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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+<html>
+<head>
+<title>
+Netlab Reference Manual mdn
+</title>
+</head>
+<body>
+<H1> mdn
+</H1>
+<h2>
+Purpose
+</h2>
+Creates a Mixture Density Network with specified architecture.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+net = mdn(nin, nhidden, ncentres, dimtarget)
+net = mdn(nin, nhidden, ncentres, dimtarget, mixtype, ...
+	prior, beta)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>net = mdn(nin, nhidden, ncentres, dimtarget)</CODE> 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 <CODE>mixtype</CODE> 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.
+
+<p>The network is initialised by a call to <CODE>mlp</CODE>, and the arguments
+<CODE>prior</CODE>, and <CODE>beta</CODE> have the same role as for that function.
+Weight initialisation uses the Matlab function <CODE>randn</CODE>
+ and so the seed for the random weight initialization can be 
+set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value.
+A specialised data structure (rather than <CODE>gmm</CODE>)
+is used for the mixture model outputs to improve
+the efficiency of error and gradient calculations in network training.
+The fields are described in <CODE>mdnfwd</CODE> where they are set up.
+
+<p>The fields in <CODE>net</CODE> are
+<PRE>
+  
+  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
+</PRE>
+
+
+<p><h2>
+Example
+</h2>
+<PRE>
+
+net = mdn(2, 4, 3, 1, 'spherical');
+</PRE>
+
+This creates a Mixture Density Network with 2 inputs and 4 hidden units.
+The mixture model has 3 components and the target space has dimension 1.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mdnpak.htm">mdnpak</a></CODE>, <CODE><a href="mdnunpak.htm">mdnunpak</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+<p>David J Evans (1998)
+
+</body>
+</html>
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