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

first hg version after svn

+function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ...
+	prior, beta)
+%MDN	Creates a Mixture Density Network with specified architecture.
+%
+%	Description
+%	NET = MDN(NIN, NHIDDEN, NCENTRES, DIMTARGET) takes the number of
+%	inputs,  hidden units for a 2-layer feed-forward  network and the
+%	number of centres and target dimension for the  mixture model whose
+%	parameters are set from the outputs of the neural network. The fifth
+%	argument MIXTYPE is used to define the type of mixture model.
+%	(Currently there is only one type supported: a mixture of Gaussians
+%	with a single covariance parameter for each component.) For this
+%	model, the mixture coefficients are computed from a group of softmax
+%	outputs, the centres are equal to a group of linear outputs, and the
+%	variances are  obtained by applying the exponential function to a
+%	third group of outputs.
+%
+%	The network is initialised by a call to MLP, and the arguments PRIOR,
+%	and BETA have the same role as for that function. Weight
+%	initialisation uses the Matlab function RANDN  and so the seed for
+%	the random weight initialization can be  set using RANDN('STATE', S)
+%	where S is the seed value. A specialised data structure (rather than
+%	GMM) is used for the mixture model outputs to improve the efficiency
+%	of error and gradient calculations in network training. The fields
+%	are described in MDNFWD where they are set up.
+%
+%	The fields in NET are
+%	  
+%	  type = 'mdn'
+%	  nin = number of input variables
+%	  nout = dimension of target space (not number of network outputs)
+%	  nwts = total number of weights and biases
+%	  mdnmixes = data structure for mixture model output
+%	  mlp = data structure for MLP network
+%
+%	See also
+%	MDNFWD, MDNERR, MDN2GMM, MDNGRAD, MDNPAK, MDNUNPAK, MLP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Currently ignore type argument: reserved for future use
+net.type = 'mdn';
+
+% Set up the mixture model part of the structure
+% For efficiency we use a specialised data structure in place of GMM
+mdnmixes.type = 'mdnmixes';
+mdnmixes.ncentres = ncentres;
+mdnmixes.dim_target = dim_target;
+
+% This calculation depends on spherical variances
+mdnmixes.nparams = ncentres + ncentres*dim_target + ncentres;
+
+% Make the weights in the mdnmixes structure null 
+mdnmixes.mixcoeffs = [];
+mdnmixes.centres = [];
+mdnmixes.covars = [];
+
+% Number of output nodes = number of parameters in mixture model
+nout = mdnmixes.nparams;
+
+% Set up the MLP part of the network
+if (nargin == 5)
+  mlpnet = mlp(nin, nhidden, nout, 'linear');
+elseif (nargin == 6)
+  mlpnet = mlp(nin, nhidden, nout, 'linear', prior);
+elseif (nargin == 7)
+  mlpnet = mlp(nin, nhidden, nout, 'linear', prior, beta);
+end
+
+% Create descriptor
+net.mdnmixes = mdnmixes;
+net.mlp = mlpnet;
+net.nin = nin;
+net.nout = dim_target;
+net.nwts = mlpnet.nwts;