diff toolboxes/FullBNT-1.0.7/netlab3.3/mdngrad.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdngrad.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,66 @@
+function g = mdngrad(net, x, t)
+%MDNGRAD Evaluate gradient of error function for Mixture Density Network.
+%
+%	Description
+%	 G = MDNGRAD(NET, X, T) takes a mixture density network data
+%	structure NET, a matrix X of input vectors and a matrix T of target
+%	vectors, and evaluates the gradient G of the error function with
+%	respect to the network weights. The error function is negative log
+%	likelihood of the target data.  Each row of X corresponds to one
+%	input vector and each row of T corresponds to one target vector.
+%
+%	See also
+%	MDN, MDNFWD, MDNERR, MDNPROB, MLPBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(net, 'mdn', x, t);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+[mixparams, y, z] = mdnfwd(net, x);
+
+% Compute gradients at MLP outputs: put the answer in deltas
+ncentres = net.mdnmixes.ncentres;
+dim_target = net.mdnmixes.dim_target;
+nmixparams = net.mdnmixes.nparams;
+ntarget = size(t, 1);
+deltas = zeros(ntarget, net.mlp.nout);
+e = ones(ncentres, 1);
+f = ones(1, dim_target);
+
+post = mdnpost(mixparams, t);
+
+% Calculate prior derivatives
+deltas(:,1:ncentres)  = mixparams.mixcoeffs - post;
+
+% Calculate centre derivatives
+long_t = kron(ones(1, ncentres), t);
+centre_err = mixparams.centres - long_t;
+
+% Get the post to match each u_jk:
+% this array will be (ntarget, (ncentres*dim_target))
+long_post = kron(ones(dim_target, 1), post);
+long_post = reshape(long_post, ntarget, (ncentres*dim_target));
+
+% Get the variance to match each u_jk:
+var = mixparams.covars;
+var = kron(ones(dim_target, 1), var);
+var = reshape(var, ntarget, (ncentres*dim_target));
+
+% Compute centre deltas
+deltas(:, (ncentres+1):(ncentres*(1+dim_target))) = ...
+                       (centre_err.*long_post)./var;
+
+% Compute variance deltas
+dist2             = mdndist2(mixparams, t);
+c                 = dim_target*ones(ntarget, ncentres);
+deltas(:, (ncentres*(1+dim_target)+1):nmixparams) = ...
+                      post.*((dist2./mixparams.covars)-c)./(-2);
+
+% Now back-propagate deltas through MLP
+g = mlpbkp(net.mlp, x, z, deltas);