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<html>
<head>
<title>
Netlab Reference Manual gpinit
</title>
</head>
<body>
<H1> gpinit
</H1>
<h2>
Purpose
</h2>
Initialise Gaussian Process model.
<p><h2>
Synopsis
</h2>
<PRE>
net = gpinit(net, trin, trtargets, prior)
net = gpinit(net, trin, trtargets, prior)
</PRE>
<p><h2>
Description
</h2>
<CODE>net = gpinit(net, trin, trtargets)</CODE> takes a Gaussian Process data structure <CODE>net</CODE>
together
with a matrix <CODE>trin</CODE> of training input vectors and a matrix <CODE>trtargets</CODE> of
training target
vectors, and stores them in <CODE>net</CODE>. These datasets are required if
the corresponding inverse covariance matrix is not supplied to <CODE>gpfwd</CODE>.
This is important if the data structure is saved and then reloaded before
calling <CODE>gpfwd</CODE>.
Each row
of <CODE>trin</CODE> corresponds to one input vector and each row of <CODE>trtargets</CODE>
corresponds to one target vector.
<p><CODE>net = gpinit(net, trin, trtargets, prior)</CODE> additionally initialises the
parameters in <CODE>net</CODE> from the <CODE>prior</CODE> data structure which contains the
mean and variance of the Gaussian distribution which is sampled from.
<p><h2>
Example
</h2>
Suppose that a Gaussian Process model is created and trained with input data <CODE>x</CODE>
and targets <CODE>t</CODE>:
<PRE>
net = gp(2, 'sqexp');
net = gpinit(net, x, t);
% Train the network
save 'gp.net' net;
</PRE>
Another Matlab program can now read in the network and make predictions on a data set
<CODE>testin</CODE>:
<PRE>
load 'gp.net';
pred = gpfwd(net, testin);
</PRE>
<p><h2>
See Also
</h2>
<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
</body>
</html>
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