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diff --git a/sourcecodes/bnt-master/netlab3.3/gpinit.m b/sourcecodes/bnt-master/netlab3.3/gpinit.m
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+function net = gpinit(net, tr_in, tr_targets, prior)
+%GPINIT	Initialise Gaussian Process model.
+%
+%	Description
+%	NET = GPINIT(NET, TRIN, TRTARGETS) takes a Gaussian Process data
+%	structure NET  together  with a matrix TRIN of training input vectors
+%	and a matrix TRTARGETS of  training target vectors, and stores them
+%	in NET. These datasets are required if the corresponding inverse
+%	covariance matrix is not supplied to GPFWD. This is important if the
+%	data structure is saved and then reloaded before calling GPFWD. Each
+%	row of TRIN corresponds to one input vector and each row of TRTARGETS
+%	corresponds to one target vector.
+%
+%	NET = GPINIT(NET, TRIN, TRTARGETS, PRIOR) additionally initialises
+%	the parameters in NET from the PRIOR data structure which contains
+%	the mean and variance of the Gaussian distribution which is sampled
+%	from.
+%
+%	See also
+%	GP, GPFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', tr_in, tr_targets);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if nargin >= 4 
+  % Initialise weights at random
+  if size(prior.pr_mean) == [1 1]
+    w = randn(1, net.nwts).*sqrt(prior.pr_var) + ...
+       repmat(prior.pr_mean, 1, net.nwts);
+  else
+    sig = sqrt(prior.index*prior.pr_var);
+    w = sig'.*randn(1, net.nwts) + (prior.index*prior.pr_mean)'; 
+  end
+  net = gpunpak(net, w);
+end
+
+net.tr_in = tr_in;
+net.tr_targets = tr_targets;