📄 gpinit.htm
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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 targetvectors, and stores them in <CODE>net</CODE>. These datasets are required ifthe corresponding inverse covariance matrix is not supplied to <CODE>gpfwd</CODE>.This is important if the data structure is saved and then reloaded beforecalling <CODE>gpfwd</CODE>.Each rowof <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 theparameters in <CODE>net</CODE> from the <CODE>prior</CODE> data structure which contains themean 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 networksave '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>⌨️ 快捷键说明
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