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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gpfwd.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm b/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm new file mode 100644 index 00000000..51fc239c --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm @@ -0,0 +1,73 @@ +<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> \ No newline at end of file |