Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual gpfwd Daniel@0: Daniel@0: Daniel@0: Daniel@0:

gpfwd Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Forward propagation through Gaussian Process. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: y = gpfwd(net, x)
Daniel@0: [y, sigsq] = gpfwd(net, x)
Daniel@0: [y, sigsq] = gpfwd(net, x, cninv)
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Daniel@0: Description Daniel@0:

Daniel@0: y = gpfwd(net, x) takes a Gaussian Process data structure net Daniel@0: together Daniel@0: with a matrix x of input vectors, and forward propagates the inputs Daniel@0: through the model to generate a matrix y of output Daniel@0: vectors. Each row of x corresponds to one input vector and each Daniel@0: row of y corresponds to one output vector. This assumes that the Daniel@0: training data (both inputs and targets) has been stored in net by Daniel@0: a call to gpinit; these are needed to compute the training Daniel@0: data covariance matrix. Daniel@0: Daniel@0:

[y, sigsq] = gpfwd(net, x) also generates a column vector sigsq of Daniel@0: conditional variances (or squared error bars) where each value corresponds to a pattern. Daniel@0: Daniel@0:

[y, sigsq] = gpfwd(net, x, cninv) uses the pre-computed inverse covariance Daniel@0: matrix cninv in the forward propagation. This increases efficiency if Daniel@0: several calls to gpfwd are made. Daniel@0: Daniel@0:

Daniel@0: Example Daniel@0:

Daniel@0: The following code creates a Gaussian Process, trains it, and then plots the Daniel@0: predictions on a test set with one standard deviation error bars: Daniel@0:

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Daniel@0: net = gp(1, 'sqexp');
Daniel@0: net = gpinit(net, x, t);
Daniel@0: net = netopt(net, options, x, t, 'scg');
Daniel@0: [pred, sigsq] = gpfwd(net, xtest);
Daniel@0: plot(xtest, pred, '-k');
Daniel@0: hold on
Daniel@0: plot(xtest, pred+sqrt(sigsq), '-b', xtest, pred-sqrt(sigsq), '-b');
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Daniel@0: See Also Daniel@0:

Daniel@0: gp, demgp, gpinit

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

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