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annotate toolboxes/FullBNT-1.0.7/netlab3.3/glmgrad.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function [g, gdata, gprior] = glmgrad(net, x, t)
wolffd@0 2 %GLMGRAD Evaluate gradient of error function for generalized linear model.
wolffd@0 3 %
wolffd@0 4 % Description
wolffd@0 5 % G = GLMGRAD(NET, X, T) takes a generalized linear model data
wolffd@0 6 % structure NET together with a matrix X of input vectors and a matrix
wolffd@0 7 % T of target vectors, and evaluates the gradient G of the error
wolffd@0 8 % function with respect to the network weights. The error function
wolffd@0 9 % corresponds to the choice of output unit activation function. Each
wolffd@0 10 % row of X corresponds to one input vector and each row of T
wolffd@0 11 % corresponds to one target vector.
wolffd@0 12 %
wolffd@0 13 % [G, GDATA, GPRIOR] = GLMGRAD(NET, X, T) also returns separately the
wolffd@0 14 % data and prior contributions to the gradient.
wolffd@0 15 %
wolffd@0 16 % See also
wolffd@0 17 % GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN
wolffd@0 18 %
wolffd@0 19
wolffd@0 20 % Copyright (c) Ian T Nabney (1996-2001)
wolffd@0 21
wolffd@0 22 % Check arguments for consistency
wolffd@0 23 errstring = consist(net, 'glm', x, t);
wolffd@0 24 if ~isempty(errstring);
wolffd@0 25 error(errstring);
wolffd@0 26 end
wolffd@0 27
wolffd@0 28 y = glmfwd(net, x);
wolffd@0 29 delout = y - t;
wolffd@0 30
wolffd@0 31 gw1 = x'*delout;
wolffd@0 32 gb1 = sum(delout, 1);
wolffd@0 33
wolffd@0 34 gdata = [gw1(:)', gb1];
wolffd@0 35
wolffd@0 36 [g, gdata, gprior] = gbayes(net, gdata);