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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/netinit.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 netinit
+</title>
+</head>
+<body>
+<H1> netinit
+</H1>
+<h2>
+Purpose
+</h2>
+Initialise the weights in a network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+net = netinit(net, prior)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+
+<p><CODE>net = netinit(net, prior)</CODE> takes a network data structure
+<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
+distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
+(weights and biases) are sampled from a single isotropic Gaussian with
+inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
+structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters
+are sampled from multiple Gaussians according to their groupings
+(defined by the <CODE>index</CODE> field) with corresponding variances
+(defined by the <CODE>alpha</CODE> field).
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="netunpak.htm">netunpak</a></CODE>, <CODE><a href="rbfprior.htm">rbfprior</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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