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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/demmdn1.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 demmdn1
+</title>
+</head>
+<body>
+<H1> demmdn1
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate fitting a multi-valued function using a Mixture Density Network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demmdn1</PRE>
+
+
+<p><h2>
+Description
+</h2>
+The problem consists of one input variable
+<CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by
+sampling <CODE>t</CODE> at equal intervals and then generating target data by
+computing <CODE>t + 0.3*sin(2*pi*t)</CODE> and adding Gaussian noise. A
+Mixture Density Network with 3 centres in the mixture model is trained
+by minimizing a negative log likelihood error function using the scaled
+conjugate gradient optimizer. 
+
+<p>The conditional means, mixing coefficients and variances are plotted
+as a function of <CODE>x</CODE>, and a contour plot of the full conditional
+density is also generated.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="scg.htm">scg</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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