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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mdninit.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
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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 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)
+
+</body>
+</html>
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