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<html><head><title>Netlab Reference Manual mdninit</title></head><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 Gaussiandistribution. 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> toinitialise 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>. Thebiases 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)</body></html>⌨️ 快捷键说明
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