06 Nov 2002
02 Dec 2002)
Pattern analysis toolbox.
|gpfwd(net, x, cninv)
function [y, sigsq] = gpfwd(net, x, cninv)
%GPFWD Forward propagation through Gaussian Process.
% Y = GPFWD(NET, X) takes a Gaussian Process data structure NET
% together with a matrix X of input vectors, and forward propagates
% the inputs through the model to generate a matrix Y of output
% vectors. Each row of X corresponds to one input vector and each row
% of Y corresponds to one output vector. This assumes that the
% training data (both inputs and targets) has been stored in NET by a
% call to GPINIT; these are needed to compute the training data
% covariance matrix.
% [Y, SIGSQ] = GPFWD(NET, X) also generates a column vector SIGSQ of
% conditional variances (or squared error bars) where each value
% corresponds to a pattern.
% [Y, SIGSQ] = GPFWD(NET, X, CNINV) uses the pre-computed inverse
% covariance matrix CNINV in the forward propagation. This increases
% efficiency if several calls to GPFWD are made.
% See also
% GP, DEMGP, GPINIT
% Copyright (c) Ian T Nabney (1996-2001)
errstring = consist(net, 'gp', x);
if ~(isfield(net, 'tr_in') & isfield(net, 'tr_targets'))
error('Require training inputs and targets');
if nargin == 2
% Inverse covariance matrix not supplied.
cninv = inv(gpcovar(net, net.tr_in));
ktest = gpcovarp(net, x, net.tr_in);
% Predict mean
y = ktest*cninv*net.tr_targets;
if nargout >= 2
% Predict error bar
ndata = size(x, 1);
sigsq = (ones(ndata, 1) * gpcovarp(net, x(1,:), x(1,:))) ...
- sum((ktest*cninv).*ktest, 2);