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