comparison toolboxes/FullBNT-1.0.7/netlab3.3/rbffwd.m @ 0:e9a9cd732c1e tip

first hg version after svn

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