function net = netinit(net, prior)%NETINIT Initialise the weights in a network.%%	Description%%	NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets%	the weights and biases by sampling from a Gaussian distribution. If%	PRIOR is a scalar, then all of the parameters (weights and biases)%	are sampled from a single isotropic Gaussian with inverse variance%	equal to PRIOR. If PRIOR is a data structure of the kind generated by%	MLPPRIOR, then the parameters are sampled from multiple Gaussians%	according to their groupings (defined by the INDEX field) with%	corresponding variances (defined by the ALPHA field).%%	See also%	MLPPRIOR, NETUNPAK, RBFPRIOR%%	Copyright (c) Ian T Nabney (1996-2001)if isstruct(prior)    if (isfield(net, 'mask'))	if find(sum(prior.index, 2)) ~= find(net.mask)	    error('Index does not match mask');	end	sig = sqrt(prior.index*prior.alpha);	% Weights corresponding to zeros in mask will not be used anyway	% Set their priors to one to avoid division by zero	sig = sig + (sig == 0);  	sig = 1./sqrt(sig);    else	sig = 1./sqrt(prior.index*prior.alpha);    end    w = sig'.*randn(1, net.nwts); elseif size(prior) == [1 1]  w = randn(1, net.nwts).*sqrt(1/prior);else  error('prior must be a scalar or a structure');end  if (isfield(net, 'mask'))    w = w(logical(net.mask));endnet = netunpak(net, w);