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| author | Daniel Wolff |
|---|---|
| date | Fri, 19 Aug 2016 13:07:06 +0200 |
| parents | |
| children |
| rev | line source |
|---|---|
| Daniel@0 | 1 <html> |
| Daniel@0 | 2 <head> |
| Daniel@0 | 3 <title> |
| Daniel@0 | 4 Netlab Reference Manual demgp |
| Daniel@0 | 5 </title> |
| Daniel@0 | 6 </head> |
| Daniel@0 | 7 <body> |
| Daniel@0 | 8 <H1> demgp |
| Daniel@0 | 9 </H1> |
| Daniel@0 | 10 <h2> |
| Daniel@0 | 11 Purpose |
| Daniel@0 | 12 </h2> |
| Daniel@0 | 13 Demonstrate simple regression using a Gaussian Process. |
| Daniel@0 | 14 |
| Daniel@0 | 15 <p><h2> |
| Daniel@0 | 16 Synopsis |
| Daniel@0 | 17 </h2> |
| Daniel@0 | 18 <PRE> |
| Daniel@0 | 19 demgp</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 <CODE>x</CODE> and one target variable |
| Daniel@0 | 26 <CODE>t</CODE>. The values in <CODE>x</CODE> are chosen in two separated clusters and the |
| Daniel@0 | 27 target data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian |
| Daniel@0 | 28 noise. Two Gaussian Processes, each with different covariance functions |
| Daniel@0 | 29 are trained by optimising the hyperparameters |
| Daniel@0 | 30 using the scaled conjugate gradient algorithm. The final predictions are |
| Daniel@0 | 31 plotted together with 2 standard deviation error bars. |
| Daniel@0 | 32 |
| Daniel@0 | 33 <p><h2> |
| Daniel@0 | 34 See Also |
| Daniel@0 | 35 </h2> |
| Daniel@0 | 36 <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> |
| Daniel@0 | 37 <b>Pages:</b> |
| Daniel@0 | 38 <a href="index.htm">Index</a> |
| Daniel@0 | 39 <hr> |
| Daniel@0 | 40 <p>Copyright (c) Ian T Nabney (1996-9) |
| Daniel@0 | 41 |
| Daniel@0 | 42 |
| Daniel@0 | 43 </body> |
| Daniel@0 | 44 </html> |