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

first hg version after svn

+function [mixparams, y, z, a] = mdnfwd(net, x)
+%MDNFWD	Forward propagation through Mixture Density Network.
+%
+%	Description
+%	 MIXPARAMS = MDNFWD(NET, X) takes a mixture density network data
+%	structure NET and a matrix X of input vectors, and forward propagates
+%	the inputs through the network to generate a structure MIXPARAMS
+%	which contains the parameters of several mixture models.   Each row
+%	of X represents one input vector and the corresponding row of the
+%	matrices in MIXPARAMS  represents the parameters of a mixture model
+%	for the conditional probability of target vectors given the input
+%	vector.  This is not represented as an array of GMM structures to
+%	improve the efficiency of MDN training.
+%
+%	The fields in MIXPARAMS are
+%	  type = 'mdnmixes'
+%	  ncentres = number of mixture components
+%	  dimtarget = dimension of target space
+%	  mixcoeffs = mixing coefficients
+%	  centres = means of Gaussians: stored as one row per pattern
+%	  covars = covariances of Gaussians
+%	  nparams = number of parameters
+%
+%	[MIXPARAMS, Y, Z] = MDNFWD(NET, X) also generates a matrix Y of the
+%	outputs of the MLP and a matrix Z of the hidden unit activations
+%	where each row corresponds to one pattern.
+%
+%	[MIXPARAMS, Y, Z, A] = MLPFWD(NET, X) also returns a matrix A  giving
+%	the summed inputs to each output unit, where each row  corresponds to
+%	one pattern.
+%
+%	See also
+%	MDN, MDN2GMM, MDNERR, MDNGRAD, MLPFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(net, 'mdn', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+% Extract mlp and mixture model descriptors
+mlpnet = net.mlp;
+mixes = net.mdnmixes;
+
+ncentres = mixes.ncentres;	% Number of components in mixture model
+dim_target = mixes.dim_target;	% Dimension of targets
+nparams = mixes.nparams;	% Number of parameters in mixture model
+
+% Propagate forwards through MLP
+[y, z, a] = mlpfwd(mlpnet, x);
+
+% Compute the postion for each parameter in the whole
+% matrix.  Used to define the mixparams structure
+mixcoeff  = [1:1:ncentres];
+centres   = [ncentres+1:1:(ncentres*(1+dim_target))];
+variances = [(ncentres*(1+dim_target)+1):1:nparams];
+
+% Convert output values into mixture model parameters
+
+% Use softmax to calculate priors
+% Prevent overflow and underflow: use same bounds as glmfwd
+% Ensure that sum(exp(y), 2) does not overflow
+maxcut = log(realmax) - log(ncentres);
+% Ensure that exp(y) > 0
+mincut = log(realmin);
+temp = min(y(:,1:ncentres), maxcut);
+temp = max(temp, mincut);
+temp = exp(temp);
+mixpriors = temp./(sum(temp, 2)*ones(1,ncentres));
+
+% Centres are just copies of network outputs
+mixcentres =  y(:,(ncentres+1):ncentres*(1+dim_target));
+
+% Variances are exp of network outputs
+mixwidths = exp(y(:,(ncentres*(1+dim_target)+1):nparams));
+
+% Now build up all the mixture model weight vectors
+ndata = size(x, 1);
+
+% Return parameters
+mixparams.type = mixes.type;
+mixparams.ncentres = mixes.ncentres;
+mixparams.dim_target = mixes.dim_target;
+mixparams.nparams = mixes.nparams;
+
+mixparams.mixcoeffs = mixpriors;
+mixparams.centres   = mixcentres;
+mixparams.covars      = mixwidths;
+