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diff --git a/sourcecodes/bnt-master/netlab3.3/netopt.m b/sourcecodes/bnt-master/netlab3.3/netopt.m
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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);