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function [net, options, varargout] = netopt(net, options, x, t, alg);
%NETOPT Optimize the weights in a network model.
%
% Description
%
% NETOPT is a helper function which facilitates the training of
% networks using the general purpose optimizers as well as sampling
% from the posterior distribution of parameters using general purpose
% Markov chain Monte Carlo sampling algorithms. It can be used with any
% function that searches in parameter space using error and gradient
% functions.
%
% [NET, OPTIONS] = NETOPT(NET, OPTIONS, X, T, ALG) takes a network
% data structure NET, together with a vector OPTIONS of parameters
% governing the behaviour of the optimization algorithm, a matrix X of
% input vectors and a matrix T of target vectors, and returns the
% trained network as well as an updated OPTIONS vector. The string ALG
% determines which optimization algorithm (CONJGRAD, QUASINEW, SCG,
% etc.) or Monte Carlo algorithm (such as HMC) will be used.
%
% [NET, OPTIONS, VARARGOUT] = NETOPT(NET, OPTIONS, X, T, ALG) also
% returns any additional return values from the optimisation algorithm.
%
% See also
% NETGRAD, BFGS, CONJGRAD, GRADDESC, HMC, SCG
%
% Copyright (c) Ian T Nabney (1996-2001)
optstring = [alg, '(''neterr'', w, options, ''netgrad'', net, x, t)'];
% Extract weights from network as single vector
w = netpak(net);
% Carry out optimisation
[s{1:nargout}] = eval(optstring);
w = s{1};
if nargout > 1
options = s{2};
% If there are additional arguments, extract them
nextra = nargout - 2;
if nextra > 0
for i = 1:nextra
varargout{i} = s{i+2};
end
end
end
% Pack the weights back into the network
net = netunpak(net, w);
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