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);endif 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);endnet.tr_in = tr_in;net.tr_targets = tr_targets;