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| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual demmdn1 | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demmdn1 | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate fitting a multi-valued function using a Mixture Density Network. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demmdn1</PRE> | |
| 20 | |
| 21 | |
| 22 <p><h2> | |
| 23 Description | |
| 24 </h2> | |
| 25 The problem consists of one input variable | |
| 26 <CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by | |
| 27 sampling <CODE>t</CODE> at equal intervals and then generating target data by | |
| 28 computing <CODE>t + 0.3*sin(2*pi*t)</CODE> and adding Gaussian noise. A | |
| 29 Mixture Density Network with 3 centres in the mixture model is trained | |
| 30 by minimizing a negative log likelihood error function using the scaled | |
| 31 conjugate gradient optimizer. | |
| 32 | |
| 33 <p>The conditional means, mixing coefficients and variances are plotted | |
| 34 as a function of <CODE>x</CODE>, and a contour plot of the full conditional | |
| 35 density is also generated. | |
| 36 | |
| 37 <p><h2> | |
| 38 See Also | |
| 39 </h2> | |
| 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> | |
| 41 <b>Pages:</b> | |
| 42 <a href="index.htm">Index</a> | |
| 43 <hr> | |
| 44 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 45 | |
| 46 | |
| 47 </body> | |
| 48 </html> |