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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/somfwd.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 somfwd
+</title>
+</head>
+<body>
+<H1> somfwd
+</H1>
+<h2>
+Purpose
+</h2>
+Forward propagation through a Self-Organising Map.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+
+d2 = somfwd(net, x)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>d2 = somfwd(net, x)</CODE> propagates the data matrix <CODE>x</CODE> through
+ a SOM <CODE>net</CODE>, returning the squared distance matrix <CODE>d2</CODE> with
+dimension <CODE>nin</CODE> by <CODE>num_nodes</CODE>.  The $i$th row represents the
+squared Euclidean distance to each of the nodes of the SOM.
+
+<p><CODE>[d2, win_nodes] = somfwd(net, x)</CODE> also returns the indices of the
+winning nodes for each pattern.
+
+<p><h2>
+Example
+</h2>
+
+<p>The following code fragment creates a SOM with a $5times 5$ map for an
+8-dimensional data space.  It then applies the test data to the map.
+<PRE>
+
+net = som(8, [5, 5]);
+[d2, wn] = somfwd(net, test_data);
+</PRE>
+
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somtrain.htm">somtrain</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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