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