Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual gp Daniel@0: Daniel@0: Daniel@0: Daniel@0:

gp Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Create a Gaussian Process. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: net = gp(nin, covarfn)
Daniel@0: net = gp(nin, covarfn, prior)
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Daniel@0: Description Daniel@0:

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net = gp(nin, covarfn) takes the number of inputs nin Daniel@0: for a Gaussian Process model with a single output, together Daniel@0: with a string covarfn which specifies the type of the covariance function, Daniel@0: and returns a data structure net. The parameters are set to zero. Daniel@0: Daniel@0:

The fields in net are Daniel@0:

Daniel@0:   type = 'gp'
Daniel@0:   nin = number of inputs
Daniel@0:   nout = number of outputs: always 1
Daniel@0:   nwts = total number of weights and covariance function parameters
Daniel@0:   bias = logarithm of constant offset in covariance function
Daniel@0:   noise = logarithm of output noise variance
Daniel@0:   inweights = logarithm of inverse length scale for each input 
Daniel@0:   covarfn = string describing the covariance function:
Daniel@0:       'sqexp'
Daniel@0:       'ratquad'
Daniel@0:   fpar = covariance function specific parameters (1 for squared exponential,
Daniel@0:    2 for rational quadratic)
Daniel@0:   trin = training input data (initially empty)
Daniel@0:   trtargets = training target data (initially empty)
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net = gp(nin, covarfn, prior) sets a Gaussian prior on the Daniel@0: parameters of the model. prior must contain the fields Daniel@0: pr_mean and pr_variance. If pr_mean is a scalar, Daniel@0: then the Gaussian is assumed to be isotropic and the additional fields Daniel@0: net.pr_mean and pr_variance are set. Otherwise, Daniel@0: the Gaussian prior has a mean Daniel@0: defined by a column vector of parameters prior.pr_mean and Daniel@0: covariance defined by a column vector of parameters prior.pr_variance. Daniel@0: Each element of prmean corresponds to a separate group of parameters, which Daniel@0: need not be mutually exclusive. The membership of the groups is defined Daniel@0: by the matrix prior.index in which the columns correspond to the elements of Daniel@0: prmean. Each column has one element for each weight in the matrix, Daniel@0: in the order defined by the function gppak, and each element Daniel@0: is 1 or 0 according to whether the parameter is a member of the Daniel@0: corresponding group or not. The additional field net.index is set Daniel@0: in this case. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: gppak, gpunpak, gpfwd, gperr, gpcovar, gpgrad

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

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