# gpinit

## Purpose

Initialise Gaussian Process model.

## Synopsis

    net = gpinit(net, trin, trtargets, prior)
    net = gpinit(net, trin, trtargets, prior)

## Description

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

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

## Example

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

    net = gp(2, 'sqexp');
    net = gpinit(net, x, t);
    % Train the network
    save 'gp.net' net;

Another Matlab program can now read in the network and make predictions on a data set `testin`:

    load 'gp.net';
    pred = gpfwd(net, testin);

## See Also

`gp`, `gpfwd`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)