gpfwd

Purpose

Forward propagation through Gaussian Process.

Synopsis

y = gpfwd(net, x)
[y, sigsq] = gpfwd(net, x)
[y, sigsq] = gpfwd(net, x, cninv)

Description

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.

Example

The following code creates a Gaussian Process, trains it, and then plots the predictions on a test set with one standard deviation error bars:


net = gp(1, 'sqexp');
net = gpinit(net, x, t);
net = netopt(net, options, x, t, 'scg');
[pred, sigsq] = gpfwd(net, xtest);
plot(xtest, pred, '-k');
hold on
plot(xtest, pred+sqrt(sigsq), '-b', xtest, pred-sqrt(sigsq), '-b');

gpfwd

Purpose

Synopsis

Description

Example

See Also