function net = mlpinit(net, prior)%MLPINIT Initialise the weights in a 2-layer feedforward network.%%	Description%%	NET = MLPINIT(NET, PRIOR) takes a 2-layer feedforward network 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%	MLP, MLPPRIOR, MLPPAK, MLPUNPAK%%	Copyright (c) Ian T Nabney (1996-2001)if isstruct(prior)  sig = 1./sqrt(prior.index*prior.alpha);  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  net = mlpunpak(net, w);