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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual gpinit
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5 </title>
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6 </head>
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7 <body>
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8 <H1> gpinit
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Initialise Gaussian Process model.
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14
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15 <p><h2>
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16 Synopsis
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17 </h2>
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18 <PRE>
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19 net = gpinit(net, trin, trtargets, prior)
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20 net = gpinit(net, trin, trtargets, prior)
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21 </PRE>
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22
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23
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24 <p><h2>
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25 Description
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26 </h2>
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27 <CODE>net = gpinit(net, trin, trtargets)</CODE> takes a Gaussian Process data structure <CODE>net</CODE>
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28 together
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29 with a matrix <CODE>trin</CODE> of training input vectors and a matrix <CODE>trtargets</CODE> of
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30 training target
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31 vectors, and stores them in <CODE>net</CODE>. These datasets are required if
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32 the corresponding inverse covariance matrix is not supplied to <CODE>gpfwd</CODE>.
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33 This is important if the data structure is saved and then reloaded before
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34 calling <CODE>gpfwd</CODE>.
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35 Each row
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36 of <CODE>trin</CODE> corresponds to one input vector and each row of <CODE>trtargets</CODE>
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37 corresponds to one target vector.
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38
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39 <p><CODE>net = gpinit(net, trin, trtargets, prior)</CODE> additionally initialises the
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40 parameters in <CODE>net</CODE> from the <CODE>prior</CODE> data structure which contains the
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41 mean and variance of the Gaussian distribution which is sampled from.
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42
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43 <p><h2>
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44 Example
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45 </h2>
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46 Suppose that a Gaussian Process model is created and trained with input data <CODE>x</CODE>
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47 and targets <CODE>t</CODE>:
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48 <PRE>
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49
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50 net = gp(2, 'sqexp');
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51 net = gpinit(net, x, t);
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52 % Train the network
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53 save 'gp.net' net;
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54 </PRE>
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55
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56 Another Matlab program can now read in the network and make predictions on a data set
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57 <CODE>testin</CODE>:
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58 <PRE>
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59
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60 load 'gp.net';
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61 pred = gpfwd(net, testin);
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62 </PRE>
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63
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64
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65 <p><h2>
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66 See Also
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67 </h2>
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68 <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE><hr>
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69 <b>Pages:</b>
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70 <a href="index.htm">Index</a>
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71 <hr>
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72 <p>Copyright (c) Ian T Nabney (1996-9)
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73
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74
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75 </body>
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76 </html> |
