Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual gpinit Daniel@0: Daniel@0: Daniel@0: Daniel@0:

gpinit Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Initialise Gaussian Process model. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: net = gpinit(net, trin, trtargets, prior)
Daniel@0: net = gpinit(net, trin, trtargets, prior)
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Daniel@0: Description Daniel@0:

Daniel@0: net = gpinit(net, trin, trtargets) takes a Gaussian Process data structure net Daniel@0: together Daniel@0: with a matrix trin of training input vectors and a matrix trtargets of Daniel@0: training target Daniel@0: vectors, and stores them in net. These datasets are required if Daniel@0: the corresponding inverse covariance matrix is not supplied to gpfwd. Daniel@0: This is important if the data structure is saved and then reloaded before Daniel@0: calling gpfwd. Daniel@0: Each row Daniel@0: of trin corresponds to one input vector and each row of trtargets Daniel@0: corresponds to one target vector. Daniel@0: Daniel@0:

net = gpinit(net, trin, trtargets, prior) additionally initialises the Daniel@0: parameters in net from the prior data structure which contains the Daniel@0: mean and variance of the Gaussian distribution which is sampled from. Daniel@0: Daniel@0:

Daniel@0: Example Daniel@0:

Daniel@0: Suppose that a Gaussian Process model is created and trained with input data x Daniel@0: and targets t: Daniel@0:

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Daniel@0: net = gp(2, 'sqexp');
Daniel@0: net = gpinit(net, x, t);
Daniel@0: % Train the network
Daniel@0: save 'gp.net' net;
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Daniel@0: Daniel@0: Another Matlab program can now read in the network and make predictions on a data set Daniel@0: testin: Daniel@0:

Daniel@0: 
Daniel@0: load 'gp.net';
Daniel@0: pred = gpfwd(net, testin);
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Daniel@0: See Also Daniel@0:

Daniel@0: gp, gpfwd

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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