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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 inputs
+through the model to generate a matrix <CODE>y</CODE> of output
+vectors.  Each row of <CODE>x</CODE> corresponds to one input vector and each
+row of <CODE>y</CODE> corresponds to one output vector.  This assumes that the
+training data (both inputs and targets) has been stored in <CODE>net</CODE> by
+a call to <CODE>gpinit</CODE>; these are needed to compute the training
+data covariance matrix.
+
+<p><CODE>[y, sigsq] = gpfwd(net, x)</CODE> also generates a column vector <CODE>sigsq</CODE> of
+conditional 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 covariance
+matrix <CODE>cninv</CODE> in the forward propagation.  This increases efficiency if
+several calls to <CODE>gpfwd</CODE> are made.  
+
+<p><h2>
+Example
+</h2>
+The following code creates a Gaussian Process, trains it, and then plots the
+predictions 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 on
+plot(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>
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