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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual demmdn1
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> demmdn1
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Demonstrate fitting a multi-valued function using a Mixture Density Network.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19 demmdn1</PRE>
Daniel@0 20
Daniel@0 21
Daniel@0 22 <p><h2>
Daniel@0 23 Description
Daniel@0 24 </h2>
Daniel@0 25 The problem consists of one input variable
Daniel@0 26 <CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by
Daniel@0 27 sampling <CODE>t</CODE> at equal intervals and then generating target data by
Daniel@0 28 computing <CODE>t + 0.3*sin(2*pi*t)</CODE> and adding Gaussian noise. A
Daniel@0 29 Mixture Density Network with 3 centres in the mixture model is trained
Daniel@0 30 by minimizing a negative log likelihood error function using the scaled
Daniel@0 31 conjugate gradient optimizer.
Daniel@0 32
Daniel@0 33 <p>The conditional means, mixing coefficients and variances are plotted
Daniel@0 34 as a function of <CODE>x</CODE>, and a contour plot of the full conditional
Daniel@0 35 density is also generated.
Daniel@0 36
Daniel@0 37 <p><h2>
Daniel@0 38 See Also
Daniel@0 39 </h2>
Daniel@0 40 <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>
Daniel@0 41 <b>Pages:</b>
Daniel@0 42 <a href="index.htm">Index</a>
Daniel@0 43 <hr>
Daniel@0 44 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 45
Daniel@0 46
Daniel@0 47 </body>
Daniel@0 48 </html>