and each pattern in which each row corresponds to a data point.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	RBF, RBFERR, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: % Check arguments for consistency
wolffd@0: errstring = consist(net, 'rbf', x);
wolffd@0: if ~isempty(errstring);
wolffd@0:   error(errstring);
wolffd@0: end
wolffd@0: 
wolffd@0: [ndata, data_dim] = size(x);
wolffd@0: 
wolffd@0: % Calculate squared norm matrix, of dimension (ndata, ncentres)
wolffd@0: n2 = dist2(x, net.c);
wolffd@0: 
wolffd@0: % Switch on activation function type
wolffd@0: switch net.actfn
wolffd@0: 
wolffd@0:   case 'gaussian'	% Gaussian
wolffd@0:     % Calculate width factors: net.wi contains squared widths
wolffd@0:     wi2 = ones(ndata, 1) * (2 .* net.wi);
wolffd@0: 
wolffd@0:     % Now compute the activations
wolffd@0:     z = exp(-(n2./wi2));
wolffd@0: 
wolffd@0:   case 'tps'		% Thin plate spline
wolffd@0:     z = n2.*log(n2+(n2==0));
wolffd@0: 
wolffd@0:   case 'r4logr'		% r^4 log r
wolffd@0:     z = n2.*n2.*log(n2+(n2==0));
wolffd@0: 
wolffd@0:   otherwise
wolffd@0:     error('Unknown activation function in rbffwd')
wolffd@0: end
wolffd@0: 
wolffd@0: a = z*net.w2 + ones(ndata, 1)*net.b2;