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1 function [h, hdata] = mlphess_weighted(net, x, t, eso_w, hdata)
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2 %MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
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3 %
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4 % Description
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5 % H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
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6 % matrix X of input values, and a matrix T of target values and returns
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7 % the full Hessian matrix H corresponding to the second derivatives of
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8 % the negative log posterior distribution, evaluated for the current
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9 % weight and bias values as defined by NET.
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10 %
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11 % [H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and
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12 % the contribution HDATA arising from the data dependent term in the
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13 % Hessian.
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14 %
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15 % H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a
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16 % matrix X of input values, and a matrix T of target values, together
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17 % with the contribution HDATA arising from the data dependent term in
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18 % the Hessian, and returns the full Hessian matrix H corresponding to
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19 % the second derivatives of the negative log posterior distribution.
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20 % This version saves computation time if HDATA has already been
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21 % evaluated for the current weight and bias values.
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22 %
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23 % See also
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24 % MLP, HESSCHEK, MLPHDOTV, EVIDENCE
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25 %
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26
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27 % Copyright (c) Ian T Nabney (1996-9)
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28
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29 % Check arguments for consistency
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30 errstring = consist(net, 'mlp', x, t);
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31 if ~isempty(errstring);
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32 error(errstring);
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33 end
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34
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35 if nargin == 4
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36 % Data term in Hessian needs to be computed
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37 hdata = datahess(net, x, t, eso_w);
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38 end
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39
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40 [h, hdata] = hbayes(net, hdata);
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41
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42 % Sub-function to compute data part of Hessian
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43 function hdata = datahess(net, x, t, eso_w)
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44
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45 hdata = zeros(net.nwts, net.nwts);
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46
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47 for v = eye(net.nwts);
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48 hdata(find(v),:) = mlphdotv_weighted(net, x, t, eso_w, v);
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49 end
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50
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51 return
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