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1 function [a, z, n2] = rbffwd(net, x)
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2 %RBFFWD Forward propagation through RBF network with linear outputs.
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3 %
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4 % Description
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5 % A = RBFFWD(NET, X) takes a network data structure NET and a matrix X
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6 % of input vectors and forward propagates the inputs through the
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7 % network to generate a matrix A of output vectors. Each row of X
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8 % corresponds to one input vector and each row of A contains the
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9 % corresponding output vector. The activation function that is used is
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10 % determined by NET.ACTFN.
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11 %
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12 % [A, Z, N2] = RBFFWD(NET, X) also generates a matrix Z of the hidden
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13 % unit activations where each row corresponds to one pattern. These
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14 % hidden unit activations represent the design matrix for the RBF. The
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15 % matrix N2 is the squared distances between each basis function centre
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16 % and each pattern in which each row corresponds to a data point.
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17 %
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18 % See also
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19 % RBF, RBFERR, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK
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20 %
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21
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22 % Copyright (c) Ian T Nabney (1996-2001)
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23
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24 % Check arguments for consistency
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25 errstring = consist(net, 'rbf', x);
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26 if ~isempty(errstring);
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27 error(errstring);
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28 end
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29
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30 [ndata, data_dim] = size(x);
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31
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32 % Calculate squared norm matrix, of dimension (ndata, ncentres)
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33 n2 = dist2(x, net.c);
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34
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35 % Switch on activation function type
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36 switch net.actfn
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37
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38 case 'gaussian' % Gaussian
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39 % Calculate width factors: net.wi contains squared widths
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40 wi2 = ones(ndata, 1) * (2 .* net.wi);
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41
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42 % Now compute the activations
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43 z = exp(-(n2./wi2));
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44
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45 case 'tps' % Thin plate spline
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46 z = n2.*log(n2+(n2==0));
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47
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48 case 'r4logr' % r^4 log r
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49 z = n2.*n2.*log(n2+(n2==0));
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50
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51 otherwise
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52 error('Unknown activation function in rbffwd')
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53 end
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54
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55 a = z*net.w2 + ones(ndata, 1)*net.b2; |
