comparison toolboxes/FullBNT-1.0.7/netlab3.3/mdninit.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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 later
+net.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