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1 function net = netinit(net, prior)
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2 %NETINIT Initialise the weights in a network.
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3 %
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4 % Description
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5 %
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6 % NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
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7 % the weights and biases by sampling from a Gaussian distribution. If
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8 % PRIOR is a scalar, then all of the parameters (weights and biases)
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9 % are sampled from a single isotropic Gaussian with inverse variance
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10 % equal to PRIOR. If PRIOR is a data structure of the kind generated by
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11 % MLPPRIOR, then the parameters are sampled from multiple Gaussians
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12 % according to their groupings (defined by the INDEX field) with
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13 % corresponding variances (defined by the ALPHA field).
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14 %
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15 % See also
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16 % MLPPRIOR, NETUNPAK, RBFPRIOR
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17 %
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18
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19 % Copyright (c) Ian T Nabney (1996-2001)
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20
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21 if isstruct(prior)
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22 if (isfield(net, 'mask'))
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23 if find(sum(prior.index, 2)) ~= find(net.mask)
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24 error('Index does not match mask');
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25 end
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26 sig = sqrt(prior.index*prior.alpha);
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27 % Weights corresponding to zeros in mask will not be used anyway
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28 % Set their priors to one to avoid division by zero
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29 sig = sig + (sig == 0);
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30 sig = 1./sqrt(sig);
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31 else
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32 sig = 1./sqrt(prior.index*prior.alpha);
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33 end
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34 w = sig'.*randn(1, net.nwts);
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35 elseif size(prior) == [1 1]
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36 w = randn(1, net.nwts).*sqrt(1/prior);
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37 else
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38 error('prior must be a scalar or a structure');
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39 end
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40
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41 if (isfield(net, 'mask'))
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42 w = w(logical(net.mask));
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43 end
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44 net = netunpak(net, w);
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45
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