📄 gpfwd.htm
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<html><head><title>Netlab Reference Manual gpfwd</title></head><body><H1> gpfwd</H1><h2>Purpose</h2>Forward propagation through Gaussian Process.<p><h2>Synopsis</h2><PRE>y = gpfwd(net, x)[y, sigsq] = gpfwd(net, x)[y, sigsq] = gpfwd(net, x, cninv)</PRE><p><h2>Description</h2><CODE>y = gpfwd(net, x)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, and forward propagates the inputsthrough the model to generate a matrix <CODE>y</CODE> of outputvectors. Each row of <CODE>x</CODE> corresponds to one input vector and eachrow of <CODE>y</CODE> corresponds to one output vector. This assumes that thetraining data (both inputs and targets) has been stored in <CODE>net</CODE> bya call to <CODE>gpinit</CODE>; these are needed to compute the trainingdata covariance matrix.<p><CODE>[y, sigsq] = gpfwd(net, x)</CODE> also generates a column vector <CODE>sigsq</CODE> ofconditional variances (or squared error bars) where each value corresponds to a pattern.<p><CODE>[y, sigsq] = gpfwd(net, x, cninv)</CODE> uses the pre-computed inverse covariancematrix <CODE>cninv</CODE> in the forward propagation. This increases efficiency ifseveral calls to <CODE>gpfwd</CODE> are made. <p><h2>Example</h2>The following code creates a Gaussian Process, trains it, and then plots thepredictions on a test set with one standard deviation error bars:<PRE>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 onplot(xtest, pred+sqrt(sigsq), '-b', xtest, pred-sqrt(sigsq), '-b');</PRE><p><h2>See Also</h2><CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="demgp.htm">demgp</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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