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<html>
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<title>
Netlab Reference Manual mdninit
</title>
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<body>
<H1> mdninit
</H1>
<h2>
Purpose
</h2>
Initialise the weights in a Mixture Density Network.
<p><h2>
Synopsis
</h2>
<PRE>
net = mdninit(net, prior)
net = mdninit(net, prior, t, options)
</PRE>
<p><h2>
Description
</h2>
<p><CODE>net = mdninit(net, prior)</CODE> takes a Mixture Density Network
<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
distribution. It calls <CODE>mlpinit</CODE> for the MLP component of <CODE>net</CODE>.
<p><CODE>net = mdninit(net, prior, t, options)</CODE> uses the target data <CODE>t</CODE> to
initialise the biases for the output units after initialising the
other weights as above. It calls <CODE>gmminit</CODE>, with <CODE>t</CODE> and <CODE>options</CODE>
as arguments, to obtain a model of the unconditional density of <CODE>t</CODE>. The
biases are then set so that <CODE>net</CODE> will output the values in the Gaussian
mixture model.
<p><h2>
See Also
</h2>
<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>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
<p>David J Evans (1998)
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