wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual gpinit wolffd@0: wolffd@0: wolffd@0: wolffd@0:

gpinit wolffd@0:

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wolffd@0: Purpose wolffd@0:

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

wolffd@0: Synopsis wolffd@0:

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

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

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

wolffd@0: Example wolffd@0:

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

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

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

wolffd@0: gp, gpfwd

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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