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author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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wolffd@0 | 1 <html> |
wolffd@0 | 2 <head> |
wolffd@0 | 3 <title> |
wolffd@0 | 4 Netlab Reference Manual demmdn1 |
wolffd@0 | 5 </title> |
wolffd@0 | 6 </head> |
wolffd@0 | 7 <body> |
wolffd@0 | 8 <H1> demmdn1 |
wolffd@0 | 9 </H1> |
wolffd@0 | 10 <h2> |
wolffd@0 | 11 Purpose |
wolffd@0 | 12 </h2> |
wolffd@0 | 13 Demonstrate fitting a multi-valued function using a Mixture Density Network. |
wolffd@0 | 14 |
wolffd@0 | 15 <p><h2> |
wolffd@0 | 16 Synopsis |
wolffd@0 | 17 </h2> |
wolffd@0 | 18 <PRE> |
wolffd@0 | 19 demmdn1</PRE> |
wolffd@0 | 20 |
wolffd@0 | 21 |
wolffd@0 | 22 <p><h2> |
wolffd@0 | 23 Description |
wolffd@0 | 24 </h2> |
wolffd@0 | 25 The problem consists of one input variable |
wolffd@0 | 26 <CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by |
wolffd@0 | 27 sampling <CODE>t</CODE> at equal intervals and then generating target data by |
wolffd@0 | 28 computing <CODE>t + 0.3*sin(2*pi*t)</CODE> and adding Gaussian noise. A |
wolffd@0 | 29 Mixture Density Network with 3 centres in the mixture model is trained |
wolffd@0 | 30 by minimizing a negative log likelihood error function using the scaled |
wolffd@0 | 31 conjugate gradient optimizer. |
wolffd@0 | 32 |
wolffd@0 | 33 <p>The conditional means, mixing coefficients and variances are plotted |
wolffd@0 | 34 as a function of <CODE>x</CODE>, and a contour plot of the full conditional |
wolffd@0 | 35 density is also generated. |
wolffd@0 | 36 |
wolffd@0 | 37 <p><h2> |
wolffd@0 | 38 See Also |
wolffd@0 | 39 </h2> |
wolffd@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> |
wolffd@0 | 41 <b>Pages:</b> |
wolffd@0 | 42 <a href="index.htm">Index</a> |
wolffd@0 | 43 <hr> |
wolffd@0 | 44 <p>Copyright (c) Ian T Nabney (1996-9) |
wolffd@0 | 45 |
wolffd@0 | 46 |
wolffd@0 | 47 </body> |
wolffd@0 | 48 </html> |