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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/som.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 som
+</title>
+</head>
+<body>
+<H1> som
+</H1>
+<h2>
+Purpose
+</h2>
+Creates a Self-Organising Map.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+
+net = som(nin, map_size)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>net = som(nin, map_size)</CODE> creates a SOM <CODE>net</CODE>
+with input dimension (i.e. data dimension) <CODE>nin</CODE> and map dimensions
+<CODE>map_size</CODE>.  Only two-dimensional maps are currently implemented.
+
+<p>The fields in <CODE>net</CODE> are
+<PRE>
+
+  type = 'som'
+  nin = number of inputs
+  map_dim = dimension of map (constrained to be 2)
+  map_size = grid size: number of nodes in each dimension
+  num_nodes = number of nodes: the product of values in map_size
+  map = map_dim+1 dimensional array containing nodes
+  inode_dist = map of inter-node distances using Manhatten metric
+</PRE>
+
+
+<p>The map contains the node vectors arranged column-wise in the first
+dimension of the array.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="somfwd.htm">somfwd</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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