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1 function net = gpinit(net, tr_in, tr_targets, prior)
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2 %GPINIT Initialise Gaussian Process model.
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3 %
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4 % Description
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5 % NET = GPINIT(NET, TRIN, TRTARGETS) takes a Gaussian Process data
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6 % structure NET together with a matrix TRIN of training input vectors
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7 % and a matrix TRTARGETS of training target vectors, and stores them
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8 % in NET. These datasets are required if the corresponding inverse
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9 % covariance matrix is not supplied to GPFWD. This is important if the
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10 % data structure is saved and then reloaded before calling GPFWD. Each
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11 % row of TRIN corresponds to one input vector and each row of TRTARGETS
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12 % corresponds to one target vector.
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13 %
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14 % NET = GPINIT(NET, TRIN, TRTARGETS, PRIOR) additionally initialises
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15 % the parameters in NET from the PRIOR data structure which contains
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16 % the mean and variance of the Gaussian distribution which is sampled
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17 % from.
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18 %
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19 % See also
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20 % GP, GPFWD
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21 %
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22
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23 % Copyright (c) Ian T Nabney (1996-2001)
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24
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25 errstring = consist(net, 'gp', tr_in, tr_targets);
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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 if nargin >= 4
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31 % Initialise weights at random
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32 if size(prior.pr_mean) == [1 1]
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33 w = randn(1, net.nwts).*sqrt(prior.pr_var) + ...
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34 repmat(prior.pr_mean, 1, net.nwts);
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35 else
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36 sig = sqrt(prior.index*prior.pr_var);
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37 w = sig'.*randn(1, net.nwts) + (prior.index*prior.pr_mean)';
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38 end
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39 net = gpunpak(net, w);
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40 end
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41
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42 net.tr_in = tr_in;
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43 net.tr_targets = tr_targets;
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