📄 gpcovar.htm
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<html><head><title>Netlab Reference Manual gpcovar</title></head><body><H1> gpcovar</H1><h2>Purpose</h2>Calculate the covariance for a Gaussian Process.<p><h2>Synopsis</h2><PRE>cov = gpcovar(net, x)[cov, covf] = gpcovar(net, x)</PRE><p><h2>Description</h2><p><CODE>cov = gpcovar(net, x)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> together witha matrix <CODE>x</CODE> of input vectors, and computes the covariancematrix <CODE>cov</CODE>. The inverse of this matrix is used when calculatingthe mean and variance of the predictions made by <CODE>net</CODE>.<p><CODE>[cov, covf] = gpcovar(net, x)</CODE> also generates the covariancematrix due to the covariance function specified by <CODE>net.covarfn</CODE>as calculated by <CODE>gpcovarf</CODE>.<p><h2>Example</h2>In the following example, the inverse covariance matrix is calculatedfor a set of training inputs <CODE>x</CODE> and is thenpassed to <CODE>gpfwd</CODE> so that predictions (with mean <CODE>ytest</CODE> andvariance <CODE>sigsq</CODE>) can be made for the test inputs<CODE>xtest</CODE>.<PRE>cninv = inv(gpcovar(net, x)); [ytest, sigsq] = gpfwd(net, xtest, cninv);</PRE><p><h2>See Also</h2><CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpcovarp.htm">gpcovarp</a></CODE>, <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</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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