function net = mdninit(net, prior, t, options)%MDNINIT Initialise the weights in a Mixture Density Network.%%	Description%%	NET = MDNINIT(NET, PRIOR) takes a Mixture Density Network NET and%	sets the weights and biases by sampling from a Gaussian distribution.%	It calls MLPINIT for the MLP component of NET.%%	NET = MDNINIT(NET, PRIOR, T, OPTIONS) uses the target data T to%	initialise the biases for the output units after initialising the%	other weights as above.  It calls GMMINIT, with T and OPTIONS as%	arguments, to obtain a model of the unconditional density of T.  The%	biases are then set so that NET will output the values in the%	Gaussian  mixture model.%%	See also%	MDN, MLP, MLPINIT, GMMINIT%%	Copyright (c) Ian T Nabney (1996-2001)%	David J Evans (1998)% Initialise network weights from prior: this gives noise around values% determined laternet.mlp = mlpinit(net.mlp, prior);if nargin > 2  % Initialise priors, centres and variances from target data  temp_mix = gmm(net.mdnmixes.dim_target, net.mdnmixes.ncentres, 'spherical');  temp_mix = gmminit(temp_mix, t, options);    ncentres = net.mdnmixes.ncentres;  dim_target = net.mdnmixes.dim_target;  % Now set parameters in MLP to yield the right values.  % This involves setting the biases correctly.    % Priors  net.mlp.b2(1:ncentres) = temp_mix.priors;    % Centres are arranged in mlp such that we have  % u11, u12, u13, ..., u1c, ... , uj1, uj2, uj3, ..., ujc, ..., um1, uM2,   % ..., uMc  % This is achieved by transposing temp_mix.centres before reshaping  end_centres = ncentres*(dim_target+1);  net.mlp.b2(ncentres+1:end_centres) = ...    reshape(temp_mix.centres', 1, ncentres*dim_target);    % Variances  net.mlp.b2((end_centres+1):net.mlp.nout) = ...    log(temp_mix.covars);end