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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual mdninit
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> mdninit
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Initialise the weights in a Mixture Density Network.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19 net = mdninit(net, prior)
Daniel@0 20 net = mdninit(net, prior, t, options)
Daniel@0 21 </PRE>
Daniel@0 22
Daniel@0 23
Daniel@0 24 <p><h2>
Daniel@0 25 Description
Daniel@0 26 </h2>
Daniel@0 27
Daniel@0 28 <p><CODE>net = mdninit(net, prior)</CODE> takes a Mixture Density Network
Daniel@0 29 <CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
Daniel@0 30 distribution. It calls <CODE>mlpinit</CODE> for the MLP component of <CODE>net</CODE>.
Daniel@0 31
Daniel@0 32 <p><CODE>net = mdninit(net, prior, t, options)</CODE> uses the target data <CODE>t</CODE> to
Daniel@0 33 initialise the biases for the output units after initialising the
Daniel@0 34 other weights as above. It calls <CODE>gmminit</CODE>, with <CODE>t</CODE> and <CODE>options</CODE>
Daniel@0 35 as arguments, to obtain a model of the unconditional density of <CODE>t</CODE>. The
Daniel@0 36 biases are then set so that <CODE>net</CODE> will output the values in the Gaussian
Daniel@0 37 mixture model.
Daniel@0 38
Daniel@0 39 <p><h2>
Daniel@0 40 See Also
Daniel@0 41 </h2>
Daniel@0 42 <CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpinit.htm">mlpinit</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE><hr>
Daniel@0 43 <b>Pages:</b>
Daniel@0 44 <a href="index.htm">Index</a>
Daniel@0 45 <hr>
Daniel@0 46 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 47 <p>David J Evans (1998)
Daniel@0 48
Daniel@0 49 </body>
Daniel@0 50 </html>