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

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function net = glminit(net, prior)
2 %GLMINIT Initialise the weights in a generalized linear model.
3 %
4 % Description
5 %
6 % NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and
7 % sets the weights and biases by sampling from a Gaussian distribution.
8 % If PRIOR is a scalar, then all of the parameters (weights and biases)
9 % are sampled from a single isotropic Gaussian with inverse variance
10 % equal to PRIOR. If PRIOR is a data structure similar to that in
11 % MLPPRIOR but for a single layer of weights, then the parameters are
12 % sampled from multiple Gaussians according to their groupings (defined
13 % by the INDEX field) with corresponding variances (defined by the
14 % ALPHA field).
15 %
16 % See also
17 % GLM, GLMPAK, GLMUNPAK, MLPINIT, MLPPRIOR
18 %
19
20 % Copyright (c) Ian T Nabney (1996-2001)
21
22 errstring = consist(net, 'glm');
23 if ~isempty(errstring);
24 error(errstring);
25 end
26 if isstruct(prior)
27 sig = 1./sqrt(prior.index*prior.alpha);
28 w = sig'.*randn(1, net.nwts);
29 elseif size(prior) == [1 1]
30 w = randn(1, net.nwts).*sqrt(1/prior);
31 else
32 error('prior must be a scalar or a structure');
33 end
34
35 net = glmunpak(net, w);
36