Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/glmderiv.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function g = glmderiv(net, x) | |
2 %GLMDERIV Evaluate derivatives of GLM outputs with respect to weights. | |
3 % | |
4 % Description | |
5 % G = GLMDERIV(NET, X) takes a network data structure NET and a matrix | |
6 % of input vectors X and returns a three-index matrix mat{g} whose I, | |
7 % J, K element contains the derivative of network output K with respect | |
8 % to weight or bias parameter J for input pattern I. The ordering of | |
9 % the weight and bias parameters is defined by GLMUNPAK. | |
10 % | |
11 | |
12 % Copyright (c) Ian T Nabney (1996-2001) | |
13 | |
14 % Check arguments for consistency | |
15 errstring = consist(net, 'glm', x); | |
16 if ~isempty(errstring) | |
17 error(errstring); | |
18 end | |
19 | |
20 ndata = size(x, 1); | |
21 if isfield(net, 'mask') | |
22 nwts = size(find(net.mask), 1); | |
23 mask_array = logical(net.mask)*ones(1, net.nout); | |
24 else | |
25 nwts = net.nwts; | |
26 end | |
27 g = zeros(ndata, nwts, net.nout); | |
28 | |
29 temp = zeros(net.nwts, net.nout); | |
30 for n = 1:ndata | |
31 % Weight matrix w1 | |
32 temp(1:(net.nin*net.nout), :) = kron(eye(net.nout), (x(n, :))'); | |
33 % Bias term b1 | |
34 temp(net.nin*net.nout+1:end, :) = eye(net.nout); | |
35 if isfield(net, 'mask') | |
36 g(n, :, :) = reshape(temp(find(mask_array)), nwts, net.nout); | |
37 else | |
38 g(n, :, :) = temp; | |
39 end | |
40 end |